Drug safety assessment by machine learning models

ABSTRACT

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment.

KEYWORDS:

• Machine learning
• random forest
• drug safety assessment
• prediction of Torsades de pointes

Disclosure statement

No potential conflict of interest was reported by the authors(s).

Disclaimer

This paper reflects the views of the authors and should not be construed to represent FDA’s views or policies.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10543406.2024.2365976.

Additional information

Funding

This project was supported in part by an appointment to the Research Participation program at the U.S. Food and Drug Administration administered by the Oak Rage Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration.