Pharma’s Cutting Edge

The linked NYT article briefly discusses one of the more salient economic points faced by sponsors and academics interested in developing predictive tests of drug efficacy with established drug classes. It’s also a point that I’ve seen very little attention paid to in the lay media. I’m taking about the issue of class effects. Predictors of efficacy when the clinical read out (i.e. time to definitive efficacy determintation) is protracted have the potential to be useful, even when the drug class is associated with low toxicity and low cost. This utility is heightened when the disease being treated is serious. CHF fits this profile tightly. Beta blockers are cheap and relatively non-toxic and CHF is serious with a protracted time between therapy initiation and definitive efficacy. But they’re an established class, with many members. It’s likely that a predictive test will exhibit specificity spill-over among class members, as the article implies. The only way to to test for such spillover is a very large clinical trial that no single sponsor will want (or be able) to conduct. Even a consortium of sponsors would not be able to study every beta blocker, and only those beta blockers that will still be under patent protection after the trial is over would be considered worthy of study by the industry.

Governments have not even begun to come to grips with the implications of this situation. In the U.S., absent a single point of responsibility for public health economic policy, agencies such as CMS, AHRQ, NIH and FDA should ideally collaborate to determine whether and how such studies should be conducted and funded. The calculus will boil down to likelihood of finding predictive markers, the cost of developing the markers (including the mega study), the predictive value of the markers and the cost to use them routinely, and the potential cost savings to the healthcare system (particularly the publicly funded portion of that system) resulting from deployment of such tests. My sense is that we are unlikely to see such mega studies in the forseeable future, as the potential risk-adjusted, discounted cost savings will be found to be marginal relative to the costs of deployment in nearly all cases. As a result, when such tests are introduced after being studied for one member of a class, researchers will undoubtedly study them retrospectively (i.e. apply the tests to case series and prospective clinical studies done for other reasons) and then apply the tests clinically (usually off-label and without insurance coverage) based on these post hoc inferences. It’s far from ideal scientifically or policy-wise, but it’s going to be the reality for many established drug classes. It’s the main reason why governments should continue to encourage predictive tests of efficacy as new members of recent and new classes are developed.