The document discusses quantitative structure-activity relationship (QSAR) modeling of drug transporters. It summarizes the evolution of QSAR models for various transporters like P-glycoprotein (P-gp) and describes approaches like pharmacophore modeling that have been used to predict transporter substrates and inhibitors. Key challenges addressed are small datasets and evaluating model predictivity. The development and validation of Bayesian classification and quantitative pharmacophore models for transporters like the apical sodium-dependent bile acid transporter (ASBT) and organic cation transporter 2 (OCTN2) are also summarized.

1. Perspective on QSAR modeling of transport Sean Ekins Collaborations in Chemistry, Jenkintown, PA. Collaborative Drug Discovery, Burlingame, CA. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland. Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ.

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

4. In Silico Pharmacology- decision process Ekins et al., BJP 152, 21–37 (2007)

7. PXR agonists CYP3A4 substrates and inhibitors P-gp substrates and inhibitors hERG blockers Hydrophobic (ring aromatic) Positive ionizable (basic Nitrogen not essential) Hydrogen bond acceptors Hydrophobic Hydrogen bond acceptors Hydrogen bond donor (occasionally) Hydrophobic (ring aromatic) Hydrogen bond acceptors Hydrogen bond donor Positive ionizable Hydrophobic (ring aromatic) Hydrogen bond acceptors Hydrogen bond donor Pharmacophore Features How to avoid interaction with the protein Attaching hydrogen bonding groups on one of the hydrophobic features, adding larger more rigid groups as well as removing central H-bond acceptors Replace metabolically labile feature with a halogen Make molecule metabolically inactive renal excreted Increase polarity (ClogP <1) Removal of one of the interactions in the hERG pharmacophore Add other groups to structure – increase bulk Decrease lipophilicity logD 7.4 Decrease number of aromatic bonds Decrease ring aromatics Decrease molecular size Ekins S and Kortagere S,, in Scheiber J and Jenkins J.L., In-silico Methods for Adverse Effect Prediction in Preclinical Drug Discovery John Wiley and Sons. Hoboken, NJ. In Press 2011

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

15. 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

16. 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

18. 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

22. 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

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

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

27. 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

28. 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

29. Proactive database searching - Prioritize compounds for testing in vitro Understand drug interactions In silico allows rapid parallel optimization vs transporters or other properties See poster W3001 B. Astorga et al. Provide novel insights into the molecular interaction of inhibitors Repurpose - reposition FDA drugs 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