Shear Stress Modeling and Machine Learning for Nanomedicine Development: A Mechanistic Pathway to Pharmacokinetic Prediction

assays cannot resolve, with the cholesterol-rigidified bilayer of Doxil and the shear-activated platforms of Korin and colleagues providing representative positive control and mechanistic evidence. We synthesize recent progress at the interface of computational fluid dynamics and machine learning. Physics-informed neural networks, deep operator networks, and graph neural networks feature prominently in this synthesis and underpin an integrated framework for mechanistically informed pharmacokinetic prediction. Reported benchmarks show surrogate models reconstructing hemodynamic fields with under 5% error relative to high-fidelity CFD while reducing data requirements 5- to 10-fold, and convolutional surrogates trained on approximately 1800 patient-specific simulations predicting time-averaged wall shear stress with 2.5% mean absolute error. These surrogate models integrate hemodynamic descriptors (TAWSS, OSI, and RRT) with distributional particle features (D10, D50, and D90). We outline the evidentiary and methodological path toward biomechanically informed nanomedicine development aligned with emerging regulatory frameworks, while acknowledging that routine regulatory acceptance of computational shear modeling remains a future milestone rather than current practice.