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 success rate.

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.