Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics
Sahil Kulkarni1, Benjamin Lin2,3, Ravi Radhakrishnan1,3
1University of Pennsylvania, Chemical and Biomolecular Engineering, Philadelphia, 19104, PA, USA.
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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