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创新研究设计优化婴儿和儿童临床药理学研究。

Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children.

机构信息

Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.

Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.

出版信息

J Clin Pharmacol. 2018 Oct;58 Suppl 10(Suppl 10):S58-S72. doi: 10.1002/jcph.1053.

Abstract

Almost half of recent pediatric trials failed to achieve labeling indications, in large part because of inadequate study design. Therefore, innovative study methods are crucial to optimizing trial design while also reducing the potential harms inherent with drug investigation. Several methods exist to optimize the amount of pharmacokinetic data collected from the smallest possible volume and with the fewest number of procedures, including the use of opportunistic and sparse sampling, alternative and noninvasive matrices, and microvolume assays. In addition, large research networks using master protocols promote collaboration, reduce regulatory burden, and increase trial efficiency for both early- and late-phase trials. Large pragmatic trials that leverage electronic health records can capitalize on central management strategies to reduce costs, enroll patients with rare diseases on a large scale, and augment study generalizability. Further, trial efficiency and safety can be optimized through Bayesian adaptive techniques that permit planned protocol changes based on analyses of prior and accumulated data. In addition to these trial design features, advances in modeling and simulation have paved the way for systems-based and physiologically based models that individualize pediatric dosing recommendations and support drug approval. Last, given the low prevalence of many pediatric diseases, collecting deidentified genetic and clinical data on a large scale is a potentially transformative way to augment clinical pharmacology research in children.

摘要

几乎一半的近期儿科试验未能达到标签适应证,这在很大程度上是由于研究设计不当。因此,创新的研究方法对于优化试验设计至关重要,同时也可以降低药物研究固有的潜在危害。有几种方法可以优化从最小体积和最少程序中收集的药代动力学数据量,包括使用机会性和稀疏采样、替代和非侵入性基质以及微量测定。此外,使用主方案的大型研究网络促进了合作,减轻了监管负担,并提高了早期和晚期试验的试验效率。利用电子健康记录的大型实用试验可以利用中央管理策略来降低成本,大规模招募患有罕见疾病的患者,并增加研究的普遍性。此外,通过贝叶斯自适应技术可以优化试验效率和安全性,该技术可以根据先前和累积数据的分析来允许计划的方案变更。除了这些试验设计特点外,建模和模拟的进步为基于系统和基于生理学的模型铺平了道路,这些模型可以为儿科剂量推荐提供个性化,并支持药物批准。最后,鉴于许多儿科疾病的患病率较低,大规模收集去识别化的遗传和临床数据是增强儿童临床药理学研究的一种潜在变革方式。

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