From the post:
An algorithm designed by US scientists to trawl through a plethora of drug interactions has yielded thousands of previously unknown side effects caused by taking drugs in combination.
The work, published today in Science Translational Medicine [Tatonetti, N. P., Ye, P. P., Daneshjou, R. and Altman, R. B. Sci. Transl. Med. 4, 125ra31 (2012).], provides a way to sort through the hundreds of thousands of ‘adverse events’ reported to the US Food and Drug Administration (FDA) each year. “It’s a step in the direction of a complete catalogue of drug–drug interactions,” says the study’s lead author, Russ Altman, a bioengineer at Stanford University in California.
From later in the post:
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.
The team also compiled a similar database looking at interactions between pairs of drugs, which yielded many more possible side effects than could be attributed to either drug alone. When the data were broken down by drug class, the most striking effect was seen when diuretics called thiazides, often prescribed to treat high blood pressure and oedema, were used in combination with a class of drugs called selective serotonin reuptake inhibitors, used to treat depression. Compared with people who used either drug alone, patients who used both drugs were significantly more likely to experience a heart condition known as prolonged QT, which is associated with an increased risk of irregular heartbeats and sudden death.
A search of electronic medical records from Stanford University Hospital confirmed the relationship between these two drug classes, revealing a roughly 1.5-fold increase in the likelihood of prolonged QT when the drugs were combined, compared to when either drug was taken alone. Altman says that the next step will be to test this finding further, possibly by conducting a clinical trial in which patients are given both drugs and then monitored for prolonged QT.
This data could be marketed to drug companies, trial lawyers (both sides), medical malpractice insurers, etc. This is an example of the data marketing I mentioned in Knowledge Economics II.