Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling

Longitudinal analyses of patient response time courses following doses of therapeutics are currently performed using pharmacokinetic/pharmacodynamic (PK/PD) methodologies, which require considerable human experience and expertise in the modelling of dynamical systems. By utilizing recent advancements in deep learning, we show that the governing differential equations can be learned directly from longitudinal patient data. In particular, we propose a novel neural-PK/PD framework that combines key pharmacological principles with neural ordinary differential equations. We applied it to an analysis of drug concentration and platelet response from a clinical dataset consisting of over 600 patients. We show that the neural-PK/PD model improves on a state-of-the-art model with respect to metrics for temporal prediction. Furthermore, by incorporating key PK/PD concepts into its architecture, the model can generalize and enable the simulations of patient responses to untested dosing regimens. These results demonstrate the potential of neural-PK/PD for automated predictive analytics of patient response time course. The response of the body to drugs follows complex dynamical processes that can be difficult to predict. Lu and colleagues combine a neural network approach with pharmacokinetic/pharmacodynamic modelling to learn these complex dynamics.