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

IF 3.9 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Chemical Engineering Pub Date : 2025-07-01 Epub Date: 2025-03-09 DOI:10.1016/j.compchemeng.2025.109081
Sahil Kulkarni , Benjamin Lin , Ravi Radhakrishnan
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Abstract

This study presents a multiscale modeling framework for simulating and predicting the behavior and biodistribution of nanoparticles (NPs), 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 (RBCFL) 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.
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机器学习实现了纳米颗粒边缘和基于生理的药代动力学的多尺度模型
本研究提出了一个多尺度模型框架,用于模拟和预测纳米颗粒(NPs)的行为和生物分布,重点研究了靶向药物递送等应用。该框架包含两个耦合模型:(1) DeepONet-enabled模拟和实验所得到的NP drift-diffusion血红层游离(RBCFL)预测NP着边和浓度配置文件以比容和容器半径为输入,建立在shear-induced血液流动的血行模型,(2)基于生理药代动力学(PBPK)模型,使用微脉管系统的预测浓度资料通知biodistribution NPs身体的不同器官。
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来源期刊
Computers & Chemical Engineering
Computers & Chemical Engineering 工程技术-工程:化工
CiteScore
8.70
自引率
14.00%
发文量
374
审稿时长
70 days
期刊介绍: Computers & Chemical Engineering is primarily a journal of record for new developments in the application of computing and systems technology to chemical engineering problems.
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