Ponting David J, Czich Andreas, Felter Susan P, Glowienke Susanne, Harvey James S, Nudelman Raphael, Schlingemann Joerg, Simon Stephanie, Smith Graham F, Teasdale Andrew, Thomas Robert
Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, United Kingdom.
Sanofi Deutschland GmbH, R&D Preclinical Safety, 65926, Frankfurt, Germany.
Regul Toxicol Pharmacol. 2025 Feb;156:105762. doi: 10.1016/j.yrtph.2024.105762. Epub 2024 Dec 9.
The carcinogenic potency categorisation approach (CPCA) has recently been introduced by health authorities. In this model, structural features from recent literature, industry proposals, and analyses performed by health authorities, provide a rapid assessment of the potential acceptable intake (AI) for a nitrosamine impurity. As with other screening regulatory values (such as the ICH M7 Threshold of Toxicological Concern), the CPCA is conservative and can be considered a de minimis risk management framework. In cases where a nitrosamine drug substance-related impurity (NDSRI) is present below the CPCA limit, the framework provides resolution from a toxicological perspective (i.e., no further toxicology studies are required). Where an NDSRI is above the CPCA limit, the framework provides for the initiation of additional activities (i.e., the CPCA is not the only possible limit). Read-across approaches are described in both the CPCA and M7 guidance and can provide a limit with more specific applicability than the general model. The use of available experimental data (in vitro or in vivo), is valuable in order to provide an even more specific limit. The CPCA provides a framework; however, data should permit changing the AI from initial structural assessment, based on increasing data, to ultimately increase precision of the AI.
卫生当局最近采用了致癌潜能分类方法(CPCA)。在此模型中,来自近期文献、行业提议以及卫生当局所做分析的结构特征,可对亚硝胺杂质的潜在可接受摄入量(AI)进行快速评估。与其他筛选监管值(如ICH M7毒理学关注阈值)一样,CPCA较为保守,可被视为一种最低风险的管理框架。若亚硝胺原料药相关杂质(NDSRI)的含量低于CPCA限值,则该框架从毒理学角度给出了解决方案(即无需进一步开展毒理学研究)。若NDSRI高于CPCA限值,该框架则要求启动其他活动(即CPCA并非唯一可能的限值)。CPCA和M7指南中均描述了交叉参照方法,与通用模型相比,该方法可提供适用性更强的限值。利用现有的实验数据(体外或体内),有助于提供更为具体的限值。CPCA提供了一个框架;然而,基于不断增加的数据,应允许根据数据变化从初始结构评估中改变AI,以最终提高AI的准确性。