药物安全研究中的综合数据科学:经验、挑战和展望。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ferran Sanz
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引用次数: 0

摘要

药物研究和开发在很大程度上取决于可用于支持项目的数据的数量和质量。通过协作和综合方法对数据的二次利用正在药物安全研究中产生有希望的结果。然而,在这些综合方法中必须克服一些挑战,例如互操作性问题、知识产权保护,以及在临床信息的情况下的个人数据保护。OMOP通用数据模型和EHDEN和DARWIN欧盟平台构成了临床领域数据共享倡议的成功范例,而eTOX、eTRANSAFE和VICT3R国际项目是毒理学研究中企业数据共享的范例。VICT3R项目利用这些共享数据生成虚拟对照组,用于非临床药物安全性评估。与药物有关的知识库整合了来自不同来源的信息,也构成了药物安全领域的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrative Data Science in Drug Safety Research: Experiences, Challenges, and Perspectives.

Pharmaceutical research and development largely depend on the quantity and quality of data that are available to support projects. The secondary use of data by means of collaborative and integrative approaches is yielding promising results in drug safety research. However, there are challenges that must be overcome in these integrative approaches, such as interoperability issues, intellectual property protection, and, in the case of clinical information, personal data safeguards. The OMOP common data model and the EHDEN and DARWIN EU platforms constitute successful examples of data sharing initiatives in the clinical domain, while the eTOX, eTRANSAFE, and VICT3R international projects are examples of corporate data sharing in toxicology research. The VICT3R project is using these shared data for generating virtual control groups to be applied in nonclinical drug safety assessment. Drug-related knowledge bases that integrate information from different sources also constitute useful tools in the drug safety domain.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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