Polypharmacology: Drug Discovery in the Era of Genomics and Proteomics

1. Polypharmacology: Drug Discovery in the Era of Genomics and Proteomics Philip E. Bourne University of California San Diego pbourne@ucsd.edu

2. Big Questions in the Lab • Can we improve how science is disseminated and comprehended? • What is the ancestry of the protein structure universe and what can we learn from it? • Are there alternative ways to represent proteins from which we can learn something new? • What really happens when we take a drug? • Can we contribute to the treatment of neglected {tropical} diseases? August 14, 2009 Valas, Yang & Bourne 2009 Current Opinions in Structural Biology 19:1-6

3. The truth is we know very little about how the major drugs we take work We know even less about what side effects they might have Drug discovery seems not to have moved into the omics era The cost of bringing a drug to market is huge >$800M The cost of failure is even higher e.g., Vioxx ~ $5Bn Fatal diseases are neglected because they do not make money Motivation

4. The truth is we know very little about how the major drugs we take work – receptors/mechanism is unknown We know even less about what side effects they might have - receptors/mechanism is unknown Drug discovery seems not to have moved into the omics era – systems biology can help but as yet is unproven The cost of bringing a drug to market is huge >$800M The cost of failure is even higher e.g., Vioxx ~ $5Bn - receptors/mechanism is unknown Fatal diseases are neglected because they do not make money – there must be a workable business model Motivation - Reasoning

5. Why Don’t we Do Better?A Couple of Observations • Gene knockouts only effect phenotype in 10-20% of cases , why? • redundant functions • alternative network routes • robustness of interaction networks • 35% of biologically active compounds bind to more than one target A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 Paolini et al. Nat. Biotechnol. 2006 24:805–815

6. Why Don’t we Do Better?A Couple of Observations • Tykerb – Breast cancer • Gleevac – Leukemia, GI cancers • Nexavar – Kidney and liver cancer • Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive Collins and Workman 2006 Nature Chemical Biology 2 689-700

7. Implications • Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized • Stated another way – The notion of one drug, one target, one disease is a little naïve in a complex system

8. How Can we Begin to Address the Problem? • Systematic screening for multiple targets by multiple drugs • Integration of knowledge from multiple sources • Analyze the impact on the complete living system • Statically • Dynamically

9. What if… • We can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale? • We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals and NCEs?

10. What Do These Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each of these • scenarios, but first the bioinformatics guts of the approach

11. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology

12. A Quick Aside – RCSB PDB Pharmacology/Drug View Mid 2010 • Establish linkages to drug resources (FDA, PubChem, DrugBank, etc.) • Create query capabilities for drug information • Provide superposed views of ligand binding sites • Analyze and display protein-ligand interactions % Similarity to Drug Molecule Has Bound Drug 100 Asp Drug Name Aspirin Mockups of drug view features RCSB PDB Ligand View Peter Rose et al

13. A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules … Dock molecules to both primary and off-targets Statistics analysis of docking score correlations Xie and Bourne 2009 Bioinformatics 25(12) 305-312 Computational Methodology

14. Characterization of the Ligand Binding Site - The Geometric Potential • Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments • Initially assign Ca atom with a value that is the distance to the environmental boundary • Update the value with those of surrounding Ca atoms dependent on distances and orientation – atoms within a 10A radius define i Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

15. Discrimination Power of the Geometric Potential • Geometric potential can distinguish binding and non-binding sites 100 0 Geometric Potential Scale Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

16. Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm Structure A Structure B L E R V K D L L E R V K D L • Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix • The maximum-weight clique corresponds to the optimum alignment of the two structures Xie and Bourne 2008 PNAS, 105(14) 5441

17. Similarity Matrix of Alignment • Chemical Similarity • Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) • Amino acid chemical similarity matrix • Evolutionary Correlation • Amino acid substitution matrix such as BLOSUM45 • Similarity score between two sequence profiles fa, fb are the 20 amino acid target frequencies of profile a and b, respectively Sa, Sb are the PSSM of profile a and b, respectively Xie and Bourne 2008 PNAS, 105(14) 5441 Computational Methodology

18. Nothing in Biology {Including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975)

19. What Do Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each of these • scenarios, but first the bioinformatics guts of the approach

20. How to Optimize a NCE • African trypanosomiasis (sleeping sickness) • Carried by the tsetse fly • Trypanosoma brucei is the active agent • Endemic to Africa • 300,000 new cases each year • Sleep cycle disturbed • Neurological phase deadly Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

21. Optimize: Find Secondary Targets of TbREL1 NCS45208 Aka Compound 1 TbREL1 – T. brucei RNA editing ligase I IC50: 1.95 ± 0.33 μM Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

22. Workflow Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

23. Mitochondrial 2-enoyl ThioesterReductase (HsETR1) • Neither FATCAT nor CLUSTALW2 judged HsETR1 to be homologous to the primary target. • Both SOIPPA and AutoDock predicted it was a secondary target. Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

24. Mitochondrial 2-enoyl Thioester Reductase (HsETR1) Measured IC50 of 33.5 M Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

25. Mitochondrial 2-enoyl Thioester Reductase (HsETR1) • HsETR1 is thought to be essential for fatty acid synthesis (FAS) type II. • In the process of optimizing Compound 1 to make it more drug-like, modifications that reduce binding to human HsETR1 may diminish unforeseen side effects. Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

26. T. bruceiUDP-galactose 4-epimerase (TbGalE) • Neither FATCAT nor CLUSTALW2 judged TbGalE to be homologous to the primary target. • AutoDock predicted it was a secondary target, and it was homologous to a protein that SOIPPA identified as a secondary target. Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

27. T. brucei UDP-galactose 4-epimerase (TbGalE) Measured IC50 of 0.7 M (Better than Compound 1 binding to TbREL?) Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

28. T. bruceiUDP-galactose 4-epimerase (TbGalE) • Like TbREL1, TbGalE (galactose metabolism) is essential for T. brucei survival. • Compound 1 inhibits two essential T. brucei enzymes. Durrant et al 2009 PLoS Comp Biol in press How to Optimize a NCE

29. What Do Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each of these • scenarios, but first the bioinformatics guts of the approach

30. The Problem with Tuberculosis • One third of global population infected • 1.7 million deaths per year • 95% of deaths in developing countries • Anti-TB drugs hardly changed in 40 years • MDR-TB and XDR-TB pose a threat to human health worldwide • Development of novel, effective, and inexpensive drugs is an urgent priority

31. Found.. Evolutionary linkage between: NAD-binding Rossmann fold S-adenosylmethionine (SAM)-binding domain of SAM-dependent methyltransferases Catechol-O-methyl transferase (COMT) is SAM-dependent methyltransferase Entacapone and tolcapone are used as COMT inhibitors in Parkinson’s disease treatment Hypothesis: Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcapone Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

32. Functional Site Similarity between COMT and InhA Entacapone and tolcapone docked onto 215 NAD-binding proteins from different species M.tuberculosisEnoyl-acyl carrier protein reductaseENR (InhA) discovered as potential new drug target InhA is the primary target of many existing anti-TB drugs but all are very toxic InhA catalyses the final, rate-determining step in the fatty acid elongation cycle Alignment of the COMT and InhA binding sites revealed similarities ... Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

33. Binding Site Similarity between COMT and InhA COMT SAM (cofactor) BIE (inhibitor) InhA NAD (cofactor) 641 (inhibitor) Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

34. Summary of the TB Story Entacapone and tolcapone shown to have potential for repositioning Direct mechanism of action avoids M. tuberculosis resistance mechanisms Possess excellent safety profiles with few side effects – already on the market In vivo support Assay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugs Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

35. Summary from the TB Alliance – Medicinal Chemistry • The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered • MIC is 65x the estimated plasma concentration • Have other InhA inhibitors in the pipeline Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

36. What Do Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each {some } of these • scenarios, but first the bioinformatics guts of the approach

37. The TB Drugome Existing Drugs 3. Protein-ligand Docking … TB Protein-drug Interactome TB Structural Proteome 2. Binding site Similarity Drug resistance mechanism New therapeutics for MDR and XDR-TB Drugome/TB Target identification 1. Structural Determination & Modeling 4.2 Network Integration Drug repurposing TB Metabolome Side effect prediction TB Genome 4.1 Network Reconstruction Bioinformatics 2009 25(12) 305-312 Multi-target strategy Kinnings et al in Preparation

38. Structural coverage of the TB proteome • High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% TB proteome homology models solved structures 2, 266 3, 996 284 1, 446 Multi-target strategy Kinnings et al in Preparation

39. Drug binding sites in the PDB • Searched the PDB for protein crystal structures bound with FDA-approved drugs • 268 drugs bound in a total of 931 binding sites No. of drugs Acarbose Darunavir Alitretinoin Conjugated estrogens Chenodiol Methotrexate No. of drug binding sites Multi-target strategy Kinnings et al in Preparation

40. SMAP p-value < 1e-5 drugs TB proteins p < 1e-7 p < 1e-6 p < 1e-5

41. Multi-target drugs? • Similarities between drug binding sites and TB proteins are found for 61/268 drugs • 41 of these drugs could potentially inhibit more than one TB protein conjugated estrogens & methotrexate No. of drugs chenodiol levothyroxine testosterone raloxifene ritonavir alitretinoin No. of potential TB targets Multi-target strategy Kinnings et al in Preparation

42. Top 5 most highly connected drugs

43. What Do Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each {some } of these • scenarios, but first the bioinformatics guts of the approach

44. The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

45. Cholesteryl Ester Transfer Protein (CETP) collects triglycerides from very low density or low density lipoproteins (VLDL or LDL) and exchanges them for cholesteryl esters from high density lipoproteins (and vice versa) A long tunnel with two major binding sites. Docking studies suggest that it possible that torcetrapib binds to both of them. The torcetrapib binding site is unknown. Docking studies show that both sites can bind to torcetrapib with the docking score around -8.0. CETP inhibitor X CETP LDL HDL Bad Cholesterol Good Cholesterol The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

46. Docking Scores eHits/Autodock The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

47. JTT705 Torcetrapib Anacetrapib JTT705 VDR – RXR FA + RAS FABP ? PPARα PPARδ ? ? PPARγ High blood pressure + JNK/IKK pathway JNK/NF-KB pathway Anti-inflammatory function Immune response to infection The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

48. Chang et al. 2009 Mol Sys Biol Submitted

49. Some Limitations • Structural coverage of the given proteome • False hits / poor docking scores • Literature searching • It’s a hypothesis – need experimental validation • Money  Limitations

50. What Do Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen • Today I will give you brief vignettes of each of these • scenarios, but first the bioinformatics guts of the approach