ADVANCEMENTS AND APPLICATIONS OF DESIGN OF EXPERIMENTS (DOE) IN PHARMACEUTICAL PROCESS OPTIMIZATION: A QUALITY BY DESIGN (QbD) PARADIGM

Modern pharmaceutical manufacturing has shifted fundamentally from a reactive, empirical model to a proactive, science-driven architecture. Central to this evolution is Design of Experiments (DoE), a structured, mathematical methodology used to determine the relationships between multi-dimensional input factors and critical process outputs. This review outlines the historical transition from traditional One-Factor-at-a-Time (OFAT) experimentation to systematic DoE frameworks under global Quality by Design (QbD) initiatives. We explore the implementation workfiows of screening designs and optimization designs, their integration with International Council for Harmonisation (ICH) guidelines, and their application across unit operations like formulation, chromatography, scale-up, and advanced bioprocessing. Finally, we analyze the integration of DoE with modern artificial intelligence (AI), machine learning, digital twins, and real-time monitoring technologies to achieve robust, high-yielding process architectures in an Industry 4.0 landscape.