AQbD review maps analytical method development from ATP to MODR and folds in ICH Q14 and Q2(R2)

The review presents AQbD as a systematic, science-based, and risk-based approach for developing analytical procedures that are robust, reliable, and fit for their intended purpose. Relative to conventional trial-and-error and one-factor-at-a-time method development, AQbD emphasizes evaluation of interactions among method variables, which conventional approaches may fail to adequately assess. This is a review-level discussion based on synthesis of AQbD concepts and workflow; the source text provides no specific experimental data or case statistics.

The review lays out a typical AQbD workflow: defining the ATP, identifying critical analytical attributes and performance characteristics, risk assessment, identifying critical method parameters, applying DoE, establishing the MODR, and developing an analytical control strategy. It organizes dispersed AQbD elements into a coherent sequence from target setting to control strategy, helping readers see how the steps relate. This is a framework-level synthesis presented as a process description; the source text offers no quantitative validation of individual steps.

The review notes that ICH Q14 provides a harmonized framework for analytical procedure development and lifecycle management, while ICH Q2(R2) establishes an updated framework for analytical procedure validation, and that together they emphasize a science- and risk-based approach extending beyond validation as a standalone activity. It links regulatory guideline updates to the AQbD concept, indicating regulatory attention to lifecycle management of analytical methods. Based on an overview of finalized guideline content; the source text does not analyze individual clauses in detail.

The review states that recent advances in AQbD include integration with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical technologies, and expects this integration to enhance method robustness, reduce experimental burden, and support continuous improvement throughout the analytical lifecycle. It positions digital and sustainable technologies as directions for AQbD, expanding the scope of possible applications. This is a forward-looking judgment expressed in expectational terms; the source text provides no empirical data on integration outcomes.

This review addresses pharmaceutical analytical method development, quality control, stability testing, and regulatory decision-making, and suits readers who want an overview of AQbD concepts, workflow, statistical tools, regulatory significance, and future directions. The source text states that the AQbD framework generally begins with the ATP and proceeds through critical analytical attributes, risk assessment, critical method parameters, DoE, the MODR, and an analytical control strategy, and it mentions that ICH Q14 and ICH Q2(R2) provide harmonized frameworks for analytical procedure development, lifecycle management, and validation. The review also states that recent advances include integration with multivariate analysis, chemometrics, automation, artificial intelligence, machine learning, green analytical chemistry, and real-time analytical technologies, and expects these integrations to enhance method robustness, reduce experimental burden, and support continuous improvement throughout the analytical lifecycle.

The reading scope is incomplete and lacks figures, references, and specific cases, so it is not possible to assess the concrete use of each statistical tool, the actual outcomes in each application setting, or the empirical support for digital and green technology integration. Questions a reader might watch include: how difficult each AQbD step is to implement in real method development, how much data and which statistical assumptions MODR establishment requires, how ICH Q14 and Q2(R2) implementation differs across regulatory regions, and what artificial intelligence, machine learning, and real-time analytical technologies actually contribute across the analytical lifecycle. These are directions for later exploration rather than conclusions already given in the source text.