Optimization of PLGA Nanoparticle Formulation via Microfluidic and Batch Nanoprecipitation Techniques

Abstract

Polymeric nanoparticles based on poly(lactic-co-glycolic acid) (PLGA) are widely used in drug delivery, yet scalable and reproducible production methods remain a major challenge. In this study, we combine experimental nanoprecipitation and computational fluid dynamics (CFD) modeling to optimize PLGA nanoparticle formulation using both traditional batch and microfluidic methods. While Design of Experiments (DoE) was used to optimize the batch process, microfluidic mixing was systematically explored by varying flow parameters such as the flow rate ratio (FRR) and total flow rate (TFR). We compared two microfluidic mixer designs with Y-junction and three-inlet junction geometries to evaluate their impact on the mixing efficiency and nanoparticle formation. Experimental results revealed that the three-inlet design produced smaller, more uniform nanoparticles with superior post-lyophilization stability. CFD simulations confirmed these findings by displaying velocity fields and PLGA concentration gradients, demonstrating significantly more homogeneous mixing and efficient interfacial contact in the three-inlet configuration. Furthermore, simulated outlet concentrations were used to predict the nanoparticle size via theoretical modeling, which closely agreed with the experimental data. This integrated approach highlights the importance of microfluidic geometry in controlling nanoparticle nucleation dynamics and provides a framework for rational design of scalable nanomedicine production systems.