As broadcast by Good Morning America last week, and picked up by over 150 news sources (according to Google), a recent study found that in mild to moderate cases of depression, a sugar pill is just as effective as many currently prescribed medications for depression. I will get to how this relates to IT, projects, and technology in a moment. But before we get there, a bit of background on the story is essential.
Here is the quote from the LA Times:
…the mere act of seeing a doctor, discussing symptoms and learning about depression probably triggers the improvements many patients experience while on medication. Only people with very severe depression receive additional benefits from drugs, said the senior author of the study, Robert J. DeRubeis, a University of Pennsylvania psychology professor…
This is not good news for America’s pharma industry, but, its not all bad news either. There was an article in Wired magazine from August 2008 that has a slightly different take on the matter. This is not exactly a new article in Wired.com, but I found it very interesting. Titled “Placebos Are Getting More Effective. Drugmakers Are Desperate to Know Why” , it discusses how the so-called Placebo Effect (Patients taking sugar pills instead of actual medicine and feeling better anyway) has become more and more effective over time. In a nutshell, the article in Wired is different from this latest story because it theorizes that while the act of seeing a doctor and discussing your symptoms and situation is powerful, the actual act of taking a pill and thinking that it will work is critical component. In any case, there are real impacts for how big pharma will do business in the future, and maybe even for how drug trials will be conducted. The increased efficacy of the placebo could cost drug companies insane amounts of money, as this is usually discovered in Phase II or Phase III trials, which is way down the development pipeline.
Ten years and billions of R&D dollars after William Potter first sounded the alarm about the placebo effect, his message has finally gotten through. In the spring, Potter, who is now a VP at Merck, helped rev up a massive data-gathering effort called the Placebo Response Drug Trials Survey. Under the auspices of the FNIH1, Potter and his colleagues are acquiring decades of trial data—including blood and DNA samples—to determine which variables are responsible for the apparent rise in the placebo effect…
It would be one thing if it was just the fact that drug companies are currently developing less useful remedies, but that doesn’t seem to be the entire story. What really caught my eye about this issue twofold:
1) It is even happening with already approved drugs. It is surprising to me that this is even when using drugs the FDA has already approved. Does this point to faulty studies, inadequate sample sizes, or actually changing effects of drugs on a population?
2) CTMS (Clinical Trial Management Software) and database technology is a major driver in the research to find out why. CTMS software, and one of its key components, EDC (Electronic Data Capture) were technological breakthroughs which reduced errors and decreased the time required for analysis of findings in clinical trials. They also had a large side-effect: after the studies were over, the data was still stored electronically and easy to access for additional analysis. The major underpinning of these findings is that large amounts of clinical trial data, either captured via CTMS systems, or eventually entered into databases, is what makes these studies possible in the first place. This underlines the true importance of technology in clinical studies, and how that technology can help investigators rapidly diagnose problems and patterns in the data, across multiple studies. Previously, something like this would not be possible. While CTMS systems are the norm at pharmaceutical companies, they are often not available for university and NIH funded studies at Academic Medical Centers. The bottom line is that as more universities install their own CTMS systems, we will get even more interesting studies comparing large data sets with interesting and important results.