Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles...

Area of Science:

• Pharmacology
• Biochemistry
• Drug Discovery

Background:

• Hepatocellular organic anion transporting polypeptides (OATPs), specifically OATP1B1, OATP1B3, and OATP2B1, are crucial for liver function and drug metabolism.
• Investigating OATP roles in liver toxicity and cancer necessitates understanding hepatic OATP-ligand interactions and selectivity.
• Current research is hindered by a lack of crystal structures, transporter promiscuity, and fragmented bioactivity data.

Purpose of the Study:

• To integrate and curate ligand bioactivity data for hepatic OATPs from multiple open-source databases.
• To analyze enriched scaffolds and identify selective, dual-, or pan-inhibitory activities.
• To reveal common and distinctive ligand features for inhibitory activity against individual hepatic OATPs.

Main Methods:

• A semiautomatic KNIME workflow was developed to integrate data from ChEMBL, UCSF-FDA TransPortal, DrugBank, Metrabase, and IUPHAR.
• Curated datasets were analyzed for enriched scaffolds and activity profiles.
• Sequential binary modeling was employed to identify ligand features associated with inhibitory activity.

Main Results:

• Highly curated datasets of hepatic OATP inhibitors and substrates were generated.
• Enriched scaffolds indicating selective, dual-, or pan-inhibitory activity were identified.
• Common and distinct ligand features influencing inhibitory activity toward OATP1B1, OATP1B3, and OATP2B1 were revealed.

Conclusions:

• The developed data integration and analysis workflows are freely available and adaptable for future research.
• The generated datasets and analytical insights will facilitate structure-based studies on hepatic OATP-ligand interactions.
• This work provides a foundation for understanding and modulating OATP function in liver health and disease.