Algorithm Finds Thousands of Unknown Drug Interaction Side Effects

ananyo writes "An algorithm designed by U.S. scientists to trawl through a plethora of drug interactions has yielded thousands of previously unknown side effects caused by taking drugs in combination (abstract). The work provides a way to sort through the hundreds of thousands of 'adverse events' reported to the U.S. Food and Drug Administration each year. The researchers developed an algorithm that would match data from each drug-exposed patient to a nonexposed control patient with the same condition. The approach automatically corrected for several known sources of bias, including those linked to gender, age and disease. The team then used this method to compile a database of 1,332 drugs and possible side effects that were not listed on the labels for those drugs. The algorithm came up with an average of 329 previously unknown adverse events for each drug — far surpassing the average of 69 side effects listed on most drug labels."

Comments

I was wondered about something

[zero.kalvin]

What kind of statistical analysis methods they use in these studies? For example we use a lot of Likelihood functions, BDT, and neural networks to get the maximum number of information out of our data. Do they use these kind of methods in there analysis ? ps, my field is astrophysics and astroparticles.

Re:not surprising

[zero.kalvin]

You can go even further, by using advanced techiniques, you can even combine several drugs to best treat certain conditions without giving the patient one larger dose of one medicine. For example if medicine X was found to react in a certain way with the insulin, and Y the fat cells in the body, while Z can catalyse the reaction of some hormone in the blood that will help. Instead of giving this person one large does of medicine A, he can be given small doses of these 4 things, and keep the harm at a minimum.

Re:not surprising

[aaaaaaargh!]

There are databases and search applications that can be made more accurate with the new data. For example, Denmark has an online system where citizens can enter the name of two drugs and get a list of possible side effects and warnings. There are also big US and European databases of this kind, although less open to the public (I believe).

Multiple testing problem?

[FhnuZoag]

I am a statistician.

I've only done a light skim of the paper, but it seems to me that the OP (but not the paper itself) is being way too positive here. Their methodology seems to be very vulnerable to false positives - with a massive database of drugs and potential adverse effects, you'd expect a *lot* of apparent side effects occuring solely by chance. For example:

"We constructed a database of 438,801 off-label side effects for 1332 drugs and 10,097 adverse events."

Supposing you are doing a hypothesis test at the standard 0.05 significance level, for each of the 1332*10097 drug-side effect combinations. Then, with naive assumptions, on a null hypothesis, you'd be picking up an average of 666k+ 'side effects' anyway, purely by chance. With the drug interactions case, this multiple testing problem gets even worse.

Now, there are ways to correct for multiple testing, but for things as large and complicated as this problem, I'm not sure the standard methods are going to cut it. At best, this study should be considered more a *filter* on the set of potential side effects, than really an enumeration of effects that are actually there. This is ignoring other issues like the placebo effect.

Are these really the result of drug interactions?

[Attila Dimedici]

I definitely see this type of data mining as a useful tool, but to what degree of surety are they that the adverse effects are caused by the drugs in question? What percentage of people taking the drugs in question have to exhibit the effect before they consider it a product of drug interaction? It appears that they consider even one occurrence of the effect that does not appear in someone with the same condition not taking the drug to be an effect of the drug. If that is true, that would reduce the usability of this analysis. However, even with that flaw, this is a very valuable study. My stepfather struggled with a respiratory problem this winter that was caused by one of the medications he was on. His doctor never admitted that the medication was the problem, but it only started to clear up after he was taken off of it and that only happened when my mom insisted. She had found information that said the drug sometimes resulted in the respiratory problem he was experiencing.