Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens.

ABSTRACT: Forecasting 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

SUBMITTER: Lu J 

PROVIDER: S-EPMC8283337 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature