Among the many vexing issues faced by companies that conduct clinical trials, at least two of them — the large number of participants needed for trials and participants’ fears they will be end up getting a placebo — can be eased by using an innovative approach to collecting comparison data called synthetic control arms.
With the skyrocketing cost of clinical trials, the proliferation of digital data, and a new FDA commitment to considering real-world data in regulatory decision making, it’s the right time to begin using synthetic control arms. Medical product development is at the brink of a new age of evidence generation, an environment that’s ripe for disruption. The next step requires risk taking, not something this industry is known for.
Synthetic control arms represent a way to take a safe (as well as time- and money-saving) leap forward into real-world evidence. They work like this: Instead of collecting data from patients recruited for a trial who have been assigned to the control or standard-of-care arm, synthetic control arms model those comparators using real-world data that has previously been collected from sources such as health data generated during routine care, including electronic health records; administrative claims data; patient-generated data from fitness trackers or home medical equipment; disease registries; and historical clinical trial data.
The benefits to the pharmaceutical industry are clear. By reducing or eliminating the need to enroll control participants, a synthetic control arm can increase efficiency, reduce delays, lower trial costs, and speed lifesaving therapies to market. Imagine a trial that needs to include have 500 participants in the treatment arm in order to demonstrate the effectiveness of a new therapy. Instead of having to recruit 1,000 patients — 500 for the active arm, 500 for the control arm — only 500 participants need to be recruited when a synthetic control arm is employed.
This kind of hybrid trial design presents a less risky way for sponsors to introduce real-world data elements into regulatory trials, and can also reduce the risk of late-stage failures by better informing go/no-go development decisions.
Fear of being assigned to placebo is one of the top reasons patients choose not to participate in clinical trials. This concern is amplified when an individual’s prognosis is poor or the current standard of care has limited effectiveness. Using a synthetic control arm instead of a standard control arm ensures that all participants receive the active treatment, eliminating concerns about treatment assignment. This addresses an important participant concern and also removes an important barrier to recruitment.
The use of synthetic control arms can also eliminate the risk of unblinding when patients lean on their disease support social networks, posting details of their treatment, progress, and side effects that could harm the integrity of the trial.
Clear regulatory precedent
While synthetic control arms may be a new concept to many, they have already been successfully used in regulatory decision-making. Roche (RHHBY), for example, met European Union coverage requirements for marketing Alecensa (alectinib) in 20 European markets using a synthetic control arm. In December 2015, Alecensa received accelerated FDA approval as a treatment for a specific form of lung cancer, and in February 2017 it was conditionally approved in the EU.
To make a pricing decision, EU authorities requested additional evidence of Alecensa’s effectiveness relative to the standard of care (ceritinib). Rather than waiting for Phase 3 results, Roche used a synthetic control arm of 67 patients to provide the necessary evidence of relative performance. The decision to use a synthetic control arm advanced coverage of Alecensa by 18 months in 20 European countries. Another example is Amgen’s use of a synthetic control arm to accelerate the approval of Blincyto (blinatumomab) for the treatment of a rare form of leukemia.
While the use of synthetic control arms has been limited to date, and in many cases has required manual chart review to generate the necessary data, there is clear regulatory precedent for using them.
Strategic hire at FDA
Flatiron Health helped pioneer the use of real-world date in clinical research. The recent appointment of Dr. Amy Abernethy, Flatiron’s former chief medical officer, as the new principal deputy commissioner at the FDA reflects the agency’s interest in and outlook about the use of real-world data. (Of note, Flatiron generated the synthetic control arm for Roche that provided evidence for the EU’s coverage decision for Alecensa.)
Abernethy’s career has been characterized by innovation to advance patient care in oncology, including the use of technology solutions. Her recent publications include a checklist to ensure the quality of regulatory-grade real-world data and a real-world evidence case study in which the authors reflect on the development of contemporaneous synthetic control arms as a use case for real-world evidence. With Abernethy’s expertise in the generation of regulatory-grade evidence from real-world data, including the use of synthetic control arms, the industry should anticipate a level of regulatory comfort with this particular hybrid design.
No silver bullet
Even with the FDA making the use of real-world data a strategic priority, synthetic control arms can’t be used across the board to replace control arms. Synthetic control arms require that the disease is predictable (think idiopathic pulmonary fibrosis) and that its standard of care is well-defined and stable. That certainly isn’t the case for every disease.
It’s also important to consider that even when information is available from real-world data sources, it may be difficult to extract or of low quality. Routinely captured health care data, such as electronic health records, are typically siloed, fragmented, and unstructured. They are also often incomplete and difficult to access. New tools and methodologies are needed to consolidate, organize, and structure real-world data to generate research-grade evidence and ensure that confounding variables are accounted for in analyses. Analytic techniques such as natural language processing and machine learning will be needed to extract relevant information from structured and unstructured data.
Despite these challenges, there is a clear and beneficial role for synthetic control arms in drug development, one that can provide low-risk/high-reward payoffs in the right situations. Perhaps the easiest entry point would be using a synthetic control arm as a comparator in Phase 2 trials for internal decision making. It’s an approach that is both business savvy for sponsors and appealing to drug developers that want to get comfortable with synthetic control arms before using them in pivotal trials.
For sponsors ready to include synthetic control arms in pivotal trials, a focus on rare diseases with small patient populations, or therapeutic areas in which the information is relatively easy to mine from real-world data sources, can help speed implementation and remove barriers to approval.
It’s time for the pharmaceutical industry to take the leap forward into using real-world data. Synthetic control arms are a good way to do this. They offer significant time and cost savings compared to traditional randomized controlled trials, and also address patients’ concerns about being randomized to placebo.
Synthetic control arms aren’t the solution to all of the challenges facing randomized trials, nor do they realize the full promise of real-world evidence in drug development. But they are an excellent place to start.