In Silico Prediction of Potential Bile Salt Export Pump Inhibitors Using Pharmacophore Models and Consensus Machine Learning

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 73% accuracy (8/11 correct predictions)

• Primary Outcome: BSEP inhibitor prediction accuracy

• Nnt Or Sensitivity: Sensitivity and specificity not explicitly reported for the validation set

• Confidence Interval: Not reported

Clinical Application

High feasibility as computational tool requiring minimal resources compared to experimental screening Relevant to TGA drug approval processes and pharmaceutical R&D in Australia; could support early-stage safety assessment before clinical trials Pharmaceutical researchers and drug developers screening for hepatotoxicity risk

Abstract

The ATP-dependent bile salt export pump (BSEP) is a transporter responsible for moving bile salts from hepatocytes into bile canaliculi. Inhibition of BSEP is a known risk factor of cholestatic-drug-induced liver injury (DILI) probably caused by the accumulation of toxic bile salts in the liver. Since DILI is a major cause for attrition during drug development and postmarketing withdrawal or black box warnings, an IC50 of BSEP higher than 25 μM is advised. Here, we describe the investigation of several in silico approaches to map potential BSEP inhibitors. A consensus machine learning classification model is presented, which accurately predicts BSEP inhibitors and can assist in the prioritization of in vitro testing. An applicability domain was derived for the model, and it was validated by in vitro measurements of predicted BSEP inhibitors where the model was able to correctly flag eight out of 11 compounds.