Development, Statistical Optimization and Characterization of Fluvastatin Loaded Solid Lipid Nanoparticles: A 32 Factorial Design Approach

Abstract

The purpose of the present research work was to design, optimize, and evaluate fluvastatin-loaded solid lipid nanoparticles (FLV-SLNPs) using 32 factorial design for enhancing the bioavailability. Fluvastatin has several disadvantages, including the low solubility and substantial first-pass metabolism resulting in a low (30%) bioavailability and a short elimination half-life. FLV-SLNPs were prepared using the nano-emulsion technique. For the optimization of the FLV-SLNPs, a total of nine formulations were prepared by varying two independent factors at three levels, using full factorial design. In this design, lipid (A) and surfactant (B) concentrations were chosen as independent factors, whereas entrapment efficiency (Y1) and in-vitro drug release (Y2) were selected as the dependent variables. Additionally, the prepared SLNPs were characterized for X-ray diffraction, Fourier transform-infrared spectroscopy, and differential scanning calorimetry. These studies revealed that there were no interactions between the drug and the selected excipients and the selected formulation components are compatible with the drug. Pharmacokinetic studies in rats confirmed significant improvement in AUC and MRT of SLNPs in comparison with the pure drug indicating the enhanced bioavailability of SLNPs. This study provides a proof-of-concept for the fact that SLNPs can be effectively developed via experimental factorial design, which requires relatively minimal experimentation.

Formulation, Optimization and Evaluation of Solid Dispersion of Deferasirox Using Factorial Design

“…This design was adopted to optimize SD-Deferasirox formulations using the Design-Expert Software. 23 In this design, the experimental trials were conducted with all the 9 possible combinations. The drug-to-polymer ratio and solvents ratio were considered as independent variables and percent yield, saturation solubility, and drug content as dependent variables.…”

Development, Characterization and Optimization of N-acetylcysteine-loaded Solid Lipid Particles: In-vitro and In-vivo Study

“…After performing the necessary dilutions, the supernatant was separated, and its content of NCA was determined using a UV spectrophotometer. 18 This was done in triplicate. The %EE can be derived using the equation: (EE) (%) = (Concentration of NCA in supernatant/ Initial NCA concentration)*100…”

“…The FT-IR spectrophotometer 18 was utilized to conduct the spectral study (PerkinElmer Spectrum One, Waltham, Massachusetts, USA). Using a resolution of 4 cm -1 , the samples were analyzed using a scanning range from 4000 to 400 cm -1 .…”

“…DSC analysis was performed on the natural drug and additional excipients to evaluate whether they were compatible with the medication. 18 The specific heat and transition enthalpies are determined using a differential scanning calorimeter. The area directly measures the heat of transition beneath the acquiring curve.…”

Design and Optimization of Solid Lipid Nanoparticles Loaded with Triamcinolone Acetonide

“…The identification of factors involved in a measured response is conducted by performing a variable number of experiments, which can be modified according to the desired resolution in the tested experimental space. The most basic approach to experimental design consists in performing a full factorial design, in which the number of factors studied is tested at two levels, usually codified as +1 (higher value) and −1 (lower value), for two-level designs, and −1, 0, and +1 for three-level designs [6,7]. This implies that the total number of runs to be performed varies as 2 n or 3 n , and, for this reason, full factorial designs are usually implied for screening a minor number of variables.…”