Optimization of protein loaded PLGA nanoparticle manufacturing parameters following a quality-by-design approach
Vanessa Sainz, Carina Peres, T. Ciman, C. Rodrigues, A. S. Viana, C. A. M. Afonso, T. Barata, S. Brocchini, M. Zloh, Rogério S. Gaspar, Helena F. Florindo and João A. Lopes


This paper discusses the development of a multivariate-based regression model for estimating the critical
attributes to establish a design-space for poly(lactic-co-glycolic acid) (PLGA) nanoparticles formulated by
a double emulsion–solvent evaporation method. A three-level, full factorial experimental design is used to
assess the impact of three different manufacturing conditions (polymer viscosity, surfactant concentration
and amount of model antigen ovalbumin) on five critical particle attributes (zeta potential, polydispersity
index, hydrodynamic diameter, loading capacity and entrapment efficiency). The optimized formulation was
achieved with a viscosity of 0.6 dl g1, a surfactant concentration of 11% (w/v) in the internal phase and
2.5% (w/w) of ovalbumin. The design-space that is satisfied for nanoparticles with the targeted attributes
was obtained with a polymer viscosity between 0.4 and 0.9 dl g1, a surfactant concentration ranging from
8 to 15% (w/v) and 2.5% (w/w) of ovalbumin. The nanoparticles were spherical and homogenous and were
extensively taken up by JAWS II murine immature dendritic cells without affecting the viability of these
phagocytic cells. Better understanding was achieved by multivariate regression to control process
manufacturing to optimize PLGA nanoparticle formulation. Utilization of multivariate regression with
a defined control space is a good tool tomeet product specifications, particularly over a narrowvariation range.


Royal Society of Chemistry