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In CPT: pharmacometrics & systems pharmacology

The gold-standard approach for modelling pharmacokinetic mediated drug-drug interactions is the use of physiologically based pharmacokinetic modelling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system specific parameters. Machine learning has the potential to be utilised for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalisability by learning from multi-case historical data and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable and require a smaller sample size of data, though insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data is available. A combined approach of using mechanistic models to highlight features that can be used for training machine learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations and compare machine learning to mechanistic modelling for drug-drug interaction risk assessment across the stages of drug discovery and development.

Gill Jaidip, Moullet Marie, Martinsson Anton, Miljković Filip, Williamson Beth, Arends Rosalinda, Pilla Reddy Venkatesh