1.1 本指南涵盖了多变量数据分析（MVDA）在支持药物开发和制造活动中的应用。MVDA是药物开发过程理解和决策的关键促成因素之一，也是使用基于科学和风险的方法进行适当验证后释放中间产品和最终产品的关键促成因素之一。 1.2 本指南的范围是提供MVDA在制药行业应用的一般指南。虽然MVDA指的是典型的实证数据分析，但其范围仅限于提供高水平的指导，并不旨在提供特定于应用的数据分析程序。本指南提供了以下方面的注意事项: 1.2.1 使用基于风险的方法（了解客观要求并评估适用状态）； 1.2.2 考虑MVDA使用的数据收集和诊断（包括数据预处理和异常值）； 1.2.3 考虑不同类型的数据分析、模型测试和验证； 1.2.4 合格且称职的人员；和 1.2.5 MVDA模型的生命周期管理。 1.3 本标准并非旨在解决与其使用相关的所有安全问题（如有）。本标准的用户有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.4 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 ====意义和用途====== 4.1 在药物开发和制造活动中产生了大量数据。 这些数据的解释变得越来越困难。单变量过程变量的单独检查是相关的，但可以通过多变量数据分析（MVDA）进行显著补充。MVDA可能特别适合于探索和处理大型异构数据集，将高维数据映射到低维表示，揭示单个数据集中多变量之间的显著相关性或跨数据集多变量之间的显著相关性。MVDA可以提取具有统计意义的信息，这些信息可以增强过程理解、过程开发中的决策、过程监控（包括产品发布）、产品生命周期管理和持续改进。 4.2 MVDA广泛应用于包括制药行业在内的各个行业。 为了获得有效的结果，MVDA模型/应用程序应包含以下内容: 4.2.1 预定义的基于风险的目标，包括一个或多个特定于应用的相关科学假设； 4.2.2 具有必要质量的足够相关数据，涵盖预期使用期间遇到的差异空间，即药物开发或药物制造，或两者兼而有之； 4.2.3 适当的数据分析和模型利用实践，包括在使用模型分析之前对所有新数据的测试、验证和鉴定的考虑； 4.2.4 经过适当培训的员工； 4.2.5 适当的标准操作程序；和 4.2.6 生命周期管理。 4.3 本指南可用于支持与药物开发和制造、制造过程性能和产品质量监控相关的数据分析活动，以及故障排除和调查事件。 数据分析的技术细节可以在科学文献中找到，数据分析的标准实践已经可用（例如实践） E1655 和 E1790 对于光谱应用，实践 E2617 用于模型验证和实践 E2474 用于利用过程分析技术）。

1.1 This guide covers the applications of multivariate data analysis (MVDA) to support pharmaceutical development and manufacturing activities. MVDA is one of the key enablers for process understanding and decision making in pharmaceutical development, and for the release of intermediate and final products after being validated appropriately using a science and risk-based approach. 1.2 The scope of this guide is to provide general guidelines on the application of MVDA in the pharmaceutical industry. While MVDA refers to typical empirical data analysis, the scope is limited to providing a high level guidance and not intended to provide application-specific data analysis procedures. This guide provides considerations on the following aspects: 1.2.1 Use of a risk-based approach (understanding the objective requirements and assessing the fit-for-use status); 1.2.2 Considerations on the data collection and diagnostics used for MVDA (including data preprocessing and outliers); 1.2.3 Considerations on the different types of data analysis, model testing, and validation; 1.2.4 Qualified and competent personnel; and 1.2.5 Life-cycle management of MVDA model. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 4.1 A significant amount of data is generated during pharmaceutical development and manufacturing activities. The interpretation of such data is becoming increasingly difficult. Individual examination of the univariate process variables is relevant but can be significantly complemented by multivariate data analysis (MVDA). MVDA may be particularly appropriate for exploring and handling large sets of heterogenous data, mapping data of high dimensionality onto lower dimensional representations, exposing significant correlations among multivariate variables within a single data set or significant correlations among multivariate variables across data sets. MVDA may extract statistically significant information which may enhance process understanding, decision making in process development, process monitoring and control (including product release), product life-cycle management, and continuous improvement. 4.2 MVDA is widely used in various industries including the pharmaceutical industry. To achieve a valid outcome, an MVDA model/application should incorporate the following: 4.2.1 A predefined risk-based objective incorporating one or more relevant scientific hypotheses specific to the application; 4.2.2 Sufficient relevant data of requisite quality covering the variance space encountered during intended use, that is, pharmaceutical development, or pharmaceutical manufacturing, or both; 4.2.3 Appropriate data analysis and model utilization practices including considerations on testing, validation, and qualification of all new data prior to using a model to analyze it; 4.2.4 Appropriately trained staff; 4.2.5 Appropriate standard operating procedures; and 4.2.6 Life-cycle management. 4.3 This guide can be used to support data analysis activities associated with pharmaceutical development and manufacturing, process performance and product quality monitoring in manufacturing, as well as for troubleshooting and investigation events. Technical details in data analysis can be found in the scientific literature and standard practices in data analysis are already available (such as Practices E1655 and E1790 for spectroscopic applications, Practice E2617 for model validation, and Practice E2474 for utilizing process analytical technology).