Simplified Kinetic Modeling for Predicting the Stability of Complex Biotherapeutics 

Stability studies are vital in biologics development, guiding formulation, packaging, and shelf life determination. Traditionally, predicting long-term stability based on short-term data has been challenging due to the complex behavior of biologics. However, recently have been demonstrated that by using simple kinetics and the Arrhenius equation, it is possible to achieve accurate long-term stability predictions for various quality attributes, including protein aggregates. This study focuses on effective modeling of aggregate predictions for diverse protein modalities, such as IgG1, IgG2, Bispecific IgG, Fc fusion, scFv, bivalent nanobodies, and DARPins, using a first-order kinetic model. Notably, findings highlight the significance of temperature selection in stability studies, enabling the identification of dominant degradation processes. Additionally, simplicity of the first-order kinetic model enhances reliability by reducing the number of parameters and samples required. The model's effectiveness was further validated across various protein formats, beyond IgG, emphasizing its broad applicability and reliability. Compared to linear extrapolation, the kinetic model provided more precise and accurate stability estimates, even with limited data points. These findings highlight the benefits of using kinetic modeling with optimal temperature selection to predict protein aggregate stability and other quality attributes, aiding biologics development and shelf-life determination.