The objective of this investigation is to evaluate compositional variations and their interaction of the solid lipid nanoparticle (SLN) formulation of risperidone using response surface methodology of design of experiment (DOE) and subsequently, characterize the SLN by non-destructive methods of analysis. Box-Behnken DOE was constructed using drug (X(1)), lipid (X(2)) and surfactant (X(3)) level as independent factors. Compritol 888 ATO and sodium lauryl sulphate were used as lipid and surfactant, respectively. The SLN was prepared by solvent evaporation method and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), fourier infrared spectroscopy (FTIR), near infrared spectroscopy (NIR) and NIR-chemical imaging (NIR-CI). Responses measured were entrapment efficiency (Y(1)), D(90) (Y(2)), zeta potential (Y(3)), burst effect (Y(4)) and cumulative release in 8h (Y(5)). Statistically significant (p < 0.05) effect of X(1) on the Y(1), Y(2), Y(3) and Y(4) were seen. FTIR revealed no interaction between risperidone and compritol 888 ATO. TEM showed spherical and smooth surface SLN. Compritol retained its crystalline nature in the SLN formulation revealed by DSC and XRD studies. Homogenous distribution of risperidone and compritol 888 ATO was revealed by NIR-CI. Principal component analysis (PCA) and partial least square (PLS) were carried out on NIR data of SLN formulation. PLS showed correlation coefficient > 0.996 for prediction and calibration model of both risperidone and compritol 888 ATO. The accuracy of models in predicting risperidone and compritol 888 ATO were 1.60% and 11.27%, respectively. In conclusion, the DOE reveals significant effect of drug loading on SLN characteristics, and chemometric models based on NIR and NIR-CI data provided non-destructive method of estimation of components of SLN.
Published by Elsevier B.V.