Roper Jason M, Griffin Troy R, Johnson George E, Kostal Jakub, Nudelman Raphael, Ott Gregory R, Voutchkova-Kostal Adelina, Niddam-Hildesheim Valerie
Teva Branded Pharmaceuticals R&D, Inc., West Chester, Pennsylvania, USA.
Swansea University Medical School, Swansea University, Swansea, UK.
Environ Mol Mutagen. 2025 Apr;66(4):155-171. doi: 10.1002/em.70012. Epub 2025 May 8.
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.
对于缺乏致癌性数据的亚硝胺类药物相关杂质(NDSRIs),可根据相对于具有致癌性数据的锚定亚硝胺的诱变性强度来估算其可接受摄入量(AI)限值。该方法将体内诱变性研究得出的起始点(PoDs)与经过验证的量子力学(QM)模型生成的计算机模拟预测相结合。对具有可靠致癌性数据的N-亚硝基二乙醇胺(NDELA)和N-亚硝基哌啶(NPIP)进行了转基因啮齿动物(TGR)基因突变试验。肝脏突变频率和基准剂量(BMD)建模提供了一个合适、稳健且精确的PoD指标。根据突变频率计算的BMD置信区间(CIs)扩展了先前报道的其他锚定亚硝胺的BMD CIs的效力范围。对于致突变性NDSRIs的癌症防护AI,可以通过将其下限TGR BMD CIs与来自具有体内基因突变和癌症生物测定结果的模型/锚定亚硝胺的BMD CIs和AI进行比较,以效力为基础进行实际计算。体内建模得到了计算机辅助发现与重新设计(CADRE)程序的支持,该程序是一个经过验证的QM模型,用于基于诱变性的潜在机制预测NDSRI致癌效力。CADRE区分了锚定亚硝胺N-亚硝基二乙胺(NDEA)和N-亚硝基二甲胺(NDMA)以及效力较低的NDELA和NPIP。对潜在反应性指数和相关物理化学性质的审查使观察到的代谢活性趋势合理化,从而预测致癌效力。利用体内-计算机模拟方法对于提高对提议的AI的信心很有价值,其中QM模型作为体内结果的机制验证。