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DRUG DISCOVERY AND DEVELOPMENT OF NATURAL PRODUCTS FROM MARINE CYANOBACTERIA AS ANTICANCER AGENTS AND GROWTH FACTOR MODULATORS

WEIJING CAI

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

To my parents and my husband

ACKNOWLEDGMENTS

I would like to express my deep appreciation and gratitude to my advisor, Dr.

Hendrik Luesch, a very passionate scientist and talented teacher. I am truly fortunate to have had the opportunity to work with him. I thank Dr. Luesch for giving me intellectual and patient guidance throughout my graduate studies.

I would also like to thank my committee members, Dr. Margaret O. James, Dr.

Yousong Ding and Dr. Brian K. Law for their time, patience and invaluable help throughout the process. I am thankful of our research collaborators: Dr. Daniel Gibson,

Dr. Soojung Seo, Dr. Jose G. Trevino, Dr. Michael H. Gerber, Dr. Long H. Dang, Dr.

Valerie J.Paul, Dr. Hartmut Derendorf and Ms. Yichao Yu for all their support in specific projects. I am also grateful to Mr. James Rocca for his extensive support of my NMR studies. I also thank the Gene Expression & Genotyping Core and Bioinformatics Core of the Interdisciplinary Center for Biotechnology Research for assistance with the transcriptome profiling, data analysis and heat map generation.

I would also like to acknowledge current and former members of the Luesch Lab

– Dr. Fatma Al-Awadhi, Dr. Mariangela Soares de Azevedo, Dr. Michelle Bousquet, Dr.

Qi-Yin Chen, Mr. Simon Dolles, Ms. Birthe Förster, Dr. Khanh Ha, Dr. Pamela Havre,

Ms. Lorelie Imperial, Dr. Dimitris Kallifidas, Ms. Sabine Kuznia, Dr. Yanxia Liu, Ms. Xiao

Liang, Ms. Danmeng Luo, Dr. James Matthews, Dr. Rana Montaser, Dr. Ranjala

Ratnayake, Dr. Lilibeth Salvador, Ms. Anna Sandner, Ms. Kara Spencer, Dr. Rui Wang and Dr. Wei Zhang for all their help at various stages of my graduate studies.

Last but not least, I want to thank my parents, Dr. Shao-Qing Cai and Dr. Xuan

Wang, and my husband, Mr. Kefu Zhang. This dissertation would not have been

possible without their love and unwavering support. I am grateful to my parents for their encouragement, patience, and guidance throughout this training program and all my life.

I would like to give special thanks to my husband for supporting me in so many ways and for sharing my wish to reach the goal.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 10

LIST OF ABBREVIATIONS ...... 13

ABSTRACT ...... 20

CHAPTER

1 GENERAL INTRODUCTION ...... 22

Natural Products in Drug Discovery and Development ...... 22 Marine Cyanobacteria: A Rich Source of Novel Bioactive Compounds ...... 23 Compounds from Marine Cyanobacteria Target Disease-associated Cell Signaling ...... 25 Growth Factor Modulators as Therapeutic Agents for the Treatment of Cancer and Chronic Wounds ...... 26 Growth Factors and Receptor Tyrosine Kinases (RTKs) ...... 26 Growth Factor Signaling as Therapeutic Target in Cancer ...... 26 Angiogenesis and Cancer ...... 27 Other Growth Factor Associated Disorders ...... 28 Methods Utilized in Present Study for Drug Discovery and Development...... 30 Discovery of Novel Compounds from Marine Cyanobacteria ...... 30 Target Identification and Mode of Action (MOA) Studies for Natural Products ...... 31 Assessment of ADME In Vitro, Pharmacokinetics (PK) and Tissue Distribution ...... 36 Research Aims ...... 37

2 DEVELOPMENT OF APRATOXINS AS ANTI-TUMOR AND ANTI- ANGIOGENESIS AGENTS, ...... 40

Introduction ...... 40 Apratoxins are Inhibitors of Cotranslational Translocation ...... 40 Synthetic Analogues of Apratoxins ...... 42 Efficacy Studies ...... 43 In Vitro Evaluation of Apratoxins as Dual Inhibitors of Angiogenesis and Cancer Cell Growth ...... 43 In Vivo Efficacy Studies of Apratoxin S9 in Colon Cancer Model ...... 50 SiRNA Screening and Combination Therapy ...... 51 Ingenuity Pathway Analysis of Sensitizers from SiRNA Screen ...... 51

DNA Repair and PARP1 are Involved in MOA of Apratoxins ...... 52 In Vivo Combination Study ...... 54 ADME In Vitro, Plasma Pharmacokinetics (PK) and Tissue Distribution Studies .... 55 In Vitro Stability of Apratoxins ...... 55 Plasma Pharmacokinetics (PK) and Tissue Distribution of Apratoxin S10 ...... 55 Summary ...... 57 Experimental Methods ...... 59 General Experimental Methods ...... 59 Cell Culture ...... 59 In Vitro Angiogenesis Assay ...... 59 Cell Viability Assay (MTT) ...... 60 Measurement of Human VEGF-A and IL-6 Secretion ...... 60 Immunoblot Analysis ...... 61 In Vivo Efficacy Study and Combination Study ...... 61 PARP Assay ...... 62 In Vitro Stability Studies...... 63 Plasma Pharmacokinetics and Tissue Distribution ...... 66

3 APRATYRAMIDE, A NEW MODULATOR OF VEGF-A AND OTHER GROWTH FACTORS FROM MARINE CYANOBACTERIA ...... 88

Introduction ...... 88 Apratyramide Induces Transcript Level of VEGF-A in HCT116 Cells ...... 88 Apratyramide Induces Transcription and Secretion Level of VEGF-A in HaCaT .... 89 Apratyramide Induces Other Wound-healing Related Growth Factors ...... 89 Mode of Action Study by Transcriptome Profiling and Ingenuity Pathway Analysis ...... 90 Cytoprotective Roles of UPR and Its Modulatory Effects on Growth Factors. .. 91 Angiogenic and Cytoprotective Roles of Individual Molecular Components in UPR and Their Modulatory Effects on VEGF-A ...... 92 Apratyramide Induces VEGF-A in a Rabbit Corneal Epithelial Ex Vivo Model ...... 94 Formulation Study of Apratyramide ...... 94 Summary ...... 95 Experimental Methods ...... 97 Cell culture ...... 97 Cell Viability Assay (MTT) ...... 97 Measurement of Human VEGF-A Secretion ...... 97 Immunoblot Analysis ...... 98 RNA Isolation and Reverse Transcription...... 98 Real-time Quantitative Polymerase Chain Reaction (qPCR) for Transcript Level Determination in HaCaT Cells ...... 98 Transcriptome Profiling ...... 99 Ex Vivo Organ Culture of Rabbit Corneas ...... 99 Evaluation of Transcript Level of VEGF-A in Ablated Corneas After Treatment with Apratyramide ...... 100 Formulation of Apratyramide ...... 100

4 PITIAMIDES A AND B: CYTOTOXIC FATTY ACID AMIDES FROM MARINE CYANOBACTERIA ...... 112

Introduction ...... 112 Isolation and Structure Determination ...... 113 Biological Evaluation ...... 116 Summary ...... 116 Experimental Methods ...... 117 Chemicals ...... 117 General Experimental Procedures ...... 117 Extraction and Isolation ...... 117 Cell Viability Assay (MTT) ...... 119

5 LAXAPHYCIN ANALOGUES FROM THE MARINE CYANOBACTERIUM HORMOTHAMNION SP...... 127

Introduction ...... 127 Structure Determination ...... 128 Absolute Configuration...... 130 Biological Evaluation ...... 131 Summary ...... 132 Experimental Methods ...... 132 General Experimental Procedures ...... 132 Structure Characterization ...... 133 Acid Hydrolysis and Chiral Amino Acid Analysis ...... 133 Advanced Marfey’s Analysis ...... 134 Cell Viability Assay (MTT) ...... 134

6 CONCLUSIONS ...... 158

APPENDIX

A STANDARD CURVES OF ARPATOXINS FOR IN VITRO PLASMA STABILITY STUDIES ...... 163

B PHASE SOLUBILITY ANALYSIS OF APRATYRAMIDE ...... 169

C NMR SPECTRA OF ISOLATED COMPOUNDS ...... 171

LIST OF REFERENCES ...... 200

BIOGRAPHICAL SKETCH ...... 221

LIST OF TABLES

Table page

2-1 Activities of apratoxins and known RTKs inhibitors on a range of cancer cells ... 68

2-2 Microsomal stability studies ...... 69

2-3 Summary statistics for the pharmacokinetic parameters on the observed concentration-time profiles ...... 70

3-1 Top up- and down- regulated genes after 12 h treatment with 30 µM apratyramide...... 102

4-1 NMR data for 1E-pitiamide B (1) and 1Z-pitiamide B (2) in CDCl3 (600 MHz)... 120

4-2 Reported optical activity of model compounds with α-ketone stereocenter and similar structure scaffold ...... 123

5-1 NMR data for 3 and laxaphycin B3 in DMSO-d6 (600 MHz) ...... 135

5-2 NMR data for 4 in CH3CN-d3 (600 MHz) ...... 140

5-3 NMR data for 4, laxaphycin A (reported) and hormothamnion A (reported) in DMSO-d6 (600 MHz) ...... 144

5-4 Chiral amino acid analysis of 3 ...... 148

5-5 Advanced Marfey’s analysis of 3 ...... 149

5-6 Chiral amino acid analysis of 4 ...... 150

5-7 Advanced Marfey’s analysis of 4 ...... 151

B-1 Phase solubility analysis ...... 169

LIST OF FIGURES

Figure page

1-1 Structures of selected bioactive compounds from marine cyanobacteria ...... 39

2-1 Evolution of apratoxins ...... 71

2-2 Evaluation of apratoxins in an in vitro angiogenesis assay ...... 72

2-3 Activity of apratoxin S10 on VEGF-A secretion ...... 74

2-4 Activity of apratoxin S10 on IL-6 secretion ...... 75

2-5 Apratoxins down-regulated multiple RTKs ...... 76

2-6 Apratoxin S9 was evaluated in HCT116 (human colon cancer cell) tumor- bearing nu/nu mice ...... 77

2-7 Cell viability of apratoxin A treatment relative to control of all genes in siRNA screen. Cell viability was indicated by luminescence signal ...... 78

2-8 IPA analysis of 178 sensitizers...... 79

2-9 Sensitizer hits with DNA repair related functions from siRNA screen ...... 82

2-10 In vivo study of apratoxin S9 in combination with AG014699 using HCT116 xenografted nu/nu mice model ...... 83

2-11 PARP activities of tumor samples ...... 84

2-12 In vitro stability of apratoxins under various conditions ...... 85

2-13 Concentrations of apratoxin S10 in plasma and tissues ...... 86

2-14 Tissue distribution histogram ...... 87

3-1 Structure of apratyramide ...... 103

3-2 Apratyramide induced VEGF-A in colon cell models ...... 104

3-3 Transcript and secretion level of VEGF-A in HaCaT cells ...... 105

3-4 Apratyramide induced transcript levels of other growth factors ...... 106

3-5 Ingenuity Pathway Analysis (IPA) for transcriptome profiling of apratyramide . 107

3-6 Networks involved in the mechanism of action of apratyramide ...... 109

3-7 Apratyramide induced VEGF-A in a rabbit corneal epithelial ex vivo model ..... 110

3-8 Solubility of apratyramide in various concentrations (0 to 40% w/v) of Captisol® ...... 111

4-1 Structures and absolute configuration of natural product pitiamide A, synthetic (7S,10R)-pitiamide A and 1E-pitiamide B (1) and 1Z-pitiamide B (2) 124

4-2 Partial structures of 1E-pitiamide B (1) with key COSY and HMBC correlations ...... 125

4-3 Antiproliferative activity of pitiamides ...... 126

5-1 Laxaphycin A-type compounds ...... 152

5-2 Laxaphycin B-type compounds ...... 153

5-3 Structure of laxaphycin B4 (3) ...... 154

5-4 Structure of laxaphycin A2 (4) ...... 155

5-5 Structure elucidation of compound 3 ...... 156

5-6 Structure elucidation of compound 4 ...... 157

B-1 Standard curve of apraytyramide for quantification...... 170

1 C-1 H NMR spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz)...... 172

C-2 COSY spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz)...... 173

C-3 TOCSY spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz) ...... 174

C-4 HSQC spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz)...... 175

C-5 HMBC spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz) ...... 176

1 C-6 H NMR spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz) ...... 177

C-7 COSY spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz) ...... 178

C-8 TOCSY spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz)...... 179

C-9 HSQC spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz)...... 180

C-10 HMBC spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz) ...... 181

1 C-11 H NMR spectrum of pitiamide A in CDCl3 (600 MHz) ...... 182

C-12 COSY spectrum of pitiamide A in CDCl3 (600 MHz)...... 183

C-13 TOCSY spectrum of pitiamide A in CDCl3 (600 MHz) ...... 184

C-14 HSQC spectrum of pitiamide A in CDCl3 (600 MHz) ...... 185

C-15 HMBC spectrum of pitiamide A in CDCl3 (600 MHz) ...... 186

1 C-16 H NMR spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 187

C-17 COSY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 188

C-18 HSQC spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 189

C-19 HMBC spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 190

C-20 TOCSY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 191

C-21 ROESY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz) ...... 192

1 C-22 H NMR spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz) ...... 193

C-23 COSY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz) ...... 194

C-24 HSQC spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz)...... 195

C-25 HMBC spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz) ...... 196

C-26 TOCSY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz)...... 197

C-27 ROESY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz) ...... 198

C-28 1D ROESY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz), saturation at δ 5.66 ppm...... 199

LIST OF ABBREVIATIONS

20 [α] D Specific optical rotation

13C NMR Carbon-13 nuclear magnetic resonance spectroscopy

1D One-dimensional

1H NMR Proton nuclear magnetic resonance spectroscopy

2D Two-dimensional

ADME Absorption, Distribution, Metabolism, and Excretion

Ala Alanine

ANOVA Analysis of variance

APCI/ESI Atmospheric pressure chemical ionization/electrospray ionization

Asp Aspartic acid

ATF4 Activating transcription factor 4

AUC Area under the concentration-time curve to infinity

AUCt Area under the concentration-time curve to the last point

BCA Bicinchoninic acid bFGF Basic fibroblast growth factor br q Broad quartet

C Concentration in g/100 mL

C=O Carbonyl calcd Calculated

CC Combinatorial chemistry

CDCl3 Deuterated chloroform cDNA Complementary deoxyribonucleic acid

CE Collision energy

CEP Collision cell entrance potential

CH2Cl2 Methylene chloride

CH3 Methyl

CL/F Clearance

Cmax The peak concentration after drug administration

COSY Correlation spectroscopy

CUR Curtain gas

CuSO4 Copper (II) sulfate

CXP Collision cell exit potential d Doublet

D- Configurational descriptor (Fisher system) db/db A mice model of diabetes dd Doublet of doublets

DFUs Diabetic foot ulcers

DICER A ribonuclease (RNase) III enzyme

DMEM Dulbecco’s Modified Eagle Medium

DMSO Dimethyl sulfoxide

DMSO-d6 Deuterated dimethyl sulfoxide

DP Declustering potential dsRNA Double-stranded RNA dt Doublet of triplets

Dtena 3,7-dihydroxy-2,5,8,8-tetramethylnonanoic acid

EGFR Epidermal growth factor receptor

ELISA Enzyme-linked immunosorbent assay

EP Entrance potential

ER Endoplasmic reticulum

ESIMS Electrospray ionization mass spectrometry

EtOAc Ethyl acetate

EtOH Ethanol

FBS Fetal bovine serum

FDA Federal Drug Administration

FGF Fibroblast growth factor

FGFR Fibroblast growth factor receptor g Gravity g Gram

GAPDH Glyceraldehyde 3-phosphate dehydrogenase

Gln Glutamine

Glu Glutamic acid

Gly Glycine

GM-CSF Granulocyte-macrophage colony stimulating factor

GS1 Gas 1

GS2 Gas 2 h Hour

HCl Hydrochloric acid

HCOOH Formic acid

HIF Hypoxia inducible factor

HMBC Heteronuclear multiple-bond correlation spectroscopy

Hmpa 2-hydroxy-3-methylpentanoic acid

HPLC-MS Tandem high pressure liquid chromatography-mass spectrometry

HPLC-UV Tandem high pressure liquid chromatography-ultraviolet spectroscopy

HRESIMS High-resolution electrospray ionization mass spectrometry

HSQC Heteronuclear single-quantum correlation spectroscopy

HTS High throughput screening i.p. Intraperitoneal i.v. Intravenous

IC50 Half-maximal inhibitory concentration

IL-6 Interleukin 6

Ile Isoleucine

IPA Ingenuity Pathway Analysis i-PrOH Isopropanol

IR Ionizing radiaion

IRE1 Inositol-requiring enzyme 1

IS Ionspray voltage

Ke Elimination rate constant m Meter m multiplet (NMR)

M Molar

MAP4K4 Mitogen-activated protein 4 kinase 4

MeCN Acetonitrile

MeOH Methanol

MHz Megahertz min Minute

MOA Mode of action moCys Modified cysteine residue

MRM Multiple reaction monitoring

MTT 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide

Na Sodium n-BuOH n-butanol

NCE New chemical entity

NH4OAc Ammonium acetate nJ Coupling constants via n bonds nM Nanomolar

N-Me-Ala N-methyl alanine

N-Me-Ile N-methyl isoleucine

NRPS Nonribosomal peptide synthetases

NSCLC Non-small cell lung cancer nu/nu Nude mice model

OMe Methoxy

O-Me-Tyr O-methyl tyrosine

PAI-1 Plaminogen activator inhibitor-1

PAR Poly (ADP-ribose)

PARP Poly (ADP-ribose) polymerase

PARP1 Poly (ADP-ribose) polymerase family, member 1

PDGF Platelet-derived growth factor

PDGFR Platelet-derived growth factor receptor

Phe Phenylalanine

PIGF Placental growth factor

PK Pharmacokinetics

PKS Polyketide synthases

Pro Proline

PUs Pressure ulcers

RCC Renal cell carcinoma rhPDGF-BB Recombinant human platelet-derived growth factor BB

RISC RNA-induced silencing complex

RNA Ribonucleic acid

RNAi RNA interference

RNC Ribosome-nascent chain complex

RTK Receptor tyrosine kinase

RT-qPCR Reverse transcription followed by quantitative polymerase chain reaction s Singlet

SAR Structure-activity relationship

SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis

Ser Serine shRNA Short hairpin RNA siRNA Small interference RNA

SR Membrane-bound receptor of SRP

SRP Signal recognition particle t1/2 Half-life

TEM Temperature

Thr Threonine

TK Tyrosine kinase

Tmax Time to reach the maximum concentration

TMZ Temozolomide

TOCSY Total correlation spectroscopy

Topo I Topoisomerase I tR Retention time

Tyr Tyrosine

UPR Unfolded protein response

Val Valine

VCAM1 Vascular cell adhesion molecule 1

VEGF-A Vascular endothelial growth factor A

VEGFR Vascular endothelial growth factor receptor

VHL Von Hippel Lindau

VUs Venous leg ulcers

Vz/F Volume of distribution

δ Chemical shift (in ppm)

λmax Wavelength maximum

μM Micromolar

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

DRUG DISCOVERY AND DEVELOPMENT OF NATURAL PRODUCTS FROM MARINE CYANOBACTERIA AS ANTICANCER AGENTS AND GROWTH FACTOR MODULATORS

Weijing Cai

August 2017

Chair: Hendrik Luesch Major: Pharmaceutical Sciences – Medicinal Chemistry

This research presented the studies of four groups of compounds from marine cyanobacteria that are at different stages of the drug discovery and development process as anticancer agents or growth factor modulators.

We first presented the early development of apratoxins as dual inhibitors against angiogenesis and tumor growth, as a logical extension of its mechanism of action of cotranslational translocation inhibition at the level of Sec61. Apratoxins showed potent anti-angiogenic activity in vitro and antiproliferative activities against a variety of cancer cell types through down-regulation of a number of RTKs and inhibition of secretion of

VEGF-A and IL-6. Apratoxin S9 significantly retarded tumor growth in colon cancer xenograft mice model. Suggested from a siRNA-based genomic drug susceptibility screen, a rational combination therapy in vivo was performed, indicating cooperative effects between a PARP inhibitor and apratoxin S9. In vitro stability, pharmacokinetics

(PK) and tissue distribution studies of apratoxins indicated their favorable stability properties as well as an unusual high enrichment in mice pancreas.

In contrast to the growth factor inhibitory effects of apratoxins, we elucidated the biological activity and MOA of a linear depsipeptide, apratyramide, as a growth factor inducer. In vitro assays using human keratinocyte (HaCaT) cells indicated that apratyramide up-regulated transcript levels of multiple growth factors and induced secretion of VEGF-A. Transcriptome analysis and sequential validation suggested its potential wound-healing properties through growth factor induction and its MOA through the UPR pathway which is functionally related to wound healing and angiogenesis. An ex vivo rabbit corneal epithelial model was applied to confirm the VEGF-A induction in this wound-healing model.

Thirdly, we described the discovery of two groups of cytotoxic compounds from marine cyanobacteria: fatty acid amides 1E-pitiamide B (1) and 1Z-pitiamide B (2) and cyclic peptides laxaphycin B4 (3) and laxaphycin A2 (4). Pitiamides were isolated through bio-assay guided fractionation. Total structure elucidation of all four compounds was performed using 1D and 2D NMR spectroscopy, mass spectrometry and enantioselective analysis of acid hydrolyzate. Biological evaluation studies indicated anticancer activities of pitiamides (1, 2) and laxapycin B4 (3) against human colon cancer cells.

CHAPTER 1 GENERAL INTRODUCTION

Natural Products in Drug Discovery and Development

Nature has evolved through at least 2.5 billion years to produce an amazingly diverse array of natural products with wide structural variation which provides possibilities for their numerous applications for the treatment of human diseases. Based on an analysis on the sources of new drugs over the period of 01/1981-12/2014, more than half of these drugs were derived from nature.1

It has been widely accepted that natural products have intriguing structures with diverse pharmacophores which provide a valuable and rich pool for the discovery of lead compounds for further development.2–5 Statistical studies indicated that natural products have higher steric complexity in structures with a larger average numbers of rings and chiral centers per molecule than synthetic compounds.6–9 The structural complexity and diversity supports the belief that natural products better exemplify the

“chemical space” of drug-like scaffolds than those of synthetic origin.10 In addition, natural products are evolved to have drug-like properties in terms of bioactivity, selectivity, protein-binding properties, favorable pharmacokinetics (PK) properties.11

Although there was a diminished interest in natural products about two decades ago with the advent of combinatorial chemistry (CC) and high throughput screening

(HTS), it is now realized that the distinctive structural diversity of natural compounds is advantageous during the production of combinatorial libraries.12 Those approaches utilizing libraries containing only synthetic compounds did not meet the initial expectations and often yield low hit rates due to limited “chemical space” of the compound libraries.11,13 Consequently, efforts have been made over the last decade to

design and produce CC libraries inspired by natural product scaffolds using semi- synthetic methods.14–18

Marine Cyanobacteria: A Rich Source of Novel Bioactive Compounds

While plants are the major and most traditional source for many natural products, novel sources of natural products were explored to discover chemical and biological novelties. With the advent of SCUBA, marine organisms were accessible within the last three decades and it’s now widely accepted that marine organisms constitute promising, abundant, and valuable sources for bioactive natural compounds. The world’s oceans cover more than 70% of the earth’s surface, which presents a huge potential for the drug discovery from marine sources.19 One successful example of marine natural product is ecteinascidin-743, approved for the treatment of refractory soft-tissue sarcomas by the European Commission in 2007.20

Marine cyanobacteria are a rich source of bioactive secondary metabolites.21,22

Twenty-percent of marine-derived drugs and clinical trial agents are likely to have cyanobacteria as predicted biosynthetic sources.23 Marine cyanobacteria produce peptides, polyketides and hybrid polyketide-polypeptides (Figure 1-1) being biosynthesized by multi-modular enzymatic systems integrating nonribosomal peptide synthetases (NRPS) and polyketide synthases (PKS) biosynthetic pathways.21 The majority of compounds isolated from marine cyanobacteria fall into three structural classes: linear peptides/depsipeptides, cyclic depsipeptides/peptides and fatty acids/fatty acid amides. There is a high degree of structural modification on these hybrid compounds, which confers structural diversity and broad spectrums of bioactivities. For instance, N- and/or O-methylation, α/β-hydroxy acids, β-amino acids, hydroxylation, incorporation of aromatic heterocycles and halogen atoms are commonly observed.21

The intriguing structural features of marine cyanobacterial compounds allow them to interact with a variety of cellular targets, which results in a broad spectrum of biological activities, including anticancer, antimicrobial, protease inhibitory, immunomodulatory and neuromodulatory properties.22,24 The cytotoxic or antiproliferative activities of marine cyanobacteria compounds are the most observed and therefore most studied.25 One reason why marine cyanobacteria may have evolved this extensive capacity to produce such cytotoxic molecules is that their survival in herbivorous environment requires a defense system to deter the feeding and inhibit digestion by diverse types of macrograzers.26–28 For example, malyngolide, isolated from cyanobacterium Lyngbya majuscula has been shown to serve as feeding deterrent to juvenile rabbitfishes and parrot fish.29 Cytotoxic or antiproliferative agents from marine cyanobacteria are promising therapeutic agents for the treatment of cancer, bacterial or fungal infections. One successful example is dolastatin 10 (Figure 1-1), a linear pentapeptide possessing potent antiproliferative activity, with pico- to nano-molar

IC50 values against various types of cancer cells through the disruption of microtubules.30,31 Dolastatin 10 was first isolated from Indian Ocean sea hare Dolabella auricularia. It was subsequently discovered that dolastatin 10 is originally produced by marine cyanobacteria, the diet source of Dolabella auricularia.32 In August 2011, an anti-CD30 monoclonal antibody-monomethyl auristatin E (dolastatin 10 analogue) conjugate was approved by FDA for the treatment of Hodgkin lymphoma (HL) and systemic anaplastic large cell lymphoma (ALCL).33 Largazole (Figure 1-1), a cyclic depsipeptide isolated from a Symploca sp., Key Largo, Florida, is another example of antiproliferative agent from marine cyanobacteria.34 Largazole is a potent class I HDAC

(Histone Deacetylase) inhibitor exhibiting potent anticancer activity in both in vivo and in vitro studies.35 In cellular environment, the thioester moiety contained in the structure of largazole is hydrolyzed by cellular proteins and the active metabolite largazole thiol is released. The free thiol group was found to chelate Zn+ in the active site of HDAC binding pocket.35,36

Compounds from Marine Cyanobacteria Target Disease-associated Cell Signaling

The mode of action of a therapeutic agent describes the events where it regulate a specific biological process, which are translated into phenotypic biological response at the level of cells, tissues or organisms.37 These processes are generally multi-level complex networks that involve single or multiple interactions with biomolecules, molecular signaling cascades being triggered as well as the subsequent biochemical pathways.

With the development of advanced screening methods and the use of model systems in the mode of action studies, the broad spectrum of bioactivities of compounds from marine cyanobacteria were linked to their modulatory effects on key disease- associated cell signaling pathways.22,38,39 For instance, lyngbyoic acid and malyngolide have been found as inhibitors of quorum sensing signaling which is a key signaling to upregulate bacterial virulence genes.40,41 Dolastatin 10 and largazole, in addition to their primary microtubule-depolymerizing and HDAC inhibiboty effects, respectively, were identified as promising inhibitors against oncogenic HIF and KRAS signaling pathways which are drivers of uncontrolled cancer cell growth.38 Furthermore, largazole has also been shown to inhibit transforming growth factor β (TGFβ) and vascular endothelial growth factor (VEGF) signaling, leading to its additional therapeutic applications in the treatment of liver fibrosis and angiogenesis associated disorders.42

Apratoxins (Figure 1-1), a group of cyclic depsipeptides, are another example of growth factor signaling modulators. Apratoxins exhibited potent antiproliferative effects against human tumor cell lines with IC50 values in nanomolar range and displayed a unique profile of cytotoxicity on NCI-60 tumor cell lines.39,43–48 Apratoxin A, the first discovered compound in the apratoxin family, was identified as an inhibitor of growth factor signaling by preventing the N-glycosylation of cancer-associated receptor tyrosine kinases (RTKs), leading to their rapid proteasomal degradation.49 The growth factor modulating effect of apratoxin A further led to the discovery of its inhibitory effect on cotranslational translocation pathways at the level of Sec61.49,50

Growth Factor Modulators as Therapeutic Agents for the Treatment of Cancer and Chronic Wounds

Growth Factors and Receptor Tyrosine Kinases (RTKs)

Growth factors are cellular signaling polypeptides that are secreted from cells to regulate growth, differentiation and metabolic homeostasis of individual cell types in higher organism.51 These growth factor act by binding and activating their cognate receptors on cell surface, named receptor tyrosine kinases (RTKs). RTKs are membrane proteins composed of extracellular transmembrane and cytoplasmic tyrosine kinase (TK) domains. Extracellular transmembrane domains bind to their ligands

(growth factors) and transmit biological signal to the cytoplasmic domains, which subsequently activate downstream signaling pathways.51–53

Growth Factor Signaling as Therapeutic Target in Cancer

In normal cells, growth factor signaling is tightly controlled to ensure they only proliferate when they are required by the body. In contrast, in cancer cells, many RTKs are mutated or structurally altered, leading to abnormal activation of growth factor

signaling.53 It has been demonstrated that these potent oncoproteins and abnormal signaling are involved in the development and progression of many human cancers.

Consequently, RTKs and their growth factor ligands have become rational targets for therapeutic intervention using humanized antibodies and small molecule inhibitors.54,55

For example, bevacizumab, a monoclonal antibody that binds secreted vascular endothelial growth factor (VEGF-A), has been approved for the treatment of colorectal cancer and lung cancer.56 Sorafenib is a small inhibitor of multiple tyrosine kinases including VEGFR, PDGFR and Raf family kinases and it has been approved for kidney cancer treatment.57,58 Trastuzumab, a monoclonal antibody, has been used for the treatment of HER-2 positive breast cancer.59 Gefitinib and erlotinib are also TKIs used for the treatment of non-small cell lung cancers (NSCLC) with EGFR overexpression.59

Angiogenesis and Cancer

Abnormal activation of growth factor signaling attribute to not only uncontrolled cancer cell growth, but also uncontrolled blood vessel formation, or angiogenesis.60

Tumor angiogenesis is a vital step for tumor development and metastasis. It has been found that, without angiogenesis, a tumor will not grow beyond 2-3 mm in size.61 The tumor cells secrete growth factors that cause new capillary to sprout, grow towards and then infiltrate into the tumor mass, supplying nutrients and oxygen and supporting the growth of hyper-proliferating tumors.

It is well-known that vascular endothelial growth factor (VEGF) is the key angiogenic regulator contributing to tumor angiogenesis. VEGF is one of the most potent angiogenic growth factor which promotes all steps in the angiogenic cascade.62

VEGF is a dimeric glycoprotein that is highly conserved and share structural homology with placental growth factor (PlGF) and platelet-derived growth factor (PDGF). The

VEGF family is made up of four isoforms: VEGF-A, VEGF-B, VEGF-C, VEGF-D and

PlGF, which is generated by alternative splicing of mRNA. VEGF-A is the most well studied angiogenic growth factor regulating both physiological and disease processes such as tumor growth, psoriasis and wound healing.63–65 VEGF-A exerts its biological function by interacting with two transmembrane tyrosine kinase receptors: VEGFR1 and

VEGFR2. VEGF-A is highly secreted from tumor mass and diffused into the tumor microenvironment and binds to its cognate receptors on endothelial cells, with VEGFR2 as the major one, leading to activation of signaling pathways involved in mediating proliferation, migration and survival of endothelial cells and promoting vascular permeability.60,66

The concept of treating cancer by inhibiting new blood vessel formation in tumors was established by Judah Folkman about 50 years ago.67 Due to the crucial rule of

VEGF-A/VEGFR signaling in tumor angiogenesis, VEGF-targeted therapy has been developed to inhibit blood vessel growth and thus starve tumors of necessary oxygen and nutrients. Currently, the first and second line treatment for these vascularized cancers include VEGF-A monoclonal antibody, bevacizumab (Avastin, Genentech), and several antiangiogenic receptor tyrosine kinase (RTK) inhibitors, including sunitinib

(Sutent, Pfizer) and sorafenib (Nexavar, Bayer/Onyx).56

Other Growth Factor Associated Disorders

In contrast to the uncontrolled growth factor signaling in tumor formation and progression, growth factors also play positive roles in many physiological processes including embryotic development, female reproduction, tissue repair and wound healing.68–71 Lack of growth factors is observed in pathological processes including chronic non-healing wounds and coronary artery disease.72,73

Wound healing is a complex biological process and consists of a series of events including inflammation, cell proliferation, tissue granulation and remodeling of the scar tissue, which involves the coordinated efforts of several cell types, such as keratinocytes, fibroblasts, endothelial cells, macrophages, and platelets.74–76 A wide variety of different growth factors and cytokines are involved in each stage of wound healing process: platelet-derived growth factors (PDGFs), vascular endothelial growth factors (VEGFs), fibroblast growth factors (FGFs), and granulocyte-macrophage colony stimulating factor (GM-CSF) and many studies have shed light on the crucial roles of these growth factors on initiating and facilitating wound healing process.75,71

Dysregulation of these growth factors could delay the wound closure and result in chronic wounds (e.g., diabetic foot ulcers [DFUs], pressure ulcers [PUs], and chronic venous leg ulcers [VUs]) which represent a major healthcare burden in the US.73,77

Despite that many efforts have been spent on the development of growth factors as therapeutic agents, to date, this field has been disappointing. There is only one

Federal Drug Administration (FDA) approved growth factor on market for the treatment of diabetic foot ulcers: recombinant human platelet-derived growth factor, rhPDGF-BB,

Becaplermin.78 There are also more growth factors in clinical trials, including VEGF, bFGF and GM-CSF.78 One of the limitations for topical administration of exogenous growth factors is the low bioavailability due to the protein nature of these growth factors.

Therefore, an alternative therapeutic method aimed at stimulating the production and secretion of endogenous growth factors from wound tissue by small molecules might be more promising.

As mentioned before, VEGF is one of the most potent angiogenic growth factor which promotes all steps in the angiogenic cascade.62 VEGF-A is the most well studied angiogenesis growth factor that regulates both physiological and disease processes, such as tumor growth, psoriasis and wound healing.63–65 VEGF-A is produced by keratinocytes, fibroblast smooth muscle cells, platelets, neutrophils and macrophages and plays an essential role in wound healing. Among these cell types, keratinocytes are thought to be a major source of VEGF-A after injury.79–81

VEGF-A stimulates angiogenesis and promotes proliferation and migration of keratinocytes.82–86 It has been found that the VEGF-A gene is up-regulated in the skin after wounding.80 Furthermore, the altered expression pattern of VEGF mRNA during skin repair in genetically diabetic (db/db) mice suggests that the impairment in VEGF synthesis and release at the wound site might contribute to chronic wounds.80 In agreement with these observations, many in vitro and in vivo studies have shown that administration of VEGF-A topically or by gene transfer accelerated experimental wound healing through stimulation of angiogenesis, re-epithelialization, collagen deposition, and synthesis and maturation of extracellular matrix.83–86 Therefore, the above information strongly suggested the therapeutic application of VEGF-A inducers in the treatment of chronic wounds.

Methods Utilized in Present Study for Drug Discovery and Development

Discovery of Novel Compounds from Marine Cyanobacteria

Drug discovery from natural product is the process of identification of new chemical entities (NCEs) of potential therapeutic value, which could be achieved through isolation from natural sources, chemical synthesis or genetic engineering. In this study, novel compounds were discovered through isolation from samples of marine

cyanobacteria. Bioactivity-guided fractionation was performed to yield pure compounds for structural characterization and biological evaluation. The total structures are determined using a combined analysis of 1D and 2D NMR, MS and chiral analysis of degradation products.

Target Identification and Mode of Action (MOA) Studies for Natural Products

It is a challenging and time-consuming process to elucidate modes of action for natural products, especially for those discovered through phenotypic screens. Although being powerful in terms of identification of therapeutic relevant biological response, phenotypic assays usually fail to provide a link between phenotypic response and the roles their structures play in delivering a unique MOA. There are many commonly used approaches for the elucidation of MOA of natural products.22 The general approaches include chemical structure analysis and transcriptional responses following treatment.87

Chemical structure analysis is the most straightforward approach based on the assumption that compounds with similar structure scaffold exert similar MOA. However, this method is biased and not applicable to compounds with unique structural features.

Transcriptome profiling

As mentioned above, transcriptional response following treatment is also a frequently used method for the reason that it requires the least amount of information from previous MOA studies, which could be easily applied to newly discovered natural products. “Gene signatures”, or a subset of genes whose differential expression can be used as markers of the activity for a given pathway or disease following compound treatment, is used to discover the links among the drug molecules, pathways and diseases.88

With the development of “omics” technologies in the past two decades, the method relying on transcriptional response following drug treatment has been expanded to transcriptomic strategies that allow a systematically and unbiased elucidation of

MOA. In many studies, mammalian cells are exposed to compounds, followed by microarray analysis to monitor global changes in transcript level.22 The experiment is usually conducted by comparing RNA samples from at least 2 groups (treatment and control). It is important to note that, in order to get informative data sets from a transcriptome profiling experiment, a number of parameters would need to be optimized for each transcriptome profiling, including cell type, concentration, and treatment duration.88

Functional genomic and siRNA-based drug susceptibility screen

In contrary to transcriptome approaches where the goal is to identify differential expressed genes based on a defined phenotype, functional genomics coupled with compound treatment is aimed at identifying phenotypes for a defined genetic manipulation.

RNA interference (RNAi) is a popular functional genomic tool that is used to target a gene of interest for functional ablation.89 RNAi is a naturally occurring phenomenon which is thought to be a mechanism that has evolved for protecting host organisms against viruses and rogue genetic elements such as transposons that use double-stranded RNA (dsRNA) for self-propagation. It was originally found in plants in

1990s where dsRNA could trigger sequence-specific gene silencing in plants and

Caenorhabditis elegans.90,91 The long dsRNA could be processed and cleaved by

DICER into small interference RNA (siRNA) duplexes. The siRNAs are then incorporated into the RNA-induced silencing complex (RISC) which contains only

single-stranded RNA that guides the sequence-specific recognition of complementary target nucleic acids and results in sequence-specific mRNA degradation. In mammalian cells, dsRNA was not effective in gene silencing because it induced the antiviral interferon response, which usually leads to cell death. Instead, the active RNA interference component siRNA could be successfully applied to sequence-specific mRNA degradation without causing interferon response.92,93 SiRNAs can either be induced into cells by transfection or generated inside of the cells by its precursors shRNA expressed by plasmids. Since its ability to induce sequence-specific gene silencing, and consequently prohibition of protein synthesis, siRNA has been used for elucidating functional roles of a certain protein where many fields could be beneficial from including anticancer drug discovery and development.94 SiRNA is a useful tool that has been wildly applied in the all stages of anticancer drug discovery and development: anticancer drug target identification, anticancer drug target validation, anticancer compound screening and drug leads optimization. For example, in primary mouse embryo fibroblasts and primary human BJ fibroblasts, suppression of plasminogen activator inhibitor-1 (PAI-1), a downstream target gene of p53, by RNA interference

(RNAi) led to escape from replicative senescence, indicating that PAI-1 is required in p53-induced replicative senescence.95 Transfection of short hairpin RNA (shRNA) for mouse Aurora-B study demonstrated that elevated Aurora-B activity augments oncogenic Ras mediated transformation by enhancing oncogenic signaling and by converting chromosome number-stable cells to aneuploid cells.96 In order to systematically and simultaneously study the contribution of most genes in mammalian cells, siRNA and shRNA libraries have been built.97,98 With the development of siRNA

and shRNA libraries, this valuable genetic tool was successfully adopted in combination with a high-throughput screening strategy to cancer drug discovery. Large scale RNAi screens are usually carried out by transfection of each siRNA into cells in separate wells, followed by monitoring the phenotypes of interest including cell viability, cell migration, proteasome functions and others. The newly identified genes with desired functions could serve as new drug targets. For example, new survival kinases were identified by transfection of a siRNA library in to HeLa cervical carcinoma cells and monitoring apoptosis.99 A library of 10,996 small interfering RNAs (targeting 5,234 human genes) was screened in a highly motile ovarian carcinoma cell line, SKOV-3, for their ability to block cell migration and mitogen-activated protein 4 kinase 4 (MAP4K4) was identified as a promigratory gene.100

The application of functional genomics has been expanded to MOA studies through integration of compound treatment with RNAi to elucidate the interactions between gene knock-down and compound treatment. The loss-of-function of genes or proteins caused by RNA interference could modify the response of cells to a drug.

These genes or their gene products could be the direct targets of the drug or indirect targets which work in parallel and potentially synergize with the target pathways. The former cases could support and validate the target identification studies. The gene hits in latter cases, on the other hand, could be further explored for drug-drug combination therapy. Inhibitors of targets of validated synergistic siRNAs could be used in combination with drug in order to enhance the drug potency as well as lower the toxicity.

In one study, a Poly (ADP-ribose) polymerase (PARP)-inhibitor sensitizing screen demonstrated that a number of kinases whose silencing strongly sensitized CAL51 cells

to PARP inhibitor which serve as a guide for rational design of combination therapy for

PARP inhibitor.101 In another study, a number of gene hits were identified in a RNAi screen using NCI-H1155 cells to sensitize lung cancer cells to paclitaxel at concentrations 1,000-fold lower than otherwise required for a significant response.102

In summary, RNAi screens have the ability to elucidate the MOA of compounds and guide the rational design of drug-drug combination therapy.

Ingenuity pathway analysis

It is not a simple task to translate the large number of data generated from transcriptome/genomic approaches into MOA of compounds. The transcriptome profiles are either compared with the profiles of compounds with known MOA, searching for similarity, or analyzed extensively to identify sub-groups of differential expressed genes that overlap with sets of genes that are associated with a particular biological function or pathways based on the knowledge from literature. 103,104 The gene hits generated from siRNA-based drug susceptibility screen could also be analyzed in a similar way.

However, this process could be time consuming and due to this pitfall, a number of computer-based data analysis programs have been developed for analysis of “omics” data. In the present research, we utilized Ingenuity Pathways Analysis (IPA, Ingenuity®

Systems, http://www.ingenuity.com), a web-delivered application that enables the discovery, visualization, and exploration of molecular interaction networks in gene expression data.

Given a gene-expression dataset, IPA provides information on upstream biological causes and probable downstream pathways or phenotypic effects on cellular and organismal biology based on a large body of literatures.105 It also gives prediction on whether such upstream regulators are activated or inhibited based on the up- and

down-regulation patterns of the expressed genes, and it identifies cause-effect relationships previously reported in the literature that are likely relevant for the biological mechanisms involved in the data.

Assessment of ADME In Vitro, Pharmacokinetics (PK) and Tissue Distribution

In the early stage of drug discovery and development, it’s important to assess the pharmacological properties of Absorption, Distribution, Metabolism, and Excretion

(ADME) of a compound and anticipate the liability of potentially unstable moieties before a large amount of effort is expended on activity optimization.106 The most commonly used in vitro ADME assays include solution stability, plasma stability, microsomal stability and cellular stability.107,108 These in vitro stability assays help prioritize a lead compound for further ADME studies, including pharmacokinetics and tissue distribution study.

Solution stability: Solution stability is generally tested for the simple reason that compounds are stored, tested and formulated in various solutions throughout the process of drug discovery and instability in solution can cause misleading in vitro and in vivo data. Also, in the GI tract, compounds are exposed to a wide variety of pHs, ranging from very acidic in the stomach and upper intestine to slightly basic in the colon.

Therefore, compounds are tested in solutions with different pHs.

Plasma stability: Despite being administered by different routes, most drugs are distributed to the target tissues through the circulatory system. Drug molecules can undergo enzymatic hydrolysis in blood in which diverse hydrolytic enzymes are present, such as cholinesterase, aldolase, lipase, dehydropeptidase, alkaline phosphatase, and acid phosphatase. Hydrolysis in plasma can lead to unexpected compound clearance and therapeutic concentrations may not be achievable in vivo. Functional groups such

as ester, amide, carbamate, lactam, lactone, sulfonamide, are susceptible to plasma degradation.

Microsomal stability: The liver is an essential metabolic organ which contains a diverse array of metabolizing enzymes with a wide specificity for binding and catalyzing metabolic reactions to drug molecules. There are two types of metabolic reactions:

Phase I, including oxidation, reduction and hydrolysis reactions and Phase II where polar molecules are conjugated to the drug molecule, making the xenobiotic compounds more polar with higher water solubility so that they are easily eliminated through kidney.

Pharmacokinetics (PK) and tissue distribution: Pharmacokinetics (PK) describes the time course of the drug concentrations in the plasma, tissue or other biological matrix. Pharmacokinetic data is also applied to determine the dose levels and frequency of administration. Pharmacokinetic data provides information of compound’s bioavailability and whether or not an effective concentration is achieved in vivo. A successful drug candidate typically possesses good bioavailability and a desirable half- life (t1/2).

Research Aims

While marine cyanobacteria produce secondary metabolites with diverse bioactivities, this study focused on compounds with anticancer and growth factor modulating activities.

The study aims are listed below:

1. Early development of apratoxins, a group of anticancer agents and inhibitors of growth factor signaling from marine cyanobacteria. We aimed to conduct studies on multiple aspects in drug development, including efficacy, MOA and ADME properties

(Chapter 2).

2. Identification of novel biological activities of discovered marine cyanobacterial compounds through monitoring their effects on growth factor signaling, followed by validation of these novel activities using in vitro and ex vivo models (Chapter 3).

3. Discovery of novel cytotoxic compounds from marine cyanobacteria. We aimed to perform bioactivity-guided purification to isolate novel compounds, followed by structure determination using a combined analysis, such as 1D and 2D NMR spectroscopy and mass spectrometry (Chapters 4, 5).

Figure 1-1. Structures of selected bioactive compounds from marine cyanobacteria.

CHAPTER 2 DEVELOPMENT OF APRATOXINS AS ANTI-TUMOR AND ANTI-ANGIOGENESIS AGENTS*,†

Introduction

Apratoxins are Inhibitors of Cotranslational Translocation

Protein secretory pathway is a process to ensure proper localization of proteins to their correct cellular destinations which is essential for the structure, organization and function of all cells.109 Cotranslational translocation is a crucial step in the secretory pathway where proteins are delivered to the endoplasmic reticulum (ER) membrane while they are still being synthesized by ribosomes.109–111 In mammalian cells, secretory proteins and membrane proteins typically contain N-terminal hydrophobic signal sequences, which correspond to the first few amino acids to be synthesized from ribosomes. As soon as the N-terminal hydrophobic signal peptides protrude from the ribosome in the cytosol, they are recognized by signal recognition particle (SRP) and the protein synthesis is stalled. The SRP binds to the ribosome-nascent chain complex

(RNC) and targets them to the ER membrane through an interaction between SRP and its membrane-bound receptor (SR). Subsequently, an ER protein translocon (Sec61 complex) recognizes the signal sequence and initiates translocation process. Once the ribosome nascent chain complex (RNC) engages the Sec61 complex, protein synthesis continues, enabling the nascent chain to be directly conveyed into the lumen of the ER where it is folded to its final conformation.

* Reproduced in part with permission from Chen, Q. Y.; Liu, Y.; Cai, W.; Luesch, H. Improved Total Synthesis and Biological Evaluation of Potent Apratoxin S4 Based Anticancer Agents with Differential Stability and Further Enhanced Activity. J. Med. Chem. 2014, 57 (7), 3011–3029. Copyright (2014) American Chemical Society.

In recent years, with the discovery of several natural products acting on cotranslational translocation pathway, cotranslational translocation has emerged as a promising therapeutic target.49,112,113 A fungus-derived cyclopeptolide HUN-7293 and its analogs, CAM741 and cotransin, are known inhibitors of cotranslational translocation that block the translocation of vascular cell adhesion molecule 1 (VCAM1), a therapeutic target of chronic inflammatory diseases.112 In addition to inflammatory diseases, cotranslational translocation inhibitors have also been shown to possess anticancer activities.49,113 Decatransin is a decadepsipeptide translocation inhibitor isolated from

Chaetosphaeia tulasneorum.113 It has been reported as a cytotoxic agent against cancer cells (IC50 value of 0.14 µM in HCT116 cells) and yeast cells (IC50 value of 2 µM in S. cerevisiae). Chemogenomic profiling in S. cerevisiae has identified the Sec61 translocon complex as the target of decatransin.

As described previously, apratoxins are potent cytotoxic agents derived from marine cyanobacteria.43–48 Apratoxin A (Figure 2-1) was first isolated from cyanobacterium Moorea bouillonii (formerly classified as Lyngbya majuscula), Apra

Harbor, Guam in 2001 by Dr. Hendrik Luesch.43 It is a cyclodepsipeptide that consists of four peptides/modified peptides (Pro, N-Me-Ile, N-Me-Ala and O-Me-Tyr) and two moieties of polyketides, modified cysteine residue (moCys) and 3,7-dihydroxy-2,5,8,8- tetramethylnonanoic acid (Dtena). Apratoxin A showed potent antiproliferative effects on human tumor cell lines with IC50 values in nanomolar range. After the discovery of apratoxin A, a series of apratoxin analogs named apratoxins B-H44–48 were isolated with comparable or weaker antiproliferative activities. Apratoxin A possesses broad- spectrum differential in vitro activity39 and its cytotoxicity is due to potent inhibition of

cotranslational translocation49 at the level of the Sec61 translocon,50 leading to both down-regulation of various receptor tyrosine kinases (RTKs) and reduced growth factor secretion.49 Apratoxin A depletes cancer cells of several cancer-associated receptor tyrosine kinases by preventing their N-glycosylation, leading to their rapid proteasomal degradation.49

Synthetic Analogues of Apratoxins

Owing to its intriguing depsipeptide scaffold and potent antiproliferative activity, apratoxin A has prompted chemists and biologists to put efforts into total syntheses and structural modifications of apratoxin A aimed at identifying apratoxin analogues with improved potency and in vivo tolerability.114–124 Our group has spent considerable efforts on improving therapeutic index of apratoxin A through medicinal chemistry, which led to apratoxins S4, S7, S8, S9 and S10 (Figure 2-1).123,124 Recognizing potential liabilities that might be responsible for the irreversible toxicity and low tolerability of apratoxin A in vivo, we previously replaced the α,β-unsaturated system at C27-C31 in apratoxin A with a corresponding saturated unit in apratoxin E which resulted in a synthetic apratoxin A/E hybrid, apratoxin S4, possessing further improved potency, in vivo selectivity and anti-tumor activity.124 An extended SAR study of apratoxins S4 (30S) and S9 (30R)123 coupled with the total synthesis of (30R)-apratoxin E (natural product) and its epimer (30S)-apratoxin E125 demonstrated an important configurational element: apratoxins containing the modified cysteine (moCys) with R configuration at C30 are more potent than the ones with S. To address the issue of the propensity for dehydration at C34-35, leading to (1) pharmacological deactivation and to (2) side products during the synthesis that would lower the synthetic yield and require HPLC purification of the intermediate, we introduced an additional methyl group at C34 which

prevented the dehydration in apratoxin S8 and led to an enhanced 3-step yield (70%) during macrocyclization and a total yield (3%) without sacrificing potency in vivo.123 A nonmethyl-C34 analogue, apratoxin S7, was also synthesized aimed at removing the chiral center and hereby simplifying the molecule. With apratoxin S8 having the highest yield during macrocyclization and S9 as the most potent apratoxin analog against human colon cancer cells,123 we logically proposed a new hybrid apratoxin S8/S9 compound, apratoxin S10 (Figure 2-1), aiming to achieve a balance between potency, metabolic and chemical stability as well as synthetic yield. As expected, the synthetic yield of apratoxin S10 is higher than that of apratoxin S8, in terms of both total yield

(4.5% vs 3%) and 3-step macrocyclization yield (80% vs 70%).

Efficacy Studies

In Vitro Evaluation of Apratoxins as Dual Inhibitors of Angiogenesis and Cancer Cell Growth

As previously mentioned, VEGF-targeted therapy, or anti-angiogenic therapy, has been developed to inhibit blood vessel growth and thus starve tumors of necessary oxygen and nutrients. Despite the efficacy of anti-angiogenic therapy, the development of drug resistance leading to transient clinical benefits and failure of antiangiogenic drugs is still a major concern. Individual responses are variable, with some patients never responding to the drugs (intrinsic resistance) and others developing resistance following a brief period of treatment (acquired resistance).

There is increasing evidence that reveals significant relationships between

Interleukin 6 (IL-6) and both tumor angiogenesis and resistance against anti- angiogenic.126–139 IL-6 is a multifunctional cytokine with a wide range of biological activities in immune regulation, hematopoiesis, inflammation and oncogenesis.140 IL-6 is

secreted by a number of different types of cells including tumor cells. In renal cancer cells, IL-6 functions as an autocrine growth factor and induces cell growth in vitro.141 It has been shown that high serum IL-6 levels in cancer patients were associated with a poor outcome and bevacizumab and sunitinib resistance.127,142 Siltuximab (CNTO 328), an anti-IL-6 chimeric monoclonal antibody, is under investigation in clinical trials for multiple types of cancers.143,144 In a phase I/II study, siltuximab stabilized disease in more than 50% of progressive metastatic renal cancer patients and was well tolerated with a favorable safety profile.143 With such compelling evidence highlighting the roles of

IL-6 in angiogenesis, it would appear to be an attractive target for combination therapy in numerous tumor types with VEGF-A-targeting drugs.

Furthermore, in some scenario, upon anti-angiogenic drug treatment, despite a strong vascular response, shrinkage of tumor tissue is negligible.145–147 Given that the development of resistance to anti-angiogenic therapy is mediated through a complex mechanism,148–150 the molecular mechanism attributed to the stabilized tumor growth despite a diminished vascular supply is still under investigation, but it is possible that tumor cells may adapt to survive in a vascular-independent manner.61 Currently, most of anti-angiogenic drugs are primarily targeting the tumor endothelium rather than tumor cells, aiming to reduce vascular density and starving the nutrient for cancer cells.151 The above information has raised the possibility that acquired resistance might be circumvented if an anti-tumor effect could be achieved simultaneously with anti- angiogenic treatment, in other words, a combination of anti-tumor and anti-angiogenic therapy would be more beneficial.

With the ability to inhibit both RTKs and growth factors, apratoxins have the necessary attributes to be such kind of dual inhibitors. As it was previously indicated, apratoxins possess broad-spectrum differential in vitro activity39 and their cytotoxicity is due to potent inhibition of cotranslational translocation49 at the level of the Sec61 translocon,50 leading to both down-regulation of various receptor tyrosine kinases and reduced growth factor secretion.49 This dual effect on RTKs and their ligands, including

VEGF and its receptor VEGFR2, give a one-two punch to cancer cells, particularly those that rely on autocrine loops. Having this striking feature, we aimed to further explore this effect on other cancer-related cell types, specifically, endothelial cells. As mentioned above, endothelial cells are enriched in VEGF receptors that recognize

VEGF secreted from tumor cells leading to endothelial cell proliferation, migration and formation of blood vessels. We proposed that apratoxins could also down-regulate

VEGFR2 on endothelial cells and through which an additional anti-angiogenic effect is achieved. Provided that apratoxins exert both anti-angiogenic and anti-tumor effect, this structure class could be a promising scaffold to develop inhibitors overcome drug resistance of anti-angiogenic therapy.

In the present study, we evaluated apratoxins’ effect on both angiogenesis and cancer cell growth.

Anti-angiogenesis activity

To evaluate apratoxins’ effect on angiogenesis, we first tested apratoxin S4, S9 and S10 in an in vitro angiogenesis model where human endothelial cells (HUVEC) were seeded in a plate pre-coated with matrigel and the growth factors and nutrients in the matrigel allow them to migrate and form tube-like structures, mimicking the process of blood vessel formation. Compared with solvent control, the presence of apratoxins

significantly inhibited the formation of tube-like structures after 14 h (Figure 2-2A).

Concentrations as low as 10 pM caused detectable diminishing of branch points, total segment length and number of junction (Figure 2-2B). Increasing concentrations of apratoxins further decreased tube-structures by both visual inspection and automatic quantifications. At concentrations higher than 100 nM, the tube-structures were completely disrupted. We have also tested known RTKs inhibitors in parallel: sunitinib, a first line anti-angiogenic drug for renal cancer, inhibited tube formation at 100 nM and 1

μM. However, its effect at concentrations below 100 nM was not observed. As expected, erlotinib, an EGFR inhibitor, did not show any anti-angiogenic effect within the tested concentration range. Cabozantinib, a multi-RTK inhibitor, although showing better effect than both sunitinib and erlotinib, still exerted a slightly weaker effect than apratoxins. These results indicated potent anti-angiogenic effects of apratoxins in vitro.

To rule out the possibility that apratoxins disrupted angiogenesis in vitro due to cytotoxicity on HUVEC cells, we monitored their effect on HUVEC cell viability using

MTT assay, which suggested a negligible effect on cell viability at that time point (Figure

2-2C). Knowing that VEGFR2 is highly expressed in endothelial cells, we next tested apratoxins’ effect on VEGFR2 expression. Consistent with the proposed mode of action,49,124 apratoxins down-regulated the expression of VEGFR2 on endothelial cells

(Figure 2-2D) which explained their anti-angiogenic effect in this assay.

Our previous studies indicated that apratoxins effectively blocked VEGF-A secretion from human colon cancer cells (HCT116).123,124 Here, we evaluated the effect of apratoxin S10 on VEGF-A secretion using highly vascularized cancer cell models: renal cancer (A498), hepatocellular carcinoma (Huh7) and neuroendocrine cancer (NCI-

H727) cells. Indeed, VEGF-A secretion from all three cell lines was blocked by apratoxin

S10 (Figure 2-3). Since IL-6 has also been implicated in angiogenesis, we evaluated the effect of apratoxin S10 on IL-6 secretion from these three cell lines. Except for NCI-

H727 cells, the other two cell lines (A498 and Huh7), produced high and detectable levels of IL-6, respectively, which were all effectively inhibited by apratoxin S10 (Figure

2-4).

The above results indicated that apratoxin S10 inhibited angiogenesis in vitro mediated through down-regulation of VEGFR2 expression of endothelial cells and it blocked secretions of VEGF-A and IL-6 from cancer cells which are considered triggers for endothelial cell proliferation, migration and blood vessel formation.

Anticancer activity against various cancer cell lines through down-regulation of RTKs

In addition to their anti-angiogenic effect, we also evaluated apratoxins for their effect on cancer cell growth. As described earlier, in some scenario, the development of resistance to anti-angiogenic therapy is possibly due to tumor cells adapted to survive in a vascular-independent manner.61 Therefore, an additional anti-tumor effect could cooperate or synergize with anti-angiogenic therapy. Here we aimed to evaluate apratoxins’ anticancer effects on various cancer cell lines.

Cancer cells from highly vascularized tumors. Angiogenesis is a complex mechanism that depends on the tumor type. Renal cell carcinoma (RCC) is a highly vascularized tumor, which is often due to a hyper-activated pro-angiogenic signaling pathways triggered by von Hippel Lindau (VHL) gene mutations.152,153 Loss-of-function of the VHL protein causes unregulated activation of hypoxia inducible factor (HIF) and overexpression of VEGF-A. In addition to RCC, other indications including

hepatocellular carcinoma and neuroendocrine carcinoma are also considered to be highly vascularized tumors, which have been the focus of development for anti- angiogenic agents. Therefore, we obtained three kinds of human cancer cells from highly vascularized tumor for the anticancer activity evaluation of apratoxins: renal cancer cells (A498, 786-O), hepatocellular carcinoma (Huh7) and neuroendocrine carcinoma (NCI-H727, QGP-1). A498 and 786-O are commonly used in RCC research, both of which harbor VHL mutations.154 Huh7 is a well-established and differentiated hepatocyte-derived cellular carcinoma cell line.155 NCI-H727 is the best differentiated available human bronchial carcinoid cell line.156 The human QGP-1 cell line is a frequently used model in pancreatic neuroendocrine tumor (PNET) research.157

Cancer cells from tumors partial sensitive or resistant to anti-angiogenic therapy. The dual effects of anti-tumor and anti-angiogenesis would be even more beneficial to those tumors that are generally partly sensitive or resistant to angiogenic therapy (e.g. colon, breast and pancreatic tumor).56 Their intrinsic resistance to anti- angiogenic therapy is possibly due to the positive VEGF regulatory effects by other growth factor signaling or oncogenes that are hyperactive in these tumor types. 158,159

For instance, the importance of the epidermal growth factor receptor (EGFR) systems in VEGF regulation and angiogenesis has been verified in pancreatic cancer.158 It has also been found that the mutation of H- or K- Ras oncogenes induces VEGF expression in various cancers such as pancreatic cancer.159 Therefore, it is possible that additional growth factor signaling or oncogenic pathways from these tumors mediate resistance to anti-angiogenic therapy through continued activation of VEGF-A signaling, supporting

the promise of a combination therapy using both anticancer and anti-angiogenic agents in these tumor types.

Many trials have assessed angiogenic inhibitors in combination with cytotoxic therapy in patients with colorectal,160 breast,161–164 or pancreatic cancer.165,166 Although not all combination trails were reported to be successful, improved response rate, progression-free survival or improved efficacy was observed in some studies.161–163,166–

169 Here, we aimed to test apratoxins against human cancer cells from partly sensitive or resistant tumor (pancreatic, colon and breast tumor). The cell lines we selected are

EC68, EC46 (primary pancreatic cancer cells), PANC-1, AsPC-1 (exocrine pancreatic cancer cells), HCT116 (colon cancer cells), MCF7 (ER positive breast cancer cells) and

MDA-MB-436 (triple negative breast cancer cells).

Apratoxins exerted potent anti-proliferative effects against all tested cancer cell lines with IC50 values in the low-nanomolar range (Table 2-1). In contrast, the three known RTK inhibitors that we tested are 2,000 ̶ 5,000 times less potent than apratoxins, with IC50 values in the micromolar range. Possible explanations for the tremendous difference in potency between apratoxins and known RTKs inhibitors are that (1) apratoxins block both RTKs and secretive factors (VEGF-A and IL-6), leading to disruption of positive feed-back autocrine loops necessary for cancer cell growth141,170,171 and (2) apratoxins inhibit a broader spectrum of RTKs, which prevents resistance through activation of alternative RTKs, and (3) efficacy in cell types with mutated (oncogenic) KRAS that confers to resistance to RTK inhibitors.

In agreement with our previous study on human colon cancer cells,123,124 apratoxins exert its potent anti-proliferative effect against these cancer cell types

through down-regulation of multiple RTKs including VEGFR2, EGFR, MET, IGF1Rβ,

RET and FGFR4 (Figure 2-5). These observations in cancer cells from anti-angiogenic partly sensitive or resistant tumors suggested the promise of apratoxins on sensitizing these tumors to angiogenic therapy by blocking growth factor signaling that may attribute to resistance. For example, EGFR is highly expressed on pancreatic cancer cells and EGFR signaling has been shown to positively regulate VEGF signaling in pancreatic cancer,158 counteracting VEGF-targeting effect of anti-angiogenic therapy.

This potential sensitizing effect is of paramount importance due to the fact that pancreatic cancer is a major unsolved health problem, with conventional cancer treatments having little impact on disease course and almost all patients who have pancreatic cancer develop metastases and die.

In terms of mode of action of apratoxins, we observed differential potency of apratoxins against different RTKs as well as VEGF-A and IL-6, among VEGF-A was the most sensitive protein. VEGFR2 and MET appeared to be less susceptible than IGF1Rβ and EGFR (Figure 2-5). These results indicated differential susceptibility of substrates of cotranslational translocation to apratoxin-mediated inhibition, as we previously proposed,124 and these differential effects also appear to be cell type dependent. This data further suggests that the apratoxin scaffold can be used to potentially modulate substrate selectivity.

In Vivo Efficacy Studies of Apratoxin S9 in Colon Cancer Model

Due to the potent in vitro activities of apratoxin S9 among all the apratoxin analogs, we aimed to further evaluated apratoxin S9 in the same HCT116 xenograft mouse model as previously performed for apratoxin S4 and S8.123,124 We administered apratoxin S9 at the previous efficacious dose for S4 and S8 (0.25 mg/kg, ∼5 μg/mouse)

daily via intraperitoneal (ip) injection for 16 days. We have previously demonstrated for apratoxin S4 and S9 that this dose down-regulated RTKs in the tumor.123,124 Apratoxin

S9 significantly retarded the tumor growth (Figure 2-6), paralleling the effects of apratoxins S4 and S8.123,124

SiRNA Screening and Combination Therapy

Ingenuity Pathway Analysis of Sensitizers from SiRNA Screen

As already stated, siRNA-based Drug Susceptibility Screen is a powerful strategy to elucidate the MOA of drug molecules. Here, we performed a siRNA-based apratoxin- sensitization screen aimed to identify the MOA of apratoxins, which could serve as a guide for further rational design of combination therapy. The screening experiment was successfully conducted previously in our lab. In brief, an arrayed siRNA library targeting

7784 human genes (4 siRNAs per gene) were obtained from commercial source.

SiRNAs were transfected into HCT116 cells for 48 h before 5 nM apratoxin A or solvent control (0.25% EtOH) was added. After 48 h of incubation, cell viability was monitored.

The siRNAs that caused reduced viability in the presence of 5 nM apratoxin A are considered as hits. High confidence sensitizing hits are the genes with two or more individual siRNA hits. From 7784 genes that were tested, inhibition of 178 gene products sensitized HCT116 cells to apratoxin A (Figure 2-7). These hits are related to multiple functional pathways including growth factors/growth factor receptors signaling pathway, secretory pathway, cell cycle progression, DNA repair pathways.

To examine the molecular functions and genetic networks, the siRNA screen data was explored using ingenuity pathways analysis (Ingenuity® Systems, http://www.ingenuity.com), a web-delivered application that enables the discovery, visualization, and exploration of molecular interaction networks in gene expression data.

The analysis revealed that these sensitizers are involved in multiple signaling pathways and networks (Figure 2-8A, B). Among the 178 hits, there is a subgroup of genes whose loss-of- function has been linked to anticancer activities (Figure 2-8C).

In addition to the apratoxin-sensitization screen, our group has also performed a similar apratoxin-resistance screen using the same siRNA library. The only difference in this screen is that 50 nM apratoxin instead of 5 nM apratoxin was treated to the siRNA- transfected cells, allowing us to identify genes that are involved in the resistance mode of apratoxins. From 7784 genes that were tested, down-regulation of 400 gene products conferred resistance of HCT116 cells to apratoxin A (Figure 2-7).

DNA Repair and PARP1 are Involved in MOA of Apratoxins

To maintain genome integrity, organisms have evolved a network of signaling pathways for DNA repair. The apratoxin-sensitization screen indicated that the loss-of- function of several DNA repair related genes could sensitize HCT116 cells to apratoxin

A (Figure 2-9). For example, MCM9 and PARP1 are two sensitizer hits in DNA repair pathway and the loss-of-function of MCM9 or PARP1 is responsible for an increased breakage of chromosomes. Therefore, the possible explanations are (1) apratoxins directly target DNA repair pathways or (2) DNA repair pathways are not direct targets of apratoxins, but the inhibitors of these pathways cooperate or synergize with apratoxins in HCT116 cells. Among these DNA repair related sensitizer hits, we were particularly interested in PARP1 (poly (ADP-ribose) polymerase family, member 1). Considering the potential off-target effects of siRNAs,172 we aimed to validate the synergistic/additive effects between PARP1 and apratoxins by performing a combination study co- administrating PARP1 inhibitors and apratoxin S9 in a HCT116 xenografted mice model.

PARP1 (poly (ADP-ribose) polymerase family, member 1) is an enzyme involved in DNA repair networks. It has been extensively studied during the past 50 years and has been applied to cancer treatment. PARP1 is an abundant nuclear enzyme that is activated by and facilitates the repair of DNA base damage and single-strand breaks.173

In response to DNA damage, PARP1 enzyme recognizes DNA single–strand breaks and binds to it through its N-terminal DNA binding domain and catalyzes the cleavage of

NAD+ into nicotinamide and ADP which lead to a large consumption of NAD+ in cells.

After the bond cleavage, PARP1 catalyzes the polymerization the ADP onto the substrate proteins including itself (auto-modification) and the activated PARP1 enzyme subsequently recruits a series of proteins that facilitate DNA repair.174

To date, alkylating agents and ionizing radiation (IR) are still the major cancer therapies that act by inducing DNA damage which subsequently activate DNA repair pathways and cause drug resistance. Therefore, PARP inhibitors have the potential to inhibit DNA repair and increase the cytotoxicity of alkylating agents and IR. Besides, it has been found that PARP1 activity is increased in certain types of tumor which also reinforce the importance of application of PARP inhibitors in cancer treatment. There is accumulating evidence that indicate the role of PARP inhibitors in enhancing the effects of DNA methylating agents including temozolomide (TMZ).175 PARP inhibitors have also been reported to potentiate topoisomerase (Topo I) inhibitors including topotecan175,176 and camptothecins.177

Efforts have been made on the discovery and development PARP inhibitors during the past three decades. AG014699, a tricyclic indole, has been approved in the

USA as monotherapy for the treatment of patients with deleterious BRCA mutation

(germline and/or somatic) associated advanced ovarian cancer who have been treated with two or more chemotherapies. It was the first PARP inhibitor to enter clinical trial for the treatment of cancer in combination of temozolomide due to its profound preclinical results in models of adult human malignancies.176,178 Phase I and Phase II trials showed that AG014699 has profound and sustained inhibition of PARP activity in surrogate normal tissues (peripheral blood lymphocytes) and tumors and enhances the response rate to temozolomide.178 Due to its outstanding preclinical and clinical trials results,

AG014699 was selected for the combination experiments with apratoxins.

In Vivo Combination Study

The combination study of AG014699 and apratoxin S9 was conducted in a

HCT116 cells xenografted mice model. The dose of AG014699 (1mg /kg) was selected based on in vivo combination studies of AG014699 with topotecan/ temozolomide.175,179

As shown in the figure (Figure 2-10), tumor growth in AG014699 treatment group is comparable with vehicle group, suggesting that AG014699 itself exerts no antitumor effect. Tumor sizes were dramatically decreased in apratoxin S9 0.25 mg/kg treatment group, indicating that apratoxin S9 has a significant antitumor effect in vivo, which is consistent with in vitro HCT116 cells data. Although AG014699 itself has almost no effect on cancer treatment, co-administration of AG014699 and apratoxin S9 induced a larger antitumor effect compared to the apratoxin S9 group. This finding suggests that

AG014699 had cooperative effect with apratoxin S9 in a HCT116 xenograft mouse model. We then performed an ELISA-based PARP assay to confirm the on-target effect of PARP1 inhibitor in vivo. As we expected, the PARP activities were found lower in tumor tissues from both combination group and PARP1 inhibitor treated group than the tissues from vehicle and apratoxin S9 alone groups (Figure 2-11).

ADME In Vitro, Plasma Pharmacokinetics (PK) and Tissue Distribution Studies

In Vitro Stability of Apratoxins

We aimed to evaluate ADME properties of apratoxins in vitro and in vivo. We first performed in vitro stability assays for apratoxin A, S4, S7, S8 and S9. Apratoxin S10 was not included because this study was conducted before the design and synthesis of arpatoxin S10. All five apratoxins were remarkably stable (t1/2 > 24 h) under aqueous conditions at physiological (pH 7.4) and lysosomal (pH 4.88) and possessed excellent plasma and cellular stability (Figure 2-12). These results suggest that C34−C35 dehydration is not a major concern under these conditions, although it was an issue during synthesis.123 Nevertheless, apratoxin S4 and apratoxin S7 were generally somewhat less stable than apratoxin S8, as expected, especially under acidic conditions (Figure 2-12B). The stability of apratoxin S9 was also slightly enhanced compared with that of apratoxin S4, suggesting that the configuration at C30 may affect the tendency to dehydrate (possibly by changing the macrocycle conformation), assuming that dehydration is indeed the mode of primary apratoxin modification.

Microsomal metabolism of all apratoxins was strongly accelerated by NADPH and stability was found to be low (t1/2 < 5 min,Table 2-2), which may suggests that certain apratoxin biotransformation products could also retain activity, considering that apratoxin S4 was extremely potent and active in vivo as well.

Plasma Pharmacokinetics (PK) and Tissue Distribution of Apratoxin S10

The in vitro stability indicated that apratoxin S8 and S9 are slightly more stable than apratoxin A and S4, suggesting apratoxin S10, the hybrid of apratoxin S8 and S9, may also possess a good stability profile. In addition to stability, apratoxin S10 is considered a lead candidate of the apratoxin family in terms of both potency and

synthetic accessibility. We therefore subjected apratoxin S10 to pharmacokinetics and tissue distribution studies in NSG mice.

The aims of this study are two folds: Firstly, apratoxins have been tested in tumor-bearing mice models by independent research groups where apratoxins were administrated through different routes and dosing strategies.47,123,180 However, the difference in pharmacokinetics of apratoxins after different routes of administration is not fully defined. The pharmacokinetics and tissue distribution properties of apratoxins are required to determine the best dosing strategy and route of administration.

Secondly, the tissue distribution pattern would provide more information on possible effective organs or sites of toxicity of apratoxins. Here, we investigated the concentrations of apratoxin S10 in plasma and various organs at various time points after intravenous (i.v.) or intraperitoneal (i.p.) administration in NSG mice.

Twenty-two NSG mice were used in this study. Following i.v. or i.p. administration of apratoxin S10, plasma and tissue samples (liver, kidney, lung, pancreas, salivary gland and brain) were collected at six different time points (10 min, 1 h, 3 h, 8 h, 24 h and 48 h). Samples were processed, followed by analysis using LC-MS to determine the concentrations of apratoxin S10. The highest peak concentration of apratoxin S10 was observed in pancreas, followed by salivary glands, spleen, liver, lung, kidney and brain

(Figure 2-13). Similar profiles were obtained between with i.v. and i.p. routes. The non- compartmental pharmacokinetic analysis was utilized to obtain the steady-state pharmacokinetic parameters of concentration-time profile (Table 2-3) using Phoenix 64

(WinNonlin 6.4) (Pharsight Corp., Sunnyvale, CA, USA). The tissue: plasma partition

coefficients (Kp), a good indicator of the extent of tissue distribution, were calculated to determine pancreas as the major distribution organ (Figure 2-14).

Summary

Apratoxins are a group of potent marine-derived cytotoxic agents that belong to a new class of anticancer agents with the mode of action of cotranslational translocation blockage at the level of sec61 tanslocon.

Owing to their high potency against cancer cells and their intriguing and non- classic mode of action, researchers have invested considerable amount of efforts on

SAR studies and discovery of new apratoxin analogs aimed at enhancing potency. Our group has designed and synthesized a series of analogues with improved potency and in vivo selectivity.

In this research, we performed efficacy, ADME and MOA studies aimed at moving forward the development of apratoxins. We evaluated the dual inhibitory effects of apratoxins against both angiogenesis and cancer cell growth using the best candidate in the apratoxin family, apratoxin S10. Apratoxin S10 inhibited angiogenesis in vitro mediated through down-regulation of VEGFR2 expression of endothelial cells and it blocked secretions of VEGF-A and IL-6 from cancer cells which are considered triggers for endothelial cell proliferation, migration and blood vessel formation. In addition to its anti-angiogenic effects, apratoxin S10 possesses potent antiproliferative effects against cancer cells from highly vascularized tumor through down-regulations of multiple RTKs. The antiproliferative effects were also observed with similar potency on cancer cells from tumors that are partially sensitive or resistant to anti-angiogenic therapy. This result suggested its potential effect on sensitizing these tumors to anti- angiogenic therapy by blocking the growth factor signaling that attribute to the

resistance. Meanwhile, we have noticed that RTKs are differentially susceptible to apratoxin S10’s inhibitory effect, suggesting apratoxins selectively inhibit different substrates in the process of cotranslational translocation through a novel mechanism that warrants further investigation.

Our group has previously performed a siRNA-based apratoxin-sensitization screen aimed at elucidating MOA as well as identifying gene targets for rational combination therapy. In the present work, we analyzed the screen hit by subjecting the sensitizer data into Ingenuity Pathway Analysis (IPA). Consistent with the known anticancer effect of apratoxins, several cancer-related signaling pathways were identified. Moreover, targeting DNA-repair associated genes including PARP was suggested to be sensitizing HCT116 cells to apratoxin A. A rational combination therapy with PARP inhibitors was designed and performed in vivo, which showed cooperative effects between the PARP inhibitor and apratoxin S9.

To evaluate the “drug-like” properties and ADME of apratoxins, we conducted in vitro stability, plasma pharmacokinetics and tissue distribution studies. All apratoxins were stable in plasma, suggesting that the ester moiety in apratoxins is resistant to the hydrolytic enzymes present in mouse plasma. Apratoxins are stable in aqueous solution at neutral or lysosomal pH, cell lysates, but not mouse liver microsomes, potentially suggesting the presence of active metabolites. In PK and tissue distribution studies in

NSG mice, the plasma concentration of apratoxin S10 peaks at 10 min and the compounds were quickly distributed into tissues, primarily pancreas. The high enrichment of apratoxin in the pancreas indicated the potential therapeutic use of apratoxins in pancreatic cancer as well as the potential site of toxicity.

Experimental Methods

General Experimental Methods

All apratoxin compounds were synthesized by Dr. Qi-Yin Chen. The siRNA screens were conducted by Dr. Yanxia Liu. AG014699, sunitinib, erlotinib and cabozantinib were obtained from Selleckchem (Houston, TX).

Cell Culture

Human renal carcinoma A498 and 786-O cells, human neuroendocrine carcinoma NCI-H727 cells and human colon adenocarcinoma HCT116 cells were purchased from ATCC (Manassas, VA). Human hepatocellular carcinoma Huh7 cells were provided by Dr. Chen Liu. Human Umbilical Vein Endothelial Cells (HUVEC, cat#

CC-2519) were purchased from Lonza. Primary pancreatic cancer cells (EC68, EC46) were provided by Dr. Trevino G. Jose. Breast cancer MDA-MB-436 cells were given by

Dr. Brian K. Law.

A498 cells were cultured in Eagle's minimum essential medium (EMEM) supplemented with 10% fetal bovine serum; Huh7, PANC-1, HCT116, MCF-7 and MDA-

MB-436 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum; 786-O, NCI-H727 and QGP-1 cells were cultured in RPMI-1640 medium, supplemented with 10% fetal bovine serum. EC68 and

EC46 cell lines were cultured in DMEM/F12K, 1:1 mixture, supplemented with 10% fetal bovine serum. HUVEC cells were cultured in EGM (Lonza cat# CC-3124). All cells were kept at 37 ºC humidified air and 5% CO2.

In Vitro Angiogenesis Assay

HUVECs (Lonza) were used at passage 4 for this assay. In vitro Angiogenesis

Assay Kit (Chemicon) was used according to the manufacturer’s recommendation.

Briefly, an ice-cold mixture of ECMatrix (50 µL per well) was transferred into a precooled

96-well plate. After the matrix solution had solidified (> 1 h incubation at 37 °C), 23, 000 cells were mixed with the appropriate inhibitor concentration (in 100 μL EGM) and plated into each well. After incubation at 37 °C for 14 h, images were captured for each well using a Nikon inverted microscope equipped with NIS-Elements software. Branch point counting was used as quantification method. Five random microscope view-fields were counted and the number of branch points was averaged. The number of junctions were analyzed by the Angiogenesis Analyzer plug-in for ImageJ (n = 5 per group).

Cell Viability Assay (MTT)

Cells were seeded in a 96-well clear bottom plate and 24 h later, cells were treated with various concentrations of the apratoxins (10 pM‒1 μM), known RTK in inhibitors or solvent control (EtOH for apratoxins and DMSO for RTK inhibitors). After 48 h of incubation, cell viability was detected using MTT according to the manufacturer’s instructions (Promega, Madison, WI). Nonlinear regression analysis was carried out using GraphPad Prism software for IC50 value calculations.

Measurement of Human VEGF-A and IL-6 Secretion

A498, Huh7 or NCI-H727 cells were seeded in a 96-well clear bottom plate. Cells were treated with various concentrations of apratoxins (10 pM‒1 μM) or solvent control

(EtOH). After 24 h incubation, culture supernatants were collected for detection of

VEGFA or IL-6 using alphaLISA kits (PerkinElmer, Waltham, MA) following the manufacturer’s instruction. Briefly, acceptor bead and anti-VEGF-A/anti-IL-6 antibody were incubated with the supernatants for 60 min firstly, donor beads were then added and incubated for another 30 min. Signal was detected using Envision (PerkinElmer).

Levels of VEGFA/IL-6 (pg/mL) were calculated using a standard curve and then normalized based on cell numbers.

Immunoblot Analysis

Cells were seeded in 6-well clear bottom plates the day before treatment. The next day, cells were treated with apratoxins or solvent control (EtOH). 24 h later, whole cell lysates were collected using PhosphoSafe buffer (EMD Chemicals, Inc, Gibbstown,

NJ). Protein concentrations were measured with the BCA Protein Assay kit (Thermo

Fisher Scientific, Rockford, IL). Lysates containing equal amounts of protein were separated by SDS polyacrylamide gel electrophoresis (4–12%), transferred to polyvinylidene difluoride membranes, probed with primary and secondary antibodies, and detected with the SuperSignal West Femto Maximum Sensitivity Substrate (Thermo

Fisher Scientific). Anti-FGFR4 antibody was obtained from Santa Cruz Biotechnology,

Inc (CA). Anti-VEGFR2, EGFR, Met, IGF1Rβ, β-actin and secondary anti-mouse and rabbit antibodies were obtained from Cell Signaling Technology, Inc (Danvers, MA).

In Vivo Efficacy Study and Combination Study

Three- to five- weeks old female nude mice (nu/nu) were obtained from Charles

River Laboratory (Wilmington, MA). One million HCT116 cells in a volume of 100 µL of

DMEM were injected subcutaneously on the left rear flank of a nude mouse to establish tumors. Tumor dimensions were measured using calipers every day and tumor volumes were calculated using the formula W2 × L× 0.5, where width (W) ≤ length (L).

Mice were injected intraperitoneally (i.p.) with 0.25 mg/kg apratoxin S9, solvent (DMSO) control, 1 mg/kg AG014699 or 0.25 mg/ kg apratoxin S9 and 1 mg/ kg AG014699 daily until the tumor size in one dimension reached 15 mm and tumor tissue was harvested.

All studies were carried out under the protocol approved by the Institutional Animal Care and Use Committee at the University of Florida.

PARP Assay

PARP activities of tumor tissue were measured using HT PARP in vivo

Pharmacokynamic Assay Kit (Trevigen). Three to four tumor tissues from each group were randomly selected for the assay. PARP activity was indicated by PAR level in tumor tissues.

The assay was conducted according to manufactural procedures. In brief, lysis buffer was added to the frozen tissue, followed by disruption the extracts by sonication on ice three times for 10 seconds each cycle. Then the samples were vortexed and allowed to stand on ice for 15 min. The samples were then moved to room temperature and 20% SDS was added to a final concentration of 1%. The samples were vortex again and incubate at 100 ºC for 5 minutes, snap-cooled on ice for 1 min and centrifuge at 10,000 x g for 2 minutes at 4 ºC. The supernatant from each sample was collected as a xenograft tumor lysate. The protein concentration of the extracts was measured using a BCA protein assay.

Test samples, PAR standards, Jurkat cell lysate standards or sample buffer (50

μL/well) were added into the pre-coated plate. The plate was then sealed and incubated overnight at 4 ºC for 16 h. After that, the plate was washed with PBST for 4 times before

PAR polyclonal detecting antibody (50 μL/well) was added and incubated at 25 ºC for 2 h. Then the plate was washed with PBST for 4 times followed by the addition of goat anti-rabbit IgG-HRP conjugate (50 μL/well) and incubation at 25 ºC for 1 h. Then the plate was washed with PBST for 4 times before a mixture of equal volumes of

PeroxyGlow™ A and B (100 μL/well) was added and immediately detected for chemiluminescent signal.

In Vitro Stability Studies

Materials and general procedures

HPLC-MS was done on a 3200 QTRAP (Applied Biosystems) equipped with a

Shimadzu (Kyoto, Japan) UFLC System. Mouse serum and harmine were purchased from Sigma-Aldrich. Pooled CD1 mouse liver (female) microsomes were purchased from XenoTech, LCC (Lenexa, KS) with protein concentrations of 20 mg/mL. HCT116 cell lysates were prepared with PhosphoSafe lysis buffer (Novagen). Protein concentration was determined by using the BCA Assay. Analysis was carried out similarly as previously described.35

Sample preparation

Stock solutions of apratoxin A, S4, S7, S8 and S9 were prepared by dissolving the compounds in ethanol to give a 1 mg/mL solution. Aliquots of these stock solutions were then obtained to afford a 40 µg/mL solution in acetonitrile. Serial dilution of the 40

µg/mL solution in acetonitrile gave standard solutions with concentrations of 25, 12.5,

2.5, 1.25, 0.25, 0.125, 0.025, and 0.0125 µg/mL. A 1 mg/mL stock solution of the internal standard harmine was prepared in ethanol, which subsequently was used to prepare 100 µg/mL solution with ethanol. An aliquot of the 100 µg/mL harmine solution was diluted to 35 ng/ml with ethyl acetate to serve as the working internal standard solution.

Plasma stability

Ten microliters of apratoxin A or apratoxin analogues (25 µg/mL) were added to

100 µL of mouse serum, and the solution was vortex-mixed for 15 s and incubated for

0.25 min to 24 h (11 time points). At the end of each incubation period, 400 µL of ethyl acetate was added to each tube, followed by 200 µL of harmine to quench the reaction and extract remaining apratoxin A or apratoxin A analogues. Samples were further incubated in a thermomixer at 27 °C (750 rpm, 5 min) and later centrifuged for 5 min at

1643 g. The ethyl acetate layer was collected and evaporated to dryness under nitrogen. Samples were reconstituted in 50 µL of acetonitrile. A volume of 10 µL of the reconstituted solution was injected into the HPLC-MS system.

Microsomal stability

Mouse liver microsomes (20 mg/mL, XenoTech LLC) were added to prewarmed phosphate buffer (100 mM, pH 7.4) at 37 °C. Apratoxin A or apratoxin analogues (3 µL) were added to the microsomal preparation followed by NADPH cofactor solution (1.3 mM NADP, 3.3 mM glucose 6-phosphate, 0.4 U/mL glucose-6-phosphate dehydrogenase, 3.3 mM MgCl2). The reaction was allowed to proceed for 3 min to 3 h

(7 time points) at 37 °C (thermomixer, 1050 rpm). The reaction was quenched by addition of ethyl acetate and subsequently spiked with harmine. The zero time point was defined by denaturing the microsomes with ethyl acetate before the addition of apratoxins. Incubation of apratoxins with microsomes alone was also performed following the same procedure to determine NADPH-dependent metabolism. The final concentration of the incubation mixture contained 0.5 mg/mL protein concentration and

1 µM apratoxins.

Cellular stability

Aliquots of HCT116 cell lysates were diluted with 25 mM Tris-HCl buffer, pH 8.0, to give a final reaction volume of 100 µL and protein concentration of 0.7 mg/mL. Cell lysate solutions were incubated with 10 µL of apratoxin A or apratoxin analogues (25

µg/mL) for 0.25 min to 24 h (nine time points). Remaining apratoxins were extracted from the reaction solution at the end of the incubation periods with ethyl acetate using the same procedure as described for the plasma stability assay.

Aqueous stability

The stability of apratoxin A, S4, S7, S8 and S9 in aqueous solution was determined in 100 mM phosphate buffer, pH 4.88, 100 mM phosphate buffer, pH 7.4.

Portions of each solution (100 µL) were spiked with 10 µL of apratoxin solution (25

µg/ml) and allowed to incubate for 0.25 min to 24 h (11 time points). The reaction was quenched at the end of each time point, and the remaining apratoxins was extracted using the ethyl acetate extraction procedure, as in the plasma stability study.

HPLC-MS parameters

Analysis of apratoxin A, S4, S7, S8 and S9 was done by using HPLC-MS

[column, Onyx Monolithic C18 (3.0 × 100 mm), Phenomenex (Torrance, CA); solvent, water (solvent A) acetonitrile (solvent B); flow rate, 0.5 ml/min; detection by electrospray ionization–MS in positive ion mode (MRM scan)]. A stepwise gradient elution was used starting at 60% B and 40% A, then increasing to 80% B at 5 min and maintained at this condition for 5 min. Parameters were optimized before analysis by using direct syringe

infusion. The retention times (tR, min; MRM ion pair) of the analytes and internal standard are as follows: harmine (2.2; 213.1→ 169.9), apratoxin A (4.7; 841.4→ 445.2), apratoxin S4 (4.1; 828.5 → 432.2), apratoxin S7 (4.05; 814.5→ 418.2), apratoxin S8

(5.2; 842.5 → 446.2), apratoxin S9 (4.9; 828.5→ 432.2). Compound-dependent parameters used were as follows: apratoxin A, declustering potential (DP) 51, entrance potential (EP) 12, collision energy (CE) 45, collision cell exit potential (CXP) 6, collision cell entrance potential (CEP) 32; and harmine, DP 56.0, EP 4.5, CE 44.0, CXP 5, CEP

16.0. Source gas parameters used were as follows: curtain gas, 15.0; collision gas low, ionspray voltage 5500; temperature, 600.0; ion source gas 1 50.0; ion source gas 2

60.0.

Quantification and data analysis

Calibration curves for apratoxin A, S4, S7, S8 and S9 in the presence of mouse serum, HCT116 cell lysates, and aqueous solutions were generated by least-square linear regression analysis of the analyte peak area and internal standard peak area ratio against the nominal concentration of the standard solutions. Examples of quantification standard curves are shown in Appendix. A linear regression analysis was performed, and the concentration of remaining apratoxins at each time point was determined through interpolation for plasma, cellular and aqueous stability experiments. The amount of remaining apratoxins with microsome incubation was determined from the

peak area ratio of apratoxins at tx (3 min to 3 h) and t0. All calculations were done by using Analyst 1.4.2 (Applied Biosystems) Quantitate Mode.

Plasma Pharmacokinetics and Tissue Distribution

Four- to Ten- weeks old NSG™, NOD scid gamma male/female mice were used in plasma pharmacokinetic and tissue distribution study. Forty mice were randomly distributed into two administration groups: 1 mg/kg apratoxin S10 treatment group (36 mice) and sham group (4 mice). Mice in treatment group was further randomly assigned in to twelve administration subgroups (3 mice per group): sacrificed at six different time points following i.v. or i.p. administration. Apratoxin S10 was formulated in 10% EtOH,

5% Tween-80, 85% saline solution (100 µL/20 g mice) for i.v. injection. It was formulated in DMSO (25 µL/20 g mice) for i.p. injection.

At each time point, mice were sacrificed and whole blood was collected immediately through cardiac puncture using 1 mL syringes. Blood was collected into heparinized tube and kept on ice. Blood sample was centrifuged at 1,000 g, 10 min. The supernatant was collected as plasma. Tissues including liver, kidney, pancreas, lung, spleen, salivary glands and brain was collected and weighed. Plasma and tissue samples were snap freezed in liquid nitrogen and storage in -80 °C until analysis.

Tissue was thawed on ice and three volumes (mL) of PBS buffer was added.

Tissue was homogenized on ice and centrifuged for 5 min, 16,000 g. Supernatant was collected as which is the tissue homogenate. Samples of plasma or tissue homogenates

(50 µL) was added into Eppendorf tubes followed by addition of 150 µL 0.067 µg/mL harmine in 1:1 acetonitrile/methanol. Sample was mixed and centrifuged at 10,000 g, 5 min at 4 °C. The supernatant was collected and evaporated to dryness under nitrogen.

Compounds were reconstituted with 50 µL acetonitrile and the obtained solution was filtered and subjected for LC-MS analysis.

PK data analysis was performed by Ms. Yichao Yu. The non-compartmental pharmacokinetic analysis was utilized using Phoenix 64 (WinNonlin 6.4) to obtain the pharmacokinetic parameters.

Table 2-1. Activities of apratoxins and known RTKs inhibitors on a range of cancer cells, IC50 (nM). Apratoxin Apratoxin Apratoxin Cabo- Cancer Type Cell lines Sunitinib Erlotinib S4 S9 S10 zantinib A498 2.07 1.18 3.35 8000 ~12500 8500 Renal 786-O 3.87 2.52 7.44 9600 ~15000 10000

Hepatocellular Huh7 0.66 0.46 0.83 4700 ~25000 4300 Highly vascularized tumors QGP-1 3.99 1.98 3.40 8100 ~7600 5300

Neuroendocrine NCI-H727 1.69 1.12 2.55 10100 > 6200 14000

EC68 2.92 1.83 4.14 18300 >9000 12100

Pancreas EC46 4.51 1.77 4.43 9800 >2400 9000

Tumors that are partial PANC-1 4.63 2.57 5.11 11300 ~13100 9600 sensitive or resistant to anti- angiogenic therapy Colon HCT116 1.43 0.69 1.47 3600 > 50,000 5000

MDA-MB- Breast 2.63 1.06 436 MCF7 2.67

IC50 (nM) values were determined after 48 h (n = 3).

Table 2-2. Microsomal stability studies. apratoxin S4 apratoxin S7 apratoxin S8 apratoxin S9 time microsomes microsomes + microsomes microsomes + microsomes microsomes + microsomes microsomes + (min) only NADPH only NADPH only NADPH only NADPH 0 100 100 100 100 100 100 100 100 3 87.05 ± 1.63 39.55 ± 1.06 66.60 ± 12.30 54.95 ± 4.74 74.10 ± 9.62 37.20 ± 1.27 83.10 ± 0.42 51.40 ± 8.06 5 70.80 ± 23.48 32.20 ± 16.12 60.50 ± 2.26 18.70 ± 1.41 77.45 ± 3.18 17.30 ± 2.55 71.40 ± 1.27 17.80 ± 0.28 15 87.20 ± 2.83 10.07 ± 1.46 76.55 ± 9.55 52.40 ± 2.40 63.45 ± 3.61 8.28 ± 2.86 45.25 ± 5.30 3.55 ± 0.25 30 73.85 ± 15.49 9.58 ± 1.03 54.80 ± 1.56 6.10 ± 1.96 74.75 ± 8.70 4.83 ± 1.05 56.05 ± 1.06 3.73 ± 0.25 60 87.10 ± 1.41 10.00 ± 0.71 54.50 ± 0.00 5.41 ± 1.09 81.25 ± 1.91 3.55 ± 0.91 48.45 ± 0.92 6.06 ± 2.40 120 55.25 ± 12.09 6.02 ± 2.11 53.85 ± 9.40 4.30 ± 0.82 70.05 ± 0.78 5.01 ± 0.23 56.25 ± 0.21 5.63 ± 0.18 Assays were done in triplicate. Values are expressed as % remaining. Mean values are shown ± S.D. The amount of remaining apratoxins was determined from the peak area ratio of apratoxins at tx (3 min to 2 h) and t0.

Table 2-3. Summary statistics for the pharmacokinetic parameters on the observed concentration-time profiles. Tissue Plasma Pancreas Parameters a Route i.v. i.p. i.v. i.p. Ke 1/h 0.089104 0.081538 0.059266 0.051394 t1/2 h 7.779053 8.500923 11.69561 13.48689 Tmax h 0.166667 0.166667 8 3 Cmax ng/mL 1072 774.6667 2348 2883.2 AUCt ngh/mL 1287.37 1251.263 45982 54531.51 AUC ngh/mL 1302.613 1292.308 49289.15 60205.31 MRT h 4.201285 6.54045 14.7971 17.4556 CL/F mL/(hkg) 767.6876 773.8096 20.28844 16.60983 Vz/F mL/kg 8615.604 9490.186 342.3309 323.1853 a Ke Elimination rate constant t1/2 Elimination half-life Tmax Time to reach the maximum concentration Cmax The peak concentration after drug administration AUCt Area under the concentration-time curve to the last point AUC Area under the concentration-time curve to infinity MRT Mean residence time CL/F Clearance Vz/F Volume of distribution

Figure 2-1. Evolution of apratoxins.

Figure 2-2. Evaluation of apratoxins in an in vitro angiogenesis assay. A) Apratoxins inhibited angiogenesis in vitro in a dose-dependent manner, determined by matrigel assay using HUVECs (scale bar 200 μm), 14 h. Three known RTK inhibitors were tested in parallel. B) Branch point counting was used as quantification method. Five random microscope view-fields were counted and the number of branch points was averaged. The number of junctions and total segment length were analyzed by the Angiogenesis Analyzer plug-in for ImageJ (n = 5 per group). C) Anti-proliferative effect of apratoxins and known RTK inhibitors on HUVECs. D) Immunoblot analysis of apratoxin S4 and S10- treated HUVECs, 14 h.

Figure 2-2. Continued.

Figure 2-3. Activity of apratoxin S10 on VEGF-A secretion. VEGF-A secretion in A) A498, B) Huh7 and C) NCI-H727 cells was detected using AlphaLISA Human VEGF-A Immunoassay Kit (PerkinElmer). Numbers represent the average of triplicates with error bars indicating SD.

Figure 2-4. Activity of apratoxin S10 on IL-6 secretion. IL-6 secretion in A) A498, and B) Huh7 cells was detected using AlphaLISA Human IL-6 Immunoassay Kit (PerkinElmer). Numbers represent the average of triplicates with error bars indicating SD.

Figure 2-5. Apratoxins down-regulated multiple RTKs. Immunoblot analysis of apratoxins-treated A) renal cancer A498 cells, B) hepatocellular carcinoma Huh7 cells, C), D) neuroendocrine cancer NCI-H727and QGP-1 cells, E) triple negative breast cancer MDA-MB-436 cells and F), G) pancreatic cancer PANC-1 and AsPC-1 cells, 24 h.

Figure 2-6. Apratoxin S9 was evaluated in HCT116 (human colon cancer cell) tumor- bearing nu/nu mice.

A B

Figure 2-7. Cell viability of apratoxin A treatment relative to control of all genes in siRNA screen. Cell viability was indicated by luminescence signal. A) viability of all individual siRNAs in apratoxin-sensitization screen. High confidence individual siRNA sensitizing hits were highlighted in green. Other individual siRNAs are also shown in grey. B) viability of all individual siRNAs in apratoxin-resistance screen. High confidence individual siRNA resistance hits were highlighted in red. Other individual siRNAs are also shown in grey.

ERK/MAPK Signaling, 7 Bladder Cancer Signaling, 7

PAK Signaling, 5

Calcium Signaling, 9 Cellular Effects of Sildenafil (Viagra), 6

HIF1α Signaling, 7 Endothelin-1 Signaling, 7

Pancreatic IL-8 Signaling, 8 Adenocarcinoma Signaling, 6 eNOS Signaling, 7

Figure 2-8. IPA analysis of 178 sensitizers. A) Top canonical pathways of sensitizers. The number indicate the number of sensitizers. B) Top molecular networks. C) Top regulator effects and downstream phenotypic effects which are associated to anticancer activities.

Figure 2-8. Continued.

Figure 2-9. Sensitizer hits with DNA repair related functions from siRNA screen. Green indicates sensitizers from the siRNA screen. In each figure, loss-of-functions of a group of sensitizers are predicted to inhibit or activate functions shown in blue and orange, respectively, based on the literatures. Blue and orange lines indicate the inhibition or activation relationships. Yellow lines indicate inconsistence of predicted relationships with literature reports.

Figure 2-10. In vivo study of apratoxin S9 in combination with AG014699 using HCT116 xenografted nu/nu mice model. Tumor volume was calculated using formula: Tumor volume = 1/2 (L x W2).

PAR level in tumor tissue

200000 180000 160000 140000 120000 100000

g protein extract protein g 80000 μ 60000 40000

pg/ 100pg/ 20000 0

Figure 2-11. PARP activities of tumor samples. PARP activity was indicated by PAR level in tumor tissues.

Figure 2-12. In vitro stability of apratoxins under various conditions. Apratoxins were incubated as indicated and extracted with ethyl acetate, subjected to LC-MS and monitored using compound-specific MRM mode with harmine as internal standard. A) Stability in aqueous solution, pH 7.40. B) Stability in aqueous solution, pH 4.88. C) Stability in mouse serum. D) Cellular stability upon exposure to HCT116 protein lysate (0.7 mg/mL).

Figure 2-13. Concentrations of apratoxin S10 in plasma and tissues. The 4-10 weeks old NSG™, NOD scid gamma male/female mice were used in tissue distribution study. Apratoxin S10 was formulated in 10% EtOH, 5% Tween-80, 85% saline solution (100 μL/ 20 g mice) for i.v. injection, 1 mg/kg. It was formulated in DMSO (25 μL /20g mice) for i.p. injection, 1 mg/kg. Tissues and plasma samples were collected at each time point and subjected to LC-MS analysis. Numbers represent the average of three mice in each group, with error bars indicating SD.

Figure 2-14. Tissue distribution histogram. Kp: tissue:plasma partition coefficients. Numbers represent the average of three mice in each group, with error bars indicating SD.

CHAPTER 3 APRATYRAMIDE, A NEW MODULATOR OF VEGF-A AND OTHER GROWTH FACTORS FROM MARINE CYANOBACTERIA

Introduction

A novel linear depsipeptide termed apratyramide was isolated from marine cyanobacterium Moorea bouillonii previously by our group. It consists of five moieties including three modified tyrosines [N-Me-Tyr, N-Me-Tyr (1-OMe) and N,N-diMe-

Tyr(OMe)], one proteinogenic amino acids (Val) and one α-hydroxy acid moiety [2- hydroxy-3-methylpentanoic acid (Hmpa)] (Figure 3-1). Its high enrichment of modified tyrosine is not commonly observed in other linear depsipeptides of similar size from marine cyanobacteria, suggesting that it may have a unique MOA.

Here, we aimed to elucidate the biological activity and mode of action of apratyramide using both in vitro and ex vivo experiments.

Apratyramide Induces Transcript Level of VEGF-A in HCT116 Cells

During our search for modulators of VEGF-A and angiogenesis from marine cyanobacteria, we previously identified apratoxins from the same organism as potent inhibitor by preventing cotranslational translocation of VEGF-A and other secreted protein.123,124 Apratyramide, however, had the opposite effect.

Using human colon cancer HCT116 cells, we observed that apratyramide up- regulatedVEGF-A, while displaying minimal cytotoxicity (Figure 3-2), suggesting its unique non-cancer related bioactivities. Fifty micro-molar apratyramide induced VEGF-A transcriptional level about 2-fold (Figure 3-2A). Apratyramide also exerted a similar effect in the corresponding normal colon cells (CCD-18Co) (Figure 3-2B) with negligible effects on cell viability (Figure 3-2C). Therefore, we aimed to further explore

apratyramide’s therapeutic applications where VEGF-A upregulation might be beneficial.

Apratyramide Induces Transcription and Secretion Level of VEGF-A in HaCaT

Since VEGF-A inducers are considered promising therapeutic agents for the treatment of chronic wounds, we proposed that apratyramide may also induce VEGF-A in normal cell types that are involved in the wound healing process or organ repair.

Thus, we logically moved on to another mammalian cellular model that is commonly used to study wound healing: human keratinocyte cells (HaCaT). As expected, VEGF-A

RNA level was induced 1.7- fold after 4 h treatment with 30 µM apratyramide (Figure

3-3 A). A greater induction effect was observed at 12 h (Figure 3-3 A). After 16 h treatment, 50 µM apratyramide increased VEGF-A transcript level by 7- fold, while 31.6

µM apratyramide induced a 5-fold increase (Figure 3-3 B). Accordingly, at secretion level, 50 µM apratyramide caused a 1.5-fold induction of secreted VEGF-A and 31.6 µM apratyramide caused a 1.3-fold induction after 24 h (Figure 3-3C) without causing cytotoxicity (Figure 3-3D). Around ninety-percent cell viability was observed at 50 µM apratyramide after 24 h (Figure 3-3D).

Apratyramide Induces Other Wound-healing Related Growth Factors

Multiple growth factors and cytokines are involved in a complex integration of signals for regulating wound healing process. They act through paracrine, autocrine, juxtacrine, or endocrine mechanisms, and regulate cell behavior through binding to specific cell surface receptors or ECM proteins and subsequently trigger a cascade of molecular events. This information prompted us to question whether apratyramide also induces other growth factors which might work cooperatively with VEGF-A during wound healing. RT-qPCR data showed that the mRNA level of PDGFB and bFGF were

all stimulated by apratyramide (Figure 3-4). Importantly, rhPDGF-BB (recombinant platelet-derived growth factor, Becaplermin) is the only FDA-approved growth factor on market for the treatment of diabetic foot ulcers.

Mode of Action Study by Transcriptome Profiling and Ingenuity Pathway Analysis

The stimulatory effect of apratyramide on multiple growth factors supported our hypothesis that apratyramide may induce wound healing through induction of growth factors. In addition, these preliminary data also suggested that HaCaT is a suitable cell model for transcriptome profiling of aprayramide for MOA elucidation. Hence, we performed microarray profiling using the Affymetrix GeneChip® Human Transcriptome

Array 2.0 and determined global changes in transcript levels in HaCaT cells treated with apratyramide. Comparative analysis identified 371 differentially expressed genes after

12 h treatment with 30 μM apratyramide (p < 0.05, FDR corrected, fold change >1.5 or

<0.67) (Figure 3-5A). Consistent with our previous data, VEGF-A appeared to be one of the most up-regulated genes (Table 3-1).

To examine the molecular functions and genetic networks, the microarray data was analyzed using Ingenuity Pathways Analysis (IPA). In accordance with our hypothesis, the global changes of transcript levels are associated with increased downstream phenotypic effects including angiogenesis, mitogenesis, differentiation of epithelial tissue and formation of skin, and decreased effects such as apoptosis of liver cells and hypoplasia of organs (Figure 3-5B).

IPA analysis of 371 microarray hits indicated the unfolded protein response

(UPR) as the top canonical pathway with a p-value of 1.45 × 10-16. The IPA also elucidated that the 371 hits were most related to a molecular network associated with the function of cellular compromise and cellular maintenance (Figure 3-6). The network

contains molecular components from UPR pathway (ATF4, INSIG1, CHOP, DNAJC3,

PP1R15A, JINK1/2), NRF2-mediated oxidative stress response signaling (ATF4,

DNAJC3, JINK1/2, Akt, HERPUD1, DNAJB9) as well as glucocorticoid receptor signaling (ADRB, Akt, JINK1/2, PEPCK, PCK2).

Cytoprotective Roles of UPR and Its Modulatory Effects on Growth Factors.

In normal cells, secretory proteins are folded and processed in the ER prior to their secretion. ER homeostasis must be maintained to ensure a proper protein-folding process. When ER homeostasis is perturbed by adverse environmental conditions like nutrient deprivation, hypoxia and inflammatory cytokines, the resultant unfolded proteins accumulate in the ER. This disruption process has been implicated in a variety of metabolic disorders such as type II diabetes (T2D). In type II diabetes (T2D), the convergence of chronic inflammatory signals and nutrient overload at the endoplasmic reticulum (ER) results in unresolved ER stress.

The unfolded protein response (UPR) pathway is a cytoprotective signaling cascade in response to endoplasmic reticulum (ER) stress in cells. Accumulating studies have documented that the UPR coordinates multiple signaling pathways and controls various physiologies in cells and the whole organism including liver development, plasma cell differentiation,181 bone development,182,183 plasma cell differentiation,184,185 normal pancreatic homeostasis186 and placental development and embryonic viability.187 Importantly, it has been found that UPR is activated after skin injury, suggesting the protective roles of UPR in rescuing wound injuries. Therefore, intervening in ER stress and modulating signaling components of UPR would provide promising therapeutics for the treatment of chronic wounds.188

Interestingly, studies have unveiled the modulatory effects of the UPR on VEGF-

A. The UPR contributes to the transcriptional and protein processing of VEGF-A in the

ER through activation of ATF4 and IRE1α, which were all up-regulated by apratyramide

(Figure 3-6).189190 These findings suggested that apratyramide may induce VEGF-A through UPR pathway. Besides up-regulating VEGF-A, the UPR has also been proven to enhance angiogenesis by up-regulating a number of other pro-angiogenic factors like

FGFs, PDGFs and IL-8.191 These observations are also in accordance with our microarray and RT-qPCR results, indicating that many other pro-angiogenic factors (e.g. bFGF, PDGFB, HB-EGF) were also up-regulated after treatment with apratyramide

(Figure 3-4, Table 3-1).

Collectively, improving ER homeostasis by activating the UPR pathway independent to ER stress may be a promising tool to accelerate wound closure, including diabetes-associated chronic wounds closure192 and apratyramide has promising attributes to be one such therapeutic agent.

Angiogenic and Cytoprotective Roles of Individual Molecular Components in UPR and Their Modulatory Effects on VEGF-A

The UPR potentially promotes wound healing and it induces VEGF-A through

ATF4 and IRE1α. These individual molecular components also directly attribute to angiogenesis or wound healing and they all independently up-regulate VEGF-A.187,193–

195

There are three transmembrane proteins on the ER membrane, ATF6, PERK and IRE1α, which are responsible for sensing ER stress from the environment and stimulate UPR. Our microarray data showed that some of the molecular components

involved in the UPR pathway were up-regulated after treatment with apratyramide, including BIP, IRE1α, XBP1, ATF4, GADD34, CHOP and others.

UPR induces VEGF-A through ATF4

ATF4 is an important transcription factor in the UPR signaling cascade which activates VEGF-A at both transcript and protein levels.193–195 ATF4 promotes bone angiogenesis by increasing VEGF expression and release in the bone environment.195 It has also been reported that ATF-4 expression was induced in smooth muscle cells after arteries injury in rats and its overexpression further enhanced the expression of VEGF-

A by an interaction between ATF-4 and a recognition element located in the VEGF-A gene.194

The microarray data as well as Western blot analysis suggested that ATF4 is activated by apratyramide at both the mRNA and protein level, which subsequently leads to the induction of the transcription of a number of its downstream molecular components: CHOP (DDIT3), SLC6A9, CHAC1, ATF3, SARS, WARS and others

(Figure 3-6).

UPR induces VEGF-A through IRE1α

IRE1α is also an ER-located transmembrane protein, which play an essential role in physiological processes including angiogenesis, placental development and embryonic viability.187,196 It has been shown that VEGF-A expression in the placenta is partially dependent on IRE1α.187 Another recent study identified the deficiency of IRE1α in type 2 diabetic db/db mice and that cell therapies using direct IRE1α gene transfer significantly accelerated cutaneous wound healing in diabetic mice through enhancing angiogenesis.196 These findings strongly suggested the therapeutic strategy for diabetic wound healing by enhancing IRE1α activity. In addition, the authors also documented

that IRE1α deletion resulted in and elevation of microRNAs, while the supply of IRE1α promoted the angiogenic potential of diabetic (bone marrow–derived progenitor cells)

BMPCs through modulating miRNA biogenesis. Accordingly, we also observed a large number of down-regulated microRNAs after 12 h treatment of apratyramide. The most significant ones are shown in Table 3-1.

Apratyramide Induces VEGF-A in a Rabbit Corneal Epithelial Ex Vivo Model

In order to evaluate apratyramide in a more physiological context, we tested it in an ex vivo rabbit corneal epithelial model, a validated wound healing model. The fresh rabbit eyes were obtained and wounds were induced on the center of cornea by a laser.

After that, eyeballs were trimmed to collect cornea tissues which were then cultured in medium with or without the presence of apraytramide. Eighteen-hour later, total RNA was collected from cornea tissues and subjected for RT-qPCR analysis. Consistent with the effect in vitro, we have detected a dose-dependent increase of VEGF-A mRNA in cornea after the treatment with apratyramide (Figure 3-7).

Formulation Study of Apratyramide

With the promising results both in vitro and ex vivo, we then aimed to improve the aqueous solubility of apratyamide for the ease of administration in vivo. We aimed to develop a formulation of apratyramide using a modified β-cyclodextrin, Captisol. Phase solubility analysis was conducted and the aqueous solubility of apratyramide was monitored in the presence of a series of concentrations of Captisol, as illustrated in

(Figure 3-8). Without pH adjustment, the aqueous solubility of apratyramide was increased by 20- fold using 40% w/v Captisol. At pH 4.44, the aqueous solubility of apratyramide is dramatically increased, possibly due to the protonation of the tertiary

amine in its structure. The aqueous solubility is further increased to15-fold compared to the solubility without pH adjustment with 40% w/v Captisol.

Summary

In this study, we aimed to evaluate the biological activities and investigate the mode of action of apratyramide, a previously isolated linear depsipeptide enriched with tyrosine moieties. This structural feature is not commonly observed among other linear peptides of similar size from marine cyanobacteria, suggesting it could possess a unique MOA.

In our screen for VEGF-A inhibitors, we used HCT116 cells. Although negligible cytotoxicity was observed, an unusual VEGF-A induction was seen upon apratyramide treatment (16 h). This intriguing data prompted us to further explore the MOA in another in vitro cell model where VEGF-A induction may be beneficial. Thus, the hypothesis has been formulated that apratyramide may induce VEGF-A at the transcriptional level which results in an induction of VEGF-A protein secretion, which could be beneficial in accelerating chronic wound closure. We tested 30 μM apratyramide in human keratinocyte HaCaT cells and observed VEGF-A induction at various time points: 1.7- fold induction at 4 h, 4-fold induction at 12 h, and 5-fold induction at 16 h. Treatment with a higher concentration of apratyramide (50 μM) led to 7- fold induction of VEGF-A.

Importantly, there was an increased amount of VEGF-A secreted into the culture medium after 24 h treatment with 50 μM apratyramide detected using AlphaLISA human

VEGF-A assay. In addition to VEGF-A, other wound-promoting growth factors were also found to be induced at the transcript level, including PDGFB and bFGF. These results supported the hypothesis of the potential wound healing properties of apratyramide.

To comprehensively evaluate the MOA of apratyramide in HaCaT and identify the mechanism by which VEGF-A was induced. we conducted a transcriptome profiling to monitor the global gene expression after 3 h and 12 h treatment with apratyramide.

The 371 differentially expressed genes after 12 h treatment with apratyramide were subjected to Ingenuity Pathway Analysis (IPA) which resulted in a strong correlation between apratyramide treatment and the UPR. UPR is a cytoprotective pathway in response to ER stress. While ER stress has been observed in many metabolic diseases, such as Type II diabetes associated chronic wounds, UPR has been considered as a protective mechanism to counteract the pathological conditions when activated independent to ER stress. Furthermore, UPR has been shown to induce

VEGF-A and many other wound healing-related growth factors through modulating

ATF4 and IRE1α, two components of UPR that were up-regulated by apratyramide.

Based on the literature evidence that ATF4 and IRE1α all individually promote angiogenesis or wound healing in vivo,187,195,196 we concluded that apratyramide may induce wound-healing related growth factors through ATF4 and IRE1α.

To evaluate apratyramide in a more physiological environment, we tested it in an ex vivo rabbit corneal epithelial model where VEGF-A induction was also observed. We then developed a formulation of apratyramide aimed at facilitating in vivo studies in the future. The formulation using a modified β-cyclodextrin in acidic pH condition has significantly improved the aqueous solubility of apratyramide.

In summary, we identified the novel bioactivities of a previously isolated linear peptide from marine cyanobacteria. It induced multiple wound-healing related growth

factors and therefore is considered as a promising candidate for wound-healing application. Its effect is potentially mediated through ATF-4 and IRE1α.

Experimental Methods

Cell culture

Human keratinocyte HaCaT cells and human colon cancer HCT116 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum at 37 ºC humidified air and 5% CO2. Human normal colon (CCD-18Co) cells (ATCC) were cultured in Eagle’s minimal essential medium (EMEM) supplemented with 10% fetal bovine serum at 37 ºC humidified air and 5% CO2.

Cell Viability Assay (MTT)

Cells were seeded in a 96-well clear bottom plate and 24 h later, cells were treated with various concentrations of apratyramide or solvent control (DMSO). After 48 h of incubation, cell viability was detected using MTT according to the manufacturer’s instructions (Promega, Madison, WI). Nonlinear regression analysis was carried out using GraphPad Prism software for IC50 value calculations.

Measurement of Human VEGF-A Secretion

HaCaT cells were seeded in a 96-well clear bottom plate. Cells were treated with various concentrations of apratyramide or solvent control (DMSO). After 24 h incubation, culture supernatants were collected for detection of VEGF-A using

AlphaLISA kits (PerkinElmer, Waltham, MA) following the manufacturer’s instruction.

Briefly, acceptor bead and anti-VEGF-A antibody were incubated with the supernatants for 60 min firstly, donor beads were added later and incubated for another 30 min.

Signal was detected using Envision (PerkinElmer). Levels of VEGF-A (pg/mL) were calculated using a standard curve.

Immunoblot Analysis

HaCaT cells were seeded in 6-well clear bottom plates the day before treatment.

The next day, cells were treated with apratyramide or solvent control (DMSO). 24 h later, whole cell lysates were collected using PhosphoSafe buffer (EMD Chemicals, Inc,

Gibbstown, NJ). Protein concentrations were measured with the BCA Protein Assay kit

(Thermo Fisher Scientific, Rockford, IL). Lysates containing equal amounts of protein were separated by SDS polyacrylamide gel electrophoresis (4–12%), transferred to polyvinylidene difluoride membranes, probed with primary and secondary antibodies, and detected with the SuperSignal West Femto Maximum Sensitivity Substrate (Thermo

Fisher Scientific). BIP, IRE1, β-actin and secondary anti-mouse and rabbit antibodies were from Cell Signaling Technology, Inc (Danvers, MA). ATF4 and HYOU1 (ORP150) antibodies were obtained from Santa Cruz (CA).

RNA Isolation and Reverse Transcription

Cells were seeded in 6-well plates at a density of 2 × 105 per well and incubated further for 24 h in growth medium prior to treatment. RNA was isolated at 3, 12 or 16 h post treatment using the RNeasy mini kit (QIAGEN, Valencia, CA). Total RNA was quantified using NanoDrop 2000. From 2 μg of total RNA, cDNA synthesis was done using SuperScript II Reverse Transcriptase (Invitrogen, Carlsbad, CA) and oligo (dT)

(Invitrogen).

Real-time Quantitative Polymerase Chain Reaction (qPCR) for Transcript Level Determination in HaCaT Cells

qPCR after reverse transcription (RT-qPCR) was performed on a 25 μL reaction solution containing a 0.3 μL aliquot of cDNA, 12.5 μL of TaqMan gene expression assay mix, 1.25 μL of probes, and 11 μL RNase-free water. qPCR was carried out on an ABI

7300 sequence detection system using the thermocycler program: 2 min at 50 °C, 10 min at 95 °C, and 15 s at 95 °C (40 cycles) and 1 min at 60 °C. Each experiment was performed in triplicate. VEGF-A (Hs00900055_m1), PDGFB (Hs00266645_m1) and bFGF (Hs00966522_m1) were used as target genes, while GAPDH (Hs02758991_g1) was used as endogenous control. Graphs and data analysis were performed using the

Prism software and analyzed using unpaired t test.

Transcriptome Profiling

The transcriptome profiling was conducted by Dr. Yanping Zhang from the Gene

Expression & Genotyping Core of the Interdisciplinary Center for Biotechnology

Research in UF. GeneChip™ Human Transcriptome Array 2.0 and GeneChip WT PLUS

Reagent Kit (Affymetrix, Santa Clara, CA) were used according to the manufacturer’s instruction. The transcriptome data analysis and heat map generation was done by Dr.

Alberto Riva from the Bioinformatics Core of the Interdisciplinary Center for

Biotechnology Research.

Ex Vivo Organ Culture of Rabbit Corneas

Wounding and trimming of rabbit corneas were conducted with the assistance of

Dr. Daniel Gibson. The central 6 mm diameter area of corneas of twelve fresh rabbit globes (Pelfreeze) was ablated to a total depth of 155 microns using a Nidek excimer laser in phototherapeutic keratectomy mode. Ablated corneas were then surgically dissected from the rabbit globes using sterile scalpel and forceps, grasping only the scleral rims and not the clear cornea. The corneas were cultured in DMEM/F-12 1:1

(Thermo Fisher) containing 40 mM HEPES, 10% FBS, 0.01 % dextran 40 (Tokyo

Chemical Industry, Co., Ltd.) and 0.025 % chondroitin sulfate (Chem-Impex

International, Inc.) in 6-well plates. Twelve corneas were randomly distributed into four

groups (three corneas in each group): DMSO (1 %), 25 μM, 50 μM or 100 μM apratyramide. Compounds or solvent were added to the media immediately after placing the corneas in the 6-well plates. The corneas were incubated for 18 h at 37°C in humidified atmosphere containing 5% CO2 before total RNA was extracted.

Evaluation of Transcript Level of VEGF-A in Ablated Corneas After Treatment with Apratyramide

RNA extraction and qRTPCR was conducted with the assistance of Dr.

Soojung Seo. The central scar-like tissue from each cornea was collected using an 8 mm punch biopsy. RNA was collected from these scar-like tissue using TRIzol Reagent

(Life Technologies) according to manufacturer's procedure. cDNA was synthesized using the iScript™ Select cDNA Synthesis Kit (Bio-Rad) according to manufacturer's procedure. The quantitative real-time polymerase chain reaction (qRTPCR) was performed using the SYBR Select Master Mix (Applied Biosystems). The relative gene expression of VEGF-A in the apratyramide treatment groups compared to DMSO control group was calculated using the 2-δCt method.

Formulation of Apratyramide

Sulfobutyl ether β-cyclodextrin sodium (Captisol®) was provided by CyDex Inc.

(Lenexa, KS). To conduct the phase solubility analysis, a 40% (w/v) solution of

Captisol® was prepared by dissolving 400 mg into a total volume of 1 mL water. The stock solution of Captisol® was serially diluted as show in Appendix. To six eppendorf tubes, sufficient amount of apratyramide was added to exceed the potential amount that could be solubilized by Captisol® (Appendix). To each tube, 40 µL of Captisol® solution with corresponding concentrations was added. Samples were sonicated and agitated for

3 days at RT using a thermomixer. Tubes were removed at the end of the agitation

100

period, followed by centrifugation. The clear supernatant was collect for analysis by measuring absorbance at 220 nM. Apratyramide standards were prepared using EtOH and a concentration-absorbance standard curve was made for quantification

(Appendix).

101

Table 3-1. Top up- and down- regulated genes after 12 h treatment with 30 µM apratyramide. Down-regulationed genes, Up-regulationed genes, FDR correction, 12 h FDR correction, 12 h

Growth factor AREG, VEGFA, HBEGF, EREG

SCGB1A1, CXCL1, CCL2, Cytokine IL1A, IL1B, MYDGF TNFSF10 DDIT3, CREB3L2, XBP1, ETV5, ATF4, JUND, ZNF165, ETV4, ATF3, MYC, GMNN, UHRF1, GTF2I, Transcription regulator NFE2L1, KLF6, SQSTM1, DAP, PREB, VGLL1, DLX5, E2F8, SMAD6, NFKB2, FOXE1, MEF2D, MAGED1, ID3, GRHL3 IRF2BP2, BHLHE40 SLC7A11, SEC24D, LCN2, SLC33A1, LDLR, SYVN1, COPG1, SLC1A5, SEC61A1, ATP1B1, ATP1B3, TFRC, Transporter SLC6A9, SEC23B, SLC39A7, STX5, NPC1, SCFD2, SLC12A2, AQP3, SLC1A4, SLC43A1, TMCO3, SEC63, SLC39A10, SLC6A6, SLC1A3 SLC50A1, SEC61B, TMED10, OSBP

Kinase TRIB3, PCK2, ERN1, NDRG1 EPHA4

G-protein coupled GPR1 P2RY2 receptor

Ion channel CLIC4, CLCN6, CACNB1 KCNJ15

Transmembrane F3 BCAM receptor

Phosphatase PLPP5, LPIN1, DUSP6, MTMR4 ALPPL2, DUSP10

Peptidase ABHD4, PRSS8, LONP1 MMP13 CYP1A1, ASNS, GFPT1, CTH, WARS, SARS, MTHFD2, PSAT1, CYP1B1, AARS, UPP1, CYP51A1, FUT3, SCD, HSPA5, CBS/CBSL, MICAL2, DHCR7, CYP4F11, HSPA8, MSH2, DHFR, ALG2, PYCR1, FKBP11, EDEM2, MARS, ESCO2, TYMS, GLUL, PCNA, PDIA4, GPT2, NCF2, EDEM1, CARS, Enzyme RHOBTB3, MCM6, POP1, PHGDH, OSTC, GARS, GMPPA, ANXA3, AKR1B10, HSPA1A/HSPA1B, IARS, PYGB, SMOX, MSMO1, CHPF, CROT, HAS2, RNF152 SHMT2, UAP1, FDFT1, SND1, GTPBP2, YARS, MVD, LSS, SDR42E1, DNASE2, NEU1, PDIA6, RAB6A, P4HB, SQLE, MTHFD1L, TXNDC11, ODC1, DTD1, ACSL3 MicroRNA MIR-3143, MIR-554, MIR-548

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Figure 3-1. Structure of apratyramide.

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Figure 3-2. Apratyramide induced VEGF-A in colon cell models. Transcript level of VEGF-A in A) HCT116 cells and B) CCD-18Co cells after 16 h treatment of apratyramide. C) Antiproliferative effect of apratyramide on HCT116 and CCD-18Co cells (48 h). Data are presented as mean + SD, *P < 0.05, **P < 0.01, ***P < 0.001 compared to control using unpaired t test (n = 3).

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Figure 3-3. Transcript and secretion level of VEGF-A in HaCaT cells. A) Transcript level of VEGF-A in HaCaT cells after 4 h and 12 h treatment with 30 µM apratyramide. B) Transcript level of VEGF-A in HaCaT cells after 16 h. C) Level of VEGF-A secretion from HaCaT after 24 h. D) Antiproliferative activity of apratyramide on HaCaT cells, 24 h. Data are presented as mean + SD, *P < 0.05, **P < 0.01, ***P < 0.001 compared to control using unpaired t test (n = 3).

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Figure 3-4. Apratyramide induced transcript levels of other growth factors. Transcript levels of A) PDGFB and B) bFGF in HaCaT cells, 16 h. Data are presented as mean + SD, *P < 0.05, **P < 0.01, ***P < 0.001 compared to control using unpaired t test (n = 3).

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A 3 h 12 h

Figure 3-5. Ingenuity Pathway Analysis (IPA) for transcriptome profiling of apratyramide. A) Heat map for transcriptional fold changes after 3 h and 12 h treatment with 30 µM apratyramide. B) Top regulator effect network.

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Figure 3-5. Continued.

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Figure 3-6. Networks involved in the mechanism of action of apratyramide. A) Molecular network of unfolded protein response and VEGF-A production. B) Validation using Western blot.

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VEGF-A Transcript

Ex Vivo Rabbit Corneal Epithelial Model l

o 5

n o

c *

v i

t 3

o 2

d n

i 1

l o

F 0 0 5 0 0 2 5 0 1 Apratyramide ( µM)

Figure 3-7. Apratyramide induced VEGF-A in a rabbit corneal epithelial ex vivo model. Data are presented as mean + SEM, *P < 0.05, **P < 0.01, ***P < 0.001 compared to control using unpaired t test (n = 3).

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Solubility of apratyramide in various concentrations (0 to 40% w/v) of Captisol® 30000

25000

20000 No pH adjustment

15000 pH = 4.44

Apratyramide Apratyramide [µM] 10000

5000

0 0 10 20 30 40 50 % (w/v) Captisol Figure 3-8. Solubility of apratyramide in various concentrations (0 to 40% w/v) of Captisol®.

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CHAPTER 4 PITIAMIDES A AND B: CYTOTOXIC FATTY ACID AMIDES FROM MARINE CYANOBACTERIA‡

Introduction

As depicted previously in Chapter 1, one prevailing lipophilic class of compounds are fatty acid amides, which are featured by the presence of an amide bond in a fatty acid chain and in some cases with incorporated halogen atoms. A number of fatty acid amides have been isolated from marine cyanobacteria over the past two decades, including pitiamide A197, malyngamides21, jamaicamides198, grenadamides199,200, hermitamides201, semiplenamides202, janthielamide A203, kimbeamides203, kalkitoxin204, taveuniamides205, besarhanamides206 and credneramides207. Bioactivities associated with these fatty acid amides include cytotoxicity, voltage-gated sodium channel blockage or activation, cannabinoid receptors binding and others.

Pitiamide A was isolated from an extract of a mixed assemblage of Lyngbya majuscula and a Microcoleus sp. found growing on intact colonies of the hard coral

Porites cylindrica on Guam.197 This compound had not been linked to biological activity yet. An analogue of pitiamide A, named pitamide B, was co-isolated with pitiamide A without full structure elucidation, but was hypothesized to be a mixture of E/Z isomers around the terminal double bond.197 Here, we describe the isolation and structure determination of these elusive isomers, 1E-pitiamide B (1) and 1Z-pitiamide B (2) using bioassay-guided fractionation along with the co-produced pitiamide A. We also describe the antiproliferative effect of pitiamides.

‡ Reproduced with permission from Cai, W.; Matthews, J. H.; Paul, V. J.; Luesch, H. Pitiamides A and B, Multifunctional Fatty Acid Amides from Marine Cyanobacteria. Planta Med. 2016, 82 (9–10), 897–902. Copyright 2016 Georg Thieme Verlag KG Stuttgart • New York.

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Isolation and Structure Determination

The cyanobacterium, a recollection of one that previously yielded pitiamide A,197 was collected on the shallow reef flat at Piti Bomb Holes, Guam, Mariana Islands. As previously noted, this iridescent cyanobacterium grows prominently on the tips of corals such as Porites cylindrica. Recently this cyanobacterium has been identified as

Hydrocoleum majus.208 Hydrocoleum is a polyphyletic group, and the taxonomy of marine cyanobacteria is under revision, so it is likely the taxonomic designation for this species will be revised. The nonpolar extract (MeOH-EtOAc, 1:1) was subjected to silica chromatography and various rounds of reversed-phase HPLC to yield pitiamide A, 1E- pitiamide B (1) and 1Z-pitiamide B (2) (Figure 4-1) using a HCT116 cancer cell growth- inhibition guided fractionation.

+ The HRESIMS spectrum of 1 showed a [M + NH4] peak at m/z 413.2926 and an isotopic peak of about one third intensity at m/z 415.2886, indicating the presence of one chlorine atom. The molecular formula of 1 was deduced as C23H38ClNO2, with 5 degrees of unsaturation. NMR analysis (Table 4-1) suggested its structural similarity to the co-isolated pitiamide A with the presence of a ketone (δC 211.2), an amide or ester linkage (δC 173.0), a terminally chlorinated conjugated diene (δC/δH 118.5/6.08,

133.7/6.40, 126.4/5.96, 135.8/5.68) and an isolated C-C double bond (δC/δH 128.6/5.40,

131.8/5.47).

Compared to pitiamide A, 1 is a homologue of pitiamide A with a 14 amu higher molecular weight corresponding to additional methyl or methylene group. However, closer inspection of the 1H NMR revealed the presence of only one methyl doublet and one methyl triplet, while pitiamide A has three methyl groups including one terminal methyl group (two doublets and one triplet). The absence of one methyl branch in 1

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suggested the presence of two additional methylenes, which are needed to account for

3 MS-based molecular formula. The JH,H coupling constants for H-1/H-2 (13.1 Hz), H-

2/H-3 (10.8 Hz), H-3/H-4 (15.2 Hz) suggested the same E,E configuration of the conjugated diene as pitiamide A. Interpretation of COSY data revealed partial structure I

(Figure 4-2) which has five consecutive methylene groups (δH 2.07, 1.39, 1.29, 1.57,

2.38) adjacent to the terminally chlorinated conjugated diene. The connection of partial structure I was also confirmed by HMBC correlations. Partial structure II (Figure 4-2) was assigned based on COSY and HMBC correlations. The HMBC correlations between H-11, H-12, H-13 and C-24 supported the attachment of the methyl group

1 (δC/δH 20.2/0.92) to the methane (δC/δH 26.5/2.06) in partial structure II. The H NMR

13 and C NMR chemical shifts for C-14/H-14 (δC/δH 37.4/ 3.18, 3.28) indicated its direct

3 attachment to a nitrogen atom. The JH,H coupling constants for H-19/H-20 (15.2 Hz) suggested an E configuration of the C-C double bond. COSY correlations combined with HMBC correlations established partial structure III (Figure 4-2). HMBC correlations between H-8, H-9, H-11 and C-10 (δC 211.2) constructed the linkage of partial structures

I and II through a keto carbonyl group (δC 211.2). HMBC correlations between H-17, H-

18 and C-16 linked partial structures II and III through an amide bond (δC 173.0).

+ The same HRESIMS pattern was obtained for compound 2 with a [M + NH4] peak at m/z 413.2943 and an isotopic peak of about one third intensity at m/z

415.2924. A same molecular formula of 2 was deduced as C23H38ClNO2, with 5 degrees of unsaturation. Further interpretation of the COSY, HSQC, HMBC and TOSCY spectra

3 indicated that 2 has an identical planar structure with 1. The JH,H coupling constants, however, revealed that the conjugated diene side chain of 2 has a different

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3 configuration from that of 1. The JH,H coupling constants for H-1/H-2 (7.0 Hz), H-2/H-3

(10.3 Hz), H-3/H-4 (15.3 Hz) suggested a Z,E configuration of the conjugated diene.

The absolute configuration was proposed as 12R for both 1 and 2 on the basis of the additivity rule of specific rotation.209 Compounds 1 and 2 have very close structures with pitiamide A with one less stereocenter. The experimental specific rotations of

(7S,10R)- and (7R,10R)- isomers of pitiamide A were reported as: [α]D +11.6° (c 0.49,

210 CHCl3) and [α]D ̶ 30.3° (c 0.71, CHCl3) respectively (Figure 4-1). In general, stereocenters more than three atoms removed from each other have negligible mixed- term contributions to the rotation of the plane of polarized light, as long as the rotation of the chain can occur freely.209 The contribution of the α-ketone stereocenter at C-7 should therefore be about +20° for S and ̶ 20° for R configuration calculated on the basis of the additivity rule. These calculated results are also consistent with the observed specific rotation of other model compounds with an α-ketone stereocenter

(Table 4-2). Therefore, the β-ketone stereocenter of pitiamide A at C-10 should have about ̶ 10° contribution for the R configuration. In contrast to pitamide A, 1 and 2 only have one β-ketone stereocenter at C-12. Compounds 1 and 2 have the same partial structure in three atom distance from the β-ketone stereocenter at C-12 as pitiamide A; we could therefore conclude that R configuration at C-12 of 1 and 2 should have contributed around -10° to their specific rotation, while S should have contributed

20 around +10°. The experimental specific rotation measurement for 1 and 2 was [α]D ̶

20 6.3° (c 0.011, CDCl3) and [α]D ̶ 8.8° (c 0.020, CDCl3) respectively, which is very close to the one we proposed for 12R-1 and 12R-2.

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Pitiamide A was reported in 1997 and isolated with pitiamide B as a mixture of unstable isomers which differ in the configuration of the chlorinated olefin. Pitiamide B had the same molecular weight as 1 and 2 evidenced by EIMS data: m/z 395, 6.7% rel. abundance [M]+; 397, 2.5% rel. abundance [M+2]+. Comparison of the NMR spectra of the original pitiamide B with spectra of 1 and 2 suggested that pitiamide B is the mixture of 1 and 2. Therefore, we refer to 1 and 2 as 1E-pitiamide B and 1Z-pitiamide B, respectively.

Biological Evaluation

Pitiamide A, 1E-pitiamide B (1) and 1Z-pitiamide B (2) were evaluated for their antiproliferative effects towards HCT116 colorectal cancer cells, with vinblastine (IC50 =

1.2 nM) as a positive control. The IC50 values are 1.1 µM, 5.1 µM and 4.5 µM, respectively (Figure 4-3). Interestingly, the dose-response curves of pitiamides B were consistently rightward shifted by 4- to 5- fold compared with that of pitiamide A, suggesting a structure-activity relationship between the pitiamide compounds. Either the absence of α-ketone methyl or the increase in length of the methylene chain caused a shift of IC50 to a higher value.

Summary

In conclusion, we identified two new pitiamide A analogues from marine cyanobacteria using bioassay-guided fractionation. Two geometric isomers related to pitiamide A, termed 1E-pitiamide B (1) and 1Z-pitiamide B (2), were isolated from a marine cyanobacterium collected from the shallow reef flat at Piti Bomb Holes, Guam,

Mariana Islands. The structures of these analogues were elucidated using 1D and 2D

NMR analysis. Pitiamide A, which had been previously described and had not been

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investigated in bioassays, was co-isolated. Pitiamides A and B were subjected to biological evaluation and they both showed antiproliferative effects on HCT116 cells with IC50 values of 1 ̶ 5 µM. However, further structure-activity relationship studies of pitiamides are warranted.

Experimental Methods

Chemicals

Benzamil (purity >98%) was purchased from Sigma Aldrich. Ionomycin (purity

>97%), glibenclamide (purity >98%) and vinblastine (purity >98%) were purchased from

Calbiochem.

General Experimental Procedures

Optical rotations were measured on a Perkin-Elmer 341 polarimeter. 1H and 2D

NMR spectra were recorded in CDCl3 on Bruker Advance II 600 MHz spectrometer equipped with a 5 mm TXI cryogenic probe using residual solvent signals (δH 7.26; δC

77.0 ppm, CDCl3) as internal standards. HSQC and HMBC experiments were optimized

1 n for JCH = 145 and JCH = 7 Hz, respectively. HRESIMS data were obtained using an

Agilent LC-TOF mass spectrometer equipped with an APCI/ESI multimode ion source detector.

Extraction and Isolation

The sample VPG 14-38 was collected on May 31, 2014 in 1 m water on the shallow reef flat at Piti Bomb Holes, Guam, Mariana Islands. The sample was a recollection of one that previously yielded pitiamide A, and was previously described as predominately Lyngbya majuscula. Recently this cyanobacterium has been identified as Hydrocoleum majus. A voucher specimen of VPG 14-38 has been retained at the

Smithsonian Marine Station. The freeze-dried sample (10.26 g) was extracted with

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MeOH-EtOAc (1:1) (2 × 300 mL, each for 24 h) and the extract (2.4 g) was suspended in water (50 mL) and partitioned with EtOAc (3 × 50 mL). The EtOAc soluble part (246.6 mg) was then subjected to silica column (230-400 mesh, i.d. 2.5 × 40 cm, 50 g) using the gradient of hexane (300 mL), 30% EtOAc in hexane (300 mL), EtOAc (300 mL),

10% MeOH in EtOAc (300 mL) and EtOAc/MeOH 1:1 (300 mL) to elute five fractions.

The EtOAc fraction (49.41 mg) was further subjected to semipreparative HPLC

(Phenomenex Luna C18, 250 × 10 mm, 5µm, 2.0 mL/min; PDA detection) using an acetonitrile-H2O linear gradient (40-100% acetonitrile for 10 min and 100 % acetonitrile for 25 min). Fractions were pooled on the basis of retention times, 1H NMR analysis and low-resolution MS measurements to afford pitiamide A (tR 17.8 min, 0.92 mg) and a mixture of 1E-pitiamide B (1) and 1Z-pitiamide B (2) (tR 18.4-19.1 min, 0.87 mg). The mixture of pitiamide B was further purified by analytical HPLC (Phenomenex Kinetex 2.6

µm PFP, 150 × 4.60 mm, 1.0 mL/min; PDA detection) using an acetonitrile-H2O linear gradient (50% acetonitrile for 10 min, 50-100% acetonitrile for 20 min and 100% acetonitrile for 5 min) to yield 1E-pitiamide B (1) (tR 16.9 min, 0.12 mg) and 1Z-pitiamide

B (2) (tR 17.6 min, 0.22 mg).

20 1 1E-pitiamide B (1): [α]D ̶ 6.3° (c 0.011, CDCl3); H NMR, COSY, TOCSY, HSQC and HMBC data, see Table 1; UV (MeOH) λmax (log ε) 235 (5.52); HRESIMS m/z [M +

+ 35 NH4] 413.2926 (calcd for C23H42 ClN2O2 413.2935), 415.2886 (calcd for

37 + C23H42 ClN2O2 415.2905) (3:1 [M + NH4] ion cluster)

20 1 1Z-pitiamide B (2): [α]D ̶ 8.8° (c 0.020, CDCl3); H NMR, COSY, TOCSY, HSQC and HMBC data, see Table 1; UV (MeOH) λmax (log ε) 235 (5.56); HRESIMS m/z [M +

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+ 35 NH4] 413.2943 (calcd for C23H42 ClN2O2 413.2935), 415.2924 (calcd for

37 + C23H42 ClN2O2 415.2905) (3:1 [M + NH4] ion cluster)

Cell Viability Assay (MTT)

HCT116 cells were cultured in Dulbecco’s modified Eagle medium (DMEM,

Invitrogen) supplemented with 10% fetal bovine serum (FBS, Hyclone) under a humidified environment with 5% CO2 at 37 °C. HCT116 (10,000) cells were seeded in

96-well plates. Cells were treated with a series of concentrations of pitiamide A or its analogues dissolved in EtOH, 24 h postseeding. Cells were incubated for an additional

48 h before the addition of the MTT reagent. Cell viability was measured according to the manufacturer’s instructions (Promega, Madison, WI, USA). Treatments were done in triplicate. Nonlinear regression analysis was carried out using GraphPad Prism software for IC50 value calculations.

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Table 4-1. NMR data for 1E-pitiamide B (1) and 1Z-pitiamide B (2) in CDCl3 (600 MHz). Position 1E-pitiamide B (1) 1Z-pitiamide B (2)

δH mult. (J δH mult. (J in in Hz) δCa COSY HMBC TOCSY Hz) δCa COSY HMBC TOCSY 6.08 d 2, 3, 4, 5, 6, 7, 2, 3, (5, 1 (13.1) 118.5 2 2, 3 8, 9 5.88 d (7.0) 116.2 2 6) 2, 3 6.4 dd (13.1, 6.25 dd 2 10.8) 133.7 1, 3 1, 3 1, 3, 4, 5, 6, 7 (10.3, 7.0) 129.9 1, 3 1, 4 3, 4, 5, 6, 7, 8 5.96 dd 6.43 ddd (15.2, 1, 2, 4, 5, 6, 7, (15.3, 1, 2, 5, 6, 7, 8, 3 10.8) 126.4 2, 4, 5 8, 9 10.3, 1.1) 124.0 2, 4, 5 1, 2, 5 9 5.68 dt 1, 2, 3, 5, 6, 7, (15.2, 8, 11a, 11b, 5.84 dt 2, 3, 5, 6, 7, 8, 4 7.1) 135.8 3, 5 2, 6 24 (15.3, 7.0) 138.5 3, 5 2, 5, 6 9 1, 2, 3, 4, 6, 7, 2.14 dt (7.5, 2, 3, 4, 6, 7, 8, 5 2.07 m 32.5 4, 6 3, 4, 6 8 7.5) 32.8 3, 4, 6 3, 4, 6 9 2, 3, 4, 5, 7, 8, 6 1.39 m 28.9 5, 7 4, 5 3, 4, 5, 7, 8, 9 1.42 m 28.9 5, 7 4, 5, 8 9 2, 3, 4, 5, 6, 8, 7 1.29 m 29.0 6, 8 8 3, 4, 5, 6, 8, 9 1.29 m 29.0 6, 8 5, 8, 9 9 7, 9, 2, 3, 4, 5, 6, 7, 8 1.57 m 23.7 7, 9 10 3, 4, 5, 6, 7, 9 1.57 m 23.7 7, 9 7, 9, 10 9 7, 8, 9 2.38 t (7.4) 43.8 8 10 6, 7, 8, 12 2.38 t (7.4) 43.8 8 7, 8, 10 4, 6, 7, 8, 12 10 211.2 211.2 2.39 dd 11b, 12, 13a, 10, 12, 11b, 12, 13a, (17.0, 13b, 14a, 2.39 dd 13, 13b, 14a, 11a 7.0) 49.7 11b, 12 10 14b, 15, 24 (17.0, 7.0) 49.7 11b, 12 24 14b, 15, 24 2.30 dd 11a, 12, 13a, 10, 12, 11a, 12, 13a, (17.0, 11a, 13b, 14a, 2.30 dd 13, 13b, 14a, 11b 7.0) 49.7 12, 10 14b, 15, 24 (17.0, 7.0) 49.7 11a, 12 24 14b, 15, 24

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Table 4-1. Continued Position 1E-pitiamide B (1) 1Z-pitiamide B (2)

δH mult. (J δH mult. (J in in Hz) δCa COSY HMBC TOCSY Hz) δCa COSY HMBC TOCSY 11a, 11b, 13a, 11a, 11a, 11b, 13a, 13b, 11a, 11b, 14a, 11b,13a, 11, 13, 13b, 14a, 12 2.06 m 26.5 24 24 14b, 24 2.06 m 26.5 13b, 24 24 14b, 15, 24 12, 13b, 11, 12, 11b, 12, 13b, 14a, 13b, 14a, 14b, 12, 13b, 14, 14a, 14b, 13a 1.43 m 36.5 14b 24 24 1.43 m 36.5 14a, 14b 24 15, 24 12, 13a, 11, 12, 11b, 12, 13a, 14a, 13a, 14a, 14b, 13a, 14a, 14, 14a, 14b, 13b 1.37 m 36.5 14b 24 25 1.37 m 36.5 14b 24 15, 24 13a, 13b, 11a, 11b, 12, 11a, 11b, 12, 14b, 13a, 13b, 13a, 13b, 13a, 13b, 14a 3.28 m 37.4 15 14b, 15, 24 3.28 m 37.4 14b, 15 12, 13 14b, 15, 24 13a, 13b, 11a, 11b, 12, 11a, 11b, 12, 14a, 13a, 13b, 13a, 13b, 13a, 13b, 14b 3.18 m 37.4 15 14a, 15, 24 3.18 m 37.4 14a, 15 12 14a, 15, 24 12, 13a, 13b, 14a, 14a, 14b, 15 5.66 14b 14a, 14b 5.68 br s 14a, 14b 24 16 173.0 173.0 16, 18, 17 2.24 t (7.6) 36.8 18 19 18, 19, 20, 21 2.24 t (7.6) 36.8 18 18, 19, 20, 21 16, 17, 16, 17, 19, 18 2.32 m 28.8 17, 19 20 17, 19, 20, 21 2.32 m 28.8 17, 19 20 17, 19, 20, 21

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Table 4-1. Continued Position 1E-pitiamide B (1) 1Z-pitiamide B (2)

δH mult. (J δH mult. (J in in Hz) δCa COSY HMBC TOCSY Hz) δCa COSY HMBC TOCSY 5.40 dt (15.2, 17, 18, 20, 21, 5.40 dt 17, 18, 20, 21, 19 6.6) 128.6 18, 20 22, 23 (15.2, 6.7) 128.6 18, 20 18, 21 22, 23 5.47 dt (15.2, 17, 18, 19, 21, 5.47 dt 17, 18, 19, 21, 20 6.6) 131.8 19, 21 22, 23 (15.2, 6.7) 131.8 19, 21 18 22, 23 20, 19, 19, 20, 1.95 dt (7.3, 22, 17, 18, 19, 20, 1.95 dt (7.3, 22, 17, 18, 19, 20, 21 7.2) 34.8 20, 22 23 22, 23 7.2) 34.8 20, 22 23 22, 23 20, 21, 20, 21, 22 1.34 m 22.5 21, 23 23 19, 20, 21, 23 1.34 m 22.5 21, 23 23 19, 20, 21, 23 17, 18, 19, 20, 17, 18, 19, 20, 23 0.87 t (7.3) 13.8 22 21, 22 21, 22 0.87 t (7.3) 13.8 22 21, 22 21, 22 11a, 11b, 12, 11a, 11b, 12, 13a, 13b, 13a, 13b, 11, 12, 14a, 14b, 11, 13, 14a, 14b, 24 0.92 d (6.7) 20.2 12 13 15 0.92 d (6.7) 20.2 12 12 15 a Deduced from HSQC and HMBC experiments

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Table 4-2. Reported optical activity of model compounds with α-ketone stereocenter and similar structure scaffold. Specific Optical Number Structure Reference Rotation

23 [α]D -17.9° (c 1.0, 1 211 CHCl3)

23 [α]D +19.6° (c 1.9, 2 212 CHCl3)

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Figure 4-1. Structures and absolute configuration of natural product pitiamide A, synthetic (7S,10R)-pitiamide A and 1E-pitiamide B (1) and 1Z-pitiamide B (2).

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Figure 4-2. Partial structures of 1E-pitiamide B (1) with key COSY and HMBC correlations.

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Figure 4-3. Antiproliferative activity of pitiamides. A) Antiproliferative activity of pitiamides on HCT116 cells. B) Antiproliferative activity of vinblastine on HCT116 cells. Vinblastine was used as a positive control.

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CHAPTER 5 LAXAPHYCIN ANALOGUES FROM THE MARINE CYANOBACTERIUM HORMOTHAMNION SP.

Introduction

As described earlier, cyclic peptides are one classic structural class of compounds isolated from marine cyanobacteria. However, only few large cyclic lipopeptides with more than ten amino acids or polyketide moieties were isolated from marine cyanobacteria. The family of laxaphycins is one example.

Laxaphycins A (Figure 5-1) and B (Figure 5-2) were isolated from fresh water cyanobacterium Anabaena laxa in 1992.213,214 The exact configurations of these two compounds were not elucidated until 1997, when laxaphycins A and B were identified again from a collection of tropical marine cyanobacterium Lyngbya majuscula.215 Since the discovery of laxaphycins A and B, several laxaphycin analogues have been isolated from marine cyanobacteria (Figure 5-1, Figure 5-2) including hormothamnion A from

Hormothamnion enteromorphoides,216 laxaphycins B2 and B3 from Lyngbya majuscula,217 lobocyclamides A-C from Lyngbya confervoids,218 and lyngbyacyclamides

A and B from Lyngbya sp..219 Related compounds from fresh water cyanobacteria include trichormamides A and B from Trichormus sp.,220 and trichormamides C and D from cf. Oscillatoria sp..221

Laxaphycins are cyclic lipopeptides featured by a rare fatty β-amino acid with a linear chain of up to 12 carbons. All laxaphycins can be separated into two groups, the laxaphycin A-type peptides (Figure 5-1), which are cyclic undecapeptides, and the laxaphycin B-type peptides (Figure 5-2), which are cyclic dodecapeptides. Laxaphycin

A-type and laxaphycin B-type peptides are generally co-produced from the same cyanobacteria.

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Laxaphycin B-type compounds exhibited antifungal activities against C. albicans

218 and C. glabrata and anticancer activity towards various cancer cells (IC50 < 2

µM)217,219–221 as well as antibacterial activities against M. tuberculosis.221 In contrast,

most of the laxaphycin A-type compounds show low cytotoxicity (IC50 >10 µM) with hormothamnin A as an exception. 214,216,217,220,221 Hormothamnin is a Z-Dhb analog of laxaphycin A. It is possible that the geometry of the Dhb unit in laxaphycin A type compounds is one of the contributing factor to its cytotoxicity. Interestingly, a synergistic effect between laxaphycin A-type and B-type peptides on cytotoxic activities against fungus and cancer cells has been found.217,218 This finding may provide an explanation for the frequently observed co-production of laxaphycin A-type and B-type peptides from a same cyanobacteria, that is these two types of compounds may work cooperatively in the organism.

Here we describe the discovery of two novel laxaphycin analogues: laxaphycin

B4 (3) (Figure 5-3) and laxaphycin A2 (4) (Figure 5-4).These two compounds were previously isolated from Hormothamnion sp. by our group. In the present study, we performed the total structure determination and biological evaluation studies.

Structure Determination

The ESIMS spectrum of compound 3 showed a [M + Na]+ peak at m/z

1463.8334. The molecular formula of was deduced as C66H116N14O12. The structure of 3 was established based on a detailed NMR interpretation of 1H NMR, 13C NMR, HSQC,

HMBC, COSY and TOCSY spectra (Table 5-1). The 1H NMR spectrum of 3 exhibited a signal pattern characteristic of a lipopeptide: a group of signals for exchangeable amide protons (δH 6.9-8.2), signals of α-protons (δH 4.0-5.0), aliphatic methylene signals (δH

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1.1-1.4) and methyl signals (δH 0.7-1.0). Eleven α-amino acids units were characterized by interpretation of COSY and TOCSY spectra: two threonines (Thr1/2), two three- hydroxyleucines (3OH-Leu 1/2), valine (Val), leucine (Leu), 4-hydroxyproline (4OH-Pro),

N-methyl isoleucine (N-Me-Ile), homoserine (Hse), glutamine (Gln), 3- hydroxyasparagine (3-OHAsn). The presence of a lipophilic β-amino acid, β- aminodecanoic acid (Ada), was evident by sequential COSY correlations between NH

(δH 7.64)/ H-3 (δH 4.07)/H2-2(δH 2.5; 2.31) as well as sequential COSY correlations from

H2-4 (δH 1.38; 1.31) to the region of highly overlapping methylene signals (H2-5 to H2-9), which correspond to five carbons in the HSQC spectrum (δC 28.7, 28.3, 25.0, 30.9,

21.8) and then to a methyl triplet (δH 0.84) (Table 5-1). The amino acid sequence was assigned based on HMBC and ROESY correlations to as 4-OHPro-Leu-Thr2-Ada-Val-3-

OHLeu1-HSe-3-OHLeu2-Gln-N-Me-Ile-3-OHAsn-Thr1. The 16 degrees of unsaturation and the molecular formula suggested that 3 was a cyclic peptide. The chemical shifts of

C1 (δC 168.6) in Thr1 indicated the presence of an amide or ester moiety and therefore the cyclic dodecapeptide ring was closed between Thr1 and 4-OHPro.

+ The molecular formula of 4 was deduced as C59H95N11O14 evident by a [M + Na] peak at m/z 1204.6930 in ESIMS spectrum. Due to the broad NMR signals observed in

DMSO-d6, 1D and 2D NMR experiments of 4 were conducted in both DMSO-d6 (Table

5-3) and CH3CN-d3 (Table 5-2). The structure of 4 was elucidated based on a combination analysis of 1H NMR, HSQC, HMBC, COSY, TOCSY and ROESY experiments (Table 5-2). COSY and TOCSY correlations of 4 revealed the presence of ten α-amino acid residues, including glycine (Gly), phenylalanine (Phe), two leucines

(Leu1/2), valine (Val), 4-hydroxyproline (4-OHPro), isoleucine (Ile), two homoserines

129

(HSe1/2), α,β-didehydro-α-aminobutyric acid (Dhb) and one β amino acid unit β- aminooctanoic acid (Aoc) (Table 5-2). The presence of β-Aoc was deduced by the

COSY, TOCSY and HSQC correlations similar as described for 3. The amino acid sequence was assigned based on interpretation of HMBC and ROESY correlations to as Dhb-4-OHPro-HSe2-Phe-Leu1-Val-Ile-Leu2-Gly-Aoc-HSe1. The molecular fomula and degree of unsaturation indicated the cyclic nature of 4. Thus, the cyclic undecapeptide ring was closed between Dhb and HSe1.

Absolute Configuration

Compound 3 (0.3 mg) was hydrolyzed with 6 N HCl (110 °C, 20 h) and the hydrolyzate was subjected to chiral HPLC-MS, revealing the presence of D-Leu, L-Thr/

L-allo-Thr, L-Gln, L-Val and trans-4OH-L-Pro/cis-4OH-D-Pro in the molecule (Table 5-4).

The exact assignment for Thr and 4-OHPro and all the other amino acids was elucidated using advanced Marfey’s analysis (Table 5-5).222–224 The L-FDLA derivative of the acid hydrolyzate of 3 was compared with FDLA derivatives of authentic standards of 3-OHLeu [(2S,3S)-3-OHLeu-L-FDLA, (2S,3S)-3-OHLeu-DL-FDLA, (2R,3S)-3-OHLeu-

L-FDLA, (2R,3S)-3-OHLeu-DL-FDLA], HSe (L-HSe-L-FDLA, L-HSe-DL-FDLA), Thr (L-

Thr-L-FDLA, L-allo-Thr-L-FDLA) and 4-OHPro (trans-4OH-L-Pro-L-FDLA /cis-4OH-D-

Pro- L-FDLA), which allowed for assignment of 2R,3S configurations for 3-OHLeu and L configurations for HSe and Thr and the assignment of trans-4OH-L-Pro (Table 5-5). A comparison of LC-MS profiles between L-FDLA and DL-FDLA derivatives of the acid hydrolyzate of 3 assigned 3R configuration for Ada (Table 5-5).218,220,222,223,225 The absolute configurations for N-Me-Ile and 3-OHAsn still remain to be assigned.

Compound 4 was hydrolyzed with 6 N HCl (110 °C, 20 h), and the hydrolyzate of

4 was subjected to chiral HPLC-MS to reveal the presence of D-Phe, L-Val, D-allo-Ile

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and trans-4OH-L-Pro/cis-4OH-D-Pro (Table 5-6). However, peaks for both L- and D-Leu were detected by chiral HPLC-MS, preventing unambiguous configurational assignment for the two Leucines (Table 5-6). The L-FDLA derivative of the acid hydrolyzate of 4 was compared with FDLA derivatives of authentic standards of HSe (L-HSe-L-FDLA, L-HSe-

DL-FDLA) and 4-OHPro (trans-4OH-L-Pro-L-FDLA /cis-4OH-D-Pro- L-FDLA), which allowed for assignment of L-HSe and trans-4OH-L-Pro (Table 5-6). LC-MS comparison between L-FDLA and DL-FDLA derivatives of the acid hydrolyzate of 4 assigned 3R configuration for Aoa (Table 5-6). The geometric configuration of Dhb was determined to be E based on 1D and 2D ROESY correlations between Dhb H3-4 (δH 1.76) and 4-

OHPro H2-5 (δH 3.62; 3.46) as well as between Dhb H3-4 (δH 1.76) and 4-OHPro H-2 (δ

218,221 H 4.65) (Table 5-2).

Collectively, compound 3 is structurally related to laxaphycin B3 with a homoserine instead of alanine at position 4. A comparison between the NMR data of compound 3 and laxaphycin B3 indicated a slight difference between the carbon chemical shifts (Table 5-1). In contrast, compound 4 is an analogue of laxaphycin A with a valine instead of Ile at position 8 (Table 5-3). Thus compounds 3 and 4 were named as laxaphycin B4 (3) and laxaphycin A2 (4), respectively.

Biological Evaluation

Owing to the anticancer activities of known laxaphycins, we subjected compounds 3 and 4 to MTT assay to evaluate their cytotoxic activities against colon cancer HCT116 cells. Laxaphycin B4 (3) showed anticancer activity against HCT116 with IC50 value of 1.7 µM, while laxaphycin A2 (4) showed low activity with IC50 value

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approximately 40 µM. These results are consistent with other laxaphycins where laxapycin B-type compounds are more potent than laxaphycin A-type compounds.

Summary

Laxaphycins are cyclic peptides bearing a fatty β-amino acid moiety with a linear chain of up to 12 carbons. Laxaphycins are classified into two groups, laxaphycin A- and B- type peptides, which differ at the numbers and compositions of amino acid moieties. The structural differences between A- and B- type peptides result in the differences of cytotoxic potency with B-type peptides more potent than A- type. We have discovered two novel laxaphycin analogues, which belong to A-type and B- type peptides, respectively. The planar structures of the two compounds were solved based on a combined interpretation of 1D and 2D NMR data and mass spectral data. The absolute configurations of subunits were determined by chiral LC-MS analysis of the hydrolyzate, Marfey’s analysis or 1D and 2D ROESY experiments. Due the their structural similarity to the known laxaphycin B3 and laxaphycin A, compounds 3 and 4 were named as laxaphycin B4 (3) (Figure 5-3) and Laxaphycin A2 (4) (Figure 5-4), respectively.

Experimental Methods

General Experimental Procedures

1 H and 2D NMR spectra in DMSO-d6 and CH3CN-d3 were recorded on a Bruker

Avance II 600 MHz spectrometer equipped with a 5 mm TXI cryogenic probe using residual solvent signals (δH 2.50; δC 39.51 ppm, DMSO-d6; δH 1.94; δC 118.69 ppm,

13 CH3CN-d3) as internal standards. C spectrum for 3 were recorded on an Agilent 600

MHz Spectrometer equipped with a 1.5 mm high-temperature superconducting cryogenic probe. HSQC experiments were optimized for 145 Hz, and HMBC

132

experiments were optimized for 7 Hz. LC−MS data were obtained using an API 3200

(Applied Biosystems) equipped with a Shimadzu LC system.

Structure Characterization

Laxaphycin B4 (3). White amorphous solid; NMR data, 1H NMR,13C NMR,

COSY, HSQC, HMBC and ROESY in DMSO-d6, see Table 5-1; HRESI/APCIMS m/z [M

+ Na]+ 1463.8334, [M + H]+ 1441.8496.

Laxaphycin A2 (4). White amorphous solid; NMR data, 1H NMR, COSY, HMQC,

1 HMBC and ROESY in CH3CN-d3, see Figure 5-2; H NMR, COSY, HSQC, HMBC in

+ + DMSO-d6, see Table 5-3; HRESI/APCIMS m/z [M + Na] 1204.6930, [M + H]

1188.7119.

Acid Hydrolysis and Chiral Amino Acid Analysis

A sample of 3 (0.3 mg) was hydrolyzed with 6 N HCl (110 °C, 20 h) and the hydrolyzate was subjected to chiral HPLC-MS [column, Chirobiotic TAG (4.6 × 250 mm), Supelco; solvent, MeOH-10mM NH4OAc (40:60, pH 5.12): flow rate, 0.5 mL/min; detection by ESIMS in positive ion mode (MRM scan)]. The retention times (tR, min;

MRM ion pair, parent→product) of the authentic amino acids and MS parameters were listed in Table 5-4.

A sample of 4 (~0.1 mg) was treated with 6 N HCl at 110 ºC for 20 h. The hydrolyzate was concentrated to dryness, reconstituted in 50 µL H2O and then analyzed by chiral HPLC [column, Chirobiotic TAG (4.6 × 250 mm), Supelco; solvent, MeOH-10 mM NH4OAc (40:60, pH 5.12): flow rate, 0.5 mL/min; detection by ESIMS in positive ion mode (MRM scan)]. The retention times (tR, min; MRM ion pair, parent→product) of the authentic standards and MS parameters are listed in Table 5-6.

133

Advanced Marfey’s Analysis

Samples of both 3 and 4 (30 μg) were subjected to acid hydrolysis, reconstituted in water. Then, 10 μL of 1 M NaHCO3 and 50 μL of 1-fluoro-2,4-dinitrophenyl-5-L- leucinamide (L-FDLA, 1% w/v in acetone) were added to 25 μL of these solutions. After heating at 40 °C for 1 h, with frequent mixing, the reaction mixtures were acidified with 5

μL 2 N HCl, concentrated to dryness and then reconstituted with 250 μL MeCN–H2O

(1:1). Amino acid standards were made into 50 mM stock solutions in water, derivatized with L-FDLA in a similar method. Standards and hydrolyzate were subjected to reversed-phase HPLC-MS analysis. Details were listed in Table 5-5 and Table 5-7.

Cell Viability Assay (MTT)

HCT116 cells were cultured in Dulbecco’s modified Eagle medium (DMEM,

Invitrogen) supplemented with 10% fetal bovine serum (FBS, Hyclone) under a humidified environment with 5% CO2 at 37 °C. HCT116 (10,000) cells were seeded in

96-well plates. Cells were treated with a series of concentrations of laxaphycins in

DMSO, 24 h postseeding. Cells were incubated for an additional 48 h before the addition of the MTT reagent. Cell viability was measured according to the manufacturer’s instructions (Promega, Madison, WI, USA). Treatments were done in triplicate. Nonlinear regression analysis was carried out using GraphPad Prism software for IC50 value calculations.

134

Table 5-1. NMR data for 3 and laxaphycin B3 in DMSO-d6 (600 MHz). 3 Laxaphycin B3

C/ C/ δ (J in H δ δ (J in Hz) COSY HMBC ROESY H δ H C H C Hz) no no β-Ada 1 171.5 β-Ada 1 171.3

2a 39.6, CH2 2.5, m 3, 2b 1, 3, 4 NH (Val) 2a 2.44

2b 2.31, m 3, 2a 1, 3, 4 NH (Val) 2b 2.30

3 45.7, CH 4.07, m 2, 4, NH 1 NH (Val) 3 45.9, CH 4.08

4a 33.4, CH2 1.38 3, 4b, 5 2 4a 33.4, CH2 1.40

4b 1.31 3, 4a, 5 5 4b

5 28.7 1.19 5b 28.7, CH2 1.24

6 28.3 1.21 6 28.5, CH2 1.20

7 25.0, CH2 1.2 7 25.2, CH2 1.20

8 30.9, CH2 1.2 8 31.1, CH2 1.20

9 21.8, CH2 1.23 10 9 21.9, CH2 1.20

10 13.7, CH3 0.84, t (7) 9 10 13.8, CH3 0.82

N 2 (Thr2), 3 7.64, d (8.9) 3 3, 1 (Thr2) NH 7.52 H (Thr2) Val 1 171.4 Val 1 171.3

NH (3- 2 58.4, CH 4.16, t (7.17) 3, NH 1, 3, 4 2 58.9, CH 4.12 OHLeu1) 3 29.4, CH 1.98, m 2, 4 2, 5 3 29.4, CH 1.98

4 18.1, CH3 0.88, d (6.77) 3 3, 5 4 18.6, CH3 0.88

5 18.8, CH3 0.85 2, 3, 4 5 18.9, CH3 0.84

N 2 (β-Ada), 3 8.17, d (7.64) 2 2, 3, 1 (β-Ada) NH 8.10 H (β-Ada)

135

Table 5-1. Continued 3 Laxaphycin B3 C/H C/H δH (J in no δC δH (J in Hz) COSY HMBC ROESY no δC Hz) 3 - 3 - OHLeu OHLeu 1 1 171.6 1 1 2 55.0, CH 4.43, m 3, NH 1, 3 NH (Hse) 2 55.2, CH 4.37 3 76.6, CH 3.50 m 2, 4, OH 4, 6 NH (Hse) 3 76.5, CH 3.50 4 30.5, CH 1.59, m 3, 5, 6 3, 5 4 30.6, CH 1.60 5 19.0, CH3 0.92, m 4 3, 4, 6 5 18.8, CH3 0.89 6 18.5, CH3 0.78, d (6.7) 4 3, 4, 5 6 18.4, CH3 0.76 NH 7.91, d (8.3) 2 2, 1 (Val) 2 (Val) NH 7.90 OH 4.83, d (7.1) 3 2, 3, 4 OH 4.90 Hse 1 171.8 Ala 1 172.5 NH (3- 2 50.8, CH 4.36, m NH 3, 4 OHLeu2) 2 49.3, CH 4.22 2, 3b, 4a, 3a 34.3, CH2 1.86 4b 1, 2, 4 3 17.7, CH3 1.32 2, 3a, 4a, 3b 34.3, CH2 1.82 4b 1, 2, 4 NH 7.87 3a, 3b, 4b, 4a 57.4, CH2 3.49, m OH 2, 3 3a, 3b, 4a, 4b 57.4, CH2 3.42, m OH 2, 3 2 (3- 7.96, br d 1, 2, 3, 1 (3- OHLeu1), 3 NH (5.6) 2 OHLeu1) (3-OHLeu1) OH OH 4.5, t (5.19) 4a, 4b 3, 4

136

Table 5-1. Continued 3 Laxaphycin B3 C/H C/H δH (J in no δC δH (J in Hz) COSY HMBC ROESY no δC Hz) 3 - OHleu 3- 2 1 171 OHleu2 1 170.6 2 55.5, CH 4.31, m 3, NH 1 NH (Gln) 2 55.6, CH 4.28 3 76.0, CH 3.44 2, OH 3 75.8, CH 3.48 4 29.8, CH 1.57, m 3, 5, 6 2, 3, 5 4 29.8, CH 1.58 5 18.5, CH3 0.9 4, 6 3, 4, 6 5 18.7, CH3 0.88 6 18.7, CH3 0.76 4, 5 3, 4, 5 6 No data 0.74 NH 7.79, d (8.9) 2 2, 1 (Hse) 2 (Hse) NH 7.61 OH 4.91, d (5.1) 3 3, 4 OH 5.05 Gln 1 173 Gln 1 172.6 3a, 3b, 2 49.2, CH 4.56, m NH 1 2 49.4, CH 4.58 2, 3b, 4a, 3a 25.4, CH2 2 4b 4, 5 3a No data 2.00 2, 3a, 4a, 3b 25.4, CH2 1.67, m 4b 4, 5 3b No data 1.64 4a 30.4, CH2 2.29, m 3a, 3b, 4b 2, 3, 5 4a No data 2.23 4b 30.4, CH2 2.17, m 3a, 3b, 4a 2, 3, 5 4b No data 2.15 5 174.8 5 174.7 2, 1 (3- 2 (3- NH 7.84, d (7.3) 2 OHLeu2) OHLeu2) NH 7.56 NH2 NH a 7.37 NH2b 5 a 7.17 NH2 NH b 6.96 NH2a 4, 5 b 6.79

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Table 5-1. Continued 3 Laxaphycin B3 δH (J C/H C/H in no δC δH (in Hz) COSY HMBC ROESY no δC Hz) N -Me-Ile 1 170.2 N -Me-Ile 1 170.1 NH (3- 2 59.7, CH 4.74, d (10.8) 3, 6 1, 3, 4, 6, N-Me OHAsn) 2 59.9, CH 4.73 3 31.5, CH 1.92 m 2, 6 3 31.8, CH 1.90 4a 23.5, CH2 1.29 3, 4b, 5 3, 6 4a No data 1.27 4b 23.5, CH2 0.91 4a, 5 3, 6 4b 0.74 5 10.0, CH3 0.77 4a 5 10.3, CH3 0.75 6 14.8, CH3 0.75, m 3 1, 2, 3, 4 6 15.0, CH3 0.74 N- Me 30.0, CH3 3.02 2, 1 (Gln) N-Me 30.2, CH3 3.01 3- OHAsn 1 169.1, C 3-OHAsn 1 169.1, C 4.64, dd 2 55.3, CH (8.25, 1.40) H-3, NH 1, 3, 4 NH (Thr1) 2 55.5, CH 4.63 3 70.0 CH 4.37, dd H-2, OH 1, 2, 4 NH (Thr1) 3 70.3, CH 4.35 4 173.6 4 173.4 NH 7.67, d (8.25) 2 2, 3, 1 (N-MeIle) 2 (N-MeIle) NH 7.66 NHa 7.30, NH2 4 NHa 7.17 NHb 7.27, NH2 3, 4 NHb OH 5.82, d (6.37) 3 2, 3, 4 OH 5.70 Thr1 1 168.6 Thr1 1 168.7 2 55.7, CH 4.46, m 3, NH 1, 3, 4 2 55.8, CH 4.46 3.88, dq 2, 4, 3 66.6, CH (11.5, 6.05) OH, 1, 2, 4 3 66.4, CH 3.90 4 18.6, CH3 1.05, d (6.13) 3, OH 2, 3 4 18.9, CH3 1.03 OH 4.93, d (4.6) 3 2, 3, 4 OH 4.89 2 (3-OHAsn), NH 7.25, d (8.0) 2 2 3 (3-OHAsn) NH 7.12

138

Table 5-1. Continued 3 Laxaphycin B3 C/H C/H δH (J in no δC δH (J in Hz) COSY HMBC ROESY no δC Hz) 4 - 4 - OHPro 1 171.3 OHPro 1 171.5 2 58.5, CH 4.44, m 3a, 3b 1, 3, 4 NH (Leu) 2 58.6, CH 4.43 2, 4, 3b, 3a 37.6, CH2 1.99 5b 1, 2, 4, 5 3a 37.7, CH 2.01 3b 37.6, CH2 1.85, m 2, 4, 3a 1, 2, 4, 5 3b 1.84 3a, 3b, 5a, 5b, 4 68.4, CH 4.32, m OH 22 4 68.5, CH 4.32 3.73, dd 5a 55.3, CH2 (10.6, 3.9) 4, 5b 3 5a 55.5, CH2 3.72 5b 55.3, CH2 3.58 d (10.7) 3a, 4, 5a 4 5b 3.58 OH 5.08, d (3.32) 4 3, 4, 5 OH 5.08 Leu 1 171.9 Leu 1 171.4 2 51.3, CH 4.33, m 3, NH 1, 3, 4 NH (Thr2) 2 51.3, CH 4.35 3 41.0, CH2 1.47, t 2, 4 1, 2, 4, 5, 6 3 41.2, CH2 1.47 4 24.0, CH 1.53, m 3, 5, 6 2, 3, 5, 6 4 24.1, CH 1.52 5 22.6, CH3 0.88 4 3, 4, 6 5 22.8, CH3 0.86 6 21.6, CH3 0.82 4 3, 4, 5 6 21.7, CH3 0.80 NH 7.95 2 1 (4-OHPro) 2 (4-OHPro) NH 7.86 Thr2 1 168.8 Thr2 1 168.7 1, 3, 4, 1 2 58.2, CH 4.09, m 3, NH (Leu) NH (β-Ada) 2 58.2, CH 4.10 3 66.1, CH 3.99, m 2, 4, OH NH (β-Ada) 3 66.4, CH 3.97 4 19.4, CH3 1.02, d (6.31) 3 3, 2 4 19.5, CH3 0.99 NH 7.78, d (9.16) 2 1 (Leu) 2 (Leu) NH 7.86 OH 4.78, d (5.37) 3 2, 3, 4 OH 4.80

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Table 5-2. NMR data for 4 in CH3CN-d3 (600 MHz).

δC δH (J in Hz) COSY HMBC ROESY β-Aoc 1 171.1 2a 41.4, CH2 1.65 3, 2b 1 NH (HSe1) 2b 41.4, CH2 1.98 3, 2a 3 45.9, CH2 4.34 NH, 2a, 2b, 4 4 36.1, CH 1.41 2, 3 5 28.9, CH2 1.25 6 6 32.2, CH2 1.32 7 32.5, CH2 1.25 8 14.4, CH3 0.87 NH 6.92, t 3 2a ( Gly) Hse1 1 2 50, CH 4.69, t 3, NH 3 34.3, CH2 1.8 2, 4 4 57.8, CH2 3.58 3, OH OH 4.22 4 NH 6.81, m 2 2a (β-Aoc ) Dhb 1 169.3 2 131.9 3 122.6, CH 5.66, q (7.2) 4 1 4 11, CH3 1.76, d (7.2) 3 3, 2 2 (4-OHPro), 5a (4-OHPro), 5b (4-OHPro)

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Table 5-2. Continued

δC δH (J in Hz) COSY HMBC ROESY 4-OHPro 1 172.1 2 60.7, CH 4.65, dd(7.97) 2, 3a, 3b 1 4 (Dhb), NH (HSe2) 3a 38.9, CH2 1.95 2 1 3b 38.9, CH2 2.39, d (13.8, 7.9) 2, 4 4 4 68.4, CH 4.36, br s 5a, 5b, 3a, 3b OH 5a 58.5, CH2 3.46, d (11) 4, 5b 4, 1 (Dhb) 5b 58.5, CH2 3.62, dd (11, 2.9) 4, 5a

Hse2 1 174.2 2 50.3, CH 4.30 NH, 3, 2 NH (Phe) 3 33.4, CH2 2.14 2 1 4a 59.1, CH2 3.42 OH, 3, 4b 4b 59.1, CH2 3.51 OH, 3, 4a 2 OH 2.74, br s 3a, 3b NH 7.154, d 2 1 (4-OHPro) 2 (4-OHPro) Phe 1 174.2 2 58.5, CH 4.31, t 3a, 3b 1 NH (Leu1) 3a 38.2, CH2 3.13, dd (13.5, 12) 2 2, 5/9 3b 38.2, CH2 3.05, dd (13.5, 3) 2 4, 5/9 4 139.4 5/9 130.8, CH 7.45 3a, 3b, 6/8 3, 6/8, 7 6/8 129.5 CH 7.29, t (7.5) 5/9, 7 7 7 127.8 CH 7.22, t (7.27) 6/8 6/8 NH 7.91, d (7.42) 2 1 (HSe2) 2 (HSe2)

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Table 5-2. Continued

δC δH (J in Hz) COSY HMBC ROESY Leu1 1 173.5 2 53.2, CH 4.30 NH, 3a, 3b 1 NH (Val) 3a 40.3, CH2 1.08 2 3b 40.3, CH2 1.33 2 4 25.2, CH 1.58 3a, 3b, 5, 6 5 23.7, CH3 0.82 4 3, 4, 6 6 20.6, CH3 0.75, d (6.6) 4 3, 5, 6 NH 6.98, d (8) 2 1 (Phe) 2 (Phe) Val 1 174.5 2 56.7, CH 4.80 NH, 3 1 NH (Ile) 3 35, CH 2.14 2, 4, 5

4 19.8, CH3 0.84, d (7) 3 2, 3, 5

5 16, CH3 0.73, d (6.9) 3 2, 3, 4

6.47, d (9.9) 2 1 (Leu1) 2 (Leu1) NH Ile 1 174.9 2 53.1, CH 4.79 NH, 3 1 NH (Leu2) 3 37.6, CH 1.94 2, 4b, 6 4a 27.7, CH2 0.91 4b 27.7, CH2 1.23 3 5 12.7, CH3 0.90 4b 6 14.8, CH3 0.83 NH 7.09, d (9.5) 2 2 (Val), 3 (Val)

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Table 5-2. Continued

δC δH (J in Hz) COSY HMBC ROESY Leu2 1 175.2 2 55.3, CH 3.92 NH, 3a, 3b 1 NH (Gly) 3a 39.9, CH2 1.56 2, 3b 2 3b 39.9, CH2 1.46 2, 3a 2, 4, 5 4 25.3, CH 1.63 3a, 3b, 5, 6 5 22.1, CH3 0.88, (d, 6.5) 4 3, 4, 6 6 22.8, CH3 0.94, (d, 6.5) 4 3, 5, 6 NH 7.16, s 2 2 (Ile), 3 (Ile) Gly 1 168.6 2a 43.6, CH2 3.91 NH, 2b 1 NH (β-Aoc) 2b 43.6, CH2 3.37 NH, 2a 1, 1 (Leu) NH 7.43 2a, 2b 2 (Leu2)

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Table 5-3. NMR data for 4, laxaphycin A (reported) and hormothamnion A (reported) in DMSO-d6 (600 MHz). 4 Laxaphycin A Hormothamnion A δ δ (J in Hz) δ δ (J in Hz) δ δ (J in Hz) C H C H C H β Aoc 1 β Aoc 1 β Aoc

2a 39.8 1.89 2a 39.9 1.69 2a 39.9 1.68

2b 1.65 2b 1.97 2b 1.90

3 44.5 4.26 3 44.9 4.27 3 44.8 4.28

4 34.8 1.33 4 34.8 1.34 4 35.1 1.35

5 28.7 1.21 5 28.8 1.23 5 30.8 1.25

6 25.0 1.23 6 25.0 1.23 6

7 30.9 1.22 7 30.7 1.23 7

8 13.7 0.85 8 13.7 0.84 8 13.9 0.89

NH NH 6.82 NH 6.80

Hse1 1 Hse1 1 Hse1 1

2 48.9 4.55 2 49.1 4.54 2 49.0 4.70

3 33.4 1.76 3 33.8 1.76 3 33.4 1.80

4 56.9 3.46 4 57.0 3.46 4 56.9 3.55

OH OH 4.42 OH 56.9 4.59

NH 7 NH 7.10 NH 7.09

Dhb 1 Dhb 1 167.3 Dhb 1 166.7

2 131.0 2 130.8 2 131.8

3 119.6 5.6 (q 7.2) 3 118.3 5.57 3 121.7 5.76

4 12.0 1.71, d (7.14) 4 12.0 1.69 4 12.5 1.75

NH 10.84 br s NH 10.75 NH 10.69

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Table 5-3. Continued 4 Laxaphycin A Hormothamnion A δ δ (J in Hz) δ δ (J in Hz) δ δ (J in Hz) C H C H C H 4-OHPro 1 4-OHPro 1 170.1 4-OHPro 1

2 59.1 4.52 2 59.1 4.51 2 59.6 4.48

3a 37.6 1.86 3a 37.8 1.92 3a 38.0 1.83

3b 37.6 2.29 3b 37.8 2.27 3b 38.0 2.25

4 68.0 4.28 4 67.9 4.28 4 68.4 4.30

OH 5.18 OH 5.03 OH 5.19

5a 57.0 3.33 5a 57 3.34 5a 57.6 3.32

5b 57.0 3.6 5b 57 3.59 5b 57.6 3.61

Hse2 1 Hse2 1 172 Hse2 1

2 48.7 4.28 2 48.9 4.27 2 48.7 4.31

3 33.6 1.99 3a 33.8 1.88 3a 33.9 1.91

3b 1.96 3b 2.01

4a 56.9 3.26 4a 57 3.34 4a 56.9 3.27

4b 56.9 3.42 4b 57 3.45 4b 56.9 3.42

OH OH OH 4.39

NH 7.17 NH 7.22 NH 7.04

Phe 1 Phe 1 171.9 Phe 1

2 56.4 4.26 2 56.1 4.28 2 56.8 4.23

3a 36.8 2.96 3a 37 2.94 3a 37.0 2.96

3b 36.8 3.06 3b 37 3.04 3b 37.0 3.04

4 138.0 4 137.8 4 138.0

5/9 128.9 7.36 5/9 126.1 7.34 5/9 129.1 7.21

6/8 128.0 7.26, t (7.62) 6/8 128 7.24 6/8 128.2 7.26

7 126.1 7.19, t (7.62) 7 129 7.18 7 126.3 7.39

NH 7.88 NH 7.79 NH 7.65

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Table 5-3. Continued 4 Laxaphycin A Hormothamnion A δ δ (J in Hz) δ δ (J in Hz) δ δ (J in Hz) C H C H C H Leu1 1 Leu1 1 171.5 Leu1 1 2 51.5 4.27 2 51.6 4.28 2 51.4 4.32 3a 39.1 1.26 3a 42.2 1.18 3a 39.5 1.03

3b 39.1 1.36 3b 42.2 1.34 3b 39.5 1.28

4 23.8 1.57 4 23.9 1.58 4 24.7 1.57

5 22.7 0.79 5 22.7 0.8 5 22.8 0.81

6 20.1 0.72 6 20.3 0.73 6 21.1 0.75

NH 7.21 NH 7.22 NH 7.22

Val 1 Ile1 1 172.2 Ile1 1

2 55.4 4.69 2 56 4.63 2 53.6 4.76

3 32.3 2.12 3 38.4 1.76 3 37.1 1.80

4 19 0.74 4 21.9 1.18 4a 26.3 1.24

4b 26.3 1.26

5 15.2 0.63 5 15.3 0.76 5 14.5 0.75

NH 8.37, d(9.5) 6 11.3 0.75 6 11.1 0.75

NH 6.61 NH 6.49

Ile 1 Ile2 1 172.4 Ile2 1

2 53.5 4.68 2 53.9 4.63 2 53.6 4.68

3 36.9 2.03 3 36.7 1.97 3 37.1 2.07

4 26.1 1.14 4 26.1 1.18 4a 26.3 1.12

4b 26.3 1.19

5 14.3 0.79 5 14.3 0.8 5 14.5 0.81

6 11.2 0.84 6 11 0.84 6 11.1 0.84

NH 8.77 NH 8.68 NH 8.44

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Table 5-3. Continued 4 Laxaphycin A Hormothamnion A δ δ (J in Hz) δ δ (J in Hz) δ δ (J in Hz) C H C H C H Leu2 1 Leu2 1 172.7 Leu2 1 2 52.8 4 2 52.6 4.03 2 53.1 4.02 3 39.2 1.55 3a 42.2 1.58 3a 39.2 1.36

1.59 39.2 1.56

4 23.9 1.57 4 23.9 1.56 4 24.1 1.63

5 21.3 0.84 5 21.2 0.83 5 21.4 0.85

6 22.4 0.9 6 22.5 0.89 6 22.6 0.92

NH 7.78 NH 8.34 NH 8.49

Gly 1 Gly 1 166.7 Gly 1

2a 42.0 3.82 2a 42.2 3.82 2a 42.3 3.26

2b 3.22 2b 3.22 2b 3.81

NH 8.55 NH 8.56 NH 8.84

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Table 5-4. Chiral amino acid analysis of 3. Retention times (min) a Amino Q3 CX CE acid MW Q1 m/z m/z DP EP CE P P L D L-allo D-allo Measured Assignment Val 117.1 117.1 72.0 31 7.5 15 4 12 8.44 14.09 8.47 L Glu 147.1 145.9 101.9 -30 -2 -20 -4 -14 6.05 7.46 6.00 L Thr 119.1 120.0 74.0 21 6.5 13 4 10 7.70 8.87 7.84 11.00 7.75 L/allo-L Leu 131.2 131.9 86.0 31 8 13 4 10 9.31 17.43 18.08 D Amino Q3 CX CE acid MW Q1 m/z m/z DP EP CE P P cis-L cis-D trans-L trans-D Measured Assignment trans-4OH- 4- L-Pro/cis- OHPro 131.0 132.0 68.0 31 4.5 27 4 12 9.14 10.58 10.9 28.6 10.80 4OH-D-Pro a Measured by LC-MS selected ion chromatogram on a chiral column (Chirobiotic TAG (250x4.6 mm), Supelco; solvent: MeOH:10mM NH4OAc (40:60, pH 5.12)); flow rate 0.5 mL/min

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Table 5-5. Advanced Marfey’s analysis of 3. Retention times (min) Amino Q3 MW Q1 m/z DP EP CE CXP CEP L D L-allo D-allo Measured Assignment acid m/z Adaa 482 480 32.0 (S) 38.8 (R) 38.4 R 13.9 14.1 19.3 21.8 3-OH-Leua 442 440 161.9 -30 -9.5 -48 -2 -22 (2S,3 22.2 2R,3S (2S,3S) (2R,3R) (2R,3S) R) Hsea 414 412 175.9 -45 -5.5 -38 -4 -20 11.0 12.0 11.0 L 9.4 7.9 (trans- trans-4OH- 4-OHProa 426 426 381.1 66 4.5 25 8 24 8.0 (cis-D) L) L-Pro 19.0 19.9 Thrb 414 412 191.9 -40 -8.0 -26 -4 -20 18.9 L (2S,3R) (2S,3S) a Measured by LC-MS selected ion chromatogram on a a reversed-phase column (Phenomenex Kinetex C18, 100 x 2.10 mm, 2.6 μm, 0.2 mL/min) with a linear gradient from 25% to 65% aqueous acetonitrile containing 0.1% formic acid over 50 min. b Measured by LC-MS selected ion chromatogram on a a reversed-phase column (Alltech Alltima C18, 250 x 4.6 mm, 5 μm, 1.0 mL/min) with a linear gradient from 25% to 65% aqueous acetonitrile containing 0.1% formic acid over 50 min.

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Table 5-6. Chiral amino acid analysis of 4. Retention times (min)

Amino MW Q1 m/z Q3 m/z DP EP CE CXP CEP L D L-allo D-allo Measured Assignment acid Vala 117.1 117.1 72.0 31 7.5 15 4 12 8.44 14.09 8.47 L

Phea 165.0 166.2 120.0 31 7.5 19 4 12 12.95 18.24 18.56 D

Ileb 131.2 131.9 86.0 31 8.0 13 4 10 17.30 95.00 19.80 75.10 75.70 D-allo-Ile 19.10 and Leub 131.2 131.9 86.0 31 8.0 13 4 10 19.00 65.70 L-Leu and D-Leu 65.80 Amino trans trans- MW Q1 m/z Q3 m/z DP EP CE CXP CEP cis-L cis-D Measured Assignment acid -L D trans-4OH-L- 4-OHPro 131.0 132.0 68.0 31 4.5 27 4 12 9.14 10.58 10.9 28.6 10.79 Pro/ cis-4OH-D- Pro a Measured by LC-MS selected ion chromatogram on a chiral column (Chirobiotic TAG (250x4.6 mm), Supelco; solvent: MeOH:10mM NH4OAc (40:60, pH 5.12)); flow rate 0.5 mL/min. b Measured by LC-MS selected ion chromatogram on a chiral column (Chirobiotic TAG (250x4.6 mm), Supelco; solvent: MeOH:10mM NH4OAc (90:10, pH 6.0)); flow rate 0.5 mL/min.

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Table 5-7. Advanced Marfey’s analysis of 4. Retention times (min) a Amino M Q1 Q3 CX CE L- D- DP EP CE L D Measured Assignment acid W m/z m/z P P allo allo 26.2 32.0 Aoa 454 452 32.0 R (S) (R) Hse 414 412 175.9 -45 -5.5 -38 -4 -20 11.0 12.0 11.0 L 9.4 7.9 (trans- trans-4OH-L- 4-OHPro 426 426 381.1 66 4.5 25 8 24 8.0 (cis-D) L) Pro a Measured by LC-MS selected ion chromatogram on a a reversed-phase column (Phenomenex Kinetex C18, 100 x 2.10 mm, 2.6 μm, 0.2 mL/min) with a linear gradient from 25% to 65% aqueous acetonitrile containing 0.1% formic acid over 50 min.

151

Figure 5-1. Laxaphycin A-type compounds.

152

Figure 5-2. Laxaphycin B-type compounds.

153

Figure 5-3. Structure of laxaphycin B4 (3).

154

Figure 5-4. Structure of laxaphycin A2 (4).

155

Figure 5-5. Structure elucidation of compound 3.

156

Figure 5-6. Structure elucidation of compound 4.

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CHAPTER 6 CONCLUSIONS

Particularly, marine cyanobacteria are a rich source of bioactive secondary metabolites.

Many structurally intriguing compounds have been discovered from marine cyanobacteria which possess unusual modes of action.

In this research, we aimed at the discovery and development of therapeutic agents from marine cyanobacteria as anticancer agents or growth factor modulators or both. Growth factors are appealing therapeutic targets in both anticancer and wound healing area. Growth factor signaling is a validated therapeutic target in cancer. For example, bevacizumab, an anti-VEGF antibody for the treatment of cancer. On the other hand, growth factor administration therapy is under development for chronic wound healing treatment. Here, we performed studies on four groups of compounds which are at different stages of drug discovery and development as anticancer agents and growth factor modulators.

We first characterized two groups of natural modified peptides, apratoxins and apratyramide, from the marine cyanobacterium Moorea bouillonii that have opposite roles in growth factor modulation, possessing anticancer and potential wound healing activities, respectively. We designed and conducted the early development of apratoxins, a series of anticancer agents discovered in our lab. Due to its unusual mode of actions of blocking cotranslational translocation pathway at the level of Sec61, leading to down-regulation of receptor tyrosine kinases (RTKs) and inhibition of their

158

cognate ligands, we further developed apratoxins as dual inhibitors against angiogenesis and cancer cell growth. Apratoxins potently inhibited angiogenesis in vitro by down-regulation of VEGFR2 on endothelial cells as well as inhibition of secretion of

VEGF-A and IL-6 from cancer cells. In vitro cell viability assays showed that apratoxins exerted potent antiproliferative effects against a variety of cancer cells, including cancer cells from highly vascularized tumors (renal, hepatocellular and neuroendocrine cancers) and tumors that are partially sensitive or resistant to anti-angiogenic therapies

(colon, breast and pancreatic cancer). Apratoxin S9 was tested in a colon cancer xenograft mice model and showed significant tumor suppression effects at the dose of

0.25 mg/ kg. The MOA of apratoxins were further elucidated based on a previously conducted siRNA-based drug susceptibility screen in HCT116 cells. Ingenuity Pathway

Analysis (IPA) indicated that there might be a chemical-genetic interaction of apratoxins with DNA-repair pathways. The PARP1 gene, encoding a key DNA-repair associated enzyme, has been identified as a significant sensitizer hit. The cooperative effects between a clinically approved PARP1 inhibitor (AG014699) and apratoxins were validated in a colon cancer xenograft mice model by co-administration of AG014699 and apratoxin S9. The combination group dramatically retarded tumor growth and its effect was improved compared to apratoxin S9 alone, while PARP1 inhibitor treatment group had negligible antitumor effects. The PARP activities were found to be reduced in tumor tissues from both combination group and PARP1 inhibitor treated group, which confirmed the on-target effect of PARP1 inhibitor in vivo. Following the MOA studies, we also conducted in vitro stability, plasma pharmacokinetics and tissue distribution to evaluate the “drug-like” properties of apratoxins. All apratoxins tested were stable in

159

vitro and apratoxin S10 administration into NSG mice through both i.v. or i.p. caused a high compound enrichment in pancreas, indicating its potential therapeutic use for pancreatic cancer as well as its potential toxicity to pancreatic tissue.

Apratyramide, on the other hand, is a growth factor inducer which has the potential to rescue chronic wounds and accelerate the wound healing process by inducing growth factor secretion in the wound area. Apratyramide is a linear depsipeptide isolated previously with unknown biological activity. We performed a series of in vitro cell-based assays to elucidate the biological activity as well as MOA.

Apratyramide induced both transcription and secretion of VEGF-A in human keratinocyte (HaCaT) cells, evident by qRT-PCR and AlphaLISA analysis. Other wound- healing related growth factors were also found to be induced at the transcriptional level, including PDGFB and bFGF. Transcriptome profiling using HaCaT cells identified 371 differential expressed genes after 12 h treatment of apratyramide. Importantly, VEGF-A and other growth factors were up-regulated, showing consistency with our previous in vitro data and supporting our hypothesis of the potential wound healing properties of apratyramide through growth factor modulation. IPA analysis indicated that apratyramide induced growth factors through or partially through UPR pathway. More specifically, apratyramide activated growth factors potentially through ATF4 and IRE1α, two molecular components in the UPR pathway that are functionally related to wound healing and angiogenesis.

In addition to the above two growth factor modulators, two series of novel cytotoxic compounds were discovered from marine cyanobacteria which are potential anticancer therapeutic agents. Pitiamides B are isomeric fatty acid amides containing

160

terminally chlorinated conjugated diene isolated through a bioactivity-guided fractionation from a marine cyanobacterium collected from Guam, Mariana Islands. The planar structures of purified compounds were established using a combination of 1D and 2D NMR spectroscopy and mass spectrometry. 1E-pitiamide B (1) and 1Z-pitiamide

B (2) are two geometric isomers which structurally assemble a known fatty acid amide, pitiamide A. The absolute configuration of the only stereocenter in pitiamides B was proposed as 12R for both 1 and 2 on the basis of the additivity rule of specific rotation.

Pitiamide A, which had been previously described and had not been investigated in bioassays, was co-isolated. Pitiamides A and B were subjected to biological evaluation and they both showed antiproliferative effects on HCT116 cells with IC50 values of 1 ̶ 5

µM. However, further structure-activity relationship studies of pitiamides are warranted.

Another group of cytotoxic compounds are laxaphycin analogues. Laxaphycins are cyclic peptides with the structural feature of a fatty β-amino acid moiety, possessing antifungal and anticancer activities. Laxaphycin A- and B- type peptides, differing at the numbers and compositions of amino acid moieties, were frequently co-isolated and has been validated to work synergistically against cancer or fungi. We have discovered two novel laxaphycin analogues, which belong to A-type and B- type peptides, respectively.

The planar structures of the two compounds were elucidated using a combined analysis of 1D and 2D NMR spectra and mass spectra. The absolute configurations of amino acid building blocks were determined by chiral LC-MS analysis and Marfey’s analysis of the acid hydrolyzate or 1D and 2D ROESY experiments of the intact molecules. Due to their structural similarity to the known laxaphycin B3 and laxaphycin A, compounds 3 and 4 were named as laxaphycin B4 (3) and Laxaphycin A2 (4), respectively.

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In conclusion, this study demonstrated that marine cyanobacteria are validated source organisms of novel bioactive secondary metabolites, yielding both structurally and pharmacologically diverse compounds that have potential applications as small molecule therapeutics in cancer and other growth factor associated disorders.

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APPENDIX A STANDARD CURVES OF ARPATOXINS FOR IN VITRO PLASMA STABILITY STUDIES

The following pages contain examples of quantification standard curves.

Calibration curves for apratoxin A, S4, S7, S8 and S9 in the presence of mouse serum were generated by least-square linear regression analysis of the analyte peak area and internal standard peak area ratio against the nominal concentration of the standard solutions.

163

Figure A-1. Standard curve of apratoxin A for plasma stability study.

164

Figure A-2. Standard curve of apratoxin S4 for plasma stability study.

165

Figure A-3. Standard curve of apratoxin S7 for plasma stability study.

166

Figure A-4. Standard curve of apratoxin S8 for plasma stability study.

167

Figure A-5. Standard curve of apratoxin S9 for plasma stability study.

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APPENDIX B PHASE SOLUBILITY ANALYSIS OF APRATYRAMIDE

Table B-1. Phase solubility analysis. Sample number Weight of Captisol® % w/v molar Apratyramide (mg) A 16 mg in 40 µL water 40.0 0.1850 4 B 8 mg in 40 µL water 20.0 0.0925 2 C 4 mg in 40 µL water 10.0 0.0462 2 D 2 mg in 40 µL water 5.0 0.0231 2 E 20 mg in 40 µL water 2.5 0.0116 2 F 21 mg in 40 µL water 0.0 0.0000 2

169

Standard Curve

1.2 y = 0.0049x + 0.0057 R² = 0.9986 1

0.8

0.6 OD

0.4

0.2

0 0 50 100 150 200 250 Aptratyramide in EtOH [µM]

Figure B-1. Standard curve of apraytyramide for quantification.

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APPENDIX C NMR SPECTRA OF ISOLATED COMPOUNDS

The following pages are the NMR spectra of isolated compounds in this study, which includes the known compound pitiamide A, novel compounds pitiamides B (1, 2), laxaphycin B4 (3) and laxaphycin A2 (4).

171

1 Figure C-1. H NMR spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz).

172

Figure C-2. COSY spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz).

173

Figure C-3. TOCSY spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz).

174

Figure C-4. HSQC spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz).

175

Figure C-5. HMBC spectrum of 1E-pitiamide B (1) in CDCl3 (600 MHz).

176

1 Figure C-6. H NMR spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz).

177

Figure C-7. COSY spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz).

178

Figure C-8. TOCSY spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz).

179

Figure C-9. HSQC spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz).

180

Figure C-10. HMBC spectrum of 1Z-pitiamide B (2) in CDCl3 (600 MHz).

181

1 Figure C-11. H NMR spectrum of pitiamide A in CDCl3 (600 MHz).

182

Figure C-12. COSY spectrum of pitiamide A in CDCl3 (600 MHz).

183

Figure C-13. TOCSY spectrum of pitiamide A in CDCl3 (600 MHz).

184

Figure C-14. HSQC spectrum of pitiamide A in CDCl3 (600 MHz).

185

Figure C-15. HMBC spectrum of pitiamide A in CDCl3 (600 MHz).

186

1 Figure C-16. H NMR spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

187

Figure C-17. COSY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

188

. Figure C-18. HSQC spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

189

Figure C-19. HMBC spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

190

Figure C-20. TOCSY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

191

Figure C-21. ROESY spectrum of laxaphycin B4 (3) in DMSO-d6 (600 MHz).

192

1 Figure C-22. H NMR spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

193

Figure C-23. COSY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

194

Figure C-24. HSQC spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

195

Figure C-25. HMBC spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

196

Figure C-26. TOCSY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

197

Figure C-27. ROESY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz).

198

Figure C-28. 1D ROESY spectrum of laxaphycin A2 (4) in MeCN-d3 (600 MHz), saturation at δ 5.66 ppm.

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BIOGRAPHICAL SKETCH

Weijing Cai was born in Beijing, China. She attended the High School Affiliated to

Renmin University of China and after graduation in 2008 she was accepted into Peking

University, Beijing, China for undergraduate studies. She obtained her bachelor’s degree in pharmaceutical sciences in 2012 and entered a doctoral program in the

Department of Medicinal Chemistry at the University of Florida. Under the mentorship of

Dr. Hendrik Luesch, she received her doctorate in pharmaceutical sciences from the

College of Pharmacy in August 2017.

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