Shelf Life Prediction Models and Statistical Approaches

Statistical Models and Prediction Approaches for Pharmaceutical Shelf Life

Posted on By

Determining the shelf life of pharmaceutical products is a critical regulatory and quality requirement. While real-time stability data under ICH conditions provides the most reliable estimate, prediction models and statistical analysis are essential for early-phase decision-making, accelerated approval, and shelf life extensions. These methods help estimate product viability over time using mathematical tools and empirical data trends, ensuring regulatory compliance and scientific accuracy.
Click to read the full article.

Shelf Life and Expiry, Shelf Life Prediction Models and Statistical Approaches

Introduction to Shelf Life Prediction Using Regression Models

Posted on By

Regression analysis helps quantify how a critical quality attribute (CQA) changes over time. Using degradation data collected from real-time or accelerated stability studies, a linear regression line is fitted to determine when the CQA reaches its specification limit. This projected time is considered the product’s shelf life under those storage conditions.
Click to read the full article.

Shelf Life and Expiry, Shelf Life Prediction Models and Statistical Approaches

Checklist for Statistical Methods in Stability-Based Shelf Life Claims

Posted on By

Statistical methods are at the heart of shelf life estimation in the pharmaceutical industry. This checklist offers a robust framework for QA and regulatory teams to ensure accuracy, transparency, and compliance in regression-based expiry claims. A well-documented, validated, and auditable approach protects both product quality and company reputation across global markets.
Click to read the full article.

Shelf Life and Expiry, Shelf Life Prediction Models and Statistical Approaches