The shelf-life of a drug product is determined conventionally based on up to 24 months of observed values for critical quality attributes from available batches prior to the regulatory approval of a new drug application. The data typically consist of three bio batches and are analyzed using the statistical method recommended in Guidance for Industry: Q1E Evaluation of Stability Data, International Council for Harmonisation (ICH). Many statistical approaches for shelf-life determination have been proposed, especially when the number of batches used is large. We compared two approaches for shelf-life determination using various number of batches. One approach is an Analysis of Covariance (ANCOVA) method by which the shelf-life is determined by the worst batch, if batches cannot be pooled and is otherwise determined by the pooled data of all batches. The other approach uses a tolerance interval based on a linear mixed-effects model. In this approach, the shelf-life is determined by the lower confidence limit of the 5th percentile of shelf lives of all batches. We compared these two approaches using simulated datasets of 3, 6, 20, and 40 batches. The ANCOVA approach is appropriate when fewer than six batches have available stability data. When the number of batches increases, the batches are less likely to be pooled, which yields an underestimation of shelf-life. The estimated shelf-life using the tolerance interval-based approach alleviates such underestimation when the number of batches is six or more.
Li et al. (Tue,) studied this question.