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Discovery of Therapeutics to Improve Quality of Life Ram Samudrala University of Washington Overall research areas PROTEIN STRUCTURE PREDICTION PROTEIN FUNCTION PREDICTION PROTEIN INTERACTION PREDICTION CASP6 prediction for T0281 4.3 Å Cα RMSD for all 70 residues PROTEOME APPLICATION INTEGRATED SYSTEMS BIOLOGY CASP6 prediction for T0271 2.4 Å Cα RMSD for all 142 residues (46% ID) http://protinfo.compbio.washington.edu http://bioverse.compbio.washington.edu Drug discovery – current approach Pink et al, September 2005 Drug discovery – our approach Computational protein docking with molecular dynamics protocol enables in silico discovery of compounds that inhibit multiple targets and diseases Several papers published; first ones in 2003 Pink et al, September 2005 Prediction of HIV-1 protease-inhibitor binding energies Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 157-166, 2005. Jenwitheesuk E, Samudrala R. BMC Structural Biology 3: 2, 2003. Ekachai Jenwitheesuk Prediction of HIV inhibitor resistance/susceptibility http://protinfo.compbio.washington.edu/pirspred/ Jenwitheesuk E, Wang K, Mittler J, Samudrala R. AIDS 18: 1858-1859, 2004. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Trends in Microbiology 13: 150-151, 2005. Ekachai Jenwitheesuk/ Kai Wang/John Mittler Set of FDA approved, experimental and naturally occurring compounds Inhibitor X Disease A Protein A… Protein A2 Protein A1 1… 2… 3… 4 Inhibitor X 5… 6… Disease B Protein B… Protein B2 Protein B1 1… 2 Inhibitor X 3… 4… 5… 6… Disease C Protein C… Protein C2 Protein C1 1… 2… 3… 4… 5 Inhibitor X 6… Disease XXX ........... Protein XXX… Protein XXX2 Protein XXX1 1… 2… 3 Inhibitor X 4… 5… 6… Binding affinity calculation using docking with dynamics protocol Multi-target multi-disease therapeutic discovery Disease A •Protein A1 •Protein A2 •Protein A3 •… •… Disease B •Protein B1 •Protein B2 •Protein B3 •… •… Disease C •Protein C1 •Protein C2 •Protein C3 •… •… Disease XXX •Protein XXX1 •Protein XXX2 •Protein XXX3 •… •… Ekachai Jenwitheesuk Identification of multi-target inhibitors against malaria Ekachai Jenwitheesuk Jenwitheesuk E, Samudrala R. Journal of the American Medical Association 294: 1490-1491, 2005. Identification of multi-strain herpesvirus inhibitors Ekachai Jenwitheesuk/Michael Lagunoff Summary of our multi-target multi-disease drug discovery efforts Dengue – 3 targets HIV – 5 targets Influenza – protease inhibitors for 4 strains Leishmania – 4 targets M. tuberculosis Plasmodium – 14 targets SARS – protease inhibitor published T. cruz – 15 targets T. brucei – 14 targets Herpesviruses – protease inhibitors for 5 strains Cancer – 31 targets Ekachai Jenwitheesuk Summary of our multi-target multi-disease drug discovery efforts Ekachai Jenwitheesuk What do we want to do: Ideal world scenario Focus on discovery of inhibitors for third world diseases Foster an “open drug” development approach where discoveries are rapidly published Build on infrastructure created by drug companies to validate and deliver therapeutics to the people who need it What do we want to do: Specifics Package HIV drug resistance prediction server into a standalone tool for use in a clinical setting Screen drugs computationally for more targets and diseases most relevant to global health Perform in vitro assays of drugs being predicted with collaborators (SBRI, UW, UCSF) Perform in vivo studies Conduct clinical trials to ensure follow through of leads Limitations Exhausting the limits of academic research Academic funding not adequate for translating these predictions into a clinical setting Possible funding models For-profit: focus on a disease of importance to industrialised nations; form a startup; obtain VC funding; build up a pipeline that includes drugs against third world diseases – misplaced focus, control issues Not-for-profit: obtain funding from granting agencies and foundations - slow, not generalisable Hybrid: focus on third world diseases; have a NFP mechanism in place to push through our leads – infrastructure can be generally used to generate revenue Funding specifics Costs are reduced due to: Computational discovery Use of preapproved drugs Lower number of failed drugs Probabily of success is higher due to: Multi-target inhibition Mechanism of action is understood Use of preapproved drugs Side effects may be predicted
