Experimental design and machine learning strategies for parameters screening and optimization of Hantzsch condensation reaction for the assay of sodium alendronate in oral solution

Abstract

An experimental design was adopted to attain the optimum reaction parameters of chemical derivatization of anhydrous sodium alendronate in an oral solution formula via Hantzsch condensation reaction. All reaction controlling variables, namely, time of reaction, temperature, reagent ratio and volume and buffer type, pH and volume were studied using the Plackett–Burman screening design to determine significant variables. Reaction temperature and pH of the buffer solution were found to be significant variables. Optimization was performed using the central composite design to get the optimum levels of these variables. Moreover, a comparison was made with artificial neural networks and support vector machines. The same results were obtained with low percentage relative error. After carrying out the spectrophotometric analysis, interferences from oral solution excipients were eliminated with a simple extraction procedure before measuring the absorbance at 340 nm. Satisfactory results of sample analysis were obtained and they were in good agreement with the label claim. A linear calibration graph of absorbance versus concentration was obtained with very low value of intercept and high value of correlation coefficient (0.9999) in the range of 2.44–34.10 μg mL⁻¹. The proposed spectrophotometric method was fully validated in accordance with ICH guidelines. Statistical comparison with a reported reference method showed similar results with respect to accuracy and precision.