{"title":"Managing NDSRIs in Pharmaceuticals: Integrating Structural Risk Assessment and Mitigation Strategies","authors":"Wisut Wichitnithad, , , Pornpoom Angsuwattana, , , Binbin Yu, , , Worathat Thitikornpong, , and , Pornchai Rojsitthisak*, ","doi":"10.1021/acs.chas.6c00012","DOIUrl":null,"url":null,"abstract":"<p >The emergence of nitrosamine drug-substance-related impurities (NDSRIs) has presented a complex and evolving challenge to pharmaceutical manufacturing and regulatory oversight. Unlike traditional nitrosamines, NDSRIs may form through multiple pathways involving active pharmaceutical ingredients (APIs), synthetic intermediates, excipients, packaging components, and degradation processes. This review integrates the current understanding of NDSRI formation with implications for pharmaceutical process development, impurity control, and lifecycle risk mitigation. Key structural alerts, including secondary and tertiary amines, piperazines, and hydrazines, are discussed in relation to nitrosating agents, oxidative stressors, and solid-state properties. Special attention is given to precursor-derived and degradation-induced nitrosation, polymorphic variability, and moisture sensitivity, all of which influence NDSRI formation during both the synthesis and formulation stages. Case examples in high-risk therapeutic classes (e.g., antihypertensive drugs, antimicrobial agents, and antidiabetic agents) illustrate diverse routes of NDSRI formation and control opportunities. Mitigation strategies are outlined, including synthetic route design, excipient selection and precursor control, crystallization optimization, and storage conditions, all framed within a tiered, life-cycle-based risk management model aligned with ICH Q9(R1), ICH Q10, and the Carcinogenic Potency Categorization Approach (CPCA). The role of in silico tools, including structure–activity relationships (SAR), quantitative SAR (QSAR), and machine learning (ML), is emphasized in supporting nitrosation risk prediction and acceptable intake (AI) estimation.</p>","PeriodicalId":73648,"journal":{"name":"Journal of chemical health & safety","volume":"33 3","pages":"350–370"},"PeriodicalIF":3.4000,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chas.6c00012","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of chemical health & safety","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.chas.6c00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/4/22 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The emergence of nitrosamine drug-substance-related impurities (NDSRIs) has presented a complex and evolving challenge to pharmaceutical manufacturing and regulatory oversight. Unlike traditional nitrosamines, NDSRIs may form through multiple pathways involving active pharmaceutical ingredients (APIs), synthetic intermediates, excipients, packaging components, and degradation processes. This review integrates the current understanding of NDSRI formation with implications for pharmaceutical process development, impurity control, and lifecycle risk mitigation. Key structural alerts, including secondary and tertiary amines, piperazines, and hydrazines, are discussed in relation to nitrosating agents, oxidative stressors, and solid-state properties. Special attention is given to precursor-derived and degradation-induced nitrosation, polymorphic variability, and moisture sensitivity, all of which influence NDSRI formation during both the synthesis and formulation stages. Case examples in high-risk therapeutic classes (e.g., antihypertensive drugs, antimicrobial agents, and antidiabetic agents) illustrate diverse routes of NDSRI formation and control opportunities. Mitigation strategies are outlined, including synthetic route design, excipient selection and precursor control, crystallization optimization, and storage conditions, all framed within a tiered, life-cycle-based risk management model aligned with ICH Q9(R1), ICH Q10, and the Carcinogenic Potency Categorization Approach (CPCA). The role of in silico tools, including structure–activity relationships (SAR), quantitative SAR (QSAR), and machine learning (ML), is emphasized in supporting nitrosation risk prediction and acceptable intake (AI) estimation.
亚硝胺类原料药相关杂质(NDSRIs)的出现给药品生产和监管监督带来了复杂和不断发展的挑战。与传统亚硝胺不同,NDSRIs可通过多种途径形成,包括活性药物成分(api)、合成中间体、赋形剂、包装成分和降解过程。这篇综述整合了目前对NDSRI形成的理解,以及对制药工艺开发、杂质控制和生命周期风险降低的影响。主要的结构警告,包括仲胺和叔胺,哌嗪和肼,讨论了与亚硝化剂,氧化应激源和固态性质的关系。特别关注前体衍生和降解诱导的亚硝化、多态变异和水分敏感性,所有这些都影响NDSRI在合成和配制阶段的形成。高危治疗类别(如降压药、抗菌药物和降糖药)的案例说明了NDSRI形成和控制机会的不同途径。概述了缓解策略,包括合成路线设计,赋形剂选择和前体控制,结晶优化和储存条件,所有这些都是在与ICH Q9(R1), ICH Q10和致癌效力分类方法(CPCA)一致的分层,基于生命周期的风险管理模型中构建的。计算机工具的作用,包括结构-活性关系(SAR),定量SAR (QSAR)和机器学习(ML),强调支持亚硝化风险预测和可接受摄入量(AI)估计。