Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease.
Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease.
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DOI：
10.1111/cts.12845
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Larkindale J
中科院分区：
文献类型：
--
作者：
Mullin AP;Corey D;Turner EC;Liwski R;Olson D;Burton J;Sivakumaran S;Hudson LD;Romero K;Stephenson DT;Larkindale J
自1983年通过《孤儿药法案》以来，对罕见病药物开发的兴趣急剧增加，2019年40%的新药批准针对孤儿适应症。然而，对自然史和疾病进展的定量了解有限，阻碍了进展，并增加了与罕见病药物开发相关的风险。使用国际数据标准可以帮助数据协调，实现数据交换，整合到更大的数据集，并定量了解疾病的自然史。美国食品药品监督管理局（FDA）要求在新药申报中使用临床数据交换联盟（CDISC）标准，以帮助该机构高效地接收、处理、审查和存档申报，并帮助整合数据以回答研究问题。这些数据库一直是生物标志物鉴定工作和监管机构认可的适用模型的核心。我们描述了通过关键路径研究所联盟开发的杜兴氏肌营养不良症和亨廷顿病CDISC治疗领域用户指南。这些指南描述了正式的数据结构和受控术语，以映射和集成来自不同来源的数据。这将提高数据收集的标准化程度，并允许整合和比较多项研究的数据。多个数据集的整合可以定量了解疾病进展，这有助于克服这些和其他罕见疾病临床试验设计中的常见挑战。最终，临床数据标准化将为患者急需的新疗法提供更快的监管批准途径。
Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit‐for‐purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington’s disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.
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发表时间：
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期刊：
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影响因子：
--
作者：
LaRocca NG;Hudson LD;Rudick R;Amtmann D;Balcer L;Benedict R;Bermel R;Chang I;Chiaravalloti ND;Chin P;Cohen JA;Cutter GR;Davis MD;DeLuca J;Feys P;Francis G;Goldman MD;Hartley E;Kapoor R;Lublin F;Lundstrom G;Matthews PM;Mayo N;Meibach R;Miller DM;Motl RW;Mowry EM;Naismith R;Neville J;Panagoulias J;Panzara M;Phillips G;Robbins A;Sidovar MF;Smith KE;Sperling B;Uitdehaag BM;Weaver J;Multiple Sclerosis Outcome Assessments Consortium (MSOAC)
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