US Food and Drug Administration (US FDA), Silver Spring, MD, USA.
Danish Medicines Agency (DKMA), Copenhagen, Denmark.
Regul Toxicol Pharmacol. 2024 Jun;150:105640. doi: 10.1016/j.yrtph.2024.105640. Epub 2024 May 14.
N-Nitrosamine impurities, including nitrosamine drug substance-related impurities (NDSRIs), have challenged pharmaceutical industry and regulators alike and affected the global drug supply over the past 5 years. Nitrosamines are a class of known carcinogens, but NDSRIs have posed additional challenges as many lack empirical data to establish acceptable intake (AI) limits. Read-across analysis from surrogates has been used to identify AI limits in some cases; however, this approach is limited by the availability of robustly-tested surrogates matching the structural features of NDSRIs, which usually contain a diverse array of functional groups. Furthermore, the absence of a surrogate has resulted in conservative AI limits in some cases, posing practical challenges for impurity control. Therefore, a new framework for determining recommended AI limits was urgently needed. Here, the Carcinogenic Potency Categorization Approach (CPCA) and its supporting scientific rationale are presented. The CPCA is a rapidly-applied structure-activity relationship-based method that assigns a nitrosamine to 1 of 5 categories, each with a corresponding AI limit, reflecting predicted carcinogenic potency. The CPCA considers the number and distribution of α-hydrogens at the N-nitroso center and other activating and deactivating structural features of a nitrosamine that affect the α-hydroxylation metabolic activation pathway of carcinogenesis. The CPCA has been adopted internationally by several drug regulatory authorities as a simplified approach and a starting point to determine recommended AI limits for nitrosamines without the need for compound-specific empirical data.
在过去的 5 年里,N-亚硝胺杂质(包括与亚硝胺原料药相关的杂质(NDSRIs))给制药行业和监管机构带来了挑战,并影响了全球药物供应。亚硝胺是一类已知的致癌物质,但 NDSRIs 带来了额外的挑战,因为许多 NDSRIs 缺乏经验数据来确定可接受的摄入量(AI)限值。从替代物进行的读通分析已被用于在某些情况下确定 AI 限值;然而,这种方法受到可用的与 NDSRIs 的结构特征相匹配的经过充分测试的替代物的限制,这些替代物通常含有多种多样的功能基团。此外,在某些情况下,由于缺乏替代物,导致 AI 限值保守,这给杂质控制带来了实际挑战。因此,迫切需要建立一种确定推荐 AI 限值的新框架。本文介绍了致癌潜能分类方法(CPCA)及其支持的科学原理。CPCA 是一种快速应用的基于结构-活性关系的方法,它将亚硝胺分为 5 类之一,每类都有相应的 AI 限值,反映了预测的致癌潜能。CPCA 考虑了 N-亚硝胺中心的α-氢的数量和分布以及影响致癌形成的α-羟化代谢激活途径的其他激活和失活结构特征。CPCA 已被几个药物监管机构采用,作为一种简化的方法和确定亚硝胺推荐 AI 限值的起点,而无需特定化合物的经验数据。