Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).

Area of Science:

• Pharmacology
• Biochemistry
• Computational Biology

Background:

• Human Organic Anion Transporter 4 (OAT4) is primarily in kidney proximal tubules, mediating organic anion transport.
• OAT4 plays a significant role in drug-drug interactions (DDIs) due to its involvement in handling various compounds.

Purpose of the Study:

• To identify novel inhibitors of OAT4 and understand OAT4-compound interactions.
• To build predictive models for OAT4 inhibition and potential DDIs.

Main Methods:

• In vitro inhibition screening of over 1400 small molecules against OAT4 using 6-carboxyfluorescein (6-CF) uptake in CHO cells.
• Determination of IC50 values for selected compounds against OAT4 substrates (estrone sulfate, ochratoxin A, 6-CF).
• Development and comparison of machine learning models for predicting OAT4 inhibition.

Main Results:

• Identified several novel OAT4 inhibitors, some not previously reported.
• Established IC50 values and compared them with FDA DDI guidance.
• Developed and validated machine learning models for predicting OAT4 inhibitor activity.

Conclusions:

• The study provides valuable experimental and computational data for understanding OAT4-mediated DDIs.
• Novel OAT4 inhibitors were discovered, expanding the toolkit for DDI research.
• Machine learning models show promise in predicting OAT4 inhibition and facilitating DDI risk assessment.