What Really Happens When I Take a Drug?

1. What Really Happens When I Take a Drug? Philip E. Bourne University of California San Diego pbourne@ucsd.edu http://www.sdsc.edu/pb Vancouver April 12, 2012

2. Big Questions in the Lab{In the spirit of Hamming} • Can we improve how science is disseminated and comprehended? • What is the ancestry and organization 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? Erren et al 2007 PLoS Comp. Biol., 3(10): e213 Motivators

3. Our Motivation • 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 Motivators

4. Our Broad Approach • Involves the fields of: • Structural bioinformatics • Cheminformatics • Biophysics • Systems biology • Pharmaceutical chemistry • L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52: 361-379 • L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and D. Frail (Eds.) Wiley and Sons. (available upon request) Disciplines Touched & 2012 Reviews

5. A Quick Aside – RCSB PDB Pharmacology/Drug View 2012 • Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB 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 Led by Peter Rose Mockups of drug view features RCSB PDB’s Drug Work RCSB PDB Team

6. A Quick Aside PDB Scope/Deliverables • Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012 • Part II: peptide derived compounds (PRD)- tbd • Part III: toxins and toxin targets (T3DB), human metabolites (HMDB) • Part IV: biotherapeutics, i.e., monoclonal antibodies • Part V: veterinary drugs (FDA Green Book) RCSB PDB’s Drug Work

7. Our Approach • We characterize a known protein-ligand binding site from a 3D structure (primary site) and search for similar sites (secondary sites) on a proteome wide scale independent of global structure similarity • We try a static and dynamic network-based approach to understand the implications of drug binding to multiple sites Methodology

8. Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications

9. Approach - Need to Start with a 3D Drug-Receptor Complex – Either Experimental or Modeled Computational Methodology

10. Some Numbers to Show Limitations TB-drugome Pf-Drugome Target gene 3996 5491 Target protein in PDB 284 136 Solved structure in PDB 749 333 Reliable homology models 1446 1236 Structure coverage 43.29% 25.02% Drugs 274 321 Drug binding sites 962 1569

11. 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

12. 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

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

14. 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 Computational Methodology

15. 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

16. ScoringThe Point is this Approach Can Now be Applied on a Proteome-wide Scale • Scores for binding site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version • Blosum45 and • b) McLachlan substitution matrices. Xie, Xie and Bourne 2009 Bioinformatics 25(12) 305-312

17. Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications

18. Nelfinavir • Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) • Nelfinavir can inhibit receptor tyrosine kinase(s) • Nelfinavir can reduce Akt activation • Our goal: • to identify off-targets of Nelfinavir in the human proteome • to construct an off-target binding network • to explain the mechanism of anti-cancer activity PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning

19. Possible Nelfinavir Repositioning

20. drug target off-target? structural proteome binding site comparison 1OHR protein ligand docking MD simulation & MM/GBSA Binding free energy calculation network construction & mapping Clinical Outcomes Possible Nelfinavir Repositioning

21. Binding Site Comparison • 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR) • Structures with SMAP p-value less than 1.0e-3 were retained for further investigation • A total 126 structures have significant p-values < 1.0e-3 Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037

22. Enrichment of Protein Kinases in Top Hits • The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease • Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets) • 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases PLoS Comp. Biol., 2011 2011 7(4) e1002037 Possible Nelfinavir Repositioning

23. Distribution of Top Hits on the Human Kinome p-value < 1.0e-4 p-value < 1.0e-3 Manning et al., Science, 2002, V298, 1912 Possible Nelfinavir Repositioning

24. Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues EGFR-DJK Co-crys ligand EGFR-Nelfinavir H-bond: Met793 with benzamide hydroxy O38 H-bond: Met793 with quinazoline N1 DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE

25. Off-target Interaction Network Identified off-target Pathway Activation Intermediate protein Cellular effect Inhibition PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning

26. Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor Joell J. Gills et al. Clinic Cancer Research September 2007 13; 5183 Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest Nelfinavir induces G1 arrest through inhibition of CDK2 activity. Such inhibition is not caused by inhibition of Akt signaling. Jiang W el al. Cancer Res. 2007 67(3) BCR-ABL is a constitutively activated tyrosine kinasethat causes chronic myeloid leukemia (CML) Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037 Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010. 9:19 Nelfinavir may inhibit BCR-ABL Possible Nelfinavir Repositioning

27. Summary • The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor • Most targets are upstream of the PI3K/Akt pathway • Findings are consistent with the experimental literature • More direct experiment is needed PLoS Comp. Biol., 2011 2011 7(4) e1002037 Possible Nelfinavir Repositioning

28. Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications

29. Case Study: Torcetrapib Side Effect • Cholesteryl ester transfer protein (CETP) inhibitors treat cardiovascular disease by raising HDL and lowering LDL cholesterol (Torcetrapib, Anacetrapib, JTT-705). • Torcetrapib withdrawn due to occasional lethal side effect, severe hypertension. • Cause of hypertension undetermined; off-target effects suggested. • Predicted off-targets include metabolic enzymes. Renal function is strong determinant of blood pressure. Causal off-targets may be found through modeling kidney metabolism.

30. Constraint-based Metabolic Modeling Flux space Metabolic network reactions Steady-state assumption S · v = 0 Perturbation constraint Flux Matrix representation of network Change in system capacity

31. Recon1: A Human Metabolic Network Global Metabolic Map Comprehensively represents known reactions in human cells Reactions (3,311) Compounds (2,712) Pathways (98) Genes (1,496) Transcripts (1,905) Proteins (2,004) Compartments (7) http://bigg.ucsd.edu (Duarte et al Proc Natl Acad Sci USA 2007)

32. metabolomic biofluid & tissue localization data metabolic network gene expression data constrain exchange fluxes preliminary model normalize & set threshold model refine based on capabilities set flux constraints GIMME metabolic influx set minimum objective flux objective function literature metabolic efflux Context-specific Modeling Pipeline

33. Predicted Hypertension Causal Drug Off-Targets *Clinically linked to hypertension.

34. Applications Thus Far • Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi) Applications

35. The Future as a High Throughput Approach…..

36. 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 Repositioning- The TB Story

37. The TB-Drugome • Determine the TB structural proteome • Determine all known drug binding sites from the PDB • Determine which of the sites found in 2 exist in 1 • Call the result the TB-drugome A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

38. 1. Determine the TB Structural 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 A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

39. 2. Determine all Known 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 A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

40. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).

41. From a Drug Repositioning Perspective • 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 A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

42. Top 5 Most Highly Connected Drugs

43. Vignette within Vignette 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 Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

44. 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

45. Acknowledgements Lei Xie Li Xie Roger Chang Bernhard Palsson Jian Wang Sarah Kinnings http://funsite.sdsc.edu