Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles of Hepatic Organic Anion Transporting Polypeptides (OATPs)

Alzbeta Tuerkova, Sankalp Jain, Barbara Zdrazil

Hepatocellular Organic Anion Transporting Polypeptides (OATP1B1, OATP1B3, and OATP2B1) are important for proper liver function and the regulation of the drug elimination process. Understanding their role in different conditions of liver toxicity and cancer requires an in depth investigation of hepatic OATP ligand interactions and selectivity. However, such studies are impeded by the lack of a crystal structure, the promiscuous nature of these transporters, and limited availability of reliable bioactivity data which is spread over different data sources in the open domain. To this end, we integrated ligand bioactivity data for hepatic OATPs from five open data sources (ChEMBL, UCSF-FDA TransPortal database, DrugBank, Metrabase, and IUPHAR) in a semi-automatic KNIME workflow. Highly curated data sets were analyzed with respect to enriched scaffolds and their activity profiles and interesting scaffold series providing indication for selective, dual, or pan-inhibitory activity toward hepatic OATPs could be extracted. In addition, a sequential binary modeling approach revealed common and distinctive ligand features for inhibitory activity toward the individual transporters. The workflows designed for integrating data from open sources, data curation, and subsequent substructure analyses are freely available and fully adaptable. The new data sets for inhibitors and substrates of hepatic OATPs as well as the insights provided by the feature and substructure analyses will guide future structure-based studies on hepatic OATP ligand interactions and selectivity.

Department of Pharmaceutical Chemistry
Journal of Chemical Information and Modeling
Publication date
Peer reviewed
Austrian Fields of Science 2012
Pharmaceutical chemistry, Computer aided design (CAD), Toxicology
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