Optimization of nanovesicular carriers of a poorly soluble drug using factorial design methodology and artificial neural network by applying quality by design approach

Abstract

The current work aims to utilize a quality by design (QbD) approach to develop and optimize nanovesicular carriers of a hydrophobic drug. Rosuvastatin calcium was used as a model drug, which suffers poor bioavailability. Several tools were used in the risk assessment study as Ishikawa diagrams. The critical process parameters (CPP) were found to be the particle size, polydispersity index, zeta potential, and entrapment efficiency. A factorial design was used in risk analysis, which was complemented with an artificial neural network (ANN); to assure its accuracy. A design space was established, with an optimized nanostructured lipid carrier formula containing 3.2% total lipid content, 0.139% surfactant, and 0.1197 mg % drug. The optimized formula showed a sustained drug release up to 72 h. It successfully lowered each of the total cholesterol, low-density lipoprotein, and triglycerides and elevated the high-density lipoprotein levels, as compared to the standard drug. Thus, the concurrent use of the factorial design with ANN using the QbD approach permitted the exploration of the experimental regions for a successful nanovesicular carrier formulation and could be used as a reference for many nanostructured drug delivery studies during their pharmaceutical development and product manufacturing.

Keywords:

• Quality by design
• neural networks
• drug delivery systems
• lipid nanoparticles
• factorial design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.