Stability modeling methodologies to enable earlier patient access.

Over recent years, confidence has been gained that predictive stability modeling approaches using statistical tools, prior knowledge and industry experience enable, in many instances, a robust and reliable shelf-life/expiry or retest period prediction for medicinal products. These science and risk-based approaches can compensate for not having a complete real-time stability data set to be included in regulatory applications at the time of initial submission and, thereby, accelerate the availability of new medicines. Examples of predictive stability modeling include accelerated stability assessment procedure (ASAP), advanced kinetic modeling (AKM), and novel modeling approaches that involve the use of Bayesian statistics and Artificial Intelligence (AI) applications such as Machine Learning (ML), with applicability to both synthetic and biological molecules.