Key Takeaways:
- The absence of diversity in clinical trial data can lead to biases and inequities in healthcare.
- Regulators like the FDA are emphasizing the need for more diverse and representative data through initiatives like the Real World Evidence Program.
- Having accurate, diverse real world data leads to more equitable and effective treatments by ensuring safety and efficacy across populations.
- Pharmaceutical companies should prioritize capturing diverse real-world data and applying advanced analytics to identify variabilities in treatment response.
In recent years, a growing understanding has emerged regarding the critical need for diversity and representation in clinical research data. Historically, certain demographic groups such as women, minorities, and the elderly have been underrepresented in many clinical trials. This lack of diversity in the underlying data can lead to significant biases and inequities when new therapies are approved and launched.
For example, a landmark study in the early 1990s showed that women had been excluded from most major clinical trials, leading to gaps in knowledge about women’s responses to medications. The study found that eight out of ten prescription drugs withdrawn from the market posed greater health risks to women than men. This exemplifies the real dangers of not gathering data across diverse populations.
More recently, the COVID-19 pandemic has further revealed disparities in health outcomes and treatment responses between different demographic groups. Regulators have emphasized the need for clinical trials that are more representative of real-world diversity. In the United States, the Food and Drug Administration (FDA) now requires inclusion of underrepresented populations in clinical trials under the Improving Representation in Clinical Trials initiative.
The FDA has also created the Real World Evidence Program to evaluate the potential use of real-world data (RWD) from sources like electronic health records, insurance claims databases, and registries. The goal is to complement data from traditional trials with more diverse, real-world information on safety, effectiveness, and treatment response variabilities across patient subgroups.
Having access to accurate, representative real-world data enables more equitable and effective treatments in several key ways:
- Identifying safety issues or side effects that disproportionately impact certain populations based on factors like age, race, or comorbidities. This allows for better labeling and monitoring.
- Ensuring adequate efficacy across all segments of the patient population. Understanding variabilities in treatment response is key for optimal dosing guidance.
- Enabling development of targeted therapies for population subgroups where the risk-benefit profile may differ, such as pregnant women.
- Avoiding biases and inequities in access to treatment. Diverse data helps prevent therapies from being indicated for only limited populations.
- Informing appropriate use criteria and payor coverage decisions based on real-world comparative effectiveness across groups.
From a regulatory compliance perspective, lack of representation in trial data can also lead to delays or rejection of new drug and device applications. The FDA has advised that drugs may not be approvable if safety and efficacy has not been demonstrated across demographics.
Looking ahead, embracing diversity and representativeness throughout the drug discovery process will be critical. Pharmaceutical companies should make gathering inclusive, real-world data a priority. Advanced analytics techniques like machine learning can then help unlock insights about treatment response variabilities within diverse patient populations.
Ultimately, leveraging diverse and representative data will lead to more equitable, effective personalized healthcare and better outcomes for all patients.
Sources:
- Improving Representation in Clinical Trials and Research: FDA’s New Efforts to Bridge the Gap – FDA
- Real-World Evidence – FDA
- Racial and Ethnic Differences in Response to Medicines: Towards Individualized Pharmaceutical Treatment – NIH
- Addressing sex, gender, and intersecting social identities across the translational science spectrum – NIH
- Utilizing Real-World Data for Clinical Trials: The Role of Data Curators – NIH
