用深度学习提升药物动力学模型效率与准确性
Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling
- 将药代动力学建模转化为序列预测，引入基础PBPK变换器
- 生成符合生理约束的虚拟患者群体，生理违规率降至0.50%
- 融合图神经网络与神经微分方程，实现跨物种连续缩放
基于生理的药代动力学（PBPK）建模是模型指导药物研发（MIDD）的核心，提供预测药物吸收、分布、代谢和排泄（ADME）的机制框架。尽管其价值显著，但大规模模拟计算成本高、复杂生物系统参数识别难、种间外推不确定性大等问题限制了应用。本文提出一个统一的科学机器学习（SciML）框架，融合机制严谨性与数据驱动灵活性。提出三项贡献：（1）基础PBPK变换器，将药代动力学预测视为序列建模任务；（2）生理约束扩散模型（PCDM），利用物理信息损失生成符合生物学规律的虚拟患者群体；（3）神经异速生长法，结合图神经网络（GNN）与神经微分方程（Neural ODEs），学习连续跨物种缩放规律。在合成数据集上的实验表明，该框架在施加约束下将生理违规率从2.00%降至0.50%，同时为加速模拟提供了路径。
原文摘要 · Abstract (English)
Physiologically Based Pharmacokinetic (PBPK) modeling is a cornerstone of model-informed drug development (MIDD), providing a mechanistic framework to predict drug absorption, distribution, metabolism, and excretion (ADME). Despite its utility, adoption is hindered by high computational costs for large-scale simulations, difficulty in parameter identification for complex biological systems, and uncertainty in interspecies extrapolation. In this work, we propose a unified Scientific Machine Learning (SciML) framework that bridges mechanistic rigor and data-driven flexibility. We introduce three contributions: (1) Foundation PBPK Transformers, which treat pharmacokinetic forecasting as a sequence modeling task; (2) Physiologically Constrained Diffusion Models (PCDM), a generative approach that uses a physics-informed loss to synthesize biologically compliant virtual patient populations; and (3) Neural Allometry, a hybrid architecture combining Graph Neural Networks (GNNs) with Neural ODEs to learn continuous cross-species scaling laws. Experiments on synthetic datasets show that the framework reduces physiological violation rates from 2.00% to 0.50% under constraints while offering a path to faster simulation.
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