Publications

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

Kulkarni S, Lin B, Radhakrishnan R. Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics. Comput Chem Eng. 2025 Jul;198:109081. doi: 10.1016/j.compchemeng.2025.109081. Epub 2025 Mar 9. PMID: 40657536; PMCID: PMC12245191.

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.