Associate Principal Scientist Merck West Point, Pennsylvania
Chemical and physical stability of drug substance and drug product are critical in the development and manufacturing of vaccines. Real-time stability studies in support of shelf-life and filing can be time-limiting and resource-intensive to pharmaceutical and biotechnology R&D. Predictive stability models enables formulation innovation and rapid development, by assessing the long-term stability of these properties with limited data and lots, ensuring the quality and efficacy of the pharmaceutical products over their shelf life. Predictive stability is demonstrated as a powerful method for assessing the shelf-life of biopharmaceutical products, such as therapeutic proteins and vaccines.
Here, a Bayesian hierarchical multi-level stability model is illustrated for a multivalent sub-unit vaccine. Product thermal stability plays a major role in deployment of vaccines particularly to regions with cold-chain challenges, while lengthy real-time stability and shelf-life supporting studies are resource-intensive and time-consuming. Hence, an accelerated model-informed stability approach is devised. The product in this case study contains six molecular types (antigens) which each target different viral genotypes of the same class of the virus. The molecular types are mixed together as a co-formulation within a given container (vial or syringe). The stability behavior of the platform vaccine was well-characterized experimentally and antigen potency was identified as a primary stability-limiting attribute. A Bayesian hierarchical statistical stability model approach was developed utilizing long-term drug product storage data through shelf life at 5 °C as well as shorter-term accelerated stability data at 25 °C and 37 °C for 30 product batches. The model was able to comprehensively assess the stability of all molecular types in the vaccine as well as covariates like container type within a singular unified model framework. Moreover, method superiority was demonstrated for this application over multiple approaches such as linear and mixed effects models. Further biophysical characterization of the formulation was assessed as additional levels of the hierarchical model (including particle size, and thermodynamic paramters like Tm). This work demonstrates that biopharmaceutical product platform knowledge from historical lots can be used in conjunction with batch-specific data from early stability timepoints to support long-term assessment for shelf-life of the stability and shelf-life indicating attribute(s), and employed to accelerating novel vaccine formulation development. These findings hold utility towards enabling accelerated patient access of future complex biotherapeutics and for supply to underserved populations.
Learning Objectives:
Understand predictive stability with a vaccine case study: Explain the concept of predictive stability (Frequentist vs Bayes) and its significance in the context of vaccine drug products.
Apply biophysical techniques in predictive models: Explain how these techniques can be incorporated with primary stability indicators to predict the stability of vaccine formulations and assess key attributes relevant to vaccine potency and stability.
Determine impact on formulation development: Discuss predictive stability impacts vaccine formulation development. Specifically, explaining how predictive stability studies guide formulation optimization, excipient selection, and storage conditions.
Evaluate risk assessment and mitigation: Discuss how predictive stability studies contribute to risk assessment by probability estimates and mitigation strategies in vaccine development and manufacturing.
Consider regulatory mitigating factors: Provide an overview of the regulatory landscape, with considerations related to predictive stability assessments for vaccines.
