RESPONSE SURFACE METHODOLOGY (RSM) AS A TOOL IN PHARMACEUTICAL FORMULATION DEVELOPMENT

Abstract

Response surface methodology (RSM) serves as a valuable tool in pharmaceutical formulation development, facilitating the optimization of drug formulations by systematically exploring the effects of multiple variables on desired responses. This methodology involves the design of experiments to generate mathematical models that predict the relationship between formulation parameters and critical quality attributes. By utilizing statistical techniques such as factorial design, central composite design, and Box-Behnken design, RSM enables the identification of optimal formulation conditions while minimizing the number of experimental trials. Across iterative experimentation and model refinement, RSM assists in understanding the complex interactions between formulation components, process variables, and product characteristics. In this review, we discuss the application of RSM in pharmaceutical formulation studies, highlighting its efficacy in optimizing drug delivery systems, enhancing product stability, and ensuring quality control. In addition, we explore recent advancements in RSM-driven approaches, including its integration with computational modeling and artificial intelligence techniques for enhanced formulation design and process optimization. Overall, RSM offers a systematic and efficient approach for developing robust pharmaceutical formulations, thereby accelerating the drug development process and improving therapeutic outcomes.