Optimizing nanoparticle-mediated drug delivery: insights from compartmental modeling via the CompSafeNano cloud platform

Abstract

The deployment of nanoparticles (NPs) for targeted drug delivery in vivo holds immense potential for enhancing therapeutic efficacy while minimizing systemic side effects. However, the complexity of biological environments, including the biological barriers that need to be crossed for effective systemic delivery, presents significant challenges in optimizing NP delivery. This study demonstrates how a simple compartmental model facilitates the simulation and analysis of NP-mediated drug delivery, supporting targeted delivery optimization. The model involves reversible transport between five compartments related to drug delivery (administration site, off-target sites, target cell vicinity, target cell interior and excreta) that determine NP dynamics, including biodistribution, degradation, and excretion processes. This approach enables the estimation of delivery efficiency and the identification of critical factors affecting NP delivery through sensitivity analysis. A case study involving PEG-coated gold NPs delivered intravenously to the lungs demonstrates the model's capacity to describe observed biodistribution patterns and highlights key parameters influencing delivery outcomes. The model is exposed as a web application that provides a user-friendly graphical interface, enabling researchers to conduct in silico experiments with the goal of optimizing delivery strategies, thereby accelerating the development of precision nanomedicine. The model is made available both as a web application, via the Enalos Cloud Platform, and as a RESTful aaplication programming interface (API), providing a user-friendly graphical interface and programmatic access, respectively, enabling researchers to integrate the model into their own computational workflows. This study illustrates how simple compartmental modelling can be employed to guide the development of targeted drug delivery systems, contributing to more effective and personalized healthcare interventions.