Experimental Design in Pharmaceutical Formulation Development: Achievements, Limitations and the Transition Toward Intelligent Optimization

Historically, pharmaceutical formulation development relied heavily on trial-and-error experimentation, which was useful for empirical progress but often provided limited mechanistic understanding and insufficient efficiency for increasingly complex drug products. The introduction of Design of Experiments (DoE) and Quality by Design (QbD) established a more systematic framework for studying formulation variables, manufacturing parameters, and Critical Quality Attributes (CQAs). Approaches such as factorial designs, response-surface methodology, and mixture designs have therefore become central to modern pharmaceutical development because they improve experimental efficiency and support the definition of design space. However, as formulations become more nonlinear, high-dimensional, and multi-objective, these classical approaches may no longer be sufficient on their own. This review examines the evolution of experimental design in pharmaceutical research, from one-factor-at-a-time experimentation to structured DoE/QbD strategies, and then to emerging intelligent optimization methods. Its central objective is to clarify when conventional DoE/QbD remains appropriate and when it should be complemented by machine learning, Bayesian optimization, digital twins, and closed-loop experimental systems. The review first summarizes the foundations and strengths of classical experimental design; then, it discusses its practical limitations in complex formulation settings, and finally evaluates how data-driven and hybrid approaches can extend pharmaceutical development. Evidence from tablets, capsules, nanocarriers, transdermal patches, and biotherapeutic systems suggests that intelligent optimization can improve predictive performance and experimental efficiency when used alongside, rather than instead of, established pharmaceutical development principles.