Disha Subramaniam, Colin Anderson-Smits, Rebecca Rubinstein, Sydney T. Thai, Rose Purcell, Cynthia Girman
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The framework guided outcome abstraction on regulatory responses.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We examined all non-oncology FDA and EMA drug and biologic approvals for first indications from 2019 to 2022 to identify those with SAT as pivotal safety or efficacy evidence. We abstracted outcomes, key study design features, regulator responses to SAT and (where applicable) ECA design, and product label content.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Among 20 SAT-based FDA approvals and 17 SAT-based EMA approvals, most common indications were progressive rare diseases with high unmet need/limited therapeutic options and a natural history without spontaneous improvement. Of the types of comparators, most were natural history cohorts (45% FDA; 47% EMA) and baseline controls (40% FDA; 47% EMA). Common critiques were of non-contemporaneous ECAs, subjective endpoints, and baseline covariate imbalance between arms.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Based on recent FDA and EMA approvals, the likelihood of regulatory success for SATs with ECAs depends on many design, analytic, and data quality considerations. Our framework is useful in early drug development when considering SAT strategies for evidence generation.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Framework for the Use and Likelihood of Regulatory Acceptance of Single-Arm Trials\",\"authors\":\"Disha Subramaniam, Colin Anderson-Smits, Rebecca Rubinstein, Sydney T. 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引用次数: 0
摘要
背景单臂临床试验(SAT)在罕见或危及生命的药物和生物制剂申报中很常见,尤其是在没有治疗方案的情况下。通过叙述性综述和专家意见,我们建立了一个框架,用于考虑可能影响监管机构使用单臂临床试验以及监管机构接受单臂临床试验的可能性,并将非肿瘤学首次适应症批准作为一个关注领域。我们系统分析了 FDA 和 EMA 将 SAT 作为关键证据的批准情况。方法我们研究了 2019 年至 2022 年 FDA 和 EMA 批准的所有非肿瘤学药物和生物制剂的首次适应症,以确定那些将 SAT 作为关键安全性或有效性证据的药物。结果在 20 项基于 SAT 的 FDA 批准和 17 项基于 SAT 的 EMA 批准中,最常见的适应症是渐进性罕见病,具有高需求未获满足/治疗选择有限的特点,且自然病史无自发改善。在比较对象类型中,大多数是自然病史队列(45% FDA;47% EMA)和基线对照(40% FDA;47% EMA)。常见的批评意见包括非同期ECA、主观终点以及两臂间基线协变量不平衡。结论根据最近FDA和EMA的批准情况,带有ECA的SAT在监管方面取得成功的可能性取决于许多设计、分析和数据质量方面的考虑因素。我们的框架对早期药物开发中考虑 SAT 证据生成策略非常有用。
A Framework for the Use and Likelihood of Regulatory Acceptance of Single-Arm Trials
Background
Single-arm clinical trials (SAT) are common in drug and biologic submissions for rare or life-threatening conditions, especially when no therapeutic options exist. External control arms (ECAs) improve interpretation of SATs but pose methodological and regulatory challenges.
Objective
Through narrative reviews and expert input, we developed a framework for considerations that might influence regulatory use and likelihood of regulatory acceptance of an SAT, identifying non-oncology first indication approvals as an area of interest. We systematically analyzed FDA and EMA approvals using SATs as pivotal evidence. The framework guided outcome abstraction on regulatory responses.
Methods
We examined all non-oncology FDA and EMA drug and biologic approvals for first indications from 2019 to 2022 to identify those with SAT as pivotal safety or efficacy evidence. We abstracted outcomes, key study design features, regulator responses to SAT and (where applicable) ECA design, and product label content.
Results
Among 20 SAT-based FDA approvals and 17 SAT-based EMA approvals, most common indications were progressive rare diseases with high unmet need/limited therapeutic options and a natural history without spontaneous improvement. Of the types of comparators, most were natural history cohorts (45% FDA; 47% EMA) and baseline controls (40% FDA; 47% EMA). Common critiques were of non-contemporaneous ECAs, subjective endpoints, and baseline covariate imbalance between arms.
Conclusion
Based on recent FDA and EMA approvals, the likelihood of regulatory success for SATs with ECAs depends on many design, analytic, and data quality considerations. Our framework is useful in early drug development when considering SAT strategies for evidence generation.