How Real-World Patient Data Can Supercharge the Clinical Trial Process

Traditional, site-based randomized controlled trials have always been, and continue to be, the gold standard of scientific research. But when it comes to developing new treatments and therapies, real-world evidence may be emerging as the best tool we have to expand the insights gained from traditional clinical trials.

The COVID-19 pandemic cast a spotlight on the clinical trial process, and just how hard it is to enroll the right people that will benefit the most from a specific treatment. And that’s exactly the challenge we can solve by using real-world patient encounters to augment the clinical trials process.

Limitations to Site-Based Clinical Trials

In the traditional clinical trial model, a pharmaceutical sponsor will work with a contract research organization (CRO) to recruit patients. That CRO will typically leverage its network of providers and test sites and identify five to ten different clinical sites where the trials will be conducted. They will then recruit through providers, direct-to-patient advertising, and outreach efforts in the regions where those sites are located to spur participation. 

Location is part of the reason why roughly 80% of clinical trials fail to meet enrollment timelines and requirements. The sites are typically located in major research hospitals in a handful of big cities, limiting access to people in more rural areas, and creating intense competition for attention and resources within a small subset of the provider population. On the drug development side, major biopharmas are often working with these same institutions, essentially double-dipping into the same patient pool. 

By focusing on a handful of institutions and existing relationships as the central unifying factor in a clinical trial, this process ignores the data that could highlight other epicenters of disease burden.

The fact is, the patients who might benefit most from a clinical trial could live anywhere. With today’s real-world patient data analytics capabilities, there is no reason to artificially limit access only to those within a reasonable commute to the test site. Armed with this information, pharmaceutical sponsors and CROs are increasingly adopting decentralized clinical trials models, which – instead of relying only on a handful of centralized sites – draw on information supplied by individual providers working within their own practices anywhere in the country.

The lynchpin making this possible is highly granular, near-real time data that tracks complete patient journeys, allowing clinical development teams to identify patients with particular diagnoses and patterns of symptoms and behaviors that make them ideal candidates for a trial. 

Adding Decentralized Trials to the Drug Development Mix

Karyopharm Therapeutics recently used this approach in a clinical trial for its cancer medicine, selinexor, to determine whether the medication's potential antiviral and anti-inflammatory properties could also be effective in treating patients with severe cases of COVID-19. Karyopharm needed to quickly and accurately identify hundreds of providers and institutions with the highest at-risk populations to target for participation – at the height of the COVID-19 outbreak crisis.

By tracking trends in COVID-19 diagnoses and related symptoms, including pneumonia, bronchitis, and other variants, Karyopharm was able to pinpoint the largest populations of at-risk patients and build its patient outreach strategy based on those facts, rather than being boxed-in by an overly narrow, location-based outreach strategy.

Even in cases where there is a more traditional trial model in place, with a principal investigator installed at a major teaching hospital, it is possible to substantially expand the field of eligible patients by augmenting traditional patient outreach with real-world patient data. In one recent example for a clinical trial taking place at a major research hospital in Atlanta, the clinical team was struggling to recruit a very specific cohort of newly diagnosed multiple myeloma patients. By expanding their search beyond the patients of the principal investigator (who had 4 patients) to include every patient who met these specific criteria within a 50-mile radius, the team was able to surface 23 potential new screening candidates and meet its enrollment goals.

A Patient-Centric Approach to Trial Recruitment

The central function of randomization in a clinical trial is to remove potential selection bias, allowing for direct comparison between two study groups. However, by limiting the patient pool based on geographic and location-based logistical constraints, the traditional, site-based clinical trial ignores a much larger population of potential participants. 

Access is not just being within five miles of a clinical trial site. It also means that a person has the ability to take time off work to participate in the trial, that they don’t need to take three buses and two subways to get there, and that they speak the same language as the investigators. These are all barriers that don’t need to exist in a world where we can, in many instances, manage the logistics of a clinical trial with data and analytics and a decentralized network of providers instead of a handful of centralized academic facilities.

Real-world patient data is making it possible for clinical development teams to extend their reach well past the insular networks of key influencers and patient populations of major research hospitals. This is a critical step forward not only for the business of drug development, but also for health equity. 

By deploying decentralized recruitment approaches based on real-world data at scale, we have the power to level the playing field, extending access to everyone who stands to benefit from a trial all while addressing one of the biggest challenges in the new drug development workflow.