Eliminating the Placebo/Comparator Arm in Clinical Trials with Advanced Analytics

Gen Li, PhD, MBA, is the president and founder of Phesi Inc., a global provider of patient-centric data software and analytics. Li has extensive experience in clinical development, having previously worked at BMS, Pfizer, and Pharmacia, where he significantly contributed to the Centre for Medicines Research (CMR) International database for pharmaceutical R&D performance. His work inspired him to create Phesi—ensuring the insights held from historical data, or data collected from planned and current clinical trials, were not lost.

In R&D today, increased insight into the drivers of disease is accelerating the development of more specific therapies. Thanks to new knowledge of biomarkers and underlying genetic causes of diseases, the next generation of therapies promises to be highly targeted and more efficacious.

As such, the clinical development industry is undertaking more trials for therapies targeting rare or rarely studied diseases and supporting precision medicine initiatives. By nature, patients with rare diseases are relatively few in number, so following the traditional randomized controlled trial model is either difficult or infeasible.

As predictive data analytics capabilities have developed further, there is a real benefit to using existing data from the real world—including from clinical trials and other sources—to model the placebo or comparator arm, minimizing and ultimately eliminating the need to assign patients to this group. Not only does this lessen patient burden, but it also helps tackle recruitment challenges by reducing the number of patients needed for a trial. Thus, applying past learnings to future trials can significantly accelerate the clinical development process.

How digital patient profiles improve the clinical trial design process

Reducing the placebo or comparator arm of a trial with data analytics requires a deep understanding of the patients targeted by the therapy being trialed. One way existing patient data can be used to streamline clinical trials is by generating digital patient profiles. These profiles provide a statistical view of patient characteristics, including demographics, diversity, comorbidities, concomitant medications, and importantly, outcomes.

With this deeper understanding, sponsors can design trials with a more comprehensive view of patient cohorts, resulting in fewer or zero protocol amendments when implementing clinical trials, which means more efficient and targeted recruitment.

Modernizing clinical trials, one step at a time

Clinical studies without a placebo or comparator arm, or with a small number of patients, are becoming more common, especially in rare diseases and oncology. But unless the right data is used to design these trials, there is the risk that therapies that are not appropriate for real-world applications will enter the market. Digital patient profiles can help mitigate this risk by showcasing how diseases and patients have evolved.

This is just the first step of using patient data to modernize the trial process. As the industry begins to realize the benefits of digital patient profiles and predictive data analytics, we will see other advanced and transformational use cases being applied in clinical trial design and operations, including creating digital twins, digital trial arms, and even simulating clinical trials—all of which will eventually accelerate the discovery of new medicines, reduce costs and patient burden, and make therapies more accessible to patients.