Target Profiles at Adenosine A2aand Dopamine D2

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DISCOVERY OF INDOLYL PIPERAZINYLPYRIMIDINES WITH DUAL-

TARGET PROFILES AT ADENOSINE A2A AND DOPAMINE D2 RECEPTORS FOR PD TREATMENT

SHAO YI-MING

(B. Sc. and M. Sc., National Taiwan University)

A THESIS SUBMITTED FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

NUS GRADUATE SCHOOL FOR INTEGRATIVE

SCIENCES AND ENGINEERING

NATIONAL UNIVERSITY OF SINGAPORE

2014

ACKNOWLEDGEMENTS

I would like to extend sincere gratitude to my supervisor, A/Prof. Giorgia

PASTORIN, for giving me an opportunity to do my Ph.D. in her laboratory. I still can clearly remember how flexible and agreeable she was to my proposals on polypharmacology when I first approached her in December of 2010. It was the freedom to pursue new ideas, such as A2A/D2 polypharmacology, offered by her that impressed me and made me decide to join her laboratory. Over the years, she has been showing enthusiasm by allocating funds, time, energy and manpower to my projects. In addition, her qualities of wisdom, modesty and considerateness have been, and will continue to be, my inspiration. I deem it an honour and a rare privilege to work with her during my Ph.D. studies.

I am also grateful to my Thesis Advisory Committee, Prof. CHEN Yu Zong and

Prof. Giampiero SPALLUTO, for their constant advices with regard to my projects. I especially thank Prof. Chen’s support and suggestions at the initial stage of A2A/D2 project. Moreover, I am appreciative of my thesis examiners,

Prof. GO Mei Lin and Prof. Brian DYMOCK, for their valuable comments on the thesis.

My appreciation also goes to Dr. Priyankar PAIRA for his guidance in organic synthesis, Dr. MA Xiaohua for computer modeling, Prof. Karl-Norbert KLOTZ for radioligand assays, Dr. ANG Wee Han and Miss TAN Geok Kheng for X- ray crystallographic analysis, Madam WONG Lai Kwai for high resolution mass spectrometry analysis, Dr. Sourabh BANERJEE and Dr. Madhavan

NALLANI for artificial cell membrane assays, Miss WANG Wei and Dr. Deron

HERR for functional assays, NUS Drug Development Unit for assays on drug-

ACKNOWLEDGEMENTS like properties and prior art search, Dr. NG Chee Hoe and A/Prof. LIM Kah-

Leong for Drosophila experiments, Mr. YANG Xuan, Dr. Sam

RAMANUJULU and Mr. SEE Cheng Shang for assistance in high-performance liquid chromatography, all members in Giorgia’s group and all people in S15

Level 5 for day-to-day operations, NGS for scholarship, as well as NUS

Pharmacy Department for research facilities.

I am thankful for my brothers in Christ, Miss Esther TAN, NUS Office of

Safety, Health & Environment, as well as my family members for their care and concern.

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TABLE OF CONTENTS

DECLARATION...... i ACKNOWLEDGEMENTS ...... ii TABLE OF CONTENTS ...... iv SUMMARY ...... viii LIST OF TABLES ...... ix LIST OF FIGURES ...... xi LIST OF SCHEMES ...... xviii LIST OF ABBREVIATIONS ...... xix

CHAPTER 1 PARKINSON’S DISEASE (PD) ...... 1 1.1 Introduction to PD ...... 1 1.2 Current PD Pharmacotherapeutics ...... 7 1.2.1 Dopamine replacement therapies ...... 7 1.2.2 Non-dopaminergic therapies ...... 10 1.3 Opportunities of Polypharmacology in Treating PD ...... 12 1.3.1 Approaches to multi-targeting ...... 12 1.3.2 Polypharmacology in PD ...... 24

1.3.2.1 An example: MAO-B inhibitor + A2A antagonist ...... 24

1.3.2.2 An opportunity: D2 agonist + A2A antagonist ...... 31 1.4 Chapter Conclusion ...... 35

CHAPTER 2 HYPOTHESIS AND AIMS ...... 36

CHAPTER 3 LIGAND-BASED VIRTUAL SCREENING AND ANALYSES ...... 40 3.1 Receptor Agonism and Antagonism ...... 40

3.1.1 Preclinical studies on two-drug cocktail (KW-6002 + a D2 agonist) ...... 42 3.1.2 Compounds capable of agonising one receptor but antagonising another ...... 43

TABLE OF CONTENTS

3.1.3 Compounds capable of agonising D2 receptor but antagonising A2A receptor ...... 45

3.1.3.1 Comparison between A2A agonism and antagonism ...... 47

3.1.3.2 Comparison between D2 agonism and antagonism ...... 51 3.2 Support Vector Machine Models ...... 54 3.2.1 Introduction ...... 55 3.2.2 Methods ...... 56 3.2.2.1 Molecular descriptors...... 56 3.2.2.2 Generation of putative inactive compounds ...... 58 3.2.2.3 Support vector machine classification ...... 58 3.2.2.4 Model validation ...... 62 3.2.3 Results and discussion ...... 64

3.2.3.1 Collection of adenosine A2A receptor antagonists ...... 64

3.2.3.2 Collection of dopamine D2 receptor agonists ...... 65 3.2.3.3 N-arylpiperazine—a privileged substructure ...... 68 3.2.3.4 Generation of putative inactive compounds ...... 71 3.2.3.5 Establishment of SVM models for virtual screening ...... 74 3.3 Tanimoto Similarity ...... 85 3.3.1 Introduction ...... 85 3.3.2 Methods ...... 87 3.3.3 Results and discussion ...... 87 3.4 Chapter Conclusion ...... 93

CHAPTER 4 DESIGN, SYNTHESIS, AND A2A RECEPTOR BINDING STUDIES OF INDOLYL PIPERAZINYLPYRIMIDINES .... 94 4.1 Design Rationale ...... 94 4.2 IPP ...... 110 4.2.1 Chemical considerations ...... 110 4.2.2 Experimental methods ...... 113 4.2.3 Results and discussion ...... 125 4.3 Indole C4~C7 Positions of IPP ...... 127 4.3.1 Chemical considerations ...... 127 4.3.2 Experimental methods ...... 128 4.3.3 Results and discussion ...... 136 4.4 Indole C2 Position of IPP ...... 141 v

TABLE OF CONTENTS

4.4.1 Chemical considerations ...... 141 4.4.2 Experimental methods ...... 144 4.4.3 Results and discussion ...... 151 4.5 Chapter Conclusion ...... 154

CHAPTER 5 FURTHER PHARMACOLOGICAL EVALUATION AND CHARACTERISATION OF COMPOUNDS 69 AND 70 ...... 156 5.1 Introduction ...... 156 5.2 Materials and Methods ...... 158 5.3 Results and Discussion ...... 165

5.3.1 Proteopolymersomes for D2 receptor binding assay ...... 165

5.3.2 Cell-based functional assay for D2 receptor ...... 170 5.3.3 Drug-like properties ...... 184 5.3.3.1 Solubility and permeability assays...... 188 5.3.3.2 Metabolism assays ...... 195 5.3.3.3 Toxicity assays ...... 207 5.3.4 Drosophila models of Parkinson’s disease ...... 215 5.4 Chapter Conclusion ...... 220

CHAPTER 6 CONCLUSIONS AND FUTURE DIRECTIONS ...... 221

BIBLIOGRAPHY ...... 235

APPENDICES ...... 282 Appendix A Reverse Transcriptase Inhibitors for Treating HIV ...... 282 Appendix B Protease Inhibitors for Treating HIV ...... 283 Appendix C Polypharmacology in Cancer Therapy ...... 284 Appendix D Polypharmacology in Antipsychotics ...... 291 Appendix E GPCR Basics ...... 299 Appendix F Basal Ganglia Motor Circuit ...... 307 Appendix G Virtual Screening ...... 310

Appendix H Classification of Adenosine A2A Receptor Antagonists ...... 315

TABLE OF CONTENTS

Appendix I Collection of Reported Adenosine A2A Receptor Antagonists 317

Appendix J Major Scaffolds of A2A Antagonists Reported ...... 321

Appendix K Collection of Reported Dopamine D2 Receptor Agonists ...... 328

Appendix L Major Scaffolds of D2 Agonists Reported ...... 332 Appendix M Hits Identified from PubChem and MDDR ...... 338 Appendix N Compounds in Clusters 1~10 ...... 347 Appendix O Crystal Structure Report for Compound 70 ...... 357 Appendix P NMR Spectra of Compounds 66-72 and 69a-72a ...... 365 Appendix Q HPLC Chromatograms of Compounds 69-72 and 69a-72a .... 375 Appendix R Radioligand Competition Assays ...... 383 Appendix S NMR Spectra of Compounds 73-83 ...... 385 Appendix T HPLC Chromatograms of Compounds 73-83 ...... 396 Appendix U NMR Spectra of Compounds 86-95 ...... 407 Appendix V HPLC Chromatograms of Compounds 90-95 ...... 416 Appendix W Western Blot Analysis ...... 421 Appendix X Flow Cytometry Measurements ...... 422 Appendix Y References for Appendices A to Y ...... 423

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SUMMARY

Parkinson’s disease (PD) is a prevalent neurodegenerative movement disorder characterised pathologically by the loss of dopaminergic neurons in the substantia nigra. The current pharmacotherapies of PD are facing limitations on side effects, such as motor fluctuations, drug-induced dyskinesia, and psychosis. This thesis seeks to address these limitations by discovering new chemical entities (NCEs) targeting adenosine hA2A and dopamine hD2 receptors as potential therapeutics of PD. Three computational methods, namely support vector machine (SVM) models, Tanimoto similarity, and hierarchical clustering, were utilised for the identification of compounds containing the [indole + piperazine + pyrimidine] (IPP). Subsequent synthesis and testing of IPP- containing compounds led to the discovery of compounds 69 and 70, which acted as hA2A binders in the radioligand competition assay (Ki = 8.71~11.2 µM) and hD2 binders in the artificial cell membrane assay (EC50 = 22.5~40.2 µM).

These two novel compounds did not exhibit mutagenicity (up to 100 µM), hepatotoxicity (up to 30 µM) and cardiotoxicity (up to 30 µM), although they suffered from poor aqueous solubility, poor membrane permeability, and metabolic instability. More importantly, in Drosophila models of PD, these two compounds showed improvement in movement and mitigation of the loss of dopaminergic neurons. Together with recommendations given for future optimisation, these two compounds have paved the way for novel PD therapeutics.

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LIST OF TABLES

1 Potential Therapeutics Development for PD ...... 37

2 Descriptor List ...... 57

3 Compound Data Sets...... 74

4 Classification Performance of SVM Models ...... 76

5 SVM-Based Virtual Screening of PubChem & MDDR Databases ...... 79

6 Known Structural Properties of Four Purchased Compounds Bearing Scaffold A ...... 84

7 Compounds A and B Represented by Molecular Fingerprint ...... 86

8 Ten Compounds Compliant with at Least One Criterion of Each Receptor ...... 106

9 Weeding-Out Analysis of Ten Compounds Obtained from Clustering ...... 109

10 Binding (Ki, nM) of Compounds 69-72 and 69a-72a at Adenosine Receptor Subtypes ...... 126

11 Binding (Ki, nM) of Compounds 73-83 at Adenosine Receptor Subtypes ...... 140

12 The Size and Lipophilicity of Hydrogen and Halogens ...... 141

13 Binding (Ki, nM) of Compounds 90-94 at Adenosine Receptor Subtypes ...... 153

14 The Potency of Ligands in Different Assay Systems ...... 169

15 Dopamine D2 Receptor Activation Assays Used in the Thesis ... 172

LIST OF TABLES

16 Summary of Three D2R Activation Assays ...... 184

17 The Aqueous Solubility and Permeability of Compounds 69 and 70 ...... 191

18 Structural Properties of Compounds 69 and 70 ...... 192

19 Metabolites of Compound 69 Identified Using LightSight Software Sorted by Retention Time...... 201

20 Metabolites of Compound 70 Identified Using LightSight Software Sorted by Retention Time...... 205

21 Structural Properties Required of CNS Drugs ...... 226

A1 Ten Kinases Targeted by Sunitinib ...... 288

LIST OF FIGURES

1 The four major dopaminergic pathways in the brain ...... 3

2 The diseased molecular events inside and outside the dopaminergic neurons ...... 5

3 Reserpine-induce rabbits before (top) and after (below) treatment of DOPA ...... 8

4 Current major dopamine replacement therapies for PD act at four points as shown in 1 to 4 ...... 9

5 The biosynthesis and breakdown of dopamine ...... 10

6 The evolution of approaches to drug discovery over the past century ...... 15

7 Single-target-based drugs (upper) are selective to a validated target responsible for a cellular mechanism, whereas polypharmacological drugs (lower) selectively bind to multiple therapeutically beneficial targets ...... 16

8 Three strategies to generate knowledge-based, dual-acting DMLs ...... 22

9 Examples of three knowledge-based DML strategies ...... 23

10 MAO-catalysed oxidative deamination of dopamine starts with the formation of iminium species ...... 24

11 Five inhibitors of monoamine oxidases ...... 25

12 Xanthine and its derivatives ...... 28

13 The metabolism of 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP) by MAO-B in the brain ...... 30

LIST OF FIGURES

14 Polypharmacological drug candidates inhibiting

MAO-B and antagonising A2A receptor ...... 30

15 Drugs (either in clinical use or under development) targeting GPCRs implicated in PD ...... 31

16 Dopamine (D1, D2) and adenosine (A2A) receptors located in the striatopallidal GABAergic neurons ...... 34

17 The sigmoid relationships between the base 10 logarithm of ligand concentration and the activity magnitude of induced receptor ...... 42

18 Four examples of prospective DMLs that antagonising one receptor but agonising another ...... 44

19 Linked DML strategy for integrating A2A antagonistic

and D2 agonistic activities into one compound ...... 46

20 Fused and merged DML strategies for integrating

A2A antagonistic and D2 agonistic activities into one compound ...... 47

21 The nitrogen-containing cores of adenosine

(an A2A agonist), caffeine (an A2A antagonist),

KW-6002 (an A2A antagonist), and ZM241385

(an A2A antagonist) are shown in the bracket ...... 49

22 Comparison of ligand-binding sites for A2A receptor in complex with ZM241385, adenosine and caffeine ...... 50

23 SAR studies on dopamine D2 agonists...... 52

24 The molecular docking pose of compounds 12 (pale green) and 14 (dark green) ...... 54

25 The commonly prescribed dopamine agonists for the treatment of PD ...... 54

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LIST OF FIGURES

26 Two data sets represented by red squares and blue circles are separated by several possible hyperplanes (green straight lines) ...... 56

27 Schematic representation of using support vector machine for binary classification of compounds ...... 61

28 Selected D2 agonists containing the aromatic piperazine scaffold ...... 67

29 Selected A2A antagonists containing the aromatic piperazine scaffold ...... 67

30 The N-arylpiperazine substructure is to mimic neurotransmitters (e.g. dopamine, serotonin and epinephrine), and can be found in many CNS drugs (e.g. aripiprazole and ziprasidone) ...... 70

31 The ligand-binding pockets of D2 receptor in complex with dopamine (A) and aripiprazole (B) ...... 71

32 The diagram illustrating how putative inactive compounds are generated ...... 73

33 The diagram of union and intersection of three sets ...... 79

34 Two largest scaffold classes identified by SVM models ...... 83

35 Four compounds with Scaffold A purchased from Enamine Ltd ...... 84

36 The Tanimoto similarity for object A and object B is calculated by the intersection divided by union ...... 87

37 The three pairs of reported compounds with Tc > 0.9 ...... 91

38 The flowchart for SVM models, virtual screening, and similarity analysis of collected compounds ...... 92

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LIST OF FIGURES

39 Hierarchical agglomerative clustering ...... 96

40 Clustering dendrogram of the collected compounds ...... 98

41 Compounds in cluster 2 ...... 99

42 The magnified area where compounds in cluster 2 are located in the clustering dendrogram ...... 99

43 Eighteen compounds (18-35) designed from cluster 1 ...... 101

44 Fifteen compounds (36-50) designed from cluster 2 ...... 102

45 Five compounds (51-55) designed from cluster 3 ...... 103

46 Seven compounds (56-62) designed from cluster 8 ...... 103

47 Two compounds (63 and 64) designed from cluster 9 ...... 104

48 Three computational tools, SVM models, Tanimoto similarity analysis, and hierarchical clustering analysis, are used to analyse collected compounds, screen databases, and identify potential scaffolds ...... 110

49 Proposed mechanisms of steps in Scheme 1 ...... 112

50 X-ray crystal structure of compound 70 ...... 113

51 The X-ray crystal structures of A2A receptor in complex with ZM241385 (blue sticks) and caffeine (yellow sticks) shows that both compounds locate in the same ligand-binding pocket ...... 127

52 Proposed compound 84 for future synthesis ...... 141

53 The catalytic cycle for synthesis of 89 ...... 143

54 Preliminary SAR profiles of IPP at hA2A receptor ...... 155

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LIST OF FIGURES

55 Two possible directions for further development of newly identified IPPs ...... 157

56 In vitro synthesis reaction and fluorescence detection ...... 166

57 The dose-response curve of compound 69 (A), compound 70 (B), and (±)-PPHT.HCl (C) with

D2R-proteopolymersomes using fluorescence polarisation (FP) competition assay with BODIPY-NAPS ...... 169

58 The simplified depiction of proteins involved

when dopamine D2 receptor is activated ...... 172

59 The formation of cAMP, a second messenger regulating a myriad of physiological functions, is catalysed by adenylyl cyclase ...... 174

60 Inhibition of cAMP accumulation induced by compound 70 in comparison with the reference compound quinpirole (Q) ...... 175

61 Principles and results of internalisation assays ...... 177

62 The model of GPCR-ADAM-EGFR crosstalk ...... 181

63 The mechanism of TGFα shedding assay for assessing GPCR activation ...... 183

64 The TGFα shedding assay for compounds 69, 70 and dopamine (DA)...... 183

65 Four major mechanisms of intestinal membrane transport ...... 194

66 Schematic diagram of PAMPA ...... 194

67 Results of metabolic stability assays ...... 199

LIST OF FIGURES

68 The product ion spectra of the parent compound 69 and its major metabolite 69-M8 ...... 202

69 The proposed buspirone metabolism in human liver microsomes ...... 203

70 The product ion spectrum of the major metabolite of 70 ...... 206

71 The proposed compounds 96-98 ...... 206

72 Diagram depicting Ames assay ...... 208

73 The number of histidine revertant colonies ...... 211

74 The cell viability assay after treatment of TAMH (A–C) and HL-1 (D–F) cells with positive controls and compounds ...... 214

75 Survival percentages of LRRK2 G2019S flies after 7 days, 10 days and 17 days of treatment with various concentrations (50 µM, 100 µM, 200 µM) of compound 69 ...... 217

76 Treatment of compounds 69 in both parkin-null (A and B) and mutant LRRK2 G2019S-expressing (C and D) flies ...... 219

77 Experimental data of 69, and estimated structural properties of 69, 99-102 ...... 230

78 Selected CNS drugs with 1,4-DAP substructure ...... 232

79 Lists of carbonyl isosteres and structural comparison of propan-2-one with 3,3-dimethyloxetane ...... 234

A1 The chemical structures of SU5416, SU6668 and sunitinib ...... 287

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LIST OF FIGURES

A2 The chemical structures of CUDC-101, niclosamide, galeterone, MDG892 and CDBT ...... 290

A3 Chemical structures of chlorpromazine (typical), clozapine (atypical) and ziprasidone (atypical) ...... 293

A4 Design of ziprasidone with dual D2/5-HT2A antagonism ...... 297

A5 The ternary complex model ...... 300

A6 Biased agonism exemplified by angiotensin II receptor type 1...... 303

A7 The mechanism of action of TRV027 ...... 303

A8 The traditional (simplified) model of basal ganglia motor circuit in the normal brain ...... 309

A9 Estimated number of compounds in various sources ...... 311

A10 The classification of virtual screening ...... 313

A11 Representative nitrogen-containing mono-heterocyclic

and bi-heterocyclic compounds as adenosine A2A receptor antagonists ...... 315

A12 Representative nitrogen-containing tri-heterocyclic

compounds as adenosine A2A receptor antagonists ...... 316

A13 Radioligand competition experiments for receptors ...... 384

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LIST OF SCHEMES

1 Synthesis of compounds 69-72 and 69a-72a ...... 111

2 Synthesis of compounds 73-83 ...... 128

3 Synthesis of compound 90 ...... 143

4 Synthesis of compounds 91-95 ...... 144

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LIST OF ABBREVIATIONS

A2A adenosine A2A receptor

2-AA 2-aminoanthracene

ADAGIO Attenuation of Disease Progression with Azilect Given Once Daily

ADAM a disintegrin and metalloprotease

ADME absorption, distribution, metabolism, and excretion

ADME-Tox absorption, distribution, metabolism, excretion and toxicity

ADR adverse drug reaction

AHF acute heart failure

API active pharmaceutical ingredient

AP-TGFα alkaline phosphatase (AP)-tagged transforming growth factor-α

AR adenosine receptor

ARB angiotensin receptor blocker

ATP adenosine triphosphate

AT1R angiotensin II type 1 receptor

AUC area under the curve

BBB blood-brain barrier

BCS Biopharmaceutics Classification System

Boc butyloxycarbonyl

BODIPY-NAPS boron-dipyrromethene N-(p- aminophenethyl)spiperone

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LIST OF ABBREVIATIONS br broad

BRET bioluminescent resonance energy transfer cAMP adenosine 3’,5’-cyclic monophosphate

CATIE Clinical Antipsychotic Trials of Intervention Effectiveness

CaV1.3 calcium channel, voltage-dependent, L type, alpha 1D subunit

CCPA 2-chloro-6-cyclopentyladenosine

CD4 cluster of differentiation 4 cGMP guanosine 3’,5’-cyclic monophosphate

CHO Chinese hamster ovary

CL clearance

CLint, in vitro in vitro intrinsic clearance

CLogD calculated logarithm of the octanol/water distribution coefficient at pH = 7.4

CLogP calculated logarithm of the octanol/water partition coefficient

CNS central nervous system

COMT catechol-O-methyltransferase

COPD chronic obstructive pulmonary disease

CPX 1,3-dipropyl-8-cyclopentylxanthine

CRPC castration-resistant prostate cancer

CSC m-chlorostyrylcaffeine

CV cross validation

CYP cytochrome P450 d doublet

LIST OF ABBREVIATIONS

2D two-dimensional

3D three-dimensional

D2 dopamine D2 receptor

DA dopamine

1,4-DAP 1,4-disubstituted aromatic piperazine

DATATOP Deprenyl And Tocopherol Antioxidative Therapy Of Parkinsonism

DDI drug-drug interaction

DEER double electron-electron resonance

DMEM Dulbecco's Modified Eagle Medium

DMF dimethylformamide

DML designed multiple ligand

DMPX 3,7-dimethyl-1-propargylxanthine

DMSO dimethylsulfoxide

DNA deoxyribonucleic acid

DOPA 3,4-dihydroxyphenylalanine

DPAT dipropylaminotetralin

EC endothelial cell

EDC 1-ethyl-3-(3- dimethylaminopropyl)carbodiimide

EDTA ethylenediaminetetraacetic acid

EGFR epidermal growth factor receptor

EGTA ethylene glycol tetraacetic acid

EL extracellular loop

ELISA enzyme-linked immunosorbent assay

EPS extrapyramidal syndrome

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LIST OF ABBREVIATIONS

ER endoplasmic reticulum

ESI electrospray ionization

F bioavailability

FBS fetal bovine serum

FDA Food and Drug Administration

FDC fixed-dose combination

FGA first generation antipsychotic

FGFR fibroblast growth factor receptor

FLIPR fluorescence imaging plate reader

FN false negative

FP false positive

FPP finished pharmaceutical product

FRET fluorescent resonance energy transfer

FRLM female rat liver microsome fu fraction unbound

GABA gamma-aminobutyric acid

GDP guanosine 5’-diphosphate

GIST gastrointestinal stromal tumour

GI tract gastrointestinal tract

GPCR G protein-coupled receptor

GPe external segment of the globus pallidus

GPi internal segment of the globus pallidus

GRK G protein-coupled receptor kinase

GSK GlaxoSmithKline

GTP guanosine 5’-triphosphate

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LIST OF ABBREVIATIONS

H helix

HAC hierarchical agglomerative clustering

HB hydrogen bond

HBA hydrogen bond acceptors

HBD hydrogen bond donor

HB-EGF heparin-binding epidermal growth factor

HBSS Hank's Balanced Salt Solution

HDAC histone deacetylase

HEK human embryonic kidney

HEMADO 2-hexyn-1-yl-N6-methyladenosine

HER2 human epidermal growth factor receptor 2 hERG human ether-à-go-go-related gene

HGP Human Genome Project

HIV human immunodeficiency virus

HMG-CoA 5-hydroxy-3-methylglutaryl-coenzyme A reductase

HNSCC head and neck squamous cell cancer

HPLC high-performance liquid chromatography

HRMS high-resolution mass spectrometry

Hsp90 heat shock protein 90

5-HT 5-hydroxytryptamine

HTS high-throughput screening

HUVEC human umbilical vein endothelial cell

IBMX 3-isobutyl-1-methylxanthine

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LIST OF ABBREVIATIONS

IBS-D diarrhea-predominant irritable bowel syndrome

IC50 the half maximal inhibitory concentration

IDP [indole + 1,4-dihydropyrazine + pyrimidine]

IgG immunoglobulin G

IGP [indole + guanidine + pyrimidine]

ILO Industry Liaison Office

IPP [indole + piperazine + pyrimidine]

Kbb brain:blood partition ratio

Kd dissociation constant

Ki dissociation constant

L-AP4 L-(+)-2-amino-4-phosphonobutyric acid

LB Lewy body

LBVS ligand-based virtual screening

Lck lymphocyte-specific protein tyrosine kinase

LCMS liquid chromatography–mass spectrometry

LDL-C low-density lipoprotein cholesterol

LID L-DOPA-induced dyskinesia

LPA lysophosphatidic acid

LRRK2 leucine-rich repeat kinase 2 m multiplet

MABA muscarinic antagonist-beta2 agonist

MAO monoamine oxidase

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LIST OF ABBREVIATIONS

MAP mean arterial pressure

MD molecular dynamics

MDDR MDL Drug Data Report

MG Minimal Glucose mGluR metabotropic glutamate receptor

MKI multikinase inhibitor

ML machine learning

MPDP+ 1-methyl-4-phenyl-2,3- dihydropyridinium species

MPP+ 1-methyl-4-phenylpyridinium species

MPPP 1-methyl-4-phenyl-4- propionoxypiperidine

MPTP 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine

MRLM male rat liver microsome mTORC1 mammalian target of rapamycin complex 1

MW molecular weight nAChR nicotinic acetylcholine receptor

NADPH the reduced form of nicotinamide adenine dinucleotide phosphate

NAM negative allosteric modulator

NCE new chemical entity

NDA New Drug Application

NECA 5’-N-ethylcarboxamidoadenosine

NF-κB nuclear factor-kappaB

NIMH National Institute of Mental Health

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LIST OF ABBREVIATIONS

NMDA N-methyl-D-aspartate

NMR nuclear meagnetic resonance

NO nitric oxide

4-NQO 4-nitroquinoline N-oxide

NSCLC non-small cell lung cancer

6-OHDA 6-hydroxydopamine

OR opioid receptor

PAM positive allosteric modulator

PAMPA parallel artificial membrane permeability assay

PARIS parkin interacting substrate

PBS phosphate-buffered saline

PC positive control

PD Parkinson’s disease

PDGF platelet-derived growth factor

PDGFR platelet-derived growth factor receptor

PDMS polydimethylsiloxane

Pe effective permeability

PET positron emission tomography

PFS progression-free survival

PGC peroxisome proliferator-activated receptor gamma coactivator

P-gp P-glycoprotein

PHCCC N-phenyl-7- (hydroxylimino)cyclopropa[b]chromen- 1a-carboxamide

PINK1 PTEN induced putative kinase 1

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LIST OF ABBREVIATIONS

PK pharmacokinetics

PKC protein kinase C

PLA proximity ligation assay pNET pancreatic neuroendocrine tumour p-NPP para-nitrophenylphosphate

PPHT.HCl 2-(N-phenethyl-N-propyl)amino-5- hydroxytetralin hydrochloride

PPHT-NH2 6-((4-aminophenethyl)(propyl)amino)- 5,6,7,8-tetrahydronaphthalen-1-ol

PSA polar surface area

Q accuracy

RBD rapid eye movement sleep behaviour disorder

RCC renal-cell carcinoma

R-PIA R-phenylisopropyl adenosine

RNS reactive nitrogen species

RO5 rule of 5

ROS reactive oxygen species

RTK receptor tyrosine kinase s singlet

SAR structure-activity relationship

SBVS structure-based virtual screening

SD standard deviation

SE sensitivity

SEM standard error of the mean

SGA second generation antipsychotic

SNc substantia nigra pars compacta

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LIST OF ABBREVIATIONS

SNr substantia nigra pars reticulate

SP specificity

SPR structure-property relationship

STAT3 signal transducer and activator of transcription 3

STN subthalamic nucleus

SV5 simian virus 5

SVM support vector machine t triplet

T1/2 half-life

TACE tumor necrosis factor-α-converting enzyme

TAMH transforming growth factor-alpha mouse hepatocyte

Tc Tanimoto coefficient td triplet of doublets

TGA third generation antipsychotic

TGFα transforming growth factor-α

TH tyrosine hydroxylase

THF tetrahydrofuran

TJ tight junction

TLC thin layer chromatography

TM transmembrane

TMN Tris-MgCl2-NaCl

TN true negative

TOF time-of-flight

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LIST OF ABBREVIATIONS

TP true positive

TPSA topological polar surface area

TTP time-to-tumour-progression

UPS ubiquitin-proteasome system

UV ultraviolet

Vd volume of distribution

VEGF vascular endothelial growth factor

VEGFR vascular endothelial growth factor receptor

VS virtual screening

VTA ventral tegmental area

WHO World Health Organization

XCC 2-(4-(2,6-dioxo-1,3-dipropyl- 2,3,4,5,6,7-hexahydro-1H-purin-8- yl)phenoxy)acetic acid

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CHAPTER 1 PARKINSON’S DISEASE (PD)

1.1 Introduction to PD

The term “Parkinson’s disease” originated from the monograph “An Essay on the Shaking Palsy” published in 1817 by the British physician James Parkinson.

At the very beginning of chapter 1 of this monograph, the shaking palsy was defined as “involuntary tremulous motion, with lessened muscular power, in parts not in action and even when supported; with a propensity to bend the trunk forwards, and to pass from a walking to a running pace: the senses and intellects being uninjured.” (Parkinson, 2002). These manifestations observed by James

Parkinson in his time have been evolved into the currently accepted classification, namely, motor and nonmotor symptoms. The cardinal motor features include tremor, rigidity, bradykinesia and postural instability. PD tremor occurs at rest, and decreases when the patient is in action, mental concentration or writing. Rigidity refers to the resistance to movement, and this stiffness may occur in the neck, shoulders, hips, wrists or ankles. Bradykinesia, which is defined as extreme slowness of movements and reflexes, is the most characteristic feature of PD, and it encompasses decreased amplitude of movement, arrests in ongoing movement, and decreased spontaneous movements such as eye blinking, swallowing, or arm swing in walking.

Generally, tremor, rigidity and bradykinesia (the typical motor triad) occur at the early stages of PD, whereas postural instability manifests as a late feature.

A PD patient with postural instability has difficulty in recovering balance to maintain an upright posture when jostled, and may result in a fall (Jankovic,

CHAPTER 1

2008). The nonmotor symptoms are divided into four categories: sleep disorders, neuropsychiatric symptoms, autonomic symptoms, and sensory symptoms. Virtually all PD patients suffer from sleep disruption. Specifically, rapid eye movement sleep behaviour disorder (RBD) and excessive daytime sleepiness affect 33% and 50% of patients, respectively. Moreover, depression, visual hallucinations, and dementia occur in 10~45%, 40%, and 40% of PD patients, respectively. Other major neuropsychiatric symptoms associated with

PD include anxiety, apathy and psychosis. Autonomic symptoms involve orthostatic hypotension, constipation, and bladder disturbances. Sensory symptoms cover olfactory dysfunction, which affects up to 90% of PD patients, ageusia, pain and paresthesia (Chaudhuri et al., 2006). Some nonmotor symptoms may precede PD motor dysfunctions by years. Currently, PD is a prevalent neurodegenerative disease, and the number of individuals with PD over age 50 was projected to be 9 million by 2030 (Dorsey et al., 2007).

The traditional view of PD pathogenesis holds that the dopaminergic neurons located in the substantia nigra pars compacta (SNc) of midbrain degenerate, resulting in the depletion of striatal dopamine. Figure 1 shows the four major dopaminergic pathways (i.e. nigrostriatal pathway, mesolimbic pathway, mesocortical pathway and tuberoinfundibular pathway) which project diffusely onto other areas of the brain and use modulatory dopamine (DA) as the neurotransmitter. The nigrostriatal pathway originates from SNc and extends its fibers into the dorsal striatum, and it is believed to be the most germane dopaminergic pathway to PD. The mesolimbic and mesocortical pathways arise from dopaminergic neurons residing in the ventral tegmental area (VTA), but they project to the limbic system (via the nucleus accumbens) and frontal cortex,

CHAPTER 1 respectively. The mesolimbic and mesocortical dopaminergic neurons are often intermingled with each other, and are therefore collectively referred to as the mesocorticolimbic system which plays an important role in reward, memory, motivation and schizophrenia (Chinta and Andersen, 2005). The tuberoinfundibular pathway starts with dopaminergic neurons in the hypothalamus, and it is involved in the inhibition of the hormone prolactin secretion (Ben-Jonathan, 1985). In the normal brain, dopamine within striatum is essential for the initiation of willed movements of the body, and this dopamine input is realised by the nigrostriatal pathway which is composed of dopaminergic neurons whose cell bodies are located in the SNc. In contrast, at the onset of PD, patients have usually lost 60% of SNc dopaminergic neurons, and the striatal DA has been depleted by 80%. Why do these neurons die?

Figure 1. The four major dopaminergic pathways in the brain (modified from thebrain.mcgill.ca) (under copyleft ).

Over the past 197 years (i.e. 1817–2014), there have been tremendous advances in the understanding of the pathogenesis associated with PD, and these studies

CHAPTER 1 have suggested that the neuronal cell death in PD is caused by an interwoven tapestry of molecular events. Since the dopaminergic neurons of the nigrostriatal pathway are surrounded by a number of non-dopaminergic neurons

(e.g. cholinergic and GABAergic neurons) and non-neuronal cells (e.g. microglia and astrocytes), it is increasingly recognised that PD pathogenesis is a combination of cell-autonomous (i.e. occurring inside the degenerative neurons) and non-cell-autonomous (i.e. occurring outside the degenerative neurons) mechanisms (Hirsch et al., 2013). All cell-autonomous mechanisms revolve around mitochondrial dysfunction. Mitochondria are intracellular organelles that produce most of the ATP for the cell by the electron transport system, but their dysfunction accounts for the production of reactive oxygen species (ROS), which play roles in ageing, ischemia-reperfusion injury, neurodegenerative diseases, and cancer. In 1998, it was identified that the mutations of parkin gene cause autosomal recessive juvenile parkinsonism

(Kitada et al., 1998). Later on, an important discovery was the identification of a new parkin interacting substrate (PARIS), which is a transcriptional repressor of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α), and it accumulates following parkin inactivation (Shin et al., 2011). PGC-1α stimulates mitochondrial electron transport as well as suppresses ROS levels, so it protects neural cells (St-Pierre et al., 2006). The benefit of inducing PGC-

1α in the PD brain was further confirmed by the analysis (Zheng et al., 2010) where 10 gene sets (encoding proteins involved in the glucose sensing, glucose utilisation, mitochondrial electron transport chain, and mitochondrial biogenesis) identified to be associated with PD were commonly regulated by

PGC-1α, the underexpression of the PGC-1α responsive genes in patients with

CHAPTER 1

PD was observed, and the overexpression of PGC-1α was shown to protect neuronal damage. Collectively, the altered Parkin-PARIS-PGC-1α pathway and its resulting defects in cellular bioenergetics have been considered to be one of the contributing mechanisms to PD pathogenesis (Figure 2).

Figure 2. The diseased molecular events inside and outside the dopaminergic neurons (Hirsch et al., 2013) (reprinted with permission).

In addition, the mitochondrial oxidant stress in the dopaminergic neurons in

SNc was found to be associated with calcium homeostatis (Chan et al., 2007;

Guzman et al., 2010). These neurons drive their rhythmic pacemaking by distinct L-type voltage-gated calcium channels with α1 subunit CaV1.3, making them distinguished from dopaminergic neurons in the VTA. This selective engagement of L-type calcium channels augments with age, and the resultant increase in cytosolic Ca2+ ions elevate oxidative stress in the mitochondria. It

CHAPTER 1 provides an explanation of why SNc dopaminergic neurons are comparatively vulnerable. Since this Ca2+ influx is not necessary for pacemaking, there have

2+ been some endeavours to develop selective antagonists of CaV1.3 L-type Ca channels as potential PD therapeutics (Chang et al., 2010; Kang et al., 2012;

Kang et al., 2013). Furthermore, the proposed mitochondrial quality-control system suggests that the defective mitochondria undergo parkin-mediated autophagy (mitophagy) through ubiquitin-proteasome system (UPS) in a

PINK1-dependent manner (Okatsu et al., 2012). If parkin/PINK1 mutates (as seen in familial PD), the build-up of and the stress caused by damaged mitochondria will eventual lead to neuronal death.

The non-cell-autonomous mechanisms include α-synuclein transfer and neuroinflammation. The presence of Lewy bodies (LBs) composed of α- synuclein deposition is often observed in the surviving neurons (both dopaminergic and non-dopaminergic neurons) of affected PD brains (Spillantini et al., 1997). The discovery that LB-like structures and α-synuclein aggregates develop in grafted neurons of a PD patient who died 14 years after transplantation implies that α-synuclein might be able to transfer between cells in PD brains (Kordower et al., 2008). How α-synuclein leaves cells, how it propagates to a new cell, and how it enters this new cell have been reviewed elsewhere (Steiner et al., 2011). For innate immune system, activated microglia cells have been found in the substantia nigra of PD patients, and it is likely that microglial activation poses harm to SNc neurons by releasing pro-inflammatory cytokines, reactive nitrogen species (RNS), and ROS (Figure 2). For adaptive immne system, evidence has shown that CD4+ T cells invaded postmortem human PD brain, and neuronal death was attenuated by the removal of CD4+ T

CHAPTER 1 cells (Brochard et al., 2009). Taken together, the pathogenesis of PD represents a complex network of events in which dopaminergic neurons, non- dopaminergic neurons and nonneuronal cells participate. The susceptibility of neurons in SNc to many stressors outlined above calls for a PD therapy capable of protecting neurons from multiple possible insults.

1.2 Current PD Pharmacotherapeutics

The development of PD therapies has been largely focused on improving motor symptoms which are caused by dopamine deficiency. Relatively few approaches are targeted to alleviate nonmotor symptoms, which are attributed to the degeneration of both dopaminergic and non-dopaminergic neurons. In addition to symptomatic alleviation, an agent that displays neuroprotective or disease-modifying properties remains an unmet clinical need. In this section, only treatments for PD motor symptoms will be reviewed.

1.2.1 Dopamine replacement therapies

In 1957, Arvid Carlsson understood that dopamine itself could not penetrate the blood-brain barrier (BBB), so he injected 3,4-dihydroxyphenylalanine (DOPA), the amino-acid precursor of dopamine, into mice and rabbits that had been treated with reserpine. The animals almost resumed normal behaviours after

DOPA injection (Figure 3) (Carlsson et al., 1957; Carlsson et al., 1958). This led him to conclude that dopamine depletion induces the PD syndromes, and that DOPA treatment restores the dopamine level. In 1970, L-DOPA was approved by the US Food and Drug Administration (FDA) for the treatment of 7

CHAPTER 1

PD. Later, research was conducted and showed that L-DOPA enters the brain through an amino acid transporter (Kageyama et al., 2000).

Figure 3. Reserpine-induce rabbits before (top) and after (below) treatment of DOPA. The figure is taken from Arvid Carlsson’s Nobel Lecture, © The Nobel Foundation 2000. Source: Nobelprize.org. Direct link: http://www.nobelprize.org/nobel_prizes/medicine/laureates/2000/carlsson- lecture.html (reprinted with permission).

Following that was the development of dopamine receptor agonists, which mimic dopamine in its postsynaptic receptors, as well as inhibitors of two dopamine-degrading enzymes, catechol-O-methyltransferase (COMT) and monoamine oxidase (MAO) isoenzyme B (Figures 4 and 5). Representative

MAO-B inhibitors and dopamine receptor agonists are listed in Figures 11 and

25, respectively. Over the past 40 years, L-DOPA remains the most effective

PD therapy for improving motor symptoms. However, several years after the introduction of L-DOPA, it was observed that patients developed motor complications, such as drug-induced dyskinesia. The mechanisms of dyskinesia

CHAPTER 1 are complex, inasmuch as they involve dopaminergic, glutamatergic, opioid, serotonergic, GABAergic, adenosine, cannabinoid, adrenergic, histaminergic, cholinergic neurotransmission systems, as well as the intracellular events (Huot et al., 2013).

Figure 4. Current major dopamine replacement therapies for PD act at four points as shown in 1 to 4. 1, L-DOPA, the precursor of dopamine; 2, dopamine receptor agonists; 3, COMT inhibitors; 4, MAO B inhibitors. The figure is modified from Lecht’s publication (Lecht et al., 2007) (reprinted with permission).

CHAPTER 1

H2N CO2H L-tyrosine

tyrosine hydroxylase (TH)

H2N CO2H L-3,4-dihydroxyphenylalanine (L-DOPA)

DOPA decarboxylase

H2N 3-hydroxytyramine (dopamine) ) T m M o on O x C ida o ( a e s m s e in ra ( e e M sf AO n a ) tr B yl th e OH OH -m O l- o ch OMe OH te a c CO2H H2N 3-methoxytyramine 3,4-dihydroxyphenylacetic acid Figure 5. The biosynthesis and breakdown of dopamine.

1.2.2 Non-dopaminergic therapies

In addition to dopamine replacement therapies, there are other potentially anti-

PD agents modulating various receptors, including adenosine A2A receptor, metabotropic glutamate receptor type 4 (mGluR4), and metabotropic glutamate receptor type 5 (mGluR5). A2A receptor is localised richly in striato-pallidal

CHAPTER 1

neurons and functionally opposes the effect of dopamine D2 receptor. Blockade of A2A receptor has been demonstrated to help restore the motor impairment of

PD (Schwarzschild et al., 2006). In addition, most metabotropic glutamate receptors (mGluRs) are found in neurons in different brain regions, such as basal ganglia, and preclinical studies have suggested that selective ligands for mGluR4 and mGluR5 are potential PD treatments. Dopaminergic neuron loss in PD patients leads to the hyperactivity (i.e. disinhibition) from the striatum to the external globus pallidus (GPe), and activating mGluR4, which is expressed in the presynaptic terminal of the striatal-pallidal projection, has been shown to reduce GABAergic transmission at this synapse (Conn et al., 2009). The hypothesis that activating mGluR4 has utility for treating PD is being tested, with encouraging results that L-(+)-2-amino-4-phosphonobutyric acid (L-AP4), a mGluR4 receptor agonist, reduced motor symptoms in rodent models of PD

(Konieczny et al., 2007), and N-phenyl-7-

(hydroxylimino)cyclopropa[b]chromen-1a-carboxamide (PHCCC) (Marino et al., 2003), a selective positive allosteric modulator (PAM) of mGluR4, provided symptomatic relief and neuroprotective effects in 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP)-lesioned mice (Battaglia et al., 2006). Also, the expression level of mGluR5 is increased in parkinsonian monkeys with L-

DOPA-induced dyskinesia (LID), and there have been several mGluR5 negative allosteric modulators (NAMs) proven to be useful to reduce LID both in preclinical models (Morin et al., 2013) and in phase 2a clinical studies (Nickols and Conn, 2014).

CHAPTER 1

1.3 Opportunities of Polypharmacology in Treating PD

In view of the fact that PD is a disease with changes in neuronal activities in the basal ganglia motor circuit on which a multitude of ionotropic and metabotropic receptors modulate excitatory/inhibitory neurotransmission, it is conceivable that a therapeutic agent specifically targeting one subtype of biological macromolecule is necessary but may not be sufficient to treat PD. Indeed, apart from PD, some other multifactorial diseases, including cancer, psychiatric diseases, bacterial infections, epilepsy, inflammation, and malaria, cannot be treated effectively by focusing only on one aberrant event. Rather, evidence has shown that many approved drugs bind to multiple proteins (Bain et al., 2007;

Peters, 2013), and there have been a number of attempts to deliberately design a compound acting at multiple relevant targets for the treatment of schizophrenia (Lowe et al., 1991), depression (Ryckmans et al., 2002),

Alzheimer’s disease (Toda et al., 2003), pain (Daniels et al., 2005) and PD

(Pretorius et al., 2008).

1.3.1 Approaches to multi-targeting

In the late 19th to the early 20th century, scientists/clinicians tested potentially therapeutic agents directly in animals or patients. For example, in 1891, Paul

Ehrlich used methylene blue in patients with malaria, and the patients were apparently cured. The continual hunt for malaria treatment led to the surprising discovery of phenothiazine, which is structurally analogous to methylene blue but not antimalarial. Minor structural modification on phenothiazine resulted in chlorpromazine that was confirmed to be antipsychotic and antimanic

CHAPTER 1

(Frankenburg and Baldessarini, 2008). Chlorpromazine was marketed in France in the autumn of 1952, and it is still a useful antipsychotic medication today

(Sneader, 2002). Imipramine, an analogue of chlorpromazine, was clinically proven to have an antidepressant therapeutic effect by Roland Kuhn, and was marketed in Switzerland in late 1957 (Lopez-Munoz and Alamo, 2009). Until

1970s, most drugs were first assessed in animal models, and researchers utilised these phenotypic assay results to direct their drug discovery programmes. The advantage of this in vivo pharmacology approach is the high relevance of the assay results to the human conditions, yet when compounds show no effect, it is often difficult to know whether this is because they do not bind to the desired target(s) or simply fail to deliver to the site of action. Also, the lack of mechanistic evaluation of toxicity aggravates the difficulty in compound optimisation. Not surprisingly, many drugs discovered in 1950s elicited side effects, which were later confirmed to be ascribable to their interactions with undesired targets (i.e. off-target effects). In 1970s, the appreciation of how pharmacological promiscuity of drugs, namely the activity of a single compound at multiple targets, leads to adverse drug reactions (ADRs) is exemplified by subtype selectivity of β-adrenergic receptors. The β1 receptor is predominantly expressed in the cardiac muscle, whereas the β2 receptor is mainly located in the lung muscle. Propranolol, which was developed by James

W. Black in 1960s, blocks both subtypes and thus causes bronchoconstriction.

The β1-selective atenolol was later developed (in 1976) to reduce the risk of bronchospastic reactions. Also, in September 1997, U.S. FDA announced withdrawal of appetite-suppressant drug fenfluramine and its active metabolite dexfenfluramine due to their risks of pulmonary hypertension and valvular heart

CHAPTER 1 disease (Abenhaim et al., 1996; Connolly et al., 1997). The later screening revealed that dexfenfluramine activated serotonin 5-HT2B receptor, which is abundant in cardiac valvular endothelium (Setola and Roth, 2005). Other frequently encountered antitargets (Vaz and Klabunde, 2008) include, but are not limited to, the human ether-à-go-go-related gene (hERG) potassium channel, blockade of which leads to cardiac arrhythmia (Sanguinetti and

Tristani-Firouzi, 2006), and adrenergic α1A receptor, whose antagonism results in adverse orthostatic hypotension. These observations on how off-target effects caused serious toxicity provide the impetus for establishing the screen of a panel of ADR-associated targets in the early phase of drug discovery to ensure drug safety (Whitebread et al., 2005), as well as for developing compounds exquisitely selective to one particular subtype of a receptor class. This reductionist, hypothesis-driven, “one disease-one target” approach (i.e. “magic bullet” proposed by Paul Ehrlich) has dominated pharmaceutical industries since 1990s. Undeniably, single-target-based drug discovery (Sams-Dodd,

2005) with the concept that modulation of one validated target by ligands with high affinity and selectivity is adequate to confer maximal efficacy and minimal toxicity has resulted in many successful drugs. The most notable example may be the development of anti-human immunodeficiency virus (HIV) drugs. Two enzymes, reverse transcriptase and protease, play important roles in the HIV replicative cycle, and each of them has been selectively targeted for chemotherapeutic intervention for more than 30 years. At present, there are 12 reverse transcriptase inhibitors (didanosine, zidovudine, stavudine, abacavir, lamivudine, zalcitabine, emtricitabine, delavirdine, etravirine, rilpivirine, efavirenz, nevirapine) (Appendix A) and 10 protease inhibitors (tipranavir,

CHAPTER 1 darunavir, fosamprenavir, atazanavir, ritonavir, saquinavir, indinavir, lopinavir, amprenavir, nelfinavir) (Appendix B) approved for clinical use (De Clercq,

2013). The design processes for these drugs rely on the high-resolution X-ray crystallographic structures of drug-enzyme complex, leading to the identification of highly potent and selective single-enzyme-targeting agents

(Zhan et al., 2013). Undoubtedly, the strategy of targeting one protein allowed for the implementation of rational drug design, and was also suitable for high- throughput screening for potential drug candidates. Hence it has been proven to be a powerful approach to discovering treatments for many diseases. However, since 2004 (Morphy et al., 2004; Roth et al., 2004), there has been an increasing awareness that the effectiveness of a drug depends not only on minimising the interactions with unintended targets but also on maximising the interactions with multiple relevant therapeutic targets. This entails the understanding of the functions of all targets in the network context of a drug, so that the knowledge on which beneficial targets are to be aimed and which detrimental targets are to be avoided may be utilised in the drug design. The changing paradigms of drug discovery are illustrated in Figures 6 and 7.

Figure 6. The evolution of approaches to drug discovery over the past century.

CHAPTER 1

Figure 7. Single-target-based drugs (upper) are selective to a validated target responsible for a cellular mechanism, whereas polypharmacological drugs (lower) selectively bind to multiple therapeutically beneficial targets.

At the beginning of the twenty-first century, data showed that the two major causes of attrition during drug development are lack of efficacy and failure to meet the toxicological standard (Kola and Landis, 2004). In a sense, these two causes correlate with intended/unintended targets discussed above, meaning that it is probable that the single-target-based strategy is responsible for lack of efficacy and some unidentified binding targets result in toxicity (Figure 7).

Obviously, a plausible solution to reduce attrition rates is to have a drug that binds to a group of therapeutically valuable targets, that is, multi-targeting, to enhance efficacy and does not bind to any safety-related antitargets to reduce side effects (Bowes et al., 2012). Whilst the elimination of interactions with unintended targets has gained appreciation since 1970s as shown in the case of adrenergic β1/β2 receptors selectivity, the practice of deliberately designing a

CHAPTER 1 compound that binds to multiple intended targets is currently being recognised.

The essentiality of a drug that binds to several beneficial proteins can be understood by the concept of biological networks which emphasise on nodes

(e.g. gene products) connected by links (e.g. protein-protein interactions). It implies that perturbing only one node usually triggers the compensatory mechanism that offsets the effect of a drug (Barabasi et al., 2011). For instance, the fbaA gene deletion in Escherichia coli MG1655 can be rescued by additional deletion of either aceA or sucAb gene to the optimal state, suggesting that knocking out a select pair of synthetically viable genes may be necessary for restoring the lost function, as well as providing a basis for inhibiting two enzymes to achieve therapeutic effectiveness (Motter et al., 2008). It has been documented that such biological robustness, which refers to the maintenance and persistence of cellular functions under genetic perturbations or environmental uncertainties (Stelling et al., 2004), is manifested in the intracellular feedback of Escherichia coli (Alon et al., 1999), the stable pattern formation of Drosophila melanogaster (von Dassow et al., 2000), the metabolic enzyme system in Salmonella enterica (Becker et al., 2006), and tumour cells

(Kitano, 2003). More importantly, a study (Mestres et al., 2008) from databases

DrugBank and WOMBAT determined that a drug binds to 2.7 targets on average, and such compound promiscuity can be further illustrated by the fact that clozapine, which is the gold-standard among antipsychotic medications, interacts with 5 dopamine receptors, 10 serotonin receptors, 5 muscarinic acetylcholine receptors, 5 α-adrenergic receptors, and 3 histamine receptors.

The multi-targeting property displayed by clozapine to mitigate or control the

CHAPTER 1 robustness of central nervous system may account for its superior efficacy in treating schizophrenia.

In general, there are two approaches to multi-targeting, that is, combination therapies and polypharmacological drugs, both of which aim at reducing the problems attendant on binding to one target tightly and selectively.

Combination therapies include (i) drug cocktails, and (ii) fixed-dose combination (FDC). Drug cocktails refer to two or more tablets, with different active pharmaceutical ingredients (APIs) in each. Reduction of side effects arosed from high-dose of each drug component by drug cocktails was shown in an analysis conducted in 2009, providing evidence that anti-bacterial, anti-viral and anti-inflammation synergies increased the safe treatment window as well as conferred greater selectivity to particular cellular phenotypes over individual drugs (Lehar et al., 2009). In contrast, FDC refers to a tablet with two or more different APIs in a fixed ratio of doses. The commercial success of FDCs can be illustrated in treatments for respiratory and cardiovascular systems.

ADVAIR, an approved combination inhaler containing fluticasone, an inhaled corticosteroid having anti-inflammatory effects, and salmeterol, a long-acting adrenergic β2 receptor agonist acting as a bronchodilator, was developed by

GlaxoSmithKline to treat asthma and chronic obstructive pulmonary disease

(COPD) with a global sales of US$8.3 billion (data in 2010), and importantly, a meta-analysis has demonstrated that the single inhaler of ADVAIR offers superior efficacy over its two components administered concurrently via separate inhalers (Nelson et al., 2003). CADUET, launched by Pfizer in 2004, is a combination of atorvastatin, an inhibitor of 5-hydroxy-3-methylglutaryl- coenzyme A reductase (HMG-CoA) to reduce the level of low-density

CHAPTER 1 lipoprotein cholesterol (LDL-C), and amlodipine, a calcium channel antagonist to lower blood pressure, and it is indicated for treating patients with hypertension and hyperlipidaemia. Studies have shown that treatment with a single-tablet CADUET led to synergistic increase compared with using either of its constituent agents in the production of nitric oxide (NO), a cardioprotective molecule in the vasculature (Jukema and van der Hoorn, 2004;

Mason et al., 2008). With regard to polypharmacological drugs (Anighoro et al.,

2014; Peters, 2013), they refer to one API, with a balanced profile of pharmacological activities to multiple therapeutic targets in an optimal ratio, formulated in a tablet. Case studies of polypharmacology in cancer therapy and antipsychotics are given in Appendices C and D, respectively. In consonance with combination therapies, several lines of evidence have suggested that polypharmacological drugs reduce side effects resulted from merely interacting with one target. (Here it is worth noting that toxicity can stem from interacting with either antitargets, as shown in Figure 7, or intended targets.) For instance, tapentadol, an analgesic which acts as µ-opioid receptor agonist and norepinephrine reuptake inhibitor, displayed less side effects seen in morphine which acts only through opioid receptor agonism (Tzschentke et al., 2007).

Moreover, rapacuronium, a neuromuscular blocking agent, was withdrawn from the market due to bronchoconstriction, with a possible mechanism for this fatal side effect being the selective antagonism of muscarinic M2 receptor shown in the radioligand displacement experiments (Jooste et al., 2003). By contrast, some other neuromuscular blocking agents (e.g. pancuronium) which are M2 and M3 dual-acting antagonists are still in clinical use.

CHAPTER 1

The advantages of polypharmacological drugs over FDCs lie in the elimination of drug-drug interactions (DDIs) and the easier regulatory approval. DDIs, which can alter pharmacokinetics through either gastrointestinal absorption, plasma protein binding, transport carrier, or liver metabolism (Ito et al., 1998), is particularly prominent in the elderly patients who live with chronic comorbidities, have reduced immunity, and use two to six medications (Stewart and Cooper, 1994). A recent examination on the issues of polypharmacy and medication-related problems facing 89 elderly HIV-positive patients with comorbid conditions, including depression, diabetes mellitus, hypertension, hyperlipidaemia and HIV-associated neurocognitive disorders, revealed that

96% patients in this study took at least five medications. The authors identified

283 potential DDIs in these 89 patients, providing evidence that older HIV patients are at a higher risk of DDIs (Greene et al., 2014). In addition, an FDC pill needs to pass more stringent regulatory criteria and takes lengthy time before marketing approval. The World Health Organization (WHO) requires that, in the submission of an FDC finished pharmaceutical product (FDC-FPP) for a marketing authorisation, quality/medical/bioavailability considerations should be made, evidence that the combination has a greater efficacy than any of the component ingredients should be provided, the safety data of the individual agents should be demonstrated, and lower cost as compared with that of individual components should be presented. This is clearly stated in WHO

Expert Committee on Specifications for Pharmaceutical Preparations, Thirty- ninth Report (published in 2005).

Like tapentadol and pancuronium, many polypharmacological drugs on the market were not prospectively designed, and their multi-targeting properties

CHAPTER 1 were in fact discovered retrospectively. A recent trend, known as designed multiple ligands (DMLs), however, has been deliberately to design ligands that selectively act on multiple targets which are deemed relevant to the pathogenesis of a given disease (Morphy et al., 2004). In general, there are three strategies—linked, fused or merged—to generate a knowledge-based DML that has activities at two targets (Figure 8). Some linked DMLs are composed of two ligands (Note: Each of the ligands is selective for their respective targets.) connected by a metabolically stable linker, whilst some are intentionally endowed with a cleavable linker for the purpose of releasing the two ligands in vivo. Linked DMLs are excellent tools for probing target combinations, and they could potentially be used as intravenous (i.v.) drugs with stronger affinity, enhanced subtype selectivity, as well as synergistic effect. As shown in Figure

9, a heterodimeric conjugate containing naltrindole (a δ-opioid receptor antagonist) and ICI-199,441 (a κ-opioid receptor agonist) linked by oligoglycines was developed as a pharmacological tool to prove the existence of opioid receptor dimerisation (Portoghese, 2001). NCX-4016, or nitroaspirin, however, consists of aspirin and an NO-releasing moiety linked by an ester- based spacer. It is metabolised by esterase to release aspirin. In fused DMLs, the two ligands are directly attached to each other, and ziprasidone may be the classic example (Appendix D). Merged DMLs, which have the maximal degree of overlap of two ligands, produce a single compound with minimal molecular weight. In fact, it was well documented that DMLs are, on average, larger in size compared to marketed oral drugs. The increase in molecular size and complexity are often associated with poor oral bioavailability (Veber et al.,

2002). (Bioavailability is defined as the extent to which an administered drug

CHAPTER 1 reaches systemic circulation unchanged, and the rate at which this occurs.)

Therefore, merging two ligands becomes the more likely way to produce an orally active DML (Morphy and Rankovic, 2006). An example of merged

DMLs is seen in fentanyl (a µ-opioid receptor agoinist) merged with agmatine

(an I2-imidazoline receptor ligand), resulting in a single compound active at both receptors (Montero et al., 2002).

Figure 8. Three strategies to generate knowledge-based, dual-acting DMLs (Morphy and Rankovic, 2008) (reprinted with permission).

LINKED DML N OH

N N Cl

HO O O N O O O Cl H H H HN N N delta-antagonist N N N (naltrindole) H H H O O O kappa-agonist (ICI-199,441)

CLEAVABLE LINKED DML FUSED DML

H N O 2 OH Cl ONO2 N N O S OH N NO-releaser NNH NH OAc

dopamine 1 CHAPTER O aspirin 23 NCX-4016 1 ziprasidone

D K 4.8 nM (antagonist) D2 IC50 > 1,000 nM 2 i 5-HT K 0.42 nM (antagonist) 5-HT1 Ki 5 nM (agonist) 2A i 5-HT2 Ki 18 nM (antagonist)

MERGED DML O O H N N NH2 N NH n N NH H2N N N NH2 H n = 6 Fentanyl Agmatine mu Ki 126 nM (mu agonist) I K 37 nM (I2 ligand) 2 i

Figure 9. Examples of three knowledge-based DML strategies.

CHAPTER 1

1.3.2 Polypharmacology in PD

1.3.2.1 An example: MAO-B inhibitor + A2A antagonist

Monoamine oxidase (MAO) catalyses the oxidation of numerous biogenic monoamine neurotransmitters (e.g. dopamine, norepinephrine, epinephrine, histamine and serotonin) in the CNS and peripheral tissues to form the corresponding aldehyde and hydrogen peroxide (Figure 10). Around 1970, one subtype of MAO that was sensitive to clorgyline (Figure 11) was defined as

MAO-A (Johnston, 1968), and the one more sensitive to pargyline and selegiline (Figure 11) was MAO-B (Knoll and Magyar, 1972). In terms of substrate specificity, MAO-A preferentially binds to serotonin, norepinephrine, and dopamine, whereas MAO-B preferentially binds to phenylethylamine and benzylamine. MAOs are located in the outer membrane of mitochondria in rodent, cat, primate and man. In the rat brain, MAO-A and MAO-B distribute mainly in the locus coerulus and raphe nuclei, respectively (Jahng et al., 1997).

The intravenous injection of 11C-labelled clorgyline and selegiline noninvasively to the human brain analysed by PET imaging showed that the distribution of MAOs was highest in striatum, thalamus and brainstem. In the

90-minute time course analysis, the magnitude of uptake for [11C]selegiline was stronger than that of [11C]clorgyline, reflecting the higher concentration of

MAO-B than MAO-A in the human brain (Fowler et al., 1987).

HO NH 2 MAO HO NH2 HO O H2O O , H H HO 2 HO HO 2 dopamine H2O2 NH4 Figure 10. MAO-catalysed oxidative deamination of dopamine starts with the formation of the iminium species 2. Subsequent hydrolysis leads to the aldehyde product.

CHAPTER 1

Cl N O N N

Cl clorgyline pargyline selegiline

NH H N 2 N H F O O

rasagiline safinamide Figure 11. Five inhibitors of monoamine oxidases. Clorgyline is a MAO-A inhibitor. Pargyline, selegiline, rasagiline and safinamide are MAO-B inhibitors.

There was no decrease in dopamine concentration observed after treatment with

60 mg/kg of MPTP, an agent used to induce PD conditions, in MAO-B knockout mice experiments, demonstrating the requirement of MAO-B for MPTP toxicity

(Grimsby et al., 1997). In view of the fact that 40% decrease in MAO-B level was evidenced in the brains of smokers (Fowler et al., 1996), a PET study used

21 non-smokers with ages from 23 to 86 years old and confirmed that MAO-B increased with ageing (Fowler et al., 1997). By contrast, in a post-mortem study using radioligands and enzyme autoradiography in 27 human subjects, no obvious increase in MAO-A concentrations in several brain areas was observed

(Saura et al., 1997). The strong association of PD with altered MAO-B, but not

MAO-A, was also revealed in the combined treatment of L-DOPA and a MAO-

B inhibitor in patients with PD (Birkmayer et al., 1975). The development of selective MAO-B inhibitors as potential PD therapeutic agents was therefore pursued. In a clinical study involving 800 PD patients carried out by Deprenyl

And Tocopherol Antioxidative Therapy Of Parkinsonism (DATATOP), the use of selegiline, a selective MAO-B inhibitor, with the dose of 10 mg/day as

CHAPTER 1 monotherapy showed the delay of disability by approximately 50% and improvement in function in the early stage of PD (Shoulson et al., 1993;

Shoulson et al., 1989). A controlled trial conducted by the Parkinson Study

Group in 472 L-DOPA-treated PD patients with motor fluctuations demonstrated that treatment (1.0 mg/day) with rasagiline (Figure 11), a selective

MAO-B inhibitor, decrease the mean daily off time (motor impairment) from baseline by 1.85 hours, suggesting the benefit of rasagiline in alleviating motor symptoms in PD patients (Parkinson Study, 2005). In addition to the symptomatic amelioration, some MAO-B inhibitors were reported to have neuroprotective effect. Known as ADAGIO (Attenuation of Disease

Progression with Azilect Given Once Daily), the delayed-start clinical study for

1176 PD patients was performed to assess the disease modifying effect of rasagiline in 2005–2006 (Olanow et al., 2009). The disease-modifying effect by early treatment with rasagiline was sustained for at least 18 month at the dose of 1 mg/day. To date, two irreversible MAO-B inhibitors, selegiline and rasagiline, have been approved by FDA as monotherapy at the early PD or as adjunct therapy with L-DOPA later in the disease. A reversible MAO-B inhibitor, salfinamide (Figure 11), has completed the phase 3 trial (Borgohain et al., 2014), and its New Drug Application (NDA) was submitted to FDA in

May 2014.

Adenosine, a purinergic modulator that regulates many physiological processes with effects on CNS, cardiovascular system, renal system, respiratory system, immunological system, gastrointestinal system, and metabolic system, exerts its action via four distinct subtypes (i.e. A1, A2A, A2B and A3) of cell-surface G protein-coupled receptors (GPCRs) (Collis and Hourani, 1993). For GPCR

CHAPTER 1

basics, readers may refer to Appendix E. Whilst the distribution of A1, A2B and

A3 subtypes in the brain is widespread, ligand binding and in situ hybridisation studies showed that A2A subtype localises primarily in striatum, olfactory tubercle, and nucleus accumbens where dopamine D2 receptor-enriched indirect pathway in the basal ganglia is present (Dunwiddie and Masino, 2001). Since adenosine A2A receptor agonists decrease the affinity of dopamine D2 receptor for dopamine (Ferre et al., 1991), antagonism of A2A receptor were presumed to have therapeutic effects in PD (Armentero et al., 2011). The consumption of caffeine (Figure 12), the dietary psychoactive substance in coffee and tea, was associated with the lower incidence of PD (Ross et al., 2000). By using

[3H]cyclohexyladenosine binding to mouse brain membranes, the correlation between stimulant effect of caffeine in locomotor activity and its competeing at adenosine receptors was demonstrated, strongly suggesting the blockade of adenosine receptors by caffeine (Snyder et al., 1981). In a more integrated level, caffeine was reported to increase rotation behaviour (Fredholm et al., 1976), increase striatal dopamine (Morgan and Vestal, 1989), show neuroprotection in

MPTP-treated mice by A2A receptor blockade (Chen et al., 2001), and more recently, a pilot open-label study confirmed motor and nonmotor benefits of caffeine (Altman et al., 2011). Caffeine-derived xanthine derivatives (Figure

12) with adenosine A2A selectivity versus other subtypes were thus developed as potential treatments for PD. With the consideration that caffeine nonselectively binds to adenosine receptors with highest affinity for A2A subtype (KD values of caffeine: hA1 = 12 µM, hA2A = 2.4 µM, hA2B = 13 µM, hA3 = 80 µM), xanthine scaffold was used to develop selective A2A antagonists.

Different modifications were made at the positions C-1, C-3, C-7 and C-8,

CHAPTER 1 resulting in 3,7-dimethyl-1-propargylxanthine (DMPX) with good aqueous solubility (Seale et al., 1988) and in its analogue 3 with higher affinity (Müller et al., 1998). Further substitution at the position C-8 with styryl group led to m- chlorostyrylcaffeine (CSC) and KW-6002 (Figure 12). In March 2013, KW-

6002 was approved in Japan as the adjunct therapy with L-DOPA in advanced stage of PD patients (Dungo and Deeks, 2013).

O O O H H N N HN N N N

O N N O N N O N N H xanthine theophylline caffeine

O O 1 H 7 N N N N 3 O N N O N N

DMPX 3 Ki(rA2A) = 16000 nM Ki(rA2A) = 105 nM

O O N styryl Cl N O N 8 N

O N N O N N O

CSC KW-6002

Ki(rA2A) = 54 nM Ki(hA2A) = 12 nM Ki(baboon MAO-B) = 81 nM Ki(hA1) = 841 nM Ki(hA2B) > 10,000 nM Ki(hA3) = 4,470 nM Figure 12. Xanthine and its derivatives.

The attenuation of striatal dopamine loss by CSC, a selective adenosine A2A receptor, in an MPTP-induced mice model of PD, together with the observation that the level of MPP+, the active neurotoxin which can be produced from the

CHAPTER 1 oxidation of MPTP catalysed by MAO-B (Figure 13), significantly reduced in

CSC-treated mice, raised the hypothesis that CSC may act as a MAO-B inhibitor. The direct effect of CSC on MAO-B was investigated by the enzyme kinetic analysis using mitochondria from mouse brain, and the results showed that CSC, but not other xanthine derivatives such as caffeine, DMPX, and 1,3- dipropyl-8-cyclopentylxanthine (CPX), potently (with Ki ~ 100 nM) and selectively (versus MAO-A) inhibited MAO-B. The inhibitory effect of CSC on

MAO-B was further substantiated in adenosine A2A knockout mice, confirming this unexpected property of MAO-B inhibition is independent of the known A2A antagonism (Chen et al., 2002). Given both A2A antagonists and MAO-B inhibitors were suggested to be potential PD treatments with symptomatic relief as well as neuroprotective effect, efforts was subsequently made to synthesise

CSC derivatives to discover better dual-acting drug candidates for PD.

Structure-activity relationship (SAR) studies revealed that the C-3 and C-4 substituents (e.g. Cl, CF3, CH3 and F) on the styryl ring of (E)-8-styrylcaffeine have a considerable impact on the MAO-B inhibition (Vlok et al., 2006). In addition to (E)-8-styrylcaffeines, (E)-2-styrylbenzimidazoles (van den Berg et al., 2007), (E,E)-8-(4-phenylbutadien-1-yl)caffeines (Pretorius et al., 2008), 9- deazaxanthines (Rivara et al., 2013), and 4H-3,1-benzothiazin-4-ones (Stossel et al., 2013) were developed as dual-acting compounds which exhibit MAO-B inhibition and antagonistic effect at A2A receptor (Figure 14).

CHAPTER 1

brain N brain N N MAO-B MAO-B

Ph Ph Ph

+ + MPTP MPDP MPP Figure 13. The metabolism of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) by MAO-B in the brain. The discovery of MPTP as a parkinsonian- inducing agent arised from a clandestine laboratory whose intention was the synthesis of 1-methyl-4-phenyl-4-propionoxypiperidine (MPPP) with MPTP as the by-product. Four individuals who used virtually pure MPTP (with trace amounts of MPPP) intravenously developed PD symptoms within one week after the injection (Langston et al., 1983). MAO is the principal brain enzyme that catalyses the 4-electron oxidation of MPTP to 1-methyl-4- phenylpyridinium species (MPP+), the active toxin interfering with mitochondrial oxidative phosphorylation which in turn causes cell death. The evidence from HPLC retention times, chemical ion mass spectra, diode array UV, coupled with the trapping agent NaCN, confirmed this oxidation proceeds via the unstable 1-methyl-4-phenyl-2,3-dihydropyridinium species (MPDP+) (Peterson et al., 1985). MPTP has been proved to be a useful agent for preclinical studies of PD.

(E)-2-styrylbenzimidazoles (E,E)-8-(4-phenylbutadien-1-yl)caffeines O

N N N O N N N CF3

Ki(baboon MAO-B) = 430 nM Ki(baboon MAO-B) = 17 nM Ki(rA2A) = 59 nM

9-deazaxanthines 4H-3,1-benzothiazin-4-ones O O N N S O

O N Cl N N H

IC50(hMAO-B) = 200 nM IC50(hMAO-B) = 35 nM Ki(hA2A) = 260 nM Ki(hA2A) = 40 nM Figure 14. Polypharmacological drug candidates inhibiting MAO-B and antagonising A2A receptor. 30

CHAPTER 1

1.3.2.2 An opportunity: D2 agonist + A2A antagonist

In addition to A2A receptor, GPCRs implicated in the pathophysiology of PD and also served as potential targets for PD drug development include dopamine receptors (Heimer et al., 2006), metabotropic glutamate receptors (mGlu4 subtype, Cuomo et al., 2009) (mGlu5 subtype, Mela et al., 2007), muscarinic acetylcholine receptors (M1 subtype, Xiang et al., 2012) (M4 subtype, Tzavara et al., 2004), and serotonin receptors (5-HT1A and 5-HT1B subtypes, Munoz et al., 2009) (5-HT2A subtype, Armentero et al., 2011) (Figure 15).

NO 2 N OH N OH N HO C P H S 2 OMe N N O S N OH NH2 OH O O O N S

rotigotine LSP1-2111 VU0255035 dopamine agonist mGlu4 agonist M1 antagonist H N

O OH OH N N O N N O N N N N Cl H H O N H

fenobam tropicamide 8-OH-DPAT CP-94253 mGlu5 antagonist M4 antagonist 5-HT1A agonist 5-HT1B agonist

OH O OMe N N N MeO O N N OMe OMe F

MDL-100907 KW-6002 5-HT2A antagonist A2A antagonist Figure 15. Drugs (either in clinical use or under development) targeting GPCRs implicated in PD.

Of these GPCRs implicated in the pathogenesis of PD, the adenosine A2A receptor represents an ideal target for developing novel therapeutics, which was described in Section 1.3.2.1. This is attributed to its high levels of expression in

CHAPTER 1 the striatum (Svenningsson et al., 1997) and, as will be described in this Section, its interactions with the dopamine D2 receptor in the striatopallidal GABAergic neurons (Ferraro et al., 2012) (Figure 16). In the 1990s, the binding experiments on the rat striatal membranes have shown that the activation of adenosine A2A receptor decreased the binding of dopamine D2 receptor to dopamine and the existence of antagonistic A2A-D2 receptor-receptor interactions on GABAergic striatopallidal neurons was suggested (Ferre et al., 1991; Fuxe et al., 1993). The

A2A-D2 heteroreceptor complexes localised in the striatum were further confirmed by their co-aggregation study (Hillion et al., 2002), and fluorescent and bioluminescent resonance energy transfer (FRET and BRET) techniques

(Canals et al., 2003). The in vivo study also corroborated the A2A-D2 heteroreceptor complexes by showing that A2A agonists caused the decrease in quinpirole-mediated yawing but did not affect the motor deficits induced by a

D1 receptor agonist (Rimondini et al., 1998). In addition, behavioural studies on

6-hydroxydopamine (6-OHDA)-lesioned rats indicated that a selective A2A antagonist (SCH 58261) potentiated turning behaviours induced by a D2 agonist

(Popoli et al., 2000), and it did not develop tolerance after repeated treatment

(Pinna et al., 2001). Recently, it was demonstrated that A2AR-D2R oligomer formation was reduced in 6-OHDA-lesioned rats using proximity ligation assay

(PLA), suggesting that A2AR-D2R oligomerisation is an emerging therapeutic target for the treatment of PD (Fernandez-Duenas et al., 2015). All these results clearly suggest that adenosine A2A receptors, which have unique CNS distribution in the striatopallidal GABAergic neurons, functionally interact with dopamine D2 receptors in an antagonistic manner. That is, antagonising A2A receptors increases D2R-mediated signalling, reduces the activity of the indirect

CHAPTER 1 pathway, and thus serves as a potential approach to treat PD. Indeed, one selective A2A antagonist, KW-6002 (Figure 12), has gone through clinical trials

(Hauser et al., 2003) and culminated in the approval in Japan in March 2013

(Dungo and Deeks, 2013).

CHAPTER 1

Figure 16. Dopamine (D1, D2) and adenosine (A2A) receptors located in the striatopallidal GABAergic neurons. In the normal state, dopamine activates D1 receptor, which in turn, recruits stimulatory Gs protein to increase the direct pathway. In contrast, the activation of D2 receptor results in the recruitment of inhibitory Gi protein so as to decrease the indirect pathway. The basal ganglia motor circuit involving direct and indirect pathways is elaborated in Appendix F. DA: dopamine; AC: adenylyl cyclase; cAMP: adenosine 3’,5’-cyclic monophosphate; PKG: protein kinase G; sGC: soluble guanylyl cyclase; NO: nitric oxide; PP1: protein phosphatase 1; DARPP-32: dopamine- and cAMP- regulated phosphoprotein, Mr 32 kDa; Cav 1.3: calcium channel, voltage- dependent, L type, alpha 1D subunit; M1: muscarinic M1 acetylcholine receptor (Calabresi et al., 2007) (reprinted with permission).

CHAPTER 1

1.4 Chapter Conclusion

In summary, literature revealed the following three facts: (i) the successful A2A antagonist KW-6002 (Figure 12) for the treatment of PD, (ii) the integration of

MAO-B inhibition and A2A antagonism in one single molecule (Figure 14), and

(iii) the strong evidence of A2A-D2 interactions in biochemical, biophysical and animal experiments (Figure 16). Combining these three facts point to an opportunity for the development of novel PD therapeutics—integration of A2A antagonism and D2 agonism in one single molecule.

CHAPTER 2 HYPOTHESIS AND AIMS

Since half of the world's older population lives in Asia, neurodegenerative diseases including Parkinson’s disease (PD) become a serious health problem that warrants investigation in greater depth in this region. Indeed, there have been huge efforts made towards the identification of new, effective and safe drugs for the successful therapy of PD. Previous approaches focused on highly selective compounds targeting individual proteins (i.e. “one compound-one target” approach). As shown in Table 1 (statistics prior to the year 2011), the small-molecule compounds in later stages of clinical development for PD were initially designed for one particular target class, such as dopamine receptor, monoamine oxidase, adenosine A2A receptor, and adrenergic α2 receptor.

Table 1. Potential Therapeutics Development for PDa (Huynh, 2011)

CHAPTER 2 a 37 Istradefylline was approved in Japan in March 2013. Reprinted with permission.

CHAPTER 2

Meanwhile, combination therapies have gained much interest, owing to the fact that heterodimerisation of adenosine A2A receptors and dopamine D2 receptors in the brain was established. In MPTP-treated marmosets (Callithrix jacchus), three combination therapies—(i) KW-6002 + quinpirole, (ii) KW-6002 +

SKF80723, and (iii) KW-6002 + L-DOPA—were tested (KW-6002: an adenosine A2A receptor antagonist; quinpirole: a dopamine D2 receptor agonist;

SKF80723: a dopamine D1 receptor agonist). The marked enhancement of anti-

PD activity was observed in the combinations of (i) and (iii). This is one example of “cocktail drugs-multiple targets” approaches (Kanda et al., 2000).

Although such multi-component therapies have been preclinically proven promising, they are often associated with side effects, due to the complicated interactions among drugs and different pharmacokinetic/pharmacodynamic profiles of each component drug. This motivated us to explore the possibility of

“one compound-multiple targets” in PD treatment.

As mentioned in Section 1.3.2.2, there are a couple of GPCRs implicated in the pathogenesis of PD, so in principle any two of them could be potentially targeted for the development of dual-acting ligands. On account of the recent approval of KW-6002 (Figure 12) (Dungo and Deeks, 2013), adenosine A2A receptor is likely to be a validated target. The second target we chose was dopamine D2 receptor. The reasons we did not attempt to integrate the activities of [A2A antagonist + L-DOPA], but of [A2A antagonist + D2 agonist], into one compound are as follows. First, whilst L-DOPA is still the gold-standard to treat

PD, there are reports where patients treated with dopamine (DA) agonists demonstrated better preservation of the nigrostriatal system than those treated with L-DOPA (Marek et al., 2002; Whone et al., 2003). Second, the antagonistic

CHAPTER 2

receptor-receptor interactions in the striatal A2A-D2 heteromers have been well documented (Fuxe et al., 2014). Thus, the hypothesis of this thesis is that antagonising A2A and agonising D2 receptors using the polypharmacological approach can lead to novel and more effective treatments for Parkinson’s disease.

To test this hypothesis, the aims of this thesis are to:

I. identify novel scaffolds that simultaneously bind to two receptors—

adenosine A2A receptor and dopamine D2 receptor—implicated in PD

pathogenesis, by computational tools and virtual screening (Chapters 3

and 4);

II. design and synthesise promising scaffolds based on the information

gathered from computational analyses (Chapter 4);

III. perform in vitro binding and functional assays of newly synthesised

compounds at these two receptor subtypes (Chapters 4 and 5);

IV. analyse drug-like properties of newly synthesised compounds in vitro

(Chapter 5); and

V. evaluate efficacy of newly synthesised compounds in Drosophila

models of PD (Chapter 5).

CHAPTER 3 LIGAND-BASED VIRTUAL

SCREENING AND ANALYSES

Dr. Ma Xiaohua in Professor Chen Yu Zong’s lab (Department of Pharmacy, National University of Singapore) performed SVM and similarity analyses of collected compounds. Radioligand binding assays for adenosine A2A receptor were done through collaboration with Professor Karl-Norbert Klotz (Institut für Pharmakologie und Toxikologie, Universität Würzburg, Germany).

3.1 Receptor Agonism and Antagonism

The concept of “receptor” originated from John Newport Langley’s findings that nicotine and curare acted upon the “muscle substance”, rather than upon the nerve-endings. The then “muscle substance” (or, “accessory substance”,

“receptive substance of the muscle”) is now known as nicotinic acetylcholine receptor (nAChR) (Langley, 1905). Based on the receptor concept, the subsequent “receptor theory” was established and is still being evolved (Maehle et al., 2002). Receptor theory provides a theoretical framework that allows quantitative assessment of receptor-drug interactions by different mathematical models, such as classical model (Ariens, 1954), operational model (Black and

Leff, 1983), two-state theory (Del Castillo and Katz, 1957), and ternary complex model (De Lean et al., 1980), all of which are based on Michaelis-Menten equation used in enzyme kinetics. The assumption of receptor theory is that the effect is proportional to the drug concentration, and such concentration-effect relationships are important for understanding drug action. Consider a receptor

R and a drug D form a receptor-drug complex RD in equilibrium,

CHAPTER 3

and the dissociation constant, KD, equals [R][D]/[RD]. Similar to how

Michaelis-Menten equation can be derived, receptor-drug relationships can be expressed as

ED/EM = e[D]/(KD + [D]) (1)

where ED is the effect produced by drug D, EM is the maximum effect produced by drug D, and e is the efficacy (or, intrinsic activity). Any drug that can induce maximum activation of a receptor (EM) if the drug concentration is sufficiently high is termed a full agonist (e = 1). Some agonists, even if they occupy all receptors at high drug concentration, are unable to elicit maximum effect. These agonists have lower efficacy (0 < e < 1), and are termed partial agonists.

Antagonists block the receptor site where the endogenous ligand binds (i.e. the orthosteric site), thus precluding the receptor from further activation by agonists

(e = 0). Inverse agonists (e < 0) refer to those compounds that stabilise less active receptor conformations and thus decrease constitutive receptor activity

(Bond and Ijzerman, 2006). Figure 17 illustrates concentration-effect curves for drugs with different efficacies at a receptor with constitutive activity (Bylund and Toews, 2014). Additional mechanisms of receptor modulation include non- competitive antagonism (i.e. an antagonist covalently and irreversibly binds at the orthosteric site) (Kenakin et al., 2006), allosteric modulation (i.e. an agent that can modulate the receptor by acting at the site other than the orthosteric site) (Kenakin, 2010), and biased agonism (i.e. an agonist that selectively activates a particular or a subset of signalling pathways) (Kenakin, 2011). For more details on allosteric modulation and biased agonism, readers are referred to Appendix E.

CHAPTER 3

Figure 17. The sigmoid relationships between the base 10 logarithm of ligand concentration and the activity magnitude of induced receptor. The upper solid curve is a full agonist capable of raising receptor activation to a maximum of 20 units from the constitutive activity of 6 units. The upper dashed curve is a partial agonist, causing an partial increase of receptor activity (6 → 13) with e = (13 – 6)/(20 – 6) = 0.5. The middle bold dotted curve is an antagonist which neither increases nor decreases the receptor activity with e = 0. The lower dashed curve is a partial inverse agonist which reduces the receptor constitutive activity by 50% (6 → 3), so its e = (3 – 6)/(6 – 0) = –0.5. The lower solid curve is a full inverse agonist which completely suppresses the receptor constitutive activity (6 → 0) with e = (0 – 6)/(6 – 0) = –1 (Bylund and Toews, 2014) (reprinted with permission).

3.1.1 Preclinical studies on two-drug cocktail (KW-6002 + a D2 agonist)

As mentioned in Chapter 2, the approach in the thesis is both to antagonise A2A receptor and to agonise D2 receptor with the aim of discovering new chemical entities (NCEs) for the treatment of PD. For early-stage PD patients, dopamine agonists are given as the first-line therapy, and when loss of efficacy occurs, L-

DOPA is introduced. However, the use of high doses of dopamine agonists and

L-DOPA results in dopaminergic side-effects, such as dyskinesia, vascular change, compulsive behaviours, psychosis and hallucinations. To minimise

CHAPTER 3

these dopaminergic side-effects, an A2A antagonist KW-6002 (Figure 12) was developed to improve wearing-off phenomena of later-stage PD patients under

L-DOPA treatment (launched by Kyowa Hakko Kirin Co., Ltd. in Japan in May

2013). This shows the synergistic effect of KW-6002 and L-DOPA (Mizuno et al., 2013). Concurrently, in view of the close interactions between A2A and D2 receptors in the basal ganglia (Figures 16 and A8), Kyowa Hakko Kirin has conducted co-administration of KW-6002 with a D2 agonist (quinpirole, ropinirole or pergolide) in marmosets. The results were promising—the KW-

6002 addition not only enhanced anti-PD activity of the D2 agonist but also avoided dose escalation of that D2 agonist (Kanda et al., 2000; Uchida et al.,

2015). It therefore gave us more confidence to test our hypothesis (Chapter 2).

3.1.2 Compounds capable of agonising one receptor but antagonising another

Despite Kyowa Hakko Kirin’s encouraging results of combination drugs (i.e. an A2A antagonist + a D2 agonist) in preclinical models of PD (vide supra), the aim of the thesis is even more challenging—to integrate A2A antagonism and D2 agonism into one compound. Prospective design of a compound acting as an agonist of one receptor and antagonist of another has been documented in various drug discovery programmes (Figure 18). The clinical candidate TD-

5959 (in phase 2b trial), an inhaled muscarinic antagonist-beta2 agonist

(MABA), was developed by the alliance agreement between Theravance

Biopharma and GlaxoSmithKline (GSK) as a bi-functional bronchodilator for patients with chronic obstructive pulmonary disease (COPD) (Hughes et al.,

CHAPTER 3

2015). Compound 4 was recently reported to be a partial agonist of M1 muscarinic acetylcholine receptor, an antagonist of dopamine D2 receptor, and an antagonist of serotonin 5-HT2A receptor, for the treatment of schizophrenia

(Szabo et al., 2015). Compound 5 modulates opioid receptors (ORs) by its dual

δ OR antagonistic/µ OR agonistic activities, and has successfully completed phase 2 trials in patients with diarrhea-predominant irritable bowel syndrome

(IBS-D) (Breslin et al., 2012). Arpromidine, a positive inotropic agent, has histamine H1-antagonistic and H2-agonistic properties through merged DML approach (Buschauer, 1989). Due to these four successful examples, it was deemed possible to find a compound that acts as an A2A antagonist and D2 agonist.

HO OMe NH O O N O H N N O N N H O N Cl N O H N S TD-5959 4

pKi(M3 antagonist) = 8.7 Ki(M1 partial agonist) = 4.2 uM pEC50(beta2 agonist) = 9.6 Ki(D2 antagonist) = 17.7 nM Ki(5-HT2A antagonist) = 5.8 nM

OMe

HO2C

N N N NH HN N O N N H H H2N NH2 HN F O 5 arpromidine pA (H antagonist) = 7.65 Ki(delta OR antagonist) = 1.3 nM 2 1 pD (H agonist) = 8.01 Ki(mu OR agonist) = 0.9 nM 2 2 Figure 18. Four examples of prospective DMLs that antagonising one receptor but agonising another.

CHAPTER 3

3.1.3 Compounds capable of agonising D2 receptor but antagonising A2A receptor

In fact, there have been reports on integrating A2A antagonistic and D2 agonistic activities into one compound using linked, fused or merged DML approaches

(Figure 8). As shown in Figure 19, 2-(4-(2,6-dioxo-1,3-dipropyl-2,3,4,5,6,7- hexahydro-1H-purin-8-yl)phenoxy)acetic acid (XCC) and 6-((4- aminophenethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol (PPHT-

NH2), acting as an A2A antagonist and a D2 agonist, respectively, were linked by Lys-Lys-[PEG/polyamide]3-Lys-Glu. This heterobivalent ligand 6 showed higher affinity to each receptor than its monovalent controls, and was used to specifically detect the occurrence of A2A-D2 heteromers in native tissues

(Soriano et al., 2009). Recently, compound 7, consisting of ZM241385 (an A2A antagonist) and ropinirole (a D2 agonist) linked by amides and ether functional groups, was developed, and exhibited potencies at the two receptors (Jorg et al.,

2015). Whilst heterobivalent ligands 6 and 7 are excellent pharmacological tools to probe receptor heteromers, they require long synthetic procedures (e.g.

19 steps for the synthesis of 7) and have unfavourable structural properties as

CNS drugs (Rankovic, 2015). The details of CNS drug requirements will be discussed in Chapter 6. Fused and merged DMLs for integrating A2A antagonistic and D2 agonistic activities into one compound are shown in Figure

20. Compound 8 was built without incorporating any spacer, and it was therefore considered as a fused ligand. Compound 9 was the ligand in which

ZM241385 merges with ropinirole, with its molecular weight less than the sum of ZM241385 and ropinirole. Both fused and merged compounds showed potencies at the two receptors. Notably, the merged ligand 9 displayed

CHAPTER 3

brain:blood partition ratio (Kbb) of 1.29, suggesting its high likelihood to be

CNS penetrant (The Kbb of ZM241385 is 0.76). The percentage of fraction unbound (% fu) of compound 9 in the brain is 15.33, indicating that sufficient amount of free compound is able to reach the site of action in the brain. (The % fu of ZM241385 in the brain is 8.49.) (Jorg et al., 2015) Hence, compound 9 developed via a merged DML strategy may be more suitable, compared to compounds 6-8, for use as a potential CNS drug.

O O O O HN H N O H2N N O NH H O 3

NH O NH O NH2 NH2 O OH HN N N O HN O N N N H O O

PPHT-NH2 (D2 agonist) 6 XCC (A2A antagonist)

KDB1(A2A antagonist) = 50 nM KDB1(D2 agonist) = 1.8 nM KDB2(D2 agonist) = 0.5 nM

H N N NH O 2 H N NN N N O N H 5 3 O N O

O N H ZM241385 (A antagonist) 7 2A

IC50(A2A antagonist) = 41 nM EC50(D2 agonist) = 501 nM ropinirole (D2 agonist) Figure 19. Linked DML strategy for integrating A2A antagonistic and D2 agonistic activities into one compound.

CHAPTER 3

O NH2 O N N N N N O H N N N H

O ZM241385 (A2A antagonist) N H 8

IC50(A2A antagonist) = 63 nM EC (D agonist) = 148 nM ropinirole (D2 agonist) 50 2

NH2 N N N O

N N N N H

ZM241385 (A2A antagonist) O N H 9

IC50(A2A antagonist) = 912 nM EC50(D2 agonist) = 88 nM Kbb = 1.29 ropinirole (D2 agonist) % fu(brain) = 15.33 Figure 20. Fused and merged DML strategies for integrating A2A antagonistic and D2 agonistic activities into one compound.

3.1.3.1 Comparison between A2A agonism and antagonism

How compounds 6-9 modulate A2A receptor as well as D2 receptor with respect to receptor function? That is, what are the structural determinants of activating and inhibiting these two receptors?

Exploration of A2A antagonists starts with xanthines. Structure-activity relationships (SARs) of xanthine-based derivatives as A2A antagonists have been extensively explored and discussed in Section 1.3.2.1 (Figure 12). One

CHAPTER 3 important information that can be extracted from these SAR studies is the contribution of styrene moiety at the 8 position to the binding and antagonism of A2A receptor (Jacobson et al., 1993). One example of these 8-styrylxanthines is 3-chlorostyrylcaffeine (CSC) which exhibits high A2A affinity (Ki for rat A2A receptor = 54 nM) and 560-fold selectivity in comparison to rat A1 receptor. In summary, the pharmacological results suggest that the introduction of propargyl

(e.g. compound 3), methyl (e.g. CSC) or ethyl (e.g. KW-6002) group at the 1 position of xanthine, methyl (e.g. CSC) or ethyl (e.g. KW-6002) group at the 3 position of xanthine, methyl group at the 7 position of xanthine, as well as aromatic ring (e.g. phenyl or thienyl) attached to an ethenyl group at the 8 position of xanthine, appear to constitute a xanthine-based potent and selective

A2A antagonist.

It is noted that the xanthine core of A2A antagonists listed in Figure 12 is structurally similar to the adenine moiety of adenosine (Figure 21). Moreover, non-xanthine-based A2A antagonists, such as ZM241385, also generally have a nitrogen-containing heterocyclic core, be it mono-heterocyclic, bi-heterocyclic or tri-heterocyclic one (Appendix H). X-ray crystal structures of A2A receptor in complex with adenosine, ZM241385 and caffeine (Figure 22) provide the structural basis for A2A agonism and antagonism. Comparison of the

ZM241385-bound with the adenosine-bound structures indicates that both triazolotriazine and adenine form hydrogen bonds with Glu169 in extracellular loop 2 (EL2) and Asn253 in helix 6 (H6), and have π-stacking with Phe168 in

EL2 in common. The main structural difference between these two structures lies in the orientations of furan and ribose groups. The furan ring of ZM241385 forms a hydrogen bond with the amide NH2 of Asn253, whereas the hydroxy

CHAPTER 3 groups on the ribose of adenosine form hydrogen bonds with Ser277, His278 in

H7, and His250 in H3. It is the inward shift of H3 and H7 that makes the A2A receptor activation happen (Lebon et al., 2011). These structural pieces of evidence are consistent with previous SAR observations on A2A agonists, that is, the alterations of the ribose, except at the 5’ position, generally result in loss of agonistic activity (Siddiqi et al., 1995). It may be speculated that the similar orientation between adenine of adenosine and triazolotriazine of ZM241385 is attributed to the common exocyclic NH2 groups. Interestingly, superposition of

ZM241385-bound and caffeine-bound A2A crystal structures reveals the coplanar mode between xanthine of caffeine and triazolotriazine of ZM241385, with the observation that the exocyclic C6 carbonyl carbon of caffeine forms a hydrogen bond with Asn253 (Figure 22) (Dore et al., 2011). The dual HB donor/acceptor property of the side chain of Asn253 in H6 seems to be crucial in the overlapping binding interactions shown by xanthines and ZM241385.

adenine

O NH2 1 N7 1 7 N N N 8 3 3 8 O N N N N OH O 5' caffeine 1' (A2A antagonist) 3' OH OH adenosine

(A2A agonist)

O NH2 1 7 OMe N N HO N N N O 3 8 O N N OMe N N N H

KW-6002 ZM241385 (A2A antagonist) (A2A antagonist) Figure 21. The nitrogen-containing cores of adenosine (an A2A agonist), caffeine (an A2A antagonist), KW-6002 (an A2A antagonist), and ZM241385 (an A2A antagonist) are shown in the bracket.

CHAPTER 3

Figure 22. Comparison of ligand-binding sites for A2A receptor in complex with ZM241385, adenosine and caffeine. The ligands (yellow and gray) and amino acid residues are depicted as sticks. Oxygen atoms are shown in red, and nitrogen atoms in blue (Dore et al., 2011; Lebon et al., 2011) (reprinted with permission).

CHAPTER 3

3.1.3.2 Comparison between D2 agonism and antagonism

SAR studies on dopamine agonists start with the preferred trans- molecular conformations of dopamine (i.e. the catechol ring and the ethylamine on the same plane), which include α- and β-conformers (Cannon, 1975) (Figure 23).

In the α-conformer, the meta-hydroxy group projects above the plane, whilst in the β-conformer it is below the plane. Conformational rigidisation results in the formation of 2-dipropylamino-5,6-dihydroxytetralin (10) and 2-dipropylamino-

6,7-dihydroxytetralin (11). Investigations on nitrogen substitutions, that is, R and R’ in compounds 10 and 11, with combinations of hydrogen, methyl, ethyl, propyl, and butyl led to an important observation that N-propyl group confers optimal dopaminergic agonist effect (Cannon et al., 1977). The position of the hydroxy groups on the catechol ring is also critically important. Generally, 5,6- dihydroxy aminotetralins showed higher dopaminergic activity than 6,7- dihydroxy derivatives, suggesting that the dopamine in its α rotameric disposition may be preferred for activating dopamine receptors. In addition, the meta-hydroxy group plays more roles in agonist-receptor interactions than the para-hydroxy group, as exemplified by the endeavours in aporphine derivatives

(Saari et al., 1974). This led to the identification of two dipropylaminotetralins

(DPATs), (S)-5-OH-DPAT and (R)-7-OH-DPAT, and their tricyclic derivatives

12 and 14. Strikingly, whilst one carbon extension of 12 (i.e. compound 13) retained its D2 agonistic activity, the corresponding analogue of 14 (i.e compound 15) were shown to be D2 inactive (Wikstrom et al., 1987). This implies that the cavity occupied by the butyl group of compound 13 is different from that surrounding the butyl chain of compound 15.

CHAPTER 3

OH OH OH HO OH HO HO

NH2 HO NH2

H H H H

dopamine (alpha-conformer) dopamine (beta-conformer) H H H H NH2 NH2

rigidisation rigidisation

OH HO 6 5 HO 6

R R 2 N HO 7 2 N R' R'

10 R=R'=n-C3H7 11 R=R'=n-C3H7

"meta-OH" preferred "meta-OH" preferred

OH 6 5 6

2 N HO 7 2 N

(S)-5-OH-DPAT (R)-7-OH-DPAT Ki(D2) = 6 nM Ki(D2) = 56 nM

OH OH 5 6 6 5 6 6

2 N 2 N HO 7 2 N HO 7 2 N

12 13 14 15 D2 agonist D2 agonist D2 agonist D2 inactive Figure 23. SAR studies on dopamine D2 agonists.

To better understand such discrepancy, D2 homology model was constructed using an agonist-bound serotonin 5-HT1B receptor (Krogsgaard-Larsen et al.,

2014). Compounds 12 and 14 were docked into this D2 receptor model (Figure

24). In agreement with the recently published D2 agonist pharmacophore model

(Malo et al., 2012), the specific receptor-ligand interactions shared by compounds 12 and 14 are: (i) the salt bridge between Asp114 in TM3 and the protonated amine, (ii) the hydrogen bond between Ser193 in TM5 and the hydroxyl group, and (iii) the edge-to-face π-π interactions between residues in

TM6 (e.g. Phe390, His393) and the aromatic ring. However, the distinct

CHAPTER 3 orientation of the alkyl group induced by the stereochemical difference of these two compounds accounts for the inactivity of compound 14. Whilst the propyl group of 12 protrudes to the extracellular loop region that is able to accommodate larger substituent, the propyl chain of 14 poses inwardly and has hydrophobic interactions with Cys118, Trp386, Phe389, Thr412, and Tyr416.

This hydrophobic cavity is known as the “propyl cleft”. The homology model studies presented here (Figure 24) complement the SAR data (Figure 23) and enhance our understanding of D2 receptor-agonist interactions. Currently, the commonly prescribed dopamine agonists for treating PD include rotigotine, pramipexole, apomorphine and ropinirole (Figure 25), all of which are either derived from DPAT or analogous to DPAT.

It was predicted that the interactions with Asp114 in TM3 and serine residues

(i.e. Ser193 and Ser197) in TM5 of D2 agonists provide the motion flexibility to TM6, which is required for receptor activation. In contrast, D2 antagonists lack strong interactions with serine residues in TM5, have minimal contact with

TM5, and prevent the motions between TM3 and TM6 (Kalani et al., 2004).

According to this prediction, which is also supported by mutation studies (Cox et al., 1992), what distinguishes D2 agonists from D2 antagonists appears to be their strong coupling between TM3 and TM5 via interactions with serine residues in TM5.

CHAPTER 3

Figure 24. The molecular docking pose of compounds 12 (pale green) and 14 (dark green). The hydrogen bonds are shown in yellow dotted lines (Krogsgaard-Larsen et al., 2014).

N OH H N 2 HO N S S NH HN N N O rotigotine pramipexole apomorphine ropinirole Figure 25. The commonly prescribed dopamine agonists for the treatment of PD. The Ki (D2R) are 13.5 nM for rotigotine (Scheller et al., 2009), 3.9 nM for pramipexole (Kvernmo et al., 2006), 32 nM for apomorphine (Hsieh et al., 2004), and 7.2 nM for ropinirole (McCall et al., 2005).

3.2 Support Vector Machine Models

In order to identify novel compounds integrating A2A antagonism and D2 agonism in one single molecule, three computational tools: (i) support vector machine (SVM) (Section 3.2), (ii) Tanimoto similarity (Section 3.3), and (iii) hierarchical clustering (Section 4.1) were utilised.

CHAPTER 3

3.2.1 Introduction

Support vector machine (SVM), based on statistical learning theory (Cortes and

Vapnik, 1995) and structural risk minimisation principle (Burges, 1998), is one of the supervised machine learning (ML) types, and its basic functionalities include classification, regression, ranking and kernel functions. When used in classification, it harnesses the power of statistics to build mathematical models based on training samples, and these models are then used to classify independent testing samples. As a classifier, it is defined by constructing an N- dimensional hyperplane that optimally separates the data into two categories.

The optimal separation is achieved by a hyperplane that has the largest distance to the nearest training data point (Figure 26). In ligand-based virtual screening

(LBVS) (Appendix G), a SVM model is generally built by training a set of confirmed active compounds, confirmed inactive compounds, and putative inactive compounds represented by molecular descriptors, and tested by an independent set of data. SVM has been proved to outperform other ML algorithms in the study on dihydrofolate reductase inhibitors (Burbidge et al.,

2001), and also excels other VS tools in terms of enrichment factors (Han et al.,

2008). In addition, SVM has been applied in the selection of compounds for testing (Warmuth et al., 2003), classification of drugs and non-drugs (Byvatov et al., 2003), and lead hopping (Saeh et al., 2005). The most relevant application of SVM to the thesis is the prediction of novel active compounds, be they single- target agents such as compounds targeting kinase (Liew et al., 2009), cytochrome P450 (Sun et al., 2011), and acetylcholinesterase (Lv and Xue,

2010), or compounds with multi-target activity (Wassermann et al., 2009).

Other SVM applications germane to drug discovery and development include

CHAPTER 3 aqueous solubility prediction (Cheng et al., 2011), ADME prediction (Shen et al., 2010), toxicity prediction (Bhavani et al., 2006), resistance prediction (Ding et al., 2013), synthetic accessibility prediction (Podolyan et al., 2010), drug- likeness prediction (Muller et al., 2005), permeability prediction (Kortagere et al., 2008), druggability prediction (Volkamer et al., 2012), and prioritisation of docking poses (Li et al., 2011).

Figure 26. Two data sets represented by red squares and blue circles are separated by several possible hyperplanes (green straight lines). High prediction performance of a SVM model requires maximum distance between the hyperplane and nearest data (Source: http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_s vm.html) (reprinted with permission).

3.2.2 Methods

3.2.2.1 Molecular descriptors

A total of 98 descriptors calculated by the MODEL programme (Li et al., 2007) were used (Table 2). These descriptors were computed from the three- dimensional structures of the compounds converted from two-dimensional structures using CORINA programme (Sadowski et al., 1994), and were inspected to ensure the correctness of chirality. The two-dimensional structure

CHAPTER 3

Table 2. Descriptor List descriptor class descriptors (number) simple molecular number of C,N,O,P,S, number of total atoms, properties (18) number of rings, number of bonds, number of non- H bonds, molecular weight, number of rotatable bonds, number of H-bond donors, number of H- bond acceptors, number of 5-member aromatic rings, number of 6-member aromatic rings, number of N heterocyclic rings, number of O heterocyclic rings, number of S heterocyclic rings

chemical properties (3) sanderson electronegativity, molecular polarisability, ALogP

molecular connectivity schultz molecular topological index, Gutman and shape (35) molecular topological index, Wiener index, Harary index, gravitational topological index, molecular path count of length 1-6, total path count, Balaban Index J, 0-2th valence connectivity index, 0-2th order delta chi index, Pogliani index, 0-2th solvation connectivity index, 1-3th order Kier shape index, 1-3th order kappa alpha shape index, Kier molecular flexibility index, topological radius, graph-theoretical shape coefficient, eccentricity, centralization, LogP from connectivity

electro-topological state sum of estate of atom type sCH3, dCH2, ssCH2, (42) dsCH, aaCH, sssCH, dssC, aasC, aaaC, sssC, sNH3, sNH2, ssNH2, dNH, ssNH, aaNH, dsN, aaN, sssN, ddsN, aOH, sOH, ssO, sSH; sum of estate of all heavy atoms, all C atoms, all hetero atoms, sum of estate of H-bond acceptors, sum of H estate of atom type HsOH, HdNH, HsSH, HsNH2, HssNH, HaaNH, HtCH, HdCH2, HdsCH, HaaCH, HCsats, HCsatu, Havin, sum of H estate of H-bond donors

of each compound was manually drawn on ChemDraw or downloaded from

PubChem and MDDR. Salts, like sodium or calcium salts, were removed. All geometries were optimised to ensure that there were no symmetry restrictions.

To ensure that each descriptor makes an unbiased contribution to predictive

CHAPTER 3 models, range scaling (Livingstone, 2000) was done before the descriptor was employed.

3.2.2.2 Generation of putative inactive compounds

The generation of putative inactive compounds started with clustering known compounds according to their molecular descriptors into different families. By using a K-means method (Bocker et al., 2006) and molecular descriptors computed from MODEL (Li et al., 2007; Xue et al., 2004), 8,993 compound families were generated from 13.56 million and 168,000 compounds in the

PubChem (Bolton et al., 2008; Wang et al., 2012) and MDL Drug Data Report

(MDDR) databases, respectively. These families are consistent with the 12,800 compound-occupying neurons (regions of topologically close structures) for

26.4 million compounds of up to 11 atoms (Fink and Reymond, 2007) and the

2,851 clusters for 171,045 natural products (Koch et al., 2005). After compound families were produced, the family to which each of the collected active and inactive compounds belongs was identified. The putative inactive compounds were generated by randomly selecting eight representative compounds from each family that did not contain collected active compounds. Specifically, it was identified that 8,000 families did not contain any of the collected 1,969 A2A antagonists and 810 D2 agonists.

3.2.2.3 Support vector machine classification

As shown in Figure 27, SVM models based on training samples separate members (biologically active compounds) from non-members (biologically 58

CHAPTER 3 inactive compounds) by a border called hyper-plane. A given compound in the testing samples is then projected unto this classification system for determining to which class it belongs according to its relative position to the hyper-plane. If it falls on the member (non-member) side, it is predicted to be an active

(inactive) compound.

In linearly separable cases, SVM constructs a hyper-plane to separate active and inactive classes of compounds with a maximum margin. A compound was represented by a vector xi composed of its molecular descriptors. The hyper- plane was constructed by finding another vector w and a parameter b that minimises w 2 and satisfied the following conditions:

wxiiby1, for  1 Class 1 (active) (2)

wxii by 1, for  1 Class 2 (inactive) (3) where yi is the class index, w is a vector normal to the hyper-plane, b / w is the perpendicular distance from the hyper-plane to the origin, and w 2 is the

Euclidean norm of w. Based on w and b, a given vector x can be classified by f(x) = sign[(w • x) + b]. A positive (negative) f(x) value indicates that the vector x belongs to the active (inactive) class.

In nonlinearly separable cases, which frequently occur in classifying compounds of diverse structures (Jorissen and Gilson, 2005), SVM projects the input vectors into a higher dimensional feature space by using a kernel function

K(xi, xj). RBF kernel, which was extensively utilised and consistently shown better performance than other kernel functions (Burbidge et al., 2001), was used.

The linear SVM was then applied to this feature space based on the following

CHAPTER 3

l 0 0 decision function: fsignyKb()xxx (ii (, i ) ), where i and b are i1 determined by maximising the Langrangian expression:

lll1 iijijij yyK(,xx ) under the conditions i  0 and iij1112

l i yi  0. i1

CHAPTER 3

Figure 27. Schematic representation of using support vector machine for binary classification of compounds. The vector (hj, vj, pj) denotes the physicochemical features (hydrophobicity, volume, polarisability) of a compound (Han et al., 2008) (reprinted with permission).

CHAPTER 3

3.2.2.4 Model validation

The process of model development can be divided into two steps.

In the first step, active and inactive (i.e. confirmed + putative) compounds were each divided into separate training, testing and independent validation sets.

Specifically, active and inactive compounds were each clustered into groups based on their distance in the molecular descriptor space by using a hierarchical clustering method (Ruecker and Ruecker, 1993). An upper-limit of the largest separation of 20 was used for each cluster. One representative compound was randomly selected from each group to form a training set that is sufficiently diverse and broadly distributed in the descriptor space. One or up to 50% of the remaining compounds in each group were randomly selected to form the testing set. The selected compounds from each group were further checked to ensure that they are distinguished from those of other groups. The remaining compounds were used as the independent validation set, which are also of reasonable level of diversity. Moreover, an analysis of the compounds in each cluster showed that the majority of the compounds in a cluster are substantially different. Thus, the testing and independent validation sets are expected to have certain level of usefulness for performing their task of fine-tuning the parameters of a SVM model and for evaluating its prediction performance.

In the second step, SVM models were trained by using the training set, and their parameters were optimised by using the testing set. The models were then internally validated using 5-fold cross validation (CV) and externally validated using independent validation sets. In 5-fold cross validation, compounds were randomly divided into five subsets with each subset being approximately equal

CHAPTER 3 size. Four subsets were used as the training set for developing a model; the remaining one was used as a testing set to evaluate the prediction performance of that model. This process was repeated five times such that every subset was used as a testing set once. The average accuracy of the five time models was used for measuring the generalisation capability of that method. In view of the limitation of cross validation in its underestimation of the prediction capability of a classification model, especially when important molecular features are present in only a minority of the compounds in the training set (Mosier and Jurs,

2002), it was suggested that an independent validation set may better gauge the predictive power of the model (Golbraikh and Tropsha, 2002). Compounds in

MDDR database were used as the independent validation set. The SVM model with the best overall performance on both the testing and independent validation sets was selected as a VS tool.

The classification performance of SVM models before virtual screening was evaluated by the numbers of true positive (TP, meaning an agent with desired pharmacological properties), true negative (TN, meaning an agent without desired pharmacological properties), false positive (FP, meaning an agent without desired pharmacological properties but wrongly predicted to be so), and false negative (FN, meaning an agent with desired pharmacological properties but wrongly predicted not to be so). In 5-fold cross validation studies, the prediction accuracy for actives and inactives was measured by sensitivity SE =

TP/(TP + FN) × 100 and specificity SP = TN/(TN + FP) × 100, respectively.

The overall performance of SVM models was measured by using prediction accuracy (Q) (Matthews, 1975).

CHAPTER 3

(4)

3.2.3 Results and discussion

3.2.3.1 Collection of adenosine A2A receptor antagonists

At the time of compound collection (i.e. January–July 2011), compound 6 had been reported but compounds 7-9 had not (Figures 19 and 20). Heterobivalent ligand 6 and its derivatives reported in 2009 (Soriano et al., 2009) were excluded from collection because of their high molecular weight ranging from 1247 to

2858. Whilst there are a number of small-molecule databases available online, such as BindingDB (Liu et al., 2007), ZINC (Irwin and Shoichet, 2005) and

ChEMBL, (Gaulton et al., 2012) for people to collect compounds with desired properties, it was decided to manually create our own data sets in view of the quality alert for public chemistry databases (Williams and Ekins, 2011).

Adenosine A2A receptor antagonists were collected from journal papers which focused on identifying antagonists for A2A receptor. The source and number of collected compounds are listed in Appendix I. Each compound with reported binding or antagonistic activity at A2A receptor therein was manually drawn by

ChemDraw (Evans, 2014), and the relevant pharmacological data were noted down on ChemFinder. A total of 1969 adenosine A2A receptor antagonists with reported binding data (inhibition constant Ki, in most cases) were collected from

69 papers, and they comprised 94 major scaffolds (Appendix J). Most scaffolds are composed of either a xanthine or nitrogen-containing heterocyclic nucleus

(Appendix H). Of these 1969 compounds, 418 compounds were tested for binding at A2A receptor and further shown to be active in advanced assays

CHAPTER 3 relevant to PD, including the ability to block cAMP generation (Katritch et al.,

2010), mouse catalepsy model (Vu et al., 2004), rat catalepsy model (Neustadt et al., 2007), as well as Ca2+ mobilisation assessment via a fluorescence imaging plate reader (FLIPR) assay (Gillespie et al., 2009).

3.2.3.2 Collection of dopamine D2 receptor agonists

Similar to the approach used in collecting A2A antagonists, manually creating our own data sets was decided. Dopamine D2 receptor agonists were collected from journal papers which focused on identifying agonists for D2 receptor. The source and number of collected compounds are listed in Appendix K. Each compound with reported binding or agonistic activity at D2 receptor therein was manually drawn by ChemDraw, and the relevant pharmacological data were noted down on ChemFinder. A total of 810 dopamine D2 receptor agonists with reported binding data (inhibition constant Ki, in most cases) were collected from

71 papers, and they comprised 78 major scaffolds (Appendix L). There was no compound found in both A2A and D2 collections, revealing that at that time there was not any reported drug-like entity with dual-acting property. This is very different from searching for dual kinase inhibitors, which was shown to be relatively easier than identifying A2A/D2 dual-acting ligands in that there is generally an existing compound that shows inhibitory activity at multiple relevant kinases and thus can be directly used for optimisation (Ma et al., 2010).

The discrepancy between kinase combinations and A2A/D2 in the availability of reported dual-acting agents might be due to the fact that the adenosine triphosphate (ATP)-binding pockets are highly conserved amongst kinases, so

CHAPTER 3 a molecular scaffold which binds one kinase usually binds another kinases. In contrast to kinases, the orthosteric sites between A2A receptor and D2 receptor are less conserved, as can be seen in A2A crystal structures in Figure 22 and the

D2 model structure in Figure 24, narrowing the chemical space that fits the binding pockets of the two receptors. Notwithstanding the challenge, analysis of these 78 scaffolds led to the observation that, in addition to the classical

DPAT core (Figure 23), many reported D2 agonists have the feature of piperazine linked directly to an aromatic ring. Selected examples of such compounds are shown in Figure 28. In light of this observation, there seemed worth revisiting the collected A2A antagonists to see whether the aromatic piperazine moiety could also be found in some A2A antagonists. Few, but not many, of reported A2A antagonists did possess the aromatic piperazine moiety linked to a bi-heterocyclic or tri-heterocyclic nucleus, as shown in Figure 29.

Of these 810 compounds, 332 compounds were tested for binding at D2 receptor and further shown to be active in advanced assays relevant to PD, including

[35S]GTPγS binding experiments in striatal membranes (Giorgioni et al., 2010), percentage of maximal efficacy (Pettersson et al., 2010), [3H]-thymidine uptake assay (Johnson et al., 2011), Ca2+ mobilisation assessment via a fluorescence imaging plate reader (FLIPR) assay (Favor et al., 2010), changes in intracellular cAMP levels (Yan et al., 2010), cGMP content in rat neostriatal membranes (Di

Stefano et al., 2005), rabbit ear artery test (Weinstock et al., 1987), electrophysiological assay (Menegatti et al., 2003), and whole cell forskolin- dependent adenylyl cyclase inhibition assay (Vangveravong et al., 2011).

CHAPTER 3

O HO

HN O N N HN N N N N N

N N N R N N O O N NH N N R' N N H N O N N

Cl N N Cl O N N O Cl H N N N N N N N NH H N O N O O N N F H O Cl N N O O N N N N O N O O Figure 28. Selected D2 agonists containing the aromatic piperazine scaffold.

F NH2 F NH2 Boc N O N N O N N N N NN N N N N

F NH2 NH2 N NNN O NNN O MeO N N F N N N N N N Figure 29. Selected A2A antagonists containing the aromatic piperazine scaffold.

CHAPTER 3

3.2.3.3 N-arylpiperazine—a privileged substructure

The observation that some D2 agonists and few A2A antagonists share the aromatic piperazine prompted me to wonder whether this common substructure could potentially be used as the starting point to construct dual-acting ligands at

A2A and D2 receptors. A recent patent research on N-phenylpiperazine derivatives published from 2006–2012 showed that the majority of their therapeutic use was for the treatment of CNS disorders, such as schizophrenia and depression. Moreover, their modes of action described therein are to target dopamine receptor, serotonin receptor, and adrenergic receptor (Maia Rdo et al., 2012). Indeed, N-arylpiperazine is a privileged moiety mimicking biogenic amines including dopamine, serotonin, and epinephrine (Figure 30). It is mainly composed of an aryl group to confer the intrinsic activity, and a basic amine

(pKa values ranging from 8 to 10) located 3-atom away from the aryl ring. A variety of aryl groups, such as phenyl, pyridinyl, pyrimidinyl, and naphthalenyl groups, were observed in the compound collection. The term “1,4-disubstituted aromatic piperazine (1,4-DAP)” was coined to address the compound having

N-arylpiperazine moiety (Kortagere et al., 2004). The basic tertiary amine in the piperazine ring is to simulate the amine group of the aforementioned neurotransmitters. Another aromatic residue is usually linked to the piperazine via a spacer, as shown in the R group in Figure 30, to control affinity and subtype selectivity. Recently, a molecular dynamics (MD) simulations study based on an active-state model of D2 receptor in complex with Gαi1 compared the binding mode of aripiprazole (DeLeon et al., 2004), which is a partial D2 agonist with 1,4-DAP moiety and is an atypical antipsychotic in the market, with that of dopamine, a full D2 agonist (Kling et al., 2014). The study shows

CHAPTER 3 that dopamine forms strong hydrogen bonds with Ser1935.42 and Ser1975.46, which brings Ser1945.43 in close proximity to His3936.55. This facilitates the formation of a hydrogen bond between Ser1945.43 and His3936.55. In contrast, although aripiprazole binds D2 receptor at the same site as where dopamine does, it causes a greater flexibility on Ser1945.43, which weakens hydrogen bonding between Ser1945.43 and His3936.55. As a result of this reduced stabilisation between TM5 and TM6, TM6 is more re-orientated by aripiprazole towards TM7, impairing the inward movement of TM5 needed for D2 receptor activation (Figure 31). This MD simulations study on the binding mode of N- arylpiperazine is consistent with Goddard’s first-principles-based prediction of

D2 receptor structure in complex with agonists and antagonists (Kalani et al.,

6.55 2004), as well as with mutation studies on His393 important for D2 receptor agonism (Tschammer et al., 2011). Importantly, it provides a plausible structural explanation for the partial, but not full, agonistic activity of aripiprazole at D2 receptor. In addition to acting as D2 partial agonists, efforts have been made to develop N-arylpiperazine derivatives as D2 antagonists, such as ziprasidone (Figures 9, A3, A4 and 30). Despite numerous SAR studies on

1,4-DAPs as ligands against dopamine (Ehrlich et al., 2009) and serotonin receptors (Salerno et al., 2014), 1,4-DAPs were neither used as a starting point to develop A2A antagonists nor fit the A2A antagonist pharmacophore model

(Wei et al., 2010). By and large, whilst literature review gave me confidence that 1,4-DAPs were very likely to bind D2 receptor, the outstanding question that remained was how they affect A2A receptor.

CHAPTER 3

OH HO Ar N N NH2 R dopamine N-arylpiperazine derivative

Cl N HN Cl N O N O NH2 H serotonin aripiprazole

D2 partial agonist (Ki = 0.45 nM)

N H OH S epinephrine N N Cl N

O ziprasidone

D2 antagonist (Ki = 4.8 nM) Figure 30. The N-arylpiperazine substructure is to mimic neurotransmitters (e.g. dopamine, serotonin and epinephrine), and can be found in many CNS drugs (e.g. aripiprazole and ziprasidone).

CHAPTER 3

Figure 31. The ligand-binding pockets of D2 receptor in complex with dopamine (A) and aripiprazole (B). Ligands and important residues stabilising ligands are shown as either balls-and-sticks or sticks. The D2 receptor is shown as ribbons. Hydrogen bonds are shown as black dotted lines (Kling et al., 2014).

3.2.3.4 Generation of putative inactive compounds

A caveat in ligand-based virtual screening is the less amounts of inactive data available than those of active data for a given receptor, resulting in high false positive rate. This was proved to be true during the collection of reported A2A and D2 ligands, and may affect the performance of SVM-based virtual screening

(Heikamp and Bajorath, 2013). Given the scarcity of experimentally confirmed non-antagonists of A2A receptor and non-agonists of D2 receptor, putative inactive compounds were generated by Prof Chen Yu Zong’s previous method

(Han et al., 2008) illustrated in Figure 32. A total of 8,000 compound families were selected as the realm of putative inactive compounds. Admittedly, it was

CHAPTER 3 definitely possible that undiscovered active compounds existed in any of these

8,000 families, which may increase the false negative rate. Such misclassified compounds were, however, expected to be less in numbers compared with true active compounds, given the extensive efforts that have been made to identify

A2A antagonists and D2 agonists over the past 30 years. Since SVM has been shown to have high tolerance to noise even at the level that the numbers of false negatives and true positives are equal (Glick et al., 2006), the impact of possible undiscovered active compounds in the putative inactive families on the reliability of SVM models was deemed to be minimal. Also, previous SVM studies employing this putative-inactive-compound approach showed the small effects of such misplacement on the identification of active compounds for many biological target classes (Han et al., 2008; Liew et al., 2009; Ma et al.,

2008). Hence, a total of 64,000 putative inactive compounds were collected from these 8,000 families. The advantages of generating putative inactive compounds for SVM models are twofold. First, it can enlarge the applicability domain of SVM models, enabling more coverage of molecular descriptors for screening large compound libraries, such as PubChem. Any compound in

PubChem whose descriptors are not within the hyper-rectangle descriptor range defined by the training set is to be excluded from classification, so it is desirable to maximise the applicability domain of SVM models used for screening databases so as not to abandon potential actives. This was particularly necessary for the thesis because the number of collected compounds in Sections 3.2.3.1 and 3.2.3.2 (ca. 2800 compounds) was much less than 13.56 million in

PubChem database that was used for virtual screening. Second, it can reduce the false positive rate of SVM models. The number of experimentally confirmed

CHAPTER 3 inactive compounds is limited versus confirmed active compounds for a given target of interest, as opposed to the distribution of true inactive and active compounds in chemical space. That is, whilst the number of reported inactive compounds is less, all true inactive compounds for a given target should be much more than all true actives. Therefore, it is not unreasonable nor unexpected that the SVM model built solely on the reported compounds without incorporating putative inactive compounds generally resulted in high false positive rate since the model is short of sufficient learning examples of inactive compounds. Both of the aforementioned advantages can be better appreciated by a published SVM model for virtual screening of lymphocyte-specific protein tyrosine kinase (Lck) inhibitors—an increase in coverage percentage of MDDR compounds (47.49 → 99.99%) and a sharp decrease in false positive rate at

MDDR screening (61.2 → 0.52%) for the SVM model endowed with putative inactive compounds (Liew et al., 2009).

Figure 32. The diagram illustrating how putative inactive compounds are generated. The blue circles indicate compound families, the green dots indicate confirmed active compounds, and the red dots indicate confirmed inactive compounds. The blue arrow means the removal of families that contain confirmed active compounds from the full set of compound families (at the left of the arrow). The reduced set of compound families (at the right of the arrow) is where putative inactive compounds are generated.

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3.2.3.5 Establishment of SVM models for virtual screening

With compound collection from literature (Sections 3.2.3.1 and 3.2.3.2) and putative inactive families (Section 3.2.3.4) at hand, two ligand-based computational tools—SVM models (Section 3.2.3.5) and Tanimoto similarity

(Section 3.3)—were used to analyse these collections.

Since MDDR covers compounds published in patent literature, 309 A2A antagonists and 173 D2 agonists extracted from MDDR were added to the collection from journal papers. Compounds from both journal papers and

MDDR were then categorised into active (i.e. either A2A antagonists or D2 agonists) and inactive (i.e. either non A2A antagonist or non D2 agonists) compounds by the cutoff values Ki ≤ 30 µM and Ki ≥ 50 µM, respectively.

Compounds with Ki values between 30 µM and 50 µM were discarded from the data sets. After the removal of duplicate compounds, the number of compounds

(i.e. confirmed actives + confirmed inactives) used for building SVM models and compound families to which these compounds belong are given in Table 3.

Table 3. Compound Data Sets

a a A2A antagonist D2 agonist binder 1,595 (412) 569 (253) functional binder 404 (138) 236 (138) MDDR 295 (155) 170 (106) inactive 96 (74) 39 (33) am(n): the number of compounds (the number of compound families)

As discussed in Section 3.2.3.4, the number of confirmed inactives were scarce

(i.e. 96 for A2A antagonists, 39 for D2 agonists), so 64,000 putative inactives

CHAPTER 3 from 8,000 compound families that did not contained confirmed actives were included for building SVM models to better represent the true distribution of actives and inactives in chemical space. Due to the differences between binding and function activities at a given GPCR, both binding and function models for each receptor were taken into consideration. For function-directed models, since the reliability of function data of compounds in MDDR was unknown, two models—one based solely on compounds collected from journal papers, and the other one based on compounds from both journal papers and MDDR—were built. Specifically, the 1,595 compounds together with (96 + putative) inactives were used for A2A binding-directed SVM model, SVMAB. The 404 compounds together with (96 + putative) inactives were used for A2A function-directed

SVM model, SVMAF1. The (404 + 295) compounds together with (96 + putative) inactives were used for A2A function-directed SVM model, SVMAF2.

Similarly, the 569 compounds together with (39 + putative) inactives were used for D2 binding-directed SVM model, SVMDB. The 236 compounds together with (39 + putative) inactives were used for D2 function-directed SVM model,

SVMDF1. The (236 + 170) compounds together with (39 + putative) inactives were used for D2 function-directed SVM model, SVMDF2 (Table 4).

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Table 4. Classification Performance of SVM Models

A2A antagonist D2 agonist SVMAB SVMDB SEa (five fold CV) 91.29% 84.18% SPb (five fold CV) 99.70% 99.82% Qc (five fold CV) 99.49% 99.65% Yield of inactive 54.17% (52/96) 33.33% (13/39) Yield of MDDR 56.95% (168/295) 44.12% (75/170) SVMAF1 SVMDF1 SE (five fold CV) 88.14% 71.63% SP (five fold CV) 99.85% 99.82% Q (five fold CV) 99.74% 99.72% Yield of inactive 88.54% (85/96) 41.03% (16/39) Yield of MDDR 34.24% (101/295) 37.65% (64/170) SVMAF2 SVMDF2 SE (five fold CV) 84.98% 79.82% SP (five fold CV) 99.87% 99.80% Q (five fold CV) 99.71% 99.67% Yield of inactive 88.54% (85/96) 38.46% (15/39) aSE: sensitivity. bSP: specificity. cQ: accuracy.

One of the objectives for a prospective model is its reliable prediction of the pharmacological properties of compounds that are not experimentally tested. It is therefore important to evaluate the predictive ability of the built model. In

Table 4, the prediction accuracy of a SVM model was assessed by sensitivity

(SE), specificity (SP) and overall accuracy (Q). SE is the ability of a model to correctly classify a truly active compound as an active one, whereas SP is the ability of a model to correctly classify a truly inactive compound as an inactive one. In other words, SE is the probability of being classified as an active compound when it is a truly active one, whilst SP is the probability of being classified as an inactive compound when it is a truly inactive one. The overall classification accuracy Q considers both active and inactive compounds. The accuracy percentages for predicting A2A actives and A2A inactives are

84.98~91.29% and 99.70~99.87%, respectively. The accuracy percentages for

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predicting D2 actives and D2 inactives are 71.63~84.18% and 99.8~99.82%, respectively. The overall prediction accuracy percentages are 99.49~99.74% and 99.65~99.72% for A2A and D2 receptors, respectively. The prediction accuracy of six SVM models here was comparable to the previous SVM model developed for screening Lck inhibitors (Liew et al., 2009). Subsequently, the models were externally validated by confirmed inactives and actives extracted from MDDR. The yield of screening is mostly 33.33~56.95% which was comparable to 22~55% that was obtained in a SVM model for the screening of a 98,400-compound library (Han et al., 2008). Remarkably, the screening of confirmed A2A inactives using SVMAF1 and SVMAF2 identified more than 88% of truly inactives, suggesting that for A2A antagonism, SVM models built on larger percentage of functional binders were more reliable in classifying inactive compounds than those built on compounds with only binding data available. Taken together, the six SVM models developed for A2A antagonists and D2 agonists performed well internally in predicting actives (SE =

84.98~91.29% for A2A, 71.63~84.18% for D2) and inactives (SP =

99.70~99.87% for A2A, 99.80~99.82% for D2), and showed acceptable prediction accuracy to external data composed of confirmed inactives and

MDDR actives.

These six SVM models were then used to screen 168,000 compounds in the

MDDR database and 13.56 million compounds in the PubChem database. The result is given in Table 5. First, for each receptor, three screens were done using one binding model and two function models of that receptor, followed by one screen using the “combined model” (i.e. the combination of one binding and two function models) of that receptor. The reason for using the combined model

CHAPTER 3 was to understand which compounds can simultaneously fit three models as well as to narrow down the number of possible hits. It was noticed that

6,100~8,000 compounds from PubChem and 230~480 compounds from MDDR fit the combined model of each receptor. The performance of these six SVM models in screening single-target agents (i.e. either A2A antagonists or D2 agonists) from PubChem and MDDR was assessed by yield (percentage of identified actives in all actives) and false positive rate (percentage of inactives identified as actives). These six SVM models can identify 43.2~73.5% yield of the reported compounds, and the false positive rates were < 1.5% and < 0.64% for MDDR and PubChem databases, respectively. Such yield performance was higher in comparison to a previous SVM model developed for predicting Lck inhibitors, and the low false positive rates resulted from the screening by these six SVM models suggests the reliability of classification. Next, three sets of

“dual model” consisting of one A2A model and one D2 model were used for screening, since the aim of the thesis was to identify ligands acting on both, rather than only one, receptors. A drastic reduction in the number of hits by 2~3 orders of magnitude was observed when using a dual model compared to a model specific to one receptor, suggesting the sparse distribution of compounds in PubChem and MDDR databases that can fit both A2A models and D2 models.

Whilst using each set of dual models resulted in few hits, no compound was identified after screening with the “combined-dual model”, namely all six models. This may suggest that a dual ligand acting as A2A antagonist and D2 agonist had not been reported. Therefore, a total of 172 hits (162 from PubChem and 10 from MDDR) coming from each set of dual models were collected. This

CHAPTER 3 was similar to the situation when the intersection of three sets was null, union of three sets became the choice (Figure 33).

Table 5. SVM-Based Virtual Screening of PubChem & MDDR Databases

A2A antagonist D2 agonist A2A antagonist + D2 agonist SVMAB SVMDB SVMAB+SVMDB PubChem 86,844 22,308 141 MDDR 1,470 1,358 2 SVMAF1 SVMDF1 SVMAF1+SVMDF1 PubChem 17,687 18,927 18 MDDR 400 997 5 SVMAF2 SVMDF2 SVMAF2+SVMDF2 PubChem 36,869 27,922 31 MDDR 366 1,730 8 SVMAB+SVMAF1+ SVMDB+SVMDF1+ SVMAB+SVMAF1+ SVMAF2 SVMDF2 SVMAF2+SVMDB+ SVMDF1+ SVMDF2 PubChem 8,073 6,164 0 MDDR 238 486 0 total hits PubChem 162 MDDR 10

Figure 33. The diagram of union and intersection of three sets (Source: Wikipedia) (ineligible for copyright).

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In Christopher A. Lipinski’s highly cited (times cited in Web of Science Core

Collection: 3,477, as at 19 February 2015) review published in 1997 (Lipinski et al., 1997) and its reprints in 2001 and 2012, favourable intestinal absorption properties (mainly aqueous solubility and permeability) of an oral drug were strongly emphasised to complement but not compromise its in vitro potency generated by HTS technique. Indeed, each drug, except for those administered by the intravenous route, need to translocate from its site of administration to the site of its intended target through systemic circulation. Its movement into the systemic circulation is called “drug absorption”. Aqueous solubility and permeability are main determinants of intestinal absorption of an orally administered drug. Aqueous solubility of a compound refers to the maximum concentration it can reach in water under a given condition, and poor aqueous solubility limits intestinal absorption of a drug. Another major factor that affects intestinal absorption of a drug is its permeability, which is defined as the velocity of drug passage through a biological membrane. In order to provide a guideline for eliminating compounds that were unlikely to be orally active in the drug discovery pipeline, Lipinski collected a library composed of 2,245 compounds that had entered phase 2 studies. The assumption was that most compounds with poor solubility and poor permeability had been weeded out before entry to phase 2. Four structural parameters associated with solubility and permeability were analysed for each compound in the library, namely (i) molecular weight (MW), (ii) lipophilicity, (iii) the number of hydrogen bond donor (HBD) groups, and (iv) the number of hydrogen bond acceptors (HBA) groups. For MW consideration, the general guiding principle was that compounds with low MW tended to favour their oral bioavailability (Navia and

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Chaturvedi, 1996). In this library, it was found that there were 89% of compounds with MW ≤ 500. Lipophilicity is the tendency of a drug to partition into a lipid matrix versus a water matrix, and it has effects on almost all areas of pharmacokinetic/pharmacodynamics properties of that drug. Lipophilicity is usually estimated by calculated logarithm of the octanol/water partition coefficient (CLogP) of a drug. In this library, it was found that there were about

90% of compounds with CLogP ≤ 5. The smaller amount of HB groups in a compound has been linked to better permeability, and particularly, the correlation of intramolecular HB bonding with increased permeability (Goetz et al., 2014) was also established. In this library, it was found that there were

92% of compounds with the sum of OHs and NHs ≤ 5, and 88% of compounds with the sum of Ns and Os ≤ 10. Based on the results derived from this library,

Lipinski proposed the “rule of 5” (RO5), stating that a compound is less likely to be absorbed if it fulfils two or more of the following conditions: MW > 500,

CLogP > 5, HBD > 5 and HBA > 10. Since its proposal in 1997, the RO5 filter has been used widely as the tool for assessing drug-like properties of a compound, and as the first step for achieving good oral bioavailability by many drug discoverers. It is possible, however, that a compound which fails the RO5 has good oral activity, and that a compound which passes the RO5 does not guarantee its oral absorption. The former exception is exemplified by cyclosporine, atorvastatin and montelukast which break more than one parameter of RO5, and such RO5-noncompliance gave an impetus to the development of a gradated instead of binary estimate of drug-likeness

(Bickerton et al., 2012). The latter exception was documented by Lipinski himself (Lipinski, 2004). Despite these limitations, RO5 is to a large extent

CHAPTER 3 useful in predicting the distribution of compounds with oral activity. Moreover, because generating compounds in tablet forms for oral administration is the main goal in CNS drug discovery (Alelyunas et al., 2010), which is also the topic of the thesis, RO5 was used to filter the 172 hits shown in Table 5. Of the

162 hits identified from PubChem, 89 fulfilled the requirements of RO5. These

89 compounds, together with the 10 hits identified from MDDR (listed in

Appendix M) were subjected for further analysis.

Of these 99 hits, the two largest scaffold classes are Scaffold A and 1,4-DAP

(Figure 34). Compounds with Scaffold A were those with 1-aza-4- azoniabicyclo[2.2.2]octane moiety, occupying 30.3% of hits. They were 30 compounds (P58, P60, and P62~P89) shown in Appendix M. Compounds with

1,4-DAP scaffold occupied 29.3% of hits. They were 29 compounds (P1, P4,

P6~P9, P15, P21, P25, P26, P28, P33, P35, P39, P44~P47, P50, P52, P55~P57,

P59, P61, M2, M3, M7 and M8) shown in Appendix M. Four representative compounds P60, P64, P82 and P88 (Figure 35) bearing Scaffold A were selected with the criterion of maximising structural diversity at R and Ar groups in Scaffold A, purchased from Enamine Ltd, and tested by in vitro radioligand displacement assay at A2A receptor (Note: The basic principle of radioligand displacement assay will be discussed in Section 4.3.). Unfortunately, these four compounds did not show binding at A2A receptor at 30~100 µM. Since the goal was to identify compounds that bind both A2A and D2 receptors, these four compounds were not proceeded to be tested against D2 receptor. These four compounds can be found in PubChem, ZINC (Irwin et al., 2012), and

ChemSpider (Pence and Williams, 2010). However, their biological annotations were not available in these databases, which may suggest that 1-aza-4-

CHAPTER 3 azoniabicyclo[2.2.2]octane was unlikely to be a binding moiety to biological targets. Even though these four compounds exhibited binding affinities to either

A2A or D2 or both receptors in vitro, the quaternary ammonium cation would hinder permeability across a biological membrane, be it the gastrointestinal (GI) barrier or the blood-brain barrier (BBB), making them unsuitable for further development. In fact, the impact of lipophilicity on BBB permeability was studied as early as late 19th century, and recently, scientists in Amgen gave the suggested range of CLogP = 2~5 for increased brain penetration (Hitchcock and

Pennington, 2006). It is obvious that the lipophilicity criteria for brain penetration (CLogP = 2~5) is more stringent than that for oral absorption

(CLogP ≤ 5) proposed by Lipinski. The basics of BBB, along with other structural properties required for optimal brain exposure, will be further discussed in Chapter 6. As shown in Table 6, the CLogP of P60, P64, P82 and

P88 were within the range of Lipinski’s RO5 but outside the preferred limit given by Amgen scientists. This implies that if the CLogP guideline given by

Amgen scientists had been used to filter the 99 hits that were obtained after RO5 filtering, the 30 compounds containing Scaffold A would not have existed in the final hits, which in turn, would have made 1,4-DAP the largest scaffold out of SVM screening.

R N Ar R NNAr N O N (30)(29) Scaffold A 1,4-DAP Figure 34. Two largest scaffold classes identified by SVM models.

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O N Cl O HN

O NH O NH N NH S O N+ ON+ ON+ ON+ N N N N

P60 P64 P82 P88 Figure 35. Four compounds with Scaffold A purchased from Enamine Ltd.

Table 6. Known Structural Properties of Four Purchased Compounds Bearing Scaffold A cpd CIDa ZINC ID MWb xLogPb,c HBDb HBAb no. of known binding targetsb P60 8717454 07613192 310.805 -2.42 1 5 0 P64 8717456 07613194 290.343 -3.19 1 6 0 P82 8717251 07612989 301.370 -3.47 1 6 0 P88 8717208 07612946 331.465 -2.44 1 5 0 aCID: PubChem Compound Identifier. bData from ZINC. cxLogP is an atom- additive method for the calculation of LogP (Wang et al., 1997).

From the viewpoint of SVM, which is a binary classification, P60, P64, P82 and

P88 were experimentally confirmed to be four false positives out of SVM model. As mentioned earlier, the average false positive rate of the six SVM models in screening either reported A2A antagonists or reported D2 agonists was about 1% (i.e. the average of 1.5% and 0.64%). False positive rate is defined as the percentage of true negatives predicted as positives, and can be formulated as FP/(FP + TN) where FP denotes false positive and TN denotes true negative.

Suppose that the number of true inactives in PubChem and MDDR is 10 million which is expected to be so in view of the much wider distribution of inactives in chemical space than that of actives for a given biological target, 1% false positive rate implies that the number of false positives is about 100,000. That is to say, as long as the number of false positives in hits is less than 100,000, the

CHAPTER 3 false positive rate of the model can be maintained within 1%. Therefore, it is definitely possible that in the 172 hits identified by six SVM models, the number of true positives (TP) is less than that of false positives. This limitation, which results in the largest scaffold identified being the inactive, can be overcame either by continually refining SVM models towards a lower false positive rate and a higher yield, or by screening target-focused compound libraries (Harris et al., 2011). The failure of Scaffold A to bind A2A receptor in vitro turned my eyes to 1,4-DAP which also occupied a fairly large portion of SVM hits. As shown in Section 3.2.3.3, the substructure 1,4-DAP is likely to be a binder at

D2 receptor whilst its role in binding A2A receptor has not been documented. In a sense, the result that a number of compounds bearing 1,4-DAP passed SVM models built on A2A antagonists suggests that there is a likelihood for A2A receptor to tolerate or accommodate 1,4-DAP, and therefore the effect of 1,4-

DAP on A2A receptor merits further investigation.

3.3 Tanimoto Similarity

The compounds collected from literature were used not only for building SVM models and outwardly screening databases (Section 3.2.3.5), but they were also inwardly examined by Tanimoto similarity (Section 3.3).

3.3.1 Introduction

Unlike substructure searching that relies on comparing many biologically active compounds for the purpose of identifying common features, similarity searching focuses on the specification of a whole compound (Willett et al., 85

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1998). A given compound is characterised by its molecular descriptors, which are used to compare with those of another compound. The resulting numerical measure could be either similarity coefficient (with maximum value for two identical objects) or distance coefficient (with a value of 0 for two identical objects) to quantify the degree of resemblance. Similarity is the opposite of distance which is defined as 1 minus similarity. Tanimoto coefficient (Tc), or

Jaccard coefficient, is the most widely used similarity coefficient in cheminformatics and is illustrated in Table 7. Imagine that there are two compounds, compound A (Cpd A) and compound B (Cpd B), and both are represented by 15 molecular descriptors. The binary bits 0 and 1 represent the absence and presence of that molecular feature, respectively. The number of bits

“on” in compound A is 6. The number of bits “on” in compound B is 8. There are 2 molecular features (red colour in Table 7) shared by both compounds. The

Tc for this pair of compounds is the number of bits in both compounds divided by the number of bits in either compound (i.e. 2/(6 + 8 – 2) = 0.17) (Figure 36, left). The Tc value ranges from zero to unity, with higher value indicating higher similarity between this pair of compounds. The Tc value of 0 indicates these two compounds have no descriptor in common. The Tc value of 1 does not guarantee the two compounds are identical but only indicates they have identical descriptors. When the intersection of a pair of compounds increases, which means these two compounds have more molecular features in common, the Tc value increases (Figure 36, right).

Table 7. Compounds A and B Represented by Molecular Fingerprint cpd A 0 0 1 0 0 1 1 0 1 0 0 1 0 0 1 cpd B 1 1 0 1 1 1 0 1 1 0 1 0 0 0 0 A ∩ B 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0

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Figure 36. The Tanimoto similarity for object A and object B is calculated by the intersection divided by union. (Left) The calculation of Tc for the case in Table 7. (Right) More molecular features in common result in a higher Tc value.

3.3.2 Methods

The similarity for a pair of compounds was computed by the Tanimoto coefficient sim(i,j) (Willett et al., 1998).

sim(i,j) = ∑ ⧸ ∑ ∑ ∑ (5) where l is the number of molecular descriptors. A compound i is considered to be similar to compound j if the corresponding sim(i,j) value is greater than the cutoff value. The cutoff values for molecular similarity analysis are typically in the range of 0.8 to 0.9 (Bostrom et al., 2006). A stricter cutoff value of 0.9 was used in the thesis.

3.3.3 Results and discussion

The preliminary inward examination of collected compounds did not show a compound that existed in A2A as well as D2 collections, so molecular similarity studies between A2A and D2 collections were pursued in order to find out the highly similar, but not identical, pairs of compounds. Instead of analysing the molecular similarity within either A2A or D2 collections to build pharmacophores for each receptor, one compound from each collection was

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picked to form a pair. In any pair, one was a collected A2A antagonist, and the other one was a collected D2 agonist. It was hoped that by computing similarity of all pairs followed by ranking each pair in decreasing order of the computed similarities would shed light on which substructural fragments were common to both collections. In contrast to SVM models, which required cutoffs for binary classification, Tanimoto similarity analyses utilised the entire data sets irrespective of the biological potency.

A Tanimoto similarity using two-dimensional fingerprints (Willett, 2006) was carried out on the basis of 1986 collected A2A antagonists (i.e. 1,595 confirmed actives, 96 confirmed inactives, 295 compounds from MDDR) and 778 collected D2 agonists (i.e. 569 confirmed actives, 39 confirmed inactives, 170 compounds from MDDR). A total of 1,545,108 (i.e. 1,986 × 778 = 1,545,108) pairs of compounds were therefore generated. Each pair is composed of an A2A antagonist and a D2 agonist. The degree of structural similarity between an A2A antagonist and a D2 agonist was calculated and shown by Tc. It was identified that 570 pairs had Tc = 0.80~0.88, and three pairs had Tc > 0.9 that is shown in

Figure 37. The Tc values were 0.91 between 16a and 17, 0.927 between 16b and 17, and 0.926 between 16c and 17. Structural analysis of these three pairs with highest Tc values out of ~1.5 million pairs revealed the common indole

(shown in blue) and pyrimidine (shown in red) groups at the two terminal ends of a molecule. Although the middle part of the molecule, which was shown as the green area, was not identical, it can be categorised as an alkyl/cycloalkyl attached to a nitrogen-containing basic moiety. Compounds 16a-c were discovered as novel ligands of A2A receptor by structure-based virtual screening, and 16a was confirmed as an A2A antagonist (Carlsson et al., 2010). The SAR

CHAPTER 3 study based on scaffold 16 was done for six analogues. Comparison of 16b with

16c indicated that the two methyl groups on pyrimidine contributed 15-fold binding affinity at A2A receptor. Either substitution with methoxy group at the indole C6 position or replacement of indole with other aryl groups, such as thiophene and anisole, did not affect A2A binding affinity, suggesting that the

NH group on the indole ring may not form hydrogen bonds with nearby residues. In a predicted A2A receptor model in complex with 16a, the guanidine was predicted to form hydrogen bonds with Asn253 in transmembrane helix 6 and Glu169 in extracellular loop 2, and one nitrogen atom on the pyrimidine forms a hydrogen bond with Asn253 (Carlsson et al., 2010). Compound 17 was developed in a programme directed towards the identification of novel D2 partial agonists (Wustrow et al., 1997). The SAR study for derivatives of 17 showed that increasing the number of carbon atoms in the middle linker from zero to one to two improve D2 binding affinity, and that the trans stereoisomer showed greater affinity than the cis stereoisomer. In addition, the piperazine-pyrimidine in 17 was replaced by piperazine-pyridine (Ki = 50 nM) and tetrahydropyridine- phenyl (Ki = 8.7 nM), both of which showed higher D2 affinity than 17.

Compound 17 belongs to N-arylpiperazine class of compound, and it is expected that the nitrogen atom attached to the middle linker forms a salt bridge with

Asp114 in TM3 of D2 receptor, as mentioned in Section 3.1.3.2. It seemed that indole and pyrimidine rings contributed to binding affinities not only with A2A receptor but also with D2 receptor, and that by building SARs for the middle

“alkyl—basic” linker against the two receptor (e.g. the effects of the alkyl chain length, the acyclic or cyclic alkyl groups, the different basic centres, on binding affinities with two receptors) it might be possible to identify a novel compound

CHAPTER 3 with dual-acting activities. It is of particular note that two different substructures, guanidine (in 16a-c) and piperazine (in 17), attached to the pyrimidine ring. Compounds 16a-c were tested only at A2A but not D2 receptors, and likewise, compound 17 was tested only at D2 but not A2A receptors. There are therefore two straightforward approaches that could be explored for the identification of dual-acting ligands, namely testing 16a-c at D2 receptor and testing 17 at A2A receptor. Should either testing show activity to a certain extent, the number of common substructures between A2A and D2 ligands would increase—from [indole + pyrimidine] to either [indole + guanidine + pyrimidine] or [indole + piperazine + pyrimidine]. Instead of doing experiments to address these queries, I resorted to SVM results for direction. In Appendix

M, compounds 16a-c or their analogues were not found, suggesting that the guanidine-pyrimidine substructure may not be tolerated by D2 receptor. In contrast, the piperazine-pyrimidine substructure was found in P1, P8, P47, M2 and M3, which were part of 29 compounds bearing 1,4-DAP. Under such guidance from SVM results, the probability of the guanidine-pyrimidine substructure showing activity at D2 receptor was expected to be lower than that of the piperazine-pyrimidine substructure showing activity at A2A receptor.

Hence, the higher probable substructures [indole + piperazine + pyrimidine] was regarded a rewarding pursuit.

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A2A ligand D2 ligand

N H R2 N N N N HN N N pyrimidine R1 N R3 indole H NH pyrimidine HN indole

1 2 3 16a R =F, R =R =Me (Ki = 0.2 uM) 1 2 3 16b R =H, R =R =Me (Ki = 0.4 uM) 1 2 3 16c R =R =R =H (Ki = 5.9 uM) 17 (Ki =161 nM)

(J. Med. Chem. 2010, 53, 3748-3755) (J. Med. Chem. 1997, 40, 250-259) Figure 37. The three pairs of reported compounds with Tc > 0.9.

At this stage, it is worth summarising how SVM models and Tanimoto similarity analysis contributed to the discovery of the substructures [indole + piperazine + pyrimidine]. Whilst the largest scaffold (Scaffold A) of the 99 hits identified by SVM models was experimentally confirmed to be inactive at A2A receptor, the second largest scaffold (1,4-DAP) with only one short of the number of Scaffold A warranted consideration. The distribution of scaffolds in the 99 hits facilitated the selection from two possibilities generated by similarity analysis. Without the inputs from SVM models, it would have been necessary to experimentally measure binding of 16a-c at D2 receptor and 17 at A2A receptor, a situation that would entail in-house synthesis of 16a-c and 17. In addition, if the computation works had stopped at SVM models without proceeding to similarity analysis, the indole group would not have been revealed as a potentially important moiety for binding two receptors. Collectively, it was the combination of SVM models and similarity analysis that gave a potential starting point for the identification of dual-acting ligands at A2A and D2 receptors

(Figure 38).

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A antagonists (1,595) 2A A antagonists (1,595) D agonists (569) 2A 2 D agonists (569) MDDR (465) 2 MDDR (465) confirmed inactives (135) confirmed inactives (135) putative inactives (64,000)

SVMAB SVMDB similarity analysis SVMAF1 SVMDF1 SVMAF2 SVMDF2

PubChem (13,560,000) MDDR (168,000) RO5

89 from PubChem 10 from MDDR

R N Ar R NNAr N O N (30)(29) Scaffold A 1,4-DAP

N linker NN N inactive at A2A N H Figure 38. The flowchart for SVM models, virtual screening, and similarity analysis of collected compounds.

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3.4 Chapter Conclusion

In summary, two computational methods—support vector machine (SVM) models, and Tanimoto similarity analysis—were adopted to analyse collected

A2A antagonists and D2 agonists from literature and MDDR database. Screening of compound collections from literature enabled the identification of the 1,4-

DAP substructure, which was a known privileged moiety accommodated by several aminergic GPCRs (including D2 receptor) and a potentially tolerable fragment for A2A receptor (Sections 3.2.3.1~3.2.3.3). The collected compounds underwent (i) binary classification by SVM models, which were subsequently used for screening millions of compounds in two databases (Sections 3.2.3.4 and 3.2.3.5), as well as (ii) examinations by molecular similarity (Section 3.3).

Scaffold A, the largest scaffold identified by SVM models, showed no binding in A2A radioligand binding studies. This led me to focus on second largest scaffold, 1,4-DAP. Subsequent Tanimoto similarity analysis not only specified that piperazine is the likely aromatic group of 1,4-DAP, but also revealed indole group as an additional moiety important for binding both A2A and D2 receptors.

Analyses of collected compounds by SVM models and molecular similarity culminated in a possible starting point for synthesis—compounds with the substructures [indole + piperazine + pyrimidine].

CHAPTER 4 DESIGN, SYNTHESIS, AND A2A

RECEPTOR BINDING STUDIES OF INDOLYL

PIPERAZINYLPYRIMIDINES

Dr. Ma Xiaohua in Professor Chen Yu Zong’s lab (Department of Pharmacy, National University of Singapore) performed cluster analysis of collected compounds. Radioligand binding assays for adenosine receptor subtypes were done through the collaboration with Professor Karl-Norbert Klotz (Institut für Pharmakologie und Toxikologie, Universität Würzburg, Germany). X-ray diffraction analysis of compound 70 was done by Tan Geok Kheng with the assistance of Dr. Ang Wee Han (Department of Chemistry, National University of Singapore). High-resolution mass spectrometry (HRMS) analysis of final compounds for biological assays was done by Wong Lai Kwai (Department of Chemistry, National University of Singapore). Dr. Priyankar Paira is appreciated for his advice on chemical synthesis.

4.1 Design Rationale

Similarity analysis described in Section 3.3 dealt with similarity between one

A2A antagonist selected from the pool of the A2A collection and one D2 agonist selected from the pool of the D2 collection, but it did not group the two sets of compounds, A2A antagonists and D2 agonists, according to their respective computed similarity ranges. One major advantage of grouping all collected compounds by their molecular features is that it can give clues about the likelihood of a compound acting as both A2A antagonist and D2 agonist. The extremity would be all collected A2A antagonists are grouped in one side and all collected D2 agonists are grouped in another side, meaning that in the realm of known ligands there is no possibility to have a compound that binds A2A and also binds D2 receptors. Cluster analysis is intended to classify a number of

CHAPTER 4 objects into different groups (called clusters) so that objects in one cluster are more similar to each other than to the objects in another cluster. In the thesis, all collected compounds were represented by molecular descriptors given in

Table 2, so compounds in the same cluster should have similar structural properties. Because compounds in a given cluster have similar structural properties, if one known A2A antagonist and one known D2 agonist are grouped in the same cluster, that A2A antagonist (D2 agonist) is likely to have structural motifs that are important in D2 (A2A) activity. The methods for clustering data include hierarchical and non-hierarchical approaches (Barnard and Downs,

1992). Hierarchical clustering generates a classification in which there is a hierarchy ranging from each datum in an individual cluster to all data in one cluster. Non-hierarchical clustering is to partition the data into a number of non- overlapping groups. Hierarchical clustering can be either agglomerative (small clusters merged into larger clusters) or divisive (larger clusters split into smaller clusters). An explanation of hierarchical agglomerative clustering (HAC) is shown in Figure 39. Suppose there are five compounds (A, B, C, D and E), each with their distinct molecular features. Initially, each compound is assigned a cluster, so there are five clusters in the beginning. The number of possible pairs is 5 × (5 – 1)/2 = 10 (i.e. A-B, A-C, A-D, A-E, B-C, B-D, B-E, C-D, C-E and

D-E). Out of these 10 pairs, the pair with highest similarity (i.e. A-D) is merged into a new cluster AD, making the number of total clusters from five to four.

The similarities between AD and each of the original clusters (i.e. B, C and E) are re-computed, and the cluster with highest similarity (i.e. C) to AD is merged with AD to become ADC. Such merging process based on similarity is repeated until all five compounds are grouped into one single cluster ADCBE. The y-

CHAPTER 4 axis in the dendrogram indicates the distance, which is 1 minus similarity

(Section 3.3). Larger distance at which two clusters merge indicates less similarity between these two clusters. This also implies that the closer the two compounds are placed in the x-axis of the dendrogram, the more similar of these two compounds. This concept is important and useful in the endeavour to identify dual-acting ligands targeting A2A and D2 receptors because the shortest x-axis distance between a known A2A antagonist and a known D2 agonist provides an opportunity to achieve the aim of the thesis.

Figure 39. Hierarchical agglomerative clustering. (Left) Compounds A~E are distributed in chemical space according to their molecular features. (Right) The dendrogram representing a hierarchy of clusters based on their molecular similarities.

To better appreciate the potential of the collected data sets towards the goal of the thesis, hierarchical clustering of collected A2A antagonists and D2 agonists was conducted. The dendrogram obtained from 98 descriptors (Table 2) is shown in Figure 40. Most A2A antagonists (shown in green colour) appeared at the lower right of the dendrogram, whereas most D2 agonists (shown in blue colour) distributed at the upper left of the dendrogram, suggesting that reported

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A2A antagonists as a whole and D2 agonists as a whole had remote similarity.

Generally, high similarity means Tc > 0.85, intermediate similarity means 0.7

< Tc ≤ 0.85, and remote similarity means 0.57 < Tc ≤ 0.7 (Zhang et al., 2015).

Such remote similarity indicated the ligand-binding pocket of A2A receptor is distinct from that of D2 receptor, accounting for the difficulty in identifying a ligand that can bind both receptor. Despite such a challenge, there were still few reported A2A antagonists clustered in the region of D2 agonists (some green fragments in the upper left region), and few reported D2 agonists clustered in the region of A2A antagonists (some blue fragments in lower right region). As mentioned earlier, the closeness in the x-axis of the dendrogram signifies the structural resemblance. Since compounds with similar structures have similar properties, which is the principle widely held by the community (Maggiora et al., 2014), it was no surprise that such structural similarity existed within reported A2A antagonists as shown in the continuous green colour in the lower left of the dendrogram, and within reported D2 agonists as shown in the continuous blue colour in the upper right of the dendrogram. What was interesting and relevant to the goal of the thesis was the green and blue areas adjacent to each other, and therefore ten representative clusters with such adjoining green-blue property were selected for analysis. Each cluster contained some A2A antagonists and some D2 agonists. Compounds in each cluster are listed in Appendix N. Common substructures shared by A2A antagonists and D2 agonists were observed in clusters 1, 2, 3, 8 and 9, but not in clusters 4, 5, 6, 7 and 10. Compounds grouped in the same cluster could be very structurally dissimilar, as shown in the case of clusters 4, 5, 6, 7 and 10, so it was necessary to look at the chemical structures of compounds after cluster analysis. This was

CHAPTER 4 attributed to the fact that clustering is descriptor-dependent. Compounds grouped in the same cluster only implies higher percentage of identical descriptors for each object in that particular cluster, but not necessarily guarantee higher structural similarity. In fact, it was well documented that different molecular descriptors affect the results of similarity calculations

(Dimova et al., 2013). The common substructures benzimidazole, indole- pyrimidine, quinoline, benzofuran and pyrimidine were identified in clusters 1,

2, 3, 8 and 9, respectively. Notably, cluster 2 (Figures 41 and 42) corresponds to the best similarity results shown in Figure 37, and the SARs of compounds therein were summarised in Section 3.3.3.

Figure 40. Clustering dendrogram of the collected compounds. A2A antagonists are shown in green, and D2 agonists are shown in blue. The main portion of A2A antagonists was distributed below the red dashed line, whereas the main portion of D2 agonists was distributed above the red dashed line.

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H H S N NH2 N N N

N N HN NH N F 1666_71_014 (A2A) H H same as 16b N N N H H N N N N HN NH N H HN NH N

1571_67_007 (A2A) 1663_71_011 (A2A) same as 16a 1668_71_016 (A2A) same as 16c

N N N N N N N N N N N N N N N N

F N N N N H H H N H 660_32_32 (D2) 664_32_30e (D2) 653_32_17a (D2) 654_32_17b (D2) 666_32_30g (D2) same as 17

N N

N N N N N N NH N N N N

N N N N N N H H H H N

230_32_30a (D2) 657_32_29a (D2) 655_32_20a (D2) 656_32_20b (D2) MDDR_221059 (D2) Figure 41. Compounds in cluster 2. Three A2A antagonists (in green) and one D2 agonist (in blue) in this cluster have highest Tanimoto similarity as shown in Figure 37.

Figure 42. The magnified area where compounds in cluster 2 are located in the clustering dendrogram.

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The result that the hierarchical clustering analysis showed five clusters (i.e. cluster 1, cluster 2, cluster 3, cluster 8, and cluster 9), in each of which a common substructure can be found in both A2A antagonists and D2 agonists, prompted us to focus on compounds in these five clusters. In Chapter 3, indole, piperazine and pyrimidine groups were identified by combining SVM models and similarity analysis to be the substructures that may have the binding capacity at both A2A and D2 receptors. Herein, the hierarchical clustering analysis led to the identification of three other potential substructures that may bind the two receptors, namely benzimidazole (from cluster 1), quinoline (from cluster 3), and benzofuran (from cluster 8). Together, there were a total of six potentially relevant substructures identified from SVM models, similarity analysis, and hierarchical clustering analysis. The design of dual-targeting ligands at A2A and D2 receptors was thus based on these six substructures. Since five of the six substructures (i.e. except piperazine) were already among clusters

1, 2, 3, 8 and 9, these five clusters together with the piperazine group were considered. A total of 47 synthetically feasible compounds 18-64 on the basis of these five clusters integrated with piperazine were designed (Figures 43–47).

The design relied on the common substructures observed in both A2A antagonists and D2 agonists within the same cluster, and on the piperazine suggested by the results from SVM models and similarity analysis. According to synthetically feasible procedures (Maggiora et al., 2014), the piperazine ring was only included in the design based on clusters 1, 2, 3 and 9. With these six substructures as the molecular skeletons, a variety of linkers with different lengths, substituents, and functional groups were appended to the skeletons.

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H N N N N N N N N F3C N N N N N H H 18 19 N N

N HN N N N N N N N N NN N N N H H 20 N 22 23 N 21

N N N N N N N N N N OH N N H H NH2 N N 24 25

N N

HN N HN N N N N N N N N N N N OH H H NH2 N 26 28 29 N 27

N N N N N N N N N N N N N N NH N H H 2 H NH2 N NH2 30 31 32

N N N N N N N N N N N N NH N N H H 2 SH NH2 H 33 34 35 Figure 43. Eighteen compounds (18-35) designed from cluster 1. The common substructure benzimidazole (red squares) in this cluster and piperazine (blue squares) were included in the design. The basic core is either 2-(piperazin-1- yl)benzimidazole (18-19, and 21-35) or 4-(piperazin-1-yl)benzimidazole (20)

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H H N H N N N Br N Br N NN NN NN N N N

36 37 38

H H H N N N

N N N NN NN NN N N N

39 40 41 H H N N H N N N NN N N NN NN N N 42 43 44

H H H N N N

N N NN NN N N N NN N 46 47 45 H H H N N N Br

NH NH NH NH NH NH HN N HN N HN N N N N

48 49 50 Figure 44. Fifteen compounds (36-50) designed from cluster 2. The common substructures indole and pyrimidine (red squares) in this cluster, as well as piperazine (blue squares) were included in the design. An indole and a pyrimidine are at the terminal ends of a compound.

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HN HN HO N N N N HO N N N N N N N N OH N OH OH

NH N N

N F F OH

51 52 53 54 55 Figure 45. Five compounds (51-55) designed from cluster 3. The common substructure quinoline (red squares) in this cluster and piperazine (blue squares) were included in the design. The basic core is either 2-methyl-8-(piperazin-1- yl)quinoline (51, and 53-55) or 2-methyl-4-(piperazin-1-yl)quinoline (52).

Br O Br O O Br O OH N N N N O O O O N N N N

56 57 58 59

O OH O OH O OH N N N O O O N N N

60 61 62 Figure 46. Seven compounds (56-62) designed from cluster 8. The common substructure benzofuran (red squares) in this cluster was included in the design. The benzofuran is at one end of a compound. The other end is either imidazole (58 and 62), 4,5-dihydro-imidazole (56, 57, 59 and 60) or benzimidazole (61).

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OMe OMe

N N H H H N N N N N N N

N N NH

63 64 Figure 47. Two compounds (63 and 64) designed from cluster 9. The common substructure pyrimidine (red squares) in this cluster and piperazine (blue squares) were included in the design. The basic cores are a pyrimidine and a N- (p-methoxyphenyl)piperazine at the ends of a compound.

Subsequently, in order to further prioritise these 47 compounds, filtering by six

SVM models generated in Section 3.2.3.5 and by Tanimoto similarity with the cutoff value Tc ≥ 0.85 was performed. Four criteria (i.e. three SVM models and

Tc ≥ 0.85) were used to evaluate each of these 47 compounds for each receptor.

This was to ensure that selected compounds for future synthesis obeyed maximal numbers of criteria suggested by all three computational tools that have been used, namely SVM models, Tanimoto similarity, and hierarchical clustering. Ten (20, 36-38, 40, 41, 44-46, and 48) out of these 47 compounds were in compliance with at least one A2A and one D2 computed criteria (Table

8), and the rest 37 compounds failed to pass any criterion. Compound 20 passed one A2A (SVM) and two D2 (SVM and similarity) criteria. Compounds 36-38,

40, 41, 44, 45 and 48 passed one A2A (similarity) and one D2 (similarity) criteria.

Compound 46 passed one A2A (similarity) and one D2 (SVM) criteria. Because these 10 compounds were the result of clustering analysis followed by SVM and similarity screening, they were expected to be most probable candidates for synthesis. It was noted that these ten candidates only covered clusters 1 and 2, but not clusters 3, 8 and 9, although they were designed from all these five clusters. This suggests that SVM and similarity screening weeded out

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CHAPTER 4 compounds contained in clusters 3, 8 and 9, which may have lower probability to be dual-acting ligands. In addition, nine (36-38, 40, 41, 44-46, and 48) of these ten compounds belonged to cluster 2, suggesting that compounds in cluster 2 have the highest likelihood to be dual-acting ligands. These nine compounds can be structurally divided into three scaffolds: (i) [indole + piperazine + pyrimidine] (IPP) (compounds 36-38, 40, 41, 44 and 45), (ii)

[indole + 1,4-dihydropyrazine + pyrimidine] (IDP) (compound 46), and (iii)

[indole + guanidine + pyrimidine] (IGP) (compound 48). Clearly, a large percentage (7 out of 9) of these nine compounds belonged to the scaffold IPP, suggesting that, among the nine compounds in cluster 2, there is a higher probability for the scaffold IPP than the rest two scaffolds to be the dual-acting ligands. This was in line with the results shown in Figure 38. Such a weeding- out analysis was that ten compounds were reduced to nine compounds by choosing cluster 2 and discarding cluster 1 (i.e. 20), followed by that the remaining nine compounds were reduced to seven compounds by choosing the main scaffold and discarding the rest two scaffolds (i.e. 46 and 48). An alternative weeding-out analysis that can also bring the focus on IPP was to check whether each scaffold possessed the features that were identified in previous SVM models (Section 3.2.3.5) and similarity analysis (Section

3.3.3)—only the scaffold IPP met both requirements (Table 9). The best result from starting with hierarchical clustering followed by the manual design and filtering with SVM models and similarity was consistent with the discovery

(Figure 38) via the combination of SVM models and similarity. This consistency gave an assurance that compounds with the scaffold IPP should be

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synthesised and tested at A2A and D2 receptors. The overall decision-making process for the subsequent chemical synthesis was charted in Figure 48.

Table 8. Ten Compounds Compliant with at Least One Criterion of Each Receptor cpd structure H cluster 1 N criteria SVMAB F C 20 3 passed (A2A) N criteria failed SVMAF1, N (A2A) SVMAF2, Tc criteria SVMDB, Tc N passed (D2) NN criteria failed SVMDF1, (D2) SVMDF2 scaffold BPPa

cluster 2 H N criteria Tc 36 passed (A2A) N criteria failed SVMAB, NN (A2A) SVMAF1, N SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

cluster 2 H N criteria Tc 37 passed (A2A) Br N criteria failed SVMAB, NN (A2A) SVMAF1, N SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

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Table 8. (Continued) Ten Compounds Compliant with at Least One Criterion of Each Receptor cpd structure H cluster 2 N criteria Tc 38 passed (A2A) N Br criteria failed SVMAB, NN N (A2A) SVMAF1, SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

H cluster 2 N criteria Tc 40 passed (A2A) N criteria failed SVMAB, NN N (A2A) SVMAF1, SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

H cluster 2 N criteria Tc 41 passed (A2A) N criteria failed SVMAB, NN N (A2A) SVMAF1, SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

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Table 8. (Continued) Ten Compounds Compliant with at Least One Criterion of Each Receptor cpd structure H cluster 2 N criteria Tc 44 passed (A2A) N criteria failed SVMAB, NN N (A2A) SVMAF1, SVMAF2 criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

H cluster 2 N criteria Tc 45 passed (A2A) criteria failed SVMAB, (A2A) SVMAF1, N SVMAF2 NN N criteria Tc passed (D2) criteria failed SVMDB, (D2) SVMDF1, SVMDF2 scaffold IPPb

H cluster 2 N criteria Tc 46 passed (A2A) N criteria failed SVMAB, NN (A2A) SVMAF1, N SVMAF2 criteria SVMDB passed (D2) criteria failed SVMDF1, (D2) SVMDF2, Tc scaffold IDPc

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Table 8. (Continued) Ten Compounds Compliant with at Least One Criterion of Each Receptor cpd structure H cluster 2 N criteria Tc 48 passed (A2A) criteria failed SVMAB, (A2A) SVMAF1, SVMAF2 criteria Tc NH passed (D2) NH criteria failed SVMDB, HN N N (D2) SVMDF1, SVMDF2 scaffold IGPd a[benzimidazole + piperazine + pyrimidine]. b[indole + piperazine + pyrimidine]. c[indole + 1,4-dihydropyrazine + pyrimidine]. d[indole + guanidine + pyrimidine].

Table 9. Weeding-Out Analysis of Ten Compounds Obtained from Clustering scaffold 1,4-DAPa? Tc > 0.9b? BPP (compound 20) Yes No IPP (compounds 36-38, Yes Yes 40, 41, 44 and 45) IDP (compound 46) No Yes IGP (compound 48) No Yes aContributed by SVM models in Section 3.2.3.5. bContributed by similarity analysis in Section 3.3.3.

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A antagonists (1,595) 2A A antagonists (1,595) D agonists (569) 2A 2 D agonists (569) MDDR (465) 2 MDDR (465) Confirmed inactives (135) Confirmed inactives (135) Putative inactives (64,000)

SVMAB SVMDB Similarity analysis Clustering SVMAF1 SVMDF1 SVM SVM AF2 DF2 C1~C10

PubChem (13,560,000) MDDR (168,000) C1, C2, C3, RO5 C8, C9 89 from PubChem 10 from MDDR C1, C2

R N Ar R NNAr C2 N N O (30)(29) Scaffold A 1,4-DAP IPP

N linker NN N inactive at A2A N H

active at A2A and D2 receptors Figure 48. Three computational tools, SVM models, Tanimoto similarity analysis, and hierarchical clustering analysis, are used to analyse collected compounds, screen databases, and identify potential scaffolds. The activities of the IPP scaffold to A2A and D2 receptors will be shown in Sections 4.2~4.4 and Chapter 5.

4.2 IPP

4.2.1 Chemical considerations

The designed compounds with IPP scaffold were synthesised (Scheme 1). The mixture of urea 65 and 2,4-pentanedione under an acidic condition resulted in the formation of 4,6-dimethylpyrimidin-2-ol hydrochloride (66) in 78% yield.

Following this, treating 66 with phosphorus oxychloride gave 2-chloro-4,6-

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CHAPTER 4 dimethylpyrimidine 67 in 91% yield (Vlad and Horvath, 2002). For the sake of minimising the formation of 2‐[4‐(4,6‐dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐

4,6‐dimethylpyrimidine, five equivalents of piperazine were used for coupling with 67 in a basic condition. With 4,6-dimethyl-2-(piperazin-1-yl)pyrimidine

(68) in hand, four indole-3-acids (i.e. indole-3-carboxylic acid, 2-methylindole-

3-acetic acid, indole-3-propionic acid, and indole-3-butyric acid) were selected for 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)-mediated amide formation to generate 69-72 with different lengths of linker. To study the effect of the carbonyl group of 69-72 on biological activities, reduction by lithium aluminum hydride (LiAlH4) was performed on 69-72 to give compounds 69a-

72a. Mechanisms of each step in Scheme 1 are proposed (Figure 49). The structural determination of 70 in the solid state was carried out by X-ray diffraction (Figure 50). The crystal was obtained by dissolving 70 in boiling

EtOAc/MeOH (1:3) followed by slow solvent evaporation and filtration. The crystallographic parameters are given in Appendix O.

Scheme 1a

OH HCl Cl O N i ii NN iii iv NN HN N H2N NH2 N 65 66 67 68

N N

N N N N N N v n n O N R1 N R1 H H

69 R1=H, n=0 69a R1=H, n=0 1 1 70 R =CH3, n=1 70a R =CH3, n=1 71 R1=H, n=2 71a R1=H, n=2 72 R1=H, n=3 72a R1=H, n=3 aReagents and conditions: (i) 2,4-pentanedione, 37% HCl, EtOH, reflux, 24 h, o 78%; (ii) POCl3, reflux, 10 h, 91%; (iii) piperazine, K2CO3, H2O, 45~50 C, 4.5 h, 93%; (iv) indole-3-acid, EDC.HCl, EtOAc, DMF, 23 → 50 oC, 4~4.5 h, o 50~74%; (v) LiAlH4, THF, 0 → 23 C, 4~21.5 h, 76~97%.

111

O H H N NH O O O O 2 65 2 NH NH H 2 2 O O O O N O N N H H N O H H N N

H O, H H2O, H H 2 H N O O OH Cl O P P Cl Cl Cl Cl Cl O Cl N NN Cl Cl NN NNH NNH NNH 2K H 66 O HCl 67 HCO P 3 O Cl H H H Al H H H Al H H H H Al H CHAPTER 4 4 CHAPTER O N 112 N O N N HN N NN H NN H NN N R N R N R N 68 H HN 69-72 H H Al O NH H O HN O H Al H R O N N N NN R N N 69a-72a

N O N HN C N R O O H NCN R O EDC Figure 49. Proposed mechanisms of steps in Scheme 1.

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Figure 50. X-ray crystal structure of compound 70.

4.2.2 Experimental methods

General. Reactions were consistently monitored by thin layer chromatography

(TLC) on silica gel (precoated 60 F254 Merck plate) and carried out under a nitrogen atmosphere. All chemicals are commercial products from Sigma

Aldrich or Alfa Aesar. Column chromatography was performed using silica gel

60 (Merck, 70–230 mesh). Compounds were dissolved in HPLC-grade MeOH for accurate mass analysis using ESI time-of-flight (TOF) ionisation mode. 1H

13 and C NMR spectra were determined in deuterated chloroform (CDCl3) or deuterated dimethylsulfoxide (DMSO-d6) using Bruker DPX Ultrashield NMR

(400 MHz) spectrometer, with chemical shifts given in parts per million (δ) downfield relative to the central peak of the solvents, and J values (coupling constants) were given in Hz. The following abbreviations were used: s = singlet, d = doublet, t = triplet, m = multiplet, br = broad, td = triplet of doublets. High-

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CHAPTER 4 performance liquid chromatography (HPLC) analysis was carried out for compounds used in biological assays. For HPLC (1): Hewlett-Packard series

1050 HPLC system equipped with a HP-1050 quaternary pump, a degasser, diode array detector, a HP-1100 autosampler, and a LiChrosorb reversed phase

C18 (5 μm) column (4.6 × 250 mm) with solvents being CH3CN/H2O. For

HPLC (2) and HPLC (3): Agilent HPLC 1200 series instrument on a Zorbax

SB-C18, 4.6 mm × 250 mm, 5 μm column with solvents being CH3CN/H2O

(0.1% v/v CF3COOH) and MeOH/H2O (0.1% v/v CF3COOH) for HPLC (2) and

HPLC (3), respectively. All HPLC samples were prepared by dissolving them in HPLC-grade MeOH. The analysis was performed at 30 °C, and the ultraviolet detection was made at wavelength 254 nm. The separations were carried out using gradient elution. The HPLC methods are as follows. HPLC (1): injection volume 5 μL, stop time 20 min, flow rate 1 mL/min, a gradient of 35 → 100%

CH3CN for the 0–17 min period and back to CH3CN/H2O (3:7) at 20 min. HPLC

(2): injection volume 20 µL, stop time 15 min, flow rate 0.5 mL/min, a gradient of 5 → 95% CH3CN for the 0–12 min period and back to CH3CN/H2O (5:95) at 15 min. HPLC (3): injection volume 20 µL, stop time 15 min, flow rate 0.5 mL/min, a gradient of 5 → 95% MeOH for the 0–12 min period and back to

MeOH/H2O (5:95) at 15 min. The purities of all the tested compounds were >

95% measured by the peak area of the product divided by that of the total peak areas.

The NMR spectra of compounds 66-72 and 69a-72a are listed in Appendix P.

The HPLC chromatograms of compounds 69-72 and 69a-72a are listed in

Appendix Q.

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4,6‐Dimethylpyrimidin‐2‐ol hydrochloride (66). To a suspension of urea

(1000 mg, 16.65 mmol) in EtOH (10 mL) were added 2,4-pentanedione (1885

µL, 18.32 mmol) and concentrated hydrochloric acid (2775 µL, 33.3 mmol), and the resulting clear, colourless solution was stirred and refluxed under N2 for

24 h. The mixture was cooled to 23 oC and filtered. The filter cake was washed with EtOH, Et2O, and dried in the vacuum oven to afford compound 66 (2096 mg, 78% yield) as an off-white crystalline solid. 1H NMR (400 MHz, DMSO- d6) δ 6.72 (s, 1H), 2.47 (s, 6H).

2‐Chloro‐4,6‐dimethylpyrimidine (67). The mixture of compound 66 (1998 mg, 12.44 mmol) and phosphorus oxychloride (20 mL) was refluxed for 10 h.

The remaining phosphorus oxychloride was evaporated to get a brown oil in the flask. The mixture was cooled in an ice bath, and concentrated aqueous KOH solution was added dropwise cautiously with stirring, until the litmus paper showed pH value is approximately 8. Diethyl ether (30 mL) was added, and the mixture was stirred for 2.5 h. The water layer was extracted with Et2O (3 × 30 mL) and EtOAc (3 × 30 mL). The organic layers were combined, washed with brine, dried over Na2SO4, filtered, and concentrated under reduced pressure.

The resulting yellow liquid was put in an ice bath under vacuum to afford compound 67 (1606 mg, 91% yield) as a yellow crystal. 1H NMR (400 MHz,

DMSO-d6) δ 7.33 (s, 1H), 2.42 (s, 6H).

4,6‐Dimethyl‐2‐(piperazin‐1‐yl)pyrimidine (68). Into a 250mL round-bottom flask were charged K2CO3 (3849 mg, 27.85 mmol), piperazine (10903 mg,

o 126.58 mmol), and H2O (180 mL). The mixture was heated at 45–50 C until it became a clear solution. Compound 67 (3610 mg, 25.32 mmol) was divided into four portions, and each portion was added into the mixture in a one-hour

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CHAPTER 4 interval. After the addition of all amounts of compound 67, the reaction mixture was cooled to 23 oC and stirred for overnight. The white precipitate was filtered, and the filtrate was collected. The filtrate was extracted with EtOAc (3 × 225 mL). Organic layers were collected, washed with brine, dried over Na2SO4, filtered, and concentrated under reduced pressure to give compound 68 (4536

1 mg, 93% yield) as a white crystal. H NMR (400 MHz, DMSO-d6) δ 6.36 (s,

1H), 3.62 (t, J = 5.2 Hz, 4H), 2.69 (t, J = 5.2 Hz, 4H), 2.20 (s, 6H).

3‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐1H‐indole (69).

To a suspension of indole-3-carboxylic acid (520 mg, 3.23 mmol) and EDC.HCl

(643 mg, 3.35 mmol) were added EtOAc (30 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound

68 (414 mg, 2.15 mmol). The reaction was heated at 50 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was washed with hexanes/EtOAc (3:1) and further purified by crystallisation from MeOH/Et2O/EtOAc to provide compound 69

1 (360 mg, 50% yield) as a white crystal. H NMR (400 MHz, DMSO-d6) δ 11.61

(br s, 1H), 7.74–7.72 (m, 2H), 7.45 (d, J = 8 Hz, 1H), 7.16 (td, J = 7.2, 1.2 Hz,

1H), 7.10 (td, J = 7.2, 1.2 Hz, 1H), 6.44 (s, 1H), 3.82–3.80 (m, 4H), 3.71–3.69

13 (m, 4H), 2.24 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 166.8 (2 × C), 165.7,

161.1, 135.7, 128.1, 126.1, 121.9, 120.3, 120.2, 111.9, 109.6, 109.1, 43.5 (2 ×

+ CH2), 40.1–38.9 (2 × CH2), 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd

+ for C19H22N5O, 336.1819; found, 336.1824 (Δ = -1.6 ppm). [M + Na] calcd for

C19H21N5NaO, 358.1638; found, 358.1639 (Δ = -0.2 ppm). HPLC (1): t = 5.6 min, 96.7% purity. HPLC (3): t = 15.5 min, 98.3% purity.

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1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(2‐methyl‐1H‐indol‐3‐ yl)ethan‐1‐one (70). To a suspension of 2-methylindole-3-acetic acid (1515 mg, 8.00 mmol) and EDC.HCl (1579 mg, 8.24 mmol) were added EtOAc (90 mL) and six drops of DMF, and the mixture was stirred for 70 min at 23 oC followed by the addition of compound 68 (1026 mg, 5.34 mmol). The reaction was heated at 45 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (3:1) to provide compound 70 (1130 mg,

1 58% yield) as a light brown crystal. H NMR (400 MHz, DMSO-d6) δ 10.80 (br s, 1H), 7.45 (d, J = 7.6 Hz, 1H), 7.22 (d, J = 7.6 Hz, 1H), 6.97 (td, J = 7.2, 1.2

Hz, 1H), 6.90 (td, J = 7.2, 1.2 Hz, 1H), 6.41 (s, 1H), 3.74 (s, 2H), 3.62–3.61 (m,

2H), 3.52–3.49 (m, 6H), 2.34 (s, 3H), 2.20 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 169.6, 166.8 (2 × C), 160.8, 135.1, 132.6, 128.2, 120.0, 118.2,

117.8, 110.3, 109.1, 104.0, 45.2, 43.3, 43.2, 41.2, 30.0, 23.6 (2 × CH3), 11.5.

+ HRMS-ESI (m/z) [M + H] calcd for C21H26N5O, 364.2132; found, 364.2123

+ (Δ = 2.4 ppm). [M + Na] calcd for C21H25N5NaO, 386.1951; found, 386.1940

(Δ = 3.0 ppm). HPLC (1): t = 9.8 min, 99.9% purity. HPLC (3): t = 15.6 min,

99.6% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐3‐(1H‐indol‐3‐ yl)propan‐1‐one (71). To a suspension of indole-3-propionic acid (518 mg,

2.74 mmol) and EDC.HCl (540 mg, 2.82 mmol) were added EtOAc (30 mL) and two drops of DMF, and the mixture was stirred for 1.5 h at 23 oC followed by the addition of compound 68 (352 mg, 1.83 mmol). The reaction was heated at 50 oC for 3 h, and solvents were evaporated to dryness under high vacuum.

Water was added, sonicated, and filtered. The filter cake was purified by

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CHAPTER 4 crystallisation from MeOH/EtOAc (1:1) to provide compound 71 (491 mg, 74%

1 yield) as a transparent crystal. H NMR (400 MHz, DMSO-d6) δ 10.76 (br s,

1H), 7.53 (d, J = 8.0 Hz, 1H), 7.32 (d, J = 8.0 Hz, 1H), 7.15 (d, J = 2.0 Hz, 1H),

7.06 (td, J = 8.0, 0.8 Hz, 1H), 6.97 (td, J = 8.0, 0.8 Hz, 1H), 6.42 (s, 1H), 3.67–

3.64 (m, 2H), 3.62–3.60 (m, 2H), 3.53–3.50 (m, 2H), 3.46–3.44 (m, 2H), 2.95

(t, J = 7.2 Hz, 2H), 2.71 (t, J = 7.2 Hz, 2H), 2.22 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 170.8, 166.8 (2 × C), 160.8, 136.2, 127.1, 122.5, 120.9, 118.3,

118.2, 113.8, 111.3, 109.1, 44.8, 43.4, 43.1, 40.9, 33.4, 23.7 (2 × CH3), 20.6.

+ HRMS-ESI (m/z) [M + H] calcd for C21H26N5O, 364.2132; found, 364.2131

+ (Δ = 0.2 ppm). [M + Na] calcd for C21H25N5NaO, 386.1951; found, 386.1949

(Δ = 0.5 ppm). HPLC (1): t = 10.2 min, 98.4% purity. HPLC (3): t = 15.6 min,

98.4% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐4‐(1H‐indol‐3‐yl)butan‐

1‐one (72). To a suspension of indole-3-butyric acid (509 mg, 2.50 mmol) and

EDC.HCl (508 mg, 2.65 mmol) were added EtOAc (30 mL) and two drops of

DMF, and the mixture was stirred for 70 min at 23 oC followed by the addition of compound 68 (317 mg, 1.65 mmol). The reaction was heated at 45–50 oC for

3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (2:1) to provide compound 72 (411 mg, 66% yield) as an

1 off-white crystal. H NMR (400 MHz, DMSO-d6) δ 10.75 (br s, 1H), 7.52 (d, J

= 8.0 Hz, 1H), 7.32 (d, J = 8.0 Hz, 1H), 7.11 (d, J = 2.4 Hz, 1H), 7.05 (td, J =

8.0, 0.8 Hz, 1H), 6.96 (td, J = 8.0, 0.8 Hz, 1H), 6.43 (s, 1H), 3.70–3.67 (m, 4H),

3.53–3.50 (m, 2H), 3.46–3.44 (m, 2H), 2.72 (t, J = 7.2 Hz, 2H), 2.41 (t, J = 7.2

Hz, 2H), 2.23 (s, 6H), 1.89 (quintet, J = 7.2 Hz, 2H). 13C NMR (100 MHz,

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DMSO-d6) δ 170.8, 166.8 (2 × C), 160.1, 136.3, 127.2, 122.3, 120.8, 118.3,

118.1, 114.2, 111.3, 109.1, 44.7, 43.5, 43.1, 40.9, 32.1, 25.6, 24.3, 23.7 (2 ×

+ CH3). HRMS-ESI (m/z) [M + H] calcd for C22H28N5O, 378.2288; found,

+ 378.2295 (Δ = -1.8 ppm). [M + Na] calcd for C22H27N5NaO, 400.2108; found,

400.2110 (Δ = -0.7 ppm). HPLC (1): t = 11.3 min, 99.7% purity. HPLC (3): t =

16.3 min, 99.1% purity.

3‐{[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]methyl}‐1H‐indole (69a).

o To a slurry of LiAlH4 (5 small spoons) in anhydrous THF (30 mL) at 0 C was added dropwise by syringe the solution of compound 69 (535 mg, 1.59 mmol) in anhydrous THF (60 mL), and the mixture was warmed to 23 oC with stirring for 21.5 h. At 0 oC, the following sequence of dropwise addition was performed with continuous stirring: H2O (2.7 mL), 15% aqueous NaOH (2.7 mL), 15% aqueous NaOH (3 mL), H2O (3 mL). The white sticky part was removed by filtration, and the filtrate was concentrated under reduced pressure. Purification by flash chromatography (1:3 → 1:1 EtOAc/hexanes) provided compound 69a

1 (391 mg, 76% yield) as a yellow solid. H NMR (400 MHz, DMSO-d6) δ 10.93

(br s, 1H), 7.64 (d, J = 7.6 Hz, 1H), 7.35 (d, J = 7.6 Hz, 1H), 7.23 (d, J = 2.4

Hz, 1H), 7.07 (td, J = 6.8, 1.2 Hz, 1H), 6.98 (td, J = 6.8, 1.2 Hz, 1H), 6.36 (s,

1H), 3.71–3.68 (m, 4H), 3.65 (s, 2H), 2.43–2.40 (m, 4H), 2.20 (s, 6H). 13C NMR

(100 MHz, DMSO-d6) δ 166.6 (2 × C), 161.1, 136.3, 127.7, 124.7, 121.0, 119.0,

118.5, 111.4, 110.6, 108.6, 53.2, 52.5 (2 × CH2), 43.4 (2 × CH2), 23.7 (2 × CH3).

+ HRMS-ESI (m/z) [M + H] calcd for C19H24N5, 322.2026; found, 322.2029 (Δ

+ = -1.0 ppm). [M + Na] calcd for C19H23N5Na, 344.1846; found, 344.1845 (Δ =

0.3 ppm). HPLC (1): t = 8.5 min, 100% purity. HPLC (3): t = 14.6 min, 97% purity.

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3‐{2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]ethyl}‐2‐methyl‐1H‐ indole (70a). To a slurry of LiAlH4 (5 small spoons) in anhydrous THF (30 mL) at 0 oC was added dropwise by syringe the solution of compound 70 (600 mg,

1.65 mmol) in anhydrous THF (35 mL), and the mixture was warmed to 23 oC with stirring for 4 h. At 0 oC, the following sequence of dropwise addition was performed with continuous stirring: H2O (6 mL), 15% aqueous NaOH (6 mL).

The white sticky part was removed by filtration, and the filtrate was concentrated under reduced pressure. Purification by flash chromatography

(25% → 60% EtOAc/hexanes) provided compound 70a (546 mg, 95% yield)

1 as a yellow solid. H NMR (400 MHz, DMSO-d6) δ 10.67 (br s, 1H), 7.39 (d, J

= 7.6 Hz, 1H), 7.21 (d, J = 7.6 Hz, 1H), 6.96 (td, J = 7.2, 1.2 Hz, 1H), 6.91 (td,

J = 7.2, 1.2 Hz, 1H), 6.39 (s, 1H), 3.74–3.72 (m, 4H), 2.83–2.79 (m, 2H), 2.46–

13 2.44 (m, 6H), 2.32 (s, 3H), 2.22 (s, 6H). C NMR (100 MHz, DMSO-d6) δ

166.6 (2 × C), 161.2, 135.1, 131.6, 128.2, 119.8, 118.0, 117.3, 110.3, 108.7,

108.2, 59.1, 52.7 (2 × CH2), 43.4 (2 × CH2), 23.7 (2 × CH3), 21.5, 11.2. HRMS-

+ ESI (m/z) [M + H] calcd for C21H28N5, 350.2339; found, 350.2345 (Δ = -1.6

+ ppm). [M + Na] calcd for C21H27N5Na, 372.2159; found, 372.2166 (Δ = -2.0 ppm). HPLC (2): t = 9.0 min, 96.7% purity. HPLC (3): t = 14.5 min, 96.3% purity.

3‐{3‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]propyl}‐1H‐indole

(71a). To a slurry of LiAlH4 (5 small spoons) in anhydrous THF (30 mL) at 0 oC was added dropwise by syringe the solution of compound 71 (752 mg, 2.07 mmol) in anhydrous THF (35 mL), and the mixture was warmed to 23 oC with stirring for 4 h. At 0 oC, the following sequence of dropwise addition was performed with continuous stirring: H2O (6 mL), 15% aqueous NaOH (6 mL).

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The white sticky part was removed by filtration, and the filtrate was concentrated under reduced pressure. Purification by flash chromatography

(25% → 50% EtOAc/hexanes) provided compound 71a (700 mg, 97% yield)

1 as a white solid. H NMR (400 MHz, DMSO-d6) δ 10.74 (br s, 1H), 7.51 (d, J

= 7.6 Hz, 1H), 7.32 (d, J = 7.6 Hz, 1H), 7.11 (d, J = 2 Hz, 1H), 7.05 (td, J = 7.2,

1.2 Hz, 1H), 6.96 (td, J = 7.2, 1.2 Hz, 1H), 6.38 (s, 1H), 3.71–3.69 (m, 4H),

2.73–2.69 (m, 2H), 2.40–2.34 (m, 6H), 2.21 (s, 6H), 1.83 (quintet, J = 7.6 Hz,

13 2H). C NMR (100 MHz, DMSO-d6) δ 166.6 (2 × C), 161.2, 136.3, 127.2,

122.2, 120.8, 118.3, 118.1, 114.4, 111.3, 108.7, 57.8, 52.8 (2 × CH2), 43.3 (2 ×

+ CH2), 27.2, 23.7 (2 × CH3), 22.5. HRMS-ESI (m/z) [M + H] calcd for

+ C21H28N5, 350.2339; found, 350.2346 (Δ = -1.9 ppm). [M + Na] calcd for

C21H27N5Na, 372.2159; found, 372.2166 (Δ = -2.0 ppm). HPLC (2): t = 9.0 min,

100% purity. HPLC (3): t = 14.4 min, 96.2% purity.

3‐{4‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]butyl}‐1H‐indole

(72a). To a slurry of LiAlH4 (5 small spoons) in anhydrous THF (30 mL) at 0 oC was added dropwise by syringe the solution of compound 72 (545 mg, 1.44 mmol) in anhydrous THF (20 mL), and the mixture was warmed to 23 oC with stirring for 4 h. At 0 oC, the following sequence of dropwise addition was performed with continuous stirring: H2O (6 mL), 15% aqueous NaOH (6 mL).

The white sticky part was removed by filtration, and the filtrate was concentrated under reduced pressure. Purification by flash chromatography

(25% → 40% EtOAc/hexanes) provided compound 72a (510 mg, 97% yield)

1 as a white solid. H NMR (400 MHz, DMSO-d6) δ 10.72 (br s, 1H), 7.50 (d, J

= 7.6 Hz, 1H), 7.32 (d, J = 7.6 Hz, 1H), 7.10 (d, J = 2.4 Hz, 1H), 7.05 (td, J =

6.8, 0.8 Hz, 1H), 6.95 (td, J = 6.8, 0.8 Hz, 1H), 6.38 (s, 1H), 3.69–3.66 (m, 4H),

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2.70 (t, J = 7.2 Hz, 2H), 2.37–2.31 (m, 6H), 2.21 (s, 6H), 1.67 (quintet, J = 7.6

13 Hz, 2H), 1.52 (quintet, J = 7.6 Hz, 2H). C NMR (100 MHz, DMSO-d6) δ 166.6

(2 × C), 161.2, 136.3, 127.2, 122.1, 120.7, 118.3, 118.0, 114.6, 111.3, 108.7,

57.8, 52.7 (2 × CH2), 43.3 (2 × CH2), 27.7, 26.2, 24.5, 23.7 (2 × CH3). HRMS-

+ ESI (m/z) [M + H] calcd for C22H30N5, 364.2496; found, 364.2497 (Δ = -0.4

+ ppm). [M + Na] calcd for C22H29N5Na, 386.2315; found, 386.2318 (Δ = -0.8 ppm). HPLC (2): t = 9.3 min, 100% purity. HPLC (3): t = 14.7 min, 98.5% purity.

Solid-State Structural Determination. X-ray diffraction data were collected with a Bruker AXS SMART APEX diffractometer using MoKα radiation at

223(2) K with the SMART suite of programs. Data were processed and corrected for Lorentz and polarisation effects using SAINT software, and for absorption effects using the SADABS software. Structural solution and refinement were then carried out using the SHELXTL suite of programs. The structure was determined by Direct Methods. Non-hydrogen atoms were located using difference maps and were given anisotropic displacement parameters in the final refinement. All H atoms were put at calculated positions using the riding model.

Radioligand Competition Binding Assays for Adenosine Receptors. The basics of radioligand competition is given in Appendix R. A two-step procedure was employed to prepare a membrane for radioligand binding from cells stably transfected with the human adenosine receptor subtypes (hA1, hA2A, hA2B and hA3 expressed on CHO cells) (Klotz et al., 1998). First, using the low-speed step (1,000g for 4 min), the cell fragments and nuclei were removed, and then crude membrane fraction from the supernatant was sedimented at 100,000g for

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30 min. Subsequently, the membrane pellet was resuspended in the specific buffer, frozen in liquid nitrogen, and stored at –80 oC. However, one step centrifugation method was used for the measurement of the adenylyl cyclase activity, in which the homogenate was sedimented for about 30 min at 54,000g.

Finally, the obtained crude membrane pellet was resuspended in 50 mM

Tris/HCl, pH 7.4 and used for the adenylyl cyclase assay immediately.

Binding Assays of Human Cloned A1, A2A, A3 Adenosine Receptors. In accordance with the procedures described previously (Klotz et al., 1985; Lohse et al., 1987), competition binding experiments for adenosine A1 receptor were carried out for 3 h at 25 oC in 200 μL of buffer containing 1 nM [3H]-CCPA,

0.2 U/mL adenosine deaminase, 20 μL of diluted membranes (50 μg of protein/assay) in 50 mM Tris/HCl, pH 7.4 and tested compounds in different concentrations. Non-specific binding was determined using theophylline 1 mM.

In a similar manner, binding of [3H]-NECA to CHO cells transfected with the human recombinant A2A adenosine receptors was performed. A mixture of the protein with a concentration of 50 μg/assay in buffer, 30 nM [3H]-NECA and tested compound in different concentrations were incubated for 3 h at 25 oC.

Non-specific binding was determined using N6-R-phenylisopropyl adenosine

(R-PIA) 100 μM (De Lean et al., 1982). Additionally, binding of [3H]HEMADO to CHO cells transfected with the human recombinant A3 adenosine receptors was carried out as described earlier (Klotz et al., 2007). The competition experiment was carried out for 3 h at 25 oC in the buffer solution containing 10 nM [3H]HEMADO, 20 μg membrane protein in 50 mM Tris-HCl, 1 mM ethylenediaminetetraacetic acid (EDTA), 10 mM MgCl2, pH 8.25 and tested compound in different concentrations. Non-specific binding was determined

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CHAPTER 4 using R-PIA 100 μM. Filtration of assay mixture was carried out to separate bound and un-bound radioactivity, through Whatman GF/B glass-fiber filters using a MicroMate 196 Cell Harvester (Packard Instrument Company). The filter bound radioactivity was calculated on Top Count (efficiency of 57%) with

Micro-Scint 20. Lastly, the Bio-Rad method (Bradford, 1976) using bovine albumin as a reference standard was employed to measure the protein concentration.

Adenylyl Cyclase Activity Assay for A2B Receptor. The potency of the compounds at hA2B receptor (expressed in CHO cells) was determined in adenylyl cyclase experiments. All these experiments were performed as described previously with minor modifications (Klotz et al., 1985; Klotz et al.,

1998). Membranes transfected with the hA2B receptor were incubated with 100 nM NECA, 150,000 cpm of [α-32P]ATP, and tested compounds in different concentrations for 20 min in the incubation mixture without ethylene glycol tetraacetic acid (EGTA) and NaCl. The IC50 values for concentration-dependent inhibition of NECA-mediated adenylyl cyclase activity caused by tested compounds were calculated.

Data Analysis. A non-linear curve fitting method in the SCT-FIT program was

3 used to calculate the 50% inhibition of binding of [ H]-NECA (IC50) produced by the different concentrations of tested compounds (in triplicate), as described previously (De Lean et al., 1982). The dissociation constant (Ki) values were calculated from IC50 by the Cheng and Prusoff equation (Cheng and Prusoff,

1973). A non-linear regression analysis using the equation for sigmoidal concentration-response curve was applied to calculate the IC50 values of adenylyl cyclase activity assay.

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4.2.3 Results and discussion

Compounds 69-72 and 69a-72a were tested in competition binding experiments at human adenosine A1, A2A, A2B and A3 receptors expressed in Chinese hamster ovary (CHO) cells (Table 10). The absence of a carbonyl group between indole and piperazine rings led to derivatives (compound 69a: hA2A AR Ki > 30 µM; compound 70a: hA2A AR Ki = 34.4 µM) displaying decreased affinity at A2A receptor, in comparison with the corresponding compounds (compound 69: hA2A AR Ki = 11.2 µM; compound 70: hA2A AR Ki = 8.71 µM) with a carbonyl group therein. This demonstrated the importance of the carbonyl group in binding hA2A receptor. In light of the A2A crystal structures in complex with antagonists (Figure 51), it was assumed that the carbonyl group of 69 and 70 acted as the hydrogen bond acceptor with Asn253 of A2A receptor.

In addition, whilst compounds 69 and 70 exhibited affinity for hA2A receptor, further extending the length of the middle linker to three or four carbon atoms resulted in a complete loss of A2A affinity (i.e. compounds 71 and 72: hA2A AR

Ki > 100 µM). This finding provided a clue that the carbon number of the linker may need to be limited within two for A2A binding activity.

125

Table 10. Binding (Ki, nM) of Compounds 69-72 and 69a-72a at Adenosine Receptor Subtypes

X N N N

N R1 N H 1 a b c d cpd R X hA1 hA2A hA2B hA3 hA1/A2A hA3/A2A 69 H C(=O) >100,000 11,200 (9,860–12,700) >20,000 >100,000 >9 >9 69a H CH2 >100,000 > 30,000 >20,000 >30,000 70 CH3 CH2C(=O) >100,000 8,710 (6,060–12,500) >20,000 >100,000 >11 >11 70a CH3 CH2CH2 >100,000 34,400 (23,800–49,900) >20,000 39,800 (32,500–48,800) >3 1.16 71 H CH2CH2C(=O) >100,000 >20,000 71a H CH2CH2CH2 >100,000 >20,000 72 H CH2CH2CH2C(=O) >100,000 >20,000 72a H CH2CH2CH2CH2 >100,000 >20,000 4 CHAPTER 126 a 3 3 Displacement of specific [ H]-2-chloro-6-cyclopentyladenosine ([ H]-CCPA) binding at human A1 receptors expressed in CHO b 3 3 cells (n=3–6). Displacement of specific [ H]-5’-N-ethylcarboxamidoadenosine ([ H]-NECA) binding at human A2A receptors c expressed in CHO cells (n=3–6). Ki values of the inhibition of NECA-stimulated adenylyl cyclase activity in CHO cells expressing d 3 6 3 hA2B receptors (n=3–6). Displacement of specific [ H]-2-hexyn-1-yl-N -methyladenosine ([ H]-HEMADO) binding at human A3 receptors expressed in CHO cells (n=3–6). Data are expressed with 95% confidence limits.

CHAPTER 4

Figure 51. The X-ray crystal structures of A2A receptor in complex with ZM241385 (blue sticks) and caffeine (yellow sticks) shows that both compounds locate in the same ligand-binding pocket. Hydrogen bonds are marked with red dashed lines (Dore et al., 2011) (reprinted with permission).

4.3 Indole C4~C7 Positions of IPP

4.3.1 Chemical considerations

In the series of compounds containing IPP scaffold shown in Table 10, compounds 69 and 70 were identified to be hA2A receptor binders with Ki of

8.71~11.2 µM and selectivity ratio > 9 over hA1 and hA3 subtypes. The next series of compounds was designed on the basis of the information obtained from

Table 10, that is, (i) the introduction of a carbonyl group between indole and piperazine rings, and (ii) limiting the linker length within two carbons. In addition, at that time, it was hypothesised that the hydrogen and methyl group at the indole C2 position, as shown in 69 and 70 respectively, did not make much difference in affinity for hA2A receptor. Studies on the substitution at the indole C4, C5, C6 and C7 positions were therefore conducted. Synthesis of

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CHAPTER 4 derivatives 73-83 was accomplished by EDC-mediated coupling (Figure 49) using various substituted indole-3-acids, as shown in Scheme 2.

Scheme 2a

R2 N R3 X N N N HN N i N R4 N R1 N H R5 68 73-83 aReagents and conditions: (i) indole-3-acid, EDC.HCl, EtOAc, DMF, 23 → 55 oC, 4.5~17 h, 43~69%.

4.3.2 Experimental methods

The general synthetic methods and radioligand binding procedures can be found under Section 4.2.2. The NMR spectra of compounds 73-83 are listed in

Appendix S. The HPLC chromatograms of compounds 73-83 are listed in

Appendix T.

3‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐4‐methoxy‐1H‐ indole (73). To a brown suspension of 4-methoxyindole-3-carboxylic acid (247 mg, 1.29 mmol) and EDC.HCl (261 mg, 1.36 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1.5 h at 23 oC followed by the addition of compound 68 (167 mg, 0.87 mmol). The reaction was heated at 45–50 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (1:1) to provide compound 73

1 (219 mg, 69% yield) as a brown powder. H NMR (400 MHz, DMSO-d6) δ

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11.40 (br s, 1H), 7.37 (d, J = 1.2 Hz, 1H), 7.09–7.02 (m, 2H), 6.55 (d, J = 7.2

Hz, 1H), 6.42 (s, 1H), 3.80–3.68 (2 × br s, 11H), 2.22 (s, 6H). 13C NMR (100

MHz, DMSO-d6) δ 166.8 (2 × C), 166.3, 161.1, 152.8, 136.9, 124.1, 122.8,

115.0, 110.4, 109.1, 105.1, 100.1, 55.1, 43.1 (2 × CH2), 40.1–38.9 (2 × CH2),

+ 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd for C20H24N5O2, 366.1925;

+ found, 366.1925 (Δ = -0.2 ppm). [M + Na] calcd for C20H23N5NaO2, 388.1744; found, 388.1739 (Δ = 1.3 ppm). HPLC (1): t = 8.7 min, 99.2% purity. HPLC

(3): t = 15.4 min, 99.3% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(5‐methoxy‐1H‐indol‐

3‐yl)ethan‐1‐one (74). To a suspension of 5-methoxyindole-3-acetic acid (306 mg, 1.49 mmol) and EDC.HCl (313 mg, 1.63 mmol) were added EtOAc (25 mL) and two drops of DMF, and the mixture was stirred for 100 min at 23 oC followed by the addition of compound 68 (193 mg, 1.01 mmol). The reaction was heated at 50 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (1:1) to provide compound 74 (222 mg,

1 58% yield) as a transparent colourless crystal. H NMR (400 MHz, DMSO-d6)

δ 10.74 (br s, 1H), 7.23 (d, J = 8.4 Hz, 1H), 7.19 (d, J = 2.4 Hz, 1H), 7.08 (d, J

= 2.4 Hz, 1H), 6.72 (dd, J = 8.8, 2.4 Hz, 1H), 6.41 (s, 1H), 3.78 (s, 2H), 3.74 (s,

3H), 3.66–3.64 (m, 2H), 3.56–3.52 (m, 6H), 2.21 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 169.6, 166.8 (2 × C), 161.1, 153.1, 131.3, 127.5, 124.2, 112.0,

111.2, 109.2, 107.8, 100.7, 55.4, 45.4, 43.4, 43.1, 41.1, 30.9, 23.7 (2 × CH3).

+ HRMS-ESI (m/z) [M + H] calcd for C21H26N5O2, 380.2081; found, 380.2078

+ (Δ = 0.8 ppm). [M + Na] calcd for C21H25N5NaO2, 402.1900; found, 402.1890

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(Δ = 2.5 ppm). HPLC (1): t = 8.5 min, 96.4% purity. HPLC (3): t = 14.8 min,

98.8% purity.

2‐(5‐Bromo‐1H‐indol‐3‐yl)‐1‐[4‐(4,6‐dimethylpyrimidin‐2‐yl)piperazin‐1‐ yl]ethan‐1‐one (75). To a suspension of 5-bromoindole-3-acetic acid (250 mg,

0.98 mmol) and EDC.HCl (199 mg, 1.04 mmol) were added EtOAc (25 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (125 mg, 0.65 mmol). The reaction was heated at

50–55 oC for 3.5 h, and solvents were evaporated to dryness under high vacuum.

Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (1:1) to provide compound 75 (128 mg, 46%

1 yield) as a white shiny crystal. H NMR (400 MHz, DMSO-d6) δ 11.12 (br s,

1H), 7.76 (d, J = 1.6 Hz, 1H), 7.33–7.30 (m, 2H), 7.17 (dd, J = 8.4, 2.0 Hz, 1H),

6.42 (s, 1H), 3.81 (s, 2H), 3.67–3.65 (m, 2H), 3.60 (s, 4H), 3.53–3.51 (m, 2H),

13 2.22 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 169.2, 166.8 (2 × C), 161.0,

134.9, 129.1, 125.4, 123.5, 121.2, 113.4, 111.1, 109.1, 108.0, 45.3, 43.4, 43.1,

+ 41.1, 30.5, 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd for C20H23BrN5O,

428.1080; found, 428.1083 (Δ = -0.5 ppm). [M + Na]+ calcd for

C20H22BrN5NaO, 450.0900; found, 450.0899 (Δ = 0.3 ppm). HPLC (1): t = 11.7 min, 97.4% purity. HPLC (3): t = 16.4 min, 99.0% purity.

3‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐5‐methoxy‐1H‐ indole (76). To a suspension of 5-methoxyindole-3-carboxylic acid (338 mg,

1.77 mmol) and EDC.HCl (381 mg, 1.99 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 75 min at 23 oC followed by the addition of compound 68 (229 mg, 1.19 mmol). The reaction was heated at 50 oC for 4.5 h, and solvents were evaporated to dryness under high vacuum.

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Water was added, sonicated, and filtered to provide compound 76 (206 mg, 47% yield) as a light yellow shiny transparent crystal. 1H NMR (400 MHz, DMSO- d6) δ 11.50 (br s, 1H), 7.68 (s, 1H), 7.33 (d, J = 8.8 Hz, 1H), 7.21 (d, J = 2.4 Hz,

1H), 6.80 (dd, J = 8.8, 2.4 Hz, 1H), 6.44 (s, 1H), 3.83–3.80 (m, 4H), 3.76 (s,

13 3H), 3.71–3.69 (m, 4H), 2.24 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 166.8

(2 × C), 165.9, 161.1, 154.3, 130.7, 128.5, 126.8, 112.7, 112.3, 109.3, 109.1,

101.8, 55.3, 43.6 (2 × CH2), 40.1–38.9 (2 × CH2), 23.7 (2 × CH3). HRMS-ESI

+ (m/z) [M + H] calcd for C20H24N5O2, 366.1925; found, 366.1930 (Δ = -1.4

+ ppm). [M + Na] calcd for C20H23N5NaO2, 388.1744; found, 388.1742 (Δ = 0.4 ppm). HPLC (1): t = 8.4 min, 99.6% purity. HPLC (3): t = 15.2 min, 99.0% purity.

2‐(5‐Bromo‐7‐fluoro‐2‐methyl‐1H‐indol‐3‐yl)‐1‐[4‐(4,6- dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]ethan‐1‐one (77). To a suspension of

(5-bromo-7-fluoro-2-methyl-1H-indol-3-yl)acetic acid (240 mg, 0.84 mmol) and EDC.HCl (232 mg, 1.21 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (108 mg, 0.56 mmol). The reaction was heated at 50 oC for 4 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (9:1) to provide compound 77 (130 mg, 50% yield) as a

1 dark brown crystal. H NMR (400 MHz, DMSO-d6) δ 11.53 (br s, 1H), 7.48 (d,

J = 1.2 Hz, 1H), 7.04 (dd, J = 10.4, 1.2 Hz, 1H), 6.43 (s, 1H), 3.75 (s, 2H), 3.66–

3.64 (m, 2H), 3.61–3.59 (m, 2H), 3.55–3.53 (m, 4H), 2.34 (s, 3H), 2.25 (s, 6H).

13 C NMR (100 MHz, DMSO-d6) δ 169.5, 167.3 (2 × C), 161.4, 148.5 (d, J =

245.7 Hz, C-F), 136.5, 133.8 (d, J = 7.3 Hz), 122.1 (d, J = 13.2 Hz, F-C-C-NH),

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117.3 (d, J = 2.9 Hz), 109.8 (d, J = 8.0 Hz), 109.6, 108.7 (d, J = 20.4 Hz, F-C-

C-H), 105.9 (d, J = 2.2 Hz), 45.5, 43.8, 43.6, 41.7, 29.7, 24.1 (2 × CH3), 11.9.

+ HRMS-ESI (m/z) [M + H] calcd for C21H24BrFN5O, 460.1143; found,

+ 460.1132 (Δ = 2.3 ppm). [M + Na] calcd for C21H23BrFN5NaO, 482.0962; found, 482.0950 (Δ = 2.5 ppm). HPLC (1): t = 13.3 min, 95.9% purity. HPLC

(3): t = 17.2 min, 96.5% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(5‐fluoro‐2‐methyl‐

1H‐indol‐3‐yl)ethan‐1‐one (78). To a suspension of (5-fluoro-2-methyl-1H- indol-3-yl)acetic acid (243 mg, 1.17 mmol) and EDC.HCl (245 mg, 1.28 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (152 mg, 0.79 mmol).

The reaction was heated at 50 oC for 4 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (6:5) to provide compound 78 (133 mg, 44% yield) as a transparent colourless crystal. 1H NMR

(400 MHz, DMSO-d6) δ 10.92 (br s, 1H), 7.22–7.19 (m, 2H), 6.79 (td, J = 9.2,

2.4 Hz, 1H), 6.41 (s, 1H), 3.73 (s, 2H), 3.63–3.62 (m, 2H), 3.52 (s, 6H), 2.33 (s,

13 3H), 2.21 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 169.4, 166.8 (2 × C),

161.0, 156.7 (d, J = 228.9 Hz, C-F), 135.0, 131.7, 128.6 (d, J = 10.2 Hz), 111.1

(d, J = 9.5 Hz), 109.1, 107.7 (d, J = 25.6 Hz, F-C-C-H), 104.5 (d, J = 4.3 Hz),

102.7 (d, J = 23.3 Hz, F-C-C-H), 45.2, 43.3, 43.2, 41.2, 29.7, 23.7 (2 × CH3),

+ 11.6. HRMS-ESI (m/z) [M + H] calcd for C21H25FN5O, 382.2038; found,

+ 382.2042 (Δ = -1.2 ppm). [M + Na] calcd for C21H24FN5NaO, 404.1857; found,

404.1852 (Δ = 1.2 ppm). HPLC (1): t = 10.1 min, 99.7% purity. HPLC (3): t =

15.8 min, 98.7% purity.

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1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(5‐fluoro‐1H‐indol‐3‐ yl)ethan‐1‐one (79). To a suspension of 5-fluoro-1H-indole-3-acetic acid (328 mg, 1.70 mmol) and EDC.HCl (345 mg, 1.80 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 2.5 h at 23 oC followed by the addition of compound 68 (218 mg, 1.14 mmol). The reaction was heated at 45–50 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (2:1) to provide compound 79

1 (237 mg, 57% yield) as a white shiny crystal. H NMR (400 MHz, DMSO-d6)

δ 11.01 (br s, 1H), 7.35–7.30 (m, 3H), 6.91 (td, J = 9.2, 2.4 Hz, 1H), 6.42 (s,

1H), 3.79 (s, 2H), 3.66–3.64 (m, 2H), 3.58 (s, 4H), 3.53–3.51 (m, 2H), 2.21 (s,

13 6H). C NMR (100 MHz, DMSO-d6) δ 169.3 (2 × C), 166.8, 161.1, 156.7 (d, J

= 229.6 Hz, C-F), 132.8, 127.5 (d, J = 10.2 Hz), 125.8, 112.3 (d, J = 9.5 Hz),

109.21 (d, J = 25.6 Hz, F-C-C-H), 109.18, 108.4 (d, J = 4.4 Hz), 103.5 (d, J =

23.3 Hz, F-C-C-H), 45.4, 43.5, 43.1, 41.1, 30.7, 23.7 (2 × CH3). HRMS-ESI

+ (m/z) [M + H] calcd for C20H23FN5O, 368.1881; found, 368.1889 (Δ = -2.2

+ ppm). [M + Na] calcd for C20H22FN5NaO, 390.1701; found, 390.1696 (Δ = 1.2 ppm). HPLC (1): t = 9.6 min, 99.4% purity. HPLC (3): t = 15.5 min, 99.0% purity.

5‐Chloro‐3‐[4‐(4,6‐dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐1H‐ indole (80). To a suspension of 5-chloroindole-3-carboxylic acid (248 mg, 1.27 mmol) and EDC.HCl (269 mg, 1.40 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (165 mg, 0.86 mmol). The reaction was heated at 45–

50 oC for 16 h, and solvents were evaporated to dryness under high vacuum.

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Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (1:1) to provide compound 80 (137 mg, 43%

1 yield) as a pink crystal. H NMR (400 MHz, DMSO-d6) δ 11.83 (br s, 1H), 7.83

(s, 1H), 7.76 (dd, J = 2.0, 0.4 Hz, 1H), 7.47 (dd, J = 8.8, 0.4 Hz, 1H), 7.17 (dd,

J = 8.4, 2.0 Hz, 1H), 6.44 (s, 1H), 3.82–3.80 (m, 4H), 3.73–3.70 (m, 4H), 2.24

13 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 167.3 (2 × C), 165.5, 161.6, 134.7,

130.0, 128.1, 125.4, 122.5, 120.1, 114.0, 109.61, 109.57, 43.9 (2 × CH2), 40.6–

+ 39.3 (2 × CH2), 24.2 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd for

+ C19H21ClN5O, 370.1429; found, 370.1433 (Δ = -0.9 ppm). [M + Na] calcd for

C19H20ClN5NaO, 392.1249; found, 392.1245 (Δ = 0.9 ppm). HPLC (1): t = 10.9 min, 99.1% purity. HPLC (3): t = 16.6 min, 98.1% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(6‐fluoro‐1H‐indol‐3‐ yl)ethan‐1‐one (81). To a suspension of 6-fluoroindole-3-acetic acid (244 mg,

1.27 mmol) and EDC.HCl (265 mg, 1.38 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 70 min at 23 oC followed by the addition of compound 68 (165 mg, 0.86 mmol). The reaction was heated at 50 oC for 4.5 h, and solvents were evaporated to dryness under high vacuum.

Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc (8:5) to provide compound 81 (179 mg, 57%

1 yield) as a light brown, clear crystal. H NMR (400 MHz, DMSO-d6) δ 10.97

(br s, 1H), 7.56 (dd, J = 8.8, 5.6 Hz, 1H), 7.24 (d, J = 2.0 Hz, 1H), 7.11 (dd, J =

10.0, 2.4 Hz, 1H), 6.86–6.81 (m, 1H), 6.42 (s, 1H), 3.81 (s, 2H), 3.66–3.63 (m,

2H), 3.58 (s, 4H), 3.53–3.50 (m, 2H), 2.21 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 169.8, 167.2 (2 × C), 161.5, 159.3 (d, J = 232.6 Hz, C-F), 136.4

(d, J = 12.4 Hz), 124.6 (d, J = 3.7 Hz), 124.5, 120.3 (d, J = 10.2 Hz), 109.6,

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108.9, 107.3 (d, J = 24.1 Hz, F-C-C-NH), 97.7 (d, J = 24.8 Hz, F-C-C-NH),

+ 45.8, 43.9, 43.6, 41.6, 31.2, 24.1 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd

+ for C20H23FN5O, 368.1881; found, 368.1879 (Δ = 0.6 ppm). [M + Na] calcd for C20H22FN5NaO, 390.1701; found, 390.1689 (Δ = 2.9 ppm). HPLC (1): t =

9.6 min, 99.8% purity. HPLC (3): t = 15.6 min, 99.0% purity.

3‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐7‐methoxy‐1H‐ indole (82). To a suspension of 7-methoxy-1H-indole-3-carboxylic acid (292 mg, 1.53 mmol) and EDC.HCl (309 mg, 1.61 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (198 mg, 1.03 mmol). The reaction was heated at 50 oC for 3.5 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered to provide compound 82

1 (215 mg, 57% yield) as an off-white solid. H NMR (400 MHz, DMSO-d6) δ

11.75 (br s, 1H), 7.59 (d, J = 2.4 Hz, 1H), 7.29 (d, J = 7.2 Hz, 1H), 7.02 (t, J =

8.0 Hz, 1H), 6.73 (d, J = 7.6 Hz, 1H), 6.44 (s, 1H), 3.93 (s, 3H), 3.81–3.78 (m,

13 4H), 3.69–3.66 (m, 4H), 2.24 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 166.8

(2 × C), 165.8, 161.1, 146.3, 127.6, 127.4, 125.9, 120.9, 112.9, 110.3, 109.1,

102.4, 55.3, 43.5 (2 × CH2), 40.1–38.9 (2 × CH2), 23.7 (2 × CH3). HRMS-ESI

+ (m/z) [M + H] calcd for C20H24N5O2, 366.1925; found, 366.1920 (Δ = 1.2 ppm).

+ [M + Na] calcd for C20H23N5NaO2, 388.1744; found, 388.1733 (Δ = 2.9 ppm).

HPLC (1): t = 9.5 min, 99.8% purity. HPLC (3): t = 15.9 min, 99.3% purity.

1‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazin‐1‐yl]‐2‐(1H‐indol‐3‐yl)ethan‐

1‐one (83). To a suspension of indole-3-acetic acid (672 mg, 3.84 mmol) and

EDC.HCl (774 mg, 4.04 mmol) were added EtOAc (30 mL) and two drops of

DMF, and the mixture was stirred for 3 h at 23 oC followed by the addition of

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CHAPTER 4 compound 68 (493 mg, 2.56 mmol). The reaction was heated at 50 oC for 3.5 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from

EtOH/EtOAc (1:1) to provide compound 83 (414 mg, 46% yield) as a white

1 crystal. H NMR (400 MHz, DMSO-d6) δ 10.90 (br s, 1H), 7.58 (d, J = 7.6 Hz,

1H), 7.34 (d, J = 8.4 Hz, 1H), 7.24 (d, J = 2.0 Hz, 1H), 7.07 (td, J = 7.4, 0.8 Hz,

1H), 6.97 (td, J = 7.4, 0.8 Hz, 1H), 6.41 (s, 1H), 3.81 (s, 2H), 3.66–3.63 (m,

2H), 3.57 (s, 4H), 3.53–3.51 (m, 2H), 2.21 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 169.4, 166.8 (2 × C), 161.0, 136.1, 127.1, 123.5, 121.1, 118.8,

118.4, 111.3, 109.1, 108.1, 45.4, 43.4, 43.1, 41.1, 30.9, 23.7 (2 × CH3). HRMS-

+ ESI (m/z) [M + H] calcd for C20H24N5O, 350.1975; found, 350.1980 (Δ = -1.4

+ ppm). [M + Na] calcd for C20H23N5NaO, 372.1795; found, 372.1794 (Δ = 0.2 ppm). HPLC (1): t = 9.2 min, 97.5% purity. HPLC (3): t = 15.3 min, 99.2% purity.

4.3.3 Results and discussion

The binding affinity of compounds 73-83 at adenosine receptor subtypes is given in Table 11. The hydrogen atoms at the indole C4, C5, C6 and C7 positions were replaced with substituent groups, including halogens and methoxy group, to study the effects on hA2A binding affinity. Compound 73 represents C4-substituted indolyl analogue, compounds 74-80 represent C5- substituted indolyl analogues, compound 81 represents C6-substituted indolyl analogue, and compound 82 represents C7-substituted indolyl analogue. In the first place, it was found that simply replacing the hydrogen (-H) at the indole

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C4, C5, C6 and C7 positions with methoxy (-OCH3), bromine (-Br), fluorine (-

F) and chlorine (-Cl) did not significantly enhance the binding affinity at hA2A receptor compared to that displayed by compounds 69 and 70. At the indole C4 position, the introduction of a methoxy group (compound 73: hA2A AR Ki = 18.8

µM) did not produce an appreciable difference in hA2A binding from the corresponding derivative without methoxy substitution (i.e. compound 69: hA2A

AR Ki = 11.2 µM). At the indole C-5 position, the effects of methoxy group, fluorine, chlorine and bromine substitution were studied. Similar to the indole

C4 position, the introduction of a methoxy group at the indole C5 position generated compounds with comparable hA2A affinity to the corresponding ones with a hydrogen atom on the same position (i.e. 74 versus 83; 76 versus 69).

There was also no noticeable variation in hA2A activity in reference to substitution with either fluorine (i.e. 78 versus 70; 79 versus 83) or chlorine (i.e.

80 versus 69). However, the presence of bromine at the indole C5 position was detrimental for affinity at hA2A receptor (e.g. compound 75: hA2A AR Ki > 100

µM in comparison with compound 83: hA2A AR Ki = 27.6 µM). In fact, halogen substitution can potentially have steric, electronic and lipophilic effects on the whole compound, thereby influencing biological activity of that compound.

Steric effects of halogenation refer to the creation of constraints or imposing certain conformations so as to either favour or disfavour ligand-receptor interactions. This is due to the larger size of halogens than that of hydrogen

(Table 12). It is therefore probable that bromine at the indole C5 position created a steric clash with adjacent residues and prevented the ligand from accessing to the ligand-binding pocket. The electronic effects of halogenation are based on the inductive electron-withdrawing property of halogens. Although halogens

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CHAPTER 4 with higher electronegativity, such as fluorine and chlorine, create dipole moment in C-X bond (X = halogen) and decrease the electron density of the indole ring, there was no apparent change in hA2A binding, excluding the electronic effect of bromine on the inactivity of 75 and 77. The contribution of a halogen to the lipophilicity of a compound can be described by Hansch’s π parameter (Table 12). The reduction of affinity by introducing bromine at the indole C5 position can then also be explained by the highly non-polar property of 75 and 77. Taken together, two possibilities account for the loss of affinity exhibited by 75 and 77: (i) the large size of bromine, and (ii) the non-polarity contributed by bromine. It was therefore speculated that the receptor environment near the indole C5 position is either a relatively small, or a relatively polar, or both, cavity. More analogues with different substituents at the indole C5 position need to be made to prove or disprove this speculation.

At the indole C6 position, the introduction of fluorine did not show significant change in hA2A affinity (i.e. 81 versus 83). Surprisingly, methoxy group substitution at the indole C7 position led to a 3-fold improvement in hA2A affinity (i.e. compound 82: hA2A AR Ki = 3.63 µM versus compound 69: hA2A

AR Ki = 11.2 µM). The enhanced binding observed in compound 82 could be attributed to either the oxygen atom of the methoxy substituent participating in hydrogen bonds as proton acceptor, or the methyl group of the methoxy substituent being stabilised by hydrophoblic interactions with neighbouring residues, or both. It was recently shown that methoxy substituent on methoxyindole isomers caused changes in the π-delocalization of the indole ring

(Lopes Jesus and Redinha, 2014), so the increased affinity exhibited by compound 82 may also due to the alteration of interactions (e.g. hydrophobic,

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CHAPTER 4 electrostatic) associated with the indole ring. It will be an interesting future direction to explore the SAR at the indole C7 position for optimising hA2A affinity.

Comparison of the hA2A binding affinity of 83 with that of 69 and 70 gave two pieces of information. First (comparing 83 with 69), a 2.5-fold reduction of affinity (i.e. compound 69: hA2A AR Ki = 11.2 µM versus compound 83: hA2A

AR Ki = 27.6 µM) was observed when an methylene group was introduced between indole and carbonyl group of 69, implying that one carbon for the linker may be optimal for binding. Second (comparing 83 with 70), the 3.2-fold decrease in affinity (i.e. compound 70: hA2A AR Ki = 8.71 µM versus compound

83: hA2A AR Ki = 27.6 µM) occurred in the absence of the methyl group at the indole C2 position, suggesting that that methyl group contributed binding to a certain extent. It can therefore be deduced that the slightly higher affinity shown by 70 than that shown by 69 was contributed by the additional methyl group of

70, not by the extra methylene group of 70. To conclude the SARs shown in

Tables 10 and 11, it seems that 2-methyl-7-methoxyindole linked to 68 by a carbonyl group is the best combination, making it attractive to synthesise 84 in the future (Figure 52).

139

Table 11. Binding (Ki, nM) of Compounds 73-83 at Adenosine Receptor Subtypes

R2 R3 X N N N R4 N R1 N H R5 73-83

1 2 3 4 5 a b c d cpd R R R R R X hA1 hA2A hA2B hA3 hA1/A2A hA3/A2A 73 H OCH3 H H H C(=O) >100,000 18,800 (15,500–22,700) >20,000 >30,000 >5 >2 74 H H OCH3 H H CH2C(=O) >100,000 18,400 (14,000–24,200) >20,000 >30,000 >5 >2 75 H H Br H H CH2C(=O) >100,000 >100,000 >20,000 >100,000 76 H H OCH3 H H C(=O) >100,000 12,700 (10,500–15,400) >20,000 >30,000 >8 >2 77 CH3 H Br H F CH2C(=O) >100,000 >100,000 >20,000 >100,000 78 CH3 H F H H CH2C(=O) >100,000 18,200 (15,500–21,200) >20,000 >30,000 >6 >2 79 H H F H H CH2C(=O) >100,000 18,700 (15,300–22,900) >20,000 >30,000 >5 >2

80 H H Cl H H C(=O) >100,000 15,500 (13,500–17,800) >20,000 19,500 (13,700–27,800) >7 1.26 4 CHAPTER

140 81 H H H F H CH2C(=O) >100,000 26,900 (20,300–35,600) >20,000 >30,000 >4 >1 82 H H H H OCH3 C(=O) >100,000 3,630 (2,010–6,570) >20,000 >30,000 >28 >8 83 H H H H H CH2C(=O) >100,000 27,600 (21,400–35,700) >20,000 >30,000 >4 >1 a 3 3 b 3 Displacement of specific [ H]-2-chloro-6-cyclopentyladenosine ([ H]-CCPA) binding at human A1 receptors expressed in CHO cells (n=3–6). Displacement of specific [ H]- 3 c 5’-N-ethylcarboxamidoadenosine ([ H]-NECA) binding at human A2A receptors expressed in CHO cells (n=3–6). Ki values of the inhibition of NECA-stimulated adenylyl d 3 6 3 cyclase activity in CHO cells expressing hA2B receptors (n=3–6). Displacement of specific [ H]-2-hexyn-1-yl-N -methyladenosine ([ H]-HEMADO) binding at human A3 receptors expressed in CHO cells (n=3–6). Data are expressed with 95% confidence limits.

CHAPTER 4

Table 12. The Size and Lipophilicity of Hydrogen and Halogensa atom van der Waals radii Hansch’s π parameter H 1.20 0.00 F 1.47 0.14 Cl 1.75 0.71 Br 1.85 0.86 aData obtained from (Bazzini and Wermuth, 2008).

info from i) Table 10 ii) comparison of 83 with 69

O 3 N N N N 2 7 H OMe N info from info from comparison of 82 with 69 comparison of 83 with 70

84 Figure 52. Proposed compound 84 for future synthesis.

4.4 Indole C2 Positions of IPP

4.4.1 Chemical considerations

The revelation of the importance of the methyl group at the indole C2 position motivated us to put a phenyl group at the same position, leading to the synthesis of compound 90 (Scheme 3). Ethyl ester 86 was readily prepared from indole-

3-acetic acid 85 in the acidic condition using EtOH as the solvent (95% yield).

N-bromosuccinimide, freshly recrystallised from water, was used to brominate compound 86 at the C2 position to yield bromoester 87 (59% yield), followed by protection of indole NH with butyloxycarbonyl (Boc) group (91% yield).

The protected indolyl bromide 88 was coupled with phenylboronic acid using

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Pd(PPh3)4 (15 mol%) as the catalyst, Na2CO3 as the base, dioxane/H2O (3:1) as the solvent, under traditional heating conditions of Suzuki reaction (Miyaura and Suzuki, 1995) to generate compound 89 in 69% yield. The catalytic cycle for Suzuki coupling is shown in Figure 53. The cycle is composed of three major steps: (i) oxidative addition, (ii) transmetalation, and (iii) reductive elimination.

Oxidative addition of aryl bromide 88 to tetrakis(triphenylphosphine)palladium(0) complex affords a stable palladium(II) complex 88a. Transmetalation refers to the transfer of the phenyl group in phenylboronic acid to palladium in 88a, forming a new palladium(II) complex 88b. Transmetalation is generally facilitated by the presence of bases, such as Na2CO3 in the synthesis of 89, which are able to quaternise the boron atom so as to enhance the nucleophilicity of B–Ph. The final step, reductive elimination, reproduces tetrakis(triphenylphosphine)palladium(0) complex and forms the desired product 89. The subsequent acid-promoted Boc deprotection, saponification, and amide formation resulted in the target compound 90 with an overall yield of 64% (3 steps). At the moment of this writing, the binding measurement of 90 at all subtypes of adenosine receptor is underway.

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Scheme 3a

O O O

OH OEt OEt i ii Br iii N N N H H H

85 86 87

O O O N OEt N N OEt N Br iv v, vi, vii N N N O O H O O 90

88 89 a Reagents and conditions: (i) conc. H2SO4, EtOH, reflux, 23 h, 95%; (ii) NBS, o o CH2Cl2, dark, 0 C, 2 h, 59%; (iii) (Boc)2O, Et3N, DMAP, CH2Cl2, 23 C, 1 h, o 91%; (iv) PhB(OH)2, Pd(PPh3)4, Na2CO3, dioxane, H2O, 80 C, 18 h, 69%; (v) o o TFA, CH2Cl2, 0 → 23 C, 28 h; (vi) KOH, H2O, MeOH, EtOH, 23 C, 19.5 h; (vii) EDC.HCl, EtOAc, DMF, 68, 23 → 50 oC, 5 h, 64% (over three steps).

0 RPh L4Pd RBr

89 (L = PPh3) 88 reductive oxidative addition elimination

R R +2 +2 L4Pd L4Pd Ph 88b 88a Br

OH (from Na2CO3)

B(OH) 4 R +2 L4Pd transmetalation OH

Ph B(OH)2 Ph B(OH)2 OH (from Na CO ) HO 2 3 Figure 53. The catalytic cycle for synthesis of 89.

An additional study at the indole C2 position was to link 68 to the indole C2 position for understanding the differences between indole C2 and C3 extensions

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in affinity at hA2A receptor. Since the importance of one carbonyl group between indole and piperazine has been shown for indole-3-substituted derivatives (summarised in Figure 52), indole-2-acids with one carbonyl group were deliberately selected to generate 91-95 (Scheme 4).

Scheme 4a

N R3 N N N i HN N N N N X H R5 68 91-95 aReagents and conditions: (i) indole-2-carboxylic acids, EDC.HCl, EtOAc, DMF, 23 → 40 oC, 7 h, 16~36%.

4.4.2 Experimental methods

The general synthetic methods and radioligand binding procedures can be found under Section 4.2.2. The NMR spectra of compounds 86-95 are listed in

Appendix U. The HPLC chromatograms of compounds 90-95 are listed in

Appendix V.

Ethyl 2-(1H-indol-3-yl)acetate (86). To a clear solution of indole-3-acetic acid

(1014 mg, 5.79 mmol) in EtOH (25 mL) was added dropwise concentrated

o H2SO4 (1 mL) at 23 C, and the mixture was refluxed under N2 for 20 h. The

TLC analysis showed that the starting material still existed in the reaction

o mixture, so another 1 mL of concentrated H2SO4 was added dropwise at 23 C.

The reaction was refluxed for another 3 h to consume the remaining starting materials. The resulting clear, light purple solution was cooled to 0 oC, and

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saturated aqueous NaHCO3 was added dropwise until the pH value was 7.

EtOAc was added, and the aqueous phase was extracted with EtOAc (3 × 30 mL). The combined organic layers were washed with brine (50 mL), dried over

Na2SO4, filtered, and concentrated under reduced pressure. Purification by flash chromatography (hexanes → 10% EtOAc/hexanes) afforded compound 86

1 (1117 mg, 95% yield) as a brown oil. H NMR (400 MHz, DMSO-d6) δ 10.93

(br s, 1H), 7.49 (d, J = 8.0 Hz, 1H), 7.35 (dt, J = 8.0, 0.8 Hz, 1H), 7.24 (d, J =

2.4 Hz, 1H), 7.08 (td, J = 6.8, 1.2 Hz, 1H), 6.98 (td, J = 6.8, 1.2 Hz, 1H), 4.07

(q, J = 7.2 Hz, 2H), 3.72 (s, 2H), 1.18 (t, J = 7.2 Hz, 3H).

Ethyl 2-(2-bromo-1H-indol-3-yl)acetate (87). To a stirred, clear, light- brownish solution of compound 86 (1114 mg, 5.49 mmol) in anhydrous CH2Cl2

(20 mL) at 0 oC in the dark was added N-bromosuccinimide (976 mg, 5.49 mmol; recrystallised from water), and the mixture was stirred at 0 oC for 2 h.

The solvent was evaporated, and the resulting purple oil was subjected to purification by flash chromatography (hexanes → 7.5% EtOAc/hexanes) afforded compound 87 (912 mg, 59% yield) as a yellow oil. 1H NMR (400 MHz,

DMSO-d6) δ 11.78 (br s, 1H), 7.46 (d, J = 8.0 Hz, 1H), 7.30 (d, J = 8.0 Hz, 1H),

7.10 (t, J = 7.2 Hz, 1H), 7.02 (t, J = 7.2 Hz, 1H), 4.07 (q, J = 7.2 Hz, 2H), 3.69

13 (s, 2H), 1.17 (t, J = 7.2 Hz, 3H). C NMR (100 MHz, DMSO-d6) δ 170.4, 136.0,

127.2, 121.6, 119.3, 118.0, 110.7, 109.9, 107.2, 60.2, 30.3, 14.1. tert-Butyl 2-bromo-3-(2-ethoxy-2-oxoethyl)-1H-indole-1-carboxylate (88).

To a stirred, clear yellow solution of compound 87 (884 mg, 3.14 mmol) and

o di-tert-butyl dicarbonate (917 mg, 4.08 mmol) in anhydrous CH2Cl2 at 23 C

(20 mL) were added triethylamine (1094 µL, 7.85 mmol) and 4-

(dimethylamino)pyridine (21 mg, 0.17 mmol), and the resulting yellow solution

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CHAPTER 4 was stirred at 23 oC for 1 h. Water was added, and the aqueous phase was extracted with CH2Cl2 (3 × 40 mL). The combined organic layers were washed with brine (50 mL), dried over Na2SO4, filtered, and concentrated under reduced pressure. Purification by flash chromatography (hexanes → 3%

EtOAc/hexanes) afforded compound 88 (1093 mg, 91% yield) as a clear oil. 1H

NMR (400 MHz, DMSO-d6) δ 7.99 (d, J = 8.4 Hz, 1H), 7.58 (d, J = 7.6 Hz,

1H), 7.34 (t, J = 7.2 Hz, 1H), 7.26 (t, J = 7.2 Hz, 1H), 4.09 (q, J = 7.2 Hz, 2H),

3.80 (s, 2H), 1.65 (s, 9H), 1.18 (t, J = 7.2 Hz, 3H). tert-Butyl 3-(2-ethoxy-2-oxoethyl)-2-phenyl-1H-indole-1-carboxylate (89).

Compound 88 (233 mg, 0.61 mmol), phenylboronic acid (113 mg, 0.92 mmol), and tetrakis(triphenylphosphine)palladium(0) (108 mg, 0.0915 mmol) were put in a two-neck round-bottom flask, and the mixture was evacuated/refilled with

N2 gas. A solution of degassed Na2CO3 (5 mL, 2M aqueous solution) and dioxane (15 mL) was added to the mixture and heated at 80 oC under nitrogen for 18 h. The mixture was cooled to 23 oC and solids were removed by filtration through Celite. The filtrate was concentrated under reduced pressure and chromatographed on silica gel (eluent: hexanes → 2.5% EtOAc/hexanes) to give compound 89 (160 mg, 69% yield) as a white solid. 1H NMR (400 MHz,

DMSO-d6) δ 8.13 (d, J = 8.4 Hz, 1H), 7.60 (d, J = 7.6 Hz, 1H), 7.51–7.42 (m,

3H), 7.39–7.35 (m, 3H), 7.29 (t, J = 7.2 Hz, 1H), 4.04 (q, J = 7.2 Hz, 2H), 3.53

(s, 2H), 1.18 (s, 9H), 1.14 (t, J = 7.2 Hz, 3H).

1-[4-(4,6-Dimethylpyrimidin-2-yl)piperazin-1-yl]-2-(2-phenyl-1H-indol-3- yl)ethan-1-one (90). To a colourless solution of compound 89 (45 mg, 0.12

o mmol) in CH2Cl2 (3 mL) at 0 C was added trifluoroacetic acid (50 µL, 0.65 mmol), and the mixture was stirred at 0 oC for 10 min before it was warmed to

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23 oC. After 1 h, trifluoroacetic acid (50 µL) was added. After 4 h, trifluoroacetic acid (100 µL) was added. After 17 h, trifluoroacetic acid (100

µL) was added. After 6 h, the TLC analysis showed the full consumption of compound 89, and the reaction mixture was cooled to 0 oC with slow stirring.

Saturated aqueous NaHCO3 was added dropwise to the mixture until the pH value was approximately 8–9. The layers were separated, and the aqueous phase was extracted with CH2Cl2 (3 × 10 mL). The combined organic layers were washed with brine (10 mL), dried over Na2SO4, filtered, and concentrated under reduced pressure to obtain an off-white solid (32 mg; ESI-MS (m/z): 280.5

[M+H]+, 278.0 [M-H]-) which was dissolved in MeOH (6 mL) and EtOH (3 mL). The aqueous 0.1M KOH solution (6 mL) was added, and the resulting slightly turbid solution was stirred at 23 oC for 19.5 h (ESI-MS (m/z): 250.0 [M-

H]-). The reaction was cooled to 0 oC, and 1N aqueous HCl was added dropwise until the pH value was approximately 7. Solvents were removed under reduced pressure to give a white solid (29 mg, 0.12 mmol) which was mixed with

EDC.HCl (30 mg, 0.16 mmol), EtOAc (12 mL), and four drops of DMF. The mixture was stirred for 2 h at 23 oC, and compound 68 (18 mg, 0.09 mmol) was added. The reaction was stirred at 50 oC for 3 h, and solvents were evaporated to dryness under high vacuum. Purification by flash chromatography (1:3 →

1:2 EtOAc/hexanes) afforded compound 90 (33 mg, 64% yield over three steps)

1 as a clear oil. H NMR (400 MHz, DMSO-d6) δ 11.27 (br s, 1H), 7.63–7.60 (m,

2H), 7.53–7.50 (m, 3H), 7.41–7.36 (m, 2H), 7.10 (td, J = 7.6, 1.2 Hz, 1H), 6.99

(td, J = 7.6, 1.2 Hz, 1H), 6.43 (s, 1H), 3.92 (s, 2H), 3.64–3.63 (m, 2H), 3.53–

13 3.50 (m, 6H), 2.22 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 169.3, 166.7 (2

× C), 160.9, 135.9, 135.3, 132.6, 128.9, 128.7 (2 × CH), 127.9 (2 × CH), 127.5,

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121.5, 118.9, 118.7, 111.1, 109.1, 105.8, 45.0, 43.3, 43.2, 41.3, 29.9, 23.7 (2 ×

+ - CH3). ESI-MS (m/z): 426.7 [M+H] , 424.8 [M-H] . HPLC (2): t = 12.9 min,

97.0% purity.

2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐1H‐indole (91).

To a suspension of indole-2-carboxylic acid (500 mg, 3.10 mmol) and EDC.HCl

(713 mg, 3.72 mmol) were added EtOAc (30 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound

68 (596 mg, 3.10 mmol). The reaction was heated at 40 oC for 6 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc to provide compound 91 (241 mg, 23% yield) as a clear crystal. 1H NMR (400

MHz, DMSO-d6) δ 11.60 (br s, 1H), 7.62 (dd, J = 8.0, 0.4 Hz, 1H), 7.43 (dd, J

= 8.0, 0.4 Hz, 1H), 7.21–7.17 (m, 1H), 7.07–7.03 (m, 1H), 6.86 (dd, J = 2.0, 0.8

Hz, 1H), 6.46 (s, 1H), 3.84 (br s, 8H), 2.25 (s, 6H). 13C NMR (100 MHz,

DMSO-d6) δ 166.9 (2 × C), 162.2, 161.0, 136.0, 129.8, 126.9, 123.3, 121.4,

119.8, 112.1, 109.2, 104.3, 43.4 (2 × CH2), 40.1–38.9 (2 × CH2), 23.7 (2 × CH3).

+ HRMS-ESI (m/z) [M + H] calcd for C19H22N5O, 336.1819; found, 336.1821

+ (Δ = -0.8 ppm). [M + Na] calcd for C19H21N5NaO, 358.1638; found, 358.1634

(Δ = 1.1 ppm). HPLC (1): t = 11.3 min, 95.0% purity. HPLC (3): t = 16.5 min,

99.3% purity.

2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐5‐fluoro‐1H‐ indole (92). To a suspension of 5-fluoroindole-2-carboxylic acid (100 mg, 0.56 mmol) and EDC.HCl (130 mg, 0.67 mmol) were added EtOAc (16 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (107 mg, 0.56 mmol). The reaction was heated at 40

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CHAPTER 4 oC for 6 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from EtOH/EtOAc to provide compound 92 (31 mg, 16% yield) as a white silky

1 crystal. H NMR (400 MHz, DMSO-d6) δ 11.72 (br s, 1H), 7.43 (dd, J = 8.8,

4.4 Hz, 1H), 7.37 (dd, J = 9.6, 2.4 Hz, 1H), 7.06 (td, J = 9.6, 2.4 Hz, 1H), 6.84

(s, 1H), 6.46 (s, 1H), 3.84 (br s, 8H), 2.25 (s, 6H). 13C NMR (100 MHz, DMSO- d6) δ 166.8 (2 × C), 161.9, 161.0, 157.1 (d, J = 231.1 Hz, C-F), 132.7, 131.6,

126.9 (d, J = 10.2 Hz), 113.3 (d, J = 9.4 Hz), 111.9 (d, J = 26.2 Hz, F-C-C-H),

109.2, 105.5 (d, J = 22.6 Hz, F-C-C-H), 104.2 (d, J = 5.1 Hz), 43.3 (2 × CH2),

+ 40.1–38.9 (2 × CH2), 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd for

+ C19H21FN5O, 354.1725; found, 354.1720 (Δ = 0.4 ppm). [M + Na] calcd for

C19H20FN5NaO, 376.1544; found, 376.1537 (Δ = 0.7 ppm). HPLC (1): t = 11.7 min, 98.0% purity. HPLC (3): t = 16.6 min, 99.7% purity.

2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐5‐methyl‐1H‐ indole (93). To a suspension of 5-methylindole-2-carboxylic acid (101 mg, 0.57 mmol) and EDC.HCl (134 mg, 0.69 mmol) were added EtOAc (16 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (113 mg, 0.57 mmol). The reaction was heated at 40 oC for 6 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc to provide compound 93 (70 mg, 35% yield). 1H NMR (400

MHz, DMSO-d6) δ 11.46 (br s, 1H), 7.38 (d, J = 0.8 Hz, 1H), 7.32 (d, J = 8.4

Hz, 1H), 7.02 (dd, J = 8.4, 1.6 Hz, 1H), 6.76 (d, J = 1.6 Hz, 1H), 6.46 (s, 1H),

13 3.84 (br s, 8H), 2.37 (s, 3H), 2.25 (s, 6H). C NMR (100 MHz, DMSO-d6) δ

167.3 (2 × C), 162.7, 161.5, 134.8, 130.2, 128.8, 127.6, 125.6, 121.1, 112.3,

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109.7, 104.3, 43.8 (2 × CH2), 40.6–9.3 (2 × CH2), 24.2 (2 × CH3), 21.6. HRMS-

+ ESI (m/z) [M + H] calcd for C20H24N5O, 350.1975; found, 350.1970 (Δ = 1.5

+ ppm). [M + Na] calcd for C20H23N5NaO, 372.1795; found, 372.1785 (Δ = 2.7 ppm). HPLC (1): t = 13.1 min, 98.9% purity. HPLC (3): t = 17.1 min, 98.3% purity.

2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐5‐methoxy‐1H‐ indole (94). To a suspension of 5-methoxyindole-2-carboxylic acid (203 mg,

1.06 mmol) and EDC.HCl (244 mg, 1.27 mmol) were added EtOAc (20 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (203 mg, 1.06 mmol). The reaction was heated at

40 oC for 6 h, and solvents were evaporated to dryness under high vacuum.

Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc to provide compound 94 (139 mg, 36%

1 yield) as a shiny, colourless, clear crystal. H NMR (400 MHz, DMSO-d6) δ

11.45 (br s, 1H), 7.32 (d, J = 8.8 Hz, 1H), 7.07 (d, J = 2.4 Hz, 1H), 6.85 (dd, J

= 8.8, 2.4 Hz, 1H), 6.76 (d, J = 1.2 Hz, 1H), 6.46 (s, 1H), 3.83 (br s, 8H), 3.76

13 (s, 3H), 2.25 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 166.9 (2 × C), 162.2,

161.1, 153.8, 131.3, 130.2, 127.2, 114.4, 113.0, 109.2, 104.1, 102.0, 55.3, 43.3

+ (2 × CH2), 40.1–38.9 (2 × CH2), 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H]

+ calcd for C20H24N5O2, 366.1925; found, 366.1925 (Δ = -0.1 ppm). [M + Na] calcd for C20H23N5NaO2, 388.1744; found, 388.1733 (Δ = 2.7 ppm). HPLC (1): t = 10.6 min, 97.2% purity. HPLC (3): t = 16.2 min, 99.2% purity.

{2‐[4‐(4,6‐Dimethylpyrimidin‐2‐yl)piperazine‐1‐carbonyl]‐1H‐indol‐7‐ yl}azinic acid (95). To a suspension of 7-nitroindole-2-carboxylic acid (204 mg, 0.99 mmol) and EDC.HCl (227 mg, 1.19 mmol) were added EtOAc (20

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CHAPTER 4 mL) and two drops of DMF, and the mixture was stirred for 1 h at 23 oC followed by the addition of compound 68 (190 mg, 0.99 mmol). The reaction was heated at 40 oC for 6 h, and solvents were evaporated to dryness under high vacuum. Water was added, sonicated, and filtered. The filter cake was purified by crystallisation from MeOH/EtOAc to provide compound 95 (137 mg, 36%

1 yield) as a light yellow, ball-like solid. H NMR (400 MHz, DMSO-d6) δ 11.77

(br s, 1H), 8.20 (d, J = 8.0 Hz, 1H), 8.15 (d, J = 8.0 Hz, 1H), 7.32 (t, J = 8.0 Hz,

1H), 7.04 (d, J = 2.4 Hz, 1H), 6.46 (s, 1H), 3.84 (br s, 4H), 3.71 (br s, 4H), 2.24

13 (s, 6H). C NMR (100 MHz, DMSO-d6) δ 166.8 (2 × C), 161.5, 161.0, 133.9,

133.0, 131.1, 130.0, 128.1, 120.2, 119.7, 109.3, 104.5, 40.1–38.9 (4 × CH2),

+ 23.7 (2 × CH3). HRMS-ESI (m/z) [M + H] calcd for C19H21N6O3, 381.1670;

+ found, 381.1662 (Δ = 1.9 ppm). [M + Na] calcd for C19H20N6NaO3, 403.1489; found, 403.1480 (Δ = 2.2 ppm). HPLC (x): t = 11.7 min, 97.3% purity. HPLC

(3): t = 16.8 min, 99.0% purity.

4.4.3 Results and discussion

Except for compound 95, the instalment of 68 at the indole C2 position through a carbonyl linker rendered derivatives (91-94) inactive at hA2A receptor (Table

13), suggesting that moiety 68 was not well-tolerated when it was extended from the indole C2 position. Notably, 7-nitro indolyl derivative 95 showed modest affinity (Ki = 29.7 µM) at hA2A receptor. A nitro group is a functional group that

(i) can act as hydrogen bond acceptors, (ii) is electron-withdrawing, and (iii) is polar in that the nitrogen atom is positively charged and the two oxygen atoms share a negative charge. Hence, the structural basis of the affinity shown by

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CHAPTER 4 compound 95 could be either (i) that the oxygen atoms form hydrogen bonding with water or nearby residues, (ii) that the polarised π-system of the indole ring leads to changes in ligand-receptor interactions, or (iii) that the charges on the nitro group result in electrostatic interactions (e.g. charge-charge interaction, charge-dipole interaction) with neighbouring residues. Apparently, 7- nitroindole-2-substituted 95 (the highest hA2A affinity in indole-2 series) is reminiscent of 7-methoxyindole-3-substituted 82 (the highest hA2A affinity in indole-3 series). Although it is tempting to assume that the methoxy group at the indole C7 position of 82 (Table 11) and the nitro group at the indole C7 position of 95 (Table 13) share the same mechanism that the electronegative oxygen atoms of methoxy and nitro groups accept hydrogens from nearby residues, more analogues with various C7 substituents on the indole-2 and indole-3 need to be made and tested for better understand the relationships between IPP scaffold and affinity at hA2A receptor. In addition to its activity at hA2A, compound 95 showed affinity at hA1 with Ki = 4.8 µM, suggesting its potential for the development of novel hA1 ligands.

From the perspective of drug safety, the enzymatic reduction of nitroaromatic compounds results in nitroso compounds, hydroxylamines, and nitro radical anions which often exert toxic effects on biological systems (Patterson and

Wyllie, 2014). For this reason, one of the compound selection criteria at the early stage of a drug discovery programme for neglected parasitic diseases was to remove unwanted groups, such as the nitro group which was considered potentially mutagenic and unsuitable for development (Brenk et al., 2008).

Thus, replacement of the nitro group in compound 95 with other substituents may be required for further development.

152

Table 13. Binding (Ki, nM) of Compounds 90-94 at Adenosine Receptor Subtypes

N R3 N N N N X H R5 91-95 3 5 a b c d cpd R R X hA1 hA2A hA2B hA3 hA1/A2A hA3/A2A 91 H H C(=O) >100,000 >100,000 >20,000 >100,000 92 F H C(=O) >100,000 >100,000 >20,000 >100,000

93 CH3 H C(=O) >100,000 >100,000 >20,000 >100,000 4 CHAPTER

153 94 OCH3 H C(=O) >30,000 >30,000 >20,000 >30,000 95 H NO2 C(=O) 4,820 (4,370–5,320) 29,700 (23,500–37,600) >20,000 >30,000 0.16 >1 a 3 3 Displacement of specific [ H]-2-chloro-6-cyclopentyladenosine ([ H]-CCPA) binding at human A1 receptors expressed in CHO b 3 3 cells (n=3–6). Displacement of specific [ H]-5’-N-ethylcarboxamidoadenosine ([ H]-NECA) binding at human A2A receptors c expressed in CHO cells (n=3–6). Ki values of the inhibition of NECA-stimulated adenylyl cyclase activity in CHO cells expressing d 3 6 3 hA2B receptors (n=3–6). Displacement of specific [ H]-2-hexyn-1-yl-N -methyladenosine ([ H]-HEMADO) binding at human A3 receptors expressed in CHO cells (n=3–6). Data are expressed with 95% confidence limits.

CHAPTER 4

4.5 Chapter Conclusion

Under guidance of SVM models (Section 3.2.3.5), Tanimoto similarity (Section

3.3.3), and hierarchical clustering analysis (Section 4.1), the scaffold [indole + piperazine + pyrimidine] (IPP) was identified. Compounds containing IPP were subsequently synthesised for the purpose of proving or disproving computational results. Competition binding experiments confirmed the binding of IPP-containing compounds at hA2A receptor in the micromolar range, with most promising candidates being 69, 70 and 82 (Ki = 4~11 µM) (Tables 10 and

11). Three important structural features of IPP-containing compounds in hA2A binding were extracted from preliminary structure-activity relationship (SAR) studies (Figure 54).

(a) At the indole C2 position, the presence of methyl group confers better

affinity than that of hydrogen.

(b) The linker bridging indole C3 and piperazine nitrogen must have a

carbonyl group. In addition, the length of that linker is limited to one or

two carbon atoms.

Out of the SAR analysis, compound 84 is proposed for future synthesis (Figure

52). In addition, phenyl substitution at the indole C2 position was carried out via the palladium-catalysed cross-coupling reaction (Scheme 3). The hA2A binding activity of compound 90 will shed light on the tolerability of hA2A receptor in steric bulkiness at the indole C2 position. Overall, IPP-containing compounds as hA2A receptor binders presented herein are unprecedented, and the SAR summary (Figure 54) has laid a foundation for future optimisation.

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Figure 54. Preliminary SAR profiles of IPP at hA2A receptor.

155

CHAPTER 5 FURTHER PHARMACOLOGICAL

EVALUATION AND CHARACTERISATION OF

COMPOUNDS 69 AND 70

Dr. Sourabh Banerjee in Dr. Madhavan Nallani’s lab (Institute of Materials Research and Engineering, A*STAR) performed dopamine D2 receptor binding assay. Miss Wang Wei and Dr. Deron Herr (Department of Pharmacology, National University of Singapore) conducted cell-based functional assays. The Drug Development Unit at the National University of Singapore was acknowledged for physicochemical evaluation, pharmacokinetic profiling, preliminary toxicology screening, and intellectual property analyses. Drosophila experiments were done by Dr. Ng Chee Hoe (National Neuroscience Institute, Singapore) with advice from Prof. Lim Kah-Leong (National Neuroscience Institute, Singapore).

5.1 Introduction

The goal of this thesis is to discover new chemical entities that act as both adenosine A2A receptor antagonist and dopamine D2 receptor. By means of compound collection from literature, in-depth analysis of collected compounds, as well as iterative synthesis-testing cycles, indolyl piperazinylpyrimidines

(IPPs) 69, 70 and 82 were identified to be novel binders at hA2A receptor in the micromolar range (Ki = 4~11 µM) (Figure 55). Whilst existing hA2A antagonists have reached nanomolar affinity (e.g. KW-6002, Ki = 12 nM, Figure 12;

ZM241385, Ki = 1.6 nM, Figure A11), the newly identified IPPs had the scaffold distinct from all known hA2A antagonists. Thus IPPs are worth a thorough exploration in the following two directions (Figure 55). First, it is interesting to optimise hA2A affinity on the basis of the SAR information summarised in Figure 54, as well as to establish the relationships of 156

CHAPTER 5

piperazinylpyrimidines to hA2A affinity. This direction is being pursued in our lab. Second, IPP was the most promising scaffold resulted from several rounds of computational filters (Figure 48). These filters covered not only A2A antagonism but also D2 agonism, ensuring that the ones which could survive to the end of the filtering process had the higher chance to simultaneously bind two receptors. The hA2A affinity displayed by 69, 70 and 82 merely validated our computational tools to a half extent (i.e. hA2A antagonism only). To fully appreciate the accuracy of computational analyses used for the thesis, it became indispensable to further test the synthesised IPPs at hD2 receptor. The second direction was pursued, and documented in Sections 5.3.1 (for hD2 binding) and

5.3.2 (for hD2 function). Since compound 82 was identified at the later stage and did not show significant difference in hA2A binding affinity compared to compounds 69 and 70 identified at the earlier stage, compounds 69 and 70 were selected for dopamine D2 receptor binding studies.

N I enhance affinity at hA2A N N N n O N R1 H R2

II test affinity at hD2 1 2 69 R =H, R =H, n=0 (hA2A Ki = 11.2 uM) 1 2 70 R =CH3, R =H, n=1 (hA2A Ki = 8.71 uM) 1 2 82 R =H, R =OCH3, n=0 (hA2A Ki = 3.63 uM) Figure 55. Two possible directions for further development of newly identified IPPs.

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5.2 Materials and Methods

Dopamine D2 Receptor Proteopolymersomes. Polymersomes (ABA and

BD21) preparation, cloning and in vitro synthesis of dopamine D2 receptor, and purification of proteopolymersomes were performed following previously described procedures (May et al., 2013). For the replacement assay, ABA- polymersomes were covalently attached to an amino-functionalised glass slide, which was then treated with isopropanol, ultrapure water, and a N2-stream. The slide was cut into small chips, and each chip was treated with the ethanolic solution of the mixture containing tetrazol(4-(2-phenyl-2H-tetrazol-5- yl)benzoic acid, N-hydroxysuccinimide and N-(3-dimethylaminopropyl)-N’- ethylcarbodiimidehydrochloride, and then incubated for one hour. ABA with

10% methacrylate was dispensed onto the polydimethylsiloxane (PDMS) stamps and incubated for one hour. The photoinducible 1,3-dipolar cycloaddition between the tetrazole and the methacrylate functional group on the polymersomes was induced by 15 min incubation under UV light (254 nm).

The chips were incubated with a 30 μM BODIPY-NAPS solution in TMN buffer for 30 min in the dark at room temperature. For determination of fluorescence intensities, the free program ImageJ was used.

Adenylyl Cyclase Inhibition. CHO cells transfected with D2R were cultured and then serum starved overnight. Cells were treated with various concentrations of ligands (25 µL of 10X solution), stimulated with forskolin

(0.5 µM) and IBMX (0.5 mM), and incubated for 15 minutes. After lysis, cAMP content was determined by ELISA.

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Immunocytochemistry. HEK 293 cells were obtained from American Type

Culture Collection (ATCC). The cell lines were grown as monolayers in humidified incubators at 37 °C with 5% CO2. Cells were cultured in DMEM,

25 mM D-glucose, 4mM L-glutamine, 1 U/mL penicillin, 1 µg/mL streptomycin, 10% fetal bovine serum (Invitrogen, Carlsbad, CA, USA). Cells were seeded onto coverslips coated with Type I collagen (Millipore, 10X dilution) and cultured overnight. Cells were transfected with 500 ng DNA plasmid of DRD2-V5 with 1 µL LFM and incubated overnight. Cells were incubated with ligands for 15 min and fixed with 4% paraformaldehyde for 30 min. After fixation, cells were incubated with V5 antibody (Invitrogen, R960-

25, 1:1000 diluted) overnight at 4 °C, with Alexa Fluor® 594 goat anti-mouse

(cat #AP192C, Millipore, Billerica, MA, USA). The coverslips were mounted using VECTASHIELD® Mounting Media. Images were acquired with microscope.

TGF Alpha Shedding. HEK 293 cells were seeded per well in a 6-well plate in

DMEM supplemented with 10% FBS, and cultured overnight. 200 ng D2R plasmid, 500 ng AP-TGFα plasmid, and 100 ng Gα protein were transfected in

HEK 293 cells, and incubated overnight. Transfected cells were detached by treatment with 1 mL 0.05% Trypsin-EDTA, and stop with DMEM + 10% FBS.

After centrifugation, the pellet was collected, resuspended in 4 mL autoclaved

PBS, and left it at room temperature for 10 min to remove the basal level of

TGFα shedding caused by trypnisation. After another round of pelleting and resuspending, cells were resuspended in 3.5 mL of Hank’s Balanced Salt

Solution (HBSS), seeded into each well in triplicates, and incubated at 37 °C for

30 min to let the cells attach to the plate. Ligands (10 µL) were added and into

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CHAPTER 5 appropriate well (10X diluted) and incubated at 37 °C for one hour. Plates were centrifuged at 190g for 5 min to separate the cells and condition media.

Condition media (80 µL) were transferred into a new plate in the same arrangement. The solution containing 80 µL 2X AP were added into each well in both condition media plate and cell plate. After incubation at 37 °C for 15 min, the basal absorbance at 405 nm for both plates was read. After another one- hour incubation, the second reading at 405 nm for both plates was taken. TGFα release was calculated according to the previously published method (Inoue et al., 2012).

Solubility Studies. The 10 µL stock solution of the ligand (10 mM in DMSO) was added to the universal aqueous buffer (pH 7.4), and the mixture was sonicated. 300 µL of the turbid mixture was transferred to the 3 wells of

MultiScreen HTS- PCF filter plate (Millipore Corp., Ireland), and the plate was covered and incubated with gentle shaking (250 rpm, 24 h) at room temperature

(22.5 ± 2.5 oC). After the period of incubation (24 h), the filter plate was placed on a vacuum manifold and the contents were filtered into a 96-well UV plate.

After filtration, 200 µL of filtrate was transferred from each well to the PP vial.

Absorbances of the solutions were quantified by HPLC-UV and read at 250 nm.

Parallel Artificial Membrane Permeability Assay. 5 µL of the 1% lecithin/dodecane (w/v) was pipetted into the well of the donor plate. 300 µL of the ligand solution (1% in DMSO, v/v) was added to the well in the donor plate

(with lecithin). 300 µL of 1X PBS buffer (containing 1% v/v DMSO) was added into the corresponding well in the acceptor plate. The donor/acceptor plate unit was covered, placed in an air-tight container and agitated in an incubator (250 rpm, 6 h) at temperature of 30.0 oC (± 2.5 oC). The concentrations of the analyte

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CHAPTER 5 present in each sample were analysed by LCMS, and the values of effective permeability (Pe) were calculated according to previously published formula

(Avdeef et al., 2007).

Metabolism Studies. Phosphate buffer (100 mM, pH 7.4) containing 1 mM

EDTA was prepared from 400 mM mono- and dibasic potassium phosphate stock solution. NADPH stock solutions (10 mM) in phosphate buffer were freshly prepared daily. Liver microsomal incubations were conducted in triplicate. Incubation mixtures consisted of 7.5 µL of 20 mg/mL FRLM and

MRLM (final: 0.3 mg microsome protein/mL), 2.5 µL of 100 µM test compound in acetonitrile (final: 3 µM), and 440 µL of 0.1 M phosphate buffer

(pH 7.4). The mixture was first shaken for 5 min for pre-incubation in a shaking water bath at 37 oC. Reaction was initiated by adding 50 µL of 10 mM NADPH to obtain a final concentration of 1 mM NADPH in the mixture. The total volume of the reaction mixture was 500 µL. For metabolic stability studies, aliquots of 50 µL of the incubation sample mixture were collected at 0, 5, 10,

15, 30, and 45 min. After collection of samples, the reaction was terminated with 100 µL of chilled acetonitrile containing the internal standard (0.01 µM).

The mixture was then centrifuged at 10,000g to remove the protein, and the supernatant was subsequently applied to LC-MS/MS analysis. Positive control

(PC) were prepared as described above, except the test compound was replaced with the known P450 substrate (midazolam, 2 µM). The samples were assayed for the degradation of midazolam to evaluate the adequacy of the experimental conditions for drug metabolism study. Negative control samples were also prepared as described above but without NADPH. In the determination of the in vitro half-life (T1/2), the peak areas of test compound were converted to parent

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CHAPTER 5 remaining percentages, using the t = 0 peak area values as 100%. The remaining percentages of the test compound were plotted against the microsomal incubation time using Microsoft Excel. Data points were determined from the average of three measurements with standard deviations as the error bars. The in vitro T1/2 (in units of min) and CLint, in vitro (in units of µL/min/mg) were calculated according to previously published formulas (Lu et al., 2006).

Statistical analysis of data was performed using Microsoft Excel. All data were presented as means ± standard deviation (SD).

Mutagenicity Test. In vitro mutagenicity was performed using a modified

Ames Test protocol according to manufacturer’s instructions (MolTox). S. typhimurium strains (TA 98 and TA100) were grown from bacterial discs in

Oxoid # 2 nutrient broth at 37 °C in a shaking incubator (~150 rpm) for about

10 h. The cultures were then measured for absorbance with a UV spectrophotometer at 660 nm and to be used at a density of approximately 1.0 to 1.2 absorbance units. For compound treatment, the top agar was melted in a hot water bath or microwave oven and 2 mL volumes were aliquoted into culture tubes. The tubes of agar were then maintained at 45 °C for at least 30 to 45 min for temperature equilibration. 100 µL of test compounds at concentrations of 1 mM were added to separate tubes containing top agar in duplicates. Additional tubes were set aside as negative (DMSO, methotrexate) and positive controls

(4-NQO and 2-AA). 500 µL of S9 mix was introduced to each tube containing either controls or test compounds. The contents were immediately mixed and decanted onto Minimal Glucose (MG) Agar Plate and swirled to obtain an even distribution of plating mixture over the agar surface. After the agar was set, the

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CHAPTER 5 plates were then incubated at 37 oC for 48 h. Images of colonies were captured and counted with the aid of ImageJ software.

Cytotoxicity Test. Immortalised hepatocyte and cardiomyocte cell lines,

TAMH (TGF-α overexpressing mouse hepatocytes) and HL-1 were used respectively as models for in vitro toxicity study. Both cells were cultured in accordance with previously described methods (Claycomb et al., 1998; Wu et al., 1994). Briefly, TAMH line between passages 21-35 were grown in serum free DMEM/Ham’s F12 (Invitrogen, Carlsbad, CA) supplemented with 5

µg/mL insulin, 5 µg/mL transferrin, 5 ng/mL selenium (Collaborative

Biomedical Products, 354351 Boston, MA), 100 nM dexamethasone, 10 mM nicotinamide and 0.1% v/v gentamicin (Invitrogen) and 0.12% sodium bicarbonate (Sigma 5671). For HL-1 cells, cell culture flasks were first coated with fibronectin/gelatin (25 µg of fibronectin in 2 mL of 0.02% gelatin in water per T25 flask (Sigma G1393 & F1141) overnight at 37 oC and the excess fluid aspirated thereafter. Cells were then maintained in Claycomb medium (Sigma

51800C) supplemented with 10% FBS (Sigma F2442), 2 mM L-glutamine

(Sigma), 10 µM norepinephrine, 100 U/ml penicillin, 100 µg/ml streptomycin and 1X non-essential amino acids. Medium was changed every 24 h. All cultures were maintained in a humidified incubator with 5% carbon dioxide/95% air at 37 oC and passaged at 70-90% confluence. TAMH cells were plated into 96-well plates at 12,000 cells per well whilst HL-1 at 15,000 cells per well and incubated overnight. The following day, test compounds were prepared from DMSO stock solutions and diluted down into working concentrations of 0.03 µM to 100 µM, keeping final DMSO concentration of

0.5% v/v. Medium was aspirated and replaced with respective compound

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CHAPTER 5 concentrations and incubated for another 24 h at 37 oC (n = 6). Cell-Titer-Glo

(Promega G7571) assay was performed according to manufacturer’s instructions. The cell-reagent mixture was then transferred to a solid white flat- bottom 96-well plate (Greiner 655207) for luminescence reading.

Luminescence was then recorded with an integration time of 0.25 second with a Tecan Infinite® M200 Microplate reader. Data are expressed as percentage of viable cells compared to DMSO-treated controls. Semi-log graphs were plotted using GraphPad Prism (La Jolla, CA, USA).

Drosophila Models of PD. Fly lines for 24B-Gal4 (muscle-specific), ddc-Gal4

(dopaminergic neuron-specific), elav-Gal4 (pan-neuronal), UAS-mito-GFP, and UAS-dAMPK-KA were purchased from Bloomington Drosophila Stock

Center. The parkin-null mutant flies were kind gifts from J. Chung and K. S.

Cho (Korea Advanced Institute of Science and Technology, Daejeon, Korea).

To generate transgenic mutant LRRK2 G2019S, cDNA containing a myc-tag at the C terminus was inserted into pUAST plasmid and microinjected into

Drosophila embryos (BestGene). Sequencing of cloned products was performed before they were microinjected into the embryos. Climbing assays were performed according to a previously described method (Ng et al., 2012).

Briefly, 20 female adult flies from each group were randomly selected after being anaesthetised and placed in a vertical plastic column (length 25 cm; diameter 1.5 cm). Age-matched normal flies were used as controls. After a 2 h recovery period from CO2 exposure, flies were gently tapped to the bottom of the column, and the number of flies that reached the top of column at 1 min was counted. Results are presented as mean ± SEM of the scores obtained from three independent experiments. To study the effect of compounds, flies were fed with

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CHAPTER 5 cornmeal-agar medium supplemented with the DMSO solution of the compound immediately at posteclosion (for parkin-null flies) or at day 35 onward (for LRRK2 mutant flies) for a period of 25 days. Immunohistochemical analysis of whole-mount adult fly brains were prepared according to published protocols (Whitworth et al., 2005) and stained with rabbit anti-TH (1:300, Pel-

Freez Biologicals) as primary antibody. The stained samples were viewed using an Olympus Fluoview Upright Confocal Microscope. DA neurons were quantified according to a published method (Whitworth et al., 2005). The size of mito-GFP puncta was measured using the ImageJ program and expressed as mean ± SD (n ≥ 10 DA neurons per experimental group). Statistical significance for all the quantitative data obtained were analysed using one-way ANOVA with Tukey’s test HSD post hoc test (*p < 0.05, **p < 0.01).

5.3 Results and Discussion

5.3.1 Proteopolymersomes for D2 receptor binding assay

Unlike the conventional method of overexpressing dopamine D2 receptor in host cells like what radioligand competition experiments do (Figure A13), the in vitro synthesised D2 receptor incorporated into block copolymer vesicles was used to determine D2 receptor binding of compounds 69 and 70 (May et al.,

2013). This alternative method for ligand-binding studies of membrane proteins overcomes the limitations of (i) cellular toxicity exerted by the cell-based radioligand binding approach (Katzen et al., 2009), (ii) loss of functional integrity in the reconstitution of GPCRs into lipid-based systems (Velez-Ruiz and Sunahara, 2011), and (iii) fragility of lipid bilayers faced by cell-free

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CHAPTER 5 nanolipoprotein particles (Nath et al., 2007). Amphiphilic block copolymers, as one of the biological membrane mimics, have superior stability to phospholipid- based membrane mimics (Srinivas et al., 2004), and they can self-assemble into vesicles, known as polymersomes (Discher et al., 1999). The polymersome- based dopamine D2 receptor binding assay is depicted in Figure 56.

Figure 56. (Left) Synthesis of proteopolymersomes was carried out by mixing dopamine D2 receptor DNA template and a given polymersome, followed by purification using centrifugal filtration. (Right) Confirmation of the insertion and proper receptor folding was assessed by using the unlabelled specific antibody, fluorescently labelled secondary antibody, and dansyl-dopamine (Modified from May et al., 2013) (reprinted with permission).

Two polymers, PMOXA20-PDMS54-PMOXA20 (one of the ABA triblock copolymers) and PBd22-PEO13 (one of the BD21 diblock copolymers), were used to generate the respective polymersomes. The cloned dopamine D2 receptor genes were mixed with the prepared polymersome to produce the desired proteopolymersome. The reaction mixtures were purified, and then

Western blot was used to confirm the incorporation of 25% D2 receptor into the two polymersomes (Appendix W). The retentates after the purification were subjected to flow cytometry using antibodies to reconfirm the incorporation

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(Appendix X). The correct conformation of D2 receptor inserted in the polymersomes was ensured by using dansyl-dopamine in solution as well as onto the glass surface. Further, the replacement of dansyl-dopamine by unlabelled dopamine in a concentration-dependent manner demonstrated the ligand specificity of the D2 receptor inserted in the polymersome.

This assay platform was used to evaluate whether compounds 69 and 70 can bind D2 receptor. Instead of dansyl-dopamine, boron-dipyrromethene N-(p- aminophenethyl)spiperone (BODIPY-NAPS) was incubated with D2R- functionalised polymersomes because spiperone was a selective D2-like antagonist with Ki of 0.06 nM for D2 receptor (Bakthavachalam et al., 1991).

The mixture was then incubated with solutions containing eight different concentrations (ranging from 3 nM to 0.3 mM) compounds 69 and 70, and the measured fluorescence intensity against ligand concentration was plotted in

Figures 57A and 57B, respectively. The dose-dependent reduction in fluorescence was observed for both compounds, indicating that the strong binding (Ki = 0.06 nM) of spiperone with D2 receptor can be competitively replaced by 69 and 70. To understand the potency context, the ability of (±)-2-

(N-phenethyl-N-propyl)amino-5-hydroxytetralin hydrochloride (i.e. (±)-

PPHT.HCl, a potent dopamine D2 receptor agonist with Ki of 13.3 nM determined by competition binding experiments with [3H]spiperone)

(Bakthavachalam et al., 1991) to replace BODIPY-NAPS was measured (Figure

57C), and the sigmoidal decrease in fluorescence with increasing concentration of (±)-PPHT.HCl was exemplary of how a D2 agonist ought to act in the D2R- functionalised polymersomes. The dose-response curves of 69 and 70 resembled that of (±)-PPHT.HCl, and the EC50 values for these three

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CHAPTER 5 compounds were determined to be 22.5 µM, 40.2 µM, and 0.2 µM, respectively

(Table 14). The 15-fold difference (0.2/0.0133 = 15) in potency of (±)-

PPHT.HCl in the radioligand binding assay (Ki = 0.0133 µM) and in the proteopolymersome assay (EC50 = 0.2 µM) was consistent with one case of kinase inhibitors in which the EC50 values were 4- to 20-fold above the Ki values

(Knight and Shokat, 2005). This leads to a speculation that the Ki (D2R) values for 69 and 70 could be in the range of 1~3 µM (22.5/15 = 1.5; 40.2/15 = 2.7), although D2 radioligand binding experiments were not performed for 69 and 70.

Treatment of denatured D2R-proteopolymersomes with PPHT.HCl was performed (Figure 57D) for comparison with Figure 57C, showing the necessity of proteopolymersomes’ integrity. Together with A2A radioligand binding data

(Tables 10 and 14), compounds 69 and 70 were demonstrated to be binders at

A2A and at D2 in the micromolar range.

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Figure 57. The dose-response curve of compound 69 (A), compound 70 (B), and (±)-PPHT.HCl (C) with D2R-proteopolymersomes using fluorescence polarisation (FP) competition assay with BODIPY-NAPS. The non-binding control (D) was achieved by denaturing the D2R-proteopolymersomes with heat, resulting in a curve that fluorescence intensity did not decrease much when the concentration of (±)-PPHT.HCl increased.

Table 14. The Potency of Ligands in Different Assay Systems

3 3 compound A2A, [ H]- D2, [ H]spiperone, D2, ACM, b NECA, Ki Ki (µM) EC50 (µM)a (µM)c (±)- N.A.d 0.0133b 0.2c PPHT.HCl 69 11.2a N.D.e (could be 22.5c 1~3)f 70 8.71a N.D.e (could be 40.2c 1~3)f aData from Table 10. bCompetition binding experiments were done using the 3 radioligand [ H]spiperone to label D2 receptor (Bakthavachalam et al., 1991; c Madras et al., 1990). ACM: artificial cell membrane; EC50: the concentration of an agonist that produces half-maximal response. Data are also shown in Figure 70. dN.A.: not available. eN.D.: not determined. fSee main texts for the speculation. 169

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5.3.2 Cell-based functional assay for D2 receptor

At this point in time, it has been shown that newly synthesised compounds 69 and 70 not only bind at A2A receptor (Table 10) but also bind at D2 receptor

(Figure 57). The next step was to evaluate whether these two compounds, after interacting with the two receptors, can give rise to changes in intracellular events so as to define their agonism or antagonism to respective receptors. As shown in Figure 58, the classical paradigm of D2R activation is mediated by heterotrimeric (i.e. α, β and γ subunits) Gi/o proteins, which is how “G protein”- coupled receptors (GPCRs) are named. In the absence of the endogenous dopamine or agonists, Gα is bound to guanosine 5’-diphosphate (GDP) and

Gβγ. Upon D2R activation, the conformational change of the receptor results in the GDP release, guanosine 5’-triphosphate (GTP) binding, and dissociation of

Gα from Gβγ. The released Gα then interacts and inhibits adenylyl cyclase, leading to the decrease in the adenosine 3’,5’-cyclic monophosphate (cAMP) production. Thus, measuring the extent to which a compound inhibits cAMP accumulation has been one of the functional assays for D2R activation (Kuhhorn et al., 2011). To prevent uncontrolled stimulation of cells by agonists, several mechanisms of signal attenuation, including agonist removal from the extracellular fluid, receptor desensitisation, receptor endocytosis, and receptor down-regulation, are generally adopted by cells (Bohm et al., 1997). Among these mechanisms, desensitisation and endocytosis are relevant to this thesis. G protein-mediated signalling is turned off (or, desensitised) by a process that involves G protein-coupled receptor kinases (GRKs) and β-arrestins. Prolonged exposure to agonists leads to GRK-catalysed D2R phosphorylation at the carboxyl intracellular loops. It is noteworthy that GRKs only recognise and

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CHAPTER 5 phosphorylate agonist-bound receptors, and that receptor phosphorylation is the first step to receptor internalisation. This rationalises the use of D2R internalisation assay to evaluate D2R activation in the thesis. After phosphorylation, β-arrestins are recruited to bind the phosphorylated receptor.

The binding of β-arrestins hinders G protein binding, thereby dampening G protein-mediated signalling (i.e desensitisation). Further, β-arrestins can continue to facilitate the endocytosis of the receptor via clathrin-coated pit, a process known as internalisation (Beaulieu and Gainetdinov, 2011). In a sense, the occurrence of signalling attenuation is indicative of the former existence of that signalling, so receptor internalisation assays have been used to investigate the agonistic function of a ligand. For instance, a study (Goggi et al., 2007) was performed for the direct visualisation of dopamine- and bromocriptine (a D2R agonist)-induced D2R internalisation using double antibody labelling followed by confocal microscopic imaging analysis. In that study, treatment of CHO cells with SKF38393 (a D1R agonist) failed to induce D2R internalisation, and an exposure to sulpiride (a D2R antagonist) prevented D2R internalisation. That is,

D2R internalisation can only be induced by its agonist rather than antagonist, validating the use of D2R internalisation assay for D2R activation. In this thesis, the in vitro functional responses upon exposing 69 or 70 to cells expressing D2R were investigated by three experiments—cAMP reduction, receptor internalisation, and TGFα shedding (Note: TGFα shedding will be introduced at the later part of this Section.). Table 15 gives a summary of the three assays.

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Figure 58. The simplified depiction of proteins involved when dopamine D2 receptor is activated (Modified from Beaulieu and Gainetdinov, 2011) (reprinted with permission).

Table 15. Dopamine D2 Receptor Activation Assays Used in the Thesis assay effector molecule cellular response cAMP reduction G protein signalling ↑ receptor internalisation β-arrestina signalling ↓ TGFα shedding G protein signalling ↑ aIn addition to the classical view that β-arrestins mediate receptor desensitisation and internalisation, β-arrestins can act as signal transducers and regulate many other cellular processes, such as receptor translocation, exocytosis, kinase regulation, transcription regulation, chemotaxis, apoptotic/anti-apoptotic signaling, receptor transactivation, and protein synthesis. Such β-arrestin-mediated signalling is beyond the scope of the thesis, and interested readers are referred to two reviews published by Prof. Lefkowitz (Lefkowitz and Shenoy, 2005; Rajagopal et al., 2010).

The first D2R activation assay used to characterise functional properties was the inhibition of cAMP accumulation. Chinese hamster ovary (CHO) cells were transfected with D2R, pre-treated with compound 70 and quinpirole, and stimulated with forskolin and 3-isobutyl-1-methylxanthine (IBMX). Quinpirole, a potent D2R agonist with a Ki of 4.8 nM (Seeman and Schaus, 1991), acted as the reference agent. The purpose of using forskolin (an activator of adenylyl cyclase) and IBMX (an inhibitor of phosphodiesterase) was identical, that is, to elevate basal levels of cAMP (Figure 59). As shown in Figure 60A, the control

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CHAPTER 5 refers to the high cAMP content resulted from the stimulation with forskolin/IBMX in the absence of either quinpirole or 70. Subsequently, different concentrations of either quinpirole or 70 were added to investigate whether, and if so, to which extent the added ligand can reverse the cAMP effect induced by forskolin/IBMX. The concentration-dependent decrease in cAMP accumulation when the D2R-expressing cells were treated with quinpirole suggests that quinpirole-induced D2R conformation caused the Gi protein to bind and inhibit adenylyl cyclase (Taussig et al., 1993). Activation with quinpirole at the concentration of 1 µM resulted in an 11% decrease (calculated by (103.97–92.63)/103.97 = 11%) in intracellular cAMP concentration with p- value of 0.0232, and the result of 1 µM quinpirole was normalised relative to the control (Figure 60B). After normalisation, it became clear that the extent to which 1 µM of quinpirole inhibited the cAMP accumulation was the desired

D2R agonistic behaviour expected of newly synthesised compounds, whereas the minus percentage of [cAMP] reduction in Figure 60B would indicate that the tested compound activates adenylyl cyclase. Three concentrations of compound 70 (1 µM, 10 µM and 100 µM) were evaluated, and their respective capability to inhibit cAMP accumulation was measured. There was no concentration-dependent response for compound 70 observed in this cAMP assay. The optimal inhibition occurred at 100 µM of 70 (p = 0.0232), which showed comparable potency to 100 nM of quinpirole. In Figure 60B, the y-axis values for all three concentrations of 70 were above zero, suggesting that compound 70 inhibited cAMP accumulation induced by forskolin/IBMX and therefore acted as a D2R agonist. The stability of D2R expression in CHO cells

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can be improved, and then it will be interesting to determine the EC50 value of

70 in inhibiting cAMP accumulation at D2R (Tschammer et al., 2011).

forskolin

NH2 NH2 NH2 activate N N N N N N N N N O O O N adenylyl cyclase N phosphodiesterase O N - O P O P O P O O O O HO P O O O- O- O- O- inhibit OH OH inhibit via Gi O P O OH OH OH O- ATPquinpirole, 70 cAMPIBMX AMP Figure 59. The formation of cAMP, a second messenger regulating a myriad of physiological functions, is catalysed by adenylyl cyclase. The hydrolysis of cAMP is catalysed by phosphodiesterase. The accumulation of cAMP can be done by treatment with either forskolin that is a diterpene isolated from Coleus forskohlii and binds at a hydrophobic pocket of adenylyl cyclase (Pinto et al., 2008), or IBMX that is an xanthine-derived inhibitor of phosphodiesterase (Wells et al., 1981). The inhibition of such accumulation can be realised by additional treatment with ligands causing Gi coupling.

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Figure 60. Inhibition of cAMP accumulation induced by compound 70 in comparison with the reference compound quinpirole (Q). The CHO cells were transfected with D2R, pre-treated with the indicated concentrations of quinpirole or compound 70, and activated with forskolin/IBMX. (A) The cAMP concentration (in the unit of picomole) measured by ELISA. (B) Normalisation to control (0%) and 1 µM of quinpirole (100%). *p<0.05. Error bars = standard error of the mean (SEM).

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The second D2R activation assay used to characterise functional properties was receptor internalisation. The D2R DNA plasmid with the epitope tag V5, encoding a peptide composed of 14 amino acids (GKPIPNPLLGLDST), was transfected into Human Embryonic Kidney (HEK) 293 cells. Subsequently, the cells were incubated with mouse anti-V5 IgG, and then with Alexa Fluor® 594- conjugated goat anti-mouse IgG (Figure 61, Left). V5 epitope was identified from the P and V proteins of simian virus 5 (SV5), and has been widely used as one of the protein tags. The mouse monoclonal antibody anti-V5 recognised the

V5 sequence with high specificity. To stain the internalisation process, a red- fluorescent dye Alexa Fluor® 594 conjugated to a polyclonal anti-mouse IgG was chosen for detection and illumination. Images were acquired by laser confocal microscope. As shown in Figure 61 (Right), V5-tagged-D2R- expressing HEK 293 cells were treated with dopamine (DA; 300 nM), 69 (0.01

µg/mL = 30 nM), 69 (0.1 µg/mL = 298 nM), 70 (0.01 µg/mL = 28 nM) and 70

(0.1 µg/mL = 275 nM) for immunocytochemistry. Without any ligand treatment, many sharp lines outlining the plasma membrane were observed, suggesting that in the quiescent state D2R was largely localised on the plasma membrane (Figure 61, control). Consistent with previous studies (Bartlett et al.,

2005), dopamine caused the D2R internalisation to a large extent, as evidenced by the great loss of red edges and the appearance of a punctum in the cytoplasm.

The extent of internalisation expected of newly synthesised compounds 69 and

70 was therefore between that of control and that of dopamine. Gratifyingly, following exposure to 69 and 70, the diminished surface labelling in conjunction with punctate intracellular patterns were observed, demonstrating the ability of

69 and 70 to promote the endocytosis of D2R. In light of results reported by

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other laboratories (Kuhhorn et al., 2011), such translocation of D2R from the plasma membrane to the cytosol induced by 69 and 70 suggested that these two compounds acted as D2R agonists.

Figure 61. (Left) The V5 (GKPIPNPLLGLDST)-tagged D2 receptor was specifically recognised by the primary antibody anti-V5, which was recognised by the secondary antibody anti-mouse. The fluorophore Alexa Fluor® 594 conjugated to the secondary antibody enabled the tracking of D2R internalisation. (Right) Ligand-induced D2R internalisation. V5-tagged D2R was normally abundant on the plasma membrane (control—negative control), but was internalised into intracellular vesicles after activation by dopamine (DA—positive control), 69 or 70.

Comparing cAMP assay with internalisation assay shows that higher concentrations of 70 were required to inhibit cAMP accumulation than to induce internalisation (Figure 60 versus 61). Whilst this discrepancy may be simply due to variation between experiment conditions, the following possibilities appear to be plausible. As mentioned earlier, the paradigmatic scheme for receptor desensitisation has been GRK-mediated phosphorylation, but one

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CHAPTER 5 recent study shows that protein kinase C (PKC)-mediated phosphorylation can also result in the attenuation of Gi protein coupling and eventually the D2R internalisation (Namkung and Sibley, 2004). PKC is one of the second messenger-activated protein kinases, and in the D2R case, PKC is activated by the second messenger cAMP. Therefore, the increase in the formation of PKC due to stimulation with 70 could enable the phosphorylation of both 70-bound and unbound D2R, leading to a number of D2R internalisation. Since both GRK- mediated (GRKs 2, 3, 5 and 6) phosphorylation (Ito et al., 1999) and PKC- mediated phosphorylation could contribute to D2R internalisation, lower concentrations of 70 was sufficient to elicit D2R internalisation (Kelly et al.,

2008), which was in contrast with the higher concentrations of 70 for the inhibition of cAMP accumulation. Alternatively, the lower ligand concentration required for D2R internalisation can be accounted for by either (i) “heterologous desensitisation”, or (ii) constitutive GRK-mediated phosphorylation.

Specifically, heterologous desensitisation is a process whereby the activation of one GPCR causes the desensitisation of another GPCR, and one relevant example of such heterologous regulation is that activation of M1 muscarinic receptor by carbachol (an M1R agonist) leads to an increase in D2R phosphorylation (Namkung and Sibley, 2004). So, it could be that compound

70 bound non-D2R receptors to activate these receptors, and the resultant second messenger-dependent kinases phosphorylated D2R. Moreover, the constitutive

D2R endocytosis has been confirmed using fluorescence microscopy and surface biotinylation (Vickery and von Zastrow, 1999). So, there is a possibility that 70-induced PKC enhanced the basal D2R internalisation. Taken together, it could be these three characteristic regulatory mechanisms associated with

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D2R—PKC phosphorylation, heterologous desensitisation, and constitutive activity—that resulted in the lesser amount of 70 required to induce internalisation compared to the inhibition of cAMP accumulation.

The third D2R activation assay used to characterise functional properties was

TGFα shedding. Similar to the conventional cAMP assay, TGFα shedding assay is to measure G protein-mediated signaling (Table 15). Ectodomain shedding occurs at many membrane-anchored proteins, such as cytokines, growth factors, and transmembrane receptors, leading to the cleavage and release of the extracellular domains (Peschon et al., 1998). The released domains, in turn, regulate the paracrine signalling of another cell in the distance (Massague and

Pandiella, 1993), sequester soluble ligands (McDermott et al., 1999), or even activate receptors of their own (Brown et al., 2000). The proteolytic processing of membrane proteins is mediated by a family of metalloproteases known as

ADAM which is also membrane-anchored (Blobel, 2005). The importance of

ADAM-mediated ectodomain shedding is shown in the regulation of epidermal growth factor receptor (EGFR) signalling (Figure 62). It is documented that transactivation of EGFR-dependent signalling upon GPCR activation requires

ADAM (Prenzel et al., 1999). Specifically, as depicted in Figure 62, the activation of M1 muscarinic acetylcholine receptor by carbachol (an M1R agonist) results in G protein-dependent intracellular signalling, and this signalling eventually phosphorylates the cytoplasmic region of ADAM (Inoue et al., 2011). The phosphorylation activates the metalloprotease activity of

ADAM to cleave the precursor of heparin-binding EGF (HB-EGF) anchored on the cell membrane. The released extracellular HB-EGF interacts with membrane-anchored EGFR and triggers a different signalling from that induced

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CHAPTER 5 by carbachol stimulation. There are therefore triple membrane-passing events occurred for signals being transduced in GPCR-induced EGFR activation: (i) the signal transduced from outside to inside the cell mediated by GPCR, (ii) the signal transduced from inside to outside the cell mediated by ADAM, and (iii) the signal transduced from outside to inside the cell mediated by EGFR. This

GPCR-ADAM-EGFR mechanism has been identified in a variety of GPCRs, and could be potentially targeted for therapeutic intervention (Paolillo and

Schinelli, 2008). In the year 2011, a group of researchers in Tohoku University reported that upon activation of P2Y5 receptor with lysophosphatidic acid

(LPA), the ADAM17 (also known as TACE)-mediated shedding of the precursor of transforming growth factor-α (TGFα) was elicited, the released

TGFα activated EGFR, and the triggered signalling led to hair follicle development (Inoue et al., 2011). This group of researchers then extended the application of TGFα shedding to almost all GPCRs, and demonstrated the usefulness of this assay system in evaluating ligand functions, including agonism, inverse agonism, and antagonism (Inoue et al., 2012). This assay has the advantage of accuracy, cost-efficiency, high-throughput capacity, and availability of equipment. Therefore, we adopted this assay for evaluating compounds 69 and 70.

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Figure 62. The model of GPCR-ADAM-EGFR crosstalk. The stimulation of GPCR results in activation of ADAM, leading to proteolytic cleavage of proHB- EGF. The released mature EGF-like ligand activates EGFR and its downstream signalling events (Fischer et al., 2003). Reproduced with permission, from O.M. Fischer, S. Hart, A. Gschwind and A. Ullrich (2013) Biochemical Society Transactions, 31, 1203-1208. © the Biochemical Society. Link: http://www.biochemsoctrans.org/bst/031/1203/bst0311203.htm

The principles of evaluating GPCR activation using TGFα is shown in Figure

63. In this assay, HEK 293 cells were transfected with dopamine D2 receptor, alkaline phosphatase (AP)-tagged TGFα, and Gα protein. The transfected cells were seeded in a 96-well plate and treated with dopamine (as the positive control), 69 and 70. After incubation for 1 h, the conditioned media were separated from cells by centrifugation. Adding para-nitrophenylphosphate (p-

NPP) (an AP substrate) followed by measurement of absorbance at the wavelength of 405 nm gave the results shown in Figure 64. Dopamine (DA) was used in low concentrations (10 nM and 1 µM), and the obvious AP-TGFα release induced by DA treatment suggests that this TGFα shedding assay was working in the D2R system, and the effects of 69 and 70 were compared with

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CHAPTER 5 that of DA. The percentage of AP-TGFα release stimulated by three concentrations (1 µM, 10 µM, 100 µM) of 69 and 70 were recorded. Whilst compound 69 at 1 µM and 10 µM showed marginal concentration-dependent responses, higher concentration (i.e. 100 µM) of 69 led to a significant reduction in the extent of AP-TGFα release. It is possible that compound 69 has nonspecific binding at receptors other than D2R, and in turn, induces additional intracellular signalling events. The crosstalk between D2R-mediated and non-

D2R-mediated signalling pathways could somehow prevent TACE from being activated by, for example, abolishing phosphorylation of TACE. In contrast, compound 70 showed a more marked dose-response relationships with the concentrations ranging from 1 µM to 100 µM, indicating that compound 70 was amenable to ectodomain shedding of AP-TGFα. In terms of AP-TGFα release, compound 70 at 100 µM gave a one-third response relative to that induced by dopamine at 10 nM. Since compound 70 showed concentration-dependent AP-

TGFα release, it would be interesting to determine EC50 value of 70. Overall, both 69 and 70 showed effects in the TGFα shedding assay in D2R-expressing cells, suggesting the capacity of the two compounds for activating D2R. Table

16 summarises the three D2R activation assays used in the thesis. Since cAMP and TGFα assays are G protein-mediated, the justification provided previously for the lower ligand concentrations in the cAMP assay than in the internalisation assay may apply to the results of the TGFα assay. In summary, compound 70 was shown to be a D2R agonist in the cAMP, internalisation and TGFα assays, and compound 69 was shown to be a D2R agonist in the internalisation and

TGFα assays.

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Figure 63. The mechanism of TGFα shedding assay for assessing GPCR activation. If a ligand were to activate a GPCR, the resulting G protein-mediated signalling would lead to the activation of TNFα converting enzyme (TACE), also known as ADAM17. TACE, acting as a sheddase, proteolytically cleaves the membrane-anchored precursor of AP-tagged TGFα to a certain extent. The AP-tagged TGFα is released to the condition media, and the extent of AP-TGFα release is measured by the formation of para-nitrophenol (p-NP) catalysed by AP. (Inoue et al., 2012) (reprinted with permission).

Figure 64. The TGFα shedding assay for compounds 69, 70 and dopamine (DA). 183

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Table 16. Summary of Three D2R Activation Assays

assay cell line ligands potency on D2R activation assessed cAMP CHO 70 100 µM of 70 ~ 100 nM of quinpirole

internalisation HEK 69 69: 30 nM, 298 nM 293 70 70: 28 nM, 275 nM

TGFα HEK 69 69: marginal effects 293 70 100 µM of 70 ~ 1/3 effect induced by 10 nM of DA

5.3.3 Drug-like properties

The drug discovery approach adopted in the thesis is to start with two validated pharmacologically relevant receptors, namely adenosine A2A and dopamine D2 receptors, followed by in vitro testing of ligand-receptor binding—a study of what the compounds do to the therapeutic targets. This approach often results in the quest for strongest receptor binding and high selectivity with a negligence of considering the journey of a drug from its site of administration to its site of action. It is very rare that a drug can be applied directly to the intended pharmacological targets for which it is designed, so more often than not a drug must first make a journey to its site of action via systemic circulation. Whilst the way for a drug to enter into systemic circulation can be many, such as through oral dosing (most common route of administration), injection into muscle, injection into subcutaneous tissue layers, rectal delivery, transdermal delivery, intranasal delivery, pulmonary delivery (Boussery et al., 2008), one common event before reaching systemic circulation is to permeate biological membranes. The process of a drug from its site of administration to systemic circulation is known as absorption, and after that, the movement of the drug

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CHAPTER 5 from one location to another (e.g. the intended tissue where therapeutic targets are located, the unintended site giving rise to adverse effects) via blood plasma refers to distribution. It is therefore the combination of the in vitro activity profiles and the performance at all biological barriers (Kerns and Di, 2008b) from the site of administration to the site of action that determine the in vivo exposure of a drug to the specific therapeutic targets.

Take an orally administered drug for example. Suppose the drug has been shown good in vitro affinity and selectivity at a particular receptor or a subset of receptors. After it is taken orally, the first barrier is the dissolution rate of the drug in the acidic stomach environment (pH 1~3) because the drug must be dissolved in the aqueous physiological media prior to passing the capillaries for absorption. Generally, drugs have limited absorption in the stomach due to (i) low surface area of stomach, (ii) low blood flow around the stomach, and (iii) rapid gastric emptying. Subsequently, the drug goes to the small intestine

(duodenum, jejunum, ileum) with pH 5~8, and the barriers here are the dissolution rate, solubility (in pH 5~8), and membrane permeability across the intestinal wall where most of the drug absorption occurs. Additional barriers in the intestine includes metabolic enzymes cytochrome P450 3A4 (CYP3A4), enzymatic hydrolysis (by peptidase, esterase, ribonuclease, phosphatase), and efflux transporter P-glycoprotein (P-gp). Overall, the barriers for a drug in the gastrointestinal (GI) tract include (i) dissolution rate, (ii) solubility, (iii) permeability, (iv) chemical instability, and (v) hydrolytic enzymes. After the drug enters into the bloodstream and before it distributes to the therapeutic target, barriers include (i) plasma protein binding, (ii) plasma enzyme hydrolysis, (iii) red blood cell binding, and (iv) blood-brain barrier (BBB), if

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CHAPTER 5 the intended target is located inside the brain. Concurrently, a portion of the drug is always eliminated from the body by biotransformation (i.e. xenobiotic metabolism taking place mainly in liver) and excretion (i.e. removal by kidney or bile), both of which also constitute barriers to effective in vivo drug concentrations at the therapeutic target. Taken together, after administration, the drug concentration in vivo increases upon absorption, and due to multiple biological barriers the concentration decreases with time as it is distributed to tissues, metabolised by enzymes, and eventually eliminated from the body

(ADME) (Figure 64). Pharmacokinetics (PK) is the time-course study of drug concentrations in the body from absorption to elimination, and it is described as what the body does to the drug. Importantly, PK parameters, including volume of distribution (Vd), area under the curve (AUC), clearance (CL), half-life (T1/2) and bioavailability (F) are useful to assess the exposure of a drug (Kerns and

Di, 2008g). For elaborate information on PK parameters, readers are referred to relevant textbooks (Rowland and Tozer, 2010; Shargel et al., 2012). PK of a drug is important because, despite good in vitro activity (e.g. affinity, selectivity), its delivery to the intended therapeutic targets in vivo could potentially be attenuated by all the aforementioned biological barriers, resulting in reduced in vivo exposure. Thus, in addition to establish structure-activity relationship (SAR) for achieving strong target binding, exploring structure- property relationship (SPR) to ensure high performance at every biological barrier is equally important and serves as a complement to SAR for the purpose of maximising drug efficacy (Di et al., 2009). That is, modifications of chemical structures can be used not only for enhancing binding affinity at the intended targets but also for improving the drug exposure to the intended targets.

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Figure 64. Schematic representation of drug absorption, distribution and elimination. Despite being “active” in in vitro assays on affinity, the drug needs to reach its site of action to exert its pharmacological effects. Thus, pharmacokinetics precedes pharmacodynamics. Upon administration, multiple barriers exist until the drug reaches the site of action, and each of these barriers can reduce the rate and extent of progressing to the target.

According to Christopher A. Lipinski, “drug-like” compounds refer to those that have “sufficiently acceptable” ADME and toxicity (ADME-Tox) properties to survive Phase 1 clinical trials (Lipinski, 2000). By this definition, it is clear that drug-likeness includes both inherent activity at the target and sufficient exposure. Notably, drug-like properties have been in some ways more linked to properties influencing ADME-Tox since Lipinski’s publications. It is documented that poor ADME-Tox properties contribute 50% of reasons for drug attrition (van de Waterbeemd and Gifford, 2003), and that those drug-like properties which have the most impact on drug discovery and development are solubility, permeability, metabolism, and transporter effects (Di et al., 2009). In the thesis, the following assays were therefore performed in vitro for compounds 69 and 70: (i) solubility (Section 5.3.3.1), (ii) permeability (Section

5.3.3.1), (iii) metabolism (Section 5.3.3.2), and (iv) toxicity (Section 5.3.3.3)

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CHAPTER 5 assays. Measuring these physicochemical (i.e. solubility, permeability), biochemical (i.e. metabolism), and toxicological properties not only reveals clues to how drug-like 69 and 70 are (Kerns and Di, 2008a) but also guides prediction and structural modification (van De Waterbeemd et al., 2001) towards optimal in vivo exposure.

5.3.3.1 Solubility and permeability assays

The importance of aqueous solubility and intestinal permeability can be illustrated by Biopharmaceutics Classification System (BCS) introduced by

Gordon L. Amidon in 1995 (Amidon et al., 1995). BCS states that solubility and permeability are key parameters dictating drug absorption, and divides drugs into four classes—(i) Class I (high solubility-high permeability drugs), (ii) Class

II (low solubility-high permeability drugs), (iii) Class III (high solubility-low permeability drugs), and (iv) Class IV (low solubility-low permeability drugs).

According to BCS, a highly soluble drug refers to the highest dose strength of a drug is soluble in ≤ 250 mL of aqueous media over a pH range of 1.0–7.5, and a highly permeable drug means the extent of absorption ≥ 90% of administered dose. In 2000, the BCS guidance was accepted and published by Food and Drug

Administration (FDA) as a standard to allow a waiver for Class I drugs (highly soluble and highly permeable) of in vivo bioavailability/bioequivalence testing.

In 2006, such waiver was extended to Class III (highly soluble but with low permeability) supported by World Health Organization (WHO) (Shah and

Amidon, 2014). BCS classification is not only useful for predicting oral absorption, a later analysis demonstrated its usefulness in predicting routes of

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CHAPTER 5 elimination: in general, (i) Class I and Class II drugs are prone to be metabolized, and (ii) Class III and Class IV drugs are primarily eliminated by kidney or bile (Wu and Benet, 2005), warranting the investigation of solubility and permeability of compounds 69 and 70.

Kinetic solubility was determined by pre-dissolving compounds in DMSO as a

10 mM stock solution followed by the addition into the aqueous buffer solution.

The turbid mixture was transferred to MultiScreen filter plates (Millipore), incubated for a fixed period of time (i.e. 3 h and 24 h), and filtered. The UV absorbance (at 250 nm) of the filtrate was measured using a UV plate reader.

According to Beer’s law, the concentration of the compound is proportional to the UV absorbance, and the results are presented in Table 17. The solubility of compound 69 was 9.07 ± 0.11 µg/mL (27.03 ± 0.32 µM) at 3 h and 4.58 ± 0.10

µg/mL (13.67 ± 0.31 µM) at 24 h. The solubility of compound 70 was 2.75 ±

0.05 µg/mL (7.55 ± 0.14 µM) at 3 h and 1.41 ± 0.01 µg/mL (3.89 ± 0.03 µM) at 24 h. The minimum solubility required of a compound depends on its permeability and dose. Specifically, for drugs with higher permeability and higher potency (i.e. lower dose to produce the pharmacological effects), lower solubility is sufficient for them to achieve complete oral absorption. On the contrary, drugs with lower permeability and lower potency (i.e. needing a higher dose to produce the pharmacological effects), higher solubility is necessary for them to achieve complete oral absorption. In short, solubility, permeability and dose of a drug are intercorrelated (Lipinski, 2000). Due to the fact that doses are generally not defined at the drug discovery stage but at the drug development stage, a solubility guideline for oral absorption is suggested for drug discovery teams: < 10 µg/mL (low solubility), 10~60 µg/mL (moderate solubility), > 60

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µg/mL (high solubility) (Kerns and Di, 2008c). Both 69 and 70 exhibited low aqueous solubility at ambient temperature in pH 7.4, and the decrease in solubility was observed as the incubation time increased from 3 h to 24 h. In addition to the comparison of solubility results with the aforementioned guideline that is mainly for absorption at the gastrointestinal (GI) tract, it would be helpful to examine the solubility profiling of marketed CNS drugs

(Alelyunas et al., 2010) since 69 and 70 were meant to treat PD, a CNS disorder.

A group of researchers in AstraZeneca measured the solubility of 98 marketed

CNS drugs, and the results showed that (i) 84% of the CNS drugs have solubility

> 100 µM, (ii) 7% of the CNS drugs have solubility < 10 µM, and (iii) the CNS drugs with solubility < 10 µM are either out of the current market or having liabilities. In light of these results, there seems an association between low solubility and attrition in CNS drugs. Thus, the poorly water-soluble compounds

69 and 70 (solubility in the range of 4~27 µM) can pose a high risk if they are further advanced and developed without performing any solubility improvement.

Zoom-in analysis of individual solubility shows the slightly better solubility of

69 than that of 70, which can be accounted for by structural properties of 69 and

70 (Table 18). For a drug solute to pass into an aqueous solution, the following steps are required: (i) the solute molecule removed from the crystal lattice, a process requiring to break solute-solute interactions; (ii) a void created in the solvent; (iii) the solute molecule inserted into the solvent, creating solute- solvent interactions. Solubilisation is energetically favourable if the energy released from solute-solvent interactions in step (iii) is larger than that used for destroying solute-solute interactions in step (i) as well as solvent-solvent

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CHAPTER 5 interactions (which is usually negligible) in step (ii). The two primary determinants of drug solubility in aqueous media are therefore associated with steps (i) and (iii). This implies that, for a series of analogous compounds such as 69 and 70, solubility is lower for those with stronger intermolecular attraction

(which can be generally indicated by higher melting point and higher molecular weight) and weaker water affinity (which can be indicated by higher partition/distribution coefficient). Thus, the higher CLogP, CLogD and MW of

70 (Table 18) resulted in its lower solubility. The structural properties shown in

Table 18 can not only explain the slight difference in solubility of 69 and 70, but can also underpin the tactics for improving solubility, which will be proposed in Chapter 6 (Walker, 2014; Williams et al., 2013).

Table 17. The Aqueous Solubility and Permeability of Compounds 69 and 70a 69 70 guidelineb solubility (µg/mL), 3 h 9.07 ± 0.11 2.75 ± 0.05 > 60 solubility (µM), 3 h 27.03 ± 0.32 7.55 ± 0.14 > 100 solubility (µg/mL), 24 h 4.58 ± 0.10 1.41 ± 0.01 > 60 solubility (µM), 24 h 13.67 ± 0.31 3.89 ± 0.03 > 100 -8 PAMPA Pe (10 cm/s), 6 h 2.47 ± 0.09 52.02 ± 1.49 > 200 -8 PAMPA Pe (10 cm/s), 16 h 9.25 ± 0.33 54.93 ± 2.78 > 200 aConditions in determining solubility: pH 7.4, temperature 22.5 ± 2.5 oC. Conditions in determining permeability: pH 7.4, temperature 30.0 ± 2.5 oC. PAMPA: parallel artificial membrane permeability assay. Pe: effective permeability. Data presented as mean ± SD (three determinations of test compound). bSee the main text for details.

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Table 18. Structural Properties of Compounds 69 and 70a

69 70 CLogP 2.14 2.27 CLogD 2.13 2.26 MW 335.4 363.5 TPSA 65.1 65.1 HBD 1 1 pKa 4.67 4.67 aCLogP: calculated partition coefficient. CLogD: calculated distribution coefficient at pH = 7.4. MW: molecular weight. TPSA: topological polar surface area (Ertl et al., 2000). HBD: number of hydrogen bond donors. pKa: negative base-10 logarithm of acid dissociation constant for the most basic centre. CLogP, CLogD, TPSA and pKa were calculated by MarvinSketch 15.1.5.0.

In addition, permeability of compounds 69 and 70 was determined (Table 17).

In the intestine, the membrane permeation is mainly mediated by four mechanisms, namely passive transcellular transport, paracellular permeation, carrier-mediated uptake, and carrier-mediate efflux (Figure 65). Passive transport is a diffusion process driven by concentration gradient resulting in the movement of drugs from one side of the lipid bilayer to the other side, and it is documented that 80~95% of marketed drugs are absorbed in the intestine via passive diffusion (Kerns et al., 2004). In paracellular permeation, small and polar drugs (e.g. salicylic acid, theophylline) pass membranes through loose junctions between cells. Carriers, also known as transporters, are transmembrane proteins that bind drug molecules, consume energy, and then move drug molecules into (by uptake transporters) or out of (by efflux transporter) a cell (Sugano et al., 2010). A non-cell-based permeability assay, known as parallel artificial membrane permeability assay (PAMPA), was developed in 1998 to specifically describe passive absorption process (Kansy et al., 1998). PAMPA has the advantages of increased throughput, reduced cost,

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CHAPTER 5 and rapid turnaround time, as well as its single mechanism of permeability (i.e. passive diffusion). The fact that the majority of drug molecules are absorbed by passive diffusion justifies the use of PAMPA for compounds 69 and 70. As shown in Figure 66, the test compound in DMSO was diluted with aqueous buffer and then placed in the donor well. To the acceptor well, with a porous filter at the bottom, was added phospholipid (i.e. lecithin) solubilised in the long-chain hydrocarbon (i.e. dodecane) that acted as the artificial membrane.

The mixture was incubated for a period of time (6 h and 16 h), and samples were taken from both donor and acceptor wells. The concentrations of test compound in both wells were derived by LCMS analysis, and the derived concentrations were converted to effective permeability (Pe) (Kerns and Di, 2008h). At pH 7.4,

-8 - the Pe values of 69 were 2.47 ± 0.09 (× 10 cm/s) at 6 h and 9.25 ± 0.33 (× 10

8 -8 cm/s) at 16 h. At pH 7.4, the Pe values of 70 were 52.02 ± 1.49 (× 10 cm/s) at 6 h and 54.93 ± 2.78 (× 10-8 cm/s) at 16 h (Table 17). A guideline for PAMPA

-6 with respect to Pe states that the compound with Pe > 2 (× 10 cm/s) is considered to have high absorption potential, the compound with Pe = 0.5~2 (×

10-6 cm/s) is considered to have medium absorption potential, and the

-6 compound with Pe < 0.5 (× 10 cm/s) is considered to have low absorption potential (Avdeef et al., 2007). Accordingly, 69 and 70 were found to be poorly permeable. Compounds with low permeability are typically detrimental to their intestinal absorption (Lipinski et al., 1997), bioavailability (Kerns and Di,

2008d), and cell-based activity (if the therapeutic target is inside the cell) (Kerns et al., 2004). For both 69 and 70, permeability increased as the incubation time increased from 6 h to 16 h. The determinants of permeability include lipophilicity of the compound (Avdeef et al., 2007), compound pKa/solution pH

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(Kerns et al., 2004), molecular weight of the compound (Di et al., 2009), as well as polarity of the compound indicated by hydrogen bond donors/acceptors and polar surface area (Veber et al., 2002). Because the biological membrane is composed of nonpolar lipid bilayers, permeability is generally higher for compounds with higher lipophilicity, un-ionisable, smaller size, and reduced polarity. The higher permeability of 70 (Table 17) is therefore the consequence of its higher CLogP and CLogD (Table 18), which may outweigh the effect of molecular weight. Possible structural modifications to improve permeability will be proposed in Chapter 6.

Figure 65. Four major mechanisms of intestinal membrane transport. A: passive transcellular transport, B: paracellular permeation, C: carrier-mediated uptake, D: carrier-mediate efflux. The light blue ovals are carriers (also known as transporters).

Figure 66. Schematic diagram of PAMPA (Mason, 2009) (reprinted with permission).

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5.3.3.2 Metabolism assays

Solubility and permeability are two major determinants of drug absorption

(Lipinski et al., 1997). After a drug is absorbed and distributed to its target macromolecules through the blood, it is then cleared from the body. Drug clearance (or, drug elimination) is done by three mechanisms: (i) hepatic biotransformation (most prevalent), (ii) biliary excretion of unchanged drugs, and (iii) renal excretion of unchanged drugs (Obach, 2013). Hepatic biotransformation takes place mostly by the enzymes in the endoplasmic reticulum (ER), and is often a bi-phasic process whereby the drug first undergoes Phase I functionalisation reactions (e.g. oxidation catalysed by cytochrome P450 enzymes or flavin monooxygenases), followed by Phase II conjugation reactions (e.g. glucuronidation catalysed by uridine 5’-diphospho- glucuronosyltransferases). Both Phase I and II reactions have a common goal— generating more polar compounds to facilitate the excretion via bile or urine.

Drug metabolism must be executed in optimal timing, that is, neither too quickly so as to ensure adequate drug efficacy, nor too late to minimise toxicity. At one end of the spectrum, when a drug is overly metabolised at the stage of first-pass intestinal metabolism or first-pass hepatic metabolism before reaching therapeutic targets, the low in vivo exposure may compromise drug efficacy. In this extreme case, the improvement in efficacy can be done by making analogues to block labile sites (Kerns and Di, 2008e, i). At the other extreme in which two drugs (e.g. drug A and drug B) are co-administered, the binding of drug A at a cytochrome P450 (CYP) isozyme results in the reduced binding of drug B at this particular isozyme, which in turn, leads to the reduced clearance of drug B. The higher concentration of drug B due to inhibited metabolism may

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CHAPTER 5 be toxic. A notable example is the co-administration of erythromycin (acting as drug A) and terfenadine (acting as drug B). Erythromycin inhibits the metabolism of terfenadine, and the accumulation of terfenadine results in cardiac arrhythmia. This results in the withdrawal of terfenadine from the market in February 1998. In this extreme case, structural modification for making analogues that decrease the binding at CYP isozymes can be done to reduce toxicity (Kerns and Di, 2008f). For the details regarding all enzymes that involve in drug metabolism, how enzymes catalyse metabolic reactions, and the possibility of generating toxic or active metabolites, readers may consult listed references (Macherey and Dansette, 2008; Obach, 2013; Testa, 2008).

The in vitro metabolic stability of compounds 69 and 70 was evaluated by liver microsomes. Liver microsomes contain CYP isozymes, flavin monooxygenases, reductases, esterases, UDP-glucuronosyltransferases, amino acid conjugases, glutathione-S-transferases, and methyltransferases. The cofactor NADPH is needed because it is a high energy molecule and provides energy for reactions catalysed by enzymes contained in the microsome. Such in vitro microsomal assay is generally the first-line and most frequently used method to assess drug clearance by Phase I enzymes, primarily by CYP isozymes. This is because approximately 60% of marketed drugs are metabolised by CYP-mediated reactions (McGinnity et al., 2004). It is also worth noting that metabolic stability is quite species-dependent, so microsomes derived from rat, mouse, and human can have variable results (Di et al., 2003).

Rat microsomes are commonly used in the early stage of drug discovery, and are therefore adopted for evaluating 69 and 70. To the mixture of a rat liver microsome (either male or female) and a test compound (either 69 or 70) was

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CHAPTER 5 added NADPH, and an aliquot of the incubation sample mixture was collected at six different time points (i.e. 0 min, 5 min, 10 min, 15 min, 30 min, 45 min).

The collected samples were centrifuged, and the supernatant portions were subjected to LC-MS/MS analysis for the quantification of the remaining parent compounds. Midazolam, a known CYP substrate, was used as the positive control. As shown in Figure 67, the half-life (T1/2) of midazolam (ca. 3~4 min) was in agreement with that reported previously in a highly cited paper (Obach,

1999) (times cited: 540, Web of Science, as at 7 April 2015), indicating that the experimental conditions used here were robust enough to describe the metabolic stability of 69 and 70. In this experiment, in vitro half-life (T1/2), which refers to the time required for the concentration of the test compound reduced by half, was used to determine in vitro intrinsic clearance (CLint, in vitro) values (Obach,

1997). Lower half-life is correlated with higher clearance. Within 10 min, both

69 and 70 were quickly metabolised with less than 30% remaining, suggesting that both compounds were metabolically unstable. The half-life values of 69 and 70 were determined to be 4.4~5.2 min and 0.80~1.26 min, respectively. In general, compounds having short half-life in the in vitro metabolic stability assay tend to suffer from low oral bioavailability (Wring et al., 2002). A useful guideline for rat microsomal stability states that compounds with in vitro intrinsic clearance < 13.2 µL/min/mg are categorised as “low clearance”, and compounds with in vitro intrinsic clearance > 71.9 µL/min/mg are categorised as “high clearance” (Cyprotex, 2015). Clearly, compounds 69 and 70 fall into

“high clearance” category, which may result in insufficient duration of action in vivo. In addition, after 5 min of incubation, the remaining percentage of 69

(i.e. 40% and 72%) was still higher than that of midazolam (i.e. 28%), whereas

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CHAPTER 5 the remaining percentage of 70 (i.e. 1% and 7%) was extremely low, suggesting that 70 is more metabolically unstable than 69. Numerous studies have shown that there is a linear relationship between the lipophilicity of a compound and its binding affinity at CYP isozymes, that is, a more lipophilic compound required lower concentrations to saturate a given CYP isozyme (Lewis et al.,

2004; Lewis and Ito, 2008). Hence it appears likely that the higher in vitro intrinsic clearance of 70 (CLint, in vitro = 1864~3089 µL/min/mg) than that of 69

(CLint, in vitro = 447~522 µL/min/mg) is the consequence of the higher CLogP and

CLogD values of compared 70 (Table 18). Moreover, for both compounds, higher clearance in the male rat liver microsome (MRLM) than that in the female rat liver microsome (FRLM) can be explained by the fact that CYP3A and

CYP2C are found only in MRLM. It is believed that MRLM could be more reflective of the enzyme profiles in human liver microsomes.

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Time PC 69-MRLM 69-FRLM 70-MRLM 70-FRLM

(min) CHAPTER 5

199 0 100% 100% 100% 100% 100% 5 28% 40% 72% 1% 7% 10 8% 6% 26% 0% 0% 15 2% 3% 3% 0% 0% 30 0% 1% 0% 0% 0% 45 0% 2% 0% 0% 0% T1/2 (min) 4.4 ± 0.3 5.2 ± 0.1 0.80 ± 0.08 1.26 ± 0.24 CLint, in vitro 522 ± 39 447 ± 10 3089 ± 353 1864 ± 356 (µL/min/mg)

Figure 67. Percentages of remaining compounds 69 (upper left) and 70 (upper right) relative to initial amounts (t = 0) in rat liver o microsomes upon incubation at 37 C for 5, 10, 15, 30 and 45 min, as well as half-life (T1/2) and in vitro intrinsic clearance CLint, in vitro values (mean ± standard deviation) at 3 µM of test compounds. PC: positive control. MRLM: male rat liver microsome. FRLM: female rat liver microsome.

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Subsequently, LightSight Software (AB Sciex) was employed to identify possible metabolites of compounds 69 (Table 19 and Figure 68) and 70 (Table

20 and Figure 70). After incubation, the LC-MS/MS analysis for the time window of 30 min led to the detection of the parent compound 69 (i.e. M19) and its 19 metabolites (i.e. M1–M18, M20), all of which were eluted before 20 min (Table 19). These 19 metabolites can be classified into 5 groups: (i) MW gain of 32 (i.e. M1, M2, M5 and M7) due to dioxygenation of 69; (ii) MW gain of 14 (i.e. M3 and M4) due to either (ii-a) methylation of 69, or (ii-b) the conversion of CH2 to keto C(=O) of 69; (iii) MW gain of 16 (i.e. M6, M8, M11–

M15, M17 and M18) due to monooxygenation of 69; (iv) MW gain of 18 (i.e.

M9 and M10) due to either (iv-a) the addition of OH and H of 69, (iv-b) hydrogenation and monooxygenation of 69, or (iv-c) demethylation and deoxygenation of 69; (v) MW loss of 2 (i.e. M16 and M20) due to dehydrogenation of 69. The amount of parent compound 69 (i.e. M19) was ca.

8% of the total analyte after incubation with microsomes, and the 19 metabolites constituted the remaining ca. 92%, which was consistent with the metabolic instability of 69 shown in Figure 67. Of all the 19 metabolites, M8 (retention time: 14.03 min, m/z 352.1), which has the gain of mass shift 15.7 relative to the protonated parent compound 69 (m/z 336.4), was found to be the predominant metabolite of 69. This peak with m/z 352.1 was expected to be a protonated monooxygenation product of 69. The fragmentation patterns of M8 and M19 (i.e. the protonated parent compound 69) are shown in Figure 68.

Compound 69 displayed an MH+ ion at m/z 336.1, and three product ions at m/z

193.1, m/z 150.1, and m/z 144.1. The prominent product ion at m/z 193.1 corresponded to fragment F1, which was formed by amide hydrolysis of 69.

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The product ion at m/z 144.1 was identified as the other product of amide hydrolysis, and corresponded to fragment F2. A moderate amount of product ion at m/z 150.1 was observed, and it corresponded to fragment F3. In the product ion spectrum of M8, the MH+ ion (m/z 352.0), which was 16 mass units higher than that of 69 (m/z 336.1), indicated an additional oxygen atom was incorporated into M8. The common product ion at m/z 144.1 observed in both parent 69 and M8 excluded the possibility that monooxygenation occurred in fragment F2. That is, monooxygenation must have occurred in fragment F1, and this surmise was supported by the presence of the product ion at m/z 209.1, corresponding to fragment F4, in highest abundance. The product ion at m/z

166.1, corresponding to fragment F5, further narrowed down the possibilities of monooxygenation positions—fragment F3 was revealed to be the moiety where monooxygenation occurred.

Table 19. Metabolites of Compound 69 Identified Using LightSight Software Sorted by Retention Time

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Figure 68. The product ion spectra of the parent compound 69 and its major metabolite 69-M8. The respective fragment ions are highlighted in yellow. The structure of 69-M8 was not confirmed.

Reviewing buspirone metabolism may give clues to the exact position of mono- oxidation on fragment F3. Buspirone (Figure 69), as mentioned in Appendix D

(Figure A4), is an anxiolytic drug approved by FDA in 1986. A group of researchers in Bristol-Myers Squibb conducted an in vitro study using human liver microsomes to profile metabolites of buspirone and determine CYP isozymes responsible for these metabolic reactions (Zhu et al., 2005). They demonstrated that the primary metabolites of buspirone are 1-PP by N- dealkylation, Bu N-oxide by N-oxidation, and 3’-OH-Bu, 5-OH-Bu and 6’-OH-

Bu by hydroxylation (Figure 69). It appears that CYP isozymes tend to perform aromatic oxidation (Meunier et al., 2004) at the C5 position of pyrimidine of 1-

PP moiety. This metabolic study on buspirone led to a hypothesis that mono- oxidation also occurred at the C5 position of pyrimidine of fragment F3 (Figure

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68). This hypothesis can be proven true only if the followings are met: (i) 69-

M8 is synthesised as the reference standard, (ii) the retention time of synthesised

69-M8 is the same as that of M8 peak (i.e. 14.03 min), and (iii) the product ion spectrum of synthesised 69-M8 is the same as that of M8 peak (i.e. the top spectrum in Figure 68).

O O OH N N N N NN O NN O N N O 3'-OH-Bu CYP3A Bu N-oxide CYP3A

N N NN O N buspirone

CYP3A CYP3A CYP3A O N NNH N O N N 1-PP NN O OH N N N 6'-OH-Bu HO NN O N 5-OH-Bu Figure 69. The proposed buspirone metabolism in human liver microsomes.

For compound 70, a total of 15 metabolites (Table 20) were detected. Unlike compound 69, the parent compound 70 did not exist in the metabolite mixtures, which was consistent with the observation in Figure 67 that compound 70 was more metabolically unstable than compound 69. These 15 metabolites can be classified into 5 groups: (i) MW gain of 48 (i.e. M1) due to trioxygenation of

70; (ii) MW gain of 14 (i.e. M2–M5, M7–M9) due to either (ii-a) methylation of 70, or (ii-b) the conversion of CH2 to keto C(=O) of 70; (iii) MW gain of 32

(i.e. M6, M10 and M11) due to dioxygenation of 70; (iv) MW gain of 16 (i.e.

M13) due to monooxygenation of 70; (v) MW loss of 2 (i.e. M12, M14 and

M15) due to dehydrogenation of 70. Of all the 15 metabolites, M11 (retention

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CHAPTER 5 time: 12.04 min, m/z 396.1), which has the gain of mass shift 31.6 relative to the calculated protonated parent compound 70 (m/z 364.5), was found to be the predominant metabolite of 70. The percentage (ca. 20%) of 70-M11 in the metabolite mixtures of 70 was approximately 20%, lower than that (ca. 50%) of

69-M8 in the metabolite mixtures of 69. This peak with m/z 396.1 was expected to be a protonated dioxygenation product of 70. The fragmentation patterns of

M11 are shown in Figure 70. The MH+ ion (m/z 396.1), which was 32 mass units higher than that of 70 (m/z 364.5), was in highest abundance, and indicated two additional oxygen atoms were incorporated into 70. The product ion at m/z

144.1 corresponded to fragment F6, and the product ion at m/z 336.0 appeared to the product of di-demethylation of 70. One of the oxygenation positions is presumed to be at the 5 position of pyrimidine in the light of studies on buspirone metabolism (Figure 69), and the region of the other oxidation position can be deduced from the presence of other product ions. First, the presence of product ion at m/z 144.1 precluded the possibility of oxygenation on F6. Second, product ions at m/z 182.2 and 225.2, although the intensities are low, provided a clue that the other oxygenation should occur on fragment F8. A previous study using tandem mass spectrometry to analyse the mixture of SCH 66712, a compound containing piperazinylpyrimidine, and CYP2D6 (Nagy et al., 2011) led to a speculation that the specific site of the other oxidation is more likely to be the piperazine ring (the red circle on 70-M11 in Figure 70), not the pyrimidine ring. This speculation was supported by some other metabolic studies of piperazine-containing drugs (Kamel et al., 2010; Miraglia et al., 2010;

Pellegatti et al., 2009). That said, similar to the discussion for compound 69

(vide supra), reference standards are needed to confirm the speculation. Possible

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CHAPTER 5 metabolites informed by the analysis of Figure 70 include amine oxide 96 and hydroxylated 97 and 98 (Figure 71), and these three proposed compounds can be the reference standard to test the speculation.

Table 20. Metabolites of Compound 70 Identified Using LightSight Software Sorted by Retention Time

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Figure 70. The product ion spectrum of the major metabolite of 70. The respective fragment ions are highlighted in yellow. The structure of 70-M11 was not confirmed.

OH OH OH N N N

N N N N N N N N N O OH HN O HN O HN O OH

96 97 98

[M+H], C21H26N5O3 [M+H], C21H26N5O3 [M+H], C21H26N5O3 Exact Mass: 396 Exact Mass: 396 Exact Mass: 396 Figure 71. The proposed compounds 96-98.

Taken together, synthesis of proposed 69-M8 is required to confirm the chemical structure of the major metabolite of 69 (i.e. M8 in Table 19), and synthesis of proposed 96-98 is also necessary to confirm the chemical structure of the major metabolite of 70 (i.e. M11 of Table 20). Future directions on the improvement of metabolic stability of 69 and 70 will be discussed in Chapter 6.

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5.3.3.3 Toxicity assays

Like what have been stated in Chapter 1, drug design and discovery projects should focus not only on enhancing the beneficial effects of drugs but also on reducing the potentially deleterious effects of drugs, which can arise from CYP inhibition (Zlokarnik et al., 2005), hERG blocking (Sanguinetti and Mitcheson,

2005), formation of reactive metabolites (Kalgutkar et al., 2005), or induction of oxidative stress (Deavall et al., 2012). The study of drug toxicity focuses on these unintended effects, also known as side effects or adverse drug reactions, which lead to the disruption of normal physiological functions. Drug toxicity accounts for approximately 30% drug attrition (Kola and Landis, 2004), so early screening of toxicity can greatly minimise the risks of costs spent in the late stages of development as well as patients’ health after drugs get marketed. In the thesis, in vitro assays for mutagenicity and cytotoxicity were performed on compounds 69 and 70.

Mutagens are substances capable of inducing transmissible damages to genetic materials, which may lead to cancer (Benigni and Bossa, 2011; Benigni et al.,

2013). One well-established method to investigate the mutagenic potential of a compound is bacterial reverse mutation assay, also known as Ames assay

(Figure 72) (Mortelmans and Zeiger, 2000). The assay measures the extent to which a given compound can revert, regain or restore the gene function as the indicator of its mutagenicity. Specifically, the initial screening of histidine mutants (His–) of Salmonella typhimurium done by Bruce N. Ames in the 1970s led to the selection of some tester strains (e.g. TA98, TA100, TA1535, TA1537,

TA1538) that are highly sensitive to various mutagens and can be reverted back to their wild types (His+) (Ames et al., 1973). The Salmonella typhimurium

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CHAPTER 5 strains with pre-existing mutations (His–) are unable to synthesise histidine (one of the growth requirements for these strains) and therefore could not form colonies in the agar plate. If the incubation of one such strain with a given chemical results in the formation of many colonies, it would suggest that the chemical induces back-mutation (His– → His+) and is therefore considered as a mutagen. But if the incubation does not result in many colonies but only few colonies comparable with those in the negative control, the observed colonies should be arising from spontaneous back-mutation but not from chemical treatment and therefore the chemical is considered as a non-mutagen.

Figure 72. Diagram depicting Ames assay. The source of this figure is Genetics: Analysis and Principles (Robert J. Brooker, McGraw-Hill Education, copyright year 2012). The figure is reproduced with permission of McGraw- Hill Education.

A previous analysis of the mutagenicity database in National Toxicology

Program (NTP) showed that using two Salmonella strains TA98 and TA100 with and without metabolic activation systems was capable of detecting 89% of

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CHAPTER 5 the mutagens (Zeiger et al., 1985), and therefore these two strains were adopted for the thesis. As shown in Figure 73, two strains, TA98 and TA100, in the presence (+S9) and absence (–S9) of a metabolising system “S9 mix”, were used to assess the mutagenicity of compounds 69 and 70. “S9 mix” is a metabolic activation system consisting of the 9000g supernatant fraction of rat liver microsomes, NADP and cofactors. Exogenous “S9 mix” was used owing to the lack of CYP metabolising enzymes in Salmonella typhimurium. Agar plates +S9 and –S9 can be used to detect pro-mutagens for cases where the native chemical is not mutagenic but its metabolites are mutagens, and direct- acting mutagens, respectively. The high number of His+ revertants induced by treatment with 4-nitroquinoline N-oxide (4-NQO) and 2-aminoanthracene (2-

AA), both of which are known mutagens and acted as diagnostic positive control chemicals, validated the reversion properties of each strain and the metabolising activity of “S9 mix” system (Maron and Ames, 1983). The metabolic activation of 2-AA into mutagens by various CYP isozymes has been documented

(Carriere et al., 1992), and this was the reason why 2-AA was used as the positive control chemical for TA98 +S9 (Figure 73B) and TA100 +S9 (Figure

73D). 4-NQO was reported to be a DNA damaging agent in the cell-based assay with no added exogenous metabolic enzymes (Dahm and Hubscher, 2002), and hence it was used as the positive control chemical for TA98 –S9 (Figure 73A) and TA100 –S9 (Figure 73C). Methotrexate was previously confirmed to be negative in Ames assay (Matsuzaki et al., 2010), so it was used as the negative control for all four incubations (Figure 73A–D). DMSO was used as the solvent control (vehicle). The number of His+ revertants induced by compounds 69 and

70 at two concentrations, 10 µM and 100 µM, was even lower than that induced

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Figure 73. The number of histidine revertant colonies grown on the agar plate that contained various chemicals and, (A) TA98 strain without “S9 mix”, (B) TA98 strain with “S9 mix”, (C) TA100 strain without “S9 mix”, and (D) TA100 strain with “S9 mix”. The colonies for blank indicates spontaneously induced revertants without treatment of any drug or solvent.

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In addition to mutagenicity, drug-induced hepatotoxicity (Lee, 2003) and cardiac toxicity (Wallace et al., 2004) are also of primary concerns in drug safety assessment. Cell viability assays using transforming growth factor-alpha mouse hepatocyte (TAMH) and HL-1 cardiomyocyte were performed to assess the cytotoxic potential of compounds 69 and 70. TAMH lines were derived from the transgenic mice overexpressing transforming growth factor alpha (TGF-α)

(Wu et al., 1994), and have been widely used as an in vitro model to estimate hepatotoxicity induced by acetaminophen (Pierce et al., 2002), tetrafluoroethylcysteine (Ho et al., 2005), flutamide (Coe et al., 2007), lapatinib, dexamethasone (Teo et al., 2012), and flupirtine (Lemmerhirt et al., 2015).

TAMH lines were treated with compounds 69 and 70 in eight different concentrations (100 µM, 33.3 µM, 11.1 µM, 3.7 µM, 1.23 µM, 0.41 µM, 0.13

µM, 0.045 µM), and the percentage of viable cells was plotted against the logarithm of ligand concentration (Figure 74B–C). Both compounds at the concentrations less than 30 µM were shown to be non-cytotoxic in TAMH lines.

Acetaminophen at high doses is known to induce hepatotoxicity (James et al.,

2003), so it was used as the positive control (Figure 74A). In parallel, HL-1 cardiac myocyte cell lines were capable of maintaining cardiac-specific phenotypes (Claycomb et al., 1998; White et al., 2004), and have been proven as useful in vitro models for studying various aspects of cardiac biology, including cardiac apoptosis (Kitta et al., 2001), cardiomyocyte cell cycle

(Lanson et al., 2000), and cardiac gene promoter function (Dai et al., 2002).

Recently, HL-1 cardiomyocytes were adopted by a group of researchers in the

United States FDA to study doxorubicin-induced cardiotoxicity (Aryal et al.,

2014), and this became the basis for using doxorubicin as the positive control

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(Figure 74D). Similar to the viability study using TAMH lines, there was no toxicity observed at ligand concentrations less than 30 µM in HL-1 cell (Figures

74E–F). The decreased viability induced by 0.045 µM of compound 69 in HL-

1 cell (Figure 74E) appeared to be a false negative result. Taken together, compounds 69 and 70 have no mutagenicity up to 100 µM and no cytotoxicity

(in hepatocytes and cardiomyocytes) up to 30 µM.

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Figure 74. The cell viability assay after treatment of TAMH (A–C) and HL-1 (D–F) cells with positive controls and compounds.

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5.3.4 In vivo Drosophila models of Parkinson’s disease

Although most PD cases occur in a sporadic manner, it has been gradually appreciated that PD can be inheritable since the identification of α-synuclein in

PD families (Polymeropoulos et al., 1997). To date, six genes are believed to conclusively link to PD, that is, SNCA (or, α-synuclein), LRRK2, Parkin,

PINK1, DJ-1, and ATP13A2. Of these six genes, mutations in Parkin and

LRRK2 have been recognised to be the predominant causes for early- (Lucking et al., 2000) and late-onset (Kumari and Tan, 2009) hereditary PD cases, respectively. Since modelling the genetics of PD in mice has proven disappointing (Duty and Jenner, 2011), genetic models in multicellular model organisms, such as Drosophila, C. elegans, and zebrafish have been developed to provide insights into the molecular events associated with PD gene mutations.

Of these genetic models, the Drosophila model gained most attentions because this organism contains highly conserved homologues of many human genes. It has been demonstrated that the premature loss of fly climbing ability was achieved by the transgenic Drosophila overexpressing mutant forms of α- synuclein (Feany and Bender, 2000), and this climbing defect can be restored by L-DOPA administration (Pendleton et al., 2002). Particularly, Parkin (Cha et al., 2005) and LRRK2 (Liu et al., 2008) mutants in Drosophila models displayed a good degree of reproducibility in the brain DA neuron reduction, validating Drosophila models being superior to mice models in producing PD- like phenotypes after PD-associated gene mutations. Importantly, pharmacological compounds can be mixed with food and readily delivered to the nervous system of flies, as they lack a well-defined BBB. In addition to the aforementioned advantages, the low cost, small size, short lifespan (about 2

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CHAPTER 5 months) and high-throughput capacity of Drosophila make this model attractive for drug testing in various diseases (Pandey and Nichols, 2011). Notably, the closest homologue of Drosophila adenosine receptor, DmAdoR, in human is hA2A receptor, so it gives confidence when using Drosophila model to assess

A2A ligands such as compounds 69 and 70. Also, DmAdoR is mainly distributed in the brain of adult flies (Kucerova et al., 2012), but as mentioned earlier, BBB permeability of compounds cannot be evaluated using Drosophila model.

Moreover, D2-like receptors (Hearn et al., 2002) have been shown to regulate locomotion in Drosophila (Draper et al., 2007). Due to the relevance of

Drosophila to human A2A and D2 receptors, this in vivo system was used to test the efficacy of compounds 69 and 70.

The high prevalence of G2019S mutant in LRRK2-associated PD cases has been well documented (Lesage et al., 2006; Ozelius et al., 2006), and importantly, studies have shown that expression of LRRK2-G2019S mutant in

Drosophila caused more severe PD phenotypes, including locomotor dysfunction, loss of dopaminergic neurons, and early mortality, than expression of wild-type LRRK2 (Liu et al., 2008). Prior to evaluating compounds 69 and

70 in the Drosophila model of PD, the effect of compound concentrations on fly viability was studied. At the ligand concentration of 500 µM, both compounds were lethal to G2019S flies (data not shown). Lower ligand concentrations were tried, and at 50 µM, whilst treatment with compound 70 still resulted in toxicity, compound 69 was able to delay the G2019S fly mortality (Figure 75). Although compounds 69 and 70 did not show in vitro toxicity (Figures 73 and 74), the higher toxicity induced by 70 than that induced by 69 in Drosophila may suggest that 69 is safer than 70 in the in vivo setting.

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In fact, “Drosophotoxicology” was coined to describe the use of Drosophila for screening neurotoxicity of chemicals, so it could be that 70 is more neurotoxic than 69 (Rand, 2010). Compound 69 at 50 µM was therefore selected for the subsequent climbing assay and quantification of dopaminergic (DA) neurons

(Figure 76).

Figure 75. Survival percentages of LRRK2 G2019S flies after 7 days, 10 days and 17 days of treatment with various concentrations (50 µM, 100 µM, 200 µM) of compound 69.

In the climbing assay (Figures 76A and 76C), the flies were tapped to the bottom of the column, and the extent of their climbing to the top was measured. To quantify DA neurons (Figures 76B and 76D), brains of flies were dissected, immunostained with anti-tyrosine hydroxylase (anti-TH) antibody, and viewed by confocal microscopy. Initially, parkin-null (pk-/-) flies were used to evaluate compound 69 (Figures 76A and 76B). Remarkably, compound 69-treated parkin-null flies not only exhibited improvement in climbing scores compared with untreated mutant flies (Figure 76A), but they also showed reduction of

DA neuron loss in the PPL1 cluster (Figure 76B), which is the cluster used widely in parkin-null flies (Whitworth et al., 2005). Next, we examined whether compound 69 can likewise ameliorate LRRK2 G2019S-induced PD

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CHAPTER 5 phenotypes, and found that compound 69-treated LRRK2 G2019S flies also displayed improvement in climbing (in a concentration-dependent manner)

(Figure 76C) as well as mitigation of DA neuron degeneration (Figure 76D).

Together, these findings suggest that compound 69 acts as a suppressor of DA neuron dysfunction in two Drosophila genetic PD models—one with transgene parkin-null which represents recessive PD, and one with transgene LRRK2

G2019S-mutant which represents dominant PD.

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Figure 76. Treatment of compounds 69 in both parkin-null (A and B) and mutant LRRK2 G2019S-expressing (C and D) flies. A: Climbing score of untreated and compound 69 (50 µM)-treated parkin-null flies. B: DA neuronal count (PPL1 cluster) of normal flies (control), parkin-null flies, and compound 69 (50 µM)-treated parkin-null flies. C: As in A, except that parkin-null flies are substituted with LRRK2 G2019S-expressing flies. Two concentrations, 50 µM and 100 µM, of 69 were tested. D: As in B, except that parkin-null flies are substituted with LRRK2 G2019S-expressing flies (*p < 0.05, **p < 0.01).

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5.4 Chapter Conclusion

In summary, in view of the hA2A binding affinity exhibited by compounds 69 and 70, these two compounds were selected for further pharmacological evaluation. In the artificial cell membrane system, both compounds showed binding to dopamine D2 receptor with EC50 of 23–40 µM. Various functional assays for D2R activation were conducted: (i) Inhibition of cAMP accumulation in CHO cells was observed upon treatment of 70, with its concentration of 100

µM showing comparable activity induced by 100 nM of quinpirole. (ii) D2R internalisation in HEK 293 cells was observed following treatment with 69 and

70 at 30 nM. (iii) TGFα shedding in HEK 293 cells was observed upon treatment of 70, with its concentration of 100 µM resulting in an effect that was approximate one-third of what can be caused by treatment with 10 nM of dopamine. All these three experiments pointed to the D2R agonistic activity of

69 and 70. Nevertheless, further characterisation showed that both compounds had poor aqueous solubility, permeability and metabolic stability, and structural properties (e.g. CLogP, CLogD, MW) that may account for these poor drug-like properties were discussed. There was no mutagenicity up to 100 µM, nor hepatotoxicity and cardiotoxicity up to 30 µM. Further in vivo testing of compound 69 in the Drosophila models of PD showed that treatment with compound 69 at 50 µM not only improved mutant-induced locomotor impairment but also mitigated DA degeneration in parkin-null as well as in

LRRK2 G2019S mutant flies. Future directions will be presented in Chapter 6.

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CHAPTER 6 CONCLUSIONS AND FUTURE

DIRECTIONS

The lower birth rate and rising life expectancy has resulted in an increase in the percentage of elderly people. The world population aged 60 and above was 841 million estimated in the year 2013, and it was projected to exceed two billion by the year 2050 (Chatterji et al., 2015). Concomitant with population ageing was the burden of diseases and public spending on healthcare. A study done in the year 2010 showed that 630,000 people in the United States had been diagnosed with Parkinson’s disease (PD), the most prevalent movement disorder in older people, and this had incurred 14.4 billion USD medical expenses (Kowal et al., 2013). Therefore, developing more effective treatments for PD is undoubtedly a rewarding endeavour.

The study reported in this thesis integrated computational tools, synthetic organic chemistry, in vitro pharmacological and drug-like evaluations, as well as Drosophila PD models to address this unmet medical need. Inspired by the significant roles of hA2A and hD2 receptors in the pathogenesis of PD and the emerging appreciation that in most cases a drug exerts its action through interacting with multiple receptors, we selected these two relevant receptors and aimed at identifying new chemical entities (NCEs) able to antagonise hA2A and concurrently agonise hD2 receptor. There would be no point in spending more resources (e.g. time, costs, manpower) to target two receptors if the resulting compounds do not show more effectiveness than those that were designed and

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CHAPTER 6 discovered by targeting either receptor alone. Hence, the challenges in the thesis are twofold: (i) whether compounds capable of binding these two receptors simultaneously and exerting respective functions (i.e. antagonism at hA2A receptor, agonism at hD2 receptor) could exist, and if so, (ii) whether the identified compounds can have higher efficacy and lower toxicity than either known hA2A antagonist or hD2 agonist administered alone. For the first challenge, it is worth mentioning that the initial aspiration was not to adopt reported binding moieties against the two receptors followed by integrating the two moieties via linked, fused or merged approaches, as shown in Figures 19 and 20. Rather, the objective was to identify “drug-like” compounds (i.e. MW

< 500) that had dual-acting properties at the two receptors. The quickest way to achieve this objective is to check whether there is a compound reported as an hA2A antagonist by one group of researchers and a hD2 agonist by a different group, and this strategy usually works in identifying multi-kinase inhibitors

(Karaman et al., 2008) because of more conserved binding pockets amongst kinase family members as opposed to distinct receptor families to which adenosine and dopamine receptors belong. Investigation of approximately 2,000 compounds published as at the year 2011 did not reveal any single chemical entity with such desired mechanisms of action, but uncovered the substructure

1,4-disubstituted aromatic piperazine (1,4-DAP) commonly observed in some reported hA2A antagonists and hD2 agonists (Section 3.2.3.3). Analysis by support vector machine (SVM) and Tanimoto similarity led to the specification of aromatic ring in 1,4-DAP being pyrimidine, as well as the importance of indole ring on the molecule (Figure 38). Cluster analysis further strengthened confidence in selecting [indole + piperazine + pyrimidine] (IPP) for synthesis

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(Figure 48). Gratifyingly, some of compounds containing IPP scaffold showed binding at hA2A in the micromolar range, and to our knowledge, there are no

IPP-containing compounds reported as hA2A binders. Compounds 69/70, bearing IPP scaffold, were also confirmed to be hD2 binders (Section 5.3.1) and hD2 agonists (Section 5.3.2). The first challenge was therefore successfully tackled, except that the hA2A antagonism of IPP-containing compounds remains to be evaluated (Future Direction 1).

For the second challenge, it is worth reviewing the only marketed A2A antagonist for PD treatment, KW-6002 (Figure 12), and four commonly prescribed dopamine agonists for PD treatment, rotigotine, pramipexole, apomorphine and ropinirole (Figure 25). KW-6002 has nanomolar binding affinity (Ki = 12 nM) at hA2A receptor, and all four dopamine agonists are also tight hD2 receptor binders with Ki = 4~32 nM. The advantage of 69/70 over

KW-6002 may be the binding at hD2 receptor, and similarly, the advantage of

69/70 over the four dopamine agonists may lie in the affinity at hA2A receptor.

Whether 69/70, with micromolar ranges of affinity at hA2A and hD2, are equal to or even superior than KW-6002, rotigotine, pramipexole, apomorphine or ropinirole needs more studies. It will be interesting to use the same set of experimental conditions in the Drosophila model to compare the extents of motor improvement and neuroprotection induced by 69/70 as well as the aforementioned five PD drugs currently used in the clinics (Future Direction

2). As one would expect, structural modifications of IPP-containing compounds towards optimal hA2A and hD2 binding should be done in parallel with advancing 69/70. The optimisation works will be guided by the structure- activity relationship (SAR) studies (Figure 54) established in Chapter 4, and

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may utilise antagonist-bound hA2A X-ray crystal structures (Congreve et al.,

2012; Dore et al., 2011; Jaakola et al., 2008; Liu et al., 2012) for molecular docking studies to understand how 69/70 interact with hA2A. Although agonist- bound hD2 X-ray crystal structures are not available, several attempts at constructing active-state hD2 models are documented (Kling et al., 2014;

Krogsgaard-Larsen et al., 2014; Malo et al., 2013) (Future Direction 3). If the optimisation can result in IPP-containing compounds with nanomolar binding affinity to both hA2A and hD2 receptors, the chance of these compounds for meeting the second challenge would definitely be higher than that of 69/70.

The novelty of compounds 69/70 was confirmed by a prior art search carried out by Saturo Global (a professional knowledge solutions firm) facilitated by

NUS Industry Liaison Office (ILO). The search was executed in databases such as PatBase, USPTO, EPO, CAplus and WIPO to identify the relevant patent references, yielding one relevant and eleven background patent references. The search was also performed on databases such as PubMed, Scirus, ScienceDirect,

Google Scholar and Google to identify relevant non-patent references, yielding six non-patent references. Careful evaluation of all these eighteen relevant references showed that the works in this thesis did not correspond to any references regarding compound structures, procedures of compound preparation, and disease applications. A response to the prior art search report was subsequently drafted and sent to Saturo Global. The response turned out to be agreeable to the firm.

Despite being novel, compounds 69/70 are plagued by low aqueous solubility, low permeability, as well as low metabolic stability, offering opportunities for improvement. Since the intended targets of 69/70 are located in dopaminergic

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CHAPTER 6 neurons in the midbrain (Figure 1), it is necessary to consider structural properties expected of a CNS drug for optimal brain exposure (Rankovic, 2015) before commencing the optimisation of drug-like properties. As described in

Section 3.2.3.5, Lipinski’s rule of 5 (RO5) has been widely adopted as the guideline in the design of oral drugs, and it was used for filtering the hits identified by support vector machine (SVM) models. In general, the criteria for

CNS drugs are more stringent than those for oral drugs (Kerns and Di, 2008) because CNS drugs are usually taken orally and need to cross both gastrointestinal (GI) barrier and blood-brain barrier (BBB). This justifies the use of RO5 in Chapter 3, since it is highly preferable that compounds discovered in the thesis are orally bioavailable and CNS penetrant.

BBB is mainly composed of endothelial cells (ECs) connected by tight junctions

(TJs), and serves as the barrier between blood capillaries and the brain parenchyma to impede the entry of foreign substances into the brain (Rubin and

Staddon, 1999). BBB is such a selective barrier that 98% of small molecule drugs do not cross it (Pardridge, 2005). For small molecules to cross BBB, they must have low molecular weight (MW) and high lipid solubility. Of the six structural properties shown in Table 18, lipid solubility is influenced by CLogP,

CLogD, TPSA, HBD and pKa. There are a number of BBB rules or guidelines summarised from the analysis of marketed CNS drugs (Ghose et al., 2012;

Pajouhesh and Lenz, 2005; Wager et al., 2010), and all rules point to a common principle that, compared to non-CNS drugs, CNS drugs tend to have lower MW

(Pardridge, 2005), higher LogP, lower PSA, fewer HBD (Mahar Doan et al.,

2002), and contain a basic amine (Kerns and Di, 2008) (Table 21). These rules provide strict cutoffs or ranges for individual structural properties and may be

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CHAPTER 6 of great utility for medicinal chemists in the optimisation of CNS drugs.

Complying with cutoff values set in these recommendations during ligand design in the earlier phase and lead optimisation in the later phase not only increases the likelihood of BBB penetration but also leads to more favourable pharmacokinetic properties that a CNS drug normally has, since these recommendations are extracted from successful (i.e. marketed) CNS drugs.

Comparison of Table 18 with Table 21 indicates that TPSA and HBD of compounds 69/70 are slightly higher than what are recommended in Table 21, suggesting that decreasing TPSA and HBD may improve brain exposure. It may not make sense to reduce HBD of 69/70 because the number is one and the NH of the indole might be necessary in receptor binding. Hence, according to Table

21, reduction of TPSA becomes one of the goals in future optimisation.

Table 21. Structural Properties Required of CNS Drugs data set cutoff values reference > 2,000 Compounds with TPSA < 60 Å2 and most analysis done by structurally basic pKa < 8 have lower chance to be P- Eli Lilly (Desai et diverse glycoprotein (P-gp) substrates. al., 2013) compounds

medicinal preferred ranges for increasing the analysis done by chemistry probability of BBB permeability: CLogP Amgen literature = 2~4, CLogD (pH 7.4) = 2~4, MW < (Hitchcock and 450, PSA < 70 Å2, HBD = 0~1 Pennington, 2006)

48 CNS What distinguishes CNS drugs and non- analysis done by drugs, 45 CNS drugs is that the former have greater GlaxoSmithKline non-CNS lipophilicity (mean value of CLogP = (Mahar Doan et drugs 3.43; range value of CLogP = 0.16~6.59; al., 2002) mean value of CLogD = 2.08; range value of CLogD = –1.34~6.57), lower PSA (mean value = 40.5 Å2; range value = 4.63~108), and fewer HBD (mean value = 0.67; range value = 0~3).

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Moreover, optimisation should also be done towards improvement in aqueous solubility, permeability and Phase I metabolic stability. The determinants of solubility and permeability were presented in Section 5.3.3.1 and listed in Table

18. Changes in these structural property determinants by chemical modification lead to changes in solubility, permeability, and metabolic stability. An important thing one needs to bear in mind would be that holistic consideration is always executed so as not to overly benefit one drug-like property (e.g. solubility) but risk the other (e.g. permeability). Table 22 shows the consensus in the medicinal chemistry community with regard to structural modification strategies to improve various drug-like properties. To improve solubility, one may try to reduce lipophilicity (Lin and Lu, 1997), reduce MW (Misra et al.,

2004), add polar groups (Kim et al., 2004), add HBD (Xie et al., 2004), or introduce ionisable groups (Li et al., 2000). Approaches to improving general permeability include heightening lipophilicity (Quan et al., 2005), lowering

MW (Palanki et al., 2000), reducing TPSA (Goodwin et al., 2001), and reducing

HBD (Desai et al., 2012), all of which are included in what CNS drugs tend to display (vide supra). For a compound having good BBB permeability, additional criteria such as bearing an aliphatic basic amine, are recommended.

Strategies to improve metabolic stability include reducing lipophilicity (Peglion et al., 2002), blocking metabolic site (Tandon et al., 2004), and replacing unstable groups (Brooks et al., 2001).

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Table 22. Changes in Structural Properties that can Improve Drug-Like Properties

lipophilicity MW TPSA HBD pKa/pH solubility ↓ ↓ ↑ ↑ ionisable group

permeability ↑ ↓ ↓ ↓ 70% of CNS drugs contain an aliphatic basic amine (Ghose et al., 2012).

metabolic ↓  block metabolic sites stability  replace unstable groups

Ideally, all criteria listed in Tables 21 and 22 are to be met in future optimisation.

However, in Table 22, some criteria in solubility and permeability are often opposed. For example, increasing lipophilicity will increase permeability but compromise solubility. To maximally but not completely meet the requirements listed in Tables 21 and 22, we propose the following as a recommendation for future optimisation. In view of the toxic effect of compound 70 in Drosophila, it is hypothesised that compound 69 has more translational value and may be prioritised over 70 in future optimisation. The structural properties of compound

69 (Table 18) are listed in Figure 77. To improve solubility, one straightforward strategy is to remove two methyl groups on pyrimidine of compound 69, leading to the proposed compound 99 which has lower lipophilicity and lower MW than compound 69 to fulfil two requirements listed in Table 22 for enhancing solubility. Next, taking into account that 70% CNS drugs (e.g. buspirone, aripiprazole) contain an aliphatic basic amine and 1,4-DAP substructure of 99 is to be maintained, a methylene group is proposed to be inserted in the amide group of 99, resulting in compound 100 which is expected to have better permeability. Subsequently, the discussion in Section 5.3.3.2 (Figure 68) shows that the most likely site of hydroxylation in compound 69 after incubation with

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CHAPTER 6 microsomes is the C5 position of pyrimidine ring, so blocking that labile site becomes the strategy for improving metabolic stability. The frequently employed substituent is the fluorine atom, as testified by many successful examples (Bohm et al., 2004; Park et al., 2001), including buspirone (Tandon et al., 2004), so the introduction of a fluorine substituent to compound 100 results in the proposed compound 101. The rationale behind fluorine substitution for enhancing metabolic stability is the following. First, the dissociation energy of C–F bond (105.4 kcal/mol) is higher than that of C–H

(98.8 kcal/mol) (O'Hagan, 2008). The strength of C–F bond can be explained by the electrostatic interaction between Cδ+ and Fδ– instead of the electron sharing as a covalent bond (Wiberg and Rablen, 1993), and therefore it is more difficult for CYP enzymes to attack C–F bond than C–H bond. Second, due to its high electronegativity, fluorine can pull electrons away from the pyrimidine of compound 101, making the pyrimidine more electron poor. Since oxidation of pyrimidine requires loss of electrons, fluorine-containing 101 with lower electron density on its pyrimidine compared to compound 100 will be less susceptible to oxidation by CYP enzymes. Comparison of 101 with 69 in terms of structural properties reveal that the major differences are lipophilicity and pKa. According to Table 22, the lower lipophilicity of 101 than that of 69 may improve solubility and metabolic stability, and creation of a basic amine in 101 may also improve permeability. Therefore, compound 101, which is presumed to have improved solubility, permeability and metabolic stability compared to compound 69, is worth trying for future optimisation.

229

N N O N O N optimise N N create a N N solubility basic amine

N N H H 69 99

Binding to A2A: Ki = 11.2 uM CLogP = 2.14 CLogP = 1.79 Binding to D2: EC50 = 22.5 uM CLogD = 2.13 CLogD = 1.79 Function to D2: internalisation, activation MW = 335.4 MW = 307.3 Poor aqueous solubility TPSA = 65.1 TPSA = 65.1 Poor permeability HBD = 1 HBD = 1 Metabolically unstable pKa = 4.67 pKa = 2.90 No mutagenicity solubility: moderate solubility: moderate No cytotoxicity in hepatocytes No cytotoxicity in cardiomyocytes Improve climbing and protect neurons in Drosophila Novelty: no same invention in prior art search 6 CHAPTER 230

F F N N N O O N N optimise O N N N N N metabolic N bioisosteric N stability replacement HN HN HN 100 101 102

CLogP = 1.83 CLogP = 1.97 CLogP = 2.38 CLogD = 1.82 CLogD = 1.96 CLogD = 1.84 MW = 321.4 MW = 339.4 MW = 367.4 TPSA = 65.1 TPSA = 65.1 TPSA = 57.3 HBD = 1 HBD = 1 HBD = 1 pKa = 5.64 pKa = 5.56 pKa = 7.78 solubility: high solubility: moderate solubility: high

Figure 77. Experimental data of 69, and estimated structural properties of 69, 99-102 (by MarvinSketch 15.1.5.0).

CHAPTER 6

Concurrently, it is noted that the estimated pKa of compound 101 is 5.56, implying that 101 would predominantly exist as the neutral species at physiological pH 7.4. A pKa distribution study done on 174 CNS drugs and 408 non-CNS drugs (Manallack, 2008) shows that: (i) 83% of CNS drugs contain at least one basic centre, and (ii) the majority of CNS drugs contain basic amines with pKa above 7, which is a requirement for interacting with Asp in TM3 of most GPCRs (Section 3.1.3.2, Figure 24). In addition, as shown in Table 21, Eli

Lilly recently analysed > 2,000 compounds and found that those compounds whose most basic pKa < 8 have lower chance to be the substrate of P- glycoprotein (P-gp) (Desai et al., 2013), an active efflux transporter expressed in BBB endothelial cells (Hitchcock, 2012). It thus follows from combining the pKa distribution with Eli Lilly’s studies that the optimum pKa range for CNS drugs is 7~8. It appears that partially positive charge on the tertiary amine of a

CNS drug favours the interactions with the carboxylate of Asp, resulting in the criterion of pKa > 7, and that increasing polarity confers higher likelihood of P- gp efflux, resulting in the criterion of pKa < 8. In fact, the pKa values of many

CNS drugs bearing 1,4-DAP substructure fall into the range of 7~8 (Figure 78).

Hence, it is deemed essential to raise the pKa of compound 101 by increasing the basicity of the tertiary amine on the piperazine (i.e. the blue-coloured N of compound 101 in Figure 77). The electron lone pairs at this nitrogen atom are influenced by the electron-withdrawing ketone group that is two bonds away, so one of the ways to increase the basicity is to replace the ketone group with an electron-donating group. But one should never forget the essentiality of that oxygen atom of the carbonyl group in hA2A binding, as discussed in Section

4.2.3 (Table 10), and this prompts a search for a replacement moiety that can

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CHAPTER 6 not only donate electrons but also retain the oxygen atom at a nearly identical position.

N Cl N N Cl N N N O O N O N H pKa = 7.62 pKa = 7.46 buspirone aripiprazole

O O S N N N Cl N N N S N O N N O N N O N N NH pKa = 7.09 pKa = 7.62 pKa = 7.14

ziprasidone O gepirone ipsapirone

F O N H O N N N N HN N N N N N O O bifeprunox elopiprazole trazodone

pKa = 7.84 pKa = 7.41 pKa = 7.09 Figure 78. Selected CNS drugs with 1,4-DAP substructure.

The terms “isosteric compounds” and “isosteres” were introduced by Irving

Langmuir in 1919, and refer to compounds that have the same number of atoms and electrons and thus similar physical properties (e.g. viscosity, heat of formation) to one another (Langmuir, 1919). For example, N2 is an isostere of

CO, and CO2 is an isostere of N2O. Langmuir’s concept of isosterism was subsequently applied to biological systems, leading to the concept of “bioisosteric replacement” proposed by Harris Friedman (Friedman, 1951). The current consensus of bioisosteres is largely shaped by Thornber: “Bioisosteres are groups or molecules which have chemical and physical similarities producing broadly similar biological properties.” (Thornber, 1979). Among the isosteres of the carbonyl group (Figure 79), the isostere that can not only donate

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CHAPTER 6 electrons but also retain the oxygen atom is oxetane, so compound 102 is proposed. Unlike cyclobutane, oxetane has a planar four-member ring (Luger and Buschmann, 1984) and is more stable than oxirane towards nucleophilic ring opening (Wolk et al., 2004). The oxetane ring is found in many natural products, such as Taxol®, oxetanocin A, bradyoxetin and oxetin. In fact, oxetanes as attractive structural motifs for drug discovery have been well documented (Burkhard et al., 2010; Wuitschik et al., 2010; Wuitschik et al.,

2008; Wuitschik et al., 2006). Structural comparison of propan-2-one with 3,3- dimethyloxetane revealed the agreement in the orientation of oxygen lone pairs, the similarities in C–C–C bond angles, as well as the difference in the distance between the oxygen atom and main chain (Figure 79). How the steric bulk of oxetane in compound 102 and elimination of π-conjugation of the carbonyl group with indole ring seen in compound 101 will influence binding at hA2A and hD2 receptors requires further investigation. Upon replacement with oxetane, CLogP is estimated to go up from 1.97 to 2.38, which is higher than that of compound 69 (i.e. 2.14), suggesting the potential of better permeability than compound 69. Importantly, the increase in pKa to the desired range 7~8 makes compound 102 partially charged in the physiological condition, thus lowering its CLogD to 1.84. This also implies that 102 should be more water soluble than 69. As mentioned earlier, lowering TPSA is one of the goals in future optimisation (Table 21), and the two methylenes of oxetane lower the

TPSA from 65.1 to 57.3. Therefore, in addition to compound 101, compound

102 (i.e. the oxetane version of 101) is also proposed as one candidate for future optimisation because of its promising structural properties (i.e. higher CLogP,

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lower TPSA and higher pKa compared to compound 69). Taken together,

Future Direction 4 is to synthesise and test compounds 99-102.

Figure 79. Lists of carbonyl isosteres and structural comparison of propan-2- one with 3,3-dimethyloxetane (Wuitschik, 2008) (reprinted with permission).

In a nutshell, this project has allowed the identification of promising scaffolds binding at hA2A and hD2 receptors. Future optimisation in terms of synthesis, pharmacological characterisation and in vivo testing will provide more insights into the value of these NCEs for the treatment of PD.

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BIBLIOGRAPHY

Abenhaim, L., Moride, Y., Brenot, F., Rich, S., Benichou, J., Kurz, X., Higenbottam, T., Oakley, C., Wouters, E., Aubier, M., et al. (1996). Appetite- suppressant drugs and the risk of primary pulmonary hypertension. International Primary Pulmonary Hypertension Study Group. N. Engl. J. Med. 335, 609-616.

Alelyunas, Y.W., Empfield, J.R., McCarthy, D., Spreen, R.C., Bui, K., Pelosi- Kilby, L., and Shen, C. (2010). Experimental solubility profiling of marketed CNS drugs, exploring solubility limit of CNS discovery candidate. Bioorg. Med. Chem. Lett. 20, 7312-7316.

Alon, U., Surette, M.G., Barkai, N., and Leibler, S. (1999). Robustness in bacterial chemotaxis. Nature 397, 168-171.

Altman, R.D., Lang, A.E., and Postuma, R.B. (2011). Caffeine in Parkinson's disease: a pilot open-label, dose-escalation study. Mov. Disord. 26, 2427-2431.

Ames, B.N., Lee, F.D., and Durston, W.E. (1973). An improved bacterial test system for the detection and classification of mutagens and carcinogens. Proc. Natl. Acad. Sci. U. S. A. 70, 782-786.

Amidon, G.L., Lennernas, H., Shah, V.P., and Crison, J.R. (1995). A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res. 12, 413-420.

Anighoro, A., Bajorath, J., and Rastelli, G. (2014). Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem. 57, 7874-7887.

Ariens, E.J. (1954). Affinity and intrinsic activity in the theory of competitive inhibition. I. Problems and theory. Arch. Int. Pharmacodyn. Ther. 99, 32-49.

235

BIBLIOGRAPHY

Armentero, M.T., Pinna, A., Ferre, S., Lanciego, J.L., Muller, C.E., and Franco, R. (2011). Past, present and future of A(2A) adenosine receptor antagonists in the therapy of Parkinson's disease. Pharmacol. Ther. 132, 280- 299.

Aryal, B., Jeong, J., and Rao, V.A. (2014). Doxorubicin-induced carbonylation and degradation of cardiac myosin binding protein C promote cardiotoxicity. Proc. Natl. Acad. Sci. U. S. A. 111, 2011-2016.

Avdeef, A., Bendels, S., Di, L., Faller, B., Kansy, M., Sugano, K., and Yamauchi, Y. (2007). PAMPA--critical factors for better predictions of absorption. J. Pharm. Sci. 96, 2893-2909.

Bain, J., Plater, L., Elliott, M., Shpiro, N., Hastie, C.J., McLauchlan, H., Klevernic, I., Arthur, J.S., Alessi, D.R., and Cohen, P. (2007). The selectivity of protein kinase inhibitors: a further update. Biochem. J. 408, 297-315.

Bakthavachalam, V., Baindur, N., Madras, B.K., and Neumeyer, J.L. (1991). Fluorescent probes for dopamine receptors: synthesis and characterization of fluorescein and 7-nitrobenz-2-oxa-1,3-diazol-4-yl conjugates of D-1 and D-2 receptor ligands. J. Med. Chem. 34, 3235-3241.

Barabasi, A.L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68.

Barnard, J.M., and Downs, G.M. (1992). Clustering of chemical structures on the basis of two-dimensional similarity measures. J. Chem. Inf. Comput. Sci. 32, 644-649.

Bartlett, S.E., Enquist, J., Hopf, F.W., Lee, J.H., Gladher, F., Kharazia, V., Waldhoer, M., Mailliard, W.S., Armstrong, R., Bonci, A., et al. (2005). Dopamine responsiveness is regulated by targeted sorting of D2 receptors. Proc. Natl. Acad. Sci. U. S. A. 102, 11521-11526.

Battaglia, G., Busceti, C.L., Molinaro, G., Biagioni, F., Traficante, A., Nicoletti, F., and Bruno, V. (2006). Pharmacological activation of mGlu4 metabotropic glutamate receptors reduces nigrostriatal degeneration in mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. J. Neurosci. 26, 7222-7229.

236

BIBLIOGRAPHY

Bazzini, P., and Wermuth, C.G. (2008). Chapter 20 - Substituent Groups. In The Practice of Medicinal Chemistry (Third Edition), C.G. Wermuth, ed. (New York: Academic Press), pp. 429-463.

Beaulieu, J.M., and Gainetdinov, R.R. (2011). The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol. Rev. 63, 182-217.

Becker, D., Selbach, M., Rollenhagen, C., Ballmaier, M., Meyer, T.F., Mann, M., and Bumann, D. (2006). Robust Salmonella metabolism limits possibilities for new antimicrobials. Nature 440, 303-307.

Benigni, R., and Bossa, C. (2011). Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem. Rev. 111, 2507-2536.

Benigni, R., Bossa, C., and Tcheremenskaia, O. (2013). Nongenotoxic carcinogenicity of chemicals: mechanisms of action and early recognition through a new set of structural alerts. Chem. Rev. 113, 2940-2957.

Ben-Jonathan, N. (1985). Dopamine: a prolactin-inhibiting hormone. Endocr. Rev. 6, 564-589.

Bhavani, S., Nagargadde, A., Thawani, A., Sridhar, V., and Chandra, N. (2006). Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs. J. Chem. Inf. Model. 46, 2478- 2486.

Bickerton, G.R., Paolini, G.V., Besnard, J., Muresan, S., and Hopkins, A.L. (2012). Quantifying the chemical beauty of drugs. Nat. Chem. 4, 90-98.

Birkmayer, W., Riederer, P., Youdim, M.B., and Linauer, W. (1975). The potentiation of the anti akinetic effect after L-dopa treatment by an inhibitor of MAO-B, Deprenil. J. Neural Transm. 36, 303-326.

Black, J.W., and Leff, P. (1983). Operational Models of Pharmacological Agonism. Proc. R. Soc. Lond. B. Biol. Sci. 220, 141-162.

237

BIBLIOGRAPHY

Blobel, C.P. (2005). ADAMs: key components in EGFR signalling and development. Nat. Rev. Mol. Cell Biol. 6, 32-43.

Bocker, A., Schneider, G., and Teckentrup, A. (2006). NIPALSTREE: a new hierarchical clustering approach for large compound libraries and its application to virtual screening. J. Chem. Inf. Model. 46, 2220-2229.

Bohm, H.J., Banner, D., Bendels, S., Kansy, M., Kuhn, B., Muller, K., Obst- Sander, U., and Stahl, M. (2004). Fluorine in medicinal chemistry. ChemBioChem 5, 637-643.

Bohm, S.K., Grady, E.F., and Bunnett, N.W. (1997). Regulatory mechanisms that modulate signalling by G-protein-coupled receptors. Biochem. J. 322, 1- 18.

Bolton, E.E., Wang, Y., Thiessen, P.A., and Bryant, S.H. (2008). Chapter 12 - PubChem: Integrated Platform of Small Molecules and Biological Activities. In Annu. Rep. Comput. Chem., A.W. Ralph, and C.S. David, eds. (Elsevier), pp. 217-241.

Bond, R.A., and Ijzerman, A.P. (2006). Recent developments in constitutive receptor activity and inverse agonism, and their potential for GPCR drug discovery. Trends Pharmacol. Sci. 27, 92-96.

Borgohain, R., Szasz, J., Stanzione, P., Meshram, C., Bhatt, M., Chirilineau, D., Stocchi, F., Lucini, V., Giuliani, R., Forrest, E., et al. (2014). Randomized trial of safinamide add-on to levodopa in Parkinson's disease with motor fluctuations. Mov. Disord. 29, 229-237.

Bostrom, J., Hogner, A., and Schmitt, S. (2006). Do structurally similar ligands bind in a similar fashion? J. Med. Chem. 49, 6716-6725.

Boussery, K., Belpaire, F.M., and Van de Voorde, J. (2008). Chapter 31 - Physiological Aspects Determining the Pharmacokinetic Properties of Drugs. In The Practice of Medicinal Chemistry (Third Edition), C.G. Wermuth, ed. (New York: Academic Press), pp. 635-654.

Bowes, J., Brown, A.J., Hamon, J., Jarolimek, W., Sridhar, A., Waldron, G., and Whitebread, S. (2012). Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug Discov. 11, 909-922.

238

BIBLIOGRAPHY

Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248-254.

Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I.H., Frearson, J., and Wyatt, P.G. (2008). Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435-444.

Breslin, H.J., Diamond, C.J., Kavash, R.W., Cai, C., Dyatkin, A.B., Miskowski, T.A., Zhang, S.P., Wade, P.R., Hornby, P.J., and He, W. (2012). Identification of a dual delta OR antagonist/mu OR agonist as a potential therapeutic for diarrhea-predominant Irritable Bowel Syndrome (IBS-d). Bioorg. Med. Chem. Lett. 22, 4869-4872.

Brochard, V., Combadiere, B., Prigent, A., Laouar, Y., Perrin, A., Beray- Berthat, V., Bonduelle, O., Alvarez-Fischer, D., Callebert, J., Launay, J.M., et al. (2009). Infiltration of CD4+ lymphocytes into the brain contributes to neurodegeneration in a mouse model of Parkinson disease. J. Clin. Invest. 119, 182-192.

Brooks, G., Burgess, W., Colthurst, D., Hinks, J.D., Hunt, E., Pearson, M.J., Shea, B., Takle, A.K., Wilson, J.M., and Woodnutt, G. (2001). Pleuromutilins. Part 1. The identification of novel mutilin 14-carbamates. Bioorg. Med. Chem. 9, 1221-1231.

Brown, M.S., Ye, J., Rawson, R.B., and Goldstein, J.L. (2000). Regulated intramembrane proteolysis: a control mechanism conserved from bacteria to humans. Cell 100, 391-398.

Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001). Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5-14.

Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121-167.

Burkhard, J.A., Wuitschik, G., Rogers-Evans, M., Muller, K., and Carreira, E.M. (2010). Oxetanes as versatile elements in drug discovery and synthesis. Angew. Chem. Int. Ed. Engl. 49, 9052-9067.

239

BIBLIOGRAPHY

Buschauer, A. (1989). Synthesis and in vitro pharmacology of arpromidine and related phenyl(pyridylalkyl)guanidines, a potential new class of positive inotropic drugs. J. Med. Chem. 32, 1963-1970.

Bylund, D.B., and Toews, M.L. (2014). Quantitative versus qualitative data: the numerical dimensions of drug action. Biochem. Pharmacol. 87, 25-39.

Byvatov, E., Fechner, U., Sadowski, J., and Schneider, G. (2003). Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43, 1882-1889.

Calabresi, P., Picconi, B., Tozzi, A., and Di Filippo, M. (2007). Dopamine- mediated regulation of corticostriatal synaptic plasticity. Trends Neurosci. 30, 211-219.

Canals, M., Marcellino, D., Fanelli, F., Ciruela, F., de Benedetti, P., Goldberg, S.R., Neve, K., Fuxe, K., Agnati, L.F., Woods, A.S., et al. (2003). Adenosine A2A-dopamine D2 receptor-receptor heteromerization: qualitative and quantitative assessment by fluorescence and bioluminescence energy transfer. J. Biol. Chem. 278, 46741-46749.

Cannon, J.G. (1975). Chemistry of dopaminergic agonists. Adv. Neurol. 9, 177-183.

Cannon, J.G., Lee, T., and Goldman, H.D. (1977). Cerebral dopamine agonist properties of some 2-aminotetralin derivatives after peripheral and intracerebral administration. J. Med. Chem. 20, 1111-1116.

Carlsson, A., Lindqvist, M., and Magnusson, T. (1957). 3,4- Dihydroxyphenylalanine and 5-hydroxytryptophan as reserpine antagonists. Nature 180, 1200.

Carlsson, A., Lindqvist, M., Magnusson, T., and Waldeck, B. (1958). On the presence of 3-hydroxytyramine in brain. Science 127, 471.

Carlsson, J., Yoo, L., Gao, Z.G., Irwin, J.J., Shoichet, B.K., and Jacobson, K.A. (2010). Structure-based discovery of A2A adenosine receptor ligands. J. Med. Chem. 53, 3748-3755.

240

BIBLIOGRAPHY

Carriere, V., de Waziers, I., Courtois, Y.A., Leroux, J.P., and Beaune, P.H. (1992). Cytochrome P450 induction and mutagenicity of 2-aminoanthracene (2AA) in rat liver and gut. Mutat. Res. 268, 11-20.

Cha, G.H., Kim, S., Park, J., Lee, E., Kim, M., Lee, S.B., Kim, J.M., Chung, J., and Cho, K.S. (2005). Parkin negatively regulates JNK pathway in the dopaminergic neurons of Drosophila. Proc. Natl. Acad. Sci. U. S. A. 102, 10345-10350.

Chan, C.S., Guzman, J.N., Ilijic, E., Mercer, J.N., Rick, C., Tkatch, T., Meredith, G.E., and Surmeier, D.J. (2007). 'Rejuvenation' protects neurons in mouse models of Parkinson's disease. Nature 447, 1081-1086.

Chang, C.C., Cao, S., Kang, S., Kai, L., Tian, X., Pandey, P., Dunne, S.F., Luan, C.H., Surmeier, D.J., and Silverman, R.B. (2010). Antagonism of 4- substituted 1,4-dihydropyridine-3,5-dicarboxylates toward voltage-dependent L-type Ca2+ channels Ca V 1.3 and Ca V 1.2. Bioorg. Med. Chem. 18, 3147- 3158.

Chatterji, S., Byles, J., Cutler, D., Seeman, T., and Verdes, E. (2015). Health, functioning, and disability in older adults--present status and future implications. Lancet 385, 563-575.

Chaudhuri, K.R., Healy, D.G., Schapira, A.H., and National Institute for Clinical, E. (2006). Non-motor symptoms of Parkinson's disease: diagnosis and management. Lancet Neurol. 5, 235-245.

Chen, J.F., Steyn, S., Staal, R., Petzer, J.P., Xu, K., Van Der Schyf, C.J., Castagnoli, K., Sonsalla, P.K., Castagnoli, N., Jr., and Schwarzschild, M.A. (2002). 8-(3-Chlorostyryl)caffeine may attenuate MPTP neurotoxicity through dual actions of monoamine oxidase inhibition and A2A receptor antagonism. J. Biol. Chem. 277, 36040-36044.

Chen, J.F., Xu, K., Petzer, J.P., Staal, R., Xu, Y.H., Beilstein, M., Sonsalla, P.K., Castagnoli, K., Castagnoli, N., Jr., and Schwarzschild, M.A. (2001). Neuroprotection by caffeine and A(2A) adenosine receptor inactivation in a model of Parkinson's disease. J. Neurosci. 21, RC143.

Cheng, T., Li, Q., Wang, Y., and Bryant, S.H. (2011). Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. J. Chem. Inf. Model. 51, 229-236.

241

BIBLIOGRAPHY

Cheng, Y., and Prusoff, W.H. (1973). Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem. Pharmacol. 22, 3099- 3108.

Chinta, S.J., and Andersen, J.K. (2005). Dopaminergic neurons. Int. J. Biochem. Cell Biol. 37, 942-946.

Claycomb, W.C., Lanson, N.A., Jr., Stallworth, B.S., Egeland, D.B., Delcarpio, J.B., Bahinski, A., and Izzo, N.J., Jr. (1998). HL-1 cells: a cardiac muscle cell line that contracts and retains phenotypic characteristics of the adult cardiomyocyte. Proc. Natl. Acad. Sci. U. S. A. 95, 2979-2984.

Coe, K.J., Jia, Y., Ho, H.K., Rademacher, P., Bammler, T.K., Beyer, R.P., Farin, F.M., Woodke, L., Plymate, S.R., Fausto, N., et al. (2007). Comparison of the cytotoxicity of the nitroaromatic drug flutamide to its cyano analogue in the hepatocyte cell line TAMH: evidence for complex I inhibition and mitochondrial dysfunction using toxicogenomic screening. Chem. Res. Toxicol. 20, 1277-1290.

Collis, M.G., and Hourani, S.M. (1993). Adenosine receptor subtypes. Trends Pharmacol. Sci. 14, 360-366.

Congreve, M., Andrews, S.P., Dore, A.S., Hollenstein, K., Hurrell, E., Langmead, C.J., Mason, J.S., Ng, I.W., Tehan, B., Zhukov, A., et al. (2012). Discovery of 1,2,4-triazine derivatives as adenosine A(2A) antagonists using structure based drug design. J. Med. Chem. 55, 1898-1903.

Conn, P.J., Christopoulos, A., and Lindsley, C.W. (2009). Allosteric modulators of GPCRs: a novel approach for the treatment of CNS disorders. Nat. Rev. Drug Discov. 8, 41-54.

Connolly, H.M., Crary, J.L., McGoon, M.D., Hensrud, D.D., Edwards, B.S., Edwards, W.D., and Schaff, H.V. (1997). Valvular heart disease associated with fenfluramine-phentermine. N. Engl. J. Med. 337, 581-588.

Cortes, C., and Vapnik, V. (1995). Support-Vector Networks. Mach. Learn. 20, 273-297.

242

BIBLIOGRAPHY

Cox, B.A., Henningsen, R.A., Spanoyannis, A., Neve, R.L., and Neve, K.A. (1992). Contributions of conserved serine residues to the interactions of ligands with dopamine D2 receptors. J. Neurochem. 59, 627-635.

Cuomo, D., Martella, G., Barabino, E., Platania, P., Vita, D., Madeo, G., Selvam, C., Goudet, C., Oueslati, N., Pin, J.P., et al. (2009). Metabotropic glutamate receptor subtype 4 selectively modulates both glutamate and GABA transmission in the striatum: implications for Parkinson's disease treatment. J. Neurochem. 109, 1096-1105.

Cyprotex (2015). http://www.cyprotex.com/admepk/in-vitro- metabolism/microsomal-stability. Accessed on 9 Apr 2015.

Dahm, K., and Hubscher, U. (2002). Colocalization of human Rad17 and PCNA in late S phase of the cell cycle upon replication block. Oncogene 21, 7710-7719.

Dai, B., Saada, N., Echetebu, C., Dettbarn, C., and Palade, P. (2002). A new promoter for alpha1C subunit of human L-type cardiac calcium channel Ca(V)1.2. Biochem. Biophys. Res. Commun. 296, 429-433.

Daniels, D.J., Kulkarni, A., Xie, Z., Bhushan, R.G., and Portoghese, P.S. (2005). A bivalent ligand (KDAN-18) containing delta-antagonist and kappa- agonist pharmacophores bridges delta2 and kappa1 opioid receptor phenotypes. J. Med. Chem. 48, 1713-1716.

De Clercq, E. (2013). The nucleoside reverse transcriptase inhibitors, nonnucleoside reverse transcriptase inhibitors, and protease inhibitors in the treatment of HIV infections (AIDS). Adv. Pharmacol. 67, 317-358.

De Lean, A., Hancock, A.A., and Lefkowitz, R.J. (1982). Validation and statistical analysis of a computer modeling method for quantitative analysis of radioligand binding data for mixtures of pharmacological receptor subtypes. Mol. Pharmacol. 21, 5-16.

243

BIBLIOGRAPHY

Deavall, D.G., Martin, E.A., Horner, J.M., and Roberts, R. (2012). Drug- Induced Oxidative Stress and Toxicity. J. Toxicol. 2012, 13.

Del Castillo, J., and Katz, B. (1957). Interaction at end-plate receptors between different choline derivatives. Proc. R. Soc. Lond. B. Biol. Sci. 146, 369-381.

DeLeon, A., Patel, N.C., and Crismon, M.L. (2004). Aripiprazole: a comprehensive review of its pharmacology, clinical efficacy, and tolerability. Clin. Ther. 26, 649-666.

Desai, P.V., Raub, T.J., and Blanco, M.J. (2012). How hydrogen bonds impact P-glycoprotein transport and permeability. Bioorg. Med. Chem. Lett. 22, 6540- 6548.

Desai, P.V., Sawada, G.A., Watson, I.A., and Raub, T.J. (2013). Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux. Mol. Pharm. 10, 1249-1261.

Di, L., Kerns, E.H., and Carter, G.T. (2009). Drug-like property concepts in pharmaceutical design. Curr. Pharm. Des. 15, 2184-2194.

Di, L., Kerns, E.H., Hong, Y., Kleintop, T.A., McConnell, O.J., and Huryn, D.M. (2003). Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates. J. Biomol. Screen. 8, 453-462.

Di Stefano, A., Sozio, P., Cacciatore, I., Cocco, A., Giorgioni, G., Costa, B., Montali, M., Lucacchini, A., Martini, C., Spoto, G., et al. (2005). Preparation and pharmacological characterization of trans-2-amino-5(6)-fluoro-6(5)- hydroxy-1-phenyl-2,3-dihydro-1H-indenes as D2-like dopamine receptor agonists. J. Med. Chem. 48, 2646-2654.

Dimova, D., Stumpfe, D., and Bajorath, J. (2013). Quantifying the fingerprint descriptor dependence of structure-activity relationship information on a large scale. J. Chem. Inf. Model. 53, 2275-2281.

Ding, B., Li, N., and Wang, W. (2013). Characterizing binding of small molecules. II. Evaluating the potency of small molecules to combat resistance based on docking structures. J. Chem. Inf. Model. 53, 1213-1222.

244

BIBLIOGRAPHY

Discher, B.M., Won, Y.Y., Ege, D.S., Lee, J.C., Bates, F.S., Discher, D.E., and Hammer, D.A. (1999). Polymersomes: tough vesicles made from diblock copolymers. Science 284, 1143-1146.

Dore, A.S., Robertson, N., Errey, J.C., Ng, I., Hollenstein, K., Tehan, B., Hurrell, E., Bennett, K., Congreve, M., Magnani, F., et al. (2011). Structure of the adenosine A(2A) receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19, 1283-1293.

Dorsey, E.R., Constantinescu, R., Thompson, J.P., Biglan, K.M., Holloway, R.G., Kieburtz, K., Marshall, F.J., Ravina, B.M., Schifitto, G., Siderowf, A., et al. (2007). Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68, 384-386.

Draper, I., Kurshan, P.T., McBride, E., Jackson, F.R., and Kopin, A.S. (2007). Locomotor activity is regulated by D2-like receptors in Drosophila: an anatomic and functional analysis. Dev. Neurobiol. 67, 378-393.

Dungo, R., and Deeks, E.D. (2013). Istradefylline: first global approval. Drugs 73, 875-882.

Dunwiddie, T.V., and Masino, S.A. (2001). The role and regulation of adenosine in the central nervous system. Annu. Rev. Neurosci. 24, 31-55.

Duty, S., and Jenner, P. (2011). Animal models of Parkinson's disease: a source of novel treatments and clues to the cause of the disease. Br. J. Pharmacol. 164, 1357-1391.

Ehrlich, K., Gotz, A., Bollinger, S., Tschammer, N., Bettinetti, L., Harterich, S., Hubner, H., Lanig, H., and Gmeiner, P. (2009). Dopamine D2, D3, and D4 selective phenylpiperazines as molecular probes to explore the origins of subtype specific receptor binding. J. Med. Chem. 52, 4923-4935.

Ertl, P., Rohde, B., and Selzer, P. (2000). Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 43, 3714-3717.

Evans, D.A. (2014). History of the Harvard ChemDraw project. Angew. Chem. Int. Ed. Engl. 53, 11140-11145.

245

BIBLIOGRAPHY

Favor, D.A., Powers, J.J., White, A.D., Fitzgerald, L.W., Groppi, V., and Serpa, K.A. (2010). 6-Alkoxyisoindolin-1-one based dopamine D2 partial agonists as potential antipsychotics. Bioorg. Med. Chem. Lett. 20, 5666-5669.

Feany, M.B., and Bender, W.W. (2000). A Drosophila model of Parkinson's disease. Nature 404, 394-398.

Fernandez-Duenas, V., Taura, J.J., Cottet, M., Gomez-Soler, M., Lopez-Cano, M., Ledent, C., Watanabe, M., Trinquet, E., Pin, J.P., Lujan, R., et al. (2015). Untangling dopamine-adenosine receptor-receptor assembly in experimental parkinsonism in rats. Dis. Model. Mech. 8, 57-63.

Ferraro, L., Beggiato, S., Tomasini, M.C., Fuxe, K., Antonelli, T., and Tanganelli, S. (2012). A(2A)/D(2) receptor heteromerization in a model of Parkinson's disease. Focus on striatal aminoacidergic signaling. Brain Res. 1476, 96-107.

Ferre, S., von Euler, G., Johansson, B., Fredholm, B.B., and Fuxe, K. (1991). Stimulation of high-affinity adenosine A2 receptors decreases the affinity of dopamine D2 receptors in rat striatal membranes. Proc. Natl. Acad. Sci. U. S. A. 88, 7238-7241.

Fink, T., and Reymond, J.L. (2007). Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 47, 342-353.

Fischer, O.M., Hart, S., Gschwind, A., and Ullrich, A. (2003). EGFR signal transactivation in cancer cells. Biochem. Soc. Trans. 31, 1203-1208.

Fowler, J.S., MacGregor, R.R., Wolf, A.P., Arnett, C.D., Dewey, S.L., Schlyer, D., Christman, D., Logan, J., Smith, M., Sachs, H., et al. (1987). Mapping human brain monoamine oxidase A and B with 11C-labeled suicide inactivators and PET. Science 235, 481-485.

Fowler, J.S., Volkow, N.D., Wang, G.J., Logan, J., Pappas, N., Shea, C., and MacGregor, R. (1997). Age-related increases in brain monoamine oxidase B in living healthy human subjects. Neurobiol. Aging 18, 431-435.

246

BIBLIOGRAPHY

Fowler, J.S., Volkow, N.D., Wang, G.J., Pappas, N., Logan, J., MacGregor, R., Alexoff, D., Shea, C., Schlyer, D., Wolf, A.P., et al. (1996). Inhibition of monoamine oxidase B in the brains of smokers. Nature 379, 733-736.

Frankenburg, F.R., and Baldessarini, R.J. (2008). Neurosyphilis, malaria, and the discovery of antipsychotic agents. Harv. Rev. Psychiatry 16, 299-307.

Fredholm, B.B., Fuxe, K., and Agnati, L. (1976). Effect of some phosphodiesterase inhibitors on central dopamine mechanisms. Eur. J. Pharmacol. 38, 31-38.

Friedman, H.L. (1951). First Symposium on Chemical-Biological Correlation, May 26-27, 1950 (Washington, DC: The National Academies Press).

Fuxe, K., Ferré, S., Snaprud, P., Euler, G.v., Johansson, B., and Ferdholm, B. (1993). Antagonistic A2a/D2 receptor interactions in the striatum as a basis for adenosine/dopamine interactions in the central nervous system Drug Development Research Volume 28, Issue 3. In Drug Development Research, pp. 374-380.

Fuxe, K., Tarakanov, A., Romero Fernandez, W., Ferraro, L., Tanganelli, S., Filip, M., Agnati, L.F., Garriga, P., Diaz-Cabiale, Z., and Borroto-Escuela, D.O. (2014). Diversity and Bias through Receptor-Receptor Interactions in GPCR Heteroreceptor Complexes. Focus on Examples from Dopamine D2 Receptor Heteromerization. Front. Endocrinol. (Lausanne) 5, 71.

Gaulton, A., Bellis, L.J., Bento, A.P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100-1107.

Ghose, A.K., Herbertz, T., Hudkins, R.L., Dorsey, B.D., and Mallamo, J.P. (2012). Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery. ACS Chem. Neurosci. 3, 50-68.

Gillespie, R.J., Bamford, S.J., Gaur, S., Jordan, A.M., Lerpiniere, J., Mansell, H.L., and Stratton, G.C. (2009). Antagonists of the human A(2A) receptor. Part 5: Highly bio-available pyrimidine-4-carboxamides. Bioorg. Med. Chem. Lett. 19, 2664-2667.

247

BIBLIOGRAPHY

Giorgioni, G., Ambrosini, D., Vesprini, C., Hudson, A., Nasuti, C., Di Stefano, A., Sozio, P., Ciampi, O., Costa, B., Martini, C., et al. (2010). Novel imidazoline compounds as partial or full agonists of D2-like dopamine receptors inspired by I2-imidazoline binding sites ligand 2-BFI. Bioorg. Med. Chem. 18, 7085-7091.

Glick, M., Jenkins, J.L., Nettles, J.H., Hitchings, H., and Davies, J.W. (2006). Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J. Chem. Inf. Model. 46, 193-200.

Goetz, G.H., Farrell, W., Shalaeva, M., Sciabola, S., Anderson, D., Yan, J., Philippe, L., and Shapiro, M.J. (2014). High throughput method for the indirect detection of intramolecular hydrogen bonding. J. Med. Chem. 57, 2920-2929.

Goggi, J.L., Sardini, A., Egerton, A., Strange, P.G., and Grasby, P.M. (2007). Agonist-dependent internalization of D2 receptors: Imaging quantification by confocal microscopy. Synapse 61, 231-241.

Golbraikh, A., and Tropsha, A. (2002). Beware of q2! J. Mol. Graph. Model. 20, 269-276.

Goodwin, J.T., Conradi, R.A., Ho, N.F., and Burton, P.S. (2001). Physicochemical determinants of passive membrane permeability: role of solute hydrogen-bonding potential and volume. J. Med. Chem. 44, 3721-3729.

Greene, M., Steinman, M.A., McNicholl, I.R., and Valcour, V. (2014). Polypharmacy, drug-drug interactions, and potentially inappropriate medications in older adults with human immunodeficiency virus infection. J. Am. Geriatr. Soc. 62, 447-453.

Grimsby, J., Toth, M., Chen, K., Kumazawa, T., Klaidman, L., Adams, J.D., Karoum, F., Gal, J., and Shih, J.C. (1997). Increased stress response and beta- phenylethylamine in MAOB-deficient mice. Nat. Genet. 17, 206-210.

Guzman, J.N., Sanchez-Padilla, J., Wokosin, D., Kondapalli, J., Ilijic, E., Schumacker, P.T., and Surmeier, D.J. (2010). Oxidant stress evoked by pacemaking in dopaminergic neurons is attenuated by DJ-1. Nature 468, 696- 700.

248

BIBLIOGRAPHY

Han, L.Y., Ma, X.H., Lin, H.H., Jia, J., Zhu, F., Xue, Y., Li, Z.R., Cao, Z.W., Ji, Z.L., and Chen, Y.Z. (2008). A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J. Mol. Graph. Model. 26, 1276-1286.

Harris, C.J., Hill, R.D., Sheppard, D.W., Slater, M.J., and Stouten, P.F. (2011). The design and application of target-focused compound libraries. Comb. Chem. High Throughput Screen. 14, 521-531.

Hauser, R.A., Hubble, J.P., Truong, D.D., and Istradefylline, U.S.S.G. (2003). Randomized trial of the adenosine A(2A) receptor antagonist istradefylline in advanced PD. Neurology 61, 297-303.

Hearn, M.G., Ren, Y., McBride, E.W., Reveillaud, I., Beinborn, M., and Kopin, A.S. (2002). A Drosophila dopamine 2-like receptor: Molecular characterization and identification of multiple alternatively spliced variants. Proc. Natl. Acad. Sci. U. S. A. 99, 14554-14559.

Heikamp, K., and Bajorath, J. (2013). Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening. J. Chem. Inf. Model. 53, 1595-1601.

Heimer, G., Rivlin-Etzion, M., Bar-Gad, I., Goldberg, J.A., Haber, S.N., and Bergman, H. (2006). Dopamine replacement therapy does not restore the full spectrum of normal pallidal activity in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine primate model of Parkinsonism. J. Neurosci. 26, 8101-8114.

Hillion, J., Canals, M., Torvinen, M., Casado, V., Scott, R., Terasmaa, A., Hansson, A., Watson, S., Olah, M.E., Mallol, J., et al. (2002). Coaggregation, cointernalization, and codesensitization of adenosine A2A receptors and dopamine D2 receptors. J. Biol. Chem. 277, 18091-18097.

Hirsch, E.C., Jenner, P., and Przedborski, S. (2013). Pathogenesis of Parkinson's disease. Mov. Disord. 28, 24-30. Hitchcock, S.A. (2012). Structural modifications that alter the P-glycoprotein efflux properties of compounds. J. Med. Chem. 55, 4877-4895.

Hitchcock, S.A., and Pennington, L.D. (2006). Structure-brain exposure relationships. J. Med. Chem. 49, 7559-7583.

249

BIBLIOGRAPHY

Ho, H.K., White, C.C., Fernandez, C., Fausto, N., Kavanagh, T.J., Nelson, S.D., and Bruschi, S.A. (2005). Nrf2 activation involves an oxidative-stress independent pathway in tetrafluoroethylcysteine-induced cytotoxicity. Toxicol. Sci. 86, 354-364.

Hsieh, G.C., Hollingsworth, P.R., Martino, B., Chang, R., Terranova, M.A., O'Neill, A.B., Lynch, J.J., Moreland, R.B., Donnelly-Roberts, D.L., Kolasa, T., et al. (2004). Central mechanisms regulating penile erection in conscious rats: the dopaminergic systems related to the proerectile effect of apomorphine. J. Pharmacol. Exp. Ther. 308, 330-338.

Hughes, A.D., Chen, Y., Hegde, S.S., Jasper, J.R., Jaw-Tsai, S., Lee, T.W., McNamara, A., Pulido-Rios, M.T., Steinfeld, T., and Mammen, M. (2015). Discovery of (R)-1-(3-((2-Chloro-4-(((2-hydroxy-2-(8-hydroxy-2-oxo-1,2- dihydroquinolin-5-yl)et hyl)amino)methyl)-5-methoxyphenyl)amino)-3- oxopropyl)piperidin-4-yl [1,1'-Biphenyl]-2-ylcarbamate (TD-5959, GSK961081, Batefenterol): First-in-Class Dual Pharmacology Multivalent Muscarinic Antagonist and beta2 Agonist (MABA) for the Treatment of Chronic Obstructive Pulmonary Disease (COPD). J. Med. Chem. 58, 2609- 2622.

Huot, P., Johnston, T.H., Koprich, J.B., Fox, S.H., and Brotchie, J.M. (2013). The pharmacology of L-DOPA-induced dyskinesia in Parkinson's disease. Pharmacol. Rev. 65, 171-222.

Huynh, T. (2011). The Parkinson's disease market. Nat. Rev. Drug Discov. 10, 571-572.

Inoue, A., Arima, N., Ishiguro, J., Prestwich, G.D., Arai, H., and Aoki, J. (2011). LPA-producing enzyme PA-PLA(1)alpha regulates hair follicle development by modulating EGFR signalling. EMBO J. 30, 4248-4260.

Inoue, A., Ishiguro, J., Kitamura, H., Arima, N., Okutani, M., Shuto, A., Higashiyama, S., Ohwada, T., Arai, H., Makide, K., et al. (2012). TGFalpha shedding assay: an accurate and versatile method for detecting GPCR activation. Nat. Methods 9, 1021-1029.

Irwin, J.J., and Shoichet, B.K. (2005). ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177-182.

250

BIBLIOGRAPHY

Irwin, J.J., Sterling, T., Mysinger, M.M., Bolstad, E.S., and Coleman, R.G. (2012). ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52, 1757-1768.

Ito, K., Haga, T., Lameh, J., and Sadee, W. (1999). Sequestration of dopamine D2 receptors depends on coexpression of G-protein-coupled receptor kinases 2 or 5. Eur. J. Biochem. 260, 112-119.

Ito, K., Iwatsubo, T., Kanamitsu, S., Ueda, K., Suzuki, H., and Sugiyama, Y. (1998). Prediction of pharmacokinetic alterations caused by drug-drug interactions: metabolic interaction in the liver. Pharmacol. Rev. 50, 387-412.

Jaakola, V.P., Griffith, M.T., Hanson, M.A., Cherezov, V., Chien, E.Y., Lane, J.R., Ijzerman, A.P., and Stevens, R.C. (2008). The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322, 1211-1217.

Jacobson, K.A., Gallo-Rodriguez, C., Melman, N., Fischer, B., Maillard, M., van Bergen, A., van Galen, P.J., and Karton, Y. (1993). Structure-activity relationships of 8-styrylxanthines as A2-selective adenosine antagonists. J. Med. Chem. 36, 1333-1342.

Jahng, J.W., Houpt, T.A., Wessel, T.C., Chen, K., Shih, J.C., and Joh, T.H. (1997). Localization of monoamine oxidase A and B mRNA in the rat brain by in situ hybridization. Synapse 25, 30-36.

James, L.P., Mayeux, P.R., and Hinson, J.A. (2003). Acetaminophen-induced hepatotoxicity. Drug Metab. Dispos. 31, 1499-1506.

Jankovic, J. (2008). Parkinson's disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79, 368-376.

Johnson, D.S., Choi, C., Fay, L.K., Favor, D.A., Repine, J.T., White, A.D., Akunne, H.C., Fitzgerald, L., Nicholls, K., Snyder, B.J., et al. (2011). Discovery of PF-00217830: aryl piperazine napthyridinones as D2 partial agonists for schizophrenia and bipolar disorder. Bioorg. Med. Chem. Lett. 21, 2621-2625.

Johnston, J.P. (1968). Some observations upon a new inhibitor of monoamine oxidase in brain tissue. Biochem. Pharmacol. 17, 1285-1297.

251

BIBLIOGRAPHY

Jooste, E., Klafter, F., Hirshman, C.A., and Emala, C.W. (2003). A mechanism for rapacuronium-induced bronchospasm: M2 muscarinic receptor antagonism. Anesthesiology 98, 906-911.

Jorg, M., May, L.T., Mak, F.S., Lee, K.C., Miller, N.D., Scammells, P.J., and Capuano, B. (2015). Synthesis and Pharmacological Evaluation of Dual Acting Ligands Targeting the Adenosine A2A and Dopamine D2 Receptors for the Potential Treatment of Parkinson's Disease. J. Med. Chem. 58, 718-738.

Jorissen, R.N., and Gilson, M.K. (2005). Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 45, 549-561.

Jukema, J.W., and van der Hoorn, J.W. (2004). Amlodipine and atorvastatin in atherosclerosis: a review of the potential of combination therapy. Expert Opin. Pharmacother. 5, 459-468.

Kageyama, T., Nakamura, M., Matsuo, A., Yamasaki, Y., Takakura, Y., Hashida, M., Kanai, Y., Naito, M., Tsuruo, T., Minato, N., et al. (2000). The 4F2hc/LAT1 complex transports L-DOPA across the blood-brain barrier. Brain Res. 879, 115-121.

Kalani, M.Y., Vaidehi, N., Hall, S.E., Trabanino, R.J., Freddolino, P.L., Kalani, M.A., Floriano, W.B., Kam, V.W., and Goddard, W.A., 3rd (2004). The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists. Proc. Natl. Acad. Sci. U. S. A. 101, 3815-3820.

Kalgutkar, A.S., Gardner, I., Obach, R.S., Shaffer, C.L., Callegari, E., Henne, K.R., Mutlib, A.E., Dalvie, D.K., Lee, J.S., Nakai, Y., et al. (2005). A comprehensive listing of bioactivation pathways of organic functional groups. Curr. Drug Metab. 6, 161-225.

Kamel, A., Obach, R.S., Colizza, K., Wang, W., O'Connell, T.N., Coelho, R.V., Jr., Kelley, R.M., and Schildknegt, K. (2010). Metabolism, pharmacokinetics, and excretion of the 5-hydroxytryptamine1b receptor antagonist elzasonan in humans. Drug Metab. Dispos. 38, 1984-1999.

252

BIBLIOGRAPHY

Kanda, T., Jackson, M.J., Smith, L.A., Pearce, R.K., Nakamura, J., Kase, H., Kuwana, Y., and Jenner, P. (2000). Combined use of the adenosine A(2A) antagonist KW-6002 with L-DOPA or with selective D1 or D2 dopamine agonists increases antiparkinsonian activity but not dyskinesia in MPTP- treated monkeys. Exp. Neurol. 162, 321-327.

Kang, S., Cooper, G., Dunne, S.F., Dusel, B., Luan, C.H., Surmeier, D.J., and Silverman, R.B. (2012). CaV1.3-selective L-type calcium channel antagonists as potential new therapeutics for Parkinson's disease. Nat. Commun. 3, 1146.

Kang, S., Cooper, G., Dunne, S.F., Luan, C.H., Surmeier, D.J., and Silverman, R.B. (2013). Structure-activity relationship of N,N'-disubstituted pyrimidinetriones as Ca(V)1.3 calcium channel-selective antagonists for Parkinson's disease. J. Med. Chem. 56, 4786-4797.

Kansy, M., Senner, F., and Gubernator, K. (1998). Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J. Med. Chem. 41, 1007-1010.

Karaman, M.W., Herrgard, S., Treiber, D.K., Gallant, P., Atteridge, C.E., Campbell, B.T., Chan, K.W., Ciceri, P., Davis, M.I., Edeen, P.T., et al. (2008). A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26, 127-132.

Katritch, V., Jaakola, V.P., Lane, J.R., Lin, J., Ijzerman, A.P., Yeager, M., Kufareva, I., Stevens, R.C., and Abagyan, R. (2010). Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J. Med. Chem. 53, 1799-1809.

Katzen, F., Peterson, T.C., and Kudlicki, W. (2009). Membrane protein expression: no cells required. Trends Biotechnol. 27, 455-460.

Kelly, E., Bailey, C.P., and Henderson, G. (2008). Agonist-selective mechanisms of GPCR desensitization. Br. J. Pharmacol. 153 Suppl 1, S379- 388.

Kenakin, T. (2010). G protein coupled receptors as allosteric proteins and the role of allosteric modulators. J. Recept. Signal Transduct. Res. 30, 313-321.

253

BIBLIOGRAPHY

Kenakin, T. (2011). Functional selectivity and biased receptor signaling. J. Pharmacol. Exp. Ther. 336, 296-302.

Kenakin, T., Jenkinson, S., and Watson, C. (2006). Determining the potency and molecular mechanism of action of insurmountable antagonists. J. Pharmacol. Exp. Ther. 319, 710-723.

Kerns, E.H., and Di, L. (2008). Chapter 2 - Advantages of Good Drug-like Properties. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 6-16.

Kerns, E.H., and Di, L. (2008). Chapter 3 - Barriers to Drug Exposure in Living Systems. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 17- 33,I.

Kerns, E.H., and Di, L. (2008). Chapter 7 - Solubility. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 56-85.

Kerns, E.H., and Di, L. (2008). Chapter 8 - Permeability. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 86-99.

Kerns, E.H., and Di, L. (2008). Chapter 10 - Blood-Brain Barrier. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 122-136.

Kerns, E.H., and Di, L. (2008). Chapter 11 - Metabolic Stability. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 137-168,II.

Kerns, E.H., and Di, L. (2008). Chapter 15 - Cytochrome P450 Inhibition. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 197-208.

Kerns, E.H., and Di, L. (2008). Chapter 19 - Pharmacokinetics. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 228-241.

254

BIBLIOGRAPHY

Kerns, E.H., and Di, L. (2008). Chapter 26 - Permeability Methods. In Drug- like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 287-298.

Kerns, E.H., and Di, L. (2008). Chapter 29 - Metabolic Stability Methods. In Drug-like Properties: Concepts, Structure Design and Methods, E.H. Kerns, and L. Di, eds. (San Diego: Academic Press), pp. 329-347.

Kerns, E.H., Di, L., Petusky, S., Farris, M., Ley, R., and Jupp, P. (2004). Combined application of parallel artificial membrane permeability assay and Caco-2 permeability assays in drug discovery. J. Pharm. Sci. 93, 1440-1453.

Kim, I.H., Morisseau, C., Watanabe, T., and Hammock, B.D. (2004). Design, synthesis, and biological activity of 1,3-disubstituted ureas as potent inhibitors of the soluble epoxide hydrolase of increased water solubility. J. Med. Chem. 47, 2110-2122.

Kitada, T., Asakawa, S., Hattori, N., Matsumine, H., Yamamura, Y., Minoshima, S., Yokochi, M., Mizuno, Y., and Shimizu, N. (1998). Mutations in the parkin gene cause autosomal recessive juvenile parkinsonism. Nature 392, 605-608.

Kitano, H. (2003). Cancer robustness: tumour tactics. Nature 426, 125.

Kitta, K., Day, R.M., Ikeda, T., and Suzuki, Y.J. (2001). Hepatocyte growth factor protects cardiac myocytes against oxidative stress-induced apoptosis. Free Radic. Biol. Med. 31, 902-910.

Kling, R.C., Tschammer, N., Lanig, H., Clark, T., and Gmeiner, P. (2014). Active-state model of a dopamine D2 receptor-Galphai complex stabilized by aripiprazole-type partial agonists. PLoS One 9, e100069.

Klotz, K.N., Cristalli, G., Grifantini, M., Vittori, S., and Lohse, M.J. (1985). Photoaffinity labeling of A1-adenosine receptors. J. Biol. Chem. 260, 14659- 14664.

Klotz, K.N., Falgner, N., Kachler, S., Lambertucci, C., Vittori, S., Volpini, R., and Cristalli, G. (2007). [3H]HEMADO--a novel tritiated agonist selective for the human adenosine A3 receptor. Eur. J. Pharmacol. 556, 14-18.

255

BIBLIOGRAPHY

Klotz, K.N., Hessling, J., Hegler, J., Owman, C., Kull, B., Fredholm, B.B., and Lohse, M.J. (1998). Comparative pharmacology of human adenosine receptor subtypes - characterization of stably transfected receptors in CHO cells. Naunyn Schmiedebergs Arch. Pharmacol. 357, 1-9.

Knight, Z.A., and Shokat, K.M. (2005). Features of selective kinase inhibitors. Chem. Biol. 12, 621-637.

Knoll, J., and Magyar, K. (1972). Some puzzling pharmacological effects of monoamine oxidase inhibitors. Adv. Biochem. Psychopharmacol. 5, 393-408.

Koch, M.A., Schuffenhauer, A., Scheck, M., Wetzel, S., Casaulta, M., Odermatt, A., Ertl, P., and Waldmann, H. (2005). Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc. Natl. Acad. Sci. U. S. A. 102, 17272-17277.

Kola, I., and Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711-715.

Konieczny, J., Wardas, J., Kuter, K., Pilc, A., and Ossowska, K. (2007). The influence of group III metabotropic glutamate receptor stimulation by (1S,3R,4S)-1-aminocyclo-pentane-1,3,4-tricarboxylic acid on the parkinsonian-like akinesia and striatal proenkephalin and prodynorphin mRNA expression in rats. Neuroscience 145, 611-620.

Kordower, J.H., Chu, Y., Hauser, R.A., Freeman, T.B., and Olanow, C.W. (2008). Lewy body-like pathology in long-term embryonic nigral transplants in Parkinson's disease. Nat. Med. 14, 504-506.

Kortagere, S., Chekmarev, D., Welsh, W.J., and Ekins, S. (2008). New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm. Res. 25, 1836-1845.

Kortagere, S., Gmeiner, P., Weinstein, H., and Schetz, J.A. (2004). Certain 1,4- disubstituted aromatic piperidines and piperazines with extreme selectivity for the dopamine D4 receptor interact with a common receptor microdomain. Mol. Pharmacol. 66, 1491-1499.

256

BIBLIOGRAPHY

Kowal, S.L., Dall, T.M., Chakrabarti, R., Storm, M.V., and Jain, A. (2013). The current and projected economic burden of Parkinson's disease in the United States. Mov. Disord. 28, 311-318.

Krogsgaard-Larsen, N., Harpsoe, K., Kehler, J., Christoffersen, C.T., Brosen, P., and Balle, T. (2014). Revision of the classical dopamine D2 agonist pharmacophore based on an integrated medicinal chemistry, homology modelling and computational docking approach. Neurochem. Res. 39, 1997- 2007.

Kucerova, L., Broz, V., Fleischmannova, J., Santruckova, E., Sidorov, R., Dolezal, V., and Zurovec, M. (2012). Characterization of the Drosophila adenosine receptor: the effect of adenosine analogs on cAMP signaling in Drosophila cells and their utility for in vivo experiments. J. Neurochem. 121, 383-395.

Kuhhorn, J., Gotz, A., Hubner, H., Thompson, D., Whistler, J., and Gmeiner, P. (2011). Development of a bivalent dopamine D(2) receptor agonist. J. Med. Chem. 54, 7911-7919.

Kumari, U., and Tan, E.K. (2009). LRRK2 in Parkinson's disease: genetic and clinical studies from patients. FEBS J. 276, 6455-6463.

Kvernmo, T., Hartter, S., and Burger, E. (2006). A review of the receptor- binding and pharmacokinetic properties of dopamine agonists. Clin. Ther. 28, 1065-1078.

Langley, J.N. (1905). On the reaction of cells and of nerve-endings to certain poisons, chiefly as regards the reaction of striated muscle to nicotine and to curari. J. Physiol. 33, 374-413.

Langmuir, I. (1919). Isomorphism, isosterism and covalence. J. Am. Chem. Soc. 41, 1543-1559.

Langston, J.W., Ballard, P., Tetrud, J.W., and Irwin, I. (1983). Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 219, 979-980.

257

BIBLIOGRAPHY

Lanson, N.A., Jr., Egeland, D.B., Royals, B.A., and Claycomb, W.C. (2000). The MRE11-NBS1-RAD50 pathway is perturbed in SV40 large T antigen- immortalized AT-1, AT-2 and HL-1 cardiomyocytes. Nucleic Acids Res. 28, 2882-2892.

Lebon, G., Warne, T., Edwards, P.C., Bennett, K., Langmead, C.J., Leslie, A.G., and Tate, C.G. (2011). Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 474, 521-525.

Lecht, S., Haroutiunian, S., Hoffman, A., and Lazarovici, P. (2007). Rasagiline - a novel MAO B inhibitor in Parkinson's disease therapy. Ther. Clin. Risk Manag. 3, 467-474.

Lee, W.M. (2003). Drug-induced hepatotoxicity. N. Engl. J. Med. 349, 474- 485.

Lefkowitz, R.J., and Shenoy, S.K. (2005). Transduction of receptor signals by beta-arrestins. Science 308, 512-517.

Lehar, J., Krueger, A.S., Avery, W., Heilbut, A.M., Johansen, L.M., Price, E.R., Rickles, R.J., Short, G.F., 3rd, Staunton, J.E., Jin, X., et al. (2009). Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat. Biotechnol. 27, 659-666.

Lemmerhirt, C.J., Rombach, M., Bodtke, A., Bednarski, P.J., and Link, A. (2015). Oxidation potentials of N-modified derivatives of the analgesic flupirtine linked to potassium KV 7 channel opening activity but not hepatocyte toxicity. ChemMedChem 10, 368-379.

Lesage, S., Durr, A., Tazir, M., Lohmann, E., Leutenegger, A.L., Janin, S., Pollak, P., Brice, A., and French Parkinson's Disease Genetics Study, G. (2006). LRRK2 G2019S as a cause of Parkinson's disease in North African Arabs. N. Engl. J. Med. 354, 422-423.

Lewis, D.F., Jacobs, M.N., and Dickins, M. (2004). Compound lipophilicity for substrate binding to human P450s in drug metabolism. Drug Discov. Today 9, 530-537.

258

BIBLIOGRAPHY

Lewis, D.F.V., and Ito, Y. (2008). Chapter 1 Cytochrome P450 Structure and Function: An Evolutionary Perspective. In Cytochromes P450: Role in the Metabolism and Toxicity of Drugs and other Xenobiotics (The Royal Society of Chemistry), pp. 3-45.

Li, L., Wang, B., and Meroueh, S.O. (2011). Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J. Chem. Inf. Model. 51, 2132-2138.

Li, Y., Zhu, Y.M., Jiang, H.J., Pan, J.P., Wu, G.S., Wu, J.M., Shi, Y.L., Yang, J.D., and Wu, B.A. (2000). Synthesis and antimalarial activity of artemisinin derivatives containing an amino group. J. Med. Chem. 43, 1635-1640.

Li, Z.R., Han, L.Y., Xue, Y., Yap, C.W., Li, H., Jiang, L., and Chen, Y.Z. (2007). MODEL-molecular descriptor lab: a web-based server for computing structural and physicochemical features of compounds. Biotechnol. Bioeng. 97, 389-396.

Liew, C.Y., Ma, X.H., Liu, X., and Yap, C.W. (2009). SVM model for virtual screening of Lck inhibitors. J. Chem. Inf. Model. 49, 877-885.

Lin, J.H., and Lu, A.Y. (1997). Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol. Rev. 49, 403-449.

Lipinski, C.A. (2000). Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235-249.

Lipinski, C.A. (2004). Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol. 1, 337-341.

Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev. 23, 3-25.

Liu, T., Lin, Y., Wen, X., Jorissen, R.N., and Gilson, M.K. (2007). BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198-201.

259

BIBLIOGRAPHY

Liu, W., Chun, E., Thompson, A.A., Chubukov, P., Xu, F., Katritch, V., Han, G.W., Roth, C.B., Heitman, L.H., AP, I.J., et al. (2012). Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337, 232-236.

Liu, Z., Wang, X., Yu, Y., Li, X., Wang, T., Jiang, H., Ren, Q., Jiao, Y., Sawa, A., Moran, T., et al. (2008). A Drosophila model for LRRK2-linked parkinsonism. Proc. Natl. Acad. Sci. U. S. A. 105, 2693-2698.

Livingstone, D.J. (2000). The characterization of chemical structures using molecular properties. A survey. J. Chem. Inf. Comput. Sci. 40, 195-209.

Lohse, M.J., Klotz, K.N., Lindenborn-Fotinos, J., Reddington, M., Schwabe, U., and Olsson, R.A. (1987). 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX)--a selective high affinity antagonist radioligand for A1 adenosine receptors. Naunyn Schmiedebergs Arch. Pharmacol. 336, 204-210.

Lopes Jesus, A.J., and Redinha, J.S. (2014). Methoxyindoles: stability and π- electron delocalization. Struct. Chem., 1-12.

Lopez-Munoz, F., and Alamo, C. (2009). Monoaminergic neurotransmission: the history of the discovery of antidepressants from 1950s until today. Curr. Pharm. Des. 15, 1563-1586.

Lowe, J.A., 3rd, Seeger, T.F., Nagel, A.A., Howard, H.R., Seymour, P.A., Heym, J.H., Ewing, F.E., Newman, M.E., Schmidt, A.W., Furman, J.S., et al. (1991). 1-Naphthylpiperazine derivatives as potential atypical antipsychotic agents. J. Med. Chem. 34, 1860-1866.

Lu, C., Li, P., Gallegos, R., Uttamsingh, V., Xia, C.Q., Miwa, G.T., Balani, S.K., and Gan, L.S. (2006). Comparison of intrinsic clearance in liver microsomes and hepatocytes from rats and humans: evaluation of free fraction and uptake in hepatocytes. Drug Metab. Dispos. 34, 1600-1605.

Lucking, C.B., Durr, A., Bonifati, V., Vaughan, J., De Michele, G., Gasser, T., Harhangi, B.S., Meco, G., Denefle, P., Wood, N.W., et al. (2000). Association between early-onset Parkinson's disease and mutations in the parkin gene. N. Engl. J. Med. 342, 1560-1567.

260

BIBLIOGRAPHY

Luger, P., and Buschmann, J. (1984). Oxetane: the first x-ray analysis of a nonsubstituted four-membered ring. J. Am. Chem. Soc. 106, 7118-7121.

Lv, W., and Xue, Y. (2010). Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Eur. J. Med. Chem. 45, 1167-1172.

Ma, X.H., Wang, R., Tan, C.Y., Jiang, Y.Y., Lu, T., Rao, H.B., Li, X.Y., Go, M.L., Low, B.C., and Chen, Y.Z. (2010). Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines. Mol. Pharm. 7, 1545-1560.

Ma, X.H., Wang, R., Yang, S.Y., Li, Z.R., Xue, Y., Wei, Y.C., Low, B.C., and Chen, Y.Z. (2008). Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J. Chem. Inf. Model. 48, 1227-1237.

Macherey, A.-C., and Dansette, P.M. (2008). Chapter 33 - Biotransformations Leading to Toxic Metabolites: Chemical Aspect. In The Practice of Medicinal Chemistry (Third Edition), C.G. Wermuth, ed. (New York: Academic Press), pp. 674-696.

Madras, B.K., Canfield, D.R., Pfaelzer, C., Vittimberga, F.J., Jr., Difiglia, M., Aronin, N., Bakthavachalam, V., Baindur, N., and Neumeyer, J.L. (1990). Fluorescent and biotin probes for dopamine receptors: D1 and D2 receptor affinity and selectivity. Mol. Pharmacol. 37, 833-839.

Maehle, A.H., Prull, C.R., and Halliwell, R.F. (2002). The emergence of the drug receptor theory. Nat. Rev. Drug Discov. 1, 637-641.

Maggiora, G., Vogt, M., Stumpfe, D., and Bajorath, J. (2014). Molecular similarity in medicinal chemistry. J. Med. Chem. 57, 3186-3204.

Mahar Doan, K.M., Humphreys, J.E., Webster, L.O., Wring, S.A., Shampine, L.J., Serabjit-Singh, C.J., Adkison, K.K., and Polli, J.W. (2002). Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. J. Pharmacol. Exp. Ther. 303, 1029-1037.

261

BIBLIOGRAPHY

Maia Rdo, C., Tesch, R., and Fraga, C.A. (2012). Phenylpiperazine derivatives: a patent review (2006 - present). Expert Opin. Ther. Pat. 22, 1169- 1178.

Malo, M., Brive, L., Luthman, K., and Svensson, P. (2012). Investigation of D(2) receptor-agonist interactions using a combination of pharmacophore and receptor homology modeling. ChemMedChem 7, 471-482, 338.

Malo, M., Persson, R., Svensson, P., Luthman, K., and Brive, L. (2013). Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor. J. Comput. Aided Mol. Des. 27, 277-291.

Manallack, D.T. (2008). The pK(a) Distribution of Drugs: Application to Drug Discovery. Perspect. Medicin. Chem. 1, 25-38.

Marek, K., Seibyl, J., Shoulson, I., Holloway, R., Kieburtz, K., McDermott, M., Kamp, C., Shinaman, A., Fahn, S., Lang, A., et al. (2002). Dopamine transporter brain imaging to assess the effects of pramipexole vs levodopa on Parkinson disease progression. J. Am. Med. Assoc. 287, 1653-1661.

Marino, M.J., Williams, D.L., Jr., O'Brien, J.A., Valenti, O., McDonald, T.P., Clements, M.K., Wang, R., DiLella, A.G., Hess, J.F., Kinney, G.G., et al. (2003). Allosteric modulation of group III metabotropic glutamate receptor 4: a potential approach to Parkinson's disease treatment. Proc. Natl. Acad. Sci. U. S. A. 100, 13668-13673.

Maron, D.M., and Ames, B.N. (1983). Revised methods for the Salmonella mutagenicity test. Mutat. Res. 113, 173-215.

Mason, B.P. (2009). High-Throughput Measurement of Physicochemical Properties. In Drug Bioavailability (Wiley-VCH Verlag GmbH & Co. KGaA), pp. 101-132.

Mason, R.P., Kubant, R., Heeba, G., Jacob, R.F., Day, C.A., Medlin, Y.S., Funovics, P., and Malinski, T. (2008). Synergistic effect of amlodipine and atorvastatin in reversing LDL-induced endothelial dysfunction. Pharm. Res. 25, 1798-1806.

262

BIBLIOGRAPHY

Massague, J., and Pandiella, A. (1993). Membrane-anchored growth factors. Annu. Rev. Biochem. 62, 515-541.

Matsuzaki, K., Harada, A., Takeiri, A., Tanaka, K., and Mishima, M. (2010). Whole cell-ELISA to measure the gammaH2AX response of six aneugens and eight DNA-damaging chemicals. Mutat. Res. 700, 71-79.

Matthews, B.W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442-451.

May, S., Andreasson-Ochsner, M., Fu, Z., Low, Y.X., Tan, D., de Hoog, H.P., Ritz, S., Nallani, M., and Sinner, E.K. (2013). In vitro expressed GPCR inserted in polymersome membranes for ligand-binding studies. Angew. Chem. Int. Ed. Engl. 52, 749-753.

McCall, R.B., Lookingland, K.J., Bedard, P.J., and Huff, R.M. (2005). Sumanirole, a highly dopamine D2-selective receptor agonist: in vitro and in vivo pharmacological characterization and efficacy in animal models of Parkinson's disease. J. Pharmacol. Exp. Ther. 314, 1248-1256.

McDermott, M.F., Aksentijevich, I., Galon, J., McDermott, E.M., Ogunkolade, B.W., Centola, M., Mansfield, E., Gadina, M., Karenko, L., Pettersson, T., et al. (1999). Germline mutations in the extracellular domains of the 55 kDa TNF receptor, TNFR1, define a family of dominantly inherited autoinflammatory syndromes. Cell 97, 133-144.

McGinnity, D.F., Soars, M.G., Urbanowicz, R.A., and Riley, R.J. (2004). Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab. Dispos. 32, 1247- 1253.

Mela, F., Marti, M., Dekundy, A., Danysz, W., Morari, M., and Cenci, M.A. (2007). Antagonism of metabotropic glutamate receptor type 5 attenuates l- DOPA-induced dyskinesia and its molecular and neurochemical correlates in a rat model of Parkinson's disease. J. Neurochem. 101, 483-497.

Menegatti, R., Cunha, A.C., Ferreira, V.F., Perreira, E.F., El-Nabawi, A., Eldefrawi, A.T., Albuquerque, E.X., Neves, G., Rates, S.M., Fraga, C.A., et al. (2003). Design, synthesis and pharmacological profile of novel dopamine D2 receptor ligands. Bioorg. Med. Chem. 11, 4807-4813.

263

BIBLIOGRAPHY

Mestres, J., Gregori-Puigjane, E., Valverde, S., and Sole, R.V. (2008). Data completeness--the Achilles heel of drug-target networks. Nat. Biotechnol. 26, 983-984.

Meunier, B., de Visser, S.P., and Shaik, S. (2004). Mechanism of oxidation reactions catalyzed by cytochrome p450 enzymes. Chem. Rev. 104, 3947- 3980.

Miraglia, L., Pagliarusco, S., Bordini, E., Martinucci, S., and Pellegatti, M. (2010). Metabolic disposition of casopitant, a potent neurokinin-1 receptor antagonist, in mice, rats, and dogs. Drug Metab. Dispos. 38, 1876-1891.

Misra, R.N., Xiao, H.Y., Kim, K.S., Lu, S., Han, W.C., Barbosa, S.A., Hunt, J.T., Rawlins, D.B., Shan, W., Ahmed, S.Z., et al. (2004). N- (cycloalkylamino)acyl-2-aminothiazole inhibitors of cyclin-dependent kinase 2. N-[5-[[[5-(1,1-dimethylethyl)-2-oxazolyl]methyl]thio]-2-thiazolyl]-4- piperidinecarboxamide (BMS-387032), a highly efficacious and selective antitumor agent. J. Med. Chem. 47, 1719-1728.

Miyaura, N., and Suzuki, A. (1995). Palladium-Catalyzed Cross-Coupling Reactions of Organoboron Compounds. Chem. Rev. 95, 2457-2483.

Mizuno, Y., Kondo, T., and Japanese Istradefylline Study, G. (2013). Adenosine A2A receptor antagonist istradefylline reduces daily OFF time in Parkinson's disease. Mov. Disord. 28, 1138-1141.

Montero, A., Goya, P., Jagerovic, N., Callado, L.F., Meana, J.J., Giron, R., Goicoechea, C., and Martin, M.I. (2002). Guanidinium and aminoimidazolinium derivatives of N-(4-piperidyl)propanamides as potential ligands for mu opioid and I2-imidazoline receptors: synthesis and pharmacological screening. Bioorg. Med. Chem. 10, 1009-1018.

Morgan, M.E., and Vestal, R.E. (1989). Methylxanthine effects on caudate dopamine release as measured by in vivo electrochemistry. Life Sci. 45, 2025- 2039.

Morin, N., Gregoire, L., Morissette, M., Desrayaud, S., Gomez-Mancilla, B., Gasparini, F., and Di Paolo, T. (2013). MPEP, an mGlu5 receptor antagonist, reduces the development of L-DOPA-induced motor complications in de novo parkinsonian monkeys: biochemical correlates. Neuropharmacology 66, 355- 364.

264

BIBLIOGRAPHY

Morphy, R., Kay, C., and Rankovic, Z. (2004). From magic bullets to designed multiple ligands. Drug Discov. Today 9, 641-651.

Morphy, R., and Rankovic, Z. (2006). The physicochemical challenges of designing multiple ligands. J. Med. Chem. 49, 4961-4970.

Morphy, R., and Rankovic, Z. (2008). Chapter 27 - Multi-target Drugs: Strategies and Challenges for Medicinal Chemists. In The Practice of Medicinal Chemistry (Third Edition), C.G. Wermuth, ed. (New York: Academic Press), pp. 549-571.

Mortelmans, K., and Zeiger, E. (2000). The Ames Salmonella/microsome mutagenicity assay. Mutat. Res. 455, 29-60.

Mosier, P.D., and Jurs, P.C. (2002). QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J. Chem. Inf. Comput. Sci. 42, 1460-1470.

Motter, A.E., Gulbahce, N., Almaas, E., and Barabasi, A.L. (2008). Predicting synthetic rescues in metabolic networks. Mol. Syst. Biol. 4, 168.

Müller, C.E., Deters, D., Dominik, A., and Pawlowski, M. (1998). Synthesis of Paraxanthine and Isoparaxanthine Analogs (1,7- and 1,9-Substituted Xanthine Derivatives). Synthesis, 1428-1436.

Muller, K.R., Ratsch, G., Sonnenburg, S., Mika, S., Grimm, M., and Heinrich, N. (2005). Classifying 'drug-likeness' with kernel-based learning methods. J. Chem. Inf. Model. 45, 249-253.

Munoz, A., Carlsson, T., Tronci, E., Kirik, D., Bjorklund, A., and Carta, M. (2009). Serotonin neuron-dependent and -independent reduction of dyskinesia by 5-HT1A and 5-HT1B receptor agonists in the rat Parkinson model. Exp. Neurol. 219, 298-307.

Nagy, L.D., Mocny, C.S., Diffenderfer, L.E., Hsi, D.J., Butler, B.F., Arthur, E.J., Fletke, K.J., Palamanda, J.R., Nomeir, A.A., and Furge, L.L. (2011). Substituted imidazole of 5-fluoro-2-[4-[(2-phenyl-1H-imidazol-5-yl)methyl]- 1-piperazinyl]pyrimidine Inactivates cytochrome P450 2D6 by protein adduction. Drug Metab. Dispos. 39, 974-983.

265

BIBLIOGRAPHY

Namkung, Y., and Sibley, D.R. (2004). Protein kinase C mediates phosphorylation, desensitization, and trafficking of the D2 dopamine receptor. J. Biol. Chem. 279, 49533-49541.

Nath, A., Atkins, W.M., and Sligar, S.G. (2007). Applications of phospholipid bilayer nanodiscs in the study of membranes and membrane proteins. Biochemistry 46, 2059-2069.

Navia, M.A., and Chaturvedi, P.R. (1996). Design principles for orally bioavailable drugs. Drug Discov. Today 1, 179-189.

Nelson, H.S., Chapman, K.R., Pyke, S.D., Johnson, M., and Pritchard, J.N. (2003). Enhanced synergy between fluticasone propionate and salmeterol inhaled from a single inhaler versus separate inhalers. J. Allergy Clin. Immunol. 112, 29-36.

Neustadt, B.R., Hao, J., Lindo, N., Greenlee, W.J., Stamford, A.W., Tulshian, D., Ongini, E., Hunter, J., Monopoli, A., Bertorelli, R., et al. (2007). Potent, selective, and orally active adenosine A2A receptor antagonists: arylpiperazine derivatives of pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidines. Bioorg. Med. Chem. Lett. 17, 1376-1380.

Ng, C.H., Guan, M.S., Koh, C., Ouyang, X., Yu, F., Tan, E.K., O'Neill, S.P., Zhang, X., Chung, J., and Lim, K.L. (2012). AMP kinase activation mitigates dopaminergic dysfunction and mitochondrial abnormalities in Drosophila models of Parkinson's disease. J. Neurosci. 32, 14311-14317.

Nickols, H.H., and Conn, P.J. (2014). Development of allosteric modulators of GPCRs for treatment of CNS disorders. Neurobiol. Dis. 61, 55-71.

Obach, R.S. (1997). Nonspecific binding to microsomes: impact on scale-up of in vitro intrinsic clearance to hepatic clearance as assessed through examination of warfarin, imipramine, and propranolol. Drug Metab. Dispos. 25, 1359-1369.

Obach, R.S. (1999). Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half- life approach and nonspecific binding to microsomes. Drug Metab. Dispos. 27, 1350-1359.

266

BIBLIOGRAPHY

Obach, R.S. (2013). Pharmacologically active drug metabolites: impact on drug discovery and pharmacotherapy. Pharmacol. Rev. 65, 578-640.

O'Hagan, D. (2008). Understanding organofluorine chemistry. An introduction to the C-F bond. Chem. Soc. Rev. 37, 308-319.

Okatsu, K., Oka, T., Iguchi, M., Imamura, K., Kosako, H., Tani, N., Kimura, M., Go, E., Koyano, F., Funayama, M., et al. (2012). PINK1 autophosphorylation upon membrane potential dissipation is essential for Parkin recruitment to damaged mitochondria. Nat. Commun. 3, 1016.

Olanow, C.W., Rascol, O., Hauser, R., Feigin, P.D., Jankovic, J., Lang, A., Langston, W., Melamed, E., Poewe, W., Stocchi, F., et al. (2009). A double- blind, delayed-start trial of rasagiline in Parkinson's disease. N. Engl. J. Med. 361, 1268-1278.

Ozelius, L.J., Senthil, G., Saunders-Pullman, R., Ohmann, E., Deligtisch, A., Tagliati, M., Hunt, A.L., Klein, C., Henick, B., Hailpern, S.M., et al. (2006). LRRK2 G2019S as a cause of Parkinson's disease in Ashkenazi Jews. N. Engl. J. Med. 354, 424-425.

Pajouhesh, H., and Lenz, G.R. (2005). Medicinal chemical properties of successful central nervous system drugs. NeuroRx 2, 541-553.

Palanki, M.S., Erdman, P.E., Gayo-Fung, L.M., Shevlin, G.I., Sullivan, R.W., Goldman, M.E., Ransone, L.J., Bennett, B.L., Manning, A.M., and Suto, M.J. (2000). Inhibitors of NF-kappaB and AP-1 gene expression: SAR studies on the pyrimidine portion of 2-chloro-4-trifluoromethylpyrimidine-5-[N-(3', 5'- bis(trifluoromethyl)phenyl)carboxamide]. J. Med. Chem. 43, 3995-4004.

Pandey, U.B., and Nichols, C.D. (2011). Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery. Pharmacol. Rev. 63, 411-436.

Paolillo, M., and Schinelli, S. (2008). Therapeutic targeting of g-protein coupled receptor-mediated epidermal growth factor receptor transactivation in human glioma brain tumors. Mini Rev. Med. Chem. 8, 1418-1428.

Pardridge, W.M. (2005). The blood-brain barrier: bottleneck in brain drug development. NeuroRx 2, 3-14.

267

BIBLIOGRAPHY

Park, B.K., Kitteringham, N.R., and O'Neill, P.M. (2001). Metabolism of fluorine-containing drugs. Annu. Rev. Pharmacol. Toxicol. 41, 443-470.

Parkinson, J. (2002). An essay on the shaking palsy. 1817. J. Neuropsychiatry Clin. Neurosci. 14, 223-236; discussion 222.

Parkinson Study, G. (2005). A randomized placebo-controlled trial of rasagiline in levodopa-treated patients with Parkinson disease and motor fluctuations: the PRESTO study. Arch. Neurol. 62, 241-248.

Patterson, S., and Wyllie, S. (2014). Nitro drugs for the treatment of trypanosomatid diseases: past, present, and future prospects. Trends Parasitol. 30, 289-298.

Peglion, J.L., Goument, B., Despaux, N., Charlot, V., Giraud, H., Nisole, C., Newman-Tancredi, A., Dekeyne, A., Bertrand, M., Genissel, P., et al. (2002). Improvement in the selectivity and metabolic stability of the serotonin 5- HT(1A) ligand, S 15535: a series of cis- and trans-2-(arylcycloalkylamine) 1- indanols. J. Med. Chem. 45, 165-176.

Pellegatti, M., Bordini, E., Fizzotti, P., Roberts, A., and Johnson, B.M. (2009). Disposition and metabolism of radiolabeled casopitant in humans. Drug Metab. Dispos. 37, 1635-1645.

Pence, H.E., and Williams, A. (2010). ChemSpider: An Online Chemical Information Resource. J. Chem. Educ. 87, 1123-1124.

Pendleton, R.G., Parvez, F., Sayed, M., and Hillman, R. (2002). Effects of pharmacological agents upon a transgenic model of Parkinson's disease in Drosophila melanogaster. J. Pharmacol. Exp. Ther. 300, 91-96.

Peschon, J.J., Slack, J.L., Reddy, P., Stocking, K.L., Sunnarborg, S.W., Lee, D.C., Russell, W.E., Castner, B.J., Johnson, R.S., Fitzner, J.N., et al. (1998). An essential role for ectodomain shedding in mammalian development. Science 282, 1281-1284.

Peters, J.U. (2013). Polypharmacology - foe or friend? J. Med. Chem. 56, 8955-8971.

268

BIBLIOGRAPHY

Peterson, L.A., Caldera, P.S., Trevor, A., Chiba, K., and Castagnoli, N., Jr. (1985). Studies on the 1-methyl-4-phenyl-2,3-dihydropyridinium species 2,3- MPDP+, the monoamine oxidase catalyzed oxidation product of the nigrostriatal toxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). J. Med. Chem. 28, 1432-1436.

Pettersson, F., Ponten, H., Waters, N., Waters, S., and Sonesson, C. (2010). Synthesis and evaluation of a set of 4-phenylpiperidines and 4- phenylpiperazines as D2 receptor ligands and the discovery of the dopaminergic stabilizer 4-[3-(methylsulfonyl)phenyl]-1-propylpiperidine (huntexil, pridopidine, ACR16). J. Med. Chem. 53, 2510-2520.

Pierce, R.H., Franklin, C.C., Campbell, J.S., Tonge, R.P., Chen, W., Fausto, N., Nelson, S.D., and Bruschi, S.A. (2002). Cell culture model for acetaminophen- induced hepatocyte death in vivo. Biochem. Pharmacol. 64, 413-424.

Pinna, A., Fenu, S., and Morelli, M. (2001). Motor stimulant effects of the adenosine A(2A) receptor antagonist SCH 58261 do not develop tolerance after repeated treatments in 6-hydroxydopamine-lesioned rats. Synapse 39, 233-238.

Pinto, C., Papa, D., Hubner, M., Mou, T.C., Lushington, G.H., and Seifert, R. (2008). Activation and inhibition of adenylyl cyclase isoforms by forskolin analogs. J. Pharmacol. Exp. Ther. 325, 27-36.

Podolyan, Y., Walters, M.A., and Karypis, G. (2010). Assessing synthetic accessibility of chemical compounds using machine learning methods. J. Chem. Inf. Model. 50, 979-991.

Polymeropoulos, M.H., Lavedan, C., Leroy, E., Ide, S.E., Dehejia, A., Dutra, A., Pike, B., Root, H., Rubenstein, J., Boyer, R., et al. (1997). Mutation in the alpha-synuclein gene identified in families with Parkinson's disease. Science 276, 2045-2047.

Popoli, P., Reggio, R., and Pezzola, A. (2000). Effects of SCH 58261, an adenosine A(2A) receptor antagonist, on quinpirole-induced turning in 6- hydroxydopamine-lesioned rats. Lack of tolerance after chronic caffeine intake. Neuropsychopharmacology 22, 522-529.

269

BIBLIOGRAPHY

Portoghese, P.S. (2001). From models to molecules: opioid receptor dimers, bivalent ligands, and selective opioid receptor probes. J. Med. Chem. 44, 2259-2269.

Prenzel, N., Zwick, E., Daub, H., Leserer, M., Abraham, R., Wallasch, C., and Ullrich, A. (1999). EGF receptor transactivation by G-protein-coupled receptors requires metalloproteinase cleavage of proHB-EGF. Nature 402, 884-888.

Pretorius, J., Malan, S.F., Castagnoli, N., Jr., Bergh, J.J., and Petzer, J.P. (2008). Dual inhibition of monoamine oxidase B and antagonism of the adenosine A(2A) receptor by (E,E)-8-(4-phenylbutadien-1-yl)caffeine analogues. Bioorg. Med. Chem. 16, 8676-8684.

Quan, M.L., Lam, P.Y., Han, Q., Pinto, D.J., He, M.Y., Li, R., Ellis, C.D., Clark, C.G., Teleha, C.A., Sun, J.H., et al. (2005). Discovery of 1-(3'- aminobenzisoxazol-5'-yl)-3-trifluoromethyl-N-[2-fluoro-4- [(2'- dimethylaminomethyl)imidazol-1-yl]phenyl]-1H-pyrazole-5-carboxyamide hydrochloride (razaxaban), a highly potent, selective, and orally bioavailable factor Xa inhibitor. J. Med. Chem. 48, 1729-1744.

Rajagopal, S., Rajagopal, K., and Lefkowitz, R.J. (2010). Teaching old receptors new tricks: biasing seven-transmembrane receptors. Nat. Rev. Drug Discov. 9, 373-386.

Rand, M.D. (2010). Drosophotoxicology: the growing potential for Drosophila in neurotoxicology. Neurotoxicol. Teratol. 32, 74-83.

Rankovic, Z. (2015). CNS drug design: balancing physicochemical properties for optimal brain exposure. J. Med. Chem. 58, 2584-2608.

Rimondini, R., Ferre, S., Gimenez-Llort, L., Ogren, S.O., and Fuxe, K. (1998). Differential effects of selective adenosine A1 and A2A receptor agonists on dopamine receptor agonist-induced behavioural responses in rats. Eur. J. Pharmacol. 347, 153-158.

Rivara, S., Piersanti, G., Bartoccini, F., Diamantini, G., Pala, D., Riccioni, T., Stasi, M.A., Cabri, W., Borsini, F., Mor, M., et al. (2013). Synthesis of (E)-8- (3-chlorostyryl)caffeine analogues leading to 9-deazaxanthine derivatives as dual A(2A) antagonists/MAO-B inhibitors. J. Med. Chem. 56, 1247-1261.

270

BIBLIOGRAPHY

Ross, G.W., Abbott, R.D., Petrovitch, H., Morens, D.M., Grandinetti, A., Tung, K.H., Tanner, C.M., Masaki, K.H., Blanchette, P.L., Curb, J.D., et al. (2000). Association of coffee and caffeine intake with the risk of Parkinson disease. JAMA 283, 2674-2679.

Roth, B.L., Sheffler, D.J., and Kroeze, W.K. (2004). Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353-359.

Rowland, M., and Tozer, T.N. (2010). Clinical pharmacokinetics and pharmacodynamics : concepts and applications, 4th edn (Philadelphia : Wolters Kluwer Health/Lippincott William & Wilkins).

Rubin, L.L., and Staddon, J.M. (1999). The cell biology of the blood-brain barrier. Annu. Rev. Neurosci. 22, 11-28.

Ruecker, G., and Ruecker, C. (1993). Counts of all walks as atomic and molecular descriptors. J. Chem. Inf. Comput. Sci. 33, 683-695.

Ryckmans, T., Balancon, L., Berton, O., Genicot, C., Lamberty, Y., Lallemand, B., Pasau, P., Pirlot, N., Quere, L., and Talaga, P. (2002). First dual NK(1) antagonists-serotonin reuptake inhibitors: synthesis and SAR of a new class of potential antidepressants. Bioorg. Med. Chem. Lett. 12, 261-264.

Saari, W.S., King, S.W., Lotti, V.J., and Scriabine, A. (1974). Synthesis and biological activity of some aporphine derivatives related to apomorphine. J. Med. Chem. 17, 1086-1090.

Sadowski, J., Gasteiger, J., and Klebe, G. (1994). Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures. J. Chem. Inf. Comput. Sci. 34, 1000-1008.

Saeh, J.C., Lyne, P.D., Takasaki, B.K., and Cosgrove, D.A. (2005). Lead hopping using SVM and 3D pharmacophore fingerprints. J. Chem. Inf. Model. 45, 1122-1133.

271

BIBLIOGRAPHY

Salerno, L., Pittala, V., Modica, M.N., Siracusa, M.A., Intagliata, S., Cagnotto, A., Salmona, M., Kurczab, R., Bojarski, A.J., and Romeo, G. (2014). Structure-activity relationships and molecular modeling studies of novel arylpiperazinylalkyl 2-benzoxazolones and 2-benzothiazolones as 5-HT(7) and 5-HT(1A) receptor ligands. Eur. J. Med. Chem. 85, 716-726.

Sams-Dodd, F. (2005). Target-based drug discovery: is something wrong? Drug Discov. Today 10, 139-147.

Sanguinetti, M.C., and Mitcheson, J.S. (2005). Predicting drug-hERG channel interactions that cause acquired long QT syndrome. Trends Pharmacol. Sci. 26, 119-124.

Sanguinetti, M.C., and Tristani-Firouzi, M. (2006). hERG potassium channels and cardiac arrhythmia. Nature 440, 463-469.

Saura, J., Andres, N., Andrade, C., Ojuel, J., Eriksson, K., and Mahy, N. (1997). Biphasic and region-specific MAO-B response to aging in normal human brain. Neurobiol. Aging 18, 497-507.

Scheller, D., Ullmer, C., Berkels, R., Gwarek, M., and Lubbert, H. (2009). The in vitro receptor profile of rotigotine: a new agent for the treatment of Parkinson's disease. Naunyn Schmiedebergs Arch. Pharmacol. 379, 73-86.

Schwarzschild, M.A., Agnati, L., Fuxe, K., Chen, J.F., and Morelli, M. (2006). Targeting adenosine A2A receptors in Parkinson's disease. Trends Neurosci. 29, 647-654.

Seale, T.W., Abla, K.A., Shamim, M.T., Carney, J.M., and Daly, J.W. (1988). 3,7-Dimethyl-1-propargylxanthine: a potent and selective in vivo antagonist of adenosine analogs. Life Sci. 43, 1671-1684.

Seeman, P., and Schaus, J.M. (1991). Dopamine receptors labelled by [3H]quinpirole. Eur. J. Pharmacol. 203, 105-109.

Setola, V., and Roth, B.L. (2005). Screening the receptorome reveals molecular targets responsible for drug-induced side effects: focus on 'fen- phen'. Expert Opin. Drug Metab. Toxicol. 1, 377-387.

272

BIBLIOGRAPHY

Shah, V.P., and Amidon, G.L. (2014). G.L. Amidon, H. Lennernas, V.P. Shah, and J.R. Crison. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability, Pharm Res 12, 413-420, 1995--backstory of BCS. The AAPS journal 16, 894- 898.

Shargel, L., Wu-Pong, S., and Yu, A.B.C. (2012). Applied Biopharmaceutics & Pharmacokinetics, 6th edn (McGraw-Hill Medical).

Shen, J., Cheng, F., Xu, Y., Li, W., and Tang, Y. (2010). Estimation of ADME properties with substructure pattern recognition. J. Chem. Inf. Model. 50, 1034-1041.

Shin, J.H., Ko, H.S., Kang, H., Lee, Y., Lee, Y.I., Pletinkova, O., Troconso, J.C., Dawson, V.L., and Dawson, T.M. (2011). PARIS (ZNF746) repression of PGC-1alpha contributes to neurodegeneration in Parkinson's disease. Cell 144, 689-702.

Shoulson, I., Fahn, S., Oakes, D., Kieburtz, K., Lang, A., Langston, J.W., LeWitt, P., Olanow, C.W., Penney, J.B., Tanner, C., et al. (1993). Effects of tocopherol and deprenyl on the progression of disability in early Parkinson's disease. N. Engl. J. Med. 328, 176-183.

Shoulson, I., Fahn, S., Oakes, D., Odoroff, C., Lang, A., Langston, J.W., LeWitt, P., Olanow, W., Penney, J.B., Tanner, C., et al. (1989). Effect of deprenyl on the progression of disability in early Parkinson's disease. N. Engl. J. Med. 321, 1364-1371.

Siddiqi, S.M., Jacobson, K.A., Esker, J.L., Olah, M.E., Ji, X.D., Melman, N., Tiwari, K.N., Secrist, J.A., 3rd, Schneller, S.W., Cristalli, G., et al. (1995). Search for new purine- and ribose-modified adenosine analogues as selective agonists and antagonists at adenosine receptors. J. Med. Chem. 38, 1174-1188.

Sneader, W. (2002). The 50th Anniversary of Chlorpromazine. Drug News Perspect. 15, 466-471.

Snyder, S.H., Katims, J.J., Annau, Z., Bruns, R.F., and Daly, J.W. (1981). Adenosine receptors and behavioral actions of methylxanthines. Proc. Natl. Acad. Sci. U. S. A. 78, 3260-3264.

273

BIBLIOGRAPHY

Soriano, A., Ventura, R., Molero, A., Hoen, R., Casado, V., Cortes, A., Fanelli, F., Albericio, F., Lluis, C., Franco, R., et al. (2009). Adenosine A2A receptor- antagonist/dopamine D2 receptor-agonist bivalent ligands as pharmacological tools to detect A2A-D2 receptor heteromers. J. Med. Chem. 52, 5590-5602.

Spillantini, M.G., Schmidt, M.L., Lee, V.M., Trojanowski, J.Q., Jakes, R., and Goedert, M. (1997). Alpha-synuclein in Lewy bodies. Nature 388, 839-840.

Srinivas, G., Discher, D.E., and Klein, M.L. (2004). Self-assembly and properties of diblock copolymers by coarse-grain molecular dynamics. Nat. Mater. 3, 638-644.

Steiner, J.A., Angot, E., and Brundin, P. (2011). A deadly spread: cellular mechanisms of alpha-synuclein transfer. Cell Death Differ. 18, 1425-1433.

Stelling, J., Sauer, U., Szallasi, Z., Doyle, F.J., 3rd, and Doyle, J. (2004). Robustness of cellular functions. Cell 118, 675-685.

Stewart, R.B., and Cooper, J.W. (1994). Polypharmacy in the aged. Practical solutions. Drugs Aging 4, 449-461.

Stossel, A., Schlenk, M., Hinz, S., Kuppers, P., Heer, J., Gutschow, M., and Muller, C.E. (2013). Dual targeting of adenosine A(2A) receptors and monoamine oxidase B by 4H-3,1-benzothiazin-4-ones. J. Med. Chem. 56, 4580-4596.

St-Pierre, J., Drori, S., Uldry, M., Silvaggi, J.M., Rhee, J., Jager, S., Handschin, C., Zheng, K., Lin, J., Yang, W., et al. (2006). Suppression of reactive oxygen species and neurodegeneration by the PGC-1 transcriptional coactivators. Cell 127, 397-408.

Sugano, K., Kansy, M., Artursson, P., Avdeef, A., Bendels, S., Di, L., Ecker, G.F., Faller, B., Fischer, H., Gerebtzoff, G., et al. (2010). Coexistence of passive and carrier-mediated processes in drug transport. Nat. Rev. Drug Discov. 9, 597-614.

Sun, H., Veith, H., Xia, M., Austin, C.P., and Huang, R. (2011). Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. J. Chem. Inf. Model. 51, 2474-2481.

274

BIBLIOGRAPHY

Svenningsson, P., Le Moine, C., Kull, B., Sunahara, R., Bloch, B., and Fredholm, B.B. (1997). Cellular expression of adenosine A2A receptor messenger RNA in the rat central nervous system with special reference to dopamine innervated areas. Neuroscience 80, 1171-1185.

Szabo, M., Lim, H.D., Herenbrink, C.K., Christopoulos, A., Lane, J.R., and Capuano, B. (2015). Proof of concept study for designed multiple ligands targeting the dopamine D2, serotonin 5-HT2A, and muscarinic M1 acetylcholine receptors. J. Med. Chem. 58, 1550-1555.

Tandon, M., O'Donnell, M.M., Porte, A., Vensel, D., Yang, D., Palma, R., Beresford, A., and Ashwell, M.A. (2004). The design and preparation of metabolically protected new arylpiperazine 5-HT1A ligands. Bioorg. Med. Chem. Lett. 14, 1709-1712.

Taussig, R., Iniguez-Lluhi, J.A., and Gilman, A.G. (1993). Inhibition of adenylyl cyclase by Gi alpha. Science 261, 218-221.

Teo, Y.L., Saetaew, M., Chanthawong, S., Yap, Y.S., Chan, E.C., Ho, H.K., and Chan, A. (2012). Effect of CYP3A4 inducer dexamethasone on hepatotoxicity of lapatinib: clinical and in vitro evidence. Breast Cancer Res. Treat. 133, 703- 711.

Testa, B. (2008). Chapter 32 - Biotransformation Reactions and Their Enzymes. In The Practice of Medicinal Chemistry (Third Edition), C.G. Wermuth, ed. (New York: Academic Press), pp. 655-673.

Thornber, C.W. (1979). Isosterism and molecular modification in drug design. Chem. Soc. Rev. 8, 563-580.

Toda, N., Tago, K., Marumoto, S., Takami, K., Ori, M., Yamada, N., Koyama, K., Naruto, S., Abe, K., Yamazaki, R., et al. (2003). A conformational restriction approach to the development of dual inhibitors of acetylcholinesterase and serotonin transporter as potential agents for Alzheimer's disease. Bioorg. Med. Chem. 11, 4389-4415.

Tschammer, N., Bollinger, S., Kenakin, T., and Gmeiner, P. (2011). Histidine 6.55 is a major determinant of ligand-biased signaling in dopamine D2L receptor. Mol. Pharmacol. 79, 575-585.

275

BIBLIOGRAPHY

Tschammer, N., Elsner, J., Goetz, A., Ehrlich, K., Schuster, S., Ruberg, M., Kuhhorn, J., Thompson, D., Whistler, J., Hubner, H., et al. (2011). Highly potent 5-aminotetrahydropyrazolopyridines: enantioselective dopamine D3 receptor binding, functional selectivity, and analysis of receptor-ligand interactions. J. Med. Chem. 54, 2477-2491.

Tzavara, E.T., Bymaster, F.P., Davis, R.J., Wade, M.R., Perry, K.W., Wess, J., McKinzie, D.L., Felder, C., and Nomikos, G.G. (2004). M4 muscarinic receptors regulate the dynamics of cholinergic and dopaminergic neurotransmission: relevance to the pathophysiology and treatment of related CNS pathologies. FASEB J. 18, 1410-1412.

Tzschentke, T.M., Christoph, T., Kogel, B., Schiene, K., Hennies, H.H., Englberger, W., Haurand, M., Jahnel, U., Cremers, T.I., Friderichs, E., et al. (2007). (-)-(1R,2R)-3-(3-dimethylamino-1-ethyl-2-methyl-propyl)-phenol hydrochloride (tapentadol HCl): a novel mu-opioid receptor agonist/norepinephrine reuptake inhibitor with broad-spectrum analgesic properties. J. Pharmacol. Exp. Ther. 323, 265-276.

Uchida, S., Soshiroda, K., Okita, E., Kawai-Uchida, M., Mori, A., Jenner, P., and Kanda, T. (2015). The adenosine A2A receptor antagonist, istradefylline enhances the anti-parkinsonian activity of low doses of dopamine agonists in MPTP-treated common marmosets. Eur. J. Pharmacol. 747, 160-165. van de Waterbeemd, H., and Gifford, E. (2003). ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192-204. van De Waterbeemd, H., Smith, D.A., Beaumont, K., and Walker, D.K. (2001). Property-based design: optimization of drug absorption and pharmacokinetics. J. Med. Chem. 44, 1313-1333. van den Berg, D., Zoellner, K.R., Ogunrombi, M.O., Malan, S.F., Terre'Blanche, G., Castagnoli, N., Jr., Bergh, J.J., and Petzer, J.P. (2007). Inhibition of monoamine oxidase B by selected benzimidazole and caffeine analogues. Bioorg. Med. Chem. 15, 3692-3702.

Vangveravong, S., Zhang, Z., Taylor, M., Bearden, M., Xu, J., Cui, J., Wang, W., Luedtke, R.R., and Mach, R.H. (2011). Synthesis and characterization of selective dopamine D(2) receptor ligands using aripiprazole as the lead compound. Bioorg. Med. Chem. 19, 3502-3511.

276

BIBLIOGRAPHY

Vaz, R.J., and Klabunde, T. (2008). Antitargets: Prediction and Prevention of Drug Side Effects (WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).

Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W., and Kopple, K.D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615-2623.

Velez-Ruiz, G.A., and Sunahara, R.K. (2011). Reconstitution of G protein- coupled receptors into a model bilayer system: reconstituted high-density lipoprotein particles. Methods Mol. Biol. 756, 167-182.

Vickery, R.G., and von Zastrow, M. (1999). Distinct dynamin-dependent and - independent mechanisms target structurally homologous dopamine receptors to different endocytic membranes. J. Cell Biol. 144, 31-43.

Vlad, G., and Horvath, I.T. (2002). Improved synthesis of 2,2'-bipyrimidine. J. Org. Chem. 67, 6550-6552.

Vlok, N., Malan, S.F., Castagnoli, N., Jr., Bergh, J.J., and Petzer, J.P. (2006). Inhibition of monoamine oxidase B by analogues of the adenosine A2A receptor antagonist (E)-8-(3-chlorostyryl)caffeine (CSC). Bioorg. Med. Chem. 14, 3512-3521.

Volkamer, A., Kuhn, D., Grombacher, T., Rippmann, F., and Rarey, M. (2012). Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 52, 360-372. von Dassow, G., Meir, E., Munro, E.M., and Odell, G.M. (2000). The segment polarity network is a robust developmental module. Nature 406, 188-192.

Vu, C.B., Peng, B., Kumaravel, G., Smits, G., Jin, X., Phadke, D., Engber, T., Huang, C., Reilly, J., Tam, S., et al. (2004). Piperazine derivatives of [1,2,4]triazolo[1,5-a][1,3,5]triazine as potent and selective adenosine A2a receptor antagonists. J. Med. Chem. 47, 4291-4299.

Wager, T.T., Chandrasekaran, R.Y., Hou, X., Troutman, M.D., Verhoest, P.R., Villalobos, A., and Will, Y. (2010). Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS Chem. Neurosci. 1, 420-434.

277

BIBLIOGRAPHY

Walker, M.A. (2014). Novel tactics for designing water-soluble molecules in drug discovery. Expert Opin. Drug Discov. 9, 1421-1433.

Wallace, K.B., Hausner, E., Herman, E., Holt, G.D., MacGregor, J.T., Metz, A.L., Murphy, E., Rosenblum, I.Y., Sistare, F.D., and York, M.J. (2004). Serum troponins as biomarkers of drug-induced cardiac toxicity. Toxicol. Pathol. 32, 106-121.

Wang, R., Fu, Y., and Lai, L. (1997). A New Atom-Additive Method for Calculating Partition Coefficients. J. Chem. Inf. Comput. Sci. 37, 615-621.

Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., Zhou, Z., Han, L., Karapetyan, K., Dracheva, S., Shoemaker, B.A., et al. (2012). PubChem's BioAssay Database. Nucleic Acids Res. 40, D400-D412.

Warmuth, M.K., Liao, J., Ratsch, G., Mathieson, M., Putta, S., and Lemmen, C. (2003). Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 43, 667-673.

Wassermann, A.M., Geppert, H., and Bajorath, J. (2009). Searching for target- selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J. Chem. Inf. Model. 49, 582-592.

Wei, J., Qu, W., Ye, Y., and Gao, Q. (2010). 3D pharmacophore based virtual screening of A2A adenosine receptor antagonists. Protein Pept. Lett. 17, 332- 339.

Weinstock, J., Gaitanopoulos, D.E., Stringer, O.D., Franz, R.G., Hieble, J.P., Kinter, L.B., Mann, W.A., Flaim, K.E., and Gessner, G. (1987). Synthesis and evaluation of non-catechol D-1 and D-2 dopamine receptor agonists: benzimidazol-2-one, benzoxazol-2-one, and the highly potent benzothiazol-2- one 7-ethylamines. J. Med. Chem. 30, 1166-1176.

Wells, J.N., Garst, J.E., and Kramer, G.L. (1981). Inhibition of separated forms of cyclic nucleotide phosphodiesterase from pig coronary arteries by 1,3- disubstituted and 1,3,8-trisubstituted xanthines. J. Med. Chem. 24, 954-958.

278

BIBLIOGRAPHY

White, S.M., Constantin, P.E., and Claycomb, W.C. (2004). Cardiac physiology at the cellular level: use of cultured HL-1 cardiomyocytes for studies of cardiac muscle cell structure and function. Am. J. Physiol. Heart Circ. Physiol. 286, H823-829.

Whitebread, S., Hamon, J., Bojanic, D., and Urban, L. (2005). Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today 10, 1421-1433.

Whitworth, A.J., Theodore, D.A., Greene, J.C., Benes, H., Wes, P.D., and Pallanck, L.J. (2005). Increased glutathione S-transferase activity rescues dopaminergic neuron loss in a Drosophila model of Parkinson's disease. Proc. Natl. Acad. Sci. U. S. A. 102, 8024-8029.

Whone, A.L., Watts, R.L., Stoessl, A.J., Davis, M., Reske, S., Nahmias, C., Lang, A.E., Rascol, O., Ribeiro, M.J., Remy, P., et al. (2003). Slower progression of Parkinson's disease with ropinirole versus levodopa: The REAL-PET study. Ann. Neurol. 54, 93-101.

Wiberg, K.B., and Rablen, P.R. (1993). Origin of the stability of carbon tetrafluoride: Negative hyperconjugation reexamined. J. Am. Chem. Soc. 115, 614-625.

Wikstrom, H., Andersson, B., Elebring, T., Svensson, K., Carlsson, A., and Largent, B. (1987). N-substituted 1,2,3,4,4a,5,6,10b- octahydrobenzo[f]quinolines and 3-phenylpiperidines: effects on central dopamine and sigma receptors. J. Med. Chem. 30, 2169-2174.

Willett, P. (2006). Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11, 1046-1053.

Willett, P., Barnard, J.M., and Downs, G.M. (1998). Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 38, 983-996.

Williams, A.J., and Ekins, S. (2011). A quality alert and call for improved curation of public chemistry databases. Drug Discov. Today 16, 747-750.

Williams, H.D., Trevaskis, N.L., Charman, S.A., Shanker, R.M., Charman, W.N., Pouton, C.W., and Porter, C.J. (2013). Strategies to address low drug solubility in discovery and development. Pharmacol. Rev. 65, 315-499.

279

BIBLIOGRAPHY

Wolk, J.L., Sprecher, M., Basch, H., and Hoz, S. (2004). Relative reactivity of three and four membered rings--the absence of charge effect. Org. Biomol. Chem. 2, 1065-1069.

Wring, S.A., Silver, I.S., and Serabjit-Singh, C.J. (2002). Automated quantitative and qualitative analysis of metabolic stability: a process for compound selection during drug discovery. Methods Enzymol. 357, 285-295.

Wu, C.Y., and Benet, L.Z. (2005). Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm. Res. 22, 11- 23.

Wu, J.C., Merlino, G., Cveklova, K., Mosinger, B., Jr., and Fausto, N. (1994). Autonomous growth in serum-free medium and production of hepatocellular carcinomas by differentiated hepatocyte lines that overexpress transforming growth factor alpha 1. Cancer Res. 54, 5964-5973.

Wuitschik, G. (2008). Oxetanes in drug discovery (Zürich: ETH).

Wuitschik, G., Carreira, E.M., Wagner, B., Fischer, H., Parrilla, I., Schuler, F., Rogers-Evans, M., and Muller, K. (2010). Oxetanes in drug discovery: structural and synthetic insights. J. Med. Chem. 53, 3227-3246.

Wuitschik, G., Rogers-Evans, M., Buckl, A., Bernasconi, M., Marki, M., Godel, T., Fischer, H., Wagner, B., Parrilla, I., Schuler, F., et al. (2008). Spirocyclic oxetanes: synthesis and properties. Angew. Chem. Int. Ed. Engl. 47, 4512-4515.

Wuitschik, G., Rogers-Evans, M., Muller, K., Fischer, H., Wagner, B., Schuler, F., Polonchuk, L., and Carreira, E.M. (2006). Oxetanes as promising modules in drug discovery. Angew. Chem. Int. Ed. Engl. 45, 7736-7739.

Wustrow, D.J., Smith, W.J., 3rd, Corbin, A.E., Davis, M.D., Georgic, L.M., Pugsley, T.A., Whetzel, S.Z., Heffner, T.G., and Wise, L.D. (1997). 3-[[(4- Aryl-1-piperazinyl)alkyl]cyclohexyl]-1H-indoles as dopamine D2 partial agonists and autoreceptor agonists. J. Med. Chem. 40, 250-259.

280

BIBLIOGRAPHY

Xiang, Z., Thompson, A.D., Jones, C.K., Lindsley, C.W., and Conn, P.J. (2012). Roles of the M1 muscarinic acetylcholine receptor subtype in the regulation of basal ganglia function and implications for the treatment of Parkinson's disease. J. Pharmacol. Exp. Ther. 340, 595-603.

Xie, L., Yu, D., Wild, C., Allaway, G., Turpin, J., Smith, P.C., and Lee, K.H. (2004). Anti-AIDS agents. 52. Synthesis and anti-HIV activity of hydroxymethyl (3'R,4'R)-3',4'-di-O-(S)-camphanoyl-(+)-cis-khellactone derivatives. J. Med. Chem. 47, 756-760.

Xue, Y., Yap, C.W., Sun, L.Z., Cao, Z.W., Wang, J.F., and Chen, Y.Z. (2004). Prediction of P-glycoprotein substrates by a support vector machine approach. J. Chem. Inf. Comput. Sci. 44, 1497-1505.

Yan, Y., Zhou, P., Rotella, D.P., Feenstra, R., Kruse, C.G., Reinders, J.H., van der Neut, M., Lai, M., Zhang, J., Kowal, D.M., et al. (2010). Potent dihydroquinolinone dopamine D2 partial agonist/serotonin reuptake inhibitors for the treatment of schizophrenia. Bioorg. Med. Chem. Lett. 20, 2983-2986.

Zeiger, E., Risko, K.J., and Margolin, B.H. (1985). Strategies to reduce the cost of mutagenicity screening with the Salmonella assay. Environ. Mutagen. 7, 901-911.

Zhan, P., Chen, X., Li, D., Fang, Z., De Clercq, E., and Liu, X. (2013). HIV-1 NNRTIs: structural diversity, pharmacophore similarity, and implications for drug design. Med. Res. Rev. 33 Suppl 1, E1-72.

Zhang, C., Tao, L., Qin, C., Zhang, P., Chen, S., Zeng, X., Xu, F., Chen, Z., Yang, S.Y., and Chen, Y.Z. (2015). CFam: a chemical families database based on iterative selection of functional seeds and seed-directed compound clustering. Nucleic Acids Res. 43, D558-565.

Zheng, B., Liao, Z., Locascio, J.J., Lesniak, K.A., Roderick, S.S., Watt, M.L., Eklund, A.C., Zhang-James, Y., Kim, P.D., Hauser, M.A., et al. (2010). PGC- 1alpha, a potential therapeutic target for early intervention in Parkinson's disease. Sci. Transl. Med. 2, 52ra73.

Zhu, M., Zhao, W., Jimenez, H., Zhang, D., Yeola, S., Dai, R., Vachharajani, N., and Mitroka, J. (2005). Cytochrome P450 3A-mediated metabolism of buspirone in human liver microsomes. Drug Metab. Dispos. 33, 500-507.

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Appendix A. Reverse Transcriptase Inhibitors for Treating HIV

O O O NH N N N NH NH N N N N N O N O N NH2 H HO O HO O HO O HO O

N3 didanosine zidovudine stavudine abacavir

O H NH2 NH2 N O NH2 S F N N O N N N N O H N O N O N HO HO HO O O S N S O HN

lamivudine zalcitabine emtricitabine delavirdine

CN Br H O N O CN NH H2N O O NN Cl CN N N N CF3 NH HN N NH

NC N etravirine rilpivirine efavirenz nevirapine

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Appendix B. Protease Inhibitors for Treating HIV

HO O OH O P O O O O N N O O H H N N O O S O OH S HO O O H O O N O N S H2N O F C NH 3 2 fosamprenavir darunavir tipranavir

O N H N N N N H O OH O HO O H H O S N N N OMe MeO N N S H H N O O O H N

atazanavir ritonavir

H OH O N H N O O 2 N H N N N H OH N N O HN O OH indinavir OH O H N N N O O HN S O O O N O NH H N O 2 amprenavir HN saquinavir HO O O H H N H HO N S NH O lopinavir NH O

nelfinavir

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Appendix C. Polypharmacology in Cancer Therapy

Sunitinib, working by blocking proliferation and angiogenesis of cancer cells for the treatment of gastrointestinal stromal tumour (GIST) (Demetri et al.,

2006), advanced renal-cell carcinoma (RCC) (Motzer et al., 2007), and pancreatic neuroendocrine tumour (pNET) (Raymond et al., 2011), is probably the best case illustrating polypharmacology in cancer therapy. Blood vessels are composed of endothelial cells and are responsible for supplying nutrients and oxygen for proper cellular functions. In normal adults, new blood vessels are formed by sprouting from pre-existing ones, and this process is called angiogenesis or neovascularisation. The vasculature in adults is quiescent and represents the long-lived system with the turnover time in years, but only under circumstances such as female reproduction or wound healing is angiogenesis turned on through a complex multistep process, which in turn, leads to formation of new vessels in a transient manner (Hanahan and Folkman, 1996;

Hanahan and Weinberg, 2000). By contrast, experiments have shown that implanted tumour cells persistently recruited new capillary blood vessels

(Gimbrone et al., 1972), and once without access to vasculature, the tumour cells underwent either apoptosis (Parangi et al., 1996) or necrosis (Brem et al.,

1976), supporting that ongoing angiogenesis is essential for the growth and metastasis of tumour cells. The pivotal role of angiogenesis in cancer and the therapeutic opportunities based on its molecular mechanism were first proposed in 1971 by Judah Folkman (Folkman, 1971). He contended that the trigger of

“angiogenic switch” from quiescence for a tumour phenotype is characterised by the increased production of positive angiogenic factors, including vascular endothelial growth factors (VEGFs) and platelet-derived growth factors

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(PDGFs), as well as down-regulation of negative angiogenic factors, such as thrombospondin (Folkman, 1995). The VEGFs and PDGFs released by tumours attract endothelial cells and pericytes, respectively. Subsequently, VEGFs and

PDGFs bind to the corresponding cell surface receptors which belong to the class of receptor tyrosine kinases (RTKs), and consequently, promote the growing of tumour blood vessels. RTKs are the cell surface receptors with protein tyrosine kinase activity, and their mechanism of activation is characterised by ligand-induced receptor oligomerisation which stabilises cytoplasmic domains and therefore triggers kinase function (Ullrich and

Schlessinger, 1990). The binding of VEGF-A to VEGF receptor subtype 2

(VEGFR-2) on the endothelial cell activates a cascade of intracellular signalling and eventually up-regulates the genes involved in the proliferation of endothelial cells (Shibuya and Claesson-Welsh, 2006). Additionally, the interaction of PDGF-B and PDGF receptor isoform β (PDGFR-β) on the pericyte was used in a study where it was proven that simultaneous targeting endothelial cells and pericytes induces tumour blood vessel regression and endothelial cell apoptosis (Erber et al., 2004).

The anti-angiogenic therapy to treat cancer relies on the principle that impaired supplies of nutrient and oxygen lead to tumour starvation. Two approaches have been adopted—inhibition of growth factors, and inhibition of the RTKs of these growth factors. The former approach is best exemplified by the first-approved

(by the US FDA in 2004) angiogenesis inhibitor, bevacizumab, which is a recombinant humanised monoclonal antibody which binds to all VEGF isoforms for the treatment of metastatic colorectal cancer, advanced non- squamous non-small cell lung cancer (NSCLC), metastatic kidney cancer, and

285

APPENDICES glioblastoma either by itself or in combination with other chemotherapies

(Ferrara et al., 2004). The latter approach will be illustrated by sunitinib which got US FDA approval for the treatment of GIST in 2006, advanced RCC in

2006, and pNET in 2011. The discovery of sunitinib perfectly represents the paradigm of deliberate dual-targeting strategy in an attempt to increase inhibitory activities against both VEGFR-2 kinase and PDGFR-β kinase (Figure

A1). Random screening of synthetic compounds using VEGFR-2 autophosphorylation assay followed by analogues synthesis led to the identification of a series of 3-substituted indolin-2-ones (Sun et al., 1998), and among them, SU5416 exhibited potent and selective VEGFR-2 inhibition, displayed inhibition VEGF-driven mitogenesis of human umbilical vein endothelial cells (HUVECs), and prevented tumour growth in mouse models of cancer (Fong et al., 1999). However, in 2002, the analysis of efficacy and safety in the phase 3 study for SU5416 failed to achieve the defined trial endpoints.

Unlike SU5416 which targets VEGFR-2 selectively, SU6668 was subsequently developed, and showed activities at three distinct members of RTK family, namely VEGFR-2, PDGFR-β, and fibroblast growth factor receptor-1 (FGFR-

1) (Sun et al., 1999), as well as suppressed angiogenesis in in vivo tumour xenograft experiments (Laird et al., 2000). Unfortunately, SU6668 had poor pharmacokinetic properties and aqueous solubility (Faivre et al., 2007; Sun et al., 2003). Analysis of crystal structure of FGFR-1 in complex with SU6668

(Laird et al., 2000) and the subsequent structure-activity relationship (SAR) studies confirmed that the substitution at the C4’ position of the pyrrole ring affects selectivity profile of kinases, ultimately culminating in the synthesis of sunitinib (Sun et al., 2003).

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CO2H O N N H N N H H O O F N N H N O H H N H SU5416 SU6668 sunitinib

VEGFR-2 IC50 1.23 uM VEGFR-2 IC50 2.43 uM VEGFR-2 IC50 0.08 uM PDGFR-beta IC50 22.9 uM PDGFR-beta IC50 0.06 uM PDGFR-beta IC50 0.002 uM FGFR-1 IC50 >100 uM FGFR-1 IC50 3.04 uM FGFR-1 IC50 2.90 uM Figure A1. The chemical structures of SU5416, SU6668 and sunitinib.

In comparison with SU6668, sunitinib showed 30 times greater activity against

VEGFR-2 and PDGFR-β in biochemical trans-phosphorylation assays, 20 times better aqueous solubility in pH 6, more than 500 times better aqueous solubility in pH 2, and reduction of protein binding. The in vivo study using mouse xenograft preclinical models also demonstrated that sunitinib exerted anti- tumour activity and inhibited the phosphorylation of VEGFR-2 and PDGFR-β

(Mendel et al., 2003). In a phase 3 clinical trial, a group of 312 patients with advanced GIST after failure of imatinib treatment due to resistance or intolerance, was randomised and given sunitinib (daily dose of 50 mg), with placebo control, in a cycle of 4-week-on/2-week-off. The efficacy was achieved with the median time-to-tumour-progression (TTP) obtained with sunitinib treatment being 4 times more than that with placebo, and the adverse events, such as fatigue, were easily managed (Demetri et al., 2006). Around almost the same time, another phase 3 trial was documented for 750 patients with metastatic RCC. Half of the patients were given sunitinib orally, and another half received interferon alfa-2a through subcutaneous injection. The results showed the significantly longer median progression-free survival (PFS) for the sunitinib group (11 months) than interferon alfa-2a group (5 months), and most

287

APPENDICES sunitinib-induced safety issues were ameliorated by dose modification (Motzer et al., 2007). Five years later, it was reported that sunitinib was used in a placebo-controlled phase 3 trial in 171 patients with advanced pNET. The efficacy was shown with the median PFS of 11.4 months, as compared with 5.5 months in the placebo group, and the safety assessment indicated several manageable adverse events which were consistent with previous trials of sunitinib (Raymond et al., 2011). The aforementioned success of three phase 3 trials of sunitinib demonstrated that the rationally designed dual-acting inhibitor against VEGFR- and PDGFR-mediated signalling resulted in an effective cancer therapy. Although sunitinib was initially thought to be specific for two kinases, VEGFR-2 and PDGFR-β, the recent comprehensive testing of its interaction patterns across the kinome by competition binding assays identified

10 kinases with dissociation constant (Kd) < 10 µM (Davis et al., 2011) (Table

A1). This is attributed to the target promiscuity of kinase inhibitors, and therefore caution should be taken when using small-molecule kinase inhibitors for assessing physiological roles of these kinases (Cohen, 2010).

Table A1. Ten Kinases Targeted by Sunitinib kinase Kd kinase Kd kinase Kd kinase Kd (µM) (µM) (µM) (µM) VEGFR-2 1.5 DRAK1 1 CSF1R 2.5 PHKG1 5.5 PDGFR-β 0.075 KIT 0.37 BIKE 5.5 PDGFR-α 0.79 FLT3 0.41 FLT1 1.8

In addition to targeting multiple kinases as shown in the example of sunitinib

(i.e. multikinase inhibitors, or MKIs for short), targeting two or more different types of proteins is also well documented for cancer polypharmacology. As

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APPENDICES shown in Figure A2, CUDC-101, currently at phase 1 clinical trial, showed activities in patients with head and neck squamous cell cancer (HNSCC) through inhibiting the epidermal growth factor receptor (EGFR), histone deacetylase (HDAC), and human epidermal growth factor receptor 2 (HER2)

(Galloway et al., 2015). Niclosamide, an old drug used in helminthosis patients, was recently shown not only to have inhibitory effects on several cancer-cell- associated signalling pathways, such as the Wnt/β-catenin pathway, the signal transducer and activator of transcription 3 (STAT3) pathway, the mammalian target of rapamycin complex 1 (mTORC1) pathway, the Notch pathway, and the nuclear factor-kappaB (NF-κB) pathway, but it also displayed anti-myeloma activities mediated by targeting mitochondria (Li et al., 2014). The in vivo anticancer efficacy of niclosamide has been demonstrated in xenograft models for colorectal cancer (Osada et al., 2011), lung cancer (You et al., 2014), and liver metastasis (Sack et al., 2011), rendering niclosamide promising for further research. Galeterone, which is under development by Tokai Pharmaceuticals, has successfully gone through phase 2 clinical trial in patients with castration- resistant prostate cancer (CRPC). Its multiple modes of action to treat CRPC is through CYP17 lyase inhibition, androgen receptor antagonism, and androgen receptor degradation (Njar and Brodie, 2015). The dual-acting agents targeting heat shock protein 90 (Hsp90) and tubulin, such as MDG892 (Knox et al., 2009) and CDBT (Zhang et al., 2013), represent another examples of cancer polypharmacology targeting different types of proteins.

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APPENDICES

MeO N NO O O 2 HO N Cl N O N H H HN Cl OH

CUDC-101 niclosamide

N OMe N O OMe O H N NH N OMe N N Cl H H S HO H2N galeterone MDG892 CDBT Figure A2. The chemical structures of CUDC-101, niclosamide, galeterone, MDG892 and CDBT.

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APPENDICES

Appendix D. Polypharmacology in Antipsychotics

Schizophrenia is one of the most debilitating neuropsychiatric disorders, and it affects about one percent of world population. It is the consequence of a combination of environmental, genetic and neurodevelopmental factors (van Os et al., 2010), and its symptoms encompass positive (hallucinations, delusions), negative (avolition, diminished emotional expression), and cognitive (reduced memory, impaired executive functions) deficits. The molecular lesions in schizophrenia include dopamine hyperfunction hypothesis (Snyder, 1976), serotonin hypothesis (Gaddum and Hameed, 1954), N-methyl-D-aspartate

(NMDA) receptor hypofunction hypothesis (Olney et al., 1999), and cholinergic hypothesis (Shekhar et al., 2008). The current antipsychotic medications for the treatment of schizophrenia involve the typical or first generation antipsychotics

(FGAs) (e.g. chlorpromazine and haloperidol), the atypical or second generation antipsychotics (SGAs) (e.g. clozapine, olanzapine, quetiapine and loxzapine), and the third generation antipsychotics (TGAs) (e.g. aripiprazole and risperidone). In general, typical antipsychotics have pronounced side effects such as tardive dyskinesia, neuroleptic malignant syndrome, and extrapyramidal syndrome (EPS), whereas atypical antipsychotics have less propensity to develop EPS (Lieberman et al., 2008).

Early antipsychotic drugs were discovered by serendipity, not by design.

Thanks to the observations that aniline dyes have sedating properties in the late

19th century, in the 1950s chemists at the French company Rhone-Poulenc synthesised a series of phenothiazine derivatives that led to the first antipsychotic drug, chlorpromazine (Figure A3), which was successfully used to treat patients with schizophrenic and manic psychoses (Delay et al., 1952).

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Chlorpromazine was originally intended to act as an anti-histamine agent but later experiments, according to the results from DrugBank database (Law et al.,

2014), showed that it interacted with 24 targets and all of which were considered to contribute to its clinical effects. Although chlorpromazine showed efficacy against positive symptoms, it was plagued by motor side effects such as EPS and tardive dyskinesia in 20% of patients. The subsequent discovery of haloperidol by Paul Janssen in 1958 based on its cataleptic activity led to the then view that Parkinson-like movement syndromes were what a “typical” antipsychotic drug should have and were considered to be indicators of antipsychotic activity. In the late 1950s, chemists at the Wander Laboratories in

Switzerland identified a subclass of tricyclic dibenzodiazepines which were pharmacologically characterised and administered to patients. In animal models, one compound, later named clozapine (Figure A3), showed antipsychotic effects similar to those of chlorpromazine but did not produce motor side effects at effective doses, which contradicted the then notion of

“typicality”, and consequently, some clinical investigators did not seriously consider proceeding with the development of this compound. After several years of clinical testing, clozapine was introduced in Europe in 1971.

Unfortunately, in 1975, it was reported that 16 Finnish patients treated with clozapine developed agranulocytosis, an acute condition characterised by the loss of white blood cells, which renders patients prone to infection. Of these 16 patients, 8 died, resulting in the withdrawal of clozapine from the market

(Idanpaan-Heikkila et al., 1975). In the ensuing years, many clozapine-treated patients showed relapse after being forced to switch medications, and it was this pressure from patients as well as lack of more effective agents that led to the

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APPENDICES reapproval of clozapine and its reintroduction to the market in 1989. Also,

30~50% of schizophrenics had suicide attempts, and clozapine treatment resulted in diminished suicidal behaviour (Meltzer et al., 2003; Meltzer and

Okayli, 1995). This led FDA to approve clozapine as a treatment against suicide in patients with schizophrenia in 2002.

N N Cl N NH N N N O Cl N Cl S N N S H

chlorpromazine clozapine ziprasidone Figure A3. Chemical structures of chlorpromazine (typical), clozapine (atypical) and ziprasidone (atypical).

After 43 years (1971–2014) since its inception in clinical use, clozapine still remains one of the most effective antipsychotic drugs to treat schizophrenia.

Indeed, two recent papers show that clozapine is superior to other antipsychotics used in the study in efficacy (Leucht et al., 2013; Lieberman et al., 2005).

During January 2001–December 2004, the Clinical Antipsychotic Trials of

Intervention Effectiveness (CATIE) sponsored by National Institute of Mental

Health (NIMH) involved 1493 patients diagnosed of schizophrenia, and these patients were initially randomly assigned five antipsychotic drugs (without clozapine). Discontinuation of treatment occurred over the 18-month period, and in the next phase study on medication switching for 99 patients, clozapine stood out with longest time until treatment discontinuation, proving that clozapine was the most effective in patients who did not improve with the first- line treatment (McEvoy et al., 2006). Moreover, in a more recent meta-analysis whose data were extracted from 212 trials between 1955 and 2012 to compare

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15 different antipsychotic drugs, the resultant evidence-based hierarchy demonstrated that clozapine was significantly more efficacious than all the other

14 drugs. Whilst clozapine has been confirmed to have some side effects, such as agranulocytosis, weight gain, or myocarditis, epidemiologic studies

(Tiihonen et al., 2009; Walker et al., 1997) suggests that none of the risks associated with clozapine should negate its current clinical use.

The sound efficacy of clozapine in alleviating the positive and negative symptoms of treatment-resistant schizophrenia (Kane et al., 1988) as well as in reducing the suicidal behaviour (Meltzer et al., 2003) prompted pharmacologists to study its pharmacodynamic properties. Clozapine is a broad sprectrum drug with a polypharmacological profile of binding to 11 serotonin receptors, 5 adrenergic receptors, 5 muscarinic receptors, 5 dopamine receptors,

2 histamine receptors, δ-opioid receptor, imidazoline I1 receptor, serotonin transporter and norepinephrine transporter (data from PDSP Ki database funded by the Psychoactive Drug Screening Program, National Institute of Mental

Health). The lack of EPS after treatment with clozapine may be attributed to its transient occupation of, and thus relatively low affinity (Ki = 180 nM, the long

3 isoform D2L, [ H]spiperone as the radioligand) (Seeman, 2006) to, dopamine D2 receptor (40~60% occupancy). This is in contrast with chlorpromazine (one of the FGAs) which has prolonged occupation of D2 receptor with >80% occupancy (Fakra and Azorin, 2012). The strong histamine H1 receptor affinity

(Ki = 2 nM) (Kroeze et al., 2003) most likely accounts for its side effect of weight gain. There is a consensus that addressing one subtype of receptors alone cannot satisfactorily explain the potency displayed, nor eliminate the side effects elicited, by clozapine. Obviously, it is the unique blend of numerous

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APPENDICES receptors involved in the mode of action of clozapine, as well as the extent to which this drug binds to different receptors as shown in the measurement of dissociation rates (Seeman and Tallerico, 1999), that contributes to its overall clinical effect.

Following the success of clozapine, many efforts have been attempted to search for more atypical antipsychotics that does not have EPS. Inspired by a paper

(McMillen and Mattiace, 1983) that described the anxiolytic drug buspirone

(Figure A4) reversed haloperidol-induced catalepsy in rats, a group of scientists at Pfizer chose 1-naphthylpiperazine (1; Figure A4), which is structurally similar to buspirone, as the starting point in search for novel atypical antipsychotics (Lowe et al., 1991). Compound 1 was reported to be a potent 5-

HT1 agonist/5-HT2 antagonist (Glennon et al., 1986). At that time, it was known that all clinical active antipsychotics share a common factor, namely, the ability to antagonise the dopaminergic system directly or indirectly (Lowe et al., 1988), so the structure of dopamine (Figure A4) was also taken into consideration in the synthesis of derivatives. The strategy to install dopamine-blocking activity to compound 1 is based on the rationale that a large lipophilic group appended to the amino group of an agonist (dopamine, in this case) binds to the accessory site of the receptor and therefore transforms the agonist to an antagonist. The modification of the black-dotted square part of compound 103 was to test the hypothesis that the hydroxyl group of the serine side chain of dopamine D2 receptor forms hydrogen bonds with catechol mimics, so both fused and appended heterocyclic groups were synthesised to replace the aryl group present in dopamine. The assay data confirmed that compound 103, substituting catechol group in dopamine with oxindole, not only achieved affinity to D2 and

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5-HT1A receptors simultaneously but it also showed efficacy by inhibition of amphetamine-induced hyperlocomotion and reversal of haloperidol-induced catalepsy after oral administration in rats. However, compound 103 showed disappointing results in monkeys.

Binding studies coupled with multivariate analysis on 20 antipsychotics led to the hypothesis that the atypicality of drugs is due to the higher antagonistic activity at serotonin 5-HT2 than at dopamine D2 receptor (i.e. D2/5-HT2 affinity ratio > 1) (Meltzer et al., 1989). In light of this, the same group of scientists at

Pfizer shifted their focus from 5-HT1A to 5-HT2 receptor, and they were also attracted by the structure of tiospirone (Figure A4). The bicyclic benzoisothiazole group (black-solid square part shown in Figure A4) in the atypical antipsychotic drug tiospirone was designed to mimic the tricyclic antipsychotic drugs (e.g. clothiapine, clozapine and loxapine) (Yevich et al.,

1986), and the affinity ratio of tiospirone to D2/5-HT2A was determined to be

3.6 which met the hypothesis of atypicality. A series of 3- benzisothiazolylpiperazines were then synthesised in order to improve D2 receptor affinity. One derivative showed favourable D2/5-HT2 ratio (4.8/0.42 =

11.4), inhibition of amphetamine-induced hypermotility, blockade of conditioned avoidance response, along with the subsequent 9 years of clinical trials and 3 years for addressing regulatory requirements. This derivative was ultimately marketed as ziprasidone in 2001 (Howard et al., 1996).

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H2N O OH dopamine N OH N NN O NNH N buspirone 1 (reverse catalepsy) 5-HT1 Ki 5 nM (agonist) 5-HT2 Ki 18 nM (antagonist) O

NN O N N S N tiospirone NH

O 103

D2 IC50 38 nM (antagonist) 5-HT1A IC50 4.87 nM 5-HT2 IC50 20 nM (antagonist)

information from D2 Cl Cl NN N N S S N NH N NH O O information from 5-HT2A ziprasidone ziprasidone

D2 Ki 4.8 nM (antagonist) 5-HT2A Ki 0.42 nM (antagonist) Figure A4. Design of ziprasidone with dual D2/5-HT2A antagonism.

The discovery of ziprasidone illustrated many challenges in the rational design of a polypharmacological drug. The dopamine hypothesis of schizophrenia suggested by Rossum in 1966 (van Rossum, 1966) proposes that the dopamine pathways in schizophrenia are hyperactive. After systematic research, evidence showed that nanomolar concentrations of FGAs prevented the binding of [3H]- dopamine (Seeman et al., 1974) and blocked dopamine D2 receptors (Seeman et al., 1975). Both post-mortem analysis (Seeman et al., 1984) and positron emission tomography (PET) imaging (Farde et al., 1986) revealing the abnormal elevation of D2 receptor density in the schizophrenic striatum further substantiated the dopamine hypothesis of schizophrenia. In parallel, serotonin

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(5-hydroxytryptamine or 5-HT) receptors joined dopamine as contributing factors to the atypical properties of SGAs. An animal study has shown that the administration of a selective 5-HT2A antagonist, MDL-100907 (Sorensen et al.,

1993), potentiated the ability of haloperidol (a D2 antagonist) on the dopamine release of medial prefrontal cortex, supporting the importance of 5-HT2A receptor antagonism in antipsychotic activities (Liegeois et al., 2002). With the information of the importance of dopamine D2 and serotonin 5-HT2A receptors on antipsychotics, Pfizer’s approach to modulating these two receptors informed by in vitro receptor-binding assays provided an example of targeted polypharmacology in antipsychotic drug discovery.

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Appendix E. GPCR Basics

G protein-coupled receptors (GPCRs), also known as seven transmembrane

(7TM) receptors, are encoded by more than 800 human genes and regulate nearly all physiological processes ranging from sensory systems such as vision, smell and taste, to regulations of behaviours, mood, immune system, inflammation, and nervous system. The Cell A communicates with the Cell B by secreting hormones or neurotransmitters which are detected by the GPCRs located in the plasma membrane of the Cell B followed by the activation of effector proteins within the Cell B to alter its function. Historically, the controversy and scepticism amongst academic community with regard to the existence of GPCRs did not cease (Ahlquist, 1973; Dale, 1943) until the direct studies on adrenergic receptors using radioligand binding techniques in the late

1970s (Williams and Lefkowitz, 1976), leading to the ternary complex model

(De Lean et al., 1980) as shown in Figure A5. In this model, the low-affinity form of the receptor refers to agonist-bound receptor (AR), whereas the high- affinity form of the receptor is a ternary complex of agonist, receptor, and the then “another membrane component (X)”. The ternary complex ARX along with guanine nucleotide (i.e. GTP) formation facilitate effector stimulation (E to E*). Later studies suggested that, in the classical role of the receptor, X is the guanine nucleotide regulatory protein (i.e. G protein) and E is the adenylate cyclase. This model also demonstrated the GPCR allostery—the binding of an agonist to the receptor enhances the affinity of G protein, as well as the binding of G protein to the receptor increases the affinity of an agonist.

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Figure A5. The ternary complex model explaining agonist binding to the β- adrenergic receptor (Lefkowitz, 2013) (reprinted with permission).

It was extraordinary that the purification of adrenergic receptors were done prior to their cDNA cloning. Thanks to affinity chromatography, the daunting projects on isolating (Dohlman et al., 1991) adrenergic receptors and validating their functions (Cerione et al., 1983) were completed in one decade. Subsequent cloning works for adrenergic receptors (Dixon et al., 1986; Kobilka et al., 1987) allowed the observation of the 7TM topology as the common structural feature of GPCRs. Site-directed mutagenesis (O'Dowd et al., 1988) and chimeric receptors (Kobilka et al., 1988) were utilised to confirm the extracellular loops of the β2-adrenergic receptor were responsible for the ligand specificity and the intracellular regions of the receptor were responsible for the specificity of G protein coupling. Large-scale production and purification of sufficient quantities of β2-adrenergic receptor (Kobilka, 1995) were later achieved, allowing biophysical studies of receptor structure by fluorescence spectroscopy

(Gether et al., 1995). The three-dimensional structures of inactive-state

(Rasmussen et al., 2007) and active-state (Rasmussen et al., 2011a; Rasmussen et al., 2011b) β2-adrenergic receptor were solved by X-ray diffraction. The attempts to capture active states of β2-adrenergic receptor by either X-ray

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APPENDICES diffraction (Rosenbaum et al., 2011), fluorescence (Yao et al., 2009) or NMR spectroscopy (Nygaard et al., 2013) revealed that agonist alone was not sufficient to stabilise the active conformation of the receptor, which is in agreement with the ternary complex model of receptor activation (Figure A5).

In addition to ligand-induced cell response, it was discovered that although cAMP was elevated when an agonist was added to the β-adrenergic-receptor- expressed cells, within one minute the cAMP returned to the basal level. With photoaffinity probes of β-adrenergic receptor, it was demonstrated that such desensitisation was associated with the receptor phosphorylation (Stadel et al.,

1983), leading to the later identification of GPCR kinases (Benovic et al., 1989) and arrestins (Lohse et al., 1990). Therefore, in the 1990s, the classical consensus in GPCR-mediated cellular regulation was that upon agonist binding to the receptor, the G protein was recruited and promoted signalling by second messenger systems. This signalling was terminated by phosphorylation of the receptor in the carboxyl terminus or in the intracellular loops by GPCR kinases, which in turn caused the binding of arrestins to interdict G protein coupling, resulting in receptor desensitisation. Interestingly, over the past decade, β- arrestin-mediated GPCR signalling was increasingly embraced, and this may represent a strategy in the discovery of novel therapeutic agents (Lefkowitz and

Shenoy, 2005). Figure A6 illustrates how a β-arrestin-biased agonist at the angiotensin II type 1 receptor (AT1R) may have chance to become an effective therapy for heart failure. Angiotensin II, acting as an unbiased agonist at AT1R, is a peptide hormone vasoconstrictor and increase the blood pressure.

Angiotensin receptor blockers (ARBs) are used to treat high blood pressure by blocking both G protein-mediated and β-arrestin-mediated pathways. Whilst G

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APPENDICES protein-mediated pathway is responsible for the hypertensive effect, the β- arrestin-mediated pathway provides benefits in cytoprotection and anti- apoptosis. Attempts were therefore made to find the biased ligand that blocks the vasoconstrictive effect mediated by G protein but promotes cardiac performance mediated by β-arrestin. TRV120027 was identified as an antagonist with respect to G protein and an agonist with respect to β-arrestin.

The cell culture experiments showed that TRV120027 selectively engaged β- arrestin2 with EC50 of 17 nM without any G protein activation (Figure A6). In rat experiment, TRV120027 reduced mean arterial pressure (MAP) (Violin et al., 2010). The intravenous infusion of TRV120027 in the first human study resulted in significant reduction of MAP in patients with elevated renin activity

(Soergel et al., 2013). The phase 2b acute heart failure (AHF) trials for

TRV120027 are currently ongoing. TRV120027 represents an excellent prototype of how the emerging paradigm of β-arrestin-mediated signalling translates into a possibly therapeutic drug (Figure A7).

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Figure A6. Biased agonism exemplified by angiotensin II receptor type 1. The dose-response curve is reproduced with permission (Violin et al., 2010).

Figure A7. TRV027 (i.e. TRV120027) was developed by Trevena, Inc. with a unique mechanism that selectively antagonises angiotensin II-stimulated G protein coupling in order to reduce blood pressure and fluid retention, whilst recruits β-arrestin in order to preserve cardiac output (Violin et al., 2013) (reprinted with permission).

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Apart from biased agonism, the increasingly complex GPCR pharmacology can be appreciated by GPCR dynamics (Manglik and Kobilka, 2014), GPCR allostery (Wootten et al., 2013) and GPCR oligomerisation (Ferre et al., 2014).

Although X-ray crystallography has provided static snapshots of the high- resolution GPCR structures with the two-state (i.e. inactive and active states) mechanism, it fails to trap ensembles of receptor conformations caused by different ligands. To study the structural plasticity of GPCRs and better understand how a given agonist stabilises specific receptor conformations to trigger a distinct or a subset of signalling pathways, biophysical tools such as double electron-electron resonance (DEER) spectroscopy (Altenbach et al.,

2008) and NMR spectroscopy (Nygaard et al., 2013) have been harnessed to confirm the conformational dynamic properties at the physiological pH. The elucidation of the dynamics in structural elements at the extracellular surface

(Bokoch et al., 2010) and intracellular loops (Kahsai et al., 2011) of GPCRs provides insights into the design of pathway-selective therapeutic agents.

Allosteric modulation of GPCRs refers to the binding of a ligand at the site which is topographically distinct from the orthosteric binding site, and could either enhance or decrease the affinity of the orthosteric agonist. Given the high structural homology in the orthosteric binding pockets amongst subtypes of a receptor class, modulators targeting the allosteric binding site within which there is relatively greater structural variance in the amino acid sequence open up new avenues for novel subtype-selective ligands. Recently, the high- resolution structures of one muscarinic acetylcholine receptor (M2 subtype) bound to a positive allosteric modulator (Kruse et al., 2013), and one metabotropic glutamate receptor (subtype 1) in complex with a negative

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APPENDICES allosteric modulator (Wu et al., 2014), were determined, and it is anticipated that the allosteric structural information will facilitate the discovery of more efficacious and safer drugs in the near future. Whilst GPCRs are traditionally viewed as monomers, where agonist binding to the receptor allosterically causes conformational change of the cytoplasmic regions, resulting in the engagement of G protein, a large body of evidence coming from biophysical studies (by using e.g. atomic force microscopy, single-molecule total internal reflectance fluorescence microscopy, and fluorescence correlation spectroscopy) over the past two decades supports the existence of higher-order GPCR homomers and heteromers in the native membrane (Bouvier, 2001). Receptor homomers

(heteromers) are defined as the complex, composed of at least two identical

(different) receptor units, with each unit being able to function on its own, that possesses unique physiological functions distinct from what can be achieved by either individual component (Ferre et al., 2009). To date, it has been confirmed that family C GPCRs (e.g. metabotropic glutamate receptors) form dimers for activation (Kniazeff et al., 2011). GPCR oligomerisation, especially heteromerisation with unique localisation, has significant pharmacological implications. Heteromer-selective antibodies have been developed for δ-µ- opioid receptor heteromers (Gupta et al., 2010), AT1-CB1 receptor heteromers

(Rozenfeld et al., 2011), δ-opioid-CB1 receptor heteromers (Bushlin et al.,

2012), and δ-κ-opioid receptor heteromers (Berg et al., 2012) to demonstrate the localisation of receptor heteromers in native tissues. In some cases, receptor heteromers are disease-specific and are only up-regulated during diseases, thus making them possible drug targets. For instance, compounds acting at

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APPENDICES

angiotensin AT1 receptor-bradykinin B2 receptor heteromers, but without affecting the homomers, can be used to treat asthma (AbdAlla et al., 2005).

In an analysis of drugs and their targets from the DrugBank database (Law et al., 2014), it was identified that approximately 36% of drugs target GPCRs

(Rask-Andersen et al., 2011). GPCRs represent ideal drug targets because they have ligand-binding sites on the outer plasma membrane and they can adopt several active conformations to trigger different cell signalling pathways.

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Appendix F. Basal Ganglia Motor Circuit

As shown in Figure A8, the basal ganglia in the vertebrate brain is composed of numerous complex subcortical nuclei with the primary afferent component being striatum, and it plays important roles in movement (Graybiel, 2005).

“Disorders of basal ganglia”, which is a synonym for “movement disorders” termed by neurologists, are categorised into hyperkinetic disorders characterised by uncontrollably rapid acts (e.g. chorea, ballism and tic), hypokinetic disorders featured by akinesia and rigidity (e.g. Parkinson’s disease), and dystonia consisting of involuntary muscle contractions (Albin et al., 1989). Striatum, as the input nucleus of the basal ganglia, receives inputs from the cerebral cortex by excitatory glutamatergic neurons. The output nuclei of the basal ganglia, which is composed of the internal segment of the globus pallidus (GPi) and substantia nigra pars reticulate (SNr), receives information from striatum via a monosynaptic direct pathway that promotes movement, and a polysynaptic indirect pathway that suppresses movement and includes the external segment of the globus pallidus (GPe) and the subthalamic nucleus

(STN). In the normal brain, the dopamine released from substantia nigra pars compacta (SNc) through dopaminergic neurons display dual effects on striatal

GABAergic neurons through different subtypes of dopamine receptors

(Beaulieu and Gainetdinov, 2011). The GABAergic neurons which project to

GPi/SNr are enriched in dopamine D1 receptors, muscarinic M4 receptors, dynorphin and substance P, whereas the GABAergic neurons which project to

GPe are enriched in dopamine D2 receptors, adenosine A2A receptors, and encephalin (Valjent et al., 2009). The binding of dopamine to the D1R- expressing GABAergic neurons increases the activity of the direct

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(striatonigral) pathway, whereas the binding of dopamine to the D2R-expressing

GABAergic neurons decreases the activity of the indirect (striatopallidal) pathway. That the same ligand (i.e. dopamine) induces opposing cell responses mediated by distinct receptor subtypes (i.e D1R and D2R) at the striatal signalling for proper motor function is in agreement with the observation that

D1R, rather than D2R, positively coupled to adenylyl cyclase (Spano et al.,

1978). The inhibition of GPi/SNr via input from the direct and indirect pathways causes paused inhibition of thalamocortical and brainstem neurons, hence facilitating movement. However, in the parkinsonian brain, the decreased dopamine input to the striatum results in the increase in GPi/SNr activity, and the resultant excessive reduced thalamocortical neurotransmission leads to motor suppression. To continue the cell responses mediated by either D1R or

D2R activation, dopamine replacement therapies, such as L-DOPA and dopamine agonists, have been used to alleviate PD motor symptoms.

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Figure A8. The traditional (simplified) model (Albin et al., 1989) of basal ganglia motor circuit in the normal brain. Green projections are glutamatergic neurons which have excitatory effects on the neurons they project to, and red projections are GABAergic neurons which have inhibitory effects on the neurons they project to. Blue projections are dopaminergic nigrostriatal neurons. GPi indicates internal segment of the globus pallidus; GPe, external segment of the globus pallidus; SNr, substantia nigra pars reticulate; SNc, substantia nigra pars compacta; and STN, subthalamic nucleus.

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Appendix G. Virtual Screening

Since the 1990s, pharmaceutical companies have made substantial investments on high-throughput screening (HTS) technology for the identification of potential compound hits. This became more obvious after the completion of

Human Genome Project (HGP) in 2003, as numerous unprecedented therapeutic targets revealed by HGP have no known ligands. Multi-well microtiter plates, fluorescence detection systems, and automation robots have been developed to screen millions of in-house compound libraries (Smith, 2002) to search for active compounds for an individual or a subset of targets. In a study on 58 drugs approved between 1991–2008 , it was shown that 19 drugs were discovered by

HTS (Perola, 2010). Recently, there were 12 drugs, such as chemokine receptor antagonists, thrombopoietin receptor agonists, and hepatitis C virus NS5A inhibitors, which got approval by FDA in 2003–2009 with origins in HTS endeavours (Macarron et al., 2011). Virtual screening (VS), which refers to the selection of potentially active compounds from a much larger chemical space compared to HTS using in silico approaches, complements HTS in hit identification, and offers many more possibilities than HTS on compound novelty. The term “virtual screening” was coined in 1997 in a search of inhibitors of trypanothione reductase by screening a database of 2500 molecular sketches, leading to one structurally distinct compound (Horvath, 1997). Since then, different VS methods have been developed, used and compared. VS shares the goal with HTS in screening compound libraries and identifying novel hits, but distinguishes from HTS in two advantageous aspects. First, whilst HTS aims to test experimentally a large number of physically existing compounds, VS leverages computational methods in the absence of tangible compounds at hand,

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APPENDICES and therefore it represents a cost-effective screening tool. Second, VS has the capacity to expand the chemical space beyond the possible in-house compound pool owned by any pharmaceutical company, or any commercially available library provided by external vendors, with an example of chemical universe database GDB-17 containing 166.4 billion drug-like compounds (Ruddigkeit et al., 2013). Figure A9 shows that VS overcomes the limitation of HTS in terms of the number of compounds screened.

Figure A9. Estimated number of compounds in various sources (Boehm, 2011) (reprinted with permission).

VS is typically divided into structure-based VS (SBVS) which utilises the three- dimensional structure of a given drug target (Ripphausen et al., 2012), and ligand-based VS (LBVS) which depends on the similarity of reported ligands at a particular drug target of interest (Ripphausen et al., 2011) (Figure A10). The two main advantages of SBVS are the structural novelty of the hits because the

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APPENDICES process does not involve the use of any pre-existing ligands, as well as the facilitation of lead optimisation through ligand-receptor complex. However,

SBVS is plagued with inevitable false-positives and false-negatives. The SBVS process is composed of two different stages: (i) the docking stage, with the attempt to obtain the accurate prediction in silico of the pose of a compound within the binding site; (ii) the scoring stage, where the in silico generated complexes are assessed by scoring functions to predict the likelihood of the ligand to actually bind to the target. Docking algorithms (Jain, 2008) and scoring functions require a compromise between prediction accuracy and time required for computation. The general consensus is that there is no perfect docking programme which excels others in all performances (e.g. docking accuracy, hit enrichment), and that the overall docking performance depends on the nature of the target protein. The challenge of the scoring function lies in the explanation of solvent effects and entropy contributions (Warren et al., 2006).

Top-ranking compounds are then selected for experimental testing. Importantly, drug-relevant GPCR crystal structures were increasingly determined by X-ray crystallography over the past seven years (2007–2014), increasing opportunities for structure-based discovery of new scaffolds to complement previous ligand- based approaches in GPCR drug discovery (Congreve et al., 2014). In contrast to SBVS, LBVS starts with the use of one or more active compounds as templates, and no details about the target are needed. In general, LBVS depends on computational descriptors or pharmacophore features, and analyses relationships between known actives and compounds in the database. It is computationally efficient and can rapidly search very large databases. It is therefore often used to sequentially filter large compound sets before more

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APPENDICES complex tools are applied. There are literally thousands of different descriptors, which are derived from the two-dimensional (2D) or three-dimensional (3D) distribution of atomic properties in compounds, or from the presence or absence of specific structural elements. In LBVS, many methods exist for the comparison of the similarity of compounds based on these descriptors—shape comparison (Hawkins et al., 2007), pharmacophore searches (Wolber et al.,

2008), and field-based similarity methods (Moffat et al., 2008). If large sets of active and inactive compounds are known, machine learning (ML) methods, which refer to nonlinear supervised learning methods such as kernel discrimination, support vector machines (SVMs), and naïve Bayesian classifiers, can be used to train models that distinguish active from inactive compounds based on their specific structural features. ML methods are capable of generating complex mappings from descriptors without restrictions on structural frameworks and without prior knowledge of structure-activity relationships (SARs) (Chen et al., 2007). On the whole, the hit lists obtained from SBVS and LBVS are generally complementary, not redundant (Krüger and Evers, 2010).

Figure A10. The classification of virtual screening. Reprinted with permission from (Ripphausen et al., 2010). Copyright (2010) American Chemical Society.

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In the thesis, only LBVS approaches will be used due to the following considerations. First, although there have been several antagonist-bound A2A receptor structures determined to date (Congreve et al., 2012; Dore et al., 2011;

Jaakola et al., 2008), a high-resolution crystal structure of D2 receptor is not available yet. In 2010, a crystal structure of D3 receptor in complex with an antagonist was solved (Chien et al., 2010). In spite of the high sequence homology between D3 and D2 receptors, the antagonist-bound D3 receptor structure represents an inactive state of the D3 receptor which cannot fully capture the precise active conformations of D2 receptor. The fully activated

GPCR conformations trapped by X-ray crystallography are heretofore reported only for adrenergic β2 receptor (Rasmussen et al., 2011), adenosine A2A receptor

(Xu et al., 2011), and muscarinic M2 acetylcholine receptor (Kruse et al., 2013).

Second, even though D2 receptor was available, virtual screening relying on

GPCR protein structures remains a challenge with much uncertainty. For instance, a molecular docking screen of 6.7 million compounds against agonist- bound A2A crystal structures in an attempt to discover novel A2A agonists was conducted recently (Rodríguez et al., 2015). After filters and prioritisation, 20 compounds were selected for experimentally testing, and intriguingly, none of them activated A2A receptor. This contrasts with using the antagonist-bound A2A receptor for molecular docking, which yielded some novel and functional chemotypes (Carlsson et al., 2010; Katritch et al., 2010). The disparity between the docking results of A2A agonists and A2A antagonists implies the more complex conformational ensembles in receptor activation compared with antagonism.

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Appendix H. Classification of Adenosine A2A Receptor Antagonists

Reported adenosine A2A antagonists are generally classified into xanthine-based

(Figure 12) and non-xanthine-based derivatives, such as those with the core structure being nitrogen-containing mono-heterocyclic, fused bi-heterocyclic

(Figure A11), and fused tri-heterocyclic derivatives (Figure A12) (Manera and

Saccomanni, 2010). Of numerous reported A2A antagonists, many showed promising results in preclinical studies (de Lera Ruiz et al., 2014), and some of them are undergoing clinical trials (Pinna, 2014)—tozadenant in phase 2b

(Hauser et al., 2014), PBF-509 (structure undisclosed) in phase 1, and V81444

(structure undisclosed) in phase 2a. Most importantly, KW-6002 (Figure 16), one of the xanthine-based A2A antagonists, was approved in Japan in March of

2013 for the treatment of PD.

Mono-heterocyclic O 1,2,4-triazole pyrazine N N 3 4N N2 H2N N 5 N H1 F ASP5854 O Ki(rA2A) = 20 nM Ki(hA2A) = 1.8 nM

Bi-heterocyclic benzothiazole purine [1,2,4]triazolo[1,5-a]pyrazine O H NH2 N N S N N N N O O H S O N N N N N NH N N O NH2 HO OMe O OH

tozadenant VER 6947

Ki(hA2A) = 5 nM Ki(hA2A) = 1.1 nM Ki(rA2A) = 1 nM Figure A11. Representative nitrogen-containing mono-heterocyclic and bi- heterocyclic compounds as adenosine A2A receptor antagonists.

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APPENDICES

[1,2,4]triazolo[1,5-c]quinazoline [1,2,4]triazolo[4,3-a]quinoxaline

NH2 NH2 N O N N N N N N N

Cl Cl CGS 15943 CP 66713

Ki(hA2A) = 0.43 nM Ki(rA2A) = 21 nM

pyrazolo[4,3-e][1,2,4]triazolo[1,5-c]pyrimidine indeno[1,2-d]pyrimidine NH 2 2HCl O NNN O N N N N N N NH2 SCH 442416 O K (hA ) = 0.5 nM K (hA ) = 4.1 nM i 2A i 2A Figure A12. Representative nitrogen-containing tri-heterocyclic compounds as adenosine A2A receptor antagonists.

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Appendix I. Collection of Reported Adenosine A2A Receptor Antagonists. paper paper no. of no. of functi- code binders onal binders 1 Chem. Rev. 2008, 108, 238-263 17 0

2 J. Med. Chem. 2010, 53, 3748-3755 26 2

3 Nat. Rev. Drug Discov. 2006, 5, 247- 4 0 264

4 Curr. Pharm. Des. 2008, 14, 1525-1552 25 0

5 ChemMedChem 2009, 4, 1010-1019 16 0

6 Curr. Top. Med. Chem. 2010, 10, 902- 7 0 922

7 Expert Opin. Ther. Pat. 2010, 20, 987- 11 0 1005

8 Handb. Exp. Pharmacol. 2009, 193, 59- 6 0 98

9 J. Med. Chem. 2010, 53, 8104-8115 1 1

10 J. Med. Chem. 2004, 47, 6218-6229 76 3

11 J. Med. Chem. 2004, 47, 4291-4299 40 20

12 J. Med. Chem. 2008, 51, 4449-4455 35 1

13 Bioorg. Med. Chem. Lett. 2007, 17, 19 17 1376-1380

14 J. Med. Chem. 2011, 54, 751-764 24 2

15 J. Med. Chem. 2000, 43, 1158-1164 37 0

16 J. Med. Chem. 2005, 48, 2009-2018 54 10

17 J. Med. Chem. 2010, 53, 1799-1809 56 10

18 J. Med. Chem. 2002, 45, 3440-3450 36 8

19 J. Med. Chem. 2008, 51, 400-406 7 3

20 J. Med. Chem. 2008, 51, 1719-1729 15 0

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APPENDICES paper paper no. of no. of functi- code binders onal binders 21 Bioorg. Med. Chem. Lett. 2008, 18, 56 3 5402-5405

22 J. Med. Chem. 2008, 51, 7099-7110 27 15

23 J. Med. Chem. 2008, 51, 1730-1739 22 2

24 Bioorg. Med. Chem. 2010, 18, 2491- 11 3 2500

25 Bioorg. Med. Chem. Lett. 2009, 19, 27 15 2664-2667

26 Bioorg. Med. Chem. 2009, 17, 6590- 70 5 6605

27 Bioorg. Med. Chem. 2006, 14, 2697- 49 0 2719

28 Bioorg. Med. Chem. Lett. 2010, 20, 37 10 6845-6849

29 Bioorg. Med. Chem. Lett. 2010, 20, 29 2 5241-5244

30 Bioorg. Med. Chem. Lett. 2010, 20, 16 8 3768-3771

31 Bioorg. Med. Chem. Lett. 2010, 20, 34 22 2864-2867

32 Bioorg. Med. Chem. Lett. 2010, 20, 36 20 2868-2871

33 Bioorg. Med. Chem. Lett. 2010, 20, 26 10 1090-1093

34 Bioorg. Med. Chem. Lett. 2010, 20, 12 12 1214-1218

35 J. Med. Chem. 2009, 52, 33-47 58 8

36 Bioorg. Med. Chem. Lett. 2009, 19, 32 6 1399-1402

37 Bioorg. Med. Chem. Lett. 2009, 19, 32 14 967-971

318

APPENDICES paper paper no. of no. of functi- code binders onal binders 38 Bioorg. Med. Chem. Lett. 2009, 19, 19 0 378-381

39 Bioorg. Med. Chem. Lett. 2008, 18, 17 9 4199-4203

40 Bioorg. Med. Chem. Lett. 2008, 18, 25 12 4204-4209

41 Bioorg. Med. Chem. Lett. 2008, 18, 67 40 2924-2929

42 Bioorg. Med. Chem. Lett. 2008, 18, 31 1 1778-1783

43 Bioorg. Med. Chem. Lett. 2008, 18, 20 0 1269-1273

44 Bioorg. Med. Chem. Lett. 2005, 15, 20 0 2119-2122

45 Bioorg. Med. Chem. Lett. 2005, 15, 21 0 1333-1336

46 Bioorg. Med. Chem. Lett. 2005, 15, 26 6 511-515

47 Bioorg. Med. Chem. 2003, 11, 5509- 21 0 5518

48 J. Med. Chem. 2009, 52, 709-717 35 1

49 Bioorg. Med. Chem. Lett. 2008, 18, 39 20 2920-2923

50 J. Med. Chem. 2002, 45, 115-126 25 0

51 Bioorg. Med. Chem. Lett. 2007, 17, 27 1 1659-1662

52 J. Med. Chem. 2005, 48, 6887-6896 9 1

53 Bioorg. Med. Chem. Lett. 2005, 15, 40 9 3670-3674

54 Bioorg. Med. Chem. Lett. 2005, 15, 13 11 3675-3678

319

APPENDICES paper paper no. of no. of functi- code binders onal binders 55 J. Med. Chem. 2003, 46, 1229-1241 28 6

56 Bioorg. Med. Chem. Lett. 2004, 14, 24 3 4831-4834

57 Bioorg. Med. Chem. Lett. 2008, 18, 25 0 2916-2919

58 Bioorg. Med. Chem. Lett. 2010, 20, 13 0 5690-5694

59 Curr. Opin. Drug Discov. Develop. 45 0 2010, 13, 466-480

60 J. Med. Chem. 2011, 54, 877-889 40 0

61 Bioorg. Med. Chem. 2009, 17, 3618- 19 0 3629

62 Bioorg. Med. Chem. 2006, 14, 7258- 40 0 7281

63 Bioorg. Med. Chem. Lett. 2011, 21, 28 4 2497-2501

64 Bioorg. Med. Chem. Lett. 2006, 16, 16 0 5993-5997

65 Bioorg. Med. Chem. Lett. 2005, 15, 44 0 4809-4813

66 Bioorg. Med. Chem. Lett. 2004, 14, 12 4 4835-4838

67 Bioorg. Med. Chem. Lett. 2004, 14, 36 0 817-821

68 Med. Chem. Commun. 2011, 2, 950-965 57 57

69 ACS Chem. Neurosci. 2011, 2, 526-535 1 1 total 1969 418

320

APPENDICES

a Appendix J. Major Scaffolds of A2A Antagonists Reported

NH2 O R3 N O R1 NN N N R4 R N X O N N Y 2 m X, Y= N, C R n m= 1,2 (1, 4) n= 1,2,3 (1, 9) Ar N R O R3 HN O 1 R N HN N N R4 NH + N 2 N N (2, 8) O N N R2 H2N N NH (4, 11) O Ar (3, 2) NH2 NH2 NH2 N N N N N O R1 R1 X N R2 N N R2 N N R Y N (4, 6) (5, 9) (10, 24) NH2 NH2 N O X N X N N O N N Y N Y N H 1 N R N R2 N (10, 45) R (10, 7) 1 NH2 R R1 N N N N O N N 2 N N R N NH2 N (12, 9) R R2 NNH (11, 40) 2 (12, 23) NH2 NH2 R' N O NNN O NN R N Ar N N N N N N N N N (13, 11) (13, 8)

321

APPENDICES

R' S O R1 O NH 1 R3 R N N N O N N H N N N N R R1 N R N R O 2 H O R (15, 23) (15, 11) (14, 14) NH2 NH2 N N N O N N N O O N N diamine N N N N N (16, 18) R (16, 4) NH 8 2 R N R1 N N N O N R1 N 7 N R N R2 N N N N 2 N H R O N N N 4 R O N R (16, 32) R4 (18, 2) (18, 12) O 7 H H R R2 N R3 R2 N R1 N N N R8 NNO NNO N N O N 1 1 3 R R R (19, 5) (20, 16) (18, 6) H R2 R N N H 1 NNO H N N N R N R1 N O N N NNO R N (22, 13) (21, 14) N

(21, 14) R N N N N H H N N N N N N R N S S N O NNO N O N O (24, 12) R2 N R3 (23, 22) (22, 11)

322

APPENDICES

Ar O O X N O2N O N N O Y NH2 O Ar N N NH2 Ar NH H N NH2 (25, 7) Ar NH X, Y= C, N (25, 24) (26, 72) O O N R3 R1 N O O NH NH N 2 X 1 S R2 HN R R2 R O R1 X=CH, N (29, 29) (30, 8) (28, 37) O O O O O Ar Ar

2 HN R2 R N R N R1 N N N NH2 NH2 (31, 34) (32, 36) O (30, 8) O O R1 N X O O N O NH S N HN R2 R S N R2 1 O H R NH (34, 14) X=O, S (33, 21) (33, 5) 3 Ar R NH2 N X N N NNN O Y 2 O Z R N N N NH2 N N X H 1 R R n X=CH, N (36, 32) R Y=CH, N n=1~3 Z=N X=O, NH, S, CH2 (35, 58) (37, 10) NH2 O R2 Ar N S N NN N R 1 N R N X N (37, 20) O (38, 19)

323

APPENDICES

NH2 NH2 O NNN O NNN O Ar N N N N R N N N N N N R2 (39, 17) (40, 25) R1 (41, 10) 2 2 N R R H X N R N X N S O Y Y N NNO N NH2 N N R3 R1 HN (41, 38) O O R1 (41, 19) (42, 7) R3 H N R2 N H N O X 2 N N 1 R NNO R N NNO NH 1 R O (42, 26) O

(43, 11) O (43, 4) NH2 NH2 N H NN NN N N 1 R O R2 R 1 NNO N R X N N (44, 20) (45, 21)

(43, 5) NH2 NH2 H R1 N N N O N N 1 N NNO N N R N N R O N (46, 26) R2 (47, 21) (48, 21)

324

APPENDICES

R2 Ar N H S N N N N R N 1 R NNO N N R (49, 39) N N N O

(48, 3) (48, 6) NH2 NH2 N O NN NNN N N O N R N R4 n N 3 N R (51, 22) R1 R2 n=2, 3 (50, 25) NH NH O 2 2 NNN N N N O Ar N N N N N N R N N N (52, 9) (51, 8) NH2 NH2 N NNN NN N O Ar N O R (53, 23) (53, 17) NH2 NH2 N NN NNN X R N O N N N (54, 13) (55, 8) 1 R NH2 HN X N N O NNN O N Y N N N N R1 2 N R 3 R2 R X, Y=N, CH (55, 17) (56, 24)

325

APPENDICES

2 O R R O NH2 N N N S N NH2 R1 N N O N R1 NNN O (60, 40) (57, 25) N N N (58, 13) O 1 O H R R1 N N N N n O O N N O N O N R2 3 R1 2 HN R R (61, 19) n=1~3 (62, 40) NH 1 2 O NH2 R N NH2 NN N N N 2 1 N R N R N N S R2 R2 N (64, 9) (63, 28) R1 (64, 3) F NH NH F F 2 2 N N N N N N N N O N O Ar R1 R N NH (65, 26) (65, 10)

F (64, 4) NH2 NH2 Ar N O N N N N N R2 N N N O N N N O n R (65, 8) n=1,2 (66, 12) H R N N Cl R N N S N N R N Cl H N (67, 12) H MeO (67, 4) (67, 14)

326

APPENDICES

NH2 N R' N S O N R'' R (68, 57) aThe (m, n) below each scaffold denotes (paper code, number of compounds).

327

APPENDICES

Appendix K. Collection of Reported Dopamine D2 Receptor Agonists paper paper no. of no. of functi- code binders onal binders 1 Chem. Rev. 2007, 107, 274-302 6 0

2 Pharmacol. Ther. 2010, 128, 229-273 7 15

3 ChemMedChem 2010, 5, 232-246 28 16

4 J. Med. Chem. 2010, 53, 2510-2520 8 4

5 J. Med. Chem. 2010, 53, 2114-2125 6 2

6 J. Med. Chem. 2009, 52, 4923-4935 7 1

7 J. Med. Chem. 2008, 51, 7806-7819 21 3

8 J. Med. Chem. 2009, 52, 6860-6870 1 0

9 Bioorg. Med. Chem. Lett. 2011, 21, 27 16 2621-2625

10 Bioorg. Med. Chem. Lett. 2010, 20, 64 23 5666-5669

11 Bioorg. Med. Chem. Lett. 2010, 20, 15 8 2983-2986

12 Bioorg. Med. Chem. Lett. 2010, 20, 1 0 2888-2891

13 Bioorg. Med. Chem. Lett. 2010, 20, 24 14 2013-2016

14 Bioorg. Med. Chem. Lett. 2009, 19, 21 6 5552-5555

15 J. Med. Chem. 2007, 50, 171-181 3 3

16 J. Med. Chem. 2005, 48, 4153-4160 8 8

17 J. Med. Chem. 2005, 48, 2646-2654 25 7

18 J. Med. Chem. 2004, 47, 3853-3864 3 2

19 J. Med. Chem. 2003, 46, 4136-4140 1 1

328

APPENDICES paper paper no. of no. of functi- code binders onal binders 20 J. Med. Chem. 2003, 46, 3210-3220 7 7

21 J. Med. Chem. 2002, 45, 3022-3031 15 1

22 J. Med. Chem. 2002, 45, 2349-2351 1 1

23 J. Med. Chem. 2000, 43, 3549-3557 4 3

24 J. Med. Chem. 2000, 43, 3005-3019 18 16

25 J. Med. Chem. 2000, 43, 599-608 2 1

26 J. Med. Chem. 1999, 42, 2007-2020 41 4

27 J. Med. Chem. 1998, 41, 4915-4917 6 6

28 J. Med. Chem. 1998, 41, 4385-4399 15 13

29 J. Med. Chem. 1998, 41, 760-771 27 3

30 J. Med. Chem. 1997, 40, 4235-4256 73 12

31 J. Med. Chem. 1997, 40, 250-259 19 4

32 J. Med. Chem. 1994, 37, 4251-4257 15 12

33 J. Med. Chem. 1994, 37, 3523-3533 18 1

34 J. Med. Chem. 1991, 34, 3235-3241 8 1

35 J. Med. Chem. 1990, 33, 3122-3124 8 1

36 J. Med. Chem. 1990, 33, 1800-1805 1 1

37 J. Med. Chem. 1990, 33, 39-44 9 2

38 J. Med. Chem. 1987, 30, 1166-1176 12 7

39 J. Med. Chem. 1984, 27, 1607-1613 1 1

40 Bioorg. Med. Chem. 2010, 18, 7085- 12 6 7091

41 Bioorg. Med. Chem. 2009, 17, 4756- 10 4 4762

42 Bioorg. Med. Chem. 2008, 16, 3438- 2 1 3444

329

APPENDICES paper paper no. of no. of functi- code binders onal binders 43 Bioorg. Med. Chem. 2006, 14, 1918- 17 1 1923

44 Bioorg. Med. Chem. 2004, 12, 4361- 11 9 4373

45 Bioorg. Med. Chem. 2003, 11, 4807- 3 2 4813

46 Bioorg. Med. Chem. 1999, 7, 2453-2456 24 24

47 Bioorg. Med. Chem. Lett. 1999, 9, 12 12 2593-2598

48 Bioorg. Med. Chem. Lett. 1998, 8, 295- 16 4 300

49 Bioorg. Med. Chem. Lett. 1993, 3, 639- 8 1 644

50 Bioorg. Med. Chem. Lett. 1991, 1, 539- 2 1 544

51 Tetrahedron 1998, 54, 7081-7108 7 0

52 Bioorg. Med. Chem. Lett. 1998, 8, 9 1 2675-2680

53 Bioorg. Med. Chem. Lett. 2002, 12, 6 5 271-274

54 Eur. J. Pharmacol. 2006, 552, 36-45 7 7

55 Expert Opin. Ther. Patents 2006, 16, 11 3 587-630

56 Org. Biomol. Chem. 2005, 3, 4077-4081 4 2

57 Org. Lett. 2005, 7, 3239-3242 2 1

58 Org. Process Res. Dev. 2009, 13, 555- 1 1 566

59 Clin. Endocrinol. 2000, 53, 53-60 2 2

330

APPENDICES paper paper no. of no. of functi- code binders onal binders 60 J. Pharmacol. Exp. Ther. 2002, 302, 1 0 381-389

61 Pharm. Res. 2003, 20, 1619-1625 1 1

62 Eur. J. Pharmacol. 2006, 552, 36-45 1 0

63 J. Pharmacol. Exp. Ther. 1989, 250, 1 1 227-235

64 J. Pharmacol. Exp. Ther. 1996, 276, 41- 1 0 48

65 J. Med. Chem. 1997, 40, 639-646 9 2

66 J. Pharmacol. Exp. Ther. 2010, 332, 1 1 190-201

67 J. Pharmacol. Exp. Ther. 2008, 326, 1 1 930-938

68 J. Pharmacol. Exp. Ther. 2004, 309, 1 1 903-920

69 Eur. J. Med. Chem. 2011, 46, 2992- 7 7 2999

70 Bioorg. Med. Chem. 2011, 19, 3502- 15 2 3511

71 J. Med. Chem. 2011, 54, 4324-4338 29 3

Total 810 332

331

APPENDICES

a Appendix L. Major Scaffolds of D2 Agonists Reported

R2 R1 OH R2

N R3 N H N HN (3, 5) R1 (2, 2) (3, 2) R3 R2 R H N NN

R1 (4, 2) (4, 2) 1 R1 N R R3 N N 2 5 R2 N R N N N R R3 R4 (5, 3) (5, 3) O N n N N N NN H N N N Cl n=1 or 5 X (6, 2) (6, 2) R2 3 H R N N N N R4 R1 N N O

(7, 4) (6, 2) R2 R N N N H2N N S N R1 N N

R3 (7, 3) (7, 6)

332

APPENDICES

R4 N R2 N Y 3 R1 N R Z X N O n H N (9, 27)

N R1 N O N O n H N R2 O HN n=0 or 3 (7, 7) O (11, 3) X H NH F N N O Y N n N Ar H N O NH H n=1,2 (10, 64) O (14, 2) H H X N O D N O 3 2 L R R4 O N R2 N R (11, 6) (11, 4) N N n R1

n=2,3 (13, 14) Cl F O O N nH N N X N O NH Y H Z (13, 3) O (14, 8) F

n N F N NH n A N B D N O H N O H NH (14, 7) O (14, 4)

333

APPENDICES

R1 R1 R2

N N R2 R1 R3 H X R2 N HO

HO (15, 3) (16, 3) (16, 5) R R2 O N S 3 H R1 R HN N X Y O HO (20, 7) (17, 24) R1 O N O N R HO N Ar S S (21, 2) R2 (21, 2) (21, 4) R1 R1 R2 R4 HO R2 N 2 R3 N R R3 R5 HO (24, 3) R1 (21, 3)

R3 (24, 5) 3 R1 R OH R1 HO N

H N 2 HO N R N O R2 H H H R1 HO HN (24, 2) R2 (24, 2) R1 (25, 2) (24, 4) X X

HN O HN O 2 N 1 N Y R R R2 R1 O (26, 17) (26, 29)

334

APPENDICES

O R1 S H HO HN N 2 YX N R HO HO (27, 3)

R3 (28, 5) R4 R3 1 R N 5 R R N N R2 R3 R2 N H (28, 3) n n=0~3 HN (29, 10) R1 (28, 3) N H H N O N O NH X O R N NH W 2 (30, 20) (30, 8) R1 N R2 (29, 16) Y V R2 R3 X H R1 N R1 HO O R N N X O n (30, 16) n R2 n=1~4 N (30, 26) Y n=0~2 (31, 17) O

HN R2 Ar H X N N N H n (33, 5)

HN n=0~2 R1 (33, 6) (32, 3)

335

APPENDICES

1 Ar R OH N R2 HO N N (33, 8)

R3 (34, 3)

R (34, 4) X R1 R2 R2 R1 H N H N N R HO R1 X O O HO R2 N (37, 9) H R (35, 8) R4 NN

(38, 12) (40, 11) R R2 R3 R2 N N Q R1 H H OH R3 N N OH HO N R4 n (41, 10) R1 (43, 17) n=1, 3 (44, 8) OH O N N X X HN N N N X O X Y HN Z (46, 3) N HN Y (45, 3) (48, 4)

(46, 20) X Ar O HN N R N Z X HN (48, 12) HN O N Ar n

n=0~2 Y (49, 8) (47, 11)

336

APPENDICES

NH R 2 HN N R N S HO O O X N N N X O H HN N R (51, 4) N (50, 2) H Y (55, 7) (52, 3) R N O

HN NH R R X N X HN O HN N HN O O Y NH (52, 5) (53, 5) n N

(55, 2) X R n=1,2,3 (70, 15) R F R1 R2 N S N R2 R1 H HO OH HO N N H HN OH R 3 (57, 2) R (65, 9) OH H N (69, 7) (56, 4)

O R

(71, 10) aThe (m, n) below each scaffold denotes (paper code, number of compounds).

337

APPENDICES

Appendix M. Hits Identified from PubChem and MDDRa

P2 P3 F F S F N N

N P5 N P6

F P4

P8 O

N N

N O N P9

N P11

HN Cl P10

338

APPENDICES

P13 P14

P12

N O

N P17 P18 O N P16 HN O

P15

O P20 N HN O P19 NH

P21

339

APPENDICES

NH2 N O N N HO N O F O F NH P24 N HN O F F NH P22 P23

Cl P25

HO HN F F N NH HON O F F P26 F P27 N

N O

P28 NH2 NH2

O O HO P29 P32 HO P31 P30

340

APPENDICES

HN Cl

O NH N NNH N 2 O N F N O O P34 N O N HN O P36

NH P33 P35

P39 P38

P37

P42

P41 P40 O

N NH NH NH HN HN HN O O O O N O O N O N

P43 N N N

N N N

P46 P45 P44

341

APPENDICES

F N

N NH N HN N NNH2 O O N N P48 N O P47 N

P49 N

Cl P50 NH

OON

P51 H H P52 P54

P53 HN NH

HN N N N

HON O N N

N N N O O S N S O O P55 P57

P56

342

APPENDICES

HN N

HON O

N P58 P60 P61 N

N+ O O- P59

N N N+ N+

O O HN NH

OO O O P65 P63 P64 P62

P69 P66 P67 P68

N N N + + N+ N N O O O HN HN HN Cl N F P71 F N P73 F P72 P70

343

APPENDICES

N N N N + + + N+ N N N O O O O HN HN HN HN Cl Cl Cl Cl F P76 P77 F N F P75 P74

N N N N N+ + N+ N N+ O Cl O HN O O Cl HN HN NH Cl Cl Cl P78 Cl Cl N P80 O O P79 P81

N N N N+ N+ N+ O O O N NH HN

N O H P82 O O P84 OO

P83 P85

N N N N+ N+ N+

O O O HN NH NH S

N P86 OO O O P89 P87 P88

344

APPENDICES

HN HN

N N N N N N

O O N N

M2 M3 M1 H H2N N N N NH N N N O N N N F F N NN N N F F O HN O N

HN OH O O M5 O M6 M4

O N

N N N N N N O O NH N 2 NH2 N N N N N Cl HN N N N N O N N F F F M9

F F M7 M8

345

APPENDICES

N N H N 2 N N

O NH

Cl M10 aThe code below each compound represents the source where it was obtained and its numbering. P and M indicate PubChem and MDDR, respectively.

346

APPENDICES

Appendix N. Compounds in Clusters 1~10 Cluster 1

NH N 2 N N N N N H HN N H

1092_47_008 (A2A) 1665_71_013 (A2A)

H H N N F3C H N N N F3C N N N N N N

N H 314_47_test2 (D ) 2 759_53_13 (D2) 758_53_12 (D2)

347

APPENDICES

Cluster 2

H H S N NH2 N N N

N N HN NH N F 1666_71_014 (A2A) H H same as 16b N N N H H N N N N HN NH N H HN NH N

1571_67_007 (A2A) 1663_71_011 (A2A) same as 16a 1668_71_016 (A2A) same as 16c

N N N N N N N N N N N N N N N N

F N N N N H H H N H 660_32_32 (D2) 664_32_30e (D2) 653_32_17a (D2) 654_32_17b (D2) 666_32_30g (D2) same as 17

N N

N N N N N N NH N N N N

N N N N N N H H H H N

230_32_30a (D2) 657_32_29a (D2) 655_32_20a (D2) 656_32_20b (D2) MDDR_221059 (D2)

348

APPENDICES

Cluster 3

HN HN HN HN HO HO HO HO

N CF3 N CF3 N CF3 N CF3 CF3 CF3 CF3 CF3

1869_59_005 (A2A) 1868_59_004 (A2A) 1868_59_004 (A2A) 1866_59_002 (A2A)

H N

N N N N N N N N N

N N N N N

OH OH OH OH O F

F F F F F MDDR_319038 (D ) 2 MDDR_319042 (D2) MDDR_319041 (D2) MDDR_319044 (D2) MDDR_319037 (D2) H H H N N N O O N N N N N

N N N N

O O NH NH

F F F F

MDDR_319043 (D2) MDDR_319039 (D2) 528_15_37 (D2) 528_15_38 (D2)

349

APPENDICES

Cluster 4

O H2N N NH2 O O HN NH2 S N N O N S N O S H N S HN N

0065_08_028 (A2A) 0318_18_014 (A2A) 1739_18_008 (A2A)

S N N H N N N

029_07_2b (D2)

350

APPENDICES

Cluster 5

NH NH H N N OH 2 S N N O N N O N N S S H2N N N H2N N N NH H Cl

1756_18_031 (A2A) 1759_18_030 (A2A) 1758_18_012 (A2A)

N N

NH2 N NH N N 2 N N O O O

N S N S NH H S

1779_18_040 (A2A) 1755_18_036 (A2A)

N N N

N N N N N N N N N N N N

HO HO S S S S S

MDDR_183949 (D2) MDDR_183723 (D2) 747_50_15 (D2) 749_50_17 (D2) 751_50_20 (D2)

351

APPENDICES

Cluster 6

N N N N N N N S O S O N NH MeO MeO NH

MeO

0622_30_032 (A2A) MDDR_341036 (A2A)

S H2N N N

MDDR_161841 (D2)

352

APPENDICES

Cluster 7

N N N N N N N N

O OMe O MeO N N N N N N N N N N O O O O

MDDR_318131 (A2A) MDDR_318130 (A2A) MDDR_318132 (A2A) MDDR_289523 (A2A)

N N N N O N H N N N N OMe N N N N O O

MDDR_297311 (D2) 424_07_1c (D2) 028_07_2a (D2)

H N H N N N SMe N N OMe N N N O N O

426_07_3b (D2) 425_07_3a (D2)

353

APPENDICES

Cluster 8

O O O H2N NH2 O NH2 O O N O O OMe H N 2 O

1765_18_046 (A2A) 1741_18_020 (A2A) 0317_18_028 (A2A) Br

NH2 NH2 N N NH O N N 2 H N H N N N O N N O O O O

1764_18_016 (A2A) 1145_49_011 (A2A) 0036_05_072 (A2A)

HO N HO N HO N HO O N O N O N

711_41_3d (D2) 264_41_3e (D2) 268_41_3k (D2)

N N H N O HO O O HO O N N HO O N

267_41_3h (D2) 266_41_3g (D2) 606_31_59c (D2)

354

APPENDICES

Cluster 9

NH2 N N MeO MeO CF3 N SMe N

N N N NH HN HN HN N HN N N NH NH OMe F OMe

1670_71_018 (A2A) 1683_03_018 (A2A) 1583_67_023 (A2A) 1570_67_006 (A2A)

N NH OMe

N N

584_30_40 (D2)

355

APPENDICES

Cluster 10

Cl Cl

N N N N H H H H N N N N N NH2 N NH2 N NH2 N NH2 O O O O N N N N

0500_27_063 (A2A) 0501_27_065 (A2A) 0518_27_064 (A2A) 0519_27_066 (A2A)

CN CN

N N H H N N N NH2 N NH2 O O N N

0510_27_083 (A2A) 0528_27_084 (A2A)

N H H N N N O O N H N N N H 668_33_11 (D2) 671_33_12d (D2)

H H N N N N O O N H N N N H MDDR_190847 (D2) 670_33_12c (D2)

H H N H N N N O NH N H H N N O 241_33_12b (D2) 669_33_12a (D2)

356

APPENDICES

Appendix O. Crystal Structure Report for Compound 70

A specimen of C21H25N5O, approximate dimensions 0.480 mm x 0.500 mm x 0.560 mm, was used for the X-ray crystallographic analysis. The X-ray intensity data were measured. The integration of the data using a monoclinic unit cell yielded a total of 23666 reflections to a maximum θ angle of 27.49° (0.77 Å resolution), of which 4263 were independent (average redundancy 5.551, completeness = 100.0%, Rint = 3.47%) and 4006 (93.97%) were greater than 2σ(F2). The final cell constants of a = 11.700(6) Å, b = 10.888(6) Å, c = 14.943(8) Å, β = 102.421(9)°, volume = 1859.0(16) Å3, are based upon the refinement of the XYZ-centroids of reflections above 20 σ(I). The calculated minimum and maximum transmission coefficients (based on crystal size) are 0.6746 and 0.7456.

The structure was solved and refined using the Bruker SHELXTL Software Package, using the space group P 1 21/c 1, with Z = 4 for the formula unit, 2 C21H25N5O. The final anisotropic full-matrix least-squares refinement on F with 251 variables converged at R1 = 4.43%, for the observed data and wR2 = 11.33% for all data. The goodness-of-fit was 1.058. The largest peak in the final difference electron density synthesis was 0.323 e-/Å3 and the largest hole was - 0.221 e-/Å3 with an RMS deviation of 0.048 e-/Å3. On the basis of the final model, the calculated density was 1.298 g/cm3 and F(000), 776 e-.

Sample and Crystal Data for Compound 70.

Identification code D584 Chemical formula C21H25N5O Formula weight 363.46 Temperature 100(2) K Wavelength 0.71073 Å Crystal size 0.480 x 0.500 x 0.560 mm Crystal system monoclinic Space group P 1 21/c 1 Unit cell dimensions a = 11.700(6) Å α = 90° b = 10.888(6) Å β = 102.421(9)° c = 14.943(8) Å γ = 90° Volume 1859.0(16) Å3 Z 4 Density (calculated) 1.298 g/cm3 Absorption coefficient 0.083 mm-1 F(000) 776

357

APPENDICES

Data Collection and Structure Refinement for Compound 70.

Theta range for data 1.78 to 27.49° collection -15<=h<=15, -14<=k<=14, - Index ranges 19<=l<=19 Reflections collected 23666 Independent reflections 4263 [R(int) = 0.0347] Max. and min. 0.7456 and 0.6746 transmission Structure solution direct methods technique Structure solution Bruker SHELXTL program Refinement method Full-matrix least-squares on F2 Refinement program SHELXL-2013 (Sheldrick, 2013) 2 2 2 Function minimised Σ w(Fo - Fc ) Data / restraints / 4263 / 0 / 251 parameters Goodness-of-fit on F2 1.058

Δ/σmax 0.001 4006 data; R1 = 0.0443, wR2 = Final R indices I>2σ(I) 0.1119 R1 = 0.0464, wR2 = all data 0.1133 2 2 2 w=1/[σ (Fo )+(0.0533P) +0.9385P] Weighting scheme 2 2 where P=(Fo +2Fc )/3 Largest diff. peak and 0.323 and -0.221 eÅ-3 hole R.M.S. deviation from 0.048 eÅ-3 mean

358

APPENDICES

Atomic Coordinates And Equivalent Isotropic Atomic Displacement Parameters (Å2) for Compound 70.

U(eq) is defined as one third of the trace of the orthogonalised Uij tensor. x/a y/b z/c U(eq)

O1 0.08563(8) 0.12335(9) 0.63467(6) 0.0234(2) N1 0.08706(9) 0.38977(9) 0.94624(7) 0.0182(2) N2 0.13692(9) 0.02887(9) 0.77185(7) 0.0173(2) N3 0.34484(9) 0.00004(10) 0.90546(7) 0.0193(2) N4 0.37742(9) 0.86624(9) 0.02942(7) 0.0176(2) N5 0.47832(9) 0.84380(9) 0.90594(7) 0.0183(2) C1 0.15574(10) 0.43251(11) 0.88904(8) 0.0167(2) C2 0.24042(10) 0.52460(11) 0.90190(8) 0.0199(2) C3 0.29696(10) 0.54741(11) 0.83131(9) 0.0216(3) C4 0.27074(11) 0.47984(12) 0.74913(9) 0.0218(3) C5 0.18572(10) 0.38908(11) 0.73600(8) 0.0184(2) C6 0.12647(10) 0.36425(10) 0.80651(8) 0.0154(2) C7 0.03660(10) 0.27869(10) 0.81696(8) 0.0157(2) C8 0.01568(10) 0.29727(11) 0.90277(8) 0.0173(2) C9 0.93337(11) 0.23369(13) 0.95122(9) 0.0245(3) C10 0.98032(10) 0.18484(11) 0.74765(8) 0.0175(2) C11 0.07089(10) 0.10841(11) 0.71337(8) 0.0168(2) C12 0.13043(10) 0.00641(11) 0.86720(8) 0.0174(2) C13 0.24184(10) 0.05149(11) 0.93203(8) 0.0183(2) C14 0.34824(10) 0.02592(11) 0.81033(8) 0.0193(2) C15 0.23949(11) 0.97329(11) 0.74735(8) 0.0199(2) C16 0.40143(10) 0.89901(11) 0.94854(8) 0.0162(2) C17 0.44026(10) 0.77268(11) 0.07256(8) 0.0188(2) C18 0.52390(11) 0.71183(11) 0.03589(8) 0.0210(3) C19 0.53925(10) 0.75046(11) 0.95066(8) 0.0191(2) C20 0.41585(12) 0.73638(14) 0.16329(9) 0.0284(3) C21 0.62432(12) 0.68809(13) 0.90336(9) 0.0272(3)

359

APPENDICES

Bond Lengths (Å) for Compound 70.

O1-C11 1.2356(15) N1-C1 1.3741(16) N1-C8 1.3786(16) N1-H1N 0.882(18) N2-C11 1.3492(16) N2-C15 1.4593(16) N2-C12 1.4637(16) N3-C16 1.3709(16) N3-C14 1.4582(16) N3-C13 1.4593(16) N4-C17 1.3380(16) N4-C16 1.3465(16) N5-C19 1.3352(16) N5-C16 1.3500(16) C1-C2 1.3937(18) C1-C6 1.4174(17) C2-C3 1.3828(18) C2-H2 0.95 C3-C4 1.4077(19) C3-H3 0.95 C4-C5 1.3860(18) C4-H4 0.95 C5-C6 1.4065(16) C5-H5 0.95 C6-C7 1.4382(16) C7-C8 1.3712(17) C7-C10 1.5024(16) C8-C9 1.4936(17) C9-H9A 0.98 C9-H9B 0.98 C9-H9C 0.98 C10-C11 1.5203(17) C10-H10A 0.99 C10-H10B 0.99 C12-C13 1.5282(17) C12-H12A 0.99 C12-H12B 0.99 C13-H13A 0.99 C13-H13B 0.99 C14-C15 1.5225(18) C14-H14A 0.99 C14-H14B 0.99 C15-H15A 0.99 C15-H15B 0.99 C17-C18 1.3884(17) C17-C20 1.4976(18) C18-C19 1.3891(18) C18-H18 0.95 C19-C21 1.5013(17) C20-H20A 0.98 C20-H20B 0.98 C20-H20C 0.98 C21-H21A 0.98 C21-H21B 0.98 C21-H21C 0.98

360

APPENDICES

Bond Angles (°) for Compound 70.

C1-N1-C8 109.30(10) C1-N1-H1N 127.0(12) C8-N1-H1N 123.4(12) C11-N2-C15 119.40(10) C11-N2-C12 126.54(10) C15-N2-C12 113.12(9) C16-N3-C14 120.08(10) C16-N3-C13 121.60(10) C14-N3-C13 112.85(9) C17-N4-C16 115.79(10) C19-N5-C16 116.19(11) N1-C1-C2 130.65(11) N1-C1-C6 107.42(11) C2-C1-C6 121.93(11) C3-C2-C1 117.81(11) C3-C2-H2 121.1 C1-C2-H2 121.1 C2-C3-C4 121.43(12) C2-C3-H3 119.3 C4-C3-H3 119.3 C5-C4-C3 120.74(11) C5-C4-H4 119.6 C3-C4-H4 119.6 C4-C5-C6 119.05(11) C4-C5-H5 120.5 C6-C5-H5 120.5 C5-C6-C1 119.03(11) C5-C6-C7 134.07(11) C1-C6-C7 106.90(10) C8-C7-C6 106.72(10) C8-C7-C10 127.24(11) C6-C7-C10 126.02(10) C7-C8-N1 109.66(10) C7-C8-C9 130.42(11) N1-C8-C9 119.92(11) C8-C9-H9A 109.5 C8-C9-H9B 109.5 H9A-C9-H9B 109.5 C8-C9-H9C 109.5 H9A-C9-H9C 109.5 H9B-C9-H9C 109.5 C7-C10-C11 111.76(10) C7-C10-H10A 109.3 C11-C10-H10A 109.3 C7-C10-H10B 109.3 C11-C10-H10B 109.3 H10A-C10-H10B 107.9 O1-C11-N2 121.56(11) O1-C11-C10 119.92(11) N2-C11-C10 118.47(10) N2-C12-C13 110.34(10) N2-C12-H12A 109.6 C13-C12-H12A 109.6 N2-C12-H12B 109.6 C13-C12-H12B 109.6 H12A-C12-H12B 108.1 N3-C13-C12 110.25(10) N3-C13-H13A 109.6 C12-C13-H13A 109.6 N3-C13-H13B 109.6 C12-C13-H13B 109.6 H13A-C13-H13B 108.1 N3-C14-C15 109.60(10) N3-C14-H14A 109.7 C15-C14-H14A 109.7 N3-C14-H14B 109.7 C15-C14-H14B 109.8 H14A-C14-H14B 108.2 N2-C15-C14 108.17(10) N2-C15-H15A 110.1 C14-C15-H15A 110.1 N2-C15-H15B 110.1 C14-C15-H15B 110.1 H15A-C15-H15B 108.4 N4-C16-N5 126.45(11) N4-C16-N3 117.47(10) N5-C16-N3 116.07(10) N4-C17-C18 122.28(11)

361

APPENDICES

N4-C17-C20 116.43(11) C18-C17-C20 121.29(11) C17-C18-C19 117.29(11) C17-C18-H18 121.4 C19-C18-H18 121.4 N5-C19-C18 121.93(11) N5-C19-C21 116.45(11) C18-C19-C21 121.62(11) C17-C20-H20A 109.5 C17-C20-H20B 109.5 H20A-C20-H20B 109.5 C17-C20-H20C 109.5 H20A-C20-H20C 109.5 H20B-C20-H20C 109.5 C19-C21-H21A 109.5 C19-C21-H21B 109.5 H21A-C21-H21B 109.5 C19-C21-H21C 109.5 H21A-C21-H21C 109.5 H21B-C21-H21C 109.5

Torsion Angles (°) For Compound 70.

C8-N1-C1-C2 179.83(12) C8-N1-C1-C6 -0.03(13) N1-C1-C2-C3 179.56(12) C6-C1-C2-C3 -0.60(17) C1-C2-C3-C4 -0.30(18) C2-C3-C4-C5 0.94(19) C3-C4-C5-C6 -0.66(18) C4-C5-C6-C1 -0.21(17) C4-C5-C6-C7 -179.45(12) N1-C1-C6-C5 -179.27(10) C2-C1-C6-C5 0.86(17) N1-C1-C6-C7 0.16(12) C2-C1-C6-C7 -179.71(10) C5-C6-C7-C8 179.08(12) C1-C6-C7-C8 -0.23(12) C5-C6-C7-C10 0.9(2) C1-C6-C7-C10 -178.39(10) C6-C7-C8-N1 0.21(13) C10-C7-C8-N1 178.35(10) C6-C7-C8-C9 -178.82(12) C10-C7-C8-C9 -0.7(2) C1-N1-C8-C7 -0.11(13) C1-N1-C8-C9 179.04(10) C8-C7-C10-C11 -127.40(12) C6-C7-C10-C11 50.40(15) C15-N2-C11-O1 9.66(17) C12-N2-C11-O1 177.78(11) C15-N2-C11-C10 -167.59(10) C12-N2-C11-C10 0.54(17) C7-C10-C11-O1 -108.21(12) C7-C10-C11-N2 69.08(14) C11-N2-C12-C13 -112.56(13) C15-N2-C12-C13 56.20(13) C16-N3-C13-C12 -98.53(13) C14-N3-C13-C12 55.33(13) N2-C12-C13-N3 -52.15(13) C16-N3-C14-C15 95.28(13) C13-N3-C14-C15 -59.02(13) C11-N2-C15-C14 110.63(12) C12-N2-C15-C14 -59.02(13) N3-C14-C15-N2 58.67(12) C17-N4-C16-N5 2.91(17) C17-N4-C16-N3 -175.43(10) C19-N5-C16-N4 -2.68(17) C19-N5-C16-N3 175.68(10) C14-N3-C16-N4 -167.58(10) C13-N3-C16-N4 -15.57(16) C14-N3-C16-N5 13.90(16) C13-N3-C16-N5 165.92(10) C16-N4-C17-C18 -0.87(17) C16-N4-C17-C20 179.10(11) N4-C17-C18-C19 -1.08(18) C20-C17-C18-C19 178.95(12) C16-N5-C19-C18 0.39(17)

362

APPENDICES

C16-N5-C19-C21 179.59(11) C17-C18-C19-N5 1.33(18) C17-C18-C19-C21 -177.82(12)

Anisotropic Atomic Displacement Parameters (Å2) for Compound 70.

2 2 *2 The anisotropic atomic displacement factor exponent takes the form: -2π [ h a U11 * * + ... + 2 h k a b U12 ]

U11 U22 U33 U23 U13 U12 O1 0.0302(5) 0.0279(5) 0.0124(4) -0.0008(3) 0.0057(3) -0.0012(4) N1 0.0224(5) 0.0203(5) 0.0122(5) -0.0004(4) 0.0045(4) 0.0017(4) N2 0.0197(5) 0.0199(5) 0.0133(5) -0.0010(4) 0.0059(4) 0.0001(4) N3 0.0211(5) 0.0229(5) 0.0149(5) 0.0034(4) 0.0063(4) 0.0055(4) N4 0.0172(5) 0.0200(5) 0.0159(5) 0.0016(4) 0.0045(4) -0.0003(4) N5 0.0178(5) 0.0217(5) 0.0154(5) -0.0017(4) 0.0034(4) 0.0014(4) C1 0.0181(5) 0.0170(5) 0.0145(5) 0.0015(4) 0.0026(4) 0.0042(4) C2 0.0210(6) 0.0185(6) 0.0181(6) -0.0015(4) -0.0005(4) 0.0018(4) C3 0.0180(5) 0.0194(6) 0.0262(6) 0.0014(5) 0.0019(5) -0.0018(4) C4 0.0210(6) 0.0231(6) 0.0228(6) 0.0034(5) 0.0081(5) 0.0001(5) C5 0.0203(5) 0.0193(6) 0.0162(5) -0.0002(4) 0.0054(4) 0.0012(4) C6 0.0157(5) 0.0157(5) 0.0141(5) 0.0014(4) 0.0017(4) 0.0024(4) C7 0.0160(5) 0.0168(5) 0.0142(5) 0.0015(4) 0.0034(4) 0.0016(4) C8 0.0177(5) 0.0183(5) 0.0158(5) 0.0015(4) 0.0037(4) 0.0027(4) C9 0.0257(6) 0.0304(7) 0.0199(6) 0.0006(5) 0.0104(5) -0.0027(5) C10 0.0172(5) 0.0200(6) 0.0143(5) 0.0010(4) 0.0014(4) -0.0013(4) C11 0.0192(5) 0.0172(5) 0.0136(5) -0.0028(4) 0.0025(4) -0.0049(4) C12 0.0214(6) 0.0170(5) 0.0156(5) 0.0024(4) 0.0079(4) 0.0015(4) C13 0.0214(6) 0.0200(6) 0.0142(5) 0.0012(4) 0.0053(4) 0.0053(4) C14 0.0212(6) 0.0227(6) 0.0156(5) 0.0045(4) 0.0076(4) 0.0036(4) C15 0.0243(6) 0.0209(6) 0.0164(5) -0.0009(4) 0.0089(4) 0.0026(5) C16 0.0152(5) 0.0182(5) 0.0145(5) -0.0009(4) 0.0019(4) -0.0013(4) C17 0.0189(5) 0.0193(6) 0.0177(5) 0.0019(4) 0.0030(4) -0.0023(4) C18 0.0214(6) 0.0195(6) 0.0214(6) 0.0025(5) 0.0027(5) 0.0038(5) C19 0.0165(5) 0.0216(6) 0.0182(5) -0.0036(5) 0.0013(4) 0.0007(4) C20 0.0288(7) 0.0331(7) 0.0255(7) 0.0126(5) 0.0108(5) 0.0056(5) C21 0.0262(6) 0.0337(7) 0.0216(6) -0.0019(5) 0.0053(5) 0.0106(5)

363

APPENDICES

Hydrogen Atomic Coordinates And Isotropic Atomic Displacement Parameters (Å2) for Compound 70.

x/a y/b z/c U(eq) H1N 0.0906(15) 0.4115(17) 1.0036(13) 0.035(4) H2 0.2587 0.5702 0.9573 0.024 H3 0.3547 0.6100 0.8384 0.026 H4 0.3118 0.4966 0.7021 0.026 H5 0.1677 0.3443 0.6802 0.022 H9A -0.1148 0.1753 0.9094 0.037 H9B -0.1173 0.2945 0.9716 0.037 H9C -0.0218 0.1895 1.0045 0.037 H10A -0.0705 0.2271 0.6951 0.021 H10B -0.0696 0.1299 0.7757 0.021 H12A 0.1204 -0.0826 0.8767 0.021 H12B 0.0618 0.0498 0.8809 0.021 H13A 0.2452 0.1423 0.9301 0.022 H13B 0.2409 0.0265 0.9956 0.022 H14A 0.4192 -0.0112 0.7956 0.023 H14B 0.3515 0.1158 0.8010 0.023 H15A 0.2405 -0.0081 0.6827 0.024 H15B 0.2374 -0.1170 0.7546 0.024 H18 0.5688 -0.3536 1.0678 0.025 H20A 0.4374 -0.1961 1.2070 0.043 H20B 0.4618 -0.3367 1.1862 0.043 H20C 0.3323 -0.2819 1.1561 0.043 H21A 0.5972 -0.3955 0.8862 0.041 H21B 0.7015 -0.3153 0.9448 0.041 H21C 0.6298 -0.2656 0.8482 0.041

Hydrogen Bond Distances (Å) And Angles (°) for Compound 70.

Donor-H Acceptor-H Donor-Acceptor Angle N1-H1N...O1 0.882(18) 2.008(18) 2.8231(19) 153.2

364

APPENDICES

Appendix P. NMR Spectra of Compounds 66-72 and 69a-72a

OH HCl NN

365

APPENDICES

N HN N N 68

366

APPENDICES

N O N N N

N H 69

N O N N N

N H 69

367

APPENDICES

N N N

N H 70

N N N

N H 70

368

APPENDICES

N O N N N

N H

N O N N N

N H

369

APPENDICES

N N N

HN O 72

N N N

HN O 72

370

APPENDICES

N N N N

N H 69a

N N N N

N H 69a

371

APPENDICES

N N N

N H 70a

N N N

N H 70a

372

APPENDICES

N N N N

N H 71a

N N N N

N H 71a

373

APPENDICES

N N N N HN

72a

N N N N HN

72a

374

APPENDICES

Appendix Q. HPLC Chromatograms of Compounds 69-72 and 69a-72a HPLC (1) for Compound 69:

DAD1 B, Sig=254,16 Ref=off (SYM\14020901.D) mAU

120 5.618 N 100 O N N N 80

40 N H 20 69 0.466 1.496 0 2.472

0 5 10 15 20 25 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.466 1.6 0.275 29 1.180 2 1.496 1.5 0.303 35.3 1.436 3 2.472 1.7 0.137 17.4 0.708 4 5.618 134.5 0.264 2376.6 96.677

HPLC (3) for Compound 69:

N O N N N

N H 69

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.803 10.138 0.736 621.742 0.985 2 12.697 8.901 0.138 79.369 0.126 3 14.262 39.981 0.128 348.322 0.552 4 15.511 3020.178 0.33 62026.6 98.291 5 17.780 3.059 0.147 29.201 0.046

375

APPENDICES

HPLC (1) for Compound 70:

DAD1 B, Sig=254,16 Ref=off (SYM\14021010.D) mAU

1200 9.847 N N 1000 N

800 N

600 O

400 N H 200 70

0 8.249 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 8.249 1.1 0.215 15.4 0.056 2 9.847 1365.8 0.293 27502.7 99.944

HPLC (3) for Compound 70:

N N N

N H 70

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.219 8.555 0.178 108.408 0.364 2 6.892 2.467 0.086 13.955 0.047 3 15.640 2552.582 0.172 29642.4 99.589

376

APPENDICES

HPLC (1) for Compound 71:

DAD1 C, Sig=254,16 Ref=360,100 (SYM\14020906.D) mAU 350 N 10.193 : 7956.64 a 300 O N Are 250 N N

200 71 150 100 N H 50

0 1.599 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 1.599 5.6 0.357 129.5 1.602 2 10.193 379.5 0.350 7956.6 98.398

HPLC (3) for Compound 71:

N O N N N

N H

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.247 11.177 0.214 160.467 1.182 2 6.856 6.483 0.134 63.112 0.465 3 15.627 1327.648 0.143 13356.8 98.354

377

APPENDICES

HPLC (1) for Compound 72:

DAD1 B, Sig=254,16 Ref=off (SYM\14020907.D) mAU

600 N 11.295 500 N N 400 N 300

200 HN O

100 72

0 2.603 16.075 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.603 2.8 0.143 26.7 0.178 2 11.295 704.2 0.31 14971.2 99.666 3 16.075 1.8 0.199 23.5 0.156

HPLC (3) for Compound 72:

N N N

HN O 72

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.254 10.728 0.215 155.383 0.669 2 6.889 5.870 0.128 54.284 0.234 3 16.289 1951.149 0.168 23033.8 99.098

378

APPENDICES

HPLC (2) for Compound 69a:

N N N N

N H 69a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 8.538 3086.417 0.169 31894 100

HPLC (3) for Compound 69a:

N N N N

N H 69a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 12.045 11.250 0.126 96.709 0.901 2 14.575 1127.835 0.138 10413.5 97.01 3 15.684 23.739 0.136 224.277 2.089

379

APPENDICES

HPLC (2) for Compound 70a:

N N N

N H 70a

peak # retention height width area area time (min) (mAU) (min) (mAU × s) (%) 1 9.033 3081.408 0.164 31264.3 96.681 2 9.544 129.115 0.117 1073.337 3.319

HPLC (3) for Compound 70a:

N N N

N H 70a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 13.580 11.343 0.124 93.510 0.412 2 14.492 2075.449 0.165 21861.500 96.269 3 14.944 79.294 0.135 753.858 3.320

380

APPENDICES

HPLC (2) for Compound 71a:

N N N N

N H 71a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 8.994 3020.587 0.168 30960.4 100

HPLC (3) for Compound 71a:

N N N N

N H 71a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.517 6.782 0.330 167.186 0.483 2 6.924 4.775 0.211 78.043 0.226 3 12.609 22.235 0.247 342.006 0.989 4 13.417 6.390 0.138 57.874 0.167 5 13.915 5.490 0.202 83.552 0.242 6 14.073 3.400 0.106 23.271 0.067 7 14.444 2719.874 0.197 33279.5 96.221 8 14.897 44.941 0.137 441.324 1.276 9 15.577 9.464 0.165 102.782 0.297 10 15.917 2.630 0.079 11.114 0.032

381

APPENDICES

HPLC (2) for Compound 72a:

N N N N HN

72a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 9.290 2956.135 0.174 31367.3 100

HPLC (3) for Compound 72a:

N N N N HN

72a

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.212 9.699 0.191 127.402 0.577 2 6.892 2.450 0.086 13.815 0.063 3 14.533 35.182 0.080 181.929 0.824 4 14.710 2035.998 0.165 21767.8 98.537

382

APPENDICES

Appendix R. Radioligand Competition Assays The evaluation of the binding affinity of synthesised compounds for adenosine receptor subtypes depended on the use of radioligands. A radioligand is a ligand that is labelled with a radioactive isotope (e.g. tritium [3H] or iodine [125I]).

Important characteristics for a good radioligand include high affinity (ca. KD ~

1 nM) and high selectivity at the receptor of interest, as well as low non-specific

(or, non-receptor) binding. A suitable radioligand is incubated with the receptor of interest for an appropriate period of time, and then the radioactivity bound to that receptor is measured. Experiments involving radioligand binding include saturation (to study the concentration of a receptor), competition (to assess the binding of an unlabelled drug), and kinetic (to determine the time-course radioligand dissociation and association) assays. Competition binding experiments measure the ability of a newly synthesised drug to compete with a specific radioligand for binding that receptor, and in the thesis, three

3 radioligands were used for human A1 ([ H]-2-chloro-6-cyclopentyladenosine),

3 3 6 A2A ([ H]-5’-N-ethylcarboxamidoadenosine) and A3 ([ H]-2-hexyn-1-yl-N - methyladenosine) subtypes. For the hA2B subtype, compound potency was determined in adenylyl cyclase assay, due to the unavailability of a radioligand suitable for hA2B receptor. The principle of radioligand competition is given in

Figure A13.

383

APPENDICES

Figure A13. Radioligand competition experiments for receptors. The experiment starts with receptor preparation by homogenising the tissue in the buffer and centrifugation to obtain the crude membrane pellet (A). The receptor- bound membranes and a suitable radioligand with a fixed concentration are incubated for a period of time to ensure that equilibrium has been reached. The removal of free radioligands from receptor-bound radioligands is usually employed by filtration (B). Non-specific binding can be determined by using an unlabelled drug that is structurally dissimilar from the radioligand (C). An unlabelled drug is tested over a concentration range. A competition curve is obtained by plotting the percentage of radioligand binding against the logarithmic concentration of the unlabelled drug (D) (Vauquelin and von Mentzer, 2007) (reprinted with permission).

384

APPENDICES

Appendix S. NMR Spectra of Compounds 73-83

N O N OMe N N

N H 73

N O N OMe N N

N H 73

385

APPENDICES

N N N

MeO O

N H 74

N N N

MeO O

N H 74

386

APPENDICES

N N N

Br O

N H 75

N N N

Br O

N H 75

387

APPENDICES

O N MeO N N N

N H 76

O N MeO N N N

N H 76

388

APPENDICES

N F N N N HN O

389

APPENDICES

N N N

F O

N H 78

N N N

F O

N H 78

390

APPENDICES

N N N

F O

N H

N N N

F O

N H

391

APPENDICES

N O N N N Cl

N H 80

N O N N N Cl

N H 80

392

APPENDICES

N N N

F N H 81

N N N

F N H 81

393

APPENDICES

N O N N N

N H OMe 82

N O N N N

N H OMe 82

394

APPENDICES

N N N H N

395

APPENDICES

Appendix T. HPLC Chromatograms of Compounds 73-83 HPLC (1) for Compound 73:

DAD1 B, Sig=254,16 Ref=off (SYM\14021004.D) mAU 600 8.688 N 500 O N 400 OMe N N

300

200 N 100 H 73 0.502 2.647 0 2.446 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.502 1.9 0.481 69.6 0.582 2 2.446 1.1 0.075 5.5 0.046 3 2.647 2.6 0.148 24.4 0.204 4 8.688 644.3 0.267 11863.5 99.168

HPLC (3) for Compound 73:

N O N OMe N N

N H 73

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.233 7.873 0.202 106.458 0.510 2 6.911 2.460 0.085 13.795 0.066 3 15.412 2035.682 0.147 20739.9 99.256 4 17.131 3.998 0.143 35.249 0.169

396

APPENDICES

HPLC (1) for Compound 74:

DAD1 B, Sig=254,16 Ref=off (SYM\14020919.D) mAU 400

8.542 N N 350 N 300 Area: 7630.82 250 N 200 MeO O 150 N 100 H 50 74 1.607 0.476 2.606 0 2.407 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.476 2 0.356 55.6 0.703 2 1.607 7 0.401 192.1 2.428 3 2.407 1.3 0.076 6.2 0.078 4 2.606 2.8 0.163 28.5 0.360 5 8.542 423.3 0.300 7630.8 96.431

HPLC (3) for Compound 74:

N N N

MeO O

N H 74

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.253 10.087 0.210 139.623 0.889 2 6.886 5.826 0.131 55.446 0.353 3 14.789 1623.977 0.137 15512.8 98.758

397

APPENDICES

HPLC (1) for Compound 75:

DAD1 B, Sig=254,16 Ref=off (SYM\14020920.D) mAU N 400 N 11.672 N 300 N

200 Br O

N 100 H 75 1.596 0.466 0 2.600 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.466 2.1 0.356 56.9 0.005 2 1.596 6.9 0.4 188.5 1.822 3 2.6 2.8 0.160 27.7 0.268 4 11.672 467 0.314 10074.2 97.361

HPLC (3) for Compound 75:

N N N

Br O

N H 75

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.249 8.438 0.201 110.906 0.525 2 6.915 2.517 0.084 14.143 0.067 3 14.760 8.823 0.134 79.748 0.377 4 16.418 1778.306 0.167 20925.3 99.031

398

APPENDICES

HPLC (1) for Compound 76:

DAD1 B, Sig=254,16 Ref=off (SYM\14021002.D) mAU 1200 8.433 O N 1000 MeO N N 800 N

600 N 400 H 76 200 0.491 2.626 0 2.442 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.491 1.8 0.445 59 0.263 2 2.442 1.3 0.060 5 0.022 3 2.626 3 0.147 27.3 0.122 4 8.433 1275.7 0.257 22363 99.59

HPLC (3) for Compound 76:

O N MeO N N N

N H 76

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.235 6.834 0.170 73.556 0.395 2 6.919 2.500 0.083 13.961 0.075 3 15.226 1848.640 0.143 18454.7 99.039 4 15.828 8.302 0.159 91.577 0.492

399

APPENDICES

HPLC (1) for Compound 77:

DAD1 B, Sig=254,16 Ref=off (SYM\14021003.D)

mAU 4 3.9 100 Br 5 13.308 80 N Area: 25

60 F N N N HN 40 O

20 77

0 0.567 2.629 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.567 2.2 0.459 82.3 3.090 2 2.629 2.9 0.149 26.8 1.006 3 13.308 108.7 0.392 2553.9 95.903

HPLC (3) for Compound 77:

N F N N N HN O

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 16.398 39.033 0.110 280.676 0.868 2 16.531 42.877 0.162 492.120 1.522 3 17.235 2464.557 0.194 31203.5 96.504 4 17.785 33.449 0.159 357.570 1.106

400

APPENDICES

HPLC (1) for Compound 78:

DAD1 B, Sig=254,16 Ref=off (SYM\14021005.D) mAU N 600 N 10.124 N 500

400 N

300 F O 200 N 100 H 78 11.713 0 2.657 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.657 2.5 0.140 22.6 0.169 2 10.124 681.5 0.286 13294.6 99.689 3 11.713 1.1 0.273 18.9 0.142

HPLC (3) for Compound 78:

N N N

F O

N H 78

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.212 9.639 0.185 126.722 0.315 2 6.891 2.605 0.089 15.284 0.038 3 15.783 2755.873 0.222 39720.5 98.697 4 16.359 19.860 0.180 249.023 0.619 5 17.119 12.730 0.156 133.256 0.331

401

APPENDICES

HPLC (1) for Compound 79:

DAD1 B, Sig=254,16 Ref=off (SYM\14021007.D) mAU N 500 9.561 N N 400 N 300 F O 200 N 100 H 13.367 0 2.614 79 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.614 2.9 0.149 26.5 0.249 2 9.561 572.5 0.270 10575 99.395 3 13.367 2.2 0.253 37.9 0.356

HPLC (3) for Compound 79:

N N N

F O

N H

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.269 6.100 0.165 58.142 0.213 2 6.900 2.227 0.084 11.801 0.043 3 15.462 2479.533 0.159 27043.5 99.009 4 16.636 17.067 0.175 200.777 0.735

402

APPENDICES

HPLC (1) for Compound 80:

DAD1 B, Sig=254,16 Ref=off (SYM\14021008.D) mAU N

1000 10.937 O N N N 800 Cl 600 N 400 H 80 200 8.521 1.540 9.271 0 2.613 6.190 16.714 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 1.540 3.3 0.069 14.8 0.060 2 2.613 2.8 0.144 24.3 0.098 3 6.190 1.2 0.204 17.3 0.070 4 8.521 5 0.251 84.5 0.342 5 9.271 3.5 0.271 64.3 0.260 6 10.937 1146.6 0.311 24514.9 99.080 7 16.714 1.4 0.247 22.3 0.090

HPLC (3) for Compound 80:

N O N N N Cl

N H 80

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.228 7.516 0.181 91.079 0.239 2 6.912 2.475 0.086 14.032 0.037 3 15.202 17.453 0.123 141.656 0.372 4 15.396 23.242 0.175 293.553 0.770 5 15.843 14.187 0.165 166.697 0.437 6 16.560 2803.495 0.200 37404.0 98.145

403

APPENDICES

HPLC (1) for Compound 81:

DAD1 B, Sig=254,16 Ref=off (SYM\14021006.D) mAU N 9.564 N 400 N

300 N

200 O

100 F N H 81 0 2.641 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.641 2.5 0.144 22.2 0.252 2 9.564 472.7 0.271 8795.7 99.748

HPLC (3) for Compound 81:

N N N

F N H 81

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.255 7.682 0.188 91.042 0.405 2 6.908 2.533 0.084 14.222 0.063 3 15.551 2123.325 0.150 22271.0 99.039 4 16.488 8.793 0.179 110.779 0.493

404

APPENDICES

HPLC (1) for Compound 82:

DAD1 B, Sig=254,16 Ref=off (SYM\14021001.D) mAU

800 9.452 N 700 O N 600 N N 500 400 300 N H 200 OMe 100 82

0 2.558 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.558 3.8 0.182 44.8 0.253 2 9.452 901.7 0.285 17689.8 99.747

HPLC (3) for Compound 82:

N O N N N

N H OMe 82

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.250 8.079 0.196 102.290 0.604 2 6.890 2.295 0.083 12.310 0.073 3 15.865 1594.066 0.151 16808.9 99.323

405

APPENDICES

HPLC (1) for Compound 83:

DAD1 B, Sig=254,16 Ref=off (SYM\14020917.D) mAU O

600 9.162 N 500

400 N N N 300 H N 200

100 83 1.602 0.445 2.602 0 2.403 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.445 3.2 0.360 85.9 0.672 2 1.602 6.8 0.423 198.8 1.555 3 2.403 1.3 0.088 7.1 0.056 4 2.602 2.9 0.161 28.6 0.224 5 9.162 670.2 0.271 12468.2 97.495

HPLC (3) for Compound 83:

N N N H N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.248 8.366 0.230 130.691 0.488 2 6.909 2.448 0.087 13.965 0.052 3 15.309 2450.733 0.159 26582.8 99.168 4 16.281 6.142 0.179 78.254 0.292

406

APPENDICES

Appendix U. NMR Spectra of Compounds 86-95

N H 86

407

APPENDICES

OEt Br N H

408

APPENDICES

OEt Br N O O

OEt

N O O

409

APPENDICES

O N N N N

N H 90

O N N N N

N H 90

410

APPENDICES

N N H N N N

411

APPENDICES

F O

N N H N N N

F O

N N H N N N

412

APPENDICES

N N H N N N

413

APPENDICES

MeO O

N N H N N N

MeO O

N N H N N N

414

APPENDICES

N N H NO2 N N N

415

APPENDICES

Appendix V. HPLC Chromatograms of Compounds 90-95 HPLC (2) for Compound 90:

O N N N N

N H 90

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 12.874 1924.688 0.155 19905.4 96.967 2 13.349 69.493 0.127 622.641 3.033

HPLC (1) for Compound 91:

DAD1 B, Sig=254,16 Ref=off (SYM\14020916.D)

mAU 4 .7 5 O 200 2

11.300 : 51 175 a re N N A 150 H 125 N 100 N N 75 6 6 50 25 91 1.592 0 0.434 Area: 180.4 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.434 3.3 0.365 88.5 1.64 2 1.592 6.8 0.442 180.5 3.35 3 11.300 220.3 0.388 5125.7 95.014

HPLC (3) for Compound 91:

N N H N N N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.227 10.831 0.203 149.800 0.481 2 6.865 6.054 0.128 56.103 0.180 3 16.474 2397.941 0.191 30963.2 99.339

416

APPENDICES

HPLC (1) for Compound 92:

DAD1 B, Sig=254,16 Ref=off (SYM\14020913.D) 9 mAU F O 400 11.65 N N H 300 N N 200 N

100 92 0 1.561 9.752 2.596 2.420 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 1.561 6.5 0.340 147.5 1.382 2 2.420 1.1 0.098 6.6 0.062 3 2.596 2.9 0.146 30.1 0.282 4 9.752 1.9 0.211 26.9 0.252 5 11.659 476.2 0.318 10462.7 98.022

HPLC (3) for Compound 92:

F O

N N H N N N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.215 5.010 0.049 2.307 0.005 2 6.892 2.406 0.086 13.567 0.031 3 16.559 2795.178 0.244 43544.8 99.733 4 17.081 9.761 0.153 100.880 0.231

417

APPENDICES

HPLC (1) for Compound 93:

DAD1 B, Sig=254,16 Ref=off (SYM\14021014.D)

mAU 8 O

300 13.05 N N 250 H 200 N 150 N 100 N 50 0 93 11.516 -50 2.776 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 2.776 3.1 0.343 79 0.759 2 11.516 2 0.248 33.9 0.326 3 13.058 411.8 0.360 10289.9 98.915

HPLC (3) for Compound 93:

N N H N N N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.250 10.587 0.211 151.576 1.239 2 6.891 5.781 0.127 53.115 0.434 3 17.149 1168.655 0.148 12024.6 98.326

418

APPENDICES

HPLC (1) for Compound 94:

DAD1 B, Sig=254,16 Ref=off (SYM\14020914.D) mAU 500 MeO O 10.620

400 N N H

300 N N 200 N

100 94 1.598 0.457 8.338 0 2.608 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.457 3 0.367 82.9 0.707 2 1.598 7.3 0.359 173.7 1.481 3 2.608 2.5 0.148 22 0.188 4 8.338 3.4 0.198 44.4 0.379 5 10.620 541.8 0.306 11405.6 97.246

HPLC (3) for Compound 94:

MeO O

N N H N N N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.246 8.918 0.206 122.000 0.758 2 6.905 2.540 0.087 14.549 0.090 3 16.151 1426.677 0.159 15968.5 99.152

419

APPENDICES

HPLC (1) for Compound 95:

DAD1 B, Sig=254,16 Ref=off (SYM\14020915.D)

mAU 7 O 11.69 500 N N H 400 NO2 N 300 N N 200

100 95 1.610 0.470 0 2.609 9.893 0 2.5 5 7.5 10 12.5 15 17.5 min peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 0.470 3.5 0.430 114.9 0.821 2 1.610 7.9 0.417 225.8 1.613 3 2.609 2.6 0.156 24.8 0.177 4 9.893 1.3 0.208 18 0.129 5 11.697 615 0.320 13618.3 97.261

HPLC (1) for Compound 95:

N N H NO2 N N N

peak # retention time height width area area (min) (mAU) (min) (mAU × s) (%) 1 6.236 9.100 0.184 106.263 0.256 2 6.860 5.511 0.128 51.084 0.123 3 16.458 29.455 0.135 260.956 0.628 4 16.798 2859.408 0.224 41131.9 98.993

420

APPENDICES

Appendix W. Western Blot Analysis

Lanes 3 and 7: Reaction mixtures before purification. Lanes 4 and 8: Filtrates after purification. Lanes 5 and 9: Resuspended retentates after purification. The distinct band at 40 kDa resulted from D2 receptor. For more information regarding Western blot experiments, readers are referred to the original paper (May et al., 2013) (reprinted with permission).

421

APPENDICES

Appendix X. Flow Cytometry Measurements

The decrease in antibody binding for blank polymersomes compared to D2R- functionalised polymersomes verified the association of polymersomes with D2R. For more information regarding flow cytometry experiments, readers are referred to the original paper (May et al., 2013) (reprinted with permission).

422

APPENDICES

Appendix Y. References for Appendices A to Y

AbdAlla, S., Abdel-Baset, A., Lother, H., el Massiery, A., and Quitterer, U. (2005). Mesangial AT1/B2 receptor heterodimers contribute to angiotensin II hyperresponsiveness in experimental hypertension. J. Mol. Neurosci. 26, 185- 192.

Ahlquist, R.P. (1973). Adrenergic receptors: a personal and practical view. Perspect. Biol. Med. 17, 119-122.

Albin, R.L., Young, A.B., and Penney, J.B. (1989). The functional anatomy of basal ganglia disorders. Trends Neurosci. 12, 366-375.

Altenbach, C., Kusnetzow, A.K., Ernst, O.P., Hofmann, K.P., and Hubbell, W.L. (2008). High-resolution distance mapping in rhodopsin reveals the pattern of helix movement due to activation. Proc. Natl. Acad. Sci. U. S. A. 105, 7439-7444.

Beaulieu, J.M., and Gainetdinov, R.R. (2011). The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol. Rev. 63, 182-217.

Benovic, J.L., DeBlasi, A., Stone, W.C., Caron, M.G., and Lefkowitz, R.J. (1989). Beta-adrenergic receptor kinase: primary structure delineates a multigene family. Science 246, 235-240.

Berg, K.A., Rowan, M.P., Gupta, A., Sanchez, T.A., Silva, M., Gomes, I., McGuire, B.A., Portoghese, P.S., Hargreaves, K.M., Devi, L.A., et al. (2012). Allosteric interactions between delta and kappa opioid receptors in peripheral sensory neurons. Mol. Pharmacol. 81, 264-272.

Boehm, M. (2011). Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries. In Virtual Screening (Wiley-VCH Verlag GmbH & Co. KGaA), pp. 1-33.

Bokoch, M.P., Zou, Y., Rasmussen, S.G., Liu, C.W., Nygaard, R., Rosenbaum, D.M., Fung, J.J., Choi, H.J., Thian, F.S., Kobilka, T.S., et al. (2010). Ligand- specific regulation of the extracellular surface of a G-protein-coupled receptor. Nature 463, 108-112.

423

APPENDICES

Bouvier, M. (2001). Oligomerization of G-protein-coupled transmitter receptors. Nat. Rev. Neurosci. 2, 274-286.

Brem, S., Brem, H., Folkman, J., Finkelstein, D., and Patz, A. (1976). Prolonged tumor dormancy by prevention of neovascularization in the vitreous. Cancer Res. 36, 2807-2812.

Bushlin, I., Gupta, A., Stockton, S.D., Jr., Miller, L.K., and Devi, L.A. (2012). Dimerization with cannabinoid receptors allosterically modulates delta opioid receptor activity during neuropathic pain. PLoS One 7, e49789.

Carlsson, J., Yoo, L., Gao, Z.G., Irwin, J.J., Shoichet, B.K., and Jacobson, K.A. (2010). Structure-based discovery of A2A adenosine receptor ligands. J. Med. Chem. 53, 3748-3755.

Cerione, R.A., Strulovici, B., Benovic, J.L., Lefkowitz, R.J., and Caron, M.G. (1983). Pure beta-adrenergic receptor: the single polypeptide confers catecholamine responsiveness to adenylate cyclase. Nature 306, 562-566.

Chen, B., Harrison, R.F., Papadatos, G., Willett, P., Wood, D.J., Lewell, X.Q., Greenidge, P., and Stiefl, N. (2007). Evaluation of machine-learning methods for ligand-based virtual screening. J. Comput. Aided Mol. Des. 21, 53-62.

Chien, E.Y., Liu, W., Zhao, Q., Katritch, V., Han, G.W., Hanson, M.A., Shi, L., Newman, A.H., Javitch, J.A., Cherezov, V., et al. (2010). Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 330, 1091-1095.

Cohen, P. (2010). Guidelines for the effective use of chemical inhibitors of protein function to understand their roles in cell regulation. Biochem. J. 425, 53-54.

Congreve, M., Dias, J.M., and Marshall, F.H. (2014). Structure-based drug design for G protein-coupled receptors. Prog. Med. Chem. 53, 1-63.

424

APPENDICES

Dale, H. (1943). Modes of drug action. General introductory address. Trans. Faraday Soc. 39, 319b-322.

Davis, M.I., Hunt, J.P., Herrgard, S., Ciceri, P., Wodicka, L.M., Pallares, G., Hocker, M., Treiber, D.K., and Zarrinkar, P.P. (2011). Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046-1051.

De Lean, A., Stadel, J.M., and Lefkowitz, R.J. (1980). A ternary complex model explains the agonist-specific binding properties of the adenylate cyclase-coupled beta-adrenergic receptor. J. Biol. Chem. 255, 7108-7117. de Lera Ruiz, M., Lim, Y.H., and Zheng, J. (2014). Adenosine A2A receptor as a drug discovery target. J. Med. Chem. 57, 3623-3650.

Delay, J., Deniker, P., and Harl, J.M. (1952). [Therapeutic use in psychiatry of phenothiazine of central elective action (4560 RP)]. Ann. Med. Psychol. (Paris) 110, 112-117.

Demetri, G.D., van Oosterom, A.T., Garrett, C.R., Blackstein, M.E., Shah, M.H., Verweij, J., McArthur, G., Judson, I.R., Heinrich, M.C., Morgan, J.A., et al. (2006). Efficacy and safety of sunitinib in patients with advanced gastrointestinal stromal tumour after failure of imatinib: a randomised controlled trial. Lancet 368, 1329-1338.

Dixon, R.A., Kobilka, B.K., Strader, D.J., Benovic, J.L., Dohlman, H.G., Frielle, T., Bolanowski, M.A., Bennett, C.D., Rands, E., Diehl, R.E., et al. (1986). Cloning of the gene and cDNA for mammalian beta-adrenergic receptor and homology with rhodopsin. Nature 321, 75-79.

Dohlman, H.G., Thorner, J., Caron, M.G., and Lefkowitz, R.J. (1991). Model systems for the study of seven-transmembrane-segment receptors. Annu. Rev. Biochem. 60, 653-688.

425

APPENDICES

Erber, R., Thurnher, A., Katsen, A.D., Groth, G., Kerger, H., Hammes, H.P., Menger, M.D., Ullrich, A., and Vajkoczy, P. (2004). Combined inhibition of VEGF and PDGF signaling enforces tumor vessel regression by interfering with pericyte-mediated endothelial cell survival mechanisms. FASEB J. 18, 338-340.

Faivre, S., Demetri, G., Sargent, W., and Raymond, E. (2007). Molecular basis for sunitinib efficacy and future clinical development. Nat. Rev. Drug Discov. 6, 734-745.

Fakra, E., and Azorin, J.M. (2012). Clozapine for the treatment of schizophrenia. Expert Opin. Pharmacother. 13, 1923-1935.

Farde, L., Hall, H., Ehrin, E., and Sedvall, G. (1986). Quantitative analysis of D2 dopamine receptor binding in the living human brain by PET. Science 231, 258-261.

Ferrara, N., Hillan, K.J., Gerber, H.P., and Novotny, W. (2004). Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer. Nat. Rev. Drug Discov. 3, 391-400.

Ferre, S., Baler, R., Bouvier, M., Caron, M.G., Devi, L.A., Durroux, T., Fuxe, K., George, S.R., Javitch, J.A., Lohse, M.J., et al. (2009). Building a new conceptual framework for receptor heteromers. Nat. Chem. Biol. 5, 131-134.

Ferre, S., Casado, V., Devi, L.A., Filizola, M., Jockers, R., Lohse, M.J., Milligan, G., Pin, J.P., and Guitart, X. (2014). G protein-coupled receptor oligomerization revisited: functional and pharmacological perspectives. Pharmacol. Rev. 66, 413-434.

Folkman, J. (1971). Tumor angiogenesis: therapeutic implications. N. Engl. J. Med. 285, 1182-1186.

Folkman, J. (1995). Angiogenesis in cancer, vascular, rheumatoid and other disease. Nat. Med. 1, 27-31.

426

APPENDICES

Fong, T.A., Shawver, L.K., Sun, L., Tang, C., App, H., Powell, T.J., Kim, Y.H., Schreck, R., Wang, X., Risau, W., et al. (1999). SU5416 is a potent and selective inhibitor of the vascular endothelial growth factor receptor (Flk- 1/KDR) that inhibits tyrosine kinase catalysis, tumor vascularization, and growth of multiple tumor types. Cancer Res. 59, 99-106.

Gaddum, J.H., and Hameed, K.A. (1954). Drugs which antagonize 5- hydroxytryptamine. Br. J. Pharmacol. Chemother. 9, 240-248.

Galloway, T.J., Wirth, L.J., Colevas, A.D., Gilbert, J., Bauman, J.E., Saba, N.F., Raben, D., Mehra, R., Ma, A.W., Atoyan, R., et al. (2015). A Phase I Study of CUDC-101, a Multitarget Inhibitor of HDACs, EGFR, and HER2, in Combination with Chemoradiation in Patients with Head and Neck Squamous Cell Carcinoma. Clin. Cancer Res. 21, 1566-1573.

Gether, U., Lin, S., and Kobilka, B.K. (1995). Fluorescent labeling of purified beta 2 adrenergic receptor. Evidence for ligand-specific conformational changes. J. Biol. Chem. 270, 28268-28275.

Gimbrone, M.A., Jr., Leapman, S.B., Cotran, R.S., and Folkman, J. (1972). Tumor dormancy in vivo by prevention of neovascularization. J. Exp. Med. 136, 261-276.

Glennon, R.A., Slusher, R.M., Lyon, R.A., Titeler, M., and McKenney, J.D. (1986). 5-HT1 and 5-HT2 binding characteristics of some quipazine analogues. J. Med. Chem. 29, 2375-2380.

Graybiel, A.M. (2005). The basal ganglia: learning new tricks and loving it. Curr. Opin. Neurobiol. 15, 638-644.

Gupta, A., Mulder, J., Gomes, I., Rozenfeld, R., Bushlin, I., Ong, E., Lim, M., Maillet, E., Junek, M., Cahill, C.M., et al. (2010). Increased abundance of opioid receptor heteromers after chronic morphine administration. Science signaling 3, ra54.

Hanahan, D., and Folkman, J. (1996). Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell 86, 353-364.

Hanahan, D., and Weinberg, R.A. (2000). The hallmarks of cancer. Cell 100, 57-70.

427

APPENDICES

Hauser, R.A., Olanow, C.W., Kieburtz, K.D., Pourcher, E., Docu-Axelerad, A., Lew, M., Kozyolkin, O., Neale, A., Resburg, C., Meya, U., et al. (2014). Tozadenant (SYN115) in patients with Parkinson's disease who have motor fluctuations on levodopa: a phase 2b, double-blind, randomised trial. Lancet Neurol. 13, 767-776.

Hawkins, P.C., Skillman, A.G., and Nicholls, A. (2007). Comparison of shape- matching and docking as virtual screening tools. J. Med. Chem. 50, 74-82.

Horvath, D. (1997). A virtual screening approach applied to the search for trypanothione reductase inhibitors. J. Med. Chem. 40, 2412-2423.

Howard, H.R., Lowe, J.A., 3rd, Seeger, T.F., Seymour, P.A., Zorn, S.H., Maloney, P.R., Ewing, F.E., Newman, M.E., Schmidt, A.W., Furman, J.S., et al. (1996). 3-Benzisothiazolylpiperazine derivatives as potential atypical antipsychotic agents. J. Med. Chem. 39, 143-148.

Idanpaan-Heikkila, J., Alhava, E., Olkinuora, M., and Palva, I. (1975). Letter: Clozapine and agranulocytosis. Lancet 2, 611.

Jain, A.N. (2008). Bias, reporting, and sharing: computational evaluations of docking methods. J. Comput. Aided Mol. Des. 22, 201-212.

Kahsai, A.W., Xiao, K., Rajagopal, S., Ahn, S., Shukla, A.K., Sun, J., Oas, T.G., and Lefkowitz, R.J. (2011). Multiple ligand-specific conformations of the beta2-adrenergic receptor. Nat. Chem. Biol. 7, 692-700.

Kane, J., Honigfeld, G., Singer, J., and Meltzer, H. (1988). Clozapine for the treatment-resistant schizophrenic. A double-blind comparison with chlorpromazine. Arch. Gen. Psychiatry 45, 789-796.

428

APPENDICES

Kniazeff, J., Prezeau, L., Rondard, P., Pin, J.P., and Goudet, C. (2011). Dimers and beyond: The functional puzzles of class C GPCRs. Pharmacol. Ther. 130, 9-25.

Knox, A.J., Price, T., Pawlak, M., Golfis, G., Flood, C.T., Fayne, D., Williams, D.C., Meegan, M.J., and Lloyd, D.G. (2009). Integration of ligand and structure-based virtual screening for the identification of the first dual targeting agent for heat shock protein 90 (Hsp90) and tubulin. J. Med. Chem. 52, 2177-2180.

Kobilka, B.K. (1995). Amino and carboxyl terminal modifications to facilitate the production and purification of a G protein-coupled receptor. Anal. Biochem. 231, 269-271.

Kobilka, B.K., Kobilka, T.S., Daniel, K., Regan, J.W., Caron, M.G., and Lefkowitz, R.J. (1988). Chimeric alpha 2-,beta 2-adrenergic receptors: delineation of domains involved in effector coupling and ligand binding specificity. Science 240, 1310-1316.

Kobilka, B.K., Matsui, H., Kobilka, T.S., Yang-Feng, T.L., Francke, U., Caron, M.G., Lefkowitz, R.J., and Regan, J.W. (1987). Cloning, sequencing, and expression of the gene coding for the human platelet alpha 2-adrenergic receptor. Science 238, 650-656.

Kroeze, W.K., Hufeisen, S.J., Popadak, B.A., Renock, S.M., Steinberg, S., Ernsberger, P., Jayathilake, K., Meltzer, H.Y., and Roth, B.L. (2003). H1- histamine receptor affinity predicts short-term weight gain for typical and atypical antipsychotic drugs. Neuropsychopharmacology 28, 519-526.

Krüger, D.M., and Evers, A. (2010). Comparison of Structure- and Ligand- Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors. ChemMedChem 5, 148-158.

Kruse, A.C., Ring, A.M., Manglik, A., Hu, J., Hu, K., Eitel, K., Hubner, H., Pardon, E., Valant, C., Sexton, P.M., et al. (2013). Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 504, 101-106.

429

APPENDICES

Laird, A.D., Vajkoczy, P., Shawver, L.K., Thurnher, A., Liang, C., Mohammadi, M., Schlessinger, J., Ullrich, A., Hubbard, S.R., Blake, R.A., et al. (2000). SU6668 is a potent antiangiogenic and antitumor agent that induces regression of established tumors. Cancer Res. 60, 4152-4160.

Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al. (2014). DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091- 1097.

Lefkowitz, R.J. (2013). A brief history of G-protein coupled receptors (Nobel Lecture). Angew. Chem. Int. Ed. Engl. 52, 6366-6378.

Lefkowitz, R.J., and Shenoy, S.K. (2005). Transduction of receptor signals by beta-arrestins. Science 308, 512-517.

Leucht, S., Cipriani, A., Spineli, L., Mavridis, D., Orey, D., Richter, F., Samara, M., Barbui, C., Engel, R.R., Geddes, J.R., et al. (2013). Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 382, 951-962.

Li, Y., Li, P.K., Roberts, M.J., Arend, R.C., Samant, R.S., and Buchsbaum, D.J. (2014). Multi-targeted therapy of cancer by niclosamide: A new application for an old drug. Cancer Lett. 349, 8-14.

Lieberman, J.A., Bymaster, F.P., Meltzer, H.Y., Deutch, A.Y., Duncan, G.E., Marx, C.E., Aprille, J.R., Dwyer, D.S., Li, X.M., Mahadik, S.P., et al. (2008). Antipsychotic drugs: comparison in animal models of efficacy, neurotransmitter regulation, and neuroprotection. Pharmacol. Rev. 60, 358- 403.

Lieberman, J.A., Stroup, T.S., McEvoy, J.P., Swartz, M.S., Rosenheck, R.A., Perkins, D.O., Keefe, R.S., Davis, S.M., Davis, C.E., Lebowitz, B.D., et al. (2005). Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med. 353, 1209-1223.

Liegeois, J.F., Ichikawa, J., and Meltzer, H.Y. (2002). 5-HT(2A) receptor antagonism potentiates haloperidol-induced dopamine release in rat medial prefrontal cortex and inhibits that in the nucleus accumbens in a dose- dependent manner. Brain Res. 947, 157-165.

430

APPENDICES

Lohse, M.J., Benovic, J.L., Codina, J., Caron, M.G., and Lefkowitz, R.J. (1990). beta-Arrestin: a protein that regulates beta-adrenergic receptor function. Science 248, 1547-1550.

Lowe, J.A., 3rd, Seeger, T.F., and Vinick, F.J. (1988). Atypical antipsychotics-- recent findings and new perspectives. Med. Res. Rev. 8, 475-497.

Macarron, R., Banks, M.N., Bojanic, D., Burns, D.J., Cirovic, D.A., Garyantes, T., Green, D.V., Hertzberg, R.P., Janzen, W.P., Paslay, J.W., et al. (2011). Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov. 10, 188-195.

Manera, C., and Saccomanni, G. (2010). A2A receptor ligands: Past, present and future trends. Curr. Top. Med. Chem. 10, 902-922.

Manglik, A., and Kobilka, B. (2014). The role of protein dynamics in GPCR function: insights from the beta2AR and rhodopsin. Curr. Opin. Cell Biol. 27, 136-143.

McEvoy, J.P., Lieberman, J.A., Stroup, T.S., Davis, S.M., Meltzer, H.Y., Rosenheck, R.A., Swartz, M.S., Perkins, D.O., Keefe, R.S., Davis, C.E., et al. (2006). Effectiveness of clozapine versus olanzapine, quetiapine, and risperidone in patients with chronic schizophrenia who did not respond to prior atypical antipsychotic treatment. Am. J. Psychiatry 163, 600-610.

McMillen, B.A., and Mattiace, L.A. (1983). Comparative neuropharmacology of buspirone and MJ-13805, a potential anti-anxiety drug. J. Neural Transm. 57, 255-265.

431

APPENDICES

Meltzer, H.Y., Alphs, L., Green, A.I., Altamura, A.C., Anand, R., Bertoldi, A., Bourgeois, M., Chouinard, G., Islam, M.Z., Kane, J., et al. (2003). Clozapine treatment for suicidality in schizophrenia: International Suicide Prevention Trial (InterSePT). Arch. Gen. Psychiatry 60, 82-91.

Meltzer, H.Y., Matsubara, S., and Lee, J.C. (1989). Classification of typical and atypical antipsychotic drugs on the basis of dopamine D-1, D-2 and serotonin2 pKi values. J. Pharmacol. Exp. Ther. 251, 238-246.

Meltzer, H.Y., and Okayli, G. (1995). Reduction of suicidality during clozapine treatment of neuroleptic-resistant schizophrenia: impact on risk- benefit assessment. Am. J. Psychiatry 152, 183-190.

Mendel, D.B., Laird, A.D., Xin, X., Louie, S.G., Christensen, J.G., Li, G., Schreck, R.E., Abrams, T.J., Ngai, T.J., Lee, L.B., et al. (2003). In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: determination of a pharmacokinetic/pharmacodynamic relationship. Clin. Cancer Res. 9, 327-337.

Moffat, K., Gillet, V.J., Whittle, M., Bravi, G., and Leach, A.R. (2008). A comparison of field-based similarity searching methods: CatShape, FBSS, and ROCS. J. Chem. Inf. Model. 48, 719-729.

Motzer, R.J., Hutson, T.E., Tomczak, P., Michaelson, M.D., Bukowski, R.M., Rixe, O., Oudard, S., Negrier, S., Szczylik, C., Kim, S.T., et al. (2007). Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N. Engl. J. Med. 356, 115-124.

Njar, V.C., and Brodie, A.M. (2015). Discovery and development of Galeterone (TOK-001 or VN/124-1) for the treatment of all stages of prostate cancer. J. Med. Chem. 58, 2077-2087.

Nygaard, R., Zou, Y., Dror, R.O., Mildorf, T.J., Arlow, D.H., Manglik, A., Pan, A.C., Liu, C.W., Fung, J.J., Bokoch, M.P., et al. (2013). The dynamic process of beta(2)-adrenergic receptor activation. Cell 152, 532-542.

O'Dowd, B.F., Hnatowich, M., Regan, J.W., Leader, W.M., Caron, M.G., and Lefkowitz, R.J. (1988). Site-directed mutagenesis of the cytoplasmic domains of the human beta 2-adrenergic receptor. Localization of regions involved in G protein-receptor coupling. J. Biol. Chem. 263, 15985-15992.

432

APPENDICES

Olney, J.W., Newcomer, J.W., and Farber, N.B. (1999). NMDA receptor hypofunction model of schizophrenia. J. Psychiatr. Res. 33, 523-533.

Osada, T., Chen, M., Yang, X.Y., Spasojevic, I., Vandeusen, J.B., Hsu, D., Clary, B.M., Clay, T.M., Chen, W., Morse, M.A., et al. (2011). Antihelminth compound niclosamide downregulates Wnt signaling and elicits antitumor responses in tumors with activating APC mutations. Cancer Res. 71, 4172- 4182.

Parangi, S., O'Reilly, M., Christofori, G., Holmgren, L., Grosfeld, J., Folkman, J., and Hanahan, D. (1996). Antiangiogenic therapy of transgenic mice impairs de novo tumor growth. Proc. Natl. Acad. Sci. U. S. A. 93, 2002-2007.

Perola, E. (2010). An analysis of the binding efficiencies of drugs and their leads in successful drug discovery programs. J. Med. Chem. 53, 2986-2997.

Pinna, A. (2014). Adenosine A2A receptor antagonists in Parkinson's disease: progress in clinical trials from the newly approved istradefylline to drugs in early development and those already discontinued. CNS Drugs 28, 455-474.

Rask-Andersen, M., Almen, M.S., and Schioth, H.B. (2011). Trends in the exploitation of novel drug targets. Nat. Rev. Drug Discov. 10, 579-590.

Rasmussen, S.G., Choi, H.J., Fung, J.J., Pardon, E., Casarosa, P., Chae, P.S., Devree, B.T., Rosenbaum, D.M., Thian, F.S., Kobilka, T.S., et al. (2011). Structure of a nanobody-stabilized active state of the beta(2) adrenoceptor. Nature 469, 175-180.

Rasmussen, S.G., Choi, H.J., Rosenbaum, D.M., Kobilka, T.S., Thian, F.S., Edwards, P.C., Burghammer, M., Ratnala, V.R., Sanishvili, R., Fischetti, R.F., et al. (2007). Crystal structure of the human beta2 adrenergic G-protein- coupled receptor. Nature 450, 383-387.

Rasmussen, S.G., DeVree, B.T., Zou, Y., Kruse, A.C., Chung, K.Y., Kobilka, T.S., Thian, F.S., Chae, P.S., Pardon, E., Calinski, D., et al. (2011). Crystal structure of the beta2 adrenergic receptor-Gs protein complex. Nature 477, 549-555.

433

APPENDICES

Raymond, E., Dahan, L., Raoul, J.L., Bang, Y.J., Borbath, I., Lombard-Bohas, C., Valle, J., Metrakos, P., Smith, D., Vinik, A., et al. (2011). Sunitinib malate for the treatment of pancreatic neuroendocrine tumors. N. Engl. J. Med. 364, 501-513.

Ripphausen, P., Nisius, B., and Bajorath, J. (2011). State-of-the-art in ligand- based virtual screening. Drug Discov. Today 16, 372-376.

Ripphausen, P., Nisius, B., Peltason, L., and Bajorath, J. (2010). Quo vadis, virtual screening? A comprehensive survey of prospective applications. J. Med. Chem. 53, 8461-8467.

Ripphausen, P., Stumpfe, D., and Bajorath, J. (2012). Analysis of structure- based virtual screening studies and characterization of identified active compounds. Future Med. Chem. 4, 603-613.

Rodriguez, D., Gao, Z.G., Moss, S.M., Jacobson, K.A., and Carlsson, J. (2015). Molecular docking screening using agonist-bound GPCR structures: probing the A2A adenosine receptor. J. Chem. Inf. Model. 55, 550-563.

Rosenbaum, D.M., Zhang, C., Lyons, J.A., Holl, R., Aragao, D., Arlow, D.H., Rasmussen, S.G., Choi, H.J., Devree, B.T., Sunahara, R.K., et al. (2011). Structure and function of an irreversible agonist-beta(2) adrenoceptor complex. Nature 469, 236-240.

Rozenfeld, R., Gupta, A., Gagnidze, K., Lim, M.P., Gomes, I., Lee-Ramos, D., Nieto, N., and Devi, L.A. (2011). AT1R-CB(1)R heteromerization reveals a new mechanism for the pathogenic properties of angiotensin II. EMBO J. 30, 2350-2363.

Ruddigkeit, L., Blum, L.C., and Reymond, J.L. (2013). Visualization and virtual screening of the chemical universe database GDB-17. J. Chem. Inf. Model. 53, 56-65.

434

APPENDICES

Sack, U., Walther, W., Scudiero, D., Selby, M., Kobelt, D., Lemm, M., Fichtner, I., Schlag, P.M., Shoemaker, R.H., and Stein, U. (2011). Novel effect of antihelminthic Niclosamide on S100A4-mediated metastatic progression in colon cancer. J. Natl. Cancer Inst. 103, 1018-1036.

Seeman, P. (2006). Targeting the dopamine D2 receptor in schizophrenia. Expert Opin. Ther. Targets 10, 515-531.

Seeman, P., Chau-Wong, M., Tedesco, J., and Wong, K. (1975). Brain receptors for antipsychotic drugs and dopamine: direct binding assays. Proc. Natl. Acad. Sci. U. S. A. 72, 4376-4380.

Seeman, P., and Tallerico, T. (1999). Rapid release of antipsychotic drugs from dopamine D2 receptors: an explanation for low receptor occupancy and early clinical relapse upon withdrawal of clozapine or quetiapine. Am. J. Psychiatry 156, 876-884.

Seeman, P., Ulpian, C., Bergeron, C., Riederer, P., Jellinger, K., Gabriel, E., Reynolds, G.P., and Tourtellotte, W.W. (1984). Bimodal distribution of dopamine receptor densities in brains of schizophrenics. Science 225, 728- 731.

Seeman, P., Wong, M., and Lee, T. (1974). Dopamine receptor-block and nigral fiber impulse-blockade by major tranquilizers. Fed. Proc. 33, 246.

Shekhar, A., Potter, W.Z., Lightfoot, J., Lienemann, J., Dube, S., Mallinckrodt, C., Bymaster, F.P., McKinzie, D.L., and Felder, C.C. (2008). Selective muscarinic receptor agonist xanomeline as a novel treatment approach for schizophrenia. Am. J. Psychiatry 165, 1033-1039.

Shibuya, M., and Claesson-Welsh, L. (2006). Signal transduction by VEGF receptors in regulation of angiogenesis and lymphangiogenesis. Exp. Cell Res. 312, 549-560.

Smith, A. (2002). Screening for drug discovery: the leading question. Nature 418, 453-459.

Snyder, S.H. (1976). The dopamine hypothesis of schizophrenia: focus on the dopamine receptor. Am. J. Psychiatry 133, 197-202.

435

APPENDICES

Soergel, D.G., Subach, R.A., Cowan, C.L., Violin, J.D., and Lark, M.W. (2013). First clinical experience with TRV027: pharmacokinetics and pharmacodynamics in healthy volunteers. J. Clin. Pharmacol. 53, 892-899.

Sorensen, S.M., Kehne, J.H., Fadayel, G.M., Humphreys, T.M., Ketteler, H.J., Sullivan, C.K., Taylor, V.L., and Schmidt, C.J. (1993). Characterization of the 5-HT2 receptor antagonist MDL 100907 as a putative atypical antipsychotic: behavioral, electrophysiological and neurochemical studies. J. Pharmacol. Exp. Ther. 266, 684-691.

Spano, P.F., Govoni, S., and Trabucchi, M. (1978). Studies on the pharmacological properties of dopamine receptors in various areas of the central nervous system. Adv. Biochem. Psychopharmacol. 19, 155-165.

Stadel, J.M., Nambi, P., Shorr, R.G., Sawyer, D.F., Caron, M.G., and Lefkowitz, R.J. (1983). Catecholamine-induced desensitization of turkey erythrocyte adenylate cyclase is associated with phosphorylation of the beta- adrenergic receptor. Proc. Natl. Acad. Sci. U. S. A. 80, 3173-3177.

Sun, L., Liang, C., Shirazian, S., Zhou, Y., Miller, T., Cui, J., Fukuda, J.Y., Chu, J.Y., Nematalla, A., Wang, X., et al. (2003). Discovery of 5-[5-fluoro-2- oxo-1,2- dihydroindol-(3Z)-ylidenemethyl]-2,4- dimethyl-1H-pyrrole-3- carboxylic acid (2-diethylaminoethyl)amide, a novel tyrosine kinase inhibitor targeting vascular endothelial and platelet-derived growth factor receptor tyrosine kinase. J. Med. Chem. 46, 1116-1119.

Sun, L., Tran, N., Liang, C., Tang, F., Rice, A., Schreck, R., Waltz, K., Shawver, L.K., McMahon, G., and Tang, C. (1999). Design, synthesis, and evaluations of substituted 3-[(3- or 4-carboxyethylpyrrol-2- yl)methylidenyl]indolin-2-ones as inhibitors of VEGF, FGF, and PDGF receptor tyrosine kinases. J. Med. Chem. 42, 5120-5130.

Sun, L., Tran, N., Tang, F., App, H., Hirth, P., McMahon, G., and Tang, C. (1998). Synthesis and biological evaluations of 3-substituted indolin-2-ones: a novel class of tyrosine kinase inhibitors that exhibit selectivity toward particular receptor tyrosine kinases. J. Med. Chem. 41, 2588-2603.

Tiihonen, J., Lonnqvist, J., Wahlbeck, K., Klaukka, T., Niskanen, L., Tanskanen, A., and Haukka, J. (2009). 11-year follow-up of mortality in patients with schizophrenia: a population-based cohort study (FIN11 study). Lancet 374, 620-627.

436

APPENDICES

Ullrich, A., and Schlessinger, J. (1990). Signal transduction by receptors with tyrosine kinase activity. Cell 61, 203-212.

Valjent, E., Bertran-Gonzalez, J., Herve, D., Fisone, G., and Girault, J.A. (2009). Looking BAC at striatal signaling: cell-specific analysis in new transgenic mice. Trends Neurosci. 32, 538-547. van Os, J., Kenis, G., and Rutten, B.P. (2010). The environment and schizophrenia. Nature 468, 203-212. van Rossum, J.M. (1966). The significance of dopamine-receptor blockade for the mechanism of action of neuroleptic drugs. Arch. Int. Pharmacodyn. Ther. 160, 492-494.

Vauquelin, G., and von Mentzer, B. (2007). Radioliogand Binding Studies. In G Protein-Coupled Receptors (John Wiley & Sons, Ltd), pp. 29-52.

Violin, J.D., DeWire, S.M., Yamashita, D., Rominger, D.H., Nguyen, L., Schiller, K., Whalen, E.J., Gowen, M., and Lark, M.W. (2010). Selectively engaging beta-arrestins at the angiotensin II type 1 receptor reduces blood pressure and increases cardiac performance. J. Pharmacol. Exp. Ther. 335, 572-579.

Violin, J.D., Soergel, D.G., Boerrigter, G., Burnett, J.C., Jr., and Lark, M.W. (2013). GPCR biased ligands as novel heart failure therapeutics. Trends Cardiovasc. Med. 23, 242-249.

Walker, A.M., Lanza, L.L., Arellano, F., and Rothman, K.J. (1997). Mortality in current and former users of clozapine. Epidemiology 8, 671-677.

Warren, G.L., Andrews, C.W., Capelli, A.M., Clarke, B., LaLonde, J., Lambert, M.H., Lindvall, M., Nevins, N., Semus, S.F., Senger, S., et al. (2006). A critical assessment of docking programs and scoring functions. J. Med. Chem. 49, 5912-5931.

Williams, L.T., and Lefkowitz, R.J. (1976). Alpha-adrenergic receptor identification by (3H)dihydroergocryptine binding. Science 192, 791-793.

437

APPENDICES

Wolber, G., Seidel, T., Bendix, F., and Langer, T. (2008). Molecule- pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov. Today 13, 23-29.

Wootten, D., Christopoulos, A., and Sexton, P.M. (2013). Emerging paradigms in GPCR allostery: implications for drug discovery. Nat. Rev. Drug Discov. 12, 630-644.

Wu, H., Wang, C., Gregory, K.J., Han, G.W., Cho, H.P., Xia, Y., Niswender, C.M., Katritch, V., Meiler, J., Cherezov, V., et al. (2014). Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Science 344, 58-64.

Xu, F., Wu, H., Katritch, V., Han, G.W., Jacobson, K.A., Gao, Z.G., Cherezov, V., and Stevens, R.C. (2011). Structure of an agonist-bound human A2A adenosine receptor. Science 332, 322-327.

Yao, X.J., Velez Ruiz, G., Whorton, M.R., Rasmussen, S.G., DeVree, B.T., Deupi, X., Sunahara, R.K., and Kobilka, B. (2009). The effect of ligand efficacy on the formation and stability of a GPCR-G protein complex. Proc. Natl. Acad. Sci. U. S. A. 106, 9501-9506.

Yevich, J.P., New, J.S., Smith, D.W., Lobeck, W.G., Catt, J.D., Minielli, J.L., Eison, M.S., Taylor, D.P., Riblet, L.A., and Temple, D.L., Jr. (1986). Synthesis and biological evaluation of 1-(1,2-benzisothiazol-3-yl)- and (1,2- benzisoxazol-3-yl)piperazine derivatives as potential antipsychotic agents. J. Med. Chem. 29, 359-369.

You, S., Li, R., Park, D., Xie, M., Sica, G.L., Cao, Y., Xiao, Z.Q., and Deng, X. (2014). Disruption of STAT3 by niclosamide reverses radioresistance of human lung cancer. Mol. Cancer Ther. 13, 606-616.

Zhang, Q., Zhai, S., Li, L., Li, X., Zhou, H., Liu, A., Su, G., Mu, Q., Du, Y., and Yan, B. (2013). Anti-tumor selectivity of a novel tubulin and HSP90 dual- targeting inhibitor in non-small cell lung cancer models. Biochem. Pharmacol. 86, 351-360.

438