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数据完整性:历史、问题及问题补救

Data Integrity: History, Issues, and Remediation of Issues.

作者信息

Rattan Anil K

机构信息

Boston Biomedical, Inc., 640 Memorial Drive, Cambridge, MA 02139, USA

出版信息

PDA J Pharm Sci Technol. 2018 Mar-Apr;72(2):105-116. doi: 10.5731/pdajpst.2017.007765. Epub 2017 Nov 20.

Abstract

Data integrity is critical to regulatory compliance, and the fundamental reason for 21 CFR Part 11 published by the U.S. Food and Drug Administration (FDA). FDA published the first guideline in 1963, and since then FDA and European Union (EU) have published numerous guidelines on various topics related to data integrity for the pharmaceutical industry. Regulators wanted to make certain that industry capture accurate data during the drug development lifecycle and through commercialization-consider the number of warning letters issued lately by inspectors across the globe on data integrity. This article discusses the history of regulations put forward by various regulatory bodies, the term adopted by regulators, the impact of not following regulations, and some prevention methods by using some simple checklists, self-audit, and self-inspection techniques. FDA uses the acronym ALCOA to define its expectations of electronic data. stands for Attributable, Legible, Contemporaneous, Original, and Accurate. ALCOA was further expanded to ALCOA Plus, and the Plus means Enduring, Available and Accessible, Complete, Consistent, Credible, and Corroborated. If we do not follow the regulations as written, then there is a huge risk. This article covers some of the risk aspects. To prevent data integrity, various solutions can be implemented such as a simple checklist for various systems, self-audit, and self-inspections. To do that we have to develop strategy, people, implement better business processes, and gain a better understanding of data lifecycle as well as technology. If one does a Google search on "What is data integrity?" the first page will give the definition of data integrity, how to learn more about data integrity, the history of data integrity, risk management of data integrity, and at the top about various U.S. Food and Drug Administration (FDA) and European Union (EU) regulations. Data integrity is nothing but about accuracy of data. When someone searches Google for some words, we expect accurate results that we can rely on. The same principle applies during the drug development lifecycle. Pharmaceutical industry ensures that data entered for various steps of drug development is accurate so that we can have confidence that the drugs produced by the industry are within some parameters. The regulations put forward by FDA and EU are not new. The first regulation was published in 1963, and after that regulators published multiple guidelines. Inspectors from both regulatory bodies inspected the industry, and they found that the data was not accurate. If pharmaceutical industry produces drugs within the stated parameters, then it is approved and available in the market for patients. If inspectors find that the data is modified, then the drug is not approved. That means revenue loss for industry and drugs not available for patients. In this article, I explain some of the remediation plans for the industry that can be applied during the drug development lifecycle pathway.

摘要

数据完整性对于法规合规至关重要,这也是美国食品药品监督管理局(FDA)发布21 CFR Part 11的根本原因。FDA于1963年发布了首份指南,自那时起,FDA和欧盟(EU)就诸多与制药行业数据完整性相关的主题发布了大量指南。监管机构希望确保行业在药物开发生命周期及商业化过程中获取准确的数据——想想全球各地检查员最近就数据完整性发出的警告信数量就知道了。本文讨论了各监管机构提出的法规历史、监管机构采用的术语、不遵守法规的影响,以及一些预防方法,如使用一些简单的清单、自我审核和自我检查技术。FDA使用首字母缩写词ALCOA来定义其对电子数据的期望。A代表可归属的、清晰可读的、同步的、原始的和准确的。ALCOA进一步扩展为ALCOA Plus,其中Plus表示持久的、可获取和可访问的、完整的、一致的、可信的和可证实的。如果我们不按照书面规定遵守法规,那么就会有巨大风险。本文涵盖了一些风险方面。为防止数据完整性问题,可以实施各种解决方案,如针对各种系统的简单清单、自我审核和自我检查。为此,我们必须制定战略、培养人员、实施更好的业务流程,并更好地理解数据生命周期以及技术。如果有人在谷歌上搜索“什么是数据完整性?”,第一页会给出数据完整性的定义、如何更多地了解数据完整性、数据完整性的历史、数据完整性的风险管理,以及顶部关于美国食品药品监督管理局(FDA)和欧盟(EU)的各种法规。数据完整性无非就是关于数据的准确性。当有人在谷歌上搜索某些词语时,我们期望得到我们可以信赖的准确结果。同样的原则适用于药物开发生命周期。制药行业确保在药物开发各个步骤输入的数据准确无误,这样我们才能相信该行业生产的药物在某些参数范围内。FDA和欧盟提出的法规并非新事物。首条法规于1963年发布,此后监管机构发布了多项指南。两个监管机构的检查员对该行业进行检查时,发现数据不准确。如果制药行业生产的药物在规定参数范围内,那么该药物就会被批准并投放市场供患者使用。如果检查员发现数据被修改,那么该药物就不会被批准。这意味着行业收入损失,患者也无法获得药物。在本文中,我解释了一些该行业可在药物开发生命周期路径中应用的补救计划。

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