有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究
Development of QSAR Models for Acute Toxicity of Organophosphorus Compounds towards Rats and Study of Toxicity Mechanism
-
摘要: 有机磷化合物(OPs)广泛分布在各种环境介质中，并对各类生物的健康有潜在的危害。本研究采用基于逐步算法(SW)和遗传算法(GA)的多元线性回归(MLR)方法，收集并筛选出53种OPs的数据集并建立其关于大鼠急性口服毒性(LD50)的定量结构活性关系(QSAR)模型。构建的SW-MLR模型的参数决定系数(R2)、留一法交叉验证系数(Q2LOO)、外部检验系数(Q2F1和Q2F2)分别为0.897、0.817、0.515和0.505，GA-MLR模型的参数分别为0.827、0.752、0.831和0.828。2个模型的统计参数表征了良好的预测能力。使用外部测试集对模型进行评估时，发现GA-MLR模型比SW-MLR模型具有更好的预测和泛化能力。此外，基于建立的模型预测了其他9种OPs的急性毒性，并辅以分子对接技术探究了其潜在的神经毒性。分子对接结果显示，其中8种OPs可以与人类丁酰胆碱酯酶结合。模型机理解释和分子对接结果显示，OPs取代基的高烷基化程度和支链长度的增加能够降低毒性。因此，建立的模型可以从OPs的分子结构上探索与毒性相关的信息，并为监管和筛选新的OPs提供基础数据。Abstract: Organophosphorus compounds (OPs) are widely distributed in various environmental media and are potentially harmful to various organisms. In this study, quantitative structure-activity relationship (QSAR) models were developed to predict the acute oral toxicity (LD50) based on the collected data of 53 OPs towards rats by using a multiple linear regression method on the basis of stepwise algorithm (SW-MLR) and genetic algorithm (GA-MLR). Coefficients of determination (R2), leave-one-out cross-validation (Q2LOO), external validation coefficients (Q2F1, and Q2F2) of the constructed SW-MLR model were 0.897, 0.817, 0.515, and 0.505, respectively, whereas those parameters of the GA-MLR model were 0.827, 0.752, 0.831, and 0.828, respectively. The statistical parameters of both models characterized good predictive power. When the models were evaluated using an external test set, the GA-MLR model was found to have better prediction and generalization capabilities than the SW-MLR model. In addition, the acute toxicity of 9 other OPs was predicted based on the established models and their potential neurotoxicity were explored with the aid of molecular docking techniques. Molecular docking results showed that 8 OPs could bind to human butyrylcholinesterase. The model mechanism explanation and docking results revealed that chain elongation and alkyl substitution can decrease the toxicity of OPs. Therefore, the developed models can explore the information related to toxicity from the molecular structure of OPs and provide basic data for regulation and screening of new OPs.
-
-
Pantelaki I, Voutsa D. Organophosphate flame retardants (OPFRs):A review on analytical methods and occurrence in wastewater and aquatic environment[J]. The Science of the Total Environment, 2019, 649:247-263 Pundir C S, Malik A, Preety. Bio-sensing of organophosphorus pesticides:A review[J]. Biosensors and Bioelectronics, 2019, 140:111348 Clercq E D. Clinical potential of the acyclic nucleoside phosphonates cidofovir, adefovir, and tenofovir in treatment of DNA virus and retrovirus infections[J]. Clinical Microbiology Reviews, 2003, 16(4):569-596 Tan X X, Luo X J, Zheng X B, et al. Distribution of organophosphorus flame retardants in sediments from the Pearl River Delta in South China[J]. The Science of the Total Environment, 2016, 544:77-84 Hou L, Jiang J Y, Gan Z W, et al. Spatial distribution of organophosphorus and brominated flame retardants in surface water, sediment, groundwater, and wild fish in Chengdu, China[J]. Archives of Environmental Contamination and Toxicology, 2019, 77(2):279-290 Zainuddin A H, Wee S Y, Aris A Z. Occurrence and potential risk of organophosphorus pesticides in urbanised Linggi River, Negeri Sembilan, Malaysia[J]. Environmental Geochemistry and Health, 2020, 42(11):3703-3715 Kim J W, Isobe T, Muto M, et al. Organophosphorus flame retardants (PFRs) in human breast milk from several Asian countries[J]. Chemosphere, 2014, 116:91-97 Mangas I, Vilanova E, Estévez J, et al. Neurotoxic effects associated with current uses of organophosphorus compounds[J]. Journal of the Brazilian Chemical Society, 2016, 27(5):809-825 Jokanovic M. Neurotoxic effects of organophosphorus pesticides and possible association with neurodegenerative diseases in man:A review[J]. Toxicology, 2018, 410:125-131 Yang F W, Zhao G P, Ren F Z, et al. Assessment of the endocrine-disrupting effects of diethyl phosphate, a nonspecific metabolite of organophosphorus pesticides, by in vivo and in silico approaches[J]. Environment International, 2020, 135:105383 Al-Salem A M, Saquib Q, Siddiqui M A, et al. Organophosphorus flame retardant (tricresyl phosphate) trigger apoptosis in HepG2 cells:Transcriptomic evidence on activation of human cancer pathways[J]. Chemosphere, 2019, 237:124519 Burke R D, Todd S W, Lumsden E, et al. Developmental neurotoxicity of the organophosphorus insecticide chlorpyrifos:From clinical findings to preclinical models and potential mechanisms[J]. Journal of Neurochemistry, 2017, 142(Suppl.2):162-177 Zhang Q, Lu M Y, Dong X W, et al. Potential estrogenic effects of phosphorus-containing flame retardants[J]. Environmental Science&Technology, 2014, 48(12):6995-7001 Zhang Q, Wang J H, Zhu J Q, et al. Potential glucocorticoid and mineralocorticoid effects of nine organophosphate flame retardants[J]. Environmental Science&Technology, 2017, 51(10):5803-5810 Busquet F, Strecker R, Rawlings J M, et al. OECD validation study to assess intra-and inter-laboratory reproducibility of the zebrafish embryo toxicity test for acute aquatic toxicity testing[J]. Regulatory Toxicology and Pharmacology, 2014, 69(3):496-511 于洋,郑玉婷,张丽丽,等.面向我国化学物质环境管理的计算毒理学模型评估思路及建设路径[J].生态毒理学报, 2021, 16(5):24-32 Yu Y, Zheng Y T, Zhang L L, et al. Evaluation thoughts of computational toxicological models and constructional path for environmental management of chemicals in China[J]. Asian Journal of Ecotoxicology, 2021, 16(5):24-32(in Chinese)
Brad R, Mayeno A N. What is computational toxicology?[J]. Methods in Molecular Biology, 2012, 929:3-7 Schultz T W, Cronin M T D, Walker J D, et al. Quantitative structure-activity relationships (QSARs) in toxicology:A historical perspective[J]. Journal of Molecular Structure:THEOCHEM, 2003, 622(1-2):1-22 Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE):Application of QSAR and molecular docking to drug discovery[J]. Current Topics in Medicinal Chemistry, 2008, 8(18):1555-1572 Alves V M, Bobrowski T, Melo-Filho C C, et al. QSAR modeling of SARS-CoV Mpro inhibitors identifies sufugolix, cenicriviroc, proglumetacin, and other drugs as candidates for repurposing against SARS-CoV-2[J]. Molecular Informatics, 2021, 40(1):e2000113 Jeon H K, Sarma S N, Kim Y J, et al. Toxicokinetics and metabolisms of benzophenone-type UV filters in rats[J]. Toxicology, 2008, 248(2-3):89-95 Zou X M, Lin Z F, Deng Z Q, et al. The joint effects of sulfonamides and their potentiator on Photobacterium phosphoreum :Differences between the acute and chronic mixture toxicity mechanisms[J]. Chemosphere, 2012, 86(1):30-35 Li F, Xie Q, Li X H, et al. Hormone activity of hydroxylated polybrominated diphenyl ethers on human thyroid receptor-beta: in vitro and in silico investigations[J]. Environmental Health Perspectives, 2010, 118(5):602-606 Hanane F, Taoufiq F, Mohamed M. Evaluation of the model prediction toxicity (LD50) for series of 42 organophosphorus pesticides[J]. Journal of Engineering Studies and Research, 2019, 25(1):30-35 Topliss J G, Edwards R P. Chance factors in studies of quantitative structure-activity relationships[J]. Journal of Medicinal Chemistry, 1979, 22(10):1238-1244 Knaak J B, Dary C C, Power F, et al. Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/PD models for human risk assessment[J]. Critical Reviews in Toxicology, 2004, 34(2):143-207 Liagkouridis I, Cousins A P, Cousins I T. Physical-chemical properties and evaluative fate modelling of'emerging'and'novel'brominated and organophosphorus flame retardants in the indoor and outdoor environment[J]. Science of the Total Environment, 2015, 524-525:416-426 Yap C W. PaDEL-descriptor:An open source software to calculate molecular descriptors and fingerprints[J]. Journal of Computational Chemistry, 2011, 32(7):1466-1474 Ambure P, Aher R B, Gajewicz A, et al." NanoBRIDGES"software:Open access tools to perform QSAR and nano-QSAR modeling[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 147:1-13 Gramatica P. Principles of QSAR models validation:Internal and external[J]. QSAR&Combinatorial Science, 2007, 26(5):694-701 Yuan L L, Li J S, Zha J M, et al. Targeting neurotrophic factors and their receptors, but not cholinesterase or neurotransmitter, in the neurotoxicity of TDCPP in Chinese rare minnow adults ( Gobiocypris rarus )[J]. Environmental Pollution, 2016, 208(Pt B):670-677 Trott O, Olson A J. AutoDock Vina:Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading[J]. Journal of Computational Chemistry, 2010, 31(2):455-461 Adasme M F, Linnemann K L, Bolz S N, et al. PLIP 2021:Expanding the scope of the protein-ligand interaction profiler to DNA and RNA[J]. Nucleic Acids Research, 2021, 49(W1):W530-W534 Hollas B. An analysis of the autocorrelation descriptor for molecules[J]. Journal of Mathematical Chemistry, 2003, 33(2):91-101 Ibrahim Z Y, Uzairu A, Abechi S. Quantum Modelling of Some Potent, Non-Toxic Antimalarial Compounds[M]. Chisinau:Scholar Press, 2019:161-166 Galvez J, Garcia R, Salabert M T, et al. Charge indexes. New topological descriptors[J]. Journal of Chemical Information and Computer Sciences, 1994, 34(3):520-525 Caballero J, Garriga M, Fernández M. 2D autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks[J]. Bioorganic&Medicinal Chemistry, 2006, 14(10):3330-3340 Adedirin O, Uzairu A, Shallangwa G A, et al. QSAR and molecular docking based design of some n-benzylacetamide as γ -aminobutyrate-aminotransferase inhibitors[J]. The Journal of Engineering and Exact Sciences, 2018, 4(1):65-84 Shi Q P, Guo W, Shen Q C, et al. In vitro biolayer interferometry analysis of acetylcholinesterase as a potential target of aryl-organophosphorus flame-retardants[J]. Journal of Hazardous Materials, 2021, 409:124999 Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics:Volume Ⅰ:Alphabetical Listing/Volume Ⅱ:Appendices, References[M]. John Wiley&Sons, 2009:41 García-Domenech R, Galvez J, de Julian-Ortiz J V, et al. Some new trends in chemical graph theory[J]. Chemical Reviews, 2008, 108(3):1127-1169 Guo J X, Wu J J, Wright J B, et al. Mechanistic insight into acetylcholinesterase inhibition and acute toxicity of organophosphorus compounds:A molecular modeling study[J]. Chemical Research in Toxicology, 2006, 19(2):209-216 Shityakov S, F rster C. In silico predictive model to determine vector-mediated transport properties for the blood-brain barrier choline transporter[J]. Advances and Applications in Bioinformatics and Chemistry, 2014, 7:23-36 Bennion B J, Lau E Y, Fattebert J L, et al. Modeling the binding of CWAs to AChE and BuChE[J]. Military Medical Science Letters, 2013, 82(3):102-114 Jończyk J, Kukułowicz J, ątka K, et al. Molecular modeling studies on the multistep reactivation process of organophosphate-inhibited acetylcholinesterase and butyrylcholinesterase[J]. Biomolecules, 2021, 11(2):169 -
点击查看大图
计量
- 文章访问数: 2625
- HTML全文浏览数: 2625
- PDF下载数: 161
- 施引文献: 0
下载:
