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Improving the assessment of the probability of success in late stage drug development
Author(s) -
Hampson Lisa V.,
Bornkamp Björn,
Holzhauer Björn,
Kahn Joseph,
Lange Markus R.,
Luo WenLin,
Cioppa Giovanni Della,
Stott Kelvin,
Ballerstedt Steffen
Publication year - 2021
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2179
Subject(s) - drug development , computer science , pharmaceutical industry , bayesian probability , risk analysis (engineering) , bayes' theorem , clinical trial , transparency (behavior) , expert opinion , data mining , operations research , medicine , drug , artificial intelligence , intensive care medicine , engineering , pharmacology , computer security , pathology
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 II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways.