Implementation of the Clinical Data Interchange Standards Consortium (CDISC)’s Standard for Exchange of Nonclinical Data (SEND) by the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) has created large quantities of SEND data sets and a tremendous opportunity to apply large-scale data analytic approaches. To fully realize this opportunity, differences in SEND implementation that impair the ability to conduct cross-study analysis must be addressed. In this manuscript, a prototypical question regarding historical control data (see Table of Contents graphic) was used to identify areas for SEND harmonization and to develop algorithmic strategies for nonclinical cross-study analysis within a variety of databases. FDA CDER’s repository of >1800 sponsor-submitted studies in SEND format was queried using the statistical programming language R to gain insight into how the CDISC SEND Implementation Guides are being applied across the industry. For each component needed to answer the question (defined as “query block”), the frequency of data population was determined and ranged from 6 to 99%. For fields populated <90% and/or that did not have Controlled Terminology, data extraction methods such as data transformation and script development were evaluated. Data extraction was successful for fields such as phase of study, negative controls, and histopathology using scripts. Calculations to assess accuracy of data extraction indicated a high confidence in most query block searches. Some fields such as vehicle name, animal supplier name, and test facility name are not amenable to accurate data extraction through script development alone and require additional harmonization to confidently extract data. Harmonization proposals are discussed in this manuscript. Implementation of these proposals will allow stakeholders to capitalize on the opportunity presented by SEND data sets to increase the efficiency and productivity of nonclinical drug development, allowing the most promising drug candidates to proceed through development.

中文翻译：

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利用 CDISC SEND 数据集的价值进行交叉研究分析：对照动物中显微发现的发生率

美国食品药品监督管理局药品评价与研究中心 (US FDA CDER) 实施临床数据交换标准联盟 (CDISC) 的非临床数据交换标准 (SEND)，创建了大量 SEND 数据集和应用大规模数据分析方法的巨大机会。为了充分利用这一机会，必须解决损害进行交叉研究分析能力的 SEND 实施差异。在这份手稿中，一个关于历史控制数据的原型问题（见目录图）被用来确定 SEND 协调的领域，并为各种数据库中的非临床交叉研究分析开发算法策略。FDA CDER 的存储库 > 使用统计编程语言 R 查询了申办者提交的 SEND 格式的 1800 项研究，以深入了解 CDISC SEND 实施指南在整个行业中的应用情况。对于回答问题所需的每个组件（定义为“查询块”），确定数据填充频率，范围为 6% 到 99%。对于填充 <90% 和/或没有受控术语的字段，评估了数据提取方法，例如数据转换和脚本开发。使用脚本的研究阶段、阴性对照和组织病理学等领域的数据提取是成功的。评估数据提取准确性的计算表明在大多数查询块搜索中具有很高的置信度。一些字段，如车辆名称、动物供应商名称、和测试设施名称不适合仅通过脚本开发进行准确的数据提取，需要额外的协调才能自信地提取数据。本手稿中讨论了协调建议。这些提议的实施将使利益相关者能够利用 SEND 数据集提供的机会来提高非临床药物开发的效率和生产力，从而使最有前途的候选药物得以继续开发。