Rare Disease Day: Enhancing clinical trial success in rare diseases

Rare diseases impact an estimated 300 million people globally, while around one in five cancers are considered a rare disease. Many of these people face an extremely poor standard of care for conditions that can be very severe – with limited treatment options available for them and their medical professionals.

Patients and their families are therefore constantly looking for treatments to improve outcomes and quality of life, but there are inherent challenges in rare disease clinical trials that need to be overcome first.

The challenges of rare disease trials

In order to bring new treatments to rare disease patients, researchers must be able to accurately assess and demonstrate the safety and efficacy of novel medicines in clinical trials – but running trials for rare diseases can be particularly challenging for a number of reasons. Although clinical trials may offer patients new treatment options, the possibility of receiving a placebo may dissuade some from participating, while many patients may then drop out of a trial if they find out that they are not receiving the experimental treatment and have instead been assigned to the control arm. There are also some ethical questions that emerge within trials where many of the patients will be assigned to the placebo, especially when a potential new treatment may offer so much hope to patients.

However, one of the most significant challenges to overcome is the small patient populations within rare diseases. It means that it can be difficult to find enough people to participate – and can be even harder to draw significant conclusions from such a small pool of patients. The clinical research space is constantly searching for ways to adapt trials in order to overcome these challenges and new technologies may offer a solution.

Essential decentralisation

There is significant excitement about the potential for new technologies to improve the drug discovery and development life cycle. Indeed, we are increasingly seeing the life sciences industry integrate digital tools into existing discovery and development processes. One particular area that is benefitting from these new tools is the clinical trials space – and researchers and patients are beginning to see tangible benefits, particularly in rare disease research.

A notable example is the increasing use of decentralisation tools within trials, which is reducing patient burden and accelerating clinical research. Decentralisation can support recruitment for trials, with a reduction in time commitment, cost, and travel required for trial participants. This is particularly important for rare disease trials, where patient populations are small and can be spread over a large geographic area, but remote monitoring and wearable devices may enable significantly more patients to participate.

In control: Synthetic control arms

Elsewhere, in order to reduce the numbers of patients required in trials, the industry has also seen an increasing use of synthetic control arm (SCA) technology in recent years. SCAs are external control arms used in clinical trials relying on historical, external data. This technology has the potential to transform rare disease research and the ability to rapidly accelerate trials and bring treatments to patients much faster.

As increasing volumes of data are collected and stored across the healthcare and life sciences industry, including in the clinical research sector, synthetic control arms present the opportunity to leverage the insights that this data offers.

SCAs allow researchers to develop comparator arms for clinical trials based on historical anonymised patient-level data. This anonymised data might be from previous trials, patient registries, or electronic health records. A key to a good synthetic control arm created from historical data is that the SCA should have the same baseline composition as the group of patients assigned to the investigational treatment. With this type of well-balanced, fair start, SCAs allow researchers to draw accurate conclusions about the efficacy and safety of an experimental treatment. This means that both researchers and patients can have confidence that any differences between the control and investigational arms are meaningful differences.

Using SCAs in clinical trials, especially trials in severe diseases with an inadequate standard of care, may allow for a reduction in the number of patients receiving a placebo – or eliminating the control arm entirely – which would have particular benefits within rare disease clinical trials. Reducing the number of patients required can significantly speed up the recruitment process for these trials while maintaining the scientific integrity of the results and mean that more patients can receive the experimental treatment. SCAs can also lead to greater trial completion rates which means life-changing medicines can be brought to patients with rare diseases much sooner while also reducing research costs, freeing more funds for further research into new drugs.

A synthetic future

As the industry continues to integrate new digital solutions, there is the possibility for greater scrutiny of these tools. This may be the case for trials using SCAs – particularly those where the SCA replaces the control arm entirely – as the industry and observers adapt to new norms and begin to trust new technologies. Research and case studies have shown, however, that SCAs are able to provide the same results as randomised control arms, validating this method and ensuring that medical professionals and patients can be confident in the outcomes of trials, including SCAs.

The success of SCAs is, however, heavily reliant on having the data available to create the control arm. For some rare diseases, there is still work to be done to ensure that enough data from appropriate sources and appropriate patients is available to researchers. However, the industry is beginning to embrace these new developments and, despite a reputation for some conservatism in some areas, there has been significant interest in synthetic control arm technology from across the pharmaceutical and life sciences industry.

SCAs present a significant opportunity within the drug development space and can bring considerable benefits for patients and researchers. Alongside careful use of trial decentralisation, SCAs may bring improved trial recruitment, the potential for higher rates of trial completion, and therefore better returns on research investment for the industry that can transform clinical research within rare diseases, particularly in areas where there is currently a high unmet need.

As we look ahead to the coming years, it is vital to ensure that the industry continues to embrace the opportunities offered by these new tools.

About the authors

Ruthie Davi is a statistician and vice president of data science at Medidata AI. She has a background in pharmaceutical clinical trials, with more than 20 years working as a statistical reviewer, team leader, and deputy division director in the Office of Biostatistics in CDER at the FDA. At Medidata AI, Davi is part of a team creating analytical tools to improve the efficiency and rigor of clinical trials, an example of which is the synthetic control work. Davi holds a PhD in Biostatistics from George Washington University.

Elizabeth Lamont, MD, MS, MMSc is a physician scientist and VP of clinical development at Medidata AI. Dr Lamont is a recognised leader in research at the interface of clinical medicine, clinical trials, and technology. After earning her medical degree from the Geisel School of Medicine at Dartmouth and training in internal medicine at the Brigham and Women’s Hospital, she went on to pursue dual fellowships in Medical Oncology and Health Services research at the University of Chicago, where she was a Robert Wood Johnson Clinical Scholar under Dr Nicholas Christakis. She earned an MSc in Health Studies from the University of Chicago and later an MMSc in Bioinformatics from Harvard Medical School. Prior to joining Medidata AI, Dr Lamont was an Associate Professor of Medicine and Health Care Policy at Harvard Medical School/Massachusetts General Hospital. Immediately prior to joining Medidata AI, she was senior medical director for research at COTA, Healthcare.