Low Dimensional Neural Ode Representations of Multi-Dose Pharmacokinetics

Overview

Pharmacokinetic (PK) modeling plays a crucial role in developing and optimizing drug therapies, enabling the prediction of drug concentration in the plasma over time and thus informing dosage regimens for maximum efficacy with minimal toxicity. Traditional PK models, while useful, often rely on discrete and linear representations that may not fully capture the complex dynamics of drug interaction within the human body. This limitation is particularly evident in scenarios involving multiple doses where the interactions between successive doses can significantly influence overall drug behavior. Neural Ordinary Differential Equations (NODEs) provide a novel computational approach by modeling the continuous dynamics of biological systems through a deep learning framework. Unlike traditional discrete models, NODEs integrate the derivative of the state with respect to time, offering a flexible and powerful tool for continuously simulating biological processes. This study presents a NODE-based model specifically tailored for the pharmacokinetics of multi-dose drug administration. The proposed NODE model utilizes a single-layer neural network with 50 neurons in the hidden layer, focusing exclusively on drug concentration without direct time input. This design ensures that the model remains invariant to shifts in time, enhancing its applicability across varied clinical settings. Simulated pharmacokinetic profiles with double dosing were generated, incorporating Gaussian noise to mimic real-world measurement variations and biological variability. After 1000 epochs, the NODE demonstrated high accuracy in predicting pharmacokinetic profiles, indicating its potential as a robust tool in pharmacology. The success of this NODE model in accurately simulating double-dose scenarios showcases its capability to enhance the precision of drug dosing guidelines, ultimately improving patient outcomes by tailoring therapies to individual pharmacokinetic responses.