Cetinyurek Yavuz Aysun, Fayyad Muhammad Bergas Nur, Jiang Ce, Brion Bouvier Florie, Beji Celine, Zebachi Sonia, Hayek Ghinwa Y, Amzal Billy, Porcher Raphael, Tanniou Julien, Roes Kit, Rodwell Laura
IQ Health Department, Radboud University Medical Center, Nijmegen, The Netherlands.
Quinten Health, Paris, France.
Clin Pharmacol Ther. 2025 Apr;117(4):967-977. doi: 10.1002/cpt.3571. Epub 2025 Jan 24.
Drug development is a lengthy process with considerable uncertainty at each milestone. Several trials are needed to progress to confirmatory evaluation and establish a positive benefit-risk balance. One of the critical milestones is the decision to progress to phase III based on phase II trial results. Use of probability of success is becoming standard in pharmaceutical companies to support this decision. However, the lack of consistency in terminology makes it difficult to assess the comparative value of different approaches. By leveraging the availability of high-quality external data (e.g., real-world data, historical clinical trial data, etc.), probability of success-based procedures may further improve decision-making. We performed a scoping review of approaches to calculate the probability of success of a phase III trial depending on the available data sources and the availability of specific endpoints. Calculation of probability of success is relatively straightforward if data for the primary endpoint of the phase III trial are also available in phase II trials. Often, phase II trials are based on biomarker or surrogate outcomes, due to challenges associated with study duration and required sample size. Probability of success-based procedures as reviewed can incorporate external data sources, for example, from clinical trials testing the same or similar drug or real-world data on the targeted population-optimizing the calculation of probability of trial success and the projected drug candidate value. We conclude the paper by reflecting on alternative approaches and ideas for uses within pharmaceutical companies and academia.
药物研发是一个漫长的过程,在每个里程碑都存在相当大的不确定性。需要进行多项试验才能推进到确证性评估,并建立积极的效益风险平衡。关键里程碑之一是根据II期试验结果决定推进到III期。在制药公司中,使用成功概率来支持这一决定正变得越来越普遍。然而,术语缺乏一致性使得难以评估不同方法的相对价值。通过利用高质量外部数据(如真实世界数据、历史临床试验数据等)的可用性,基于成功概率的程序可能会进一步改善决策。我们根据可用数据源和特定终点的可用性,对计算III期试验成功概率的方法进行了范围审查。如果III期试验主要终点的数据在II期试验中也可用,那么成功概率的计算相对简单。通常,由于与研究持续时间和所需样本量相关的挑战,II期试验基于生物标志物或替代结局。本文所审查的基于成功概率的程序可以纳入外部数据源,例如来自测试相同或类似药物的临床试验或目标人群的真实世界数据,从而优化试验成功概率和候选药物预期价值的计算。我们通过思考制药公司和学术界内部使用的替代方法和想法来结束本文。