Premium
Using accelerated drug stability results to inform long‐term studies in shelf life determination
Author(s) -
Faya Paul,
Seaman John W.,
Stamey James D.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7663
Subject(s) - term (time) , prior probability , bayesian probability , computer science , stability (learning theory) , shelf life , product (mathematics) , confidence region , econometrics , drug , confidence interval , statistics , reliability engineering , mathematics , medicine , machine learning , artificial intelligence , pharmacology , engineering , mechanical engineering , physics , geometry , quantum mechanics
In the pharmaceutical industry, the shelf life of a drug product is determined by data gathered from stability studies and is intended to provide consumers with a high degree of confidence that the drug retains its strength, quality, and purity under appropriate storage conditions. In this paper, we focus on liquid drug formulations and propose a Bayesian approach to estimate a drug product's shelf life, where prior knowledge gained from the accelerated study conducted during the drug development stage is used to inform the long‐term study. Classical and nonlinear Arrhenius regression models are considered for the accelerated conditions, and two examples are given where posterior results from the accelerated study are used to construct priors for a long‐term stability study.