NC state lecture jan 2012

1. Sean Ekins, M.Sc, Ph.D., D.Sc. Collaborations in Chemistry, Fuquay-Varina, NC. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland. 215-687-1320 [email_address] Computational Approaches to Toxicology

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.

5. Consider Absorption, Distribution, Metabolism, Excretion and Toxicology properties earlier in Drug Discovery Combine in silico, in vitro and in vivo data - Approach equally applicable to consumer products and getting information on chemicals. Ekins et al., Trends Pharm Sci 26: 202-209 (2005)

7. Why Use Computational Models For Toxicology ? Goal of a model – Alert you to potential toxicity, enable you to focus efforts on best molecules – reduce risk Selection of model – trade off between interpretability, insights for modifying molecules, speed of calculation and coverage of chemistry space – applicability domain Models can be built with proprietary, open and commercial tools software (descriptors + algorithms) + data = model/s Human operator decides whether a model is acceptable

9. The future: crowdsourced drug discovery Williams et al., Drug Discovery World, Winter 2009

10. Key enablers: Hardware is getting smaller 1930’s 1980s 1990s Room size Desktop size Not to scale and not equivalent computing power – illustrates mobility Laptop Netbook Phone Watch

15. L. Carlsson,et al., BMC Bioinformatics 2010, 11: 362 MetaPrint 2D in Bioclipse- free metabolism site predictor Uses fingerprint descriptors and metabolite database to learn frequencies of metabolites in various substructures

20. * * Maximum likelihood NHR phylogeny Ekins et al., BMC Evol Biol. 8(1):103 (2008) * * * * *

22. Human r=0.7 Zebrafish r=0.8 Mouse r=0.8 Rabbit r=0.8 Chicken r=0.7 TCDD (green) and 5  -pregnane-3,20-dione (grey) Ekins et al., BMC Evol Biol 8(1):103 (2008) Pharmacophores show PXR evolution Rat r=0.7

24. Pharmacophores, nuclear receptors and evolution

31. Fingolimod (Gilenya) for MS (EMEA and FDA) Paliperidone for schizophrenia Pirfenidone for Idiopathic pulmonary fibrosis Roflumilast for pulmonary disease Predictions for newly approved EMEA compounds Can we get DILI data for these?

33. hOCTN2 – Organic Cation transporter Pharmacophore Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

34. Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009) +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine

35. 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

36. 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

37. Could all pharmas share their data as models with each other? Increasing Data & Model Access Ekins and Williams, Lab On A Chip, 10: 13-22, 2010.

42. Merck KGaA Combining models may give greater coverage of ADME/ Tox chemistry space and improve predictions? Model coverage of chemistry space Lundbeck Pfizer Merck GSK Novartis Lilly BMS Allergan Bayer AZ Roche BI Merk KGaA

43. Ekins et al., Trends Pharm Sci 26: 202-209 (2005) Converging Technologies Ekins et al., Trends Pharm Sci 26: 202-209 (2005)

44. PathwayStudio Pathway / Network/ Database Software Available Ekins et al., in High Content Screening , Eds. Giuliano, Taylor & Haskin (2006)

47. Measure Xu JJ, Ekins S, McGlashen M and Lauffenburger D, in Ekins S and Xu JJ, Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools, P351-379, 2009. 4M Systems Biology Manipulate Model Mine

49. Green solvents App

50. Green Solvents App Bad Good www.scimobileapps.com

51. Mobile Apps for Drug Discovery Clark et al., submitted 2011

53. How Could Green Chemistry Benefit From These Models? Chem Rev. 2010 Oct 13;110(10):5845-82 … Nature 469, 6 Jan 2011