Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity

• Published in: Contemporary clinical trials Vol. 142; p. 107559
• Main Authors: Barrett, Jeffrey S., Lasater, Kara, Russell, Scott, McCune, Susan, Miller, Timothy M., Sibbald, David
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2024
• Subjects: Collaboration; Digital research environment (DRE); Drug development; Platform trial; Study design; Study design; Digital research environment (DRE); Platform trial; Drug development; Collaboration
• ISSN: 1551-7144; 1559-2030; 1559-2030
• DOI: 10.1016/j.cct.2024.107559

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.