ArticleInternational journal of clinical pharmacology and therapeutics2025
Prediction of silica nanoparticle biodistribution using a calibrated physiologically based model: Unbound fraction and elimination rate constants for the kidneys and phagocytosis identified as major determinants.
Article in International journal of clinical pharmacology and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Nanotechnology-Enabled Precision Therapy for Lung Cancer in Never-Smokers.Pharmaceutics · 2026Review
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Authors and funding
6 authors.
Funding
Abstract
objectivesThis study aimed to develop a minimal physiologically based pharmacokinetic (mPBPK) model to predict the biodistribution of silica nanoparticles (SiNPs) and evaluate how variations in surface charge, size, porosity, and geometry influence their systemic disposition. MATERIALS AND
methodsThe mPBPK model was calibrated using in vivo pharmacokinetic data from mice administered aminated, mesoporous, and rod-shaped SiNPs. Human data were collected from clinical trial data from Cornell dots. The mPBPK model incorporated physiological parameters and nanoparticle-specific characteristics to simulate SiNP biodistribution and was built in Matlab 2024a. Global sensitivity analysis identified influential parameters, including the unbound fraction and elimination rate constants for the kidneys and mononuclear phagocyte system (MPS). The model was extrapolated to predict human pharmacokinetics, with accuracy evaluated using Pearson correlation coefficients. Non-compartmental analysis (NCA) assessed organ-specific accumulation and biodistribution patterns.
resultsGlobal sensitivity analysis revealed that the unbound fraction and elimination rate constants for the kidneys and MPS were major determinants of SiNP biodistribution. NCA indicated that aminated SiNPs initially accumulated in the liver, spleen, and kidneys but redistributed due to their high unbound fraction, while mesoporous SiNPs localized in the lungs. Rod-shaped SiNPs exhibited high lung exposure. The extrapolated model showed high predictive accuracy, with Pearson correlation coefficients of 0.98 for mice and 0.99 for humans.
conclusionThe mPBPK model effectively predicts the pharmacokinetics of diverse SiNPs, offering insights to optimize nanoparticle-based drug delivery systems and facilitating their translation from preclinical models to clinical applications.
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