(89a) Advancing the Narrative Around Predictive Stability – a Kinetic Study of Ophthalmic Liquid Formulation Stability for Pilocarpine Solutions

While both data-driven and first principles models can be useful in describing chemical phenomena, the rooting of first principles models in the fundamental physics that describes chemical reactions can allow for both more predictive and more accurate extrapolation of a formulation’s inherent chemical stability. In this talk, we will discuss the stability of pilocarpine in aqueous solutions. Pilocarpine is a muscarinic acetylcholine agonist and is the active pharmaceutical ingredient (API) in ophthalmic liquid formulations used in conditions such as presbyopia, increased intraocular pressure, and angle-closure glaucoma. To successfully formulate this API into a doseable liquid ophthalmic formulation, building an understanding into its chemical stability is essential. Further, due to the extended cycle time of performing stability studies at representative storage conditions, it is highly desirable to build an understanding of how the formulation’s stability may progress using an accelerated stability program.

Performing targeted stability studies at elevated temperatures in representative container closures provides an opportunity to understand the inherent chemical pathways that lead to pilocarpine degradation, particularly through epimerization to form isopilocarpine and lactone hydrolysis to form pilocarpic acid. These chemical transformations also impact other CQA’s, such as the solution’s pH, over time. Further, by comparing closures, we assessed the impact of mass transfer through the packaging via diffusion as well as looking at potential surface effects associated with the chosen packaging for the stability study. By building a thorough understanding of the mechanisms that promote pilocarpine degradation, a highly predictive model was generated that accounts for the autocatalytic decomposition of pilocarpine as well as the solution’s pH over time in the aqueous formulation. This model, which was trained on several months of accelerated stability conditions, could then be used to predict up to three years of ambient temperature stability with very good performance.