Bayesian methods in design and analysis of stability studies

Stability testing is required by regulation to obtain marketing approval for any new drug
product and ICH Q1 guidance is followed worldwide for that purpose. Based on experimental
results, a unique shelf-life and storage instructions applicable to the future batches of the
drug product manufactured and packaged under similar conditions should be established.
Recently, the entire pharmaceutical regulatory framework has been changing towards a
science and risk-based paradigm for process and product development.
As a consequence, stability studies are facing the emergence of new issues related to
quality risk management going from the quanti cation of the risk of Out-of-Speci cation
products in future production, the determination of a shelf-life that guarantees acceptable
risks to both patients and manufacturers, the de nition of a robust set of storage conditions
under which the labelled shelf-life remains valid, to the validation of a process modi cation
and so on. Statistical modelling plays a prominent role in the design and analysis of stability studies. The focus of this chapter is on the bene ts of using the Bayesian framework to manage risks and make decisions, in the place of the commonly used traditional frequentist methods to support the evaluation of stability data. Adequate formalization of the stability problem is addressed in this chapter together with proposals of generic designs and models. The pitfalls of the commonly used frequentist approaches are highlighted. Then the Bayesian modelling framework and how it overcomes the limitations of the frequentist methods by enabling a natural, exible and reliable quality risk management are presented and illustrated by a case study of Bayesian modelling of stability data.