Managing NDSRIs in Pharmaceuticals: Integrating Structural Risk Assessment and Mitigation Strategies

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.

Authors

Institutions

Publication Details

Journal
ACS Chemical Health & Safety
Published
2026-04-22
DOI
https://doi.org/10.1021/acs.chas.6c00012
Primary Topic
Pharmacovigilance and Adverse Drug Reactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Managing NDSRIs in Pharmaceuticals: Integrating Structural Risk Assessment and Mitigation Strategies

Binbin Yu, Pornchai Rojsitthisak, Wisut Wichitnithad, Worathat Thitikornpong et al.
ACS Chemical Health & Safety
Pharmacovigilance and Adverse Drug Reactions
article

Managing NDSRIs in Pharmaceuticals: Integrating Structural Risk Assessment and Mitigation Strategies

Binbin Yu, Pornchai Rojsitthisak, Wisut Wichitnithad, Worathat Thitikornpong, Pornpoom Angsuwattana
article en

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.

ACS Chemical Health & Safety
Chulalongkorn University (TH), N. V. Pharma (India) (IN), Pharmaceutical Biotechnology (Czechia) (CZ), Taizhou University (CN)
Chulalongkorn University
Climate action
Openalex Percentile: Top 11%
Pharmacovigilance and Adverse Drug Reactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.