Structural Bioinformatics in Drug Discovery

1. Structural Bioinformatics in Drug Discovery Melissa Passino

2. Structural Bioinformatics • What is SBI? “Structural bioinformatics is a subset of bioinformatics concerned with the use of biological structures – proteins, DNA, RNA, ligands etc. and complexes thereof to further our understanding of biological systems.” http://biology.sdsc.edu/strucb.html

3. SBI in Drug Design and Discovery • SBI can be used to examine: • drug targets (usually proteins) • binding of ligands ↓ “rational” drug design (benefits = saved time and $$$)

4. Traditional Methods of Drug Discovery natural (plant-derived) treatment for illness/ailments ↓ isolation of active compound (small, organic)

5. synthesis of compound ↓ manipulation of structure to get better drug (greater efficacy, fewer side effects) Aspirin

6. Modern Methods of Drug Discovery NEW and IMPROVED! What’s different? • Drug discovery process begins with a disease(rather than a treatment) • Use disease model to pinpoint relevant genetic/biological components (i.e. possible drug targets)

7. Modern Drug Discovery disease→genetic/biological target ↓ discovery of a “lead” molecule - design assay to measure function of target - use assay to look for modulators of target’s function ↓ high throughput screen (HTS) - to identify “hits” (compounds with binding in low nM to low μM range)

8. Modern Drug Discovery small molecule hits ↓ manipulate structure to increase potency i.e. decrease Ki to low nM affinity ↓ *optimization of lead molecule into candidate drug* fulfillment of required pharmacological properties: potency, absorption, bioavailability, metabolism, safety ↓ clinical trials

9. Interesting facts... • Over 90% of drugs entering clinical trials fail to make it to market • The average cost to bring a new drug to market is estimated at $770 million

10. Impact of Structural Bioinformatics on Drug Discovery Fig 1 & 2 Fauman et al. • Speeds up key steps in DD process by combining aspects of bioinformatics, structural biology, and structure-based drug design

11. Identifying Targets: The “Druggable Genome”

12. human genome Problems with toxicity, specificity, and difficulty in creating potent inhibitors eliminate the first 3 categories... polysaccharides nucleic acids proteins lipids

13. human genome “druggable genome” = subset of genes which express proteins capable of binding small drug-like molecules polysaccharides nucleic acids proteins lipids proteins with binding site

14. Relating druggable targets to disease... Analysis of pharm industry reveals: • Over 400 proteins used as drug targets • Sequence analysis of these proteins shows that most targets fall within a few major gene families (GPCRs, kinases, proteases and peptidases) Fig. 3, Fauman et al.

15. Assessing Target Druggability • Once a target is defined for your disease of interest, SBI can help answer the question: Is this a “druggable” target? • Does it have sequence/domains similar to known targets? • Does the target have a site where a drug can bind, and with appropriate affinity?

16. Other roles for SBI in drug discovery • Binding pocket modeling • Lead identification • Similarity with known proteins or ligands • Chemical library design / combinatorial chemistry • Virtual screening • *Lead optimization* • Binding • ADMET

17. SBI in cancer therapy:MMPIs

18. Inability to control metastasis is the leading cause of death in patients with cancer (Zucker et al. Oncogene. 2000, 19, 6642-6650.) • Matrix metalloproteinase inhibitors (MMPIs) are a newer class of cancer therapeutics • can prevent metastasis (but not cytotoxic); may also play role in blocking tumor angiogenesis (growth inhibition) • Used to treat “major” cancers: lung, GI, prostate

19. What is an MMP? • Family of over 20 structurally related proteinases • Principal substrates: • protein components of extracellular matrix (collagen, fibronectin, laminin, proteoglycan core protein) • Functions: • Breakdown of connective tissue; tissue remodeling • Role in cancer: • Increased levels/activity of MMPs in area surrounding tumor

20. Brown PD. Breast Cancer Res Treat1998, 52, 125-136.

21. Whittaker et al. Chem. Rev.1999, 99, 2735-2776

22. MMP-1,3,8 MMP-2 MMP-7 MMP-10 to 13,19,20 MMP-9 Whittaker et al. Chem. Rev.1999, 99, 2735-2776 MMP-14 to 17

23. MMP catalysis “metallo” in MMP = zinc →catalytic domain contains 2 zinc atoms Whittaker et al. Chem. Rev.1999, 99, 2735-2776

24. Peptidic inhibitors • Structure based design • based on natural substrate collagen • zinc binding group • Poor Ki values, not very selective (inhibit other MPs) Brown PD. Breast Cancer Res Treat1998, 52, 125-136.

25. Peptidic hydroxamate inhibitors • Specificity for MMPs over other MPs • Better binding (low nM Ki) • But poor oral bioavailability

26. A (not very) long time ago, in a town (not too) far away… …lived a company named Agouron… …and this company had a dream, a dream to design a nonpeptidic hydroxamate inhibitor of MMPs…

27. ...so they made some special crystals… used x-ray crystallography/3D structure of recombinant human MMPs bound to various inhibitors ↓ to determine key a.a. residues, ligand substituents needed for binding Gelatinase A http://www.rcsb.org/pdb/

28. …and used the magic of structural bioinformatics to design many, many nonpeptidic hydroxylates. anti-metastasis oral bioavailabity anti-growth Ki repeat…

29. Results… AG3340 “Prinomastat” • Good oral bioavailability • Selective for specific MMPs • may implicate their roles in certain cancers

30. Prinomastat • Evidence showing prevention of lung cancer metastasis in rat and mice models • Clinical trials →non small cell lung cancer →hormone refractory prostate cancer …stopped at Phase 3 (Aug 2000) because did not show effects against late stage metastasis

31. Morals of the story… • SBI can be used as basis for lead discovery and optimization • MMPs are good targets for chemotherapy to help control metastasis… …but MMPIs must be combined with other cytotoxic drugs to get maximum benefits, and used at earliest stage possible