Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Int J Mol Sci. 2018 Oct 3;19(10):3015. doi: 10.3390/ijms19103015.
To better understand the mechanism of in vivo toxicity of -nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q² = 0.7533, R² = 0.8071, Q² = 0.7041 and R² = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C⁻O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.
为了更好地理解体内毒性的 - 亚硝化合物（NNCs）的机制，使用 80 种与大鼠急性口服毒性数据（50%致死剂量浓度，LD）相关的 NNCs 的毒性数据来建立定量构效关系（QSAR）和分类模型。量子化学方法计算描述符和 Dragon 描述符相结合，以描述所有化合物的分子信息。遗传算法（GA）和多元线性回归（MLR）分析相结合，以建立 QSAR 模型。使用指纹和机器学习方法建立分类模型。通过内部和外部验证技术评估所有建立模型的质量和预测性能。得到了包含八个分子描述符的最佳基于 GA-MLR 的 QSAR 模型，其 Q² = 0.7533、R² = 0.8071、Q² = 0.7041 和 R² = 0.7195。QSAR 研究的结果表明，NNCs 的急性口服毒性主要取决于三个因素，即极化率、电离势（IP）以及 C⁻O 键的存在/不存在和频率。对于分类研究，使用 MACCS 键指纹与人工神经网络（ANN）算法相结合获得了最佳模型。分类模型表明，包括腈、杂氮非碱性、烷基氯和含胺片段在内的几个代表性亚结构是 NNCs 高毒性的主要贡献者。总体而言，NNCs 大鼠急性口服毒性的 QSAR 和分类模型表现出令人满意的预测能力。结果为理解体内 NNCs 的毒性机制提供了深入了解，这可能用于对哺乳动物的 NNCs 毒性进行初步评估。
