Umbrella? Basket? Discover 4 Types of Adaptive Clinical Trials And Their Benefits

By Jessica Cordes, Clinical Excellence GmbH

Clinical trials are crucial for advancing medical science and enhancing patient outcomes. They are the best way to test how safe and effective new interventions are, such as drugs, devices, vaccines, or procedures, before they are allowed for general use. Clinical trials also can measure the effectiveness of current treatments, find the best methods to prevent or detect diseases, or assess the effect of health policies or programs.

Traditional clinical trial designs, such as randomized controlled trials (RCTs), have many challenges. They often need many participants, a lot of time, and strict protocols that make the results less relevant and useful. Also, traditional designs may not deal with the complexity and diversity of many diseases well, especially in precision medicine, where treatments are customized to the patients and their diseases.

New and creative ways of designing clinical trials are needed that can deal with some of the challenges of traditional designs and make clinical trials more flexible, efficient, and relevant.

The FDA’s Complex Innovative Trial Design (CID) Paired Meeting Program promotes using CID methods for the final stages of drug development and fosters innovation by allowing the FDA to publicly discuss the trial designs from the program, including trial designs for medical products that the FDA has not approved yet.

Adaptive Clinical Trial Designs And Real-world Examples

1. Complex Innovative Clinical Trial Designs

In complex innovative clinical trials, one or more features of the clinical trial can be changed in advance based on the data collected from the subjects in the clinical trial. These features may include sample size, randomization ratio, treatment assignment, or eligibility criteria.

Bayesian methods need different criteria for making decisions. The criteria influence the design of a clinical trial and its ability to infer from the data. Before starting, it is important to predefine not only the statistical method for testing hypotheses that will be used but also the rule for changing (i.e., the number of participants or the assignment of treatments).

Krendyukov et al. searched ClinicalTrials.gov for adaptive trials in rare cancer and surveyed 3,200 oncologists online. The survey asked doctors who treat patients about the value of different evidence, adaptive trial designs, and surrogate endpoints for clinical decisions.

The survey found that 97% of responders valued adaptive Phase 2/3 trials with small sample sizes as much as randomized trials (82%) in rare cancer. Most oncologists (80%) agreed that surrogate endpoints could replace overall survival. They preferred adaptive designs with futility, interim, sample size, and randomization adaptations (Krendyukov et al., 2021). Therefore, some rare cancer trials could benefit from using adaptive clinical trials and surrogate endpoints.

A method of creating innovative clinical trial designs that are complex is to use information from prior studies in the design and analysis of a new clinical trial. This can include improving the efficiency of Phase 3 clinical trials by using control data from Phase 2 clinical trials. In this situation, outcome data from a control group in a Phase 2 clinical trial is used in the estimation of treatment differences from a subsequent Phase 3 clinical trial. As an example, the PANORAMA-HF clinical trial (Prospective Trial to Assess the Angiotensin Receptor Blocker Neprilysin Inhibitor LCZ696 Versus Angiotensin-Converting Enzyme Inhibitor for the Medical Treatment of Pediatric Heart Failure) tested how sacubitril/valsartan affected clinical outcomes in children with heart failure. The Phase 2/3 clinical trial had an adaptive, seamless, two-part design. It measured how single doses of sacubitril/valsartan were absorbed and worked (Part 1), and how sacubitril/valsartan compared with enalapril twice a day for 52 weeks in effectiveness and safety (Part 2) in children with heart failure due to low left ventricular function with two working ventricles. A new global rank measure of severity was used in a novel clinical trial design. Patients were split into three age groups and functional categories for analysis (Shaddy et al., 2023).

Sequential Multiple Assignment Randomized Trials (SMARTs)

SMARTs help to develop adaptive interventions by testing different options at each stage. A SMART has several intervention stages, and each stage matches one of the key decisions in the adaptive intervention. In a SMART, patients experience various stages and get randomly assigned to one of several treatment options at each stage. An adaptive intervention changes the type and/or amount of treatment according to the patient’s characteristics and/or current progress (e.g., response, adherence) to improve outcomes that are important for their health. This design can change or even stop treatment arms. It is used in early clinical trials to test different doses.

As an example, patients with breast cancer and pain joined the clinical trial and got one of two treatments randomly. They got a second random treatment based on how they responded. If they responded well, they got a lower dose or no more treatment. If they responded poorly, they got a higher dose, lower dose, or no more treatment. The second random treatment had a 50% chance for each option. Patients did not know which treatment was better for pain reduction (Kelleher et al., 2017).

Adaptive Enrichment Design

An adaptive enrichment design (Rosenblum et al., 2017) allows one to hypothesize that the intervention will have a stronger effect on a certain segment of the patient population. For example, this subgroup could be defined by a characteristic that is related to their age, genes, or disease and that is likely to influence the drug's effectiveness. In this case, a design could be used that enables adaptive modifications to the patient population based on interim results that contrast different groups.

For instance, a clinical trial may have recruited patients from the overall stroke patient population until a midway review, when it will determine based on set criteria to either continue recruiting the overall population or only recruit the targeted group. Data from before and after the midway review can be used to estimate the treatment effect in the targeted group.

Group Sequential Clinical Trials

These clinical trials have one or more planned interim analyses of comparative data with predetermined criteria for stopping the clinical trial. They may have rules for terminating the clinical trial when the evidence of effectiveness is sufficient for regulatory decision-making or when the evidence indicates that the clinical trial is unlikely to show effectiveness, which is often called stopping for futility.

The REMAP-CAP was a clinical trial that tested various treatments for pneumonia in ICU patients in different countries (Angus et al., 2020). The design adapted to how patients responded to treatments and used data collected from routine care. It used a statistical model to evaluate multiple disease states and therapies at the same time and in sequence, meaning that patients received two or more treatment regimens after each other. The design also adjusted the eligibility criteria, treatment doses, and the allocation of patients to each arm over time.

2. Platform Clinical Trials

Platform clinical trials evaluate several treatments for a single disease or condition using one overall protocol, with a shared control group and common infrastructure. The I-SPY 2 clinical trial was a platform clinical trial that tested different drugs for breast cancer in the neoadjuvant setting (Barker et al., 2009). Patients got a treatment regimen based on their breast cancer molecular subtype, and the clinical trial preferred arms with better efficacy signs for that subtype. This was done by updating the predictive probabilities of the drug in different subtypes as the data came in. The clinical trial stopped a treatment regimen if the drug reached a set level of efficacy or futility for one or more subtypes or if it reached a maximum number of patients.

3. Basket Clinical Trials

Basket clinical trials test one treatment for different diseases or subtypes of a disease that have a common biomarker or molecular characteristic. The NCI-MATCH clinical trial is a complicated study that uses different targeted therapies for advanced cancers based on their genetic changes. Patients with treatable genetic changes are put into one of over 30 groups, which include new therapies and drugs used for other cancer types (Flaherty et al., 2021). The NCI-MATCH was similar to other clinical trials that use tumor sequencing to match patients to therapies based on separate sub-studies.

4. Umbrella Clinical Trials

Umbrella clinical trials evaluate different therapies for one disease or subtype of a disease that has distinct biological markers or molecular characteristics (i.e. a specific gene expression). The BATTLE clinical trial was a type of clinical trial that matched four targeted therapies to patients with advanced NSCLC who did not respond to previous chemotherapy according to their molecular profiles (Kim et al., 2011).

The Benefits of Adaptive Trials

Adaptive clinical trials can change some parts of the clinical trial based on the data that comes in. This makes them more flexible and efficient for testing different interventions in complex and changing situations, such as a pandemic. Adaptive clinical trials can give quick and trustworthy evidence to guide clinical actions and policies.

Adaptive and innovative clinical trial designs can:

• speed up and improve the development of new therapies by using fewer patients, time, and resources to get reliable results
• make research more ethical by exposing fewer patients to risky or ineffective interventions and increasing the benefit for patients and society
• use real-world data and evidence from various sources and settings to make the findings more general and useful
• test multiple interventions, combinations, doses, or biomarkers at the same time in the same clinical trial, avoiding separate clinical trials and finding the best treatment for each patient or subgroup
• support personalized or precision medicine by finding the most responsive patients or subgroups based on their features
• encourage collaboration and coordination among different stakeholders to ensure the quality, validity, and acceptability of the clinical trial results and their use in practice and policy
• offer more flexibility and adaptability to deal with the uncertainty and complexity of the research questions and the changing science and clinical situation
• enhance learning and decision-making throughout the clinical trial by using methods, analyses, or techniques to update the information and use the data as it comes
• increase the transparency and credibility of the research by stating the clinical trial goals, hypotheses, endpoints, criteria, and rules for adaptation and reporting them in a clear protocol and plan
• have some challenges and limitations, such as the need for planning, simulation, and validation of the design, the higher operational and logistical complexity, the possibility of bias or confounding, and the regulatory and ethical standards and requirements.

References:

Regulatory Documents:

1. FDA Guidance for Industry: “Interacting with the FDA on Complex Innovative Trial Designs for Drugs and Biological Products”
2. FDA Guidance for Industry: “Adaptive Designs for Clinical Trials of Drugs and Biologics” (Nov 2019)
3. FDA Guidance for Industry: “Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products” (March 2019)
4. FDA Guidance for Industry: “Master Protocols: Efficient Clinical Trial Design Strategies To Expedite Development of Oncology Drugs and Biologics” (March 2022)
5. Complex Innovative Trial Designs Pilot Program, https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ucm617212.htm

Scientific Literature:

1. Angus DC, Berry S, Lewis RJ, Al-Beidh F, Arabi Y, van Bentum-Puijk W, Bhimani Z,
2. Bonten M, Broglio K, Brunkhorst F, Cheng AC, Chiche JD, De Jong M, Detry M, Goossens H, Gordon A, Green C, Higgins AM, Hullegie SJ, Kruger P, Lamontagne F, Litton E, Marshall J, McGlothlin A, McGuinness S, Mouncey P, Murthy S, Nichol A, O’Neill GK, Parke R, Parker J, Rohde G, Rowan K, Turner A, Young P, Derde L, McArthur C, and Webb SA, 2020, The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) Stud. Ann Am Thorac Soc Vol 17(7), 879–891
3. Baker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA and Esserman LJ, 2009, I-SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy, Journal of Clinical Pharmacology & Therapeutics, 86, 1, 97–100.
4. DiMasi JA, Grabowski HG, Hansen RW, 2016, Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20-33
5. Flaherty KT, Gray RJ, Chen AP, Li S, McShane LM, Patton D, Hamilton SR, Williams PM, Iafrate AJ, Sklar J, 2021, Molecular Landscape and Actionable Alterations in a Genomically Guided Cancer Clinical Trial: National Cancer Institute Molecular Analysis for Therapy Choice (NCI MATCH). J. Clin. Oncol. 38, 3883–3894.
6. Hatfield I, Allison A, Flight L, Julious SA, and Dimairo M, 2016, Adaptive designs undertaken in clinical research: A review of registered clinical trials. Trials, 17, 150.
7. Kelleher SA, Dorfman CS, Plumb Vilardaga JC, Majestic C, Winger J, Gandhi V, et al. Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART. Contemp Clin Trials. 2017;57:51–7.
8. Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR, Tsao A, Stewart DJ, Hicks ME, M.D., Erasmus J, Gupta S, M.D., Alden CM, Liu S, Tang X, Khuri FR, Tran HT, Johnson BE, Heymach JV, Mao L, Fossella F, Kies M, Papadimitrakopoulou V, Davis SE, Lippman SM, and Hong WK, 2011, The BATTLE Trial: Personalizing Therapy for Lung Cancer. Cancer Discov. 1(1), 44–53
9. Krendyukov A, Singhvi S, and Zabransky M, 2021, Value of Adaptive Trials and Surrogate Endpoints for Clinical Decision-Making in Rare Cancers. Frontiers in Oncology, Volume 11, Article 636561
10. PREVAIL II Writing Group, 2016, A Randomized, Controlled Trial of ZMapp for Ebola Virus Infection, N Engl J Med. 2016 October 13; 375(15): 1448–1456
11. Reckamp KL, Redman MW, Dragnev KH, Minichiello K, Villaruz LC, Faller B, Al Baghdadi T, Hines S, Everhart L, Highleyman L, Papadimitrakopoulou V, Neal JW, Waqar SN, Patel JD, Gray JE, MD13; Gandara DR, Kelly K, and Herbst RS, Phase II Randomized Study of Ramucirumab and Pembrolizumab Versus Standard of Care in Advanced Non–Small-Cell Lung Cancer Previously Treated With Immunotherapy—Lung-MAP S1800A. J Clin Oncol 40:2295-2307
12. Rosenblum M, and Hanley DF, 2017, Adaptive Enrichment Designs for Stroke Clinical Trials, Volume 48(7), 2017; 2021-2025
13. Shaddy R, Garito T, Zhang S, Burch M, Kantor PF, Kocun M, Solar-Yohay S, Bonnet D, 2023, Baseline Characteristics of Pediatric Patients With Heart Failure Due to Systemic Left Ventricular Systolic Dysfunction in the PANORAMA-HF Trial. Circ Heart Fail. 16(3):e009816.
14. Sydes, MR, Spears MR, Mason MD, Clark NW, Dearnaley DP, de Bono JS, Attard G, Chowdhury S, Cross W, Gillessen S, James ND, Jones RJ, Parker CC, Ritchie AWS, Russell JM, Thalmann GN, Millman R, Lee SC, & Parmar MKB, 2018, Adding abiraterone or docetaxel to long-term hormone therapy for prostate cancer: Directly randomized data from the STAMPEDE multi-arm, multi-stage platform protocol. Annals of Oncology, 29(5), 1235–1248

About The Author:

Jessica Cordes started her clinical operations career in 2009, working at various companies including Big Pharma and several small to midsize biotech companies. She gained extensive experience on different levels from country study management, global study management, and, since 2018, leadership in clinical operations. During her time at Medigene and Immatics, she structured the clinical operations department, built cohesive global teams, and implemented GCP and ATMP-compliant processes. For more than 12 years, she has been working in oncology clinical trials (including hemato-oncology as well as solid tumors) and with ATMPs since 2018. Since 2023, she has been working as an independent consultant and trainer, supporting small companies in building their clinical operations group and setting up their clinical trials for success. She also issues a clinical research bi-weekly newsletter and hosts a quarterly discussion.