A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination

Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf life claims and expediting the path to clinical implementation. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, flexible error modeling and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or of limited quality. This is illustrated by modeling accelerated stability data from two solid dosage forms and is further examined by means of artificial data subsets and simulated data.

Solid State Kinetics of Nitrosation Using Native Sources of Nitrite

While many reactive species are known to cause N-nitrosation, trace nitrite (NO₂^-), which may be present in several excipients, is a source of nitrosating agents in pharmaceutical formulations. In this study we have found that the salt form of NO₂^- can influence the favored nitrosation conditions and final amount of nitrosamine being formed. Using native levels of NO₂^-, most likely present as ammonium nitrite (NH₄NO₂), in microcrystalline cellulose, we have determined the kinetics of nitrosamine formation in solid state with dimethylamine substrate present in metformin, used as model compound. It was found that the competing degradation of NH₄NO₂ into N₂ and H₂O limited the amount of nitrosamine formation to a great extent. Empirically modelling the kinetic data predicted reaching at maximum 1.6% conversion over a hypothetical 3-year shelf-life. These results also showed that using other sources of NO₂^- as spiking reagents, such as NaNO₂, may lead to unrealistic worst-case situations when the main form of NO₂^- in the drug product (DP) under evaluation may be NH₄NO₂. As well, measuring NO₂^- in freshly manufactured excipients containing NO₂^- potentially as NH₄NO₂ may lead to biased high NO₂^- content, which is not representative of the actual amounts present at the time of DP manufacture.