Use of Factorial Designs to Reduce Stability Studies for Parenteral Drug Products: Determination of Factor Effects via Accelerated Stability Data Analysis

<b>Objectives:</b> This study explores the potential of factorial analysis as an alternative strategy for optimizing stability study designs for registration batches-an approach not currently addressed in ICH Q1D, which focuses solely on bracketing and matrixing. The objective is to assess the reliability of stability designs reduced based on factorial analysis and the extent to which long-term stability testing can be reduced using this approach. <b>Methods:</b> To determine the feasibility of applying factorial analysis for stability study design reduction while preserving the reliability of stability assessments, three parenteral dosage forms were selected. Stability data under both accelerated and long-term storage conditions were analyzed. Factorial analysis was applied to the accelerated data to identify critical factors influencing stability (e.g., filling volume, orientation). Based on these findings, long-term study designs were strategically reduced, and the validity of these reductions was confirmed through regression analysis of long-term data. <b>Results:</b> Factorial analysis revealed key factors significantly affecting stability, including batch, orientation, filling volume, and drug substance supplier. The analysis identified the worst-case scenarios and, based on this, proposed a drastic reduction in the long-term stability study designs for three tested parenteral drug products. The regression analysis results confirmed the usefulness of factorial analysis for the reduction of long-term stability testing of tested parenteral drug products for at least 50%. <b>Conclusions:</b> This study demonstrates that factorial analysis of accelerated stability data is a valuable tool for optimizing long-term stability study designs for parenteral pharmaceutical dosage forms. The findings suggest that this approach could complement existing ICH Q1D strategies, offering the pharmaceutical industry a scientifically sound method to streamline stability programs, reduce costs, and accelerate development timelines while maintaining product quality, safety, and efficacy.