Predicting Success of Clinical Trials

In spite of huge progress in understanding disease biology and technological advances in patient selection and clinical study design, the failure rates of clinical trials are still very high. According to Hay et al. [1] the probability of drugs in phase III to get approved across different indications was only 50%. This is noteworthy in light of the fact that most drugs that make it into phase III have successfully completed phase II, this implies that they met the primary efficacy endpoint and had an acceptable safety profile. There are several possible reasons for this discrepancy between phase II and III success, sometimes drug company sponsors have such a strong financial and strategic interest to advance projects into phase III that they are willing to accept or even overlook obvious issues and risks. Alternatively, the phase II results may have simply been false positive as phase II studies are not always powered for significance, or the power may have been too low. In some rare cases, phase II may have been skipped altogether, this happens especially in indications where patients are enrolled in phase I, such as oncology. For clinical phases I and II the likelihood of approval is much lower (10.4%, 16.2%). These issues are exacerbated by the common practice to use adjusted historical success probabilities for go/no go decisions, although such historical averages can at best give a sense of direction. If agency issues and historical data not reflecting the candidates real profile come together, one gets a dangerous mixture leading to risky clinical trials. The effect of a failed phase III can be disastrous for smaller biotech companies (and their investors) but also larger companies are sometimes seriously affected and pushed into merger situations as a consequence of high profile phase III failures. But it is most disappointing for those patients who saw the participation in a clinical study as a real chance of improving their condition.