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首页> 外文期刊>The Journal of Clinical Pharmacology: Official Journal of the American College of Clinical Pharmacology >Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children
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Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children

机译：创新研究设计在婴幼儿优化临床药理学研究

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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.
机译：最近一半的儿科试验未能在很大程度上实现标签适应症，因为研究设计不足。因此，创新的研究方法对于优化试验设计至关重要，同时还降低了药物调查固有的潜在危害。存在几种方法以优化从最小可能的体积收集的药代动力学数据以及最少的程序，包括使用机会主义和稀疏采样，替代和非侵入性矩阵和微肺测定。此外，使用硕士协议的大型研究网络促进协作，降低监管负担，增加早期和后期试验的试验效率。利用电子健康记录的大型务实试验可以利用中央管理策略来降低成本，以大规模的含有罕见疾病的患者纳入患者，增强研究概括。此外，可以通过贝叶斯的自适应技术优化试验效率和安全性，这些技术允许计划的协议根据先前和累计数据的分析而变化。除了这些试验特征外，建模和仿真的进步已经为基于系统的和生理基础的模型铺平了个性化小贩的推荐和支持药物批准。最后，鉴于许多儿科疾病的患病率低，提高了大规模的临委遗传和临床数据是增强儿童临床药理学研究的潜在变革方式。

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