Pharmacophores and Their Applications
Thierry Langer Prestwick Chemical, France
28 Aug 2013 Acknowledgements
Markus Böhler Yasmin Aristei Oliver Funk Fabian Bendix Johannes Kirchmair Martin Biely Eva Maria Krovat Sharon D. Bryant Daniela Ladstätter Alois Dornhofer Christian Laggner Gökhan Ibis Patrick Markt Robert Kosara Konstantin Poptodorov Thua Huynh Buu Thi Hoang Claudia Schieferer Thomas Seidel Daniela Schuster Gerhard Wolber Theodora Steindl Hermann Stuppner Rémy D. Hoffmann Judith Rollinger Nicolas Triballeau-Hugounencq Caroline Bliem Kareen Chuang ✝
T. Langer, KSSCI 2013 Acknowledgements
Markus Böhler Yasmin Aristei Oliver Funk Fabian Bendix Johannes Kirchmair Martin Biely Eva Maria Krovat Sharon D. Bryant Daniela Ladstätter Alois Dornhofer Christian Laggner Gökhan Ibis Patrick Markt Robert Kosara Konstantin Poptodorov Thua Huynh Buu Thi Hoang Claudia Schieferer Thomas Seidel Daniela Schuster Gerhard Wolber Theodora Steindl Hermann Stuppner Rémy D. Hoffmann Judith Rollinger Nicolas Triballeau-Hugounencq Caroline Bliem Kareen Chuang ✝
T. Langer, KSSCI 2013 Prestwick Chemical’s Team
T. Langer, KSSCI 2013 Outline
• Introduction – The attrition problem in drug discovery • Computational methods for medicinal chemists
– What are our challenges in medicinal chemistry ?
– What do we really need to make a good drug candidate ? • Pharmacophore-based Ligand Activity Profiling – High throughput model generation – Parallel virtual screening & target fishing examples
T. Langer, KSSCI 2013 Dramatic Situation in Big Pharma ?
• R&D expenditure raising • New drug approvals decreasing
Billion $ T. Langer, KSSCI 2013 Dramatic Situation in Big Pharma ?
• R&D expenditure raising 120 • New drug approvals decreasing 100
80
60
40
20 2002 2003 2004 2006 2006 2007 2008 0 2009 2010 2011 Billion $ T. Langer, KSSCI 2013 Dramatic Situation in Big Pharma ?
• R&D expenditure raising 120 • New drug approvals decreasing 100
80
60
40
20 2002 2003 2004 2006 2006 2007 2008 0 2009 2010 2011 Billion $ T. Langer, KSSCI 2013 A True Productivity Problem ?
T. Langer, KSSCI 2013 A True Productivity Problem ? • New drugs approved since 1950 ... • 1103 Small molecules • 119 Biologicals
T. Langer, KSSCI 2013 A True Productivity Problem ? • New drugs approved since 1950 ... • 1103 Small molecules • 119 Biologicals
T. Langer, KSSCI 2013 A True Productivity Problem ? • New drugs approved since 1950 ... • 1103 Small molecules • 119 Biologicals
T. Langer, KSSCI 2013 Costs Per New Drug MioUSD
T. Langer, KSSCI 2013 Costs Per New Drug
Average (n=73): 4.34 billion USD (!!!) MioUSD
T. Langer, KSSCI 2013 Costs Per New Drug
Average (n=73): 4.34 billion USD (!!!) MioUSD
T. Langer, KSSCI 2013 An Interesting Article To Read ... An Interesting Article To Read ...
Nat. Rev. Drug Discov., 8, 959-968 (2009) Costs Attributed To Different Stages
Investment per phase of drug discovery and development for one successful drug (USD millions)
G.M. Milne, Jr., Annual Reports in Medicinal Chemistry, 2003, 38, 383-396
T. Langer, KSSCI 2013 Costs Attributed To Different Stages
Investment per phase of drug discovery and development for one successful drug (USD millions)
G.M. Milne, Jr., Annual Reports in Medicinal Chemistry, 2003, 38, 383-396
T. Langer, KSSCI 2013 What are we talking about today?
T. Langer, KSSCI 2013 What are we talking about today?
• How to use smart computational approaches to support & direct medicinal chemistry efforts
T. Langer, KSSCI 2013 What are we talking about today?
• How to use smart computational approaches to support & direct medicinal chemistry efforts • Focus on – Hit identification by virtual screening – Decision support for med. chem. optimization – Liability prediction for development
T. Langer, KSSCI 2013 Expectations: The Medicinal Chemist
• Answer the most important question !
T. Langer, 2012 T. Langer, KSSCI 2013 Expectations: The Medicinal Chemist
• Answer the most important question !
Tell me: Which molecule shall I prepare next ?
T. Langer, 2012 T. Langer, KSSCI 2013 Expectations: The Biologist
• Answer the most important question !
T. Langer, 2012 T. Langer, KSSCI 2013 Expectations: The Biologist
• Answer the most important question !
Tell me all about the pathway & related kinetics !
T. Langer, 2012 T. Langer, KSSCI 2013 The Computational Chemist ... The Computational Chemist ...
• Wants to explain biological data based on oversimplified models The Computational Chemist ...
• Wants to explain biological data based on oversimplified models
• Wants to find the right answers to all the medicinal chemist’s questions The Computational Chemist ...
• Wants to explain biological data based on oversimplified models
• Wants to find the right answers to all the medicinal chemist’s questions
• Wants to help the medicinal chemist by designing great molecules The Computational Chemist ...
DOOMED TO ?
T. Langer, KSSCI 2013 T. Langer, KSSCI 2013 Where to begin ?
T. Langer, KSSCI 2013 What Do Medicinal Chemists Really Need ?
T. Langer, KSSCI 2013 What Do Medicinal Chemists Really Need ?
• Reliable decision support tools
T. Langer, KSSCI 2013 What Do Medicinal Chemists Really Need ?
• Reliable decision support tools • Easy to use, easy to understand
T. Langer, KSSCI 2013 What Do Medicinal Chemists Really Need ?
• Reliable decision support tools • Easy to use, easy to understand • Need for speed
T. Langer, KSSCI 2013 What Do Medicinal Chemists Really Need ?
• Reliable decision support tools • Easy to use, easy to understand • Need for speed • Solid science behind Tanimoto Coefficient Free Energy Calculations 3D QSAR QM/MM 2D Fingerprints Docking MEPs Shape Similarity
Self OrganizingPharmacophores Neural Networks Support Vector Machine QSAR Similarity Index CoMFA
T. Langer, KSSCI 2013 What Is A Pharmacophore ?
“A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supra- molecular interactions with a specific biological target and to trigger (or block) its biological response.”
C.-G. Wermuth et al., Pure Appl. Chem. 1998, 70: 1129-1143
T. Langer, KSSCI 2013 Feature-based Pharmacophore Models Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic inter- actions, hydrophobic regions, coordination to metal ions ...
T. Langer, KSSCI 2013 Feature-based Pharmacophore Models Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic inter- actions, hydrophobic regions, coordination to metal ions ...
T. Langer, KSSCI 2013 Feature-based Pharmacophore Models Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic inter- actions, hydrophobic regions, coordination to metal ions ...
T. Langer, KSSCI 2013 Feature-based Pharmacophore Models Totality of universal chemical features that represent a defined binding mode of a ligand to a bio-molecular target Features: Electrostatic interactions, H-bonding, aromatic inter- actions, hydrophobic regions, coordination to metal ions ...
T. Langer, KSSCI 2013 The Usual Virtual Screening Protocol
10x molecules against one target
results in a hit list
T. Langer, KSSCI 2013 Why Not Do This ?
T. Langer, KSSCI 2013 Why Not Do This ? 10x molecules against 10x targets
... needs a large number of models !
T. Langer, KSSCI 2013 Our Aim: Predict Activity Pattern ...
• Modeling of all relevant targets – responsible for drug action and side effects – build feature-based pharmacophore models
• Compile all models (+ relevant info) into a database – Activity profiling of leads / drug candidates – Determination of side effects / bio-hazards
• Use this system for development of novel interesting lead molecules and drug candidates
T. Langer, KSSCI 2013 Why Use Pharmacophore Models ?
• Universal - Pharmacophore models represent chemical functions that are essential for binding to the target • Computationally Efficient - Due to their simplicity, they are suitable for large scale virtual screening (>109 compounds, also in parallel settings) • Comprehensive & Editable - Selectivity-tuning by adding or omitting chemical feature constraints, information can be easily traced back - Ideal interface between modeling and medicinal chemistry
T. Langer, KSSCI 2013 How To Build Pharmacophore Models ?
• Starting from ligand information – Exploration of conformational space – Multiple superimpositioning experiments – DISCO, Catalyst, Phase, MOE, Galahad, LigandScout ... • Starting from 3D target information – GRID interaction fields: Convert regions of high interaction energy into pharmacophore point locations & constraints [S. Alcaro et al., Bioinformatics 22, 1456-1463, 2006]
– Start from target-ligand complex: Convert interaction pattern into pharmacophore point locations & constraints
[G. Wolber et al., J. Chem. Inf. Model. 45, 160-169, 2005]
T. Langer, KSSCI 2013 How To Build Pharmacophore Models ?
• Starting from ligand information – Exploration of conformational space – Multiple superimpositioning experiments – DISCO, Catalyst, Phase, MOE, Galahad, LigandScout ... • Starting from 3D target information – GRID interaction fields: Convert regions of high interaction energy into pharmacophore point locations & constraints [S. Alcaro et al., Bioinformatics 22, 1456-1463, 2006]
– Start from target-ligand complex: Convert interaction pattern into pharmacophore point locations & constraints
[G. Wolber et al., J. Chem. Inf. Model. 45, 160-169, 2005]
T. Langer, KSSCI 2013 Let’s have a look ...
T. Langer, KSSCI 2013 T. Langer, KSSCI 2013 T. Langer, KSSCI 2013 LigandScout 1.0: Implemented Procedure
• Detect ligand and clean-up the binding site in the protein (all amino acids within 7Å distance from the ligand) • Interpret hybridization status and bond types in the ligand • Perform chemical feature recognition for the ligand (H-bond donor, H-bond acceptor, positive ionizable, negative ionizable, hydrophobic, aromatic ring, metal ion coordination) • Search for corresponding chemical features of the protein • Add interaction features to the model only if a corresponding feature pair is found in the complex • Add excluded volume spheres for opposite hydrophobic features
G. Wolber, T. Langer: J. Chem. Inf. Model. 45 , 160-169 (2005)
T. Langer, KSSCI 2013 LigandScout 3.1 Graphical User Interface
T. Langer, KSSCI 2013 LigandScout 3.1 Graphical User Interface
T. Langer, KSSCI 2013 vHTS: We Need Speed & Accuracy
• Design a new alignment algorithm • No upper limit for number of features acceptable – high number of features will give good selectivity • No exponential growth of search time with growing number of features • No graph matching necessary ...
T. Langer, KSSCI 2013 vHTS: We Need Speed & Accuracy
• Design a new alignment algorithm • No upper limit for number of features acceptable – high number of features will give good selectivity • No exponential growth of search time with growing number of features • No graph matching necessary ...
Work exclusively in the pharmacophore domain !
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
Wrong Correct
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
Methotrexate Dihydrofolate
Wrong Correct
Bystroff, Oatley, Kraut: Biochemistry. (1990) 29, 3263-77 Böhm, Klebe, Kubinyi: Wirkstoffdesign (1999) p. 320f
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
1RX2
1RB3
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
1RX2
1RB3
T. Langer, KSSCI 2013 Alignment By Pharmacophore Points
1RX2
1RB3
T. Langer, KSSCI 2013 Pharmacophoric Alignment
molecule best 3D rotation Super- pharmacophore pairing (Kabsch) position
Is pairing valid? If not, remove invalid pairs and retry
Wolber G. et al., J. Comput.-Aided Mol. Des. 20: 773-388 (2006)
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
Hungarian Matcher (Marrying Problem)
T. Langer, KSSCI 2013 How To Find The Best Pairs ...
Hungarian Matcher (Marrying Problem)
[Edmods 1965] Matching and a Polyhedron with 0-1 Vertices. J. Res. NBS 69B (1965), 125-30 [nonbipartite application] [Kuhn 1955] The Hungarian method for the Assignment Problem. Noval Research Quarterly, 2 (1955) [bipartite variant] [Richmond 2004] Application to chemistry: N. Richmond et al. Alignment of 3D molecules using an image recognition algorithm. J. Mol. Graph. Model. 23 (2004) 199-209
T. Langer, KSSCI 2013 Hungarian Matching
• How to define the pharmacophore feature matching cost (similarity)?
– Use only few feature types – Calculate similarity by defining geometric relations
=> Solution: Encode geometry and nature of the neighborhood into each feature description
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor Donor Lipophilic
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor 0 | 0 | 1 Donor Lipophilic
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor 0 | 0 | 1 Donor 0 | 1 | 1 Lipophilic
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor 0 | 0 | 1 Donor 0 | 1 | 1 Lipophilic 0 | 1 | 1
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor 0 | 0 | 1 Donor 0 | 1 | 1 Lipophilic 0 | 1 | 1
T. Langer, KSSCI 2013 Typed Distance Shells
Acceptor 0 | 0 | 1 Donor 0 | 1 | 1 Lipophilic 0 | 1 | 1
T. Langer, KSSCI 2013 Distance Characteristics
Result: Best matching pairs for each feature
T. Langer, KSSCI 2013 Distance Characteristics
Result: Best matching pairs for each feature
1
1
T. Langer, KSSCI 2013 Distance Characteristics
Result: Best matching pairs for each feature
1
2 1
2
T. Langer, KSSCI 2013 Distance Characteristics
Result: Best matching pairs for each feature
4 1 5 5 3
2 1 3 4 2
T. Langer, KSSCI 2013 Distance Characteristics
Result: Best matching pairs for each feature
4 1 5 5 3
2 1 3 4 2
Final step: 3D rotation using Kabsch algorithm
T. Langer, KSSCI 2013 Flexible Alignment • Generation of conformer ensemble (OMEGA 2.0) • Alignment experiment on bio-active conformation
1ke8 1rb3 1xp0
Wolber G. et al., J. Comput.-Aided Mol. Des. 20: 773-388 (2006)
T. Langer, KSSCI 2013 vHTS Screening Strategies
• Align everything? - 5 Mio comp. * 50 conf. * 50 ms= 144 CPU days -> too long • Fingerprint only - quick & dirty, loss of resolution & loss of predictivity • Safe early failure strategy - Bloom filtering based on fingerprints - Accurate fitting of remaining compounds
T. Langer, 2012 T. Langer, KSSCI 2013 Virtual Screening
Early Early Early 3D alignment Compound 1 fail filter fail filter fail filter
Early Early Early 3D alignment Compound 2 fail filter fail filter fail filter
Early Early Early 3D alignment Compound 3 fail filter fail filter fail filter
Early Early Early 3D alignment Compound n fail filter fail filter fail filter ....
T. Langer, KSSCI 2013 Fingerprinting strategy
• Pre-store conformations • Pre-compute and store as much as possible (2pt, 3pt) • Store fingerprints for each conformation and for the whole molecule ...
T. Langer, 2012 T. Langer, KSSCI 2013 Multiconformational Database Size
• 1 Mio compounds ca 7.5 Gb • 13kB per compound or 273 byte per conformation • 75% of the space is needed for fingerprints • Overall very compact
T. Langer, KSSCI 2013 What Did We Reach With LigandScout ?
T. Langer, KSSCI 2013 What Did We Reach With LigandScout ?
• Higher accuracy than any other pharmacophore- based screening tool
T. Langer, KSSCI 2013 What Did We Reach With LigandScout ?
• Higher accuracy than any other pharmacophore- based screening tool • Higher speed than any other pharmacophore- based screening tool
T. Langer, KSSCI 2013 What Did We Reach With LigandScout ?
• Higher accuracy than any other pharmacophore- based screening tool • Higher speed than any other pharmacophore- based screening tool • Let’s have a look …
T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Performance Analysis Using ROC Curves
T. Langer, 2012 T. Langer, KSSCI 2013 Screening Metrics: PDE5
TP FP EF AUC (of 51) (of 1803) Catalyst 21 239 2.94 0.632 Phase 10 74 4.33 0.560 MOE 29 1473 0.70 0.356 LigandScout 10 0 36.4 0.592
TP ... true positives FP ... false positives EF ... enrichment factor (TP/(TP+FP))/(A/(A+I)) AUC ... area under the (ROC) curve
T. Langer, 2012 T. Langer, KSSCI 2013 Ligand-based: Amine-binding GPCRs
• Study published by Evers et al. @ Sanofi-Aventis J. Med. Chem. 48, 5448-65, (2005)
• Compared different VS techniques for – Alpha1A receptor antagonists – 5HT2A receptor antagonists – D2 receptor antagonists – M1 receptor antagonists
T. Langer, 2012 T. Langer, KSSCI 2013 Amine-binding GPCRs EF1%
5HT2A: LigandScout 14 Phase 10 Catalyst 10 MOE 10 A1AA: LigandScout 16 Phase 16 Catalyst 14 MOE 4
D2: LigandScout 10 Phase 6 Catalyst 0 MOE 4
M1:: LigandScout 18 Phase 8 Catalyst 16 MOE 6
0 5 10 15 20 T. Langer, 2012 T. Langer, KSSCI 2013 True and False Positives LigandScout 100%
75%
50%
25%
0% 5HT2A M1 D2 A1AA True and False Positives
LigandScout Phase 100% 100%
75% 75%
50% 50%
25% 25%
0% 0% 5HT2A M1 D2 A1AA 5HT2A M1 D2 A1AA
Catalyst MOE 100% 100%
75% 75%
50% 50%
25% 25%
0% 0% 5HT2A M1 D2 A1AA 5HT2A M1 D2 A1AA Average True Positive Rate
T. Langer, 2012 T. Langer, KSSCI 2013 First Summary
• Universal method for accurate feature-based pharmacophore generation • Highly selective models will retrieve low number of false positives / false negatives • High enrichment factor can be obtained • Application for in silico ligand profiling and target fishing possible ?
T. Langer, KSSCI 2013 Ligand Profiling Case Study
- 5 viral targets
- 50 pharmacophore models
- 100 antiviral compounds
Will their activity profiles be predicted correctly ?
T. Langer, KSSCI 2013 Ligand Profiling: Targets
Target Disease Function Mechanism HIV protease HIV infection, Cleavage of gag and gag-pol Inhibition at active site AIDS precursor polyproteins into functional viral proteins
HIV reverse transcriptase HIV infection, Synthesis of a virion DNA, Inhibition at allosteric site (RT) AIDS integration into host DNA and transcription
Influenza virus Influenza Viral envelope glycoprotein, Inhibition at active site neuraminidase (NA) cleave sialic acid residues for viral release
Human rhinovirus (HRV) Common cold Attachment to host cell receptor, Binding in hydrophobic pocket coat protein viral entry, and uncoating (capsid stabilization)
Hepatitis C virus (HCV) Hepatitis C Viral replication, transcription of Inhibition at various allosteric RNA polymerase genomic RNA sites
T. Langer, KSSCI 2013 Results Matrix
T. Steindl et al., J. Chem. Inf. Model., 46, 2146-2157 (2006)
T. Langer, KSSCI 2013 Ligand-directed Analysis
Ratio ≥ 1 90% of the compounds correctly predicted
Ratio < 1 8% more often predicted for one specific false target than for correct one
for 2% of the compounds no activity prediction possible
T. Langer, KSSCI 2013 Pharmacophore-directed Analysis
HIV protease HIV RT Influenza NA HRV coat protein HCV polymerase 1 2 3
HIV protease
HIV RT
Influenza NA
HRV coat protein
HCV polymerase 1 2 3
T. Langer, KSSCI 2013 Pharmacophore-directed Analysis
HIV protease HIV RT Influenza NA HRV coat protein HCV polymerase 1 2 3
HIV protease
HIV RT
Influenza NA
HRV coat protein
HCV polymerase 1 2 3
Model with lowest selectivity: Models retrieving no false positives 70% of actives (HIV RT), but 75% from one specific false target (HRV coat protein) 40% active and 60% inactive compounds in hit list
T. Langer, KSSCI 2013 First Conclusion ...
• First published examples of applications of extensive parallel screening approach based on pharmacophores • Multitude of pharmacophore models (up to several thousand …) • Large set of molecules (up to several million …)
• Results indicate • Correct assignment of selectivity in most cases • Independent of search algorithms used
• Fast, scalable in silico activity profiling is now possible !
T. Langer, KSSCI 2013 First Conclusion ...
• First published examples of applications of extensive parallel screening approach based on pharmacophores • Multitude of pharmacophore models (up to several thousand …) • Large set of molecules (up to several million …)
• Results indicate • Correct assignment of selectivity in most cases • Independent of search algorithms used
• Fast, scalable in silico activity profiling is now possible !
T. Langer, KSSCI 2013 Inte:Ligand’s Pharmacophore Database
~ 320 unique targets ready to use*
• Represented in ~ 210 ligand-based pharmacophore models ~ 2500 structure-based pharmacophore models
• Covering a selection of all major therapeutic classes • Contains anti-target models for finding adverse effects • Categorized according to the pharmacological target
* out of ~650 categorized by August 2013
T. Langer, KSSCI 2013 Other Application Examples
• Pharmacophores for – drug repositioning – target fishing for natural products – predicting ligands for protein-protein interfaces – in silico fragment-based screening
T. Langer, KSSCI 2013 Drug Repositioning
T. Langer, KSSCI 2013 LigandScout Model of ABCG2 Inhibitors
Yinxiang Wei, Yuanfang Ma, Qing Zhao, et al., Mol Cancer Ther 2012, 11, 1693-1702.
T. Langer, KSSCI 2013 Inhibiting ABCG2 With Sorafenib
… at a concentration up to 2,5µM/L no cytotoxic effect was observed ...
…. led us to conclude that sorafenib behaves like ABCG2 degradation- induced inhibitor. Sorafenib may, therefore, be a good candidate for MDR chemosensitizing agent.
Yinxiang Wei, Yuanfang Ma, Qing Zhao, et al., Mol Cancer Ther 2012, 11, 1693-1702.
T. Langer, KSSCI 2013 Target Fishing With Natural Products
T. Langer, KSSCI 2013 Target Fishing With Natural Products
Small molecule new chemical entities organized by source/year (N =974). David J. Newman, and Gordon M. Cragg, J. Nat. Prod., 2007, 70, 461-477
T. Langer, KSSCI 2013 Target Fishing With Natural Products
• Target identification for leoligin, the major lignan from the plant Edelweiss (Leontopodium alpinum Cass.),
• Construction of a 3D multiconformational molecular structure of this compound
• Pharmacophore-based ligand affinity profiling
• Isolation and biological testing
K. Duwensee, et al., Atherosclerosis (2011) 219, 109-115
T. Langer, KSSCI 2013 Target Fishing With Natural Products
• Target identification for leoligin, the major lignan from the plant Edelweiss (Leontopodium alpinum Cass.),
• Construction of a 3D multiconformational molecular structure of this compound
• Pharmacophore-based ligand affinity profiling
• Isolation and biological testing
K. Duwensee, et al., Atherosclerosis (2011) 219, 109-115
T. Langer, KSSCI 2013 Target Fishing With Natural Products
• Target identification for leoligin, the major lignan from the plant Edelweiss (Leontopodium alpinum Cass.),
• Construction of a 3D multiconformational molecular structure of this compound
• Pharmacophore-based ligand affinity profiling
• Isolation and biological testing
K. Duwensee, et al., Atherosclerosis (2011) 219, 109-115
T. Langer, KSSCI 2013 Pharmacophore Profiling of Leoligin
Leoligin matches the pharmacophore model encoding for the interaction site of cholesteryl ester transfer protein (CETP)
K. Duwensee, et al., Atherosclerosis (2011) 219, 109-115
T. Langer, KSSCI 2013 Biological Testing
Leoligin enhances the activity of Leoligin activates CETP in vivo human and rabbit CETP in vitro (7 days test with CETP when applied in subnanomolar transgenic mice, leoligin dosed concentration orally, 1 mg/kg) (control compound: Probucol) K. Duwensee, et al., Atherosclerosis (2011) 219, 109-115
T. Langer, KSSCI 2013 Pharmacophores To Identify PPIs
A. Voet et al., Curr Pharm Design, 2013, May 29 T. Langer, KSSCI 2013 Pharmacophores To Identify PPIs
L. De Luca et al., ChemMedChem, 2009, 4, 1311 – 1316
T. Langer, KSSCI 2013 Pharmacophores To Identify PPIs
V. Corradi et al., BMCL, 2010, 20, 6133 – 6137
T. Langer, KSSCI 2013 Pharmacophores To Identify PPIs
V. Corradi et al., BMCL, 2010, 20, 6133 – 6137
T. Langer, KSSCI 2013 Pharmacophores To Identify PPIs
V. Corradi et al., BMCL, 2010, 20, 6133 – 6137
T. Langer, KSSCI 2013 A Recent Prestwick Success Story
T. Langer, KSSCI 2013 Protein Protein Interfaces Interesting targets for drug discovery ? – Problem: Large contact surfaces with limited number of hot spots for specific interactions – Can small molecules disturb such an interface ?
T. Langer, KSSCI 2013 Protein Protein Interfaces Interesting targets for drug discovery ? – Problem: Large contact surfaces with limited number of hot spots for specific interactions – Can small molecules disturb such an interface ?
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Dömling, A. Curr. Opin. Chem. Biol. 2008, 12, 281-291.
T. Langer, KSSCI 2013 Described p53/mdm2 Inhibitors
T. Langer, KSSCI 2013 Described p53/mdm2 Inhibitors
T. Langer, KSSCI 2013 Described p53/mdm2 Inhibitors
Dömling, A. Curr. Opin. Chem. Biol. 2008, 12, 281-291.
T. Langer, KSSCI 2013 Structure-based Pharmacophore
• p53/mdm2 complex structure (PDB: 1YCR)
T. Langer, KSSCI 2013 Comparison with JNJ-26854165
T. Langer, KSSCI 2013 Comparison with JNJ-26854165
T. Langer, KSSCI 2013 Comparison with JNJ-26854165
T. Langer, KSSCI 2013 Comparison with JNJ-26854165
T. Langer, KSSCI 2013 Comparison with JNJ-26854165
T. Langer, KSSCI 2013 Pharmacophores for FBDD ?
T. Langer, KSSCI 2013 Pharmacophores for FBDD ?
T. Langer, KSSCI 2013 Pharmacophores for FBDD ?
T. Langer, KSSCI 2013 A Recent FBDD Success Story
T. Langer, KSSCI 2013 FBDD Strategy @ Prestwick
• Use set of smart, recombinable fragments • Perform pharmacophore-based screening • Recombine fragments in silico • Synthesize the highest ranked solutions – IP situation – Fit for the target – Chemical tractability – Physicochemical properties
T. Langer, KSSCI 2013 The Prestwick Chemical Fragments
• Privileged structure concept • Smart fragmentation of drug molecules
T. Langer, KSSCI 2013 The Prestwick Chemical Fragments
• Privileged structure concept • Smart fragmentation of drug molecules
T. Langer, KSSCI 2013 The Prestwick Chemical Fragments
• Privileged structure concept • Smart fragmentation of drug molecules
T. Langer, KSSCI 2013 The Prestwick Chemical Fragments
• Privileged structure concept • Smart fragmentation of drug molecules
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered
first hits synthesized & delivered
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered
first hits synthesized & delivered first hits validated: low µM affinity
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered
first hits synthesized & delivered first hits validated: low µM affinity hit to lead
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered nomination of lead
first hits synthesized & delivered first hits validated: low µM affinity hit to lead lead optimization
T. Langer, KSSCI 2013 FBD Strategy @ Prestwick
Months 1 2 3 4 5 6 7 8 9 10 11 12 13
PhD 1 (CADD)
PhD 2
Technician
hit list delivered nomination of lead
First hits had already novel structures, were patentable first hits synthesized & delivered first hits validated: low µM affinity hit to lead lead optimization
T. Langer, KSSCI 2013 Fragment Screening in LigandScout
• Normal virtual screening mode
–Match all requested query features
–Useful for ‘fragment linking’ approach
• Fragment screening mode
–Match a defined minimum number of features
–Optimized algorithm for partial matching
–Useful for ‘growing fragments’ strategy
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Linking’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Fragment Screening: ‘Growing’
T. Langer, KSSCI 2013 Conclusions
• Pharmacophore-based virtual screening and ligand activity profiling are straightforward and rapid methods for the identification of new and better lead compounds • Combined with informatics-based molecular building tools, optimized design of novel and promising compounds becomes feasible • Assessment of risks in later development stages becomes possible on a rational & transparent basis
T. Langer, KSSCI 2013 Thank you for your attention
T. Langer, KSSCI 2013 Thank you for your attention
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T. Langer, KSSCI 2013