Representation In Clinical Research Calls For A New Approach

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Increasing clinical trial participant representation in clinical research is critical to developing new medicines that are safe and effective for patients in need. However, historical clinical research, from which the industry draws insights for future studies, often lacks diverse data, perpetuating imbalances and leaving key patient populations disenfranchised.

It doesn’t have to be this way—provided our industry is willing to rethink current approaches, implement more community or patient-centric outreach programs and invest in smarter use of new technologies.

Understanding Disease Impact: The First Step To Greater Representation

The FDA is urging the industry to enhance diversity and inclusion in clinical trials, issuing guidance in 2022 on drug development approaches to facilitate these goals. Recommendations include making trials more accessible with reduced visit frequencies, flexible scheduling and digital communication tools, alongside early engagement with patient advocacy groups to design appealing study protocols.

Identifying patients for clinical research is particularly challenging as drug developers target diseases affecting highly specialized patient groups. Current trials focus on developing therapies for patients with specific disease markers, genetic abnormalities or subsets of conditions to demonstrate therapy efficacy.

Moderna ran into representation issues in 2020 while testing its Covid-19 vaccine. The company hired contractors to find 30,000 participants for its clinical trial, but these participants were mainly white. At the time, Covid-19 infected Black Americans at three times the rate of white Americans. To its credit, Moderna paused the trial until it had boosted the Black patient participation to nearly 10% and the Hispanic participation to around 20%—a more representative patient pool.

Real-world data (RWD), beyond just historical clinical research data, can be leveraged to identify patients who meet specific eligibility criteria for new studies. RWD consists of datasets representing care delivery episodes and longitudinal patient interactions among groups of patients with the same disease.

Recent technologies enable anonymization, allowing patients or patient groups to be uniquely tracked through their health journeys without revealing their identities. Additionally, RWD can be used to identify and meet the diversity, equity and inclusion (DEI) goals that the FDA requires for new studies.

How Researchers Can Avoid Misrepresentation In Clinical Trials

It’s not enough to simply identify clinical trial sites in areas with more diverse populations. These sites might not have previous experience running clinical trials, or they might encounter challenges in recruiting eligible patients. That’s why the clinical research industry must drive awareness of a recruiting clinical trial for both doctors and their patients while working to overcome inherent mistrust of the healthcare industry.

The general perception of pharmaceutical companies still lags behind the perception of companies in other industries. According to the 2024 Edelman Trust Barometer, "Scientists are still trusted—but increasingly subject to public scrutiny. To build trust in expert recommendations, explain the research, engage in dialogue, and harness peer voices as advocates."

Some patients, especially those living with rare diseases, have had to become strong advocates for clinical research to fuel the pursuit of new therapies. Many have formed organizations, raised funds, lobbied regulators and even directed their own clinical trials.

Research organizations should collaborate closely with advocacy groups to mitigate distrust and misinformation among patients, aligning with FDA regulations to strengthen alliances and meet diversity goals. Expanding clinical trials internationally offers another avenue to enhance diversity, leveraging countries with diverse patient populations and addressing globally prevalent diseases and conditions effectively.

Big Data: A Blessing And A Curse

Researchers can also consider new ways to improve representation using systematic data analysis. Today, there are ever-growing volumes of data available to help trial sponsors identify specific patient populations and meet DEI goals aligned with FDA-requested diversity action plans. However, managing all this data is difficult.

According to a 2021 study by Tufts University, "Phase III clinical trials currently generate an average of 3.6 million data points, three times the data collected by late stage trials 10 years ago." Lokavant’s internal analysis suggests that these clinical trial datasets will skyrocket to seven times that of 2011 by 2030.

Today’s avalanche of data presents both opportunities and challenges. Effectively analyzing increasingly disparate data types and volumes is crucial, especially with the FDA’s new DEI requirements for clinical trials, which elevate the importance and complexity of data analysis.

Small to midsize biopharma companies may find compliance with these FDA rules cost-prohibitive, potentially hindering innovation. Leveraging advanced data approaches, such as technologies that centralize various data types, can significantly enhance these efforts by predicting challenges like inadequate patient recruitment within feasible timelines.

A Prescription For More Representative Clinical Trials

Despite the issues and complexity, industry leaders can make clinical trials more patient-friendly and increase representation. Here are my recommendations:

• Patient Feedback: This informs trial improvements. For instance, decentralized data collection, popularized during the pandemic, eases participant burden, enhancing recruitment and retention.
  
  
• Community Outreach: Implement community outreach programs to leverage local networks among diverse patient groups. The Yale School of Medicine’s Cultural Ambassadors program, which links investigators directly to resources in the community for outreach and education, is a great example.
  
  
• Unbiased Data: Leverage technologies that enable access to unbiased datasets. While technology can’t sway individual perceptions, preconceived notions, mistrust and biases, it can do a lot to improve how we design clinical trials and monitor them in real time.
  
  
• Measuring Twice, And Cutting Once: When designing clinical trials, use data-driven feasibility to target optimal trial sites and geographies for better population representation.
  
  

The aforementioned patient-centric approaches can make it easier for patients across diverse populations to participate in clinical research, thereby creating a formula to end decades of failed attempts (registration required) to boost trial representation, as well as helping drug developers comply with FDA diversity requirements.

The desired end result will be the rapid development of safe and efficacious medicines that benefit those most in need.