Everyone in the industry knows how clinical trials for efficacy are usually done: You take your compound at the dose you worked
out in Phase I, establish your "null hypothesis" (typically that your drug is no better than a placebo or the current standard
of care) and you start collecting data, in the hopes that you'll fail to prove it. Everything stays carefully blinded. The
investigators have no idea what they're administering and the patients have no idea what they're taking until a pre-determined
endpoint—to do otherwise would destroy the statistics. At the end, the data are brought together and worked up while everyone
waits to see if the efficacy measures reached significance, or, to be more precise, if you failed to prove lack of efficacy
by a statistically significant amount. If you did, then things go on according to plan, and if you didn't, you can start thinking
about a different patient population or dosing protocol and start the process again. (Naturally, you can also think about
giving up completely.)
It's been clear for some time that this system is far from optimal. A widely noted survey by Accenture provided some alarming
figures a few years ago: Eighty-nine percent of all drug candidates from the initiation of Phase I through FDA approval failed
in the clinic. These figures, it should be noted, cover the 1990s, which makes them unlikely to have improved significantly
since then. The Accenture study provided another reason for worry, suggesting that the primary reasons for failure actually
changed during the period surveyed. Pharmacokinetic (PK) and bioavailability problems gave way to efficacy as the largest
hurdle, while toxicological problems increased their share. Clearly, any techniques that could give an earlier read on these
issues would be valuable.
If the writer's definition of a novel is a long work of fiction that has something wrong with it, the clinician's definition
of a trial is a large body of drug data in humans that should have been collected differently—at a different dose, in different
patients, with different endpoints, for a different length of time. In too many cases, the chief result of a trial is to show
that the trial itself was set up wrong, in ways that only became clear after the data were unblinded. Did the numbers show
that your dosage was suboptimal partway into a two-year trial? Too bad—you probably weren't allowed to know that. Were several
arms of your study obviously pointless from the start? Even if you knew, what could you do about it without harming the validity
of the whole effort? Over the last few years, such concerns have stimulated an unprecedented amount of work on new approaches.
Ideas have come from industry, academia, and regulatory agencies (such as FDA's Critical Path initiative). Given the situation,
anything has the potential to help—if 10 percent of the trials that fail today were to succeed, that would nearly double today's
A common theme in these efforts has been a move toward adaptive clinical trials. The term "adaptive" covers a lot of ground,
from methods that are already fairly standard in certain clinical areas to controversial new designs that require sophisticated
modeling and massive computing power. They have several things in common: First, they offer a potential solution to pharma's
twin problems of too-long trials and too-low success rates. They are, in many cases, a good fit with the goals of FDA's Critical
Path initiative and could allow companies to see the benefits of a new generation of biomarkers currently under development.
Most important, after decades of adaptive techniques requiring a level of computing power that put them out of reach, they
have started to look practical.
All these factors working together mean that adaptive trials aren't just for propeller-heads anymore. In the coming decade
or so, they're one of the issues that need to be top-of-mind for the whole executive suite, as a driver of new processes and
timelines, as a hot-spot on the budget (where big dollars can be spent—and saved), and as a battleground where public policy
on drug safety and efficacy will be fought out.
What are adaptive trials? How do they work? And why do they matter so much? Let's take a look at the basics.