In 2020, the average clinical trial cycle was 93.4 months. By 2024, it jumped to 100.4 months.
Seven plus months. In the context of new drug development, where every month carries a sizable financial cost and time patients spend waiting for treatments, that time increase is significant.
The lengthening of clinical trial timelines isn't news; the research community has watched this trend unfold over decades. What's changed is the sense of urgency: as timelines approach the nine-year mark, the question has to be "what are we going to do about it?"
The forces driving longer timelines are interconnected, compounding, and — in some cases — a consequence of the very ambitions that make modern clinical research so powerful.
As our scientific understanding deepens, so does the appetite for more sophisticated trial designs. Adaptive protocols, complex eligibility criteria, multi-arm studies, and demanding biomarker requirements have all become more common.
Complexity has costs. More intricate protocols create more opportunities for deviation and site burden. Restrictive inclusion/exclusion criteria narrow the eligible patient population, making the recruitment of healthy volunteers and patients more difficult.
Before a single patient is enrolled, months can slip away. Contract negotiations, IRB reviews, and site activation processes are necessary steps — and each is a potential bottleneck.
Across a multi-site study, these delays stack. A site that takes four months to activate instead of six weeks delays enrollment at that location and also sets back the entire study's momentum.
The clinical research workforce is under strain. High coordinator turnover, investigator burnout, and a growing number of concurrent trials competing for the same site resources have created a capacity crunch that no amount of planning can fully insulate against. When sites are stretched thin, everything slows: patient recruitment, data entry, query resolution, and protocol adherence.
Longer trials mean higher costs across every line item:
For smaller biotechs, every additional month can be existential. For larger organizations, extended timelines constrain capital, limit portfolio flexibility, and slow pipeline velocity.
Patients in trials, particularly those with serious or rare conditions, often have limited alternatives. Extended trial durations mean:
For those not yet enrolled, every month of delay is a month longer before a new treatment might reach them.
A rigorous review of inclusion and exclusion criteria — with an honest lens on which requirements are essential and which are reflexive caution — can expand the eligible patient population without compromising data integrity. Similarly, data collection should be proportionate to what's needed to answer questions that are critical to the study.
Minimizing "bad complexity" is one of the highest-impact changes a sponsor can make before a trial even begins.
Study startup is one of the highest-leverage areas for time savings. Helpful approaches include:
These disciplines require organizational commitment to implement them consistently.
AI can help predict which clinical research sites will perform and which patients are most likely to be eligible and willing to participate. With access to real-world data, EHR integrations, and enrollment modeling tools, sponsors can make better decisions earlier, before expensive site activation is underway.
Getting site selection right the first time prevents the costly cycle of adding sites mid-study to compensate for underperforming ones.
Sites can offer insights into which protocol elements will create friction for their patient population, which operational requirements will strain their team, and what would make a study more attractive among the trials competing for their attention. Engaging sites during protocol development, not after finalization, supports scientifically sound and operationally executable protocols, reducing amendments and accelerating activation.
All of the above strategies converge on the same outcome: faster enrollment, faster study completion, and faster access to FDA-approved treatments for the patients who need them most.
At Remington-Davis (RDI), we operate as an extension of the sponsor team across the full clinical trial lifecycle, focusing on the areas where execution has the greatest impact.
The clinical trial community has long accepted extended timelines as an unavoidable feature of rigorous science. In many cases, there's room to do better. That's where the opportunity lies, and where RDI adds value.
Learn more about how we can help support you at any stage of the clinical trial lifecycle.
Phase III trials are typically the longest because they involve large patient populations, complex protocols, and extensive regulatory oversight required for approval. These studies also face greater challenges with patient recruitment and retention over extended periods, which can further impact timelines.
While Phase III trials remain the longest, timelines are increasing across all phases due to greater protocol complexity, stricter regulatory requirements, and ongoing recruitment and retention challenges. Phase I trials and Phase II trials are becoming more data-intensive, while Phase IV trials can extend over many years as they monitor long-term safety and real-world outcomes.
Phase I trials are generally the shortest, typically lasting several months, as they involve small participant groups and focus primarily on a drug's safety and dosing. Timelines may extend if additional cohorts are needed to assess dosing or emerging adverse events.
Phase II studies may take several months to a couple of years, depending on how quickly patients can be enrolled and how long it takes to observe efficacy signals. More complex endpoints and increased data collection requirements can also slow timelines and clinical trial results.
Phase IV trials are often open-ended and can last for many years, as they monitor long-term safety and real-world effectiveness in the human body after FDA approval. Their duration depends on the study objectives and regulatory commitments.
Preclinical studies typically take several months to a few years, depending on the complexity of the therapy and the extent of laboratory and animal testing required. Timelines can be extended by the need to demonstrate safety, toxicology, and dosing data before a therapy is approved to enter human clinical trials.