The promise and reality of therapeutic discovery from large cohorts.
The promise and reality of therapeutic discovery from large cohorts.
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来自大型队列的治疗发现的前景和现实。
DOI：
10.1172/jci129196
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
E. Melamud;Nick van Bruggen;Garret A. FitzGerald;Claude Bernard;D. L. Taylor;A. Sethi;M. Cule;A. Baryshnikova;D. Saleheen
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文献类型：
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作者：
E. Melamud;Nick van Bruggen;Garret A. FitzGerald;Claude Bernard;D. L. Taylor;A. Sethi;M. Cule;A. Baryshnikova;D. Saleheen
快速数据采集的技术进步已将医学生物学转变为数据挖掘领域，其中新的数据集通常通过日益复杂的统计模型进行剖析和分析。可以在单个大数据集中生成和测试许多假设，甚至可以从统计上将很小的影响与大量噪音区分开来。另一方面，治疗干预措施的发展速度要慢得多。它们是通过仔细随机和良好控制的实验确定的，并明确规定结果作为检验单一假设的主要机制。在这种范式中，只能测试一小部分干预措施，甚至更小的部分最终被认为治疗成功。在这篇综述中，我们提出了利用大队列数据来指导靶点选择和新疗法随机试验设计的策略。最终，大数据和实验医学方法的结合应该旨在降低临床试验的失败率，并加快和降低药物开发的成本。
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.