Early clinical drug product shelf-life setting using accelerated predictive stability and metabolite data for impurity qualification: A case study

Highlights•The importance of integrating results and information from all stability activities to inform the next steps in a drug development process is emphasized.•Predictions from the accelerated stability testing (APS) model were successfully applied and matched against the real-time stability data of the final drug product composition.•Confidence gained by shelf-life predictions allowed for adjustment of the clinical stability protocol, enabling the reduction of testing points and climates.•The APS model provided crucial information on the shelf-life limiting attributes, thereby informing limits in the drug product initial clinical specification.•The shelf-life limiting degradation product could be considered qualified, justified by available metabolite data, thus underscoring the importance of qualification of impurities.AbstractThis case study demonstrates how knowledge of degradation products together with predictions can establish a lean stability strategy using the accelerated predictive stability (APS) principles. Applying all available data for AZD4831, (R)-1-(2-(1-aminoethyl)-4-chlorobenzyl)-2-thioxo-2,3-dihydro-1H-pyrrolo[3,2-d]pyrimidin-4(5H)-one, a reliable predictive model was developed despite minor differences in technical batch tablet compositions. Early forced degradation studies were performed to map potential degradation pathways. The insights from these studies guided the design of an APS study, which in turn inform on a suitable clinical stability program, initial specification and shelf-life. The use of APS predictions of degradants as well as total impurities highlighted at an early stage, when designing the clinical stability program, the opportunity to identify which degradation product that would be shelf-life limiting. Hence, it was possible to guide the development stability activities and set an initial shelf-life of a tablet formulation. The presented study displays the importance of combining several sources of information in drug development, e.g., potential degradation pathways, accelerated stability, stability program design, metabolite data, and specification limits.Graphical abstract