从可提取到暴露数据:对外推算法的灵敏度分析,重点是USP 665
Armin Hauk1, Alexander Wildschütz2, Ina Pahl1
1Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, Göttingen 37079, Germany.
概括
外推算法准确地预测一次性使用系统 (SUS) 中的工艺设备相关的漏物 (PERL). 这些方法可靠地评估PERL暴露,即使输入数据不同,也能确保安全.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
背景情况:
- 一次性使用系统 (SUS) 越来越多地用于生物制药制造.
- 从工艺设备中评估潜在的漏物对于患者安全至关重要.
研究的目的:
- 从可提取数据中评估用于预测工艺设备相关漏物 (PERL) 的算法.
- 评估这些算法的适用性,以确定SUS和组件中的PERL暴露.
主要方法:
- 测试了算法的稳定性和对可提取数据变化的敏感性.
- 利用标准化可提取协议 (USP 665) 的数据来确定短时间和长时间的接触时间.
- 分析了短时间和长时间接触时间可提取数据的推断算法.
主要成果:
- 从SUS和组件中推断出的数据适用于安全评估.
- 算法对输入数据偏差不敏感,这些偏差在逐渐减少的情况下传播.
- 抽取的数据不会在特定的实验条件下 (例如,更高的表面积与体积比率) 系统地低估潜在的PERL暴露.
结论:
- 外推算法为PERL及其在SUS中的暴露提供了可靠的预测.
- 将从半极有机溶液 (例如,乙醇) 中提取的数据纳入可以提高PERL暴露计算.
- 经过验证的算法支持对使用SUSs的制药制造工艺进行可靠的安全评估.
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