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Pharmaceutical Informatics and Computer-Aided Drug Discovery Sangtae Kim Executive Director, Morgridge Institute for Research CDS&E Distinguished Seminar.

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Presentation on theme: "Pharmaceutical Informatics and Computer-Aided Drug Discovery Sangtae Kim Executive Director, Morgridge Institute for Research CDS&E Distinguished Seminar."— Presentation transcript:

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

2 Twin institutes under one roof on 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

5

6 IP Portfolios University Healthcare Delivery

7 IP Portfolios University Healthcare Delivery

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

10 CDS&E: Enabling Role of Data in 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: Major Targets of 21 st 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. Fabian, M.A., et al., A small molecule-kinase interaction map for clinical kinase inhibitors. Nat. Biotech. 2005, 23(3): p  Gleevec™(Novartis); Iressa™(AstraZeneca); Tarceva™(Roche); Sutent™(Pfizer); Arxxant™(Lilly); … plus more

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

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 by the dehydronic force gromacs simulation package NVT Ensemble TIP3p water model PME electrostatics Nose Hoover thermostat 100 equilibrium runs Computation details:

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

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

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

19 Dehydrons & Wrapperones™ in Pharmaceutical Informatics Gleevec™/imatinib on the Cover of Time Magazine 2001 High-Throughput-Computing improves anti-cancer drugs Change research paradigm from “generating lead generation” to “optimizing lead optimization”! 1 st generation drug candidates (tweaks) 2 nd generation drug candidates (wrapperones™) Success factors enabled by collaboratory environment Distinguished Investigator: Ariel Fernandez (Aug. 2011) Re-designing better, next generation anti-cancer drugs: selective wrapping deduced from dehydronic patterns. A. Fernandez at entry to H.F. DeLuca Forum Photo taken Feb (seminar visit) 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 “results” Ultimate: enable sharing of sensitive data The Future Societal / Regulatory factors

21 Closing Thoughts 1925 – Harry Steenbock Vitamin D by Irradiation


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