Adapt Or Die: Best Practices In Designing An Adaptive, Open-Label RCT Phase 3 Oncology Trial

By Lew Bender, CEO, Intensity Therapeutics

In an era far, far away, clinical research was dominated by fixed, traditional trial designs. In these studies, the focus was on maintaining a strict, unchanging protocol. Today, at least in oncology, especially in early-stage studies, the focus is on adaptive trial design, whereby the research is designed to learn and adapt to reach an outcome quicker. Urgent medical needs and more complex, evolving drug technologies are driving the change. There are several types of adaptive designs that have emerged:

• Early Stopping (Futility or Efficacy) Studies: If the drug is performing exceptionally well (efficacy) or clearly failing (futility), sponsors can stop the trial early to save time and protect patients. Interim looks are designed into the protocol and amendments to adjust doses or other parameters are common.
• Sample Size Re-estimation: If the initial estimate of how many patients were needed was too low, the sponsor can expand the trial midway to ensure it has enough statistical power to reach a conclusion.  For example, in the multi-cohort Keynote-001 Phase 1B study, Merck initially studied pembrolizumab in advanced solid tumors. Merck later added dose expansion cohorts for melanoma and non-small cell lung cancer, ultimately enrolling 1,200 patients and leading to their obtaining several approvals.
• Response-Adaptive Randomization: More future patients are assigned to the treatment arm that is currently performing better.
• "Drop-the-Loser" (Pick-the-Winner): In studies testing multiple doses or drugs, the underperforming arms are dropped, and resources are shifted to the most promising ones.
• Seamless Phase 2/3: Two phases are combined into one study. The first part identifies the best dose (Phase 2), and the second part immediately tests that dose for final approval (Phase 3).

Adaptive Trials Application In Cancer Research

Adaptive trials have found a home in cancer research because cancer treatment rarely involves a single agent and cancer is multifactorial in its nature. Treatments are based on biomarkers, especially genetic mutations of a patient’s particular cancer. Cancer is complicated and life-threatening, meaning researchers must find better regimens. The various treatment combinations continue to evolve as new agents are discovered and tried. Often, cancer treatment involves several complex regimens and schedules, even for the same cancer. For example, to treat lymphoma, ABVD (adriamycin, doxorubicin, bleomycin, and vinblastine) is used to treat the Hodgkin's type, whereas CHOP (cyclophosphamide, hydroxydaunorubicin, which is doxorubicin, Oncovin [vincristine], and prednisone) is used for non-Hodgkin's. While similar in some aspects, ABVD and CHOP are very different regimens designed for the specific subtype of lymphoma being treated. In recent years, newer types of drugs, biologics (cytokines, antibodies, immunotherapies, and antibody-drug conjugates), cell therapies, long-acting cytokine formulations, and intratumoral agents have added to the already complex set of drugs. In fact, AVBD and CHOP consist entirely of older chemotherapeutic agents.

Using adaptive studies can be effective to determine effective combinations and improve outcomes sooner. Adaptive studies must incorporate plausible rationality for any proposed study combination based on each drug’s mechanism of action. To justify a combination, there should be synergy between the components. For regulators and IRBs to approve a trial, the combinations of agents (new and old) must make sense. Once proposed, the key design considerations – adaptivity, randomization, control selection, operational complexity, and outcome expectations – must be clearly elucidated to investigators. An adaptive study must balance operational efficiency with interpretability and regulatory rigor. The limitations of traditional fixed-Phase 3 designs in oncology, the increasing pressure to accelerate timelines while maintaining evidentiary standards, the efficiency gains (sample size re-estimation, early stopping, etc.), and ethical considerations (reducing exposure to inferior therapy) have all necessitated the use of adaptive trial designs.

Other Considerations For Adaptive Study Design

In addition to the rationale for choosing test articles, sponsors must address several other issues in the adaptive (and any) protocol. Each test article may have its own unique route of administration, which has implications for blinding, number of clinic visits, dosing schedules, and site training. Each product may have different requirements for safety monitoring, immunogenicity, and handling. The schedule of event table of each cohort needs to be carefully thought through as those tables have implications for the onset of action, durability of response, and safety.

Adaptive trial types often depend on whether the drug being tested as first-, second-, or third-line therapy, or as a rescue. There are different levels of competition for where in the patient treatment line-up the drug is being used. Competing trials strongly affect recruitment and the size of the available patient population at the country and site levels. Costs can vary significantly even at sites in the same city.  More changes are common in adaptive trials and different sites can charge more or less for making more amendments.

In RCTs, the target population must be the same across all cohorts evaluated. Regulatory agencies can only assess a drug or regimen’s effectiveness in populations that match. Therefore, stratification and randomization programs in adaptive trials are critical to reducing bias and producing high-quality data.

Study Considerations And Rationales

Adaptive studies are expected to provide information as they gather it. When writing the protocol, sponsors need to foresee where changes may be needed and draft accordingly.

Stratifying Strategies

Many factors can affect how a study stratifies patients.  Selecting strong prognostic factors such as disease severity, age, biomarkers, gender, etc., to ensure balanced treatment groups is critical. Recruitment factors also affect stratification. For example, in RCTs, various randomization ratios may be considered. For a potent new medicine, a 1:1 ratio may achieve results more quickly; however, a 2:1 ratio could increase enrollment as patients seek the new medicine. Researchers may have to consider numerous stratification strategies during the trial, such as increasing the study size or adding biomarkers, to improve the odds of success. However, biomarkers add cost and time and, if unvalidated, may be risky. The new agent, if early results show strong potency, could potentially be used with or without the standard of care. However, if the new agent increases toxicity, then the regimen with the standard of care may be too harsh for patients to remain on the study.  

Deciding To Blind Or Unblind

Sponsors should also consider whether the study should be open-label or blinded. Blinding can eliminate bias, but it may be challenging to implement, especially when several routes of administration are needed between study arms. The more definitive the endpoint, the more acceptable an open-label study can become. Sponsors should ensure that information is carefully managed to reduce individual investigators' bias. Proper randomization can reduce bias in open-label studies. If comparators in the study are the only alternative to the new treatment, they can help reduce bias and post-randomization dropouts. If the study’s endpoint is more definitive, such as overall survival versus progression-free survival (PFS), then open-label studies might be acceptable. If the study uses a RECIST-based metric such as PFS, then sponsors should leverage a central review and blinded assessment to reduce variability among sites. While overall survival requires no central review, it takes longer to achieve than PFS and would likely be more costly. IRBs that periodically examine safety and efficacy are important for reassuring investigators that the study is not futile or unsafe. This is where adaptive studies can help, as interim looks can reduce time to achieving a more definitive endpoint.

Meeting Early With Regulators

Finally, sponsors must reach early alignment with health authorities on the study design for registration studies. Bias mitigation, independent review committees, and objective endpoints are critical. The FDA and global regulatory agencies' expectations for adaptive Phase 3 trials are that the trials are safe for participants, have low to no bias, and are conducted the same way at all sites. Regulators expect dosing in the various treatment cohorts to be rigorous and independent of location.

Types Of Adaptations And The Statistical Framework

Adaptive studies can necessitate allowances in the statistical analysis plan. Sample size re-estimation, interim efficacy or futility analyses, and population enrichment or cohort expansion are the tools of adaptive studies. These techniques allow the generation of information that permits the trial to adapt. Good interim analysis planning, a proper understanding of the timing of data readouts, alpha costs, and defining the role of the independent data monitoring committees are vital to a good adaptive design. Pharmacovigilance, risk management and mitigation, and site management are vital for every study, but especially so for adaptive trial design. Monitoring should be frequent and queries be addressed quickly, as sponsors must have accurate, near-real-time data to make informed decisions and adaptations.

Operational And Logistical Challenges 

Adaptive trials require experienced sites and investigators with a broad understanding of various imaging technologies, dosing techniques, handling procedures for novel investigational medicinal products (IMP), unusual dispensing protocols from the pharmacy, and the capability to support adaptive trial requirements. Additionally, sponsors may need to help sites reduce and eliminate paperwork so patients can enroll quickly and keep them enrolled. Patient retention is critical, and sponsors should work with sites to ensure patients are kept informed about activities necessary for their care as part of the trial.

Conclusion

Adaptive trial design is reshaping the clinical research landscape by delivering greater operational efficiency, improved efficacy outcomes, and enhanced affordability for the sponsor across the drug development life cycle. By allowing preplanned modifications based on interim data, these designs accelerate timelines and increase the likelihood that promising therapies reach patients more quickly. As development costs continue to rise and regulatory expectations evolve, adaptive methodologies are emerging as a practical solution to improving overall success rates while maintaining scientific integrity. Increasing collaboration between regulators, sponsors, and technology providers has further solidified adaptive trials as a cornerstone of modern clinical strategy, especially in cancer. In 2026, the biotech and pharma industries can expect broader regulatory acceptance and deeper integration of real-time data analytics and more AI-driven decision tools in adaptive studies and protocol preparations.

About The Author:

Lew Bender is the founder and CEO of Intensity Therapeutics. He has more than 32 years of biopharmaceutical leadership experience and has helped take products leveraging novel drug delivery techniques from discovery through product approval. Lew founded Intensity and its foundational science in his basement. Including INTS, he has been CEO of two other public biotech companies, a personalized medicine company (IG), and a drug delivery company (Emisphere). During his career, Lew held numerous positions in addition to the CEO role, including head of business development, manufacturing, process development, regulatory affairs, and quality control.