Objectives: This article explores the applicability of the accelerated stability assessment program (ASAP) in stability studies for parenteral medications. Conventional stability testing requires extensive evaluation over the entire shelf life of a product, which can be very time-consuming. In contrast, ASAP provides an efficient approach to support drug product development and expedite regulatory procedures. Methods: The study involved subjecting the medication to different stress and long-term stability conditions and monitoring the formation of degradation products. A systematic methodology was employed to evaluate the stress stability data of the parenteral medication using various designs (full and reduced). ASAP models were then developed from these data and assessed using the statistical parameters R2 (coefficient of determination) and Q2 (predictive relevance). To validate the accuracy of the models, the predicted levels of degradation products from each of the 13 models were compared with the actual long-term stability results using the relative difference parameter. Results: The results confirmed the suitability of the evaluated full model and 11 reduced models for predicting degradation products, except for the two-temperature model, demonstrating the effectiveness of ASAP in stability studies and providing reliable predictions. However, the three-temperature model was identified as the most appropriate model for the parenteral medication under investigation. The statistical analyses showed high R2 and Q2 values, indicating robust model performance and predictive accuracy. Consequently, we applied the selected model on various formulations, demonstrating the suitability of the model and impurity levels below the ICH specification limit. Conclusions: This research enhances understanding of how ASAP designs can be applied to stability studies for parenteral medications and demonstrates the significance of the application of ASAP during drug product development to expedite the initiation of procedures and implement post-approval variations.
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