Formulation and Systematic Optimisation of Polymeric Blend Nanoparticles via Box–Behnken Design

Background/Objectives: Despite the advantages of polycaprolactone (PCL) for drug delivery, it still lacks effective approaches to enhance its encapsulation of drugs. Blending PCL with less hydrophobic polymers can tailor physicochemical properties to overcome these limitations. This study, for the first time, integrates two beneficial approaches—polymer blending and Box–Behnken design (BBD) optimisation—to develop PCL-based blend nanoparticles (NPs) with enhanced encapsulation efficiency (EE), controlled particle size, and improved stability through surface charge modulation. Methods: Drug-loaded blend NPs were developed using a double emulsion method, with different polymer ratios. A BBD model was employed to investigate the influential factors that control the size, charge, and EE. Results: Blending PCL with a less hydrophobic polymer significantly improved EE, achieving 60.96% under optimal conditions. The BBD model successfully predicted conditions for obtaining NPs with optimum size, negative charge, and enhanced drug encapsulation. The drug amount was identified as the most influential factor for EE, while polymer amounts significantly impacted size and charge. Conclusions: Careful control of polymer ratios, drug amount, and surfactant levels was shown to significantly influence particle size, surface charge, and EE, with the balanced 50:50 PCL:PLGA blend achieving optimal physicochemical performance. Using the BBD, the study identified the predicted optimal formulation consisting of 162 mg polymer blend, 8.37 mg drug, and 8% surfactant, which is expected to yield NPs with a size of 283.06 nm, zeta potential of −31.54 mV, and EE of 70%. The application of BBD allowed systematic evaluation of the factors and their interactions, providing robust predictive models.