A talk given at the Accelrys UGM in Jersey City May 17 2011

1. Using Discovery Studio For Modeling Human Drug Transporters Sean Ekins Collaborations in Chemistry, Jenkintown, PA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland.

2. Transporter models in ADME Absorption Distribution Excretion Metabolism Chang, C. and Swaan, P. in Ekins S, Computer Applications in Pharmaceutical Research and Development, pp495-512, 2006 PEPT1 ASBT OCTN2 NT MDR (P-gp, MRP, BCRP) OATP OAT OCT MCT NTCP BSEP BBB CHT PEPT1

3. Nature Reviews Drug Discovery 9 , 215–236 (1 March 2010)

10. MRP BCRP P-gp Molecule Databases In vitro testing hPEPT Transporter Pharmacophores or other model types Feedback of new substrates or inhibitors More Transporters - More Models 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

11. 95% 5% >95% 5% Bile acid pool: 3-5g Circulate 6-10 daily Total turnover rate: 20-30g Lost in feces: < 0.5g Enterohepatic Circulation Expressed at high levels in the terminal ileum where it mediates bile acid recovery. studies have implicated secondary bile acids as important in the development of colorectal cancer. hASBT inhibition results in increased colonic exposure to cytotoxic secondary bile acids. an association between a polymorphism in the SLC10A2 gene and the risk of colorectal adenomatous polyps. The human Apical Sodium-dependent Bile Acid Transporter Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009

13. Computational Models for ASBT - process 38 Bayesian models + validation with test sets Test set 1 N= 30 from same lab Test set 2 N = 19 from literature sources

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. Molecular function class fingerprints of maximum diameter 6 (FCFP_6), + simple interpretable descriptors leaving 20% out 100 times, ROC was 0.78; concordance 72.5%; specificity 81.0%; Sensitivity 58.1%. Bayesian and pharmacophore perform similarly > 80% correct with n= 30 test set All models perform poorly with literature test set Bayesian Model hASBT +ve -ve Dihydropyridine substructure Zheng X, et al., Mol Pharm, 6: 1591-1603, 2009

21. Possible Association between Clinical Rhabdomyolysis and hOCTN2 Inhibition Diao, Ekins, and Polli, Pharm Res, 26, 1890, (2009)

22. +ve -ve hOCTN2 quantitative pharmacophore and Bayesian model Diao et al., Mol Pharm, 7: 2120-2131, 2010 r = 0.89 vinblastine cetirizine emetine

23. 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

24. 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

25. hOCTN2 Substrates Data from Polli lab (conjugates) and literature 161 ± 50 Valproyl-glycolic acid-L-carnitine 58.5 ± 8.7 Ketoprofen-glycine-L-carnitine 77.0 ± 4.0 Ketoprofen-L-carnitine 257 ± 57 Naproxen-L-carnitine 132 ± 23 Valproyl-L-carnitine 53 Ipratropium 26 Mildronate 9 Acetyl-L-carnitine 5.3 L-carnitine Km (microM) Substrate

26. Substrate Common feature Pharmacophore ---Used CAESAR and excluded volumes Inhibitor Hypogen pharmacophore Overlap of pharmacophores RMSD 0.27 Angstroms hOCTN2 Pharmacophores

27. Proactive database searching - Prioritize compounds for testing in vitro In silico allows rapid parallel optimization vs transporters or other properties Partial overlap of OCTN2 of substrate and inhibitor pharmacophores Work continuing on 3 additional transporters with academic collaborators Discovery Studio Pharmacophore and Bayesian components enable fast model building and database searching – in vitro data generation is rate limiting step Provide novel insights into the molecular interactions with transporters Summing up

30. 2D Similarity search with “hit” from transporter screening Export database and use for 3D searching with a pharmacophore or other model for transporter Suggest approved drugs for testing - may also indicate other uses if it is present in more than one database Suggest in silico hits for in vitro screening Key databases of structures and bioactivity data FDA drugs database Could future transporter models help Repurpose FDA drugs