A tool employing mathematical modeling predicts the long-term stability of products, particularly pharmaceuticals and other perishable goods, under stressed conditions. This predictive modeling uses data from short-term experiments conducted at elevated temperatures and humidity to extrapolate shelf life under normal storage conditions. For example, data from a three-month study at 40C might project a product’s stability over two years at 25C.
Rapid shelf-life estimation offers significant advantages, reducing the time and cost associated with traditional stability studies. This approach allows manufacturers to bring products to market faster, optimize formulations for enhanced durability, and minimize potential waste due to expiration. Historically, real-time stability studies were the standard, requiring lengthy observation periods. Predictive modeling offers a more efficient, cost-effective alternative without compromising accuracy.
