An international group of scientists has demonstrated a new tool for personalized medicine that makes it possible to predict nearly any adverse reaction an individual might have to drugs. Rather than being based on genetic screening, which up to now has been the dominant approach to personalized medicine, the new test relies on profiling an individual’s metabolic products.
Called pharmaco-metabonomics, the technique involves screening urine for metabolites: small molecules that are involved in or produced by the metabolic processes that sustain an organism.
Besides predicting adverse drug reactions, pharmaco-metabonomics also has the potential to determine more effective dose levels for each individual. “There is no genetic technique that can do that,” says Jeremy Nicholson of Imperial College London, the researcher who led the investigation.
The urine test represents a promising new approach that could speed up the slow progress in realizing the potential of personalized medicine. Indeed, efforts to develop tools to tailor drug treatments for individual patients, based on their genes or the proteins they produce, have recently come up against some serious limitations. Since these genomic and proteomic tools rely on genetic factors to predict drug reactions, they fail to take into account environmental factors.
“This is a severe drawback,” says Andy Hall, a pharmacogenomics researcher and director of the Northern Institute for Cancer Research at the University of Newcastle-upon-Tyne in England. “The dream of finding single predictor genes for drug response and toxicity is largely unfounded,” he says.
Nicholson’s proof-of-principle experiment, however, suggests that the new technique is sensitive to not just genetic factors but also the all-important environmental influences. Diet, age, fitness, and the presence of other drugs in the bloodstream can all produce changes in a person’s metabolism, changes that can influence how a drug reacts, says Nicholson.
Published in the current issue of the scientific journal Nature, the technique stems from an increasingly popular diagnostic tool called metabolomics. By screening for hundreds of metabolites, it is possible to create profiles of diseases that can then be used for diagnostic purposes.
But using metabolomics as a predictive tool has never been done before, says Rima Kaddurah-Daouk, a research scientist at Duke University in Durham, NC. “We had already determined that metabolic signatures vary with individuals,” she says. But no one had proved that this variation could be used predictively, she says.
Working with researchers in Sweden, France, and at the pharmaceutical company Pfizer, Nicholson’s group analyzed the metabolites in the urine of 65 rats, both before and after they received a lethal dose of acetaminophen. This was done using nuclear magnetic resonance (NMR) spectroscopy, a diagnostic technique that rapidly quantifies the presence of organic compounds by applying powerful magnetic fields to detect the spin in any hydrogen atoms present within these molecules.
The data derived from this technique was then used to create a predictive model based on as many as 1,000 different metabolites. The researchers found that certain metabolic signatures could indeed be used to predict the severity of liver damage caused by the drug.
In the United States, a large proportion of the drugs withdrawn each year by the Food and Drug Administration are removed because of side effects, which often affect only a small proportion of people. Now it may be possible to screen out these few people, so the majority can benefit from the drugs, while at the same time the minority will be protected against negative reactions.
“In many ways this is ground breaking,” says Kaddurah-Daouk. The fact that it captures both environmental and genetic factors makes it highly attractive – and it’s likely to prompt a huge amount of interest from drugs companies, she says.
Others agree. A lot of companies have invested in proteomics and genomics in the belief that they will help to identify biomarkers of disease, says Mike Milburn, chief scientific officer of Metabolon, which runs a metabolite screening and analysis service in Durham, North Carolina. “Now many of them are already looking to metabolomics as a way of complementing that data.”
There is still a long way to go, however. First, a new predictive model would need to be developed for humans, says Nicholson, although it would not be like starting from scratch. “We are not there yet, but this is the first step to personalized health care,” he says.
The 50-year-old problem that eludes theoretical computer science
A solution to P vs NP could unlock countless computational problems—or keep them forever out of reach.
The moon didn’t die as early as we thought
Samples from China’s lunar lander could change everything we know about the moon’s volcanic record.
Forget dating apps: Here’s how the net’s newest matchmakers help you find love
Fed up with apps, people looking for romance are finding inspiration on Twitter, TikTok—and even email newsletters.
Inside the machine that saved Moore’s Law
The Dutch firm ASML spent $9 billion and 17 years developing a way to keep making denser computer chips.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.