摘要：SummaryForecasting pharmacokinetics (PK) for individual patients is a fundamental problem in clinical pharmacology. One key challenge is that PK models constructed using data from one dosing regimen must predict PK data for different dosing regimen(s). We propose a deep learning approach based on neural ordinary differential equations (neural-ODE) and tested its generalizability against a variety of alternative models. Specifically, we used the PK data from two different treatment regimens of trastuzumab emtansine. The models performed similarly when the training and the test sets come from the same dosing regimen. However, for predicting a new treatment regimen, the neural-ODE model showed substantially better performance. To date, neural-ODE is the most accurate PK model in predicting untested treatment regimens. This study represents the first time neural-ODE has been applied to PK modeling and the results suggest it is a widely applicable algorithm with the potential to impact future studies.Graphical abstractDisplay OmittedHighlights•We describe the first application of neural-ODE to pharmacokinetics modeling•The model is accurate and predicts well across dosing regimens•The model generalizes well compared to alternate machine and deep learning modelsPharmacological parameters; Bioinformatics; Pharmacoinformatics; Machine learning
