Talk at Yale University April 26th 2011: Applying Computational Modelsfor Toxicology, Drug Discovery and Beyond
1. Applying Computational Models for Toxicology, Drug Discovery and Beyond Sean Ekins Collaborations in Chemistry, Jenkintown, PA. Collaborative Drug Discovery, Burlingame, CA. Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD.
2. … mathematical learning will be the distinguishing mark of a physician from a quack… Richard Mead A mechanical account of poisons in several essays 2nd Edition, London, 1708.
3. A decade ago we had limited data for modeling Now we are inundated with it What can we do with it?
5. Pharma reached a productivity tipping point Cost of drug development high Failure in clinic due to toxicity How to predict earlier
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7. Ekins et al., Trends Pharm Sci 26: 202-209 (2005) The Iterative ADME/Tox Optimization Process “ Drug discovery & development needs to be more like engineering” Janet Woodcock, FDA – PharmaDiscovery May 10 2006
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11. Drug Examples for DILI + and - Troglitazone DILI + Pioglitazone DILI - Rosiglitzone DILI - Sulindac DILI + Aspirin DILI - Diclofenac DILI + Xu et al., Toxicol Sci 105: 97-105 (2008).
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17. Bayesian machine learning Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Bayesian classification is a simple probabilistic classification model. It is based on Bayes’ theorem h is the hypothesis or model d is the observed data p ( h ) is the prior belief (probability of hypothesis h before observing any data) p ( d ) is the data evidence (marginal probability of the data) p ( d|h ) is the likelihood (probability of data d if hypothesis h is true) p ( h|d ) is the posterior probability (probability of hypothesis h being true given the observed data d ) A weight is calculated for each feature using a Laplacian-adjusted probability estimate to account for the different sampling frequencies of different features. The weights are summed to provide a probability estimate
18. Features in DILI + Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Avoid Long aliphatic chains Phenols Ketones Diols -methyl styrene Conjugated structures Cyclohexenones Amides ?
19. Features in DILI - Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
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22. Training vs test set PCA Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Yellow = test Blue = training
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24. SMARTS FIlters Smartsfilter kindly provided by Dr. Jeremy Yang (University of New Mexico, Albuquerque, NM, http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter). Substructure Alerts used to filter libraries – remove reactive groups etc. Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010
25. SMARTS Filters vs Rule of 5 Ekins and Freundlich, Pharm Res, In press 2011 Correlation between the number of SMARTS filter failures and the number of Lipinski violations for different types of rules sets with FDA drug set from CDD (N = 2804) Suggests # of Lipinski violations may also be an indicator of undesirable chemical features that result in reactivity
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28. MRP BCRP P-gp Molecule Databases In vitro testing hPEPT Transporter Pharmacophores or other model types Feedback of new substrates or inhibitors In silico and in vitro screening for Transporters Ekins, in Ecker G and Chiba P, Transporters as drug carriers, John Wiley and Sons. P215-227, 2009. MRP BCRP P-gp Molecule Databases In vitro testing hPEPT Transporter Pharmacophores Feedback of new substrates or inhibitors
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30. Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)
31. +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine
32. hOCTN2 quantitative pharmacophore and Bayesian model Bayesian Model - Leaving 50% out 97 times external ROC 0.90 internal ROC 0.79 concordance 73.4%; specificity 88.2%; sensitivity 64.2%. Lab test set (N = 27) Bayesian model has better correct predictions (> 80%) and lower false positives and negatives than pharmacophore (> 70%) Predictions for literature test set (N=32) not as good as in house – mean max Tanimoto similarity were ~ 0.6 Diao et al., Mol Pharm, 7: 2120-2131, 2010 PCA used to assess training and test set overlap
33. Among the 21 drugs associated with rhabdomyolysis or carnitine deficiency, 14 (66.7%) provided a C max/ K i ratio higher than 0.0025. Among 25 drugs that were not associated with rhabdomyolysis or carnitine deficiency, only 9 (36.0%) showed a C max / K i ratio higher than 0.0025. Rhabdomyolysis or carnitine deficiency was associated with a C max / K i value above 0.0025 (Pearson’s chi-square test p = 0.0382). limitations of C max / K i serving as a predictor for rhabdomyolysis -- C max / K i does not consider the effects of drug tissue distribution or plasma protein binding. hOCTN2 association with rhabdomyolysis Diao et al., Mol Pharm, 7: 2120-2131, 2010
34. Proactive database searching - Prioritize compounds for testing in vitro Understand drug interactions In silico allows rapid parallel optimization vs transporters or other properties Provide novel insights into the molecular interaction of inhibitors Repurpose - reposition FDA drugs Summing up
39. Receptor model for PXR obtained using Raptor (5D-QSAR) Bayesian model Ekins S, Kortagere S, Iyer M, Reschly EJ, Lill MA, Redinbo MR and Krasowski MD, PLoS Comp Biol 5: e1000594 (2009). A C T I V E I N A C T I V E
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43. How Could Green Chemistry Benefit From These Models? Chem Rev. 2010 Oct 13;110(10):5845-82
44. Where Can We Apply Models In Green Chemistry? … N AT U R E, 4 6 9: 6 JA N 2 0 1 1
45. Models are cheaper N AT U R E, 4 6 9: 6 JA N 2 0 1 1 Is this experimental prediction or computational prediction?
46. … ^ a Chemist -"I think if you study-if you learn too much of what others have done, you may tend to take the same direction as everybody else"- Jim Henson
57. Pfizer Merck GSK Novartis Lilly BMS Could combining models give greater coverage of ADME/ Tox chemistry space and improve predictions? Lundbeck Allergan Bayer AZ Roche BI Merk KGaA Expanding computational model coverage of chemical space
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Hinweis der Redaktion
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
The process of ADME/tox can now be viewed as an iterative process were molecules may be assessed against many properties early on before selecting molecules for clinical trials. These endpoints may be complex like toxicity.