  The longitudinal analysis of patient response time course following doses of
therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic
(PK/PD) methodologies, which requires significant human experience and
expertise in the modeling of dynamical systems. By utilizing recent
advancements in deep learning, we show that the governing differential
equations can be learnt 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
upon 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.
