A Comparative Mathematical Analysis of Drug Release from Lipid-Based Nanoparticles

Mathematical modeling of drug release from drug delivery systems is crucial for understanding and optimizing formulations. This research provides a comparative mathematical analysis of drug release from lipid-based nanoparticles. Drug release profiles from various types of lipid nanoparticles, including liposomes, nanostructured lipid carriers (NLCs), solid lipid nanoparticles (SLNs), and nano/micro-emulsions (NEMs/MEMs), were extracted from the literature and used to assess the suitability of eight conventional mathematical release models. For each dataset, several metrics were calculated, including the coefficient of determination (R²), adjusted R², the number of errors below certain thresholds (5%, 10%, 12%, and 20%), Akaike information criterion (AIC), regression sum square (RSS), regression mean square (RMS), residual sum of square (rSS), and residual mean square (rMS). The Korsmeyer-Peppas model ranked highest among the evaluated models, with the highest adjusted R² values of 0.95 for NLCs and 0.93 for other liposomal drug delivery systems. The Weibull model ranked second, with adjusted R² values of 0.92 for liposomal systems, 0.94 for SLNs, and 0.82 for NEMs/MEMs. Thus, these two models appear to be more effective in forecasting and characterizing the release of lipid nanoparticle drugs, potentially making them more suitable for upcoming research endeavors.