  There are several steps to confirming the safety and efficacy of a new
medicine. A sequence of trials, each with its own objectives, is usually
required. Quantitative risk metrics can be useful for informing decisions about
whether a medicine should transition from one stage of development to the next.
To obtain an estimate of the probability of regulatory approval, pharmaceutical
companies may start with industry-wide success rates and then apply to these
subjective adjustments to reflect program-specific information. However, this
approach lacks transparency and fails to make full use of data from previous
clinical trials. We describe a quantitative Bayesian approach for calculating
the probability of success (PoS) at the end of phase II which incorporates
internal clinical data from one or more phase IIb studies, industry-wide
success rates, and expert opinion or external data if needed. Using an example,
we illustrate how PoS can be calculated accounting for differences between the
phase IIb data and future phase III trials, and discuss how the methods can be
extended to accommodate accelerated drug development pathways.
