Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks

• URL: http://arxiv.org/abs/2405.00166v1
• Date: Tue, 30 Apr 2024 19:31:31 GMT
• Title: Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
• Authors: Imran Nasim, Adam Nasim,
• Abstract summary: We introduce PKINNs, a novel purely data-driven neural network model. PKINNs efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures. The resulting models are both interpretable and explainable through Symbolic Regression methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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