Adaptive clinical trial

An adaptive clinical trial is a clinical trial that evaluates a medical device or treatment by observing participant outcomes (and possibly other measures, such as side-effects) on a prescribed schedule, and modifying parameters of the trial protocol in accord with those observations. The adaptation process generally continues throughout the trial, as prescribed in the trial protocol. Modifications may include dosage, sample size, drug undergoing trial, patient selection criteria and "cocktail" mix.[1] In some cases, trials have become an ongoing process that regularly adds and drops therapies and patient groups as more information is gained.[2] Importantly, the trial protocol is set before the trial begins; the protocol pre-specifies the adaptation schedule and processes. The aim of an adaptive trial is to more quickly identify drugs or devices that have a therapeutic effect, and to zero in on patient populations for whom the drug is appropriate.[3] A key modification is to adjust dosing levels.[2] Traditionally, non-adverse patient reactions are not considered until a trial is completed.[3][4]

The problem of adaptive clinical trial design is more or less exactly the bandit problem as studied in the field of reinforcement learning.

## Contents

* 1 History
* 2 FDA Adaptive Trial Design Guidance
* 3 Bayesian designs
* 4 Logistics
* 5 Disease targets
* 5.1 Breast cancer
* 5.1.1 I-SPY 1
* 5.1.2 I-SPY 2
* 5.2 Alzheimer's
* 6 Risks
* 7 See also
* 8 References
* 9 Sources
* 10 External links

## History

In 2004, a Strategic Path Initiative was introduced by the United States’ Food and Drug Administration (FDA) to modify the way drugs travel from lab to market. This initiative aimed at dealing with the high attrition levels observed in the clinical phase. It also attempted to offer flexibility to investigators to find the optimal clinical benefit without affecting the study's validity. Adaptive clinical trials initially came under this regime.[2]

The FDA issued draft guidance on adaptive trial design in 2010.[3] In 2012, the President's Council of Advisors on Science and Technology (PCAST) recommended that FDA "run pilot projects to explore adaptive approval mechanisms to generate evidence across the lifecycle of a drug from the premarket through the postmarket phase." While not specifically related to clinical trials, the Council also recommended that FDA "make full use of accelerated approval for all drugs meeting the statutory standard of addressing an unmet need for a serious or lifethreatening disease, and demonstrating an impact on a clinical endpoint other than survival or irreversible morbidity, or on a surrogate endpoint, likely to predict clinical benefit."[5]

In the 2007–2009 period, the Department of Biostatistics at the M. D. Anderson Cancer Center was running 89 Bayesian adaptive trials, 36% of the total designed by the faculty.[6]

## FDA Adaptive Trial Design Guidance

The FDA adaptive trial design guidance [1] is a 50-page document covering wide-ranging and important topics “such as ... what aspects of adaptive design trials (i.e., clinical, statistical, regulatory) call for special consideration, ...when to interact with FDA while planning and conducting adaptive design studies, ... what information to include in the adaptive design for FDA review, and ... issues to consider in the evaluation of a completed adaptive design study.” Attempts have been made to excerpt the guidance and make it more accessible [2].

## Bayesian designs

According to FDA guidelines, an adaptive Bayesian clinical trial can involve:[7]

* Interim looks to stop or to adjust patient accrual
* Interim looks to assess stopping the trial early either for success, futility or harm
* Reversing the hypothesis of non-inferiority to superiority or vice versa
* Dropping arms or doses or adjusting doses
* Modification of the randomization rate to increase the probability that a patient is allocated to the most appropriate treatment (or Arm in the Multi-Armed Bandit model)

The Bayesian framework Continuous Individualized Risk Index which is based on dynamic measurements from cancer patients can be effectively used for adaptive trial designs.

## Logistics

The logistics of managing traditional, fixed format clinical trials are quite complex. Adapting the design as results arrive adds to the complexity of design, monitoring, drug supply, data capture and randomization.[2] However, according to PCAST "One approach is to focus studies on specific subsets of patients most likely to benefit, identified based on validated biomarkers. In some cases, using appropriate biomarkers can make it possible to dramatically decrease the sample size required to achieve statistical significance—for example, from 1500 to 50 patients."[8]

## Disease targets

### Breast cancer

An adaptive trial design enabled two experimental breast cancer drugs to deliver promising results after just six months of testing, far shorter than usual. Researchers assessed the results while the trial was in process and found that cancer had been eradicated in more than half of one group of patients. The trial, known as I-Spy 2, tested 12 experimental drugs.[3]

#### I-SPY 1

For its predecessor I-SPY 1, 10 cancer centers and the National Cancer Institute (NCI SPORE program and the NCI Cooperative groups) collaborated to identify response indicators that would best predict survival for women with high-risk breast cancer. During 2002–2006, the study monitored 237 patients undergoing neoadjuvant therapy before surgery. Iterative MRI and tissue samples monitored the biology of patients to chemotherapy given in a neoadjuvant setting, or presurgical setting. Evaluating chemotherapy's direct impact on tumor tissue took much less time than monitoring outcomes in thousands of patients over long time periods. The approach helped to standardize the imaging and tumor sampling processes, and led to miniaturized assays. Key findings included that tumor response was a good predictor of patient survival, and that tumor shrinkage during treatment was a good predictor of long-term outcome. Importantly, the vast majority of tumors identified as high risk by molecular signature. However, heterogeneity within this group of women and measuring response within tumor subtypes was more informative than viewing the group as a whole. Within genetic signatures, level of response to treatment appears to be a reasonable predictor of outcome. Additionally, its shared database has furthered the understanding of drug response and generated new targets and agents for subsequent testing.[9]

#### I-SPY 2

I-SPY 2 is an adaptive clinical trial of multiple Phase 2 treatment regimens combined with standard chemotherapy. I-SPY 2 linked 19 academic cancer centers, two community centers, the FDA, the NCI, pharmaceutical and biotech companies, patient advocates and philanthropic partners. The trial is sponsored by the Biomarker Consortium of the Foundation for the NIH (FNIH), and is co-managed by the FNIH and QuantumLeap Healthcare Collaborative. I-SPY 2 was designed to explore the hypothesis that different combinations of cancer therapies have varying degrees of success for different patients. Conventional clinical trials that evaluate post-surgical tumor response require a separate trial with long intervals and large populations to test each combination. Instead, I-SPY 2 is organized as a continuous process. It efficiently evaluates multiple therapy regimes by relying on the predictors developed in I-SPY 1 that help quickly determine whether patients with a particular genetic signature will respond to a given treatment regime. The trial is adaptive in that the investigators learn as they go, and do not continue treatments that appear to be ineffective. All patients are categorized based on tissue and imaging markers collected early and iteratively (a patient's markers may change over time) throughout the trial, so that early insights can guide treatments for later patients. Treatments that show positive effects for a patient group can be ushered to confirmatory clinical trials, while those that do not can be rapidly sidelined. Importantly, confirmatory trials can serve as a pathway for FDA Accelerated Approval. I-SPY 2 can simultaneously evaluate candidates developed by multiple companies, escalating or eliminating drugs based on immediate results. Using a single standard arm for comparison for all candidates in the trial saves significant costs over individual Phase 3 trials. All data are shared across the industry.[9] As of January 2016[update] I-SPY 2 is comparing 11 new treatments against 'standard therapy', and is estimated to complete in Sept 2017.[10] By mid 2016 several treatments had been selected for later stage trials.[11]

### Alzheimer's

Researchers plan to use an adaptive trial design to help speed development of Alzheimer's disease treatments, with a budget of 53 million euros. The first trial under the initiative was expected to begin in 2015 and to involve about a dozen companies.[3]

| Parts of this medicine (those related to section) need to be updated. Please update this medicine to reflect recent events or newly available information. (July 2016)

|

## Risks

Shorter trials may not reveal longer term risks, such as a cancer's return.[3]

## See also

* Clinical and Translational Science Awards
* National Center for Advancing Translational Sciences
* Multiple Myeloma Research Consortium
* Clinical trial protocol
* Adverse Event Reporting System
* Food and Drug Administration Amendments Act of 2007
* Clinical study design
* Markov chain Monte Carlo
* WinBUGS

## References

1. ↑ Brennan 2013.
2. ↑ 2.0 2.1 2.2 2.3 "Adaptive Clinical Trials for Overcoming Research Challenges". News-medical.net. 16 September 2013. http://www.news-medical.net/whitepaper/20130916/Adaptive-Clinical-Trials-for-Overcoming-Research-Challenges.aspx.
3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 Wang, Shirley S. (2013-12-30). "Health: Scientists Look to Improve Cost and Time of Drug Trials - WSJ.com". Online.wsj.com. https://www.wsj.com/news/articles/SB10001424052702304361604579290572010514840?KEYWORDS=adaptive+clinical+trial.
4. ↑ Peter W. Huber (12 November 2013). The Cure in the Code: How 20th Century Law Is Undermining 21st Century Medicine. Basic Books. ISBN 978-0-465-06981-1. https://books.google.com/books?id=lU4TAAAAQBAJ.
5. ↑ President's Council of Advisors on Science and Technology 2012, p. xiii.
6. ↑ Carlin 2009, p. 7.
7. ↑ Spiegelhalter 2010, p. 3.
8. ↑ President's Council of Advisors on Science and Technology 2012, p. 21.
9. ↑ 9.0 9.1 President's Council of Advisors on Science and Technology 2012, p. 21-22.
10. ↑ I-SPY 2 TRIAL: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer
11. ↑ Novel Agents are Targeting Drivers of TNBC - Several drug candidates in I-SPY2 have 'graduated' to later-phase studies. June 2016

## Sources

* Kurtz, Esfahani, Scherer (July 2019). "Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction". https://www.sciencedirect.com/science/article/pii/S0092867419306397.
* President's Council of Advisors on Science and Technology (September 2012). "Report To The President on Propelling Innovation in Drug Discovery, Development and Evaluation". Executive Office of the President. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast-fda-final.pdf.
* Brennan, Zachary (2013-06-05). "CROs Slowly Shifting to Adaptive Clinical Trial Designs". Outsourcing-pharma.com. http://www.outsourcing-pharma.com/Clinical-Development/CROs-Slowly-Shifting-to-Adaptive-Clinical-Trial-Designs.
* Spiegelhalter, David (April 2010). "Bayesian methods in clinical trials: Has there been any progress?". http://www.mrc-bsu.cam.ac.uk/CHTMR/DJS_forweb.pdf.
* Carlin, Bradley P. (March 25, 2009). "Bayesian Adaptive Methods for Clinical Trial Design and Analysis". http://www.mbswonline.com/upload/presentation_Brad4-27-2009-15-29-36.pdf.

## External links

* Gottlieb K. (2016) The FDA adaptive trial design guidance in a nutshell - A review in Q&A format for decision makers. PeerJ Preprints 4:e1825v1 [3]
* Coffey, C. S.; Kairalla, J. A. (2008). "Adaptive clinical trials: Progress and challenges". Drugs in R&D 9 (4): 229–242. doi:10.2165/00126839-200809040-00003. PMID 18588354.
* Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER) (February 2010). "Adaptive Design Clinical Trials for Drugs and Biologics". Food and Drug Administration. https://www.fda.gov/downloads/Drugs/.../Guidances/ucm201790.pdf.
* Yi, Cheng; Yu, Shen. "Bayesian Adaptive Designs for Clinical Trials". M. D. Anderson. http://www.mdanderson.org/education-and-research/departments-programs-and-labs/departments-and-divisions/division-of-quantitative-sciences/research/biostats-utmdabtr-012-04.pdf.
* Berry, Scott M.; Carlin, Bradley P.; Lee, J. Jack; Muller, Peter (20 July 2010). Bayesian Adaptive Methods for Clinical Trials. CRC Press. ISBN 978-1-4398-2551-8. https://books.google.com/books?id=_Wz0RlH6NssC. Berry on BAMCT on YouTube
* Press, W. H. (2009). "Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research". Proceedings of the National Academy of Sciences 106 (52): 22387–92. doi:10.1073/pnas.0912378106. PMID 20018711.

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