Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
This addresses the time-consuming model derivation problem in drug discovery, offering a potential enhancement for model-informed approaches with large datasets.
The study tackled the labor-intensive process of deriving pharmacometric models by introducing PKINNs, a data-driven neural network that discovers multi-compartment structures and forecasts derivatives, making models interpretable through Symbolic Regression.
Pharmacometric models are pivotal across drug discovery and development, playing a decisive role in determining the progression of candidate molecules. However, the derivation of mathematical equations governing the system is a labor-intensive trial-and-error process, often constrained by tight timelines. In this study, we introduce PKINNs, a novel purely data-driven pharmacokinetic-informed neural network model. PKINNs efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures, reliably forecasting their derivatives. The resulting models are both interpretable and explainable through Symbolic Regression methods. Our computational framework demonstrates the potential for closed-form model discovery in pharmacometric applications, addressing the labor-intensive nature of traditional model derivation. With the increasing availability of large datasets, this framework holds the potential to significantly enhance model-informed drug discovery.