The notion makes sense medically. It should make sense economically too — less drug gets wasted on people who won’t benefit and who might incur expensive side-effects in the bargain. That’s the promise of personalized medicine, and it’s why new drugs focusing on more specific populations than their competitors should have reimbursability advantages.
But all things are rarely equal.
Some highly popular drugs – Abbott Laboratories’ Humira, for example – generate a ton of rebate income for payers. Moving to a more targeted rheumatoid arthritis therapy makes sense in the abstract, but could certainly lose a payer a chunk of change it had been counting on if its preference for the new drug violates its contract with Abbott.
And then there’s science, which sometimes seems to follow no rules at all. For example, Eli Lilly showed in its development of Plavix-competitor Effient that up to 30% of people don’t effectively activate Plavix thanks to a mutation in the CYP2C19 gene responsible for making the enzyme required to metabolize the drug. In individuals with this mutation, therefore, Plavix shouldn’t work.
Medco seized on the notion that testing patients for the CYP2C19 mutation could be used to guide treatment choices. In this case they showed that Effient, which Lilly had proven superior to Plavix in its head-to-head trial, was in fact no better in the 70% of patients who didn’t have the CYP2C19 mutation. The economic incentives for the study were obvious: Medco stood to make a lot of money from the soon-to-be generic Plavix – or lose a lot if Lilly succeeded in switching Plavix users to Effient.
Somewhat counterintuitively helping Medco’s case: the FDA slapped a black-box warning on Plavix, suggesting doctors test for the mutation and consider alternatives if a patient turned out to be a poor CYP2C19 metabolizer.
Given such data, the CYP219 test had the makings of a perfect companion diagnostic. Sales of the test rose dramatically and Effient’s launch was a dud.
But it’s hardly that simple: a meta-analysis recently published in JAMA, and summarized in this late December Wall Street Journal story, shows that even poor metabolizers do just as well on Plavix, in terms of cardiac events, as people without the mutation. The FDA, claims Cleveland Clinic cardiologist Steve Nissen in a JAMA editorial, was irrationally exuberant in approving the diagnostic – it didn’t wait for the outcomes data.
None of this helps Effient, of course, but it sure calls into question the idea that personalized medicine works like it’s supposed to.
Meanwhile, a New York Times story detailed the economic challenges of developing companion diagnostics – the fact that FDA requires a reliable test, but that diagnostic and pharmaceutical companies have very different financial incentives for developing one. Drug companies make big margins on drugs, and are thus willing to take significant R&D risks; diagnostic companies generally make relatively slim margins – and thus need supplemental funding to do any risky development.
Or they charge what looks like a huge price for the test, like Monogram Bioscience’s $2000 test for the HIV drug Selzentry – a marketplace failure, says the Times, because competitive HIV drugs didn’t require this kind of expensive testing. Meanwhile, certainly cognizant of the Selzentry story, some influential drug marketers don’t push companion diagnostics lest they shrink the market.
The net result: few companion diagnostics exist outside of cancer (where high prices can make up for limited patient populations). And even in cancer, they’re rare: a total of three drug/diagnostic combinations have been approved simultaneously.
All of which is to say that targeting a drug for a specialist population is a good criterion for reimbursability. But it isn’t common and, until we can solve the economic, bureaucratic and scientific hurdles, won’t be any time soon.