The rate of late stage clinical trial failures is the single biggest determinant of returns on pharmaceutical R&D. The lion’s share of discovery and development costs come at the end of the process, and if those trials fail (whether for safety or lack of efficacy), all the capital invested up to that point is lost.
The entire early development process, therefore, is designed to de-risk those large and expensive pivotal trials that can lead to approval and sales. Smaller and cheaper clinical studies are meant to predict the outcome of the larger, more costly ones to come.
But the track record of the industry at achieving this ideal is patchy. Precisely what the success rate of late-stage trials is remains something of a debate, not least because such trials are a heterogeneous bunch. Many such trials are performed with a drug known to work safely in one indication, to support label expansion. Others are conducted with drugs that replicate the mechanism of action of another agent already proven in the chosen indication. Such trials, one might imagine have a disproportionately higher chance of success than the first late-stage trials of an agent with an untried mechanism of action.
Hence, while the oft-quoted figure for late-stage trial success is around 50%, the track record for novel, first-in-class agents is considerably lower: DrugBaron previously estimated it to be below 25%. Whatever the precise figure, the current failure rate is unexpectedly, and unwelcomely, high.
I say “unexpectedly” because the statistical framework used in clinical research might be expected to yield a higher predicted success rate. Early-stage (phase 2) clinical trials typically adopt a success criterion of p<0.05 on the primary end-point. Most people understand the concept of p values: superficially at least, a p value below 0.05 suggests there was less than a 5% chance that the effect seen in the trial was due to chance alone (and a whopping 95% or greater chance that the drug had been effective).
To the uninitiated, at least, that might suggest that when the successful drugs in Phase 2 are moved into Phase 3 less than 5% of them should fail (the unlucky few for whom the apparently significant effects seen in Phase 2 were, actually, due to chance alone).
But there are other factors – many of them well known – that decrease the chances for the pivotal trials, even for a drug with robust efficacy in Phase 2. For a start, many Phase 3 trials are only powered to detect a clinically-relevant effect 80 or 90% of the time, even if it really is there. So that will yield, at least, some additional failures, although the parallel program of Phase 3 …
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