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. 2025 Jul:198:109081.
doi: 10.1016/j.compchemeng.2025.109081. Epub 2025 Mar 9.

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

Sahil Kulkarni  1 , Benjamin Lin  2   3 , Ravi Radhakrishnan  1   3
Affiliations

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

Sahil Kulkarni et al. Comput Chem Eng. 2025 Jul.

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.

Keywords: DeepONet; Fokker–Planck; Hematocrit; Margination; Nanoparticles; PBPK.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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