生物药物分析方法开发中的质量设计: 从当前观点到实际应用
Analytical quality by design in biopharmaceutical method development: from current perspectives to practical applications
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摘要: 生物药物(biologics) 近年来在全球范围内迅速发展, 其分析技术涵盖分子结构表征、纯度与杂质分析、功能及稳定性评价等多个方面, 是确保生物药物质量的关键。质量源于设计(quality by design, QbD) 作为一种系统化方法, 在制药行业中被广泛应用于生产工艺的优化和确保最终产品的质量可靠性, 而基于QbD框架衍生发展的分析质量源于设计(analytical quality by design, AQbD) 则为所开发的产品“分析方法”的质量赋予信心和提供保障。AQbD是以定义分析目标概况(analytical target profile, ATP) 为起点, 通过风险评估识别和分析关键方法属性(critical method attributes, CMAs) 及关键方法参数(critical method parameters, CMPs), 并利用实验设计(design of experiments, DoE) 构建数学模型探索CMPs与CMAs之间的关系, 以建立方法参数的可操作设计区域(method operable design region, MODR) 和分析方法控制策略。与基于传统理念开发的分析方法相比, 基于AQbD理念开发的分析方法在MODR范围内具有更高的稳健性, 能够减少因分析方法所导致的超趋势或不合格结果的产生, 从而实现监管灵活性并降低分析成本。本文综述了AQbD的工作流程及其在生物药物分析方法开发中的应用现状, 并探讨了该领域实施AQbD的机遇与挑战。Abstract: Biologics have experienced rapid development on a global scale in recent years. Their analytical techniques, encompassing molecular structural characterization, purity and impurities analyses, function and stability assessment, play a crucial role in ensuring biologics' quality. Quality by design (QbD), as a systematic approach, has been widely adopted in the pharmaceutical industry to optimize manufacturing processes and ensure the reliability of final product quality. Derived from QbD, analytical quality by design (AQbD) ensures the quality of analytical methods by providing confidence and assurance in their development. AQbD begins with defining the analytical target profile (ATP) and employs risk assessment to identify and analyze critical method attributes (CMAs) and critical method parameters (CMPs). By design of experiments (DoE), mathematical models are constructed to explore the relationships between CMPs and CMAs, ultimately establishing the method operable design region (MODR) and an analytical control strategy. Compared to traditional analytical method development, AQbD-based methods offer greater robustness within the MODR, reducing the occurrence of out-of-trend (OOT) or out-of-specification (OOS) results due to method-related factors. This, in turn, enhances regulatory flexibility and reduces analytical costs. This review outlines the AQbD workflow, discusses its current applications in biologics analytical method development, and explores the opportunities and challenges associated with its implementation in this field.
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