Optimization of extended-release hydrophilic matrix tablets by support vector regression

In this work, support vector regression (SVR) was applied to the optimization of extended release from swellable hydrophilic pentoxifylline matrix-tablets and compared to multiple linear regression (MLR).

Binary mixtures comprising ethylcellulose and sodium alginate were used as the matrix-former. The matrix-former : drug weight ratio and the percentage of sodium alginate in the matrix-former were the formulation factors (independent variables) and the percentages of drug release at four different time intervals were the responses (dependent variables). Release was determined according to United States Pharmacopeia 31 for 11 pentoxifylline matrix-tablet formulations of different independent variable levels and the corresponding results were used as tutorial data for the construction of an optimized SVR model. Six additional checkpoint matrix-tablet formulations, within the experimental domain, were used to validate the external predictability of SVR and MLR models.

It was found that the constructed SVR model fitted better to the release data than the MLR model (higher coefficients of determination, R( 2), lower prediction error sum of squares, narrower range of residuals, and lower mean relative error), outlining its advantages in handling complex nonlinear problems. Superimposed contour plots derived by using the SVR model and describing the effects of polymer and sodium alginate content on pentoxifylline release showed that formulation of optimal release profiles, according to United States Pharmacopeia limitations, could be located at drug : matrix ratio of 1 and sodium alginate content 25% w/w in the matrix-former.

The results indicate the high potential for SVR in formulation development and Quality by Design.