Continuous Clinical Trials: History, Hype, and How to Make Them Work

Continuous Clinical Trials: History, Hype, and How to Make Them Work

Contributed Commentary by Jennifer Duff, Merative

April 3, 2026 | Food and Drug Administration Commissioner Marty Makary has been signaling a potential change to how clinical trials are conducted. Since assuming the Commissioner role, he has championed "continuous trials"  to accelerate drug development, calling the current phased approach inefficient.  

Continuous trials is a catchall label for designs that minimize stoppages between phases, adapt in real time to accumulating evidence, and operate under a durable master infrastructure—often using adaptive, seamless, and platform methods. These designs didn’t appear overnight; they’re the culmination of decades of innovation (group sequential monitoring, Bayesian interim decisions, seamless phase 2/3, and master protocols).

During COVID-19, large adaptive platform trials demonstrated that the model can accelerate learning at scale (e.g., REMAP-CAP, ISPY, TOGETHER). Makary is mainstreaming the term continuous trials, arguing that modern technology and cloud endpoints should enable ongoing, regulator-visible trials with fewer phase breaks and shorter review cycles.

Is this a game-changer? Potentially yes, but only if sponsors pair innovative design with rigorous data governance, validation, and auditability.

For many small- to mid-sized life sciences companies, this approach may sound out of reach given the investment in time and technology needed to realize the vision. But the technology required to run successful continuous trials is available today. Organizations that prepare for continuous trial design will have a head start on compliance and a competitive advantage by bringing therapies to patients more efficiently.

Below, I review the historical arc, what ‘continuous’ really means, whether Makary’s vision can deliver, and the technology capabilities sponsors will need to realize the promise: shorter cycle times and improved outcomes without compromising regulatory acceptability.

A Brief History: From Interim Looks to Living Platforms

For decades, the clinical trial process has been defined by distinct, sequential steps: Phase I for safety, Phase II for preliminary efficacy, and Phase III for confirmatory data. Each phase is a separate study, requiring its own protocol, application, and data analysis before the next phase can begin.

Some argue that this approach can slow the pace of innovation. 

However, from the 1970s through the ‘90s, interim monitoring and group sequential methods demonstrated that trials could be adapted ethically (stopping early for futility or benefit) without inflating error rates, setting the stage for adaptive decision-making.

In the early 2000s, the application of Bayesian methods introduced predictive probabilities and continuous monitoring, enabling flexible, information-driven pauses and resumptions rather than rigid schedules. Then, with the introduction of seamless phase 2/3 trials for oncology research, it became normalized to carry data and sites forward across development stages, shrinking “dead time” between phases.

Also from the early 2000s, the introduction of master protocols - basket, umbrella, and especially platform trials—created durable designs that add/drop arms over time based on specific outcomes during the trial. The FDA and European Medicines Agency published guidance and primers amid the rapid rise in the prevalence of master protocols, driven by COVID-19, as platform trials (e.g., REMAP-CAP, TOGETHER) demonstrated rapid, iterative evidence generation at a global scale.

Collectively, these advances are the technical backbone of continuous trials: ongoing, adaptive, infrastructure-reusing programs rather than one-off, start-stop studies.

What Do Continuous Trials Mean In 2026?

While there’s no single statutory definition, recent FDA remarks and trade coverage frame continuous trials as reducing idle time, decoupling review workstreams (e.g., early CMC review), and making phases less siloed. Policy context matters: The FDA’s 2019 Adaptive Design Guidance (final) and ICH E20 (Step 2 draft, 2025) codify acceptable adaptive methods for confirmatory trials. Any continuous approach must comply with these principles on type I error control, trial integrity, and pre-specification. Thus, continuous trials are an operationalization of seamless/adaptive designs, not a replacement for statistical or research rigor.

Are Continuous Trials A Game-Changer?

There is a case to be made that continuous trials have been, and with broader adoption, can be a game changer for research.

Historical application of continuous trials models has proven shorter development cycles by eliminating pauses between phases (seamless 2/3), adding/removing arms without new startups (platform), and streamlining regulatory review (CMC earlier; data as a Part 2).
Continuous trials do show higher information yield: common controls, response adaptive randomization, and Bayesian modeling increase efficiency, often with fewer patients and more relevant subgroups. ISPY 2 and other platforms illustrate this at scale.
And continuous trials can support learning-based medicine: CTTI and EMA highlight embedding trials into care (EHR/eSource) and using master protocols to continuously generate evidence—supporting a broader vision for the benefits of continuous trials in healthcare.

Especially for small- to mid-sized life sciences companies, where agility is already essential to operate, it will be easier to adapt to this iterative model and achieve meaningful, near-term efficiency gains. When operating in a financially constrained environment, which is much more common for small- to mid-sized life sciences companies, a streamlined research methodology delivered at a reasonable price and use of modern technology solutions will deliver significant bottom-line benefits.

However, continuous trials are not a silver bullet. Regulatory requirements still reflect the need for pre-specification, integrity safeguards, and error control. Continuous must fit inside those guardrails and does not provide a free-for-all research paradigm.

The operational complexity of continuous trials is also not trivial. Data standards, governance across multiple arms and vendors, and the reality of the trial network demand robust infrastructure and management. And finally, and especially relevant now, agency bandwidth/acceptance may be limited. Even with artificial intelligence used to optimize regulatory reviews, the benefits are yet to be fully realized or understood. While Makary promotes faster, decoupled reviews, some observers and staff express concern about the feasibility, resources, and legal authorities for ultra-expedited pathways.

Continuous trials can be a game-changer provided sponsors invest in a valid adaptive design, continuous data readiness (clean, auditable, ALCOA+), and platform-grade operations. Otherwise, “continuous” risks being a slogan rather than a transformation.

The Five Capabilities Needed For Continuous Trials

The move toward continuous trials is an opportunity. FDA leaders envision a future where regulators can monitor trial endpoints in the cloud as they occur, dramatically shortening review times and integrating into the idea of living healthcare systems. Here are five foundational capabilities organizations need to strengthen to thrive in this new environment. 

Frame the concept of use and align all stakeholders:
Choose platform vs. umbrella/basket and define endpoints, timing, and interim rules consistent with FDA 2019 and ICH E20. Then ensure alignment up front across all aspects of the business and research, including sites, clinical operations, data management, and partner providers.

Given how long a continuous trial can run, there should be a heightened focus on identifying stakeholders who can oversee the trial from start to finish. It is also key to establish strong transition plans to align multiple stakeholders who will likely be involved throughout the study.
Ensure advanced statistical and operational agility  An adaptive trial is only as good as the statistical methods that underpin it. The design requires careful planning to control for Type I error rates and ensure the analysis of combined data from different phases is statistically sound.  Using technology that enables easy, real-time, and immediate data access from sites, labs, participants, and specialists is vital to ensuring appropriate decisions are made during the trial.

Operationally, this requires more than just technology; it requires a team that is comfortable with the dynamic nature of these trials. The ability to quickly implement protocol amendments, manage site communications, and adjust logistics based on interim data is paramount. This operational agility, supported by robust technology, is what separates successful continuous trials from those that struggle with their own complexity.
Leverage the right technology framework A white paper by Friends of Cancer Research outlined operational considerations for seamless clinical trial designs and said, “Seamless designs can enable faster transitions from dose escalation to expansion, incorporate data from early-phase patients into later analyses, and can embed randomization, maximizing learning from each patient.”
The core of a successful continuous trial is technology that supports these efforts and scales seamlessly from one phase to the next. Managing a trial that adapts and expands in real time is nearly impossible with disconnected systems. 
Integrate regulatory compliance Align with FDA 2019 and ICH E20 on controlling Type I error, preserving blinding, and ensuring decision independence. In addition, continuous trials add a layer of regulatory complexity, as requirements can vary by region and continue to evolve.

Systems must be designed with flexibility to address these different standards within a single trial framework. This means having built-in controls and visible audit trails to demonstrate compliance to multiple regulatory bodies simultaneously, ensuring that an accelerated trial doesn't encounter obstacles during submission. 
Ensure flexible, real-time data capabilities 
Operationalize as a “continuous data” program: eSource ingestion, EHR integration, common controls, and real-time data quality monitoring must be consistent with the vision for learning health systems.

The clinical trial technology stack must be able to compile critical data points from disparate sources, like ePRO, labs, and imaging, in real time. This provides the continuous stream of data needed for ongoing safety signaling, endpoint monitoring, and interim analyses that drive adaptive decision-making.

Preparing Today for Continuous Trials

As industry conversations about continuous trials gain momentum, life science companies should invest in the capabilities to run faster, more efficient trials. For small to mid-sized pharma companies, it offers a path to level the playing field, accelerate development, and reduce the financial burden of bringing new therapies to market.

Makary’s continuous trials vision is in the right direction: the biggest wins come from erasing idle time, reusing infrastructure, and acting on data as it arrives. But design excellence and data integrity, not just cloud dashboards, will separate real game changers from experiments. The science (adaptive/seamless/platform), the policy (FDA 2019; ICH E20), and the operations (platform-grade solutions) are now aligned enough to deliver shorter cycles and better outcomes at scale.

By investing in the right technology and processes, organizations can transform their clinical development programs and prepare for the opportunities that come with continuous trials.

Jennifer Duff is Executive Vice President and General Manager of Zelta. She has more than 27 years of experience in the life sciences industry with a specialization in enabling and scaling industry-leading services and technology solutions for biopharma clients. Jennifer has collaborated with teams across the life sciences industry, from large to mid-sized companies to many of the major technology providers, over her career. She has an MBA in Biotechnology and Healthcare Management.  She can be reached at Jennifer.Duff@merative.com.