The Case Against Race-Based Quotas in Pharmaceutical Trials

Abstract

In 2020, while over 5,000 people worldwide were dying from COVID-19 every day, Moderna intentionally slowed down enrollment of its vaccine clinical trial to focus on more minority representation among study participants. The Moderna CEO even stated that diversity among research subjects “matters more to us than speed.” This decision happened absent any scientific reason for believing that race would affect the safety or efficacy of the vaccine. This counterintuitive decision by Moderna to value scientifically irrelevant racial demographics over that of saving lives was likely the result of pressure from the National Institutes of Health (NIH). Francis Collins, director of the NIH and Anthony Fauci’s boss, threatened to withhold approval of the Moderna vaccine if it did not increase minority representation. The issue of racial demographics in pharmaceutical research is one that evokes misinformation, even among those in the scientific community. As demonstrated by the Moderna example, the consequences can be significant.

This Article is the first to offer a comprehensive case against using racial quotas in pharmaceutical studies by providing a detailed examination of the arguments for and against the practice. Part I provides background information, such as the current racial classification system, calls for racial quotas in pharmaceutical trials, and the troubling history of combining race and scientific investigation. Part II examines the cautionary tale of BiDil, the first drug authorized by the U.S. Food and Drug Administration (FDA) for use in only Black people. BiDil demonstrates how unnecessarily racializing pharmaceutical trials results in statistical, legal, scientific, and sociological problems. Part III lays out the arguments against racial quotas. This includes discussions of the unscientific nature of the practice; why using DNA is far superior; how it promotes harmful stereotypes; how it uses race as a proxy for disadvantage; how it is based on a reductionist, monolithic assumption of racial groups; issues of autonomy; the problematic nature of combining race with corporate profiteering; the inefficiencies of the practice; recruitment issues created; and unique problems with international research. Part IV provides the legal analysis, concluding that racial quotas in pharmaceutical trials likely would not satisfy the strict scrutiny standard for two independent reasons. Part V evaluates the alleged benefits of racial quotas and demonstrates that when properly understood they are insignificant in comparison to the disadvantages. Finally, Part VI weighs the evidence to arrive at a conclusion and considers future implications. While this Article presents a cumulative case against the proposed practice of racial quotas in pharmaceutical trials, many of the same arguments presented are also applicable to the currently mandated practice of acquiring and reporting racial data of pharmaceutical trial participants.

This Article provides a framework for assessing the legal and pragmatic implications not just for pharmaceutical trial quotas but also for other racial-classification issues in health care. This is a valuable resource not only for opponents of racial quotas but also for advocates. For example, this Article provides numerous race-neutral alternatives for consideration. And the strong case against racial quotas helps facilitate a refocus of efforts away from merely ameliorating the end results of health care disparities and instead targeting the root causes. Evidence suggests that this redirected focus on root causes is more effective at producing positive change. In this way, rejecting these quotas is not in conflict with addressing health disparities; rather, it is beneficial to it. This Article will hopefully serve as a catalyst for future research regarding best practices on how pragmatic; legal; and diversity, equity, and inclusion considerations can synergistically exist.

Note:
Funding Information: None.

Conflict of Interests: None.