非参数过程能力分析的规范限制规范及其应用在清洁验证与AI启用监控模型的清洁验证中
Xiande Yang1, Wendy Lou2, Mohamed Chan3
1Manufactruing Science and Technology, Apotex Inc., North York, ON, Canada. xyang1@apotex.com.
AAPS PharmSciTech
|January 26, 2026
概括
这项研究引入了一种新的USL-规范化方法来清理验证数据,使药品制造业能够进行强大的分析和AI集成. 它提高了清洁过程验证和监控系统的可靠性.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 化学工程是化学工程的重要组成部分.
- 监管科学 监管科学
背景情况:
- 制药设备清洁验证 (CV) 数据往往受到左边审查和限制,阻碍了传统的统计分析.
- 现有的方法在异质的最高规范限度 (USL) 和小样本大小方面扎,影响AI模型培训.
- 监管指南要求严格的清洁验证,需要先进的数据分析技术.
研究的目的:
- 开发一种可靠的方法来分析多个USL的清洁验证数据.
- 为了改善统计分析,使异质CV数据集的聚合和标准化成为可能.
- 支持人工智能驱动系统的开发,用于持续清洁过程的验证和监控.
主要方法:
- 提出了一种USL-规范化方法,将残留物测量标准化为统一的USL_Pct = 100.
- 介绍了KDEDPonUSLND算法,用于在规范化数据上计算清洁过程关键质量属性 (CQA) 级上限能力指数 (Ppu).
- 开发了CQAWWC_BAKEDPonUSLND和CQAWP_BAKEDPonUSLND模型,用于人工智能驱动的CV第三阶段监控.
主要成果:
- 美国国语标准化方法有效地汇总了美国国语异质数据集.
- 与传统方法相比,KDEDPonUSLND算法在非参数分析方面表现出更好的稳定性.
- 开发的AI模型为先进的,符合监管的清洁过程验证提供了基础.
结论:
- USL-规范化和KDEDPonUSLND方法为分析具有挑战性的简历数据提供了强大的解决方案.
- 这种方法促进了对人工智能应用的历史简历数据的有效使用.
- 拟议的人工智能驱动系统增强了制药清洁过程的验证和监控,与监管预期保持一致.
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