Prediction of long-term stability of high-concentration formulations to support rapid development of antibodies against SARS-CoV-2.

Long-term stability of antibody therapeutics is required to ensure their safety and efficacy when administered to patients. However, obtaining shelf life supporting, long-term stability data are often a limiting factor for new drug candidates starting clinical trials. Predictive stability, which uses short-term accelerated stability data and kinetic modeling to forecast long-term storage stability, has the potential to provide justification to support establishing shelf life, although its application for biologics has only recently gained traction. We have developed empirical models for key stability-indicating quality attributes of high-concentration IgG1 liquid formulations. Using short-term accelerated stability data and Arrhenius-based approaches, including Arrhenius plotting and global fitting, we applied empirical kinetics to predict the long-term stability of seven anti-SARS-CoV-2 antibodies. Arrhenius plotting determines kinetics by plotting the reaction rate logarithm against inverse temperature, while global fitting simultaneously fits a model with data at multiple temperatures to comprehensively understand kinetics. These approaches were used to fit empirical kinetics to short-term data to predict long-term stability, leveraging stability data collected at shelf life storage conditions (5°C) and at least 1 month of accelerated stability data at three temperatures within 25-40°C. Model accuracy was demonstrated using long-term (up to 36 months) storage stability data at 5°C. The approach was applied successfully in anti-SARS-CoV-2 antibody drug development to enable rapid regulatory Investigational New Drug and Investigational Medicinal Product Dossier filings and support shelf life justification where limited shelf life stability data were available at the time of filing. Our results show that successful long-term stability predictions and shelf life estimation can be achieved with high accuracy using 1 month of accelerated stability data, which may be especially beneficial for rapid response programs with severely constrained development timelines. Thus, the described model demonstrates how predictive stability models can, in addition to enabling earlier decision-making in drug development, also be used to justify product shelf life in regulatory submissions, enabling faster patient access to life-saving drug products.