In modern pharmaceutical development, the increasing complexity of drug substances, formulations, and regulatory expectations has rendered traditional one-factor-at-a-time (OFAT) approaches to analytical method development inefficient and increasingly impractical. Consequently, analytical development in the twenty-first century is shifting toward systematic data-driven strategies based on Design of Experiments (DoE) and predictive modeling, in alignment with the principles of Analytical Quality by Design (AQbD). Software tools such as MODDE® and DryLab® exemplify this transition by enabling multivariate evaluation of critical method parameters, quantitative definition of design spaces, and prediction of chromatographic performance across wide operational ranges. Although numerous studies report the successful application of DoE-based optimization or predictive retention modeling as standalone approaches, a growing body of evidence, particularly from pharmaceutical applications, demonstrates the advantages of their integrated use for the development of robust, stability-indicating analytical methods. This review provides a comprehensive overview of AQbD-based stability-indicating method development with emphasis on the combined application of MODDE® and DryLab®, examining their roles in systematic risk assessment, statistical modeling, and predictive simulation to enhance method robustness, reduce experimental burden, and support regulatory flexibility within a scientifically justified method operable design region (MODR). In addition, emerging perspectives on artificial intelligence- and machine learning-assisted retention prediction are discussed as natural extensions of current software-assisted AQbD frameworks, highlighting future directions toward more efficient, knowledge-driven, and digitally enabled analytical method development.
Karpicarov et al. (Thu,) studied this question.