Pharmaceutical Informatics and Computer-Aided Drug Discovery Sangtae Kim Executive Director, Morgridge Institute for Res

1. Pharmaceutical Informatics and Computer-AidedDrug Discovery Sangtae Kim Executive Director, Morgridge Institute for Research CDS&E Distinguished Seminar Series at Rutgers – October, 10, 2011

2. Twin institutes under one roofon the UW-Madison campus

3. Vision Inspired by Wisconsin Idea Strengthen Wisconsin as world class center for research and commercialization to improve economy and lives of citizens. Collaboration Spark research collaborations across the sciences that accelerate breakthrough discoveries to improve human health Interaction Foster interaction between public and private research that breaks down barriers between researchers, labs & scientific disciplines Community Develop vibrant public space on campus that builds community and engages the public in the sciences and humanities

4. At center of campus science sites

6. HealthcareDelivery IP Portfolios University

7. HealthcareDelivery IP Portfolios University

8. OutlineThe Priorities for the Pharma-Informatics Department • Create an information highway from bio- discovery to delivery, from the promise of genomics to the fruits of personalized medicine (population segmentation). • Systems critique of the R&D Pipeline. • Focus research resources on new and better methods at the bottlenecks in the discovery and development of new drugs, e.g., lead optimization.

9. Informatics’ new frontier Why pharmaceutical informatics? Value (log scale) $ $1 per mg. Phase II clinical trials 104 $100 per kg. PharmaceuticalInformatics Pharma/Biotech R&D Timeline

10. CDS&E: Enabling Roleof Datain Computer-Aided Drug Design • Evolution of two distinct branches of computational biology • Molecule wriggling (solving differential equations of biochemical physics) • Data miners (informatics) • New generation trained to do both • Limitations of each branch • Example:

11. Protein Kinases:MajorTargets of21st century • Constituents of cell signaling pathways • Phosphorylation of other proteins • Cancer, Inflammation, Diabetes, … • e.g. MAPK, CDK2, EGFR, PKA, etc. Largest enzyme family in the genome: 518 members with 7 sub-families. 11

12. Big Pharma’s Kinase Interaction Map • High throughput assay, M. Fabian et al. (Ambit Biosciences) • 113 kinases & 17 kinase inhibitors • approved drugs, candidates in clinical trials, research compounds. = Gleevec™(Novartis); Iressa™(AstraZeneca); Tarceva™(Roche); Sutent™(Pfizer); Arxxant™(Lilly); … plus more Fabian, M.A., et al., A small molecule-kinase interaction map for clinical kinase inhibitors. Nat. Biotech. 2005, 23(3): p. 329-336.

13. Protein Kinase Inhibitors: Selectivity Kinases are cross reactive because of structure, fold conservation. Inhibitory impact across sub-families! Gleevec®, aCancerdrug, also effective againstDiabetes!! Targeted against ABL kinase but inhibits PDGF also. Some inhibitors (poisons) bind through non-conserved features. Pattern is not aligned with evolutionand thus not a low hanging fruit for simpler informatics tools. 13

14. Protein Kinase Inhibitors: Selectivity Kinases are cross reactive because of structure, fold conservation. Inhibitory impact across sub-families! Recent (2006) advance in aligning the pattern of reactivity across sub-families: A. Fernandez & S. Maddipati, J. Med. Chem. 14

15. Partially wrapped hydrogen bonds (dehydrons) attract hydrophobic groups to get completely wrapped bythe dehydronic force Computation details: gromacs simulation package NVT Ensemble TIP3p water model PME electrostatics Nose Hoover thermostat 100 equilibrium runs

16. Packing differences vs. Pharmacological differences Theory Fernandez & Maddipati J. Med. Chem. 2006 Experiments Fabian et al. Nat. Biotech 2005 16

17. Previous Example: in principle, a hand-off “results” “hand-off” Database of results Simulation runs Implication: progress via collaboration

18. When Hand-Offs are Not Possible “results” Informatics on the characteristics of the entire run Simulation runs Implication: education and training

19. Dehydrons & Wrapperones™ in Pharmaceutical Informatics • High-Throughput-Computing improves anti-cancer drugs • Change research paradigm from “generating lead generation” to “optimizing lead optimization”! • 1st generation drug candidates (tweaks) • 2nd generation drug candidates (wrapperones™) • Success factors enabled bycollaboratory environment • Distinguished Investigator: Ariel Fernandez (Aug. 2011) Gleevec™/imatinib on the Cover of Time Magazine 2001 A. Fernandez at entry to H.F. DeLuca Forum Photo taken Feb. 2011 (seminar visit) Re-designing better, next generation anti-cancer drugs: selective wrapping deduced from dehydronic patterns. Machine learning expert S. Maddipati (right) co-advised by S. Kim and A. Fernandez. Also shown: R. Nandigam now at Aspen Tech Photo taken summer 2007

20. The Future “results” Ultimate: enable sharing of sensitive data Societal / Regulatory factors

21. Closing Thoughts 1925 – Harry Steenbock Vitamin D by Irradiation