The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data

• Published in: Therapeutic innovation & regulatory science Vol. 58; no. 3; pp. 443 - 455
• Main Authors: Zou, Kelly H., Vigna, Chelsea, Talwai, Aniketh, Jain, Rahul, Galaznik, Aaron, Berger, Marc L., Li, Jim Z.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2024; Springer Nature B.V
• Subjects: Clinical medicine; Clinical trials; Clinical Trials as Topic; Decision making; Disease; Drug Development; Drug Safety and Pharmacovigilance; Enrollments; FDA approval; Humans; Medicine; Observational studies; Pharmaceuticals; Pharmacotherapy; Pharmacy; Research Design; Review; Standard of care; United States > US; Decentralized clinical trials; Patient diversity; Real-world data; Clinical trial; Record linkage; External control arm; Real-world evidence
• ISSN: 2168-4790; 2168-4804; 2168-4804
• DOI: 10.1007/s43441-024-00627-4

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects’ data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.