A major challenge in drug development is figuring out what might go wrong. During the development process, a new drug might be given to a few thousand people, maybe fewer if it’s for a rare or orphan disease – just enough to tell whether it does what the researchers think it will and to establish its short-term safety.
Once a drug is approved and available to the public, and out of the controlled laboratory or clinical trial environment, regulators rely on a mix of surveillance, reporting (by doctors and patients) and data mining to catch problems.
But these methods can fall short when it comes to rare side effects, drug-drug interactions or adverse events that arise only after patients have been on a drug for a long time. It can be years before doctors and regulators gather enough data and address safety problems with label warnings, revised prescribing guidelines or, in extreme cases, removal from the market.
So while detection works to a point, wouldn’t it be better if we could predict adverse drug events before a drug even hits the market? …