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Updated: Oct 15, 2026

A General Method for Detecting Nitrosamide Formation in the In Vitro Metabolism of Nitrosamines by Cytochrome P450s
Published on: September 25, 2017
Molecular Toxicological Mechanisms of Pharmaceutical Nitrosamines: DNA Damage, Metabolic Activation, Carcinogenesis,
Pradip Karale1, Vishal Chabukswar1, Ishvari Avachar1
1Department of Pharmaceutics, Sanjivani College of Pharmaceutical Education & Research (Autonomous), Savitribai Phule Pune University, Kopargaon, Maharashtra 423603, India.
Abstract:
In light of the valsartan recalls in 2018, nitrosamine impurities have become some of the most important pharmaceutical quality concerns to be seen in recent years, as NDMA and other nitrosamines have been discovered in angiotensin receptor blockers, metformin, ranitidine, and many other medicines. Nitrosamine impurities fall under the IARC classification of probable human carcinogens (Group 2A), and although present in very small amounts, these have prompted stricter standards for their presence, as set by the FDA, EMA, MHRA, PMDA, and Health Canada. The present review provides an overview of the current mechanistic knowledge within four interrelated areas. The first aspect to be discussed is that of the chemistry of nitrosamine generation. Second, the mechanisms underlying the metabolic activation via cytochrome P450 are explained in detail with emphasis on the key role played by α-hydroxylation, isoform specificities (with CYP2E1 and CYP2A6 enzymes being the most important players) and the mechanistic fork leading to either toxication through dealkylation or detoxification through denitrosation and hence determining the genotoxicity potential of the chemical. Third, the DNA adduct formation and mutagenicity associated with the nitrosamine compound is connected to the structure-based risk assessment systems like the Carcinogenic Potency Categorization Approach (CPCA). Fourth, analytical detection methods such as high resolution mass spectrometry, which can operate with sub-part per billion sensitivity levels, and computational prediction techniques are discussed as indispensable supplements to the mechanism-based risk assessment.
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