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外推法作为儿科药物研发中的默认策略。

Extrapolation as a Default Strategy in Pediatric Drug Development.

作者信息

Gamalo Margaret, Bucci-Rechtweg Christina, Nelson Robert M, Vanh Linh, Porcalla Ariel, Thackray Helen, Cooner Freda, Cutler Lauren, Friend Danielle, Portman Ron

机构信息

Global Biometrics and Data Management - GPD I&I Statistics, 500 Arcola Rd, Collegeville, PA, 19426, USA.

Global Head for Pediatric and Maternal Health Policy, Novartis Pharmaceuticals, 1 Health Plaza, East Hanover, NJ, 07936, USA.

出版信息

Ther Innov Regul Sci. 2022 Nov;56(6):883-894. doi: 10.1007/s43441-021-00367-9. Epub 2022 Jan 10.

DOI:10.1007/s43441-021-00367-9
PMID:35006587
Abstract

Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185-1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper's position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.

摘要

儿科药物研发比成人药物研发滞后约8年(穆卢盖塔等人,《儿科临床》,2017年,第64卷,第6期,第1185 - 1196页)。在此背景下,人们提出了许多激励措施、法规和创新技术来解决儿科患者用药方面的差距。其中一种策略是从参照人群推断疗效。目前,当儿科人群和参照人群的疾病进程及预期治疗反应足够相似时,通过提供支持药物在儿童中有效使用的证据来证明推断的合理性。本文的观点是,尽管存在不确定性,但儿科药物研发项目最初应假定一定程度的推断是可行的。推断的可用程度处于一个连续区间,该区间代表了通过生成新的儿科证据需要解决的不确定性。在解决这些不确定性时,推断策略应反映在决定让儿童承担新药风险这一决策上可容忍的不确定程度。这种关于可容忍不确定程度的判断应因具体情况而异(例如疾病严重程度、现有治疗选择),并且可以融入儿科药物研发原型中,以确定所需研究的范围以及是否考虑同时开展针对成人和青少年的研发。

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本文引用的文献

1
The Use of Pediatric Extrapolation to Avoid Unnecessary Pediatric Clinical Trials.利用儿科外推法避免不必要的儿科临床试验。
Am J Bioeth. 2020 May;20(4):114-116. doi: 10.1080/15265161.2020.1730489.
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Exposure-Response Assessment in Pediatric Drug Development Studies Submitted to the US Food and Drug Administration.向美国食品和药物管理局提交的儿科药物开发研究中的暴露-反应评估。
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Rivaroxaban compared with standard anticoagulants for the treatment of acute venous thromboembolism in children: a randomised, controlled, phase 3 trial.
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The Role of Master Protocols in Pediatric Drug Development.主协议在儿科药物开发中的作用。
Ther Innov Regul Sci. 2022 Nov;56(6):895-902. doi: 10.1007/s43441-022-00448-3. Epub 2022 Sep 1.
利伐沙班与标准抗凝剂治疗儿童急性静脉血栓栓塞症的比较:一项随机、对照、3期试验
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Developmental Pharmacodynamics and Modeling in Pediatric Drug Development.儿科药物开发中的发育药理学和建模。
J Clin Pharmacol. 2019 Sep;59 Suppl 1(Suppl 1):S87-S94. doi: 10.1002/jcph.1482.
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Epidemiology of Asthma in Children and Adults.儿童和成人哮喘的流行病学
Front Pediatr. 2019 Jun 18;7:246. doi: 10.3389/fped.2019.00246. eCollection 2019.
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Incorporating Innovative Techniques Toward Extrapolation and Efficient Pediatric Drug Development.采用创新技术促进儿科药物的外推和高效研发。
Ther Innov Regul Sci. 2019 Sep;53(5):567-578. doi: 10.1177/2168479019842541. Epub 2019 May 13.
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Current Epidemiology, Etiology, and Burden of Acute Skin Infections in the United States.目前美国急性皮肤感染的流行病学、病因和负担。
Clin Infect Dis. 2019 Apr 8;68(Suppl 3):S193-S199. doi: 10.1093/cid/ciz002.
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Commentary on the EMA Reflection Paper on the use of extrapolation in the development of medicines for paediatrics.EMA 关于在儿科药物开发中使用外推法的评论文件解读。
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How to use prior knowledge and still give new data a chance?如何运用先验知识,同时仍给新数据机会?
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