A physics-informed neural network framework for quantitative analysis of transcytosis and physical diffusion in an in vitro BBB

Nanoparticles traverse the blood-brain barrier (BBB) through passive diffusion and vesicular transcytosis, but the quantitative contributions of these routes remain difficult to determine. Here, we combine a controlled in-vitro human BBB model (hCMEC/D3 Transwells) with a physics-informed neural network (PINN) to interpret transport kinetics and estimate paracellular and vesicular components. Monodisperse polystyrene nanoparticles (20, 50 and 120 nm) showed low polydispersity, stable ζ-potential and minimal cytotoxicity. Intact monolayers displayed high TEER and low tracer permeability, whereas TNF-α induced reversible junctional opening. Apical-to-basolateral transport increased with junctional loosening and remained size-dependent; clathrin and dynamin inhibition reduced flux without altering TEER or tracer passage. A mass-balance-constrained PINN incorporating a TEER-linked permeability term reproduced transport profiles and generalized to combined perturbation (TNF-α + chlorpromazine). Under our conditions, the model suggested that vesicular uptake represented the major route, with a smaller diffusion component that increased during junctional disruption and clathrin inhibition. Overall, this combined experimental-computational approach provides a practical framework for pathway-informed evaluation of nanoparticle transport across the BBB.