ANALYTICAL QUALITY BY DESIGN (AQBD): CURRENT ADVANCES AND FUTURE PERSPECTIVES

Analytical methods play a vital role in pharmaceutical development, quality control, stability testing, and regulatory decision-making, as the reliability of drug product identity, strength, purity, safety, and stability depends on the quality of analytical data generated. Conventional analytical method development has traditionally followed trial-and-error and one-factor-at-a-time approaches, which can require extensive experimentation and may fail to adequately evaluate interactions among method variables. Analytical Quality by Design (AQbD) has emerged as a systematic, science-based, and risk-based approach for developing analytical procedures that are robust, reliable, and fit for their intended purpose. The AQbD framework generally begins with defining an Analytical Target Profile (ATP), followed by identification of critical analytical attributes and performance characteristics, risk assessment, identification of critical method parameters, application of Design of Experiments (DoE), establishment of a Method Operable Design Region (MODR), and development of an appropriate analytical control strategy. The recently finalized ICH Q14 guideline provides a harmonized framework for analytical procedure development and lifecycle management, while ICH Q2(R2) establishes an updated framework for analytical procedure validation. Together, these guidelines emphasize a science- and risk-based approach extending beyond validation as a standalone activity. Recent advances in AQbD include integration with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical technologies. This review discusses the fundamental concepts, workflow, statistical tools, applications, regulatory significance, advantages, challenges, and future perspectives of AQbD in pharmaceutical analysis. The integration of AQbD with digital and sustainable technologies is expected to enhance method robustness, reduce experimental burden, and support continuous analytical procedure improvement throughout the analytical lifecycle.