Data-Driven Prediction of Nanoparticle Biodistribution from Physicochemical Descriptors.

ABSTRACT: Nanoparticles have gained significant attention in biomedicine, electronics, and environmental science due to their unique physicochemical properties, which critically influence their absorption, distribution, metabolism, and excretion behavior in biological systems. However, predicting nanoparticle biodistribution and pharmacokinetics remains challenging due to the complexity of biological systems and the reliance on animal-derived data for physiologically based pharmacokinetic (PBPK) modeling. To address these limitations, this study integrates PBPK modeling with quantitative structure-activity (QSAR) relationship principles and multivariate linear regression (MLR) to develop a predictive framework for nanoparticle biodistribution based solely on physicochemical properties, using biodist

SUBMITTER: Wu J 

PROVIDER: S-EPMC12312149 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature