Using N-Nitrosodiethanolamine (NDELA) and N-Nitrosopiperidine (NPIP) Transgenic Rodent Gene Mutation Data and Quantum Mechanical Modeling to Derive Potency-Based Acceptable Intakes for NDSRIs Lacking Robust Carcinogenicity Data.
Journal:
Environmental and molecular mutagenesis
Published Date:
May 8, 2025
Abstract
Acceptable intake (AI) limits for nitrosamine drug substance related impurities (NDSRIs) that lack carcinogenicity data could be estimated from mutagenic potency relative to anchor nitrosamines with carcinogenicity data. This approach integrates points of departure (PoDs) derived from in vivo mutagenicity studies with in silico predictions generated by a validated quantum-mechanical (QM) model. N-nitrosodiethanolamine (NDELA) and N-nitrosopiperidine (NPIP), with AIs derived from robust carcinogenicity data, were tested in the transgenic rodent (TGR) gene mutation assay. Liver mutant frequency and benchmark dose (BMD) modeling provided a suitable, robust, and precise PoD metric. BMD confidence intervals (CIs) calculated from mutant frequency expanded the potency range of previously reported BMD CIs for other anchor nitrosamines. Cancer-protective AIs for mutagenic NDSRIs can be pragmatically calculated on a potency basis by comparing their lower bound TGR BMD CIs with the BMD CIs and AIs derived from model/anchor nitrosamines that have results for in vivo gene mutation and cancer bioassays. In vivo modeling was supported by the Computer-Aided Discovery and RE-design (CADRE) program, a validated QM model for predicting NDSRI carcinogenic potency based on the underlying mechanism of mutagenicity. CADRE distinguished between anchor nitrosamines N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA) and the less potent NDELA and NPIP. Scrutiny of underlying reactivity indices and relevant physicochemical properties rationalized the observed trend in metabolic activity and thus predicted carcinogenic potency. Leveraging the in vivo-in silico approach is valuable in gaining confidence in the proposed AIs, whereby the QM model serves as mechanistic validation of in vivo results.
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