Application of statistical design to evaluate critical process parameters and optimize formulation technique of polymeric nanoparticles

In advanced medication, drug-loaded polymeric nanoparticles (NPs) appeared as a novel drug delivery system with lots of advantages over conventional medicines. Despite all the advantages, NPs do not gain popularity for manufacturing hurdles. The study focused on the formulation difficulties and implementation of statistical design to establish an effective model for manufacturing NPs. In this study, physico-chemical properties of the drug and polymer (PLGA) were incorporated to understand the mechanistic insights of nanoformulations. Primarily, the process controlling parameters were screened by Plackett–Burman design and the critical process parameters (Cpp) were further fabricated by Box–Behnken design (BBD). The TLM-PLGA-NPs (telmisartan loaded PLGA NPs) exhibited particle size, encapsulation efficiency and zeta potential of 232.4 nm, 79.21% and −9.92 mV respectively. The NPs represented drug loading of 76.31%. Korsmeyer–Peppas model (R(2) = 0.925) appeared to be the best fitted model for in vitro release kinetics of NPs. The model identified Fickian diffusion of TLM from the polymeric nanoparticles. The ANOVA results of variables indicate that BBD is a suitable model for the development of polymeric NPs. The study successfully identified and evaluated the correlation of significant parameters that were directly or indirectly influencing the formulations which deliberately produce desired nanoparticles with the help of statistical design.