Skip to Content

Personalized Medicine in Brief

The one-size-fits-all approach to medicine is heading for the dust bin.
February 11, 2002

The label on a bottle of Tylenol couldn’t be more explicit: if you’re under age 12, take one; over 12, take two. Height, weight, gender, race, personal history, temperament-none of that matters. That’s the traditional, one-size-fits-all approach to medicine. But whether it’s an over-the-counter drug like acetaminophen, a prescription serotonin reuptake inhibitor, or even a potentially toxic HIV drug like ritonavir, one thing is obvious: when it comes to side-effects, one size definitely does not fit all.

Clearly, this isn’t news to the medical community-nor to the consumer. As early as 1909, researchers noted a relationship between family history and drug response. But until recently, science lacked the tools to identify those links.

But now that we have a draft of the human genome sequence, we’ve entered into an era in which drug discovery is being governed increasingly by an emerging science called pharmacogenomics, or more simply, personalized medicine. Rather than seeking a single blockbuster drug for all people with “condition X,” researchers are narrowing their focus, looking to design drugs that focus on genetic variations among individuals. In addition, some companies are also looking at distinct populations, in such places as Iceland and Newfoundland, where tightly knit populations consist of many generations of extended families and diseases often run in recognizable patterns.

In Medicine Gets Personal,” Marc Wortman gives the 30,000-foot view of the state of pharmacogenomics research, describing the coming progression from using “genetic data to avoid adverse reactions” to tailoring medications to individuals. Beyond that, the next step is to understand how an individual’s genetic makeup responds to the environment, diet and lifestyle choices, so that physicians can then predict an individual’s susceptibility to a given disease-and prescribe the appropriate personalized drug-with even finer accuracy. Herbert Chase of Yale Medical School draws a picture of where this could take us: “We will know from a drop of blood that a patient has 14 of 19 genes for high blood pressure, and we have 172 drugs that will interact with that.”

One company to be reckoned with as this new era begins is Millennium Pharmaceuticals, of Cambridge, MA. In Medicine’s New Millennium,” CEO Mark Levin asserts, “In order to be a successful pharmaceutical company five and 10 years from now, each company’s going to have to understand not only the genome but the proteome, have the ability to pick the right targets by understanding molecular medicine, and know how to take the right targets forward into clinical trials.” Millennium’s first drug, an anticancer medication called Campath that is geared toward people who haven’t responded to more traditional therapies, was approved in May 2001 by the FDA. And Levin says Millennium has more products in the pipeline that will combine genetic testing with therapy.

In Your Genetic Destiny for Sale,” Technology Review contributing writer Gary Taubes explores why population studies are a primary area in which companies are seeking the connections between genes and various diseases. While this has led, for example to, the discovery of the gene responsible for Huntington’s disease, it has also caused a backlash against companies like deCODE genetics, a biotech firm compiling a database containing medical information for Iceland’s entire population. This article belongs to a package that includes an in-depth interview with deCODE CEO Kari Stefansson, who explains in Population Inc.,” how traditional science has fallen short of the target and why his company’s database is both viable and necessary.

Clearly, we are many years away from being able to spit into a test tube, see a printout of our genetic profile in minutes, and have our physician prescribe a drug tailored to the results. But as Technology Review’s focus on pharmacogenomics indicates, the move to “personalize” the medicine we take is moving into high gear.

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.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

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.