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基于机器学习的纳米颗粒边缘化和生理学药代动力学多尺度模型

Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics.

作者信息

Kulkarni Sahil, Lin Benjamin, Radhakrishnan Ravi

机构信息

University of Pennsylvania, Chemical and Biomolecular Engineering, Philadelphia, 19104, PA, USA.

University of Pennsylvania, Department of Biology, Philadelphia, 19104, PA, USA.

出版信息

Comput Chem Eng. 2025 Jul;198. doi: 10.1016/j.compchemeng.2025.109081. Epub 2025 Mar 9.

Abstract

This study presents a multiscale modeling framework for simulating and predicting the behavior and biodistribution of nanoparticles (), focusing on applications such as targeted drug delivery. The framework encompasses two coupled models: (1) a DeepONet-enabled Fokker-Planck equation to model the NP drift-diffusion in the red-blood cell-free layer () that predicts NP margination and concentration profiles taking hematocrit and vessel radius as inputs, built on top of a hemorheological model of shear-induced blood flow and (2) a physiologically based pharmacokinetic (PBPK) model that uses the predicted concentration profiles in microvasculature to inform the biodistribution of NPs across different organ in the body.

摘要

本研究提出了一个多尺度建模框架,用于模拟和预测纳米颗粒的行为及生物分布,重点关注靶向给药等应用。该框架包含两个耦合模型:(1)一个基于深度算子网络(DeepONet)的福克-普朗克方程,用于模拟无红细胞层中纳米颗粒的漂移扩散,该方程以血细胞比容和血管半径为输入,预测纳米颗粒的边缘化和浓度分布,其建立在剪切诱导血流的血液流变学模型之上;(2)一个基于生理的药代动力学(PBPK)模型,该模型利用微脉管系统中预测的浓度分布来确定纳米颗粒在体内不同器官中的生物分布。

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