Do you mention it to your doctor? // Complain about it in an online forum? // Tweet about it? // When you do, researchers are listening.
On the Trail of Drug Risks
ILLUSTRATION BY MATT DORFMAN
There’s a long and winding road to Food and Drug Administration approval for a new drug. First the medication has to be tested in animals, followed by three phases of human clinical trials with progressively higher hurdles. Ultimately, a manufacturer must demonstrate that a new drug is safe and effective, that its benefits outweigh its risks, that labeling is accurate, and that it can be produced with consistent quality and purity.
Most would-be therapies don’t make it through that gauntlet, and though there are widely varying estimates of the expense of getting a new drug approved, in 2009 the Tufts Center for the Study of Drug Development pegged the average at more than $1 billion. Yet almost no one would argue that today’s system is adequate to ensure that new drugs really work as they are supposed to, and that unexpected side effects won’t make the cure worse than the disease. Clinical trials routinely exclude the very young and the old, pregnant women, patients with multiple diseases and those taking medications that might interact with the drug being tested. The biggest problem is how few people get the therapy and how short a time they take it. The acid test comes after approval, when the population exposed to the drug expands exponentially.
The FDA agency’s process of “pharmacovigilance” is long and involved and is estimated to detect fewer than 10 in 100 adverse reactions to prescription drugs. The poster child for what’s wrong with post-approval monitoring—the arthritis drug Vioxx—won approval after clinical trials during which some 5,000 people took it. And though cardiovascular events were high on the list of side effects reported during the trial, it wasn’t clear whether Vioxx itself was the cause. Then, during the first six months that the drug was available, there were 2.5 million prescriptions filled, and that number would swell to 105 million during the five years it was on the market. Having huge numbers of people taking Vioxx was the real test, and it’s estimated to have caused at least 88,000 heart attacks before it was pulled from shelves in late 2004. More recently, there have been unexpected problems with the diabetes drug Avandia, which appears to increase cardiovascular risk, and cholesterol- lowering statin drugs that increase the risk of muscular injury when taken with certain HIV or hepatitis C drugs.
Already, more than two million injuries, hospitalizations and deaths each year are attributed to prescription drug reactions, and the situation could be getting worse. “Adverse event reports” to the FDA from physicians, patients and drug companies about medication side effects tripled from 2000 to 2010, with 758,890 reports filed in 2010, compared with 266,866 a decade earlier. Part of that reflects an aging population taking more prescription medicines. Some 30% of elderly patients are on six or more drugs, and about a third of harmful drug reactions result from problems with multiple prescriptions.
“With the number of people using prescription drugs and the number of drugs being consumed per individual rising, adverse drug events could go up exponentially,” says Nigam Shah, an assistant professor of biomedical informatics at Stanford University. “We need active surveillance to monitor drugs almost in real time so that we can reduce how long patients are at risk.”
Where could that real-time intelligence come from? Shah is among several scientists who believe that at least part of the answer lies in the Internet and social media. Four out of five adult Internet users go online to research ailments and prescription drugs. In addition, many people blog about their conditions; network with others through chat rooms, Facebook and other online communities; or may tweet about their illnesses. Now researchers are investigating whether those data channels can be harnessed to provide timely red flags about prescription drugs and predict harmful side effects.
To extract nuggets of useful information from billions of bytes of data, scientists must filter out spam and other “noise”; make sense of Web users’ phrasings, jargon and misspellings; and link comments to potential side effects. Researchers have constructed complex models that use algorithms and natural language processing to scour Websites and detect key patterns or relationships between words in social media posts.
Web searches are another potential source of useful data, and patients’ electronic medical records can also be mined. The FDA is aggressively exploring these new channels, in part to comply with the Food and Drug Administration Amendments Act of 2007, which called for the agency to establish a new drug safety surveillance system. It will likely take a combination of several kinds of heightened monitoring to provide an effective post-approval system, and many of the latest efforts are in very early stages. Yet Shah is optimistic that at least some of the current research will pay off. “The good news is that this is an exciting time of innovation,” he says.