Skip to Content

Race and Personalized Medicine

Are drugs that seek to serve a specific population changing our concept of race?
December 20, 2006

Last year, the U.S. Food and Drug Administration made the landmark decision to approve the first race-based drug. Clinical trials showed that Bidil, a combination drug treatment for heart failure, significantly improved survival rates in a group of self-reported black patients. While some applauded the move for focusing on an underserved population, others countered that race is a poor proxy for the genetic variation that likely underlies response to the drug. Sandra Soo-Jin Lee, a medical anthropologist at Stanford University, says the Bidil approval is just one example of how genetic research is shaping society’s perception of race. She explains the trend in a paper appearing in the January issue of Clinical Pharmacology & Therapeutics and spoke this week with Technology Review.

Technology Review: Has genomic research changed our conception of race?

Sandra Soo-Jin Lee: We’ve been witnessing a pendulum switch. Previously, social and life scientists agreed race is best understood as a product of socioeconomic circumstances–who is included in one category as opposed to another is largely tied to the political history of groups over time. While there was a great deal of concurrence of race as a societal construction, now, with genomic technologies, there seems to be a rethinking of race as a biological phenomenon. In scientific research, researchers use race as a way of characterizing differences between groups.

TR: What role does race play in pharmacogenomics?

SL: Pharmacogenomics has really promised this idea of tailoring medicines to individuals, but we’re not quite there yet. It’s not cost-effective to sequence the genome of all individuals. In lieu of that, race becomes an easy proxy. It becomes a way of filtering individuals into populations that might have certain genetic variants.

Race also becomes an easy method for marketing a product. It is easily recognizable in terms of a specific market segment. It becomes attractive for companies to use that to segment the market and tailor products to specific markets.

TR: Previous research has shown that more genetic variation exists within race-based populations than between populations. Given that finding, is it ever appropriate to do race-based medical studies?

SL: That finding is very powerful–it does speak to the idea that race is not a good proxy for genetics. But I think race can be a valuable variable when looking at racism or inequalities in health care. I don’t want to say that race doesn’t exist. It does exist on a very visceral level for many people. But the point of this research is to disavow the idea that race is embedded in the genome and to get us away from thinking about racial biology.

TR: Bidil is a high-profile example of race-based medicine. What do you think of the clinical trials of that drug and the FDA’s decision to approve Bidil with race-specific labeling?

SL: The Bidil study is a very interesting case. There was actually very little pharmacogenomics in that story. It is a combination therapy of two substances already available as treatment for heart disease. In early trials, scientists found they weren’t able to produce significant results that the FDA required for approval. When they went back to the original data and pulled out all the information on African-American patients enrolled in the trial, they found a significant treatment difference. Based on that information, the FDA told Nitromed, the drug maker, to do another study exclusively on African Americans. That trial ultimately showed a significant reduction in mortality for those on Bidil.

Whether the FDA should have labeled that drug for African Americans is an open question. Since the approval, Nitromed has sponsored its own study to identify the genetic variation associated with [the drug’s effectiveness]. According to initial reports, it is quite prevalent in African Americans–about 60 percent. But it’s also fairly prevalent in those of European descent–about 40 percent. That begs the question of whether it should really be labeled only for African Americans. It’s a cautionary tale for us to carefully consider.

TR: Do you think scientists should stop using race in pharmacogenomics studies?

SL: We should think carefully about what the implications might be when thinking about race embedded in genetics. So I think we should ask researchers why they are including race when they do it. Race is often described as merely a first step on the road towards individualized therapies–a way station. However, it is often the case that racial findings are reported without any further interrogation of how race is serving as a proxy for other factors. The focus remains on race, which further reifies the notion that race is somehow genetic. For example, if a researcher were to conduct a pharmacogenomic study and found that one racially identified group had a significantly better drug response than another, one would hope that further studies on the reason for this difference would be conducted to see whether there were underlying factors at play. With Bidil, the FDA could have done a genetic study before they put a race-specific label on the drug. Then we could think about the results with much more complexity.

TR: One of the opposing arguments for this idea is that requiring such studies will lengthen the time frame to get a drug approved. Do you think the societal dangers of approving race-based drugs override slowing the approval process?

SL: There is no question that drugs that prove more effective than standard therapies should be made available to those who could benefit from them. However, rapid developments in genomic technologies have created new questions that should be addressed carefully. Institutions such as the FDA are in the important position of providing some guidance on how issues of race, genetics, and medicine should be addressed. At a minimum, we should explore what social costs may be involved. An interdisciplinary dialogue on the implications of continuing down the road of racially targeted medicines might help us avoid some of the mistakes we have made in the past where race and genes were conflated.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.