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Reducing Risk and Improving Efficacy of Clinical Trials: the Adaptive Design
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Reducing Risk and Improving Efficacy of Clinical Trials: the Adaptive Design

Surabhi Dangi-Garimella, PhD
A clinical trial is a massive investment for the drug manufacturer—an investment of time, effort, and funds that are channeled into preclinical research to identify a target and the right molecule for the target. Then, of course, the actual costs of conducting a trial are enormous.

When a molecule fails to achieve the expected end point in phase 3, which according to a recent report happens in 62% of oncology trials,1 it represents a significant financial and scientific loss,

not just for the company but also for the patient who is deprived of a potentially beneficial therapy. Frequently, the trial is successful, but the key to success would be identifying the right patient population or suitable readouts—by using tools such as biomarkers—at predetermined points in a trial. In this scenario, the adaptive clinical trial can prove extremely beneficial.

According to an FDA guideline, an adaptive clinical study is one that includes a prospectively planned opportunity to modify one or more specified aspects of the study design and hypothesis based on an analysis of data, usually at the interim period.2 The traditional fixed trial design—the prevalent and historic design approach—is very restrictive, and can involve a fair bit of guesswork by the trial design team on dose range, patient population, duration and frequency of treatment etc.3 “In an adaptive trial, instead of driving down a hill with your eyes closed, you open your eyes and adjust the metrics accordingly,” said Donald Berry, PhD, professor, Department of Biostatistics at the MD Anderson Cancer Center and owner of Berry Consultants, in an interview with the company Research Insight.4 Berry has designed more than 500 unique adaptive trials for medical device, biotech, and pharmaceutical companies.5 The adaptive design allows flexibility—knowledge gained from the accruing data can be analyzed at specific points in a trial, resulting in a smart design, efficient use of resources, and increased precision, although they are a lot more work to create.4

What Are the Different Types of Adaptations?

The dynamic adaptations that can be implemented in a trial include modifying/redefining end points, adjusting statistical boundaries, dropping doses and/or drug combinations, adaptive randomization, and identifying patient subpopulations that would benefit from a particular therapy.

Data acquired can be immediately analyzed and then used to include or exclude patient subpopulations in a particular trial. Additionally, sample size re-estimation and early termination are other potential advantages of an adaptive design.5

James Bolognese, MStat, senior director at Cytel Consulting, said in a telephone interview with Evidence-Based Oncology, “Two key criteria that need to be considered when applying an adaptive design are the expected recruitment rate of patients and the time after treatment when a primary end point (like a good biomarker) can be observed. For example, if the end point is mortality at 1 year after treatment, but if recruitment stops at 6 months, then there’s a need to identify a biomarker as a surrogate readout at an earlier time point, such as 2 months.” However, Bolognese notes that while a large trial without adaptation could be feasible for a big pharmaceutical company seeking to complete a traditional design earlier, a more cost-conscious smaller company might economize by extending the recruitment period to instead run a smaller but longer trial.

Cytel Consulting, a division of Cytel Inc, provides expert advice on innovative clinical program development, with a focus on adaptive trial design, implementation, and regulatory interactions,

across a wide range of therapeutic areas. Cytel Inc provides software and clinical research services to improve success rates in the medical drug and device industry.6

An instance in which adaptive design helped stop a drug trial early, following observed benefit in phase 3, was the PREVAIL trial for Xtandi (enzalutamide), which is being developed by Medivation/Astellas for metastatic prostate cancer. The premise of the PREVAIL trial was to evaluate the drug as first-line therapy in chemotherapy-naïve men with metastatic prostate cancer who

had not responded to androgen-deprivation therapy.7 Another example is the RESONATE study conducted by Pharmacyclics to compare its drug ibrutinib with the monoclonal antibody ofatumumab in patients with relapsed or refractory chronic lymphocytic leukemia.

The trial was stopped early in phase 3 after an interim analysis showed improved progression-free survival (PFS) as well as overall survival in patients administered ibrutinib.8 Although early trial termination can prove economical, it may not ultimately be in the best interests of patients or even in the best interests of the drug manufacturer, as was observed with the COU-AA-302 study conducted by Johnson & Johnson for Zytiga (abiraterone) in prostate cancer patients.9 The study was stopped early citing efficiency, but it was not conducted long enough to prove that the drug did indeed provide a survival advantage.

Biomarkers and Personalized Medicine

According to the Biomarkers Definitions Working Group, a biomarker is a characteristic that is objectively measured and evaluated as an indicator of a normal biological process, a pathogenic process, or a pharmacologic response to a therapeutic intervention.10 A biomarker can be diagnostic, predictive, and potentially usable as a metabolism or outcomes marker.The significance of biomarkers in disease prognosis, treatment, treatment response, and relapse (especially in oncology) is well established. Monitoring a biomarker can validate a particular drug’s mechanism of action (MOA) and also identify the patient population most likely to benefit from it.11 Biomarkers can be significant in establishing a drug’s MOA during preclinical development.

Subsequently, when the drug is evaluated in clinical trials, the significance of biomarkers in patient selec-tion can grow substantially, especially during phase 2 trials. Between-patient tumor heterogeneity—mutations in different genes (eg, ER-positive or HER2-positive breast cancer), or different sites of mutations in the same gene (eg, codon 12 vs codon 13 mutations in KRas in non-small cell lung cancer)—has long been appreciated, and is primarily responsible for patient selection in clinical trials. Current efforts, though, are aimed at developing methods for accurately identifying patients most likely to respond to treatment and targeting the treatment accordingly.12

The biomarker-strategy design, a fairly popular trial design among clinicians, is conducted by randomizing patients to a control arm (standard treatment independent of biomarker status) or

a biomarker-directed treatment arm. However, if there are data of sufficient quality emphasizing the importance of a particular biomarker, an enriched trial, which only recruits patients with the

biomarker status, would prove more efficient.12 The outcome of such a trial would definitely be beneficial to the patient and also to the company sponsoring the trial.

A biomarker can add greatly to the value of a trial, noted Jacqueline A. Hall, PhD, a member of the PathoBiology group at the European Organisation for Research and Treatment of Cancer

(EORTC) and author of a recent paper in Lancet Oncology on a risk assessment approach to integrating biomarkers in clinical trials, in an e-mail response. “Including a biomarker can make or break your clinical trial. The value added by including a biomarker in trial design depends on the drug being evaluated and the specific role of the biomarker in the trial, but if done well, can improve the chances of a successful trial.” She continued, “Adding biomarkers into trials is not always straightforward, and needs to be well managed or it could lead to problems in the conduct of the trial later on.” Hall went on to explain that a biomarker could either be an integral part of the trial design—eg, for deciding in which arm of the trial the patient participates—or it could be an “add-on,” to be analyzed later in samples collected during the trial.

The multiple approaches are associated with different challenges, and thus differently impact trial operations and the patients enrolled. For example, if an experimental biomarker (with limited associated evidence for use) is to be included in a trial, there would be an increased risk of using such a biomarker for patient selection, creating an added complication in trial design.

An overview of the influence of biomarkers in the treatment strategy for lung cancer was recently highlighted in a presentation at the 19th Annual Conference of the National Comprehensive

Cancer Network, held March 2014 in Hollywood, Florida.13 Leora Horn, MD, MSc, assistant professor of medicine at Vanderbilt-Ingram Cancer Center in Nashville, Tennessee, presented statistics showing the improved response obtained with the use of targeted therapy, including data that showed an improvement in PFS from 5 months to 8.5 months in EGFR-mutation–positive patients administered EGFR-directed therapy. Additionally, encouraging results were obtained with the PD-1 inhibitor nivolumab in PD-L1–expressing nonsmall cell lung cancer patients. Survival

rates with nivolumab were 42% at 1 year and 24% at 2 years, with limited side effects.13

Successful Implementation of Biomarkers in Trials

Incorporating biomarkers into clinical trials is complicated by numerous factors: tumor heterogeneity, subclonal variation, sample handling and processing, assay validity, biomarker validation,

bioinformatics, and appropriate trial design. Consequently, the quality of study designs that integrate biomarkers is variable or there may be other logistical challenges that may result in delays or study closures. Relatively few biomarkers, then, stand a chance of clinical application.14

According to Hall, advance planning and a risk mitigation strategy would help safeguard against failures. However, she also recommends regular monitoring of the results “to spot data that

may be off.” “Including more mature or gold standard biomarkers to fall back on, in parallel with highly exploratory markers, would be one solution. Another option is to choose another design

so that the biomarker is used to stratify the statistical analysis rather than for patient recruitment.”

How Do You Design an Adaptive Trial?

The adaptive design has proved to be a significant cost saving for companies, and one that does not compromise on quality or patient health. The key is to include data analysis while the trial

is ongoing, in order to make changes based on patient response to the therapy or therapies. The trial design incorporates flexibility that can fine-tune drug dosage early on, promoting an  effective and economical trial. Additionally, since each patient is a resource for making modifications, the adaptive trial could essentially use a much smaller patient population that could still generate suitable data, for additional savings of time and costs.15

According to Bolognese, “Specific adaptive designs are utilized for each phase of a clinical development. Doseescalation studies are used in phase 1, especially in oncology trials, essentially

due to drug toxicity issues. Dose-finding design is employed during phase 2 studies, while group sequential and/or sample size re-estimation designs, which allow for patient recruitment increase or interim analysis to stop a trial early, are used in phase 3.”

 
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