Dynamic Graph Neural Network for Data-Driven Physiologically Based Pharmacokinetic Modeling
This provides a scalable, equation-free alternative to traditional physiologically based pharmacokinetic modeling for drug development in preclinical and clinical research, though it is incremental as it builds on existing graph neural network methods.
The study tackled the problem of predicting drug concentration dynamics across organs in pharmacokinetic modeling by proposing a Dynamic Graph Neural Network that models physiological connections as recurrent message-passing processes, achieving an R^2 of 0.9342, RMSE of 0.0159, and MAE of 0.0116, outperforming MLP and LSTM baselines.
Physiologically Based Pharmacokinetic (PBPK) modeling plays a critical role in drug development by predicting drug concentration dynamics across organs. Traditional approaches rely on ordinary differential equations with strong simplifying assumptions, which limit their adaptability to nonlinear physiological interactions. In this study, we explore data-driven alternatives for PBPK prediction using deep learning. Two baseline architectures - a multilayer perceptron (MLP) and a long short-term memory (LSTM) network - are implemented to capture molecular and temporal dependencies, respectively. To incorporate inter-organ interactions, we propose a Dynamic Graph Neural Network (Dynamic GNN) that models physiological connections as recurrent message-passing processes between organs. Experimental results demonstrate that the proposed Dynamic GNN achieves the highest predictive performance among all models, with an R^2 of 0.9342, an RMSE of 0.0159, and an MAE of 0.0116. In comparison, the MLP baseline obtains an R^2 of 0.8705 and the LSTM achieves 0.8059. These results highlight that explicitly modeling the spatial and temporal dependencies of organ interactions enables more accurate and generalizable drug concentration prediction. The Dynamic GNN provides a scalable, equation-free alternative to traditional PBPK formulations and demonstrates strong potential for data-driven pharmacokinetic modeling in preclinical and clinical research.