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

Area of Science:

• Materials Science
• Nanotechnology
• Polymer Chemistry

Background:

• Polycaprolactone (PCL) shows promise for drug delivery but requires improved drug encapsulation.
• Blending PCL with less hydrophobic polymers offers a strategy to enhance physicochemical properties.
• Integrating polymer blending with Box-Behnken design (BBD) optimization addresses limitations in PCL-based nanoparticles.

Purpose of the Study:

• To develop PCL-based blend nanoparticles (NPs) with enhanced encapsulation efficiency (EE).
• To control particle size and improve stability through surface charge modulation.
• To optimize NP formulation using a BBD approach.

Main Methods:

• Drug-loaded blend NPs were fabricated using a double emulsion method with varying polymer ratios.
• A Box-Behnken design (BBD) was employed to identify key factors influencing NP size, charge, and EE.
• Statistical modeling was used to predict optimal formulation parameters.

Main Results:

• Blending PCL with a less hydrophobic polymer significantly increased EE, reaching 60.96% under optimal conditions.
• The BBD model accurately predicted conditions for optimal NP size, negative surface charge, and enhanced EE.
• Drug amount was the primary driver for EE, while polymer ratios significantly affected NP size and surface charge.

Conclusions:

• Controlled polymer ratios, drug loading, and surfactant concentrations are crucial for optimizing NP characteristics.
• A 50:50 PCL:PLGA blend demonstrated superior physicochemical performance.
• The BBD identified an optimal formulation predicting NPs with a size of 283.06 nm, zeta potential of -31.54 mV, and 70% EE.