Gelatin nanoparticles for NSAID systemic administration: Quality by design and artificial neural networks implementation

The present study evaluates the preparation of systemic administrated NSAID gelatin nanoparticles with the aid of quality by design and artificial neural networks (ANNs). Specifically, two different preparation techniques (i.e. nanoprecipitation and two-step desolvation) were implemented for the formulation of diclofenac sodium (DLC) gelatin nanoparticles (GNs). Preliminary screening experiments showed that in the case of nanoprecipitation the best compromise (in terms of achieving both small particle size and high encapsulation efficiency) was the use of poloxamer 407 (as stabilizer) and acetone (as non-solvent), while in the case of two-step desolvation significant effect had the use of acetone, gelatin type and bloom number (type B with bloom 150 was selected for further evaluation). Implementation of a central composite experimental design (CCD), showed that in the case of nanoprecipitation the optimum formulation can be achieved at high poloxamer, high gelatin and moderate to high glutaraldehyde (GTA used for crosslinking) concentrations, while in the case of two-step desolvation high gelatin and GTA concentrations are needed. Artificial neural networks (ANN) implementation showed significantly improved prediction ability compared to MLR, while verification experiments showed good agreement between the ANN predicted and the experimentally obtained results. SEM analysis of the optimum suggested formulations showed nanoparticles with smooth surface, while powder X-ray diffraction (XRD) analysis showed the formation of amorphously dispersed systems, and Fourier transform infrared spectroscopy (FTIR) revealed the presence of molecular interactions irrespectively of the preparation method followed. A slightly faster release profile was observed in the case of nanoprecipitation based GNs, while all formulations followed biphasic release profile.