Goal: To create a predictive system for the formation of storage-induced impurities in drug formulations through the integration of mechanism-based and risk-based methodologies. Purpose: The objective of this paper is to explore less-researched aspects of impurity formation, including microenvironment variability, interplay between the drug substance, excipients, and packaging material, as well as the development of new prediction tools based on artificial intelligence, hybrid methods, and digital twinning. Finding: It can be seen that routine stability testing does not take into account local changes within the microenvironment of the drugs, e.g., pH fluctuations and water content gradients, or conditions during transport that significantly increase impurity generation. Several new factors such as leachates, fluctuating temperatures, and mechanical forces were found to be highly effective in inducing chemical changes. Predictive models have proven to be highly accurate in predicting impurity profiles and pathways of degradation. Results: The application of predictive analytics in conjunction with QbD and risk assessment techniques such as FMEA ensures that critical risks are identified early and robust control strategies are developed. Real-time monitoring solutions using smart packaging and sensor technology provide additional ways to manage impurity risks during the entire lifetime of the product. Conclusion: A multi-pronged solution combining knowledge of mechanisms, predictive models, and risk-based controls presents a paradigm shift in the reduction of storage-driven reactive impurities. Using this kind of approach will not only benefit products in terms of stability but also promote regulatory advances towards the adoption of predictive stability guidelines. Keywords: Predictive stability modeling; Reactive impurities; Heterogeneity of microenvironments; Digital twin; Risk-based control strategies.
Shende et al. (Fri,) studied this question.