The Application of Response Surface Methodology (RSM) In the Computational Optimization of Sustained Release (SR) For Phenothiazine Derivative Matrix Tablet

Abstract

Introduction: This study explores the use of Response Surface Methodology (RSM), a statistical optimization technique, to optimize the SR properties of prochlorperazine maleate (PCM) matrix tablets. PCM is a phenothiazine derivative used for treating schizophrenia, nausea, and vomiting. Sustained-release formulations offer extended drug delivery, potentially improving patient compliance and reducing side effects. RSM helps identify optimal combinations of critical formulation factors influencing drug release, such as polymer type and concentration, filler type, and drug/polymer ratio. The study likely involves designing experiments based on chosen RSM designs (e.g., Box-Behnken) with varying factor levels. Formulate SR tablets with different factor combinations. Evaluating the drug release profiles of each tablet formulation. Analyzing data using RSM software to build mathematical models relating factors to drug release and identifying optimal factor combinations that maximize desired release characteristics. Objective: The ongoing research purpose to improve the advancement of a sustained release tablet containing Phenothiazine derivative PCM loaded matrix. This is achieved by utilizing DoE as a computational method to statistically validate the formulation. Methodology: Basically two methods involved in the formulation using direct compression; a simple and efficient method where all dry ingredients are blended and directly compressed into tablets. Results: The obtained outcomes closely matched the anticipated values derived from the experimental setup. The enhanced PCM matrix tablets demonstrated a prolonged and uniform discharge of PCM over a span of 6 hours. Conclusion: This study Based on a 23 FD, response surface methodology was used to successfully develop the current research of PCM matrix tablets for sustained release application. The corresponding contour plots and 3D response methodology represent the important variables for the optimisation process.