Bethe Ullrich, Pana Zoi D, Drosten Christian, Goossens Herman, König Franz, Marchant Arnaud, Molenberghs Geert, Posch Martin, Van Damme Pierre, Cornely Oliver A
Institute of Translational Research, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Herderstrasse 52, 50931, Cologne, Germany.
Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD) and Excellence Center for Medical Mycology (ECMM), Department I of Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Infection. 2024 Dec;52(6):2135-2144. doi: 10.1007/s15010-024-02347-1. Epub 2024 Jul 17.
WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines.
The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article.
An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms.
A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.
世界卫生组织假定在全球临床试验生态系统中应用适应性设计特征。然而，适应性平台试验（APT）方法在疫苗临床研究中尚未得到广泛采用。
VACCELERATE联盟组织了一次为期两天的研讨会，以讨论APT方法在非大流行以及大流行条件下疫苗试验中的适用性。本文总结了讨论的核心要点。
一种“始终活跃”的APT似乎非常适合提高疫苗研究的效率和速度。基于积累的APT试验数据进行持续学习，可在试验过程中进行预先计划的调整。鉴于相对复杂的设计，在APT的所有阶段使所有利益相关者保持一致至关重要。疫苗试验建模在大流行紧急情况之前和期间都至关重要。各种推理范式都是可能的（频率学派、似然法或贝叶斯法）。在大流行间期的重点可能是行业试验留下的研究空白。为了在紧急情况下启动，需要储备并定期更新针对未知病原体的综合征设计的模板疾病X方案。疫苗APT的治理应完全融入超国家大流行应对机制。
一系列广泛的适应性特征可应用于疫苗平台试验。更快地生成知识伴随着试验设计复杂性的增加。设计复杂性不应妨碍在试验地点的简单执行。持续生成的证据代表了一种投资回报，将获得社会对可持续资金的支持。适应性设计特征将自然地融入疫苗平台试验中。
