The FDA has announced two concrete steps towards making real-time clinical trials a reality: successful initiation of two proof‑of‑concept studies and a planned pilot programme starting this summer. Both initiatives focus on early‑phase trials, traditionally the bottleneck of drug development due to high uncertainty, small patient populations and slow decision‑making. The core idea is simple but radical: instead of sending cleaned data in batches with long delays, key endpoints and safety signals are streamed to the FDA in close to real time.
AstraZeneca and Amgen studies
The proof‑of‑concept trials involve AstraZeneca and Amgen. AstraZeneca’s TRAVERSE study is a Phase 2 multi‑site trial in treatment‑naïve mantle cell lymphoma, run with centres such as MD Anderson and the University of Pennsylvania. Amgen’s STREAM‑SCLC is a Phase 1b trial in limited‑stage small cell lung cancer, with site selection still ongoing. For both programmes, the FDA agreed with sponsors on predefined criteria for real‑time signal reporting and has already received and validated signals from the AstraZeneca trial via Paradigm Health, demonstrating that the technical framework for live data sharing is feasible.
Pilot’s design study
In parallel, the agency has published a Request for Information on a broader real‑time trials pilot, explicitly linked to AI‑enabled optimisation of early‑phase trials. Stakeholders are invited to comment on the pilot’s design, implementation, evaluation metrics and success criteria, with comments open until 29 May 2026. The FDA plans to finalise selection criteria in July and choose pilot participants in August, signalling an unusually tight implementation timeline. Behind this speed is a strategic goal: moving from discrete, phase‑by‑phase development towards more continuous trials, where gaps between phases are minimised because regulators already see key insights as the study progresses.
Practical consequences for sponsors
For sponsors, this shift could have several practical consequences. Real‑time visibility may allow earlier go/no‑go or dose‑adjustment decisions, faster identification of safety issues, and more agile protocol adaptations. At the same time, it raises the bar for data infrastructure, quality and governance: sites and sponsors must be able to capture, clean and transmit data at near‑live speed, and AI tools used for monitoring or signal detection will themselves come under regulatory scrutiny. The early‑phase focus means oncology and other high‑risk, high‑innovation areas are likely to move first – but if the pilot succeeds, expectations for “traditional” trials may change as well.
Future of data capture in clinical trials
For MedTech and diagnostics companies running or supporting drug trials, this development is worth watching closely. Devices used for data capture, remote monitoring or imaging could become critical enablers of real‑time trial architectures. At the same time, sponsors used to US‑only trials may find that the expectations built through this pilot start influencing global development strategies, including EU studies. Being ready with interoperable systems, robust near‑real‑time data pipelines and clear AI validation narratives may soon become a competitive advantage – not just a nice slide in the R&D strategy deck.
How Pure Clinical can help?
Pure Clinical can help sponsors assess whether their current processes and systems are ready for real‑time data flow in practice: from the choice of data‑capturing devices, through data architecture, to validation of algorithms and preparation of coherent documentation for FDA review. It is also worth stressing that real‑time or near real‑time trials are simply not possible without a strong operational backbone. You need a CRO with robust infrastructure and truly experienced project managers who understand both the regulatory expectations and the realities of running complex, data‑intensive studies. Pure Clinical offers exactly this combination: a CRO team used to coordinating high‑complexity projects, aligning sponsors, sites and data vendors, and keeping timelines and data quality under control even when trial designs and monitoring models become more dynamic.