Design of Experiments in the Formulation and Optimization of Sustained Release Matrix Tablets: A Review

Sustained release (SR) matrix tablets are widely employed oral drug delivery systems due to their formulation simplicity, cost-effectiveness, and ability to maintain therapeutic drug concentrations over extended periods. However, the development of robust SR matrix formulations is challenging, as drug release behavior is governed by multiple interrelated formulation and process variables, including polymer characteristics, drug physicochemical properties, excipient interactions, and manufacturing conditions. Conventional one-factor-at-a-time approaches are inefficient for such complex systems, as they fail to identify interaction effects and often lead to suboptimal formulations. Design of Experiments (DoE) has emerged as a systematic and statistically sound approach for the formulation and optimization of sustained release matrix tablets. This review provides a comprehensive overview of DoE concepts and their application in SR matrix tablet development. Various experimental design strategies, including full and fractional factorial designs, Central Composite Design, Box–Behnken Design, and Taguchi orthogonal arrays, are critically discussed with respect to their roles in screening critical variables, developing predictive models, and optimizing drug release profiles. Particular emphasis is placed on mechanistic insights derived from DoE-based studies, especially in understanding polymer hydration, gel layer formation, diffusion- and erosion-controlled release mechanisms, drug–polymer interactions, and tablet microstructure. The integration of DoE within the Quality by Design framework and its alignment with regulatory guidelines, such as ICH Q8(R2), Q9, and Q10, are also highlighted, demonstrating its importance in defining design space and ensuring consistent product quality. Furthermore, the review discusses key implementation challenges, including experimental complexity, scale-up considerations, and statistical interpretation, while outlining future perspectives involving artificial intelligence, machine learning, digital twins, and continuous manufacturing. Overall, this review underscores the essential role of DoE in enabling robust, predictive, and regulatory-compliant development of sustained release matrix tablets.