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Abstract Background Analytical Quality by Design (AQbD) has emerged as a pivotal paradigm in the realm of analytical chemistry, revolutionizing the approach to method development and validation. This innovative strategy integrates principles of Quality by Design (QbD) into analytical procedures, aiming to ensure the quality and robustness of analytical methods. The traditional approach to method development often involves a trial-and-error process, where parameters are adjusted until satisfactory results are obtained. However, this approach can be time-consuming, resource-intensive, and may lack reproducibility. AQbD addresses these challenges by providing a systematic framework for method development that emphasizes understanding the relationship between critical process parameters (CPPs) and critical quality attributes (CQAs). Discussion Analytical Quality by design (AQbD) presents an innovative approach to creating and validating analytical procedures, aimed at achieving quality measurements within the method operable design region (MODR). The QbD approach to analytical development is proactive, methodical, and risk-based significantly helps in acquiring an in-depth knowledge of how critical process parameters (CPPs) affect analytical performances, measured by critical quality attributes (CQAs). Experiment design (DoE) is an essential part of QbD, functioning to carry out the response surface analysis and screening process, and ultimately to enable the definition of the multidimensional region of the successful operating ranges of the CPPs, known as the design space (DS). The purpose of this article is to offer a thorough description of the QbD approach's method development process and its implementation in analytical procedure validation, to produce high-quality output, employing statistical analysis in conjunction with other designing tools. Conclusion Analytical Quality by design (AQbD) offers a systematic and proactive approach to method development in analytical procedures. By focusing on method resilience and incorporating statistical analysis and experiment design (DoE), AQbD enables the production of high-quality outputs, while minimizing out of trend (OOT) and out of specification (OOS) results. This innovative strategy enhances the reliability and reproducibility of analytical methods, ultimately leading to improved outcomes across various industries.
Bairagi et al. (Fri,) studied this question.