Improving the assessment of the probability of success in late stage drug development

TL;DR

This paper introduces a Bayesian method to more accurately estimate the probability of regulatory approval for drugs by integrating clinical trial data, industry success rates, and expert opinions, improving decision-making in late-stage drug development.

Contribution

It presents a transparent Bayesian framework that combines multiple data sources to better assess success probabilities in late-stage drug development, including extensions for accelerated pathways.

Findings

The Bayesian approach improves success probability estimates.

Incorporates internal trial data with industry success rates.

Can be extended for accelerated development pathways.

Abstract

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.