Immuno-Oncology Studies: Optimizing Design, Recruitment and Execution

The rise of immunotherapy has been meteoric — there are now well more than 1,000 immuno-oncology (IO) trials ongoing according to clinicaltrials.gov. Finding and enrolling the appropriate patients for these potentially revolutionary treatments has presented a profound challenge, one that was recently covered in the aptly titled New York Times article: A Cancer Conundrum: Too Many Drug Trials, Too Few Patients. Another piece of the puzzle is clinical trial design, which can be especially elaborate when testing combination treatments in IO. Exacerbating these issues, IO trials are an increasingly competitive race to market. There is great value assigned to reducing development times and being the first drug approved within a class or for a specific indication.

This blog article discusses the current state of immuno-oncology studies, strategies for enhancing patient recruitment, the role of companion diagnostics and solutions for dealing with the complexity of IO combination studies.

Realizing Rapid Progress With the New “Phase I/II/III” in Immuno-Oncology

The most noticeable shift in oncology drug development has occurred in clinical trial design. Recent trends point toward sponsors no longer designing oncology Phase I trials in isolation, but rather implementing a Phase I/II design. Once a safe dose is identified, biomarker-selected expansion cohorts may be added, providing an accelerated look at both efficacy and safety, and ultimately, supporting the registration of the drug. This rapid cohort expansion approach along with breakthrough therapy designation, and accelerated approval opportunities using Phase II studies for drug registration are becoming the new norm in oncology.

The use of a companion diagnostic in a trial can provide additional efficiency and outcomes benefits. By identifying the right patients for cohort selection and patient stratification, a companion diagnostic can help deliver the highest possible efficacy and further reduce the drug development timeline.

Leveraging a Companion Diagnostic to Evaluate Efficacy

AstraZeneca’s TAGRISSO® (osimertinib), a specific inhibitor for epidermal growth factor receptor (EGFR), is one example of how a companion diagnostic helped accelerate development and approval process¹. In Phase I, patients with one prior EGFR treatment were randomized in the dose-escalation phase, used to assess pharmacokinetics, pharmacodynamics and efficacy. In this phase of the study efficacy was increased in NSCLC patients whose tumor was positive for a specific EGFR mutation, T790M.

Those studies were confirmed in a phase II study for patients who had previously failed a standard first line therapy, with the continued observation that patients with a T790M mutation showed the best response.

With the incorporation of a companion diagnostic, they identified the patients with T790M and compared osimertinib against the standard of care, platinum-pemetrexed. Researchers observed that osimertinib doubled the progression-free survival from four months to eight months. As a result, the treatment was granted accelerated approval by the FDA. Remarkably, this was only two and a half years after treating the first patient, progress that was supported in part by rapidly identifying the patients that would mostly benefit from the treatment.

Removing Pain Points in Patient Recruitment

Given that patient recruitment consumes nearly 40% of a trial’s costs, and anywhere from 20-60% of the total clinical development timeline, sponsors are eager to find more efficient solutions to find and enroll patients. This is especially true in oncology studies, where only 3% of cancer patients are enrolled in trials and half of all sites under-enroll (including 11% that fail to enroll a single patient).

Recognizing this issue as an opportunity to leverage data analytics and bio informatics capabilities with both public and proprietary trial data, we have been supporting IO clinical trials with the Xcellerate® Informatics Suite. For example, if a sponsor wants to run a non-small cell lung cancer (NSCLC) study, for example, the Xcellerate Forecasting & Site Selection tool can evaluate global data of NSCLC incidence.

Filtering by region or even a specific country or city, the team can determine how many potential patients are in any given area and cross-reference that with investigator performance and recruitment percentiles. By modeling multiple scenarios to compare recruitment speed, site activation, cost and complexity, sponsors can select the best sites to support their study, optimizing patient recruitment and improving operational performance.

These tools, coupled with diagnostic data from Labcorp, sponsors can view the clinical trial opportunity and potential patient population based on biomarker results, such as PD-1/PD-L1 status. These data allow sponsors to not only see where patients of high expression are being tested, but also view the levels of expression of the molecule, valuable information that can guide the inclusion/exclusion criteria for the protocol and promote smarter and more efficient trial design.

Tackling the Complexity of Combination Trials

Recent research has examined the additive effects of IO treatments, such as nivolumab combined with ipilimumab in melanoma patients. While these studies have revealed promising results, they often require novel and complex trial designs to pick the best combinations.

Decision points appear at many junctions, such as at initial tumor evaluation to identify specific markers, ongoing tumor assessment under the iRECIST guidelines, and then options to randomize, continue treatment based on changes in the tumor size or even introduce a combination treatment and repeat with the next iteration. Very quickly, the complexity of these studies can explode.

Companion diagnostics can help handle some of the challenges faced in these trials. An adaptive two-stage population-enriched design is one example, as published by Bhatt and Mehta in the New England Journal of Medicine². Here, patients are stratified into subgroups and then placed in treatment or control arms. At interim analysis, the study is either stopped if there is no response in either group, continued if there is a response in both groups, or the non-responding subgroup can be reassigned to the responding group and increase its number of events. This grouping and regrouping process can help sponsors reach their final analysis faster, an important factor for reaching a crowded market.

As the next wave of combination trials in immuno-oncology emerges, sponsors face a variety of choices and decision points. With the help of companion diagnostic assays, historical investigator/clinical trial data and appropriate trial strategies, we hope to enable faster clinical development and regulatory approval to ultimately help patients get access to more effective, targeted oncology treatments.

References:

¹Pasi A. Jänne, M.D., Ph.D., et.al. “AZD9291 in EGFR Inhibitor-Resistant Non-Small-Cell Lung Cancer”. New England Journal of Medicine. 2015;376:629-640.

² Deepak L. Bhatt, M.D., M.P.H., and Cyrus Mehta, Ph.D. “Adaptive Designs for Clinical Trials”. New England Journal of Medicine. 2016;375:65-74.

Immuno-Oncology Studies: Optimizing Design, Recruitment and Execution

Realizing Rapid Progress With the New “Phase I/II/III” in Immuno-Oncology

Leveraging a Companion Diagnostic to Evaluate Efficacy

Removing Pain Points in Patient Recruitment

Tackling the Complexity of Combination Trials

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