In Vitro Release Kinetics and Mathematical Modeling of Ribes nigrum Extract-Loaded PLGA/Chitosan Nanoparticles

ObjectiveThe objective of this study was to improve the controlled release performance of Ribes nigrum L. (blackcurrant; RN) extract by encapsulating it within polymer-based nanoparticulate delivery systems and to evaluate their in vitro release kinetics.SignificanceNanoparticulate delivery systems offer improving stability and sustained release of plant-derived bioactive compounds with limited stability or bioavailability. Understanding release mechanisms through kinetic modeling is critical for rational formulation design, particularly for future mucosal delivery applications requiring prolonged local release.MethodThree RN-loaded nanoparticle formulations were prepared: chitosan-based nanoparticles (RN-CS), PLGA-based nanoparticles (RN-PLGA), and chitosan-coated PLGA nanoparticles (CS-RN-PLGA). Particle size, polydispersity index, and zeta potential were characterized using dynamic light scattering and electrophoretic mobility measurements. In vitro release studies were conducted over extended periods, and release profiles were fitted to eight kinetic models: Zero-order, First-order, Higuchi, Korsmeyer-Peppas, Weibull, Baker-Lonsdale, Hixson-Crowell, and Peppas-Sahlin.ResultsThe CS-RN-PLGA formulation demonstrated the most favorable sustained release profile, achieving approximately 90% cumulative release over 300 h. This formulation exhibited near zero-order release behavior (R² = 0.9796) and followed anomalous transport kinetics, indicating a combined contribution of diffusion and polymer relaxation mechanisms.ConclusionThese findings demonstrate the value of comprehensive kinetic modeling in the design of nanoparticulate delivery systems and indicate that RN-loaded hybrid polymeric nanoparticles may be promising candidates for sustained-release mucosal delivery platforms. Further biological, stability, and in vivo studies are required to evaluate their therapeutic relevance and safety under physiologically relevant conditions.