The document discusses applying computational models to problems in toxicology, drug discovery, and beyond. It summarizes recent work using machine learning models and other in silico techniques to predict drug-induced liver injury (DILI) and interactions with transporters like hOCTN2. Models were able to classify compounds as DILI-positive or negative with over 75% accuracy when tested on external datasets. The techniques discussed could help prioritize compounds for further testing and filter libraries to avoid reactive or toxic features.

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?

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

5. Pharma reached a productivity tipping point Cost of drug development high Failure in clinic due to toxicity How to predict earlier

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

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).

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

22. Training vs test set PCA Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010 Yellow = test Blue = training

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

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

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

38. Khandelwal et al., Chem Res Toxicol, 21:1457-67 (2008)

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

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

47. Some observations

48. Computational modeling – from simple to complex models with more data Ekins et a., Xenobiotica, 37:1152-1170, 2007

49. Need to think about more than one property at a time Multi-objective optimisation Ekins, Honeycutt and Metz, Drug Disc Today, 15: 451-460, 2010

50. Abbott – evolving molecules using ADME multi-objective optimization Ekins, Honeycutt and Metz, Drug Disc Today, 15: 451-460, 2010

51. Could Green Chemistry Modeling Benefit from Collaboration? Modelers Modelers Modelers Modelers

53. More collaborations, integrating models into scientific social networks Drug Disc Today, 14: 261-270, 2009

54. Could all pharmas share their data as models with each other?

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