Advanced Experiment Design Strategies for Drug Development

The design of experiments (DOE) has been introduced to pharmaceutical research to ensure quality drug products under the framework of quality by design. DOE has critical roles in systematic investigation and optimization of pharmaceutical formulation, processing, and analytical method development. With the emergence of artificial intelligence (AI), this review provides a comprehensive overview of traditional and advanced experiment design strategies, drawing attention to methodologies as applied in drug development literature. Classical approaches such as factorial, screening, response surface, mixture, Taguchi, and optimal designs were discussed, highlighting their use in pharmaceutical applications. In addition, advanced strategies, including Bayesian optimization and active learning, are reviewed for their promising application in efficient process optimization and data acquisition for machine learning modeling. Utilizing an AI‐assisted literature screening tool, we analyzed 592 pharmaceutical publications from 2020 to 2024. DOE methodologies, purposes, and application areas of collected literature were manually extracted and curated for analysis. Current research gaps at the transitioning period of AI‐guided experimentation have been identified. We conclude by outlining future directions for experiment design to accelerate drug development research, emphasizing the need for more efficient, accessible, and customized strategies.