Advances in In silico predictive models for DDI prediction: Implications and practical applications in drug discovery

Advances in machine learning and artificial intelligence have recently extended to the quantitative prediction of drug-drug interaction (DDI). Because DDIs arise from diverse mechanisms and the required level of predictive accuracy varies with both the endpoint and the stage of drug development, evaluating their significance and deciding what is needed demand unusually broad expertise-ranging from fundamental biology all the way to state-of-the-art machine-learning methods. In this review, DMPK scientists with expertise in machine learning survey and critique the most recent literature covering the following DDI categories: Cytochrome P450 (CYP) substrates, CYP competitive and time-dependent inhibition, CYP induction, non-CYP substrates, non-CYP inhibition, transporter substrates, transporter inhibition, and cutting-edge predictive algorithms based on deep learning applied for the task of DDIs. For each category we summarize current in silico methodologies and their performance, and we provide expert opinions on how these tools can be optimally incorporated into contemporary drug-discovery workflows.