Identification of bile salt export pump inhibitors using machine learning: Predictive safety from an industry perspective

Artificial Intelligence in the Life Sciences

Grégori Gerebtzoff¹  Raquel Rodríguez-Pérez² 

[1] Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002 Basel, Switzerland;Corresponding author.;

关键词: Predictive safety;    Machine learning;    Model evaluation;    Decision-making;    Drug-induced liver injury (DILI);    Liver toxicity;   

DOI  :  

来源: DOAJ

【 摘 要 】

Bile salt export pump (BSEP) is a transporter that moves bile salts from hepatocytes into bile canaliculi. BSEP inhibition can result in the toxic accumulation of bile salts in the liver, which has been identified as a risk factor of drug-induced liver injury (DILI). Since DILI is a frequent cause of drug withdrawals from the market or failings in drug development, in vitro BSEP activity is measured with the [3H]taurocholate uptake assay and a half-maximal inhibitory concentration (IC50) higher than 30 µM is advised. Herein, a machine learning classification model was developed to accurately detect BSEP inhibitors and help in the prioritization of in vitro testing. Regression models for the numerical prediction of IC50 values were also generated. Classification and regression models for BSEP inhibition have been evaluated on realistic settings, which is critical prior to ML-based decision making in drug discovery programs. This work illustrates how predictive safety can help in early toxicity risk assessment and compound prioritization by leveraging Novartis historical experimental data.

【 授权许可】

Unknown