Pavčnik Lara, Bohanec Simona, Trdan Lušin Tina, Roškar Robert
Sandoz Development Center Slovenia, Lek Pharmaceuticals d.d., Verovškova 57, SI-1526 Ljubljana, Slovenia.
Department of Biopharmaceutics and Pharmacokinetic, Faculty of Pharmacy, University of Ljubljana, SI-1000 Ljubljana, Slovenia.
Pharmaceutics. 2025 Aug 18;17(8):1067. doi: 10.3390/pharmaceutics17081067.
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. 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. 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%. 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.
本研究探索了因子分析作为优化注册批次稳定性研究设计的替代策略的潜力——这是国际协调会议（ICH）Q1D目前未涉及的一种方法，该指南仅关注分组和矩阵设计。目的是评估基于因子分析简化后的稳定性设计的可靠性，以及使用该方法可减少长期稳定性测试的程度。为了确定在保持稳定性评估可靠性的同时应用因子分析简化稳定性研究设计的可行性，选择了三种注射剂剂型。分析了加速和长期储存条件下的稳定性数据。对加速数据应用因子分析以识别影响稳定性的关键因素（例如，灌装体积、放置方向）。基于这些发现，从策略上简化了长期研究设计，并通过对长期数据的回归分析确认了这些简化的有效性。因子分析揭示了显著影响稳定性的关键因素，包括批次、放置方向、灌装体积和原料药供应商。该分析确定了最坏情况，并据此提议大幅减少三种受试注射用药品的长期稳定性研究设计。回归分析结果证实了因子分析对于减少受试注射用药品长期稳定性测试至少50%的有用性。本研究表明，加速稳定性数据的因子分析是优化注射用药物剂型长期稳定性研究设计的宝贵工具。研究结果表明，这种方法可以补充现有的ICH Q1D策略，为制药行业提供一种科学合理的方法来简化稳定性计划、降低成本并加快开发时间表，同时保持产品质量、安全性和有效性。
