A Bootstrap Approach for Pharmaceutical Accelerated Stability Prediction

• Activity Number: 432
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Zhewen Fan*+
• Companies: Abbvie
• Keywords: Accelerated Stability Prediction ; Arrhenius Modeling ; Residual Bootstrap

Abstract

The storage condition of temperature and relative humidity of a pharmaceutical product can have a large impact on its shelf life. Traditionally, the shelf life of a product is calculated from stability studies at the long term storage conditions of the targeted temperature and relative humidity. Accelerated aging process with a range of elevated temperatures and humidities has enabled rapid shelf-life estimation, which is desirable in the early phases of drug product development. We propose a residual bootstrap method based on a modified moisture-adjusted Arrhenius model for accelerated stability datasets and use it to predict the shelf life under long term targeted storage conditions. The confidence intervals and the goodness-of-fit of the method are compared with the conventional one-stage Arrhenius model. The method is illustrated with a real life data set.