Regression Quantitative Structure-toxicity Relationship of Pesticides on Fishes

• Autorzy: Yang Bowen , Dang Limin , Chen Cong , Li Mingwang

Identyfikatory

DOI

10.54740/ros.2024.026

• Języki publikacji: EN

Abstrakty

EN

Pesticide usage reaches several million metric tons annually worldwide, and the effects of pesticides on non-target species, such as various fishes in aquatic environments, have resulted in serious concerns. Predicting pesticide aquatic toxicity to fish is of great significance. In this paper, 20 molecular descriptors were successfully used to develop a regression quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity logLC50 of a large data set consisting of 1106 pesticides on fishes by using a general regression neural network (GRNN) algorithm. The optimal GRNN model produced correlation coefficients R of 0.8901 (rms = 0.6910) for the training set, 0.8531 (rms = 0.7486) for the validation set, and 0.8802 (rms = 0.6903) for the test set, which are satisfactory compared with other models in the literature, although a large data set of toxicity logLC50 was used in this work.

Słowa kluczowe

EN

toxicity; pesticide; QSTR; general regression neural network

• Wydawca: Politechnika Koszalińska
• Czasopismo: Rocznik Ochrona Środowiska
• Rocznik: 2024
• Tom: Tom 26
• Strony: 264--272
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Yang Bowen

• College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China

autor

Dang Limin

• College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China

• https://orcid.org/0000-0001-7625-1410

autor

Chen Cong

• College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China

autor

Li Mingwang

• College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China

Bibliografia

• Cachot, J. (2014). Assessment of pollution in the Bizerte lagoon (Tunisia) by the combined use of chemical and biochemical markers in mussels, Mytilus gallo-provincialis. Marine Pollution Bulletin, 84, 379-390. https://doi.org/10.1016/j.marpolbul.2014.05.002
• DeLorenzo, M.E., Scott, G.I., Ross, P.E. (2001). Toxicity of pesticides to aquatic microorganisms: a review. Environmental Toxicology and Chemistry, 20, 84-98. https://doi.org/10.1002/etc.5620200108
• Fang, Z., Yu, X., Zeng, Q. (2022). Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis. Toxicology, 480, 153325. https://doi.org/10.1016/j.tox.2022.153325
• Galimberti, F., Moretto, A., Papa, E. (2020). Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets. Water Research, 174, 115583. https://doi.org/10.1016/j.watres.2020.115583
• Golmohammadi, H., Safdari, M. (2010). Quantitative structure–property relationship prediction of gas-to-chloroform partition coefficient using artificial neural network. Microchemical Journal, 95(2), 140-151. https://doi.org/10.1016/j.microc.2009.10.019
• Jia, Q., Liu, T., Yan, F., Wang, Q. (2020). Norm index–based QSAR model for acute toxicity of pesticides toward rainbow trout. Environmental Toxicology and Chemistry, 39(2), 352-358. https://doi.org/10.1002/etc.4621
• Isah, H.M., Sawyerr, H.O., Raimi, M.O., Bashir, B.G., Haladu, S., Odipe, O.E. (2020). Assessment of Commonly Used Pesticides and Frequency of Self-Reported Symptoms on Farmers Health in Kura, Kano State, Nigeria. Journal of Education and Learning Management, 1(1), 31-54. http//dx.doi.org/10.46410/jelm.2020.1.1.05
• Li, F., Fan, D., Wang, H., Yang, H., Li, W., Tang, Y., Liu, G. (2017). In silico prediction of pesticide aquatic toxicity with chemical category approaches. Toxicology Research, 6, 831. https://doi.org/10.1039/c7tx00144d
• Khan, K., Khan, P.M., Lavado, G., Valsecchi, C., Pasqualini, J., Baderna, D., Marzo, M., Lombardo, A., Roy, K., Benfenati, E. (2019). QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors. Chemosphere, 229, 8-17. https://doi.org/10.1016/j.chemosphere.2019.04.204
• Masand, V.H., Zaki, M.E.A., Al-Hussain, S.A., Ghorbal, A.B., Akasapu, S., Lewaa, I., Ghosh, A., Jawarkar, R.D. (2021). Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata. Aquatic Toxicology, 239, 105962. https://doi.org/10.1016/j.aquatox.2021.105962
• Mit, C., Bado-Nilles, A., Daniele, G., Giroud, B., Vulliet, E., Beaudouin, R. (2022). The toxicokinetics of bisphenol A and its metabolites in fish elucidated by a PBTK model. Aquatic Toxicology, 247, 106174. https://doi.org/10.1016/j.aquatox.2022.106174
• Mo, L.-Y., Yuan, B.-K., Zhu, J., Qin, L.-T., Dai, J.-F. (2022). QSAR Models for Predicting Additive and Synergistic Toxicities of Binary Pesticide Mixtures on Scenedesmus Obliquus. Chinese Journal Structural Chemistry, 41(3), 2203166-2203177. https://doi.org/10.14102/j.cnki.0254-5861.2011-3306
• Schmidt, S., Schindler, M., Faber, D., Hager, J. (2021). Fish early life stage toxicity prediction from acute daphnid toxicity and quantum chemistry. SAR and QSAR in Environmental Research, 32(2), 151-174. https://doi.org/10.1080/1062936X.2021.1874514
• Sullivan, K.M., Manuppello, J.R., Willett, C.E. (2014). Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing. SAR and QSAR in Environmental Research, 25, 357-365. https://doi.org/10.1080/1062936X.2014.907203
• Önlü, S., Saçan, M.T. (2017). An in silico approach to cytotoxicity of pharmaceuticals and personal care products on the rainbow trout liver cell line RTL-W1. Environmental Toxicology and Chemistry, 36(5), 1162-1169. https://doi.org/10.1002/etc.3663
• Pandey, S.K., Ojha, P.K., Roy, K. (2020). Exploring QSAR models for assessment of acute fish toxicity of envi-ronmental transformation products of pesticides (ETPPs). Chemosphere, 252, 126508. https://doi.org/10.1016/j.chemosphere.2020.126508
• Roy, K., Ambure, P., Aher, R.B. (2017). How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?. Chemometrics and Intelligent Laboratory Systems, 162, 44-54. http://dx.doi.org/10.1016/j.chemolab.2017.01.010
• Talete srl (2012). DRAGON (software for molecular descriptor calculation) Version 6.0. http://www.talete.mi.it/
• Toropov, A.A., Toropova, A.P., Marzo, M., Dorne, J.L., Georgiadis, N., Benfenati, E. (2017). QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's Open-FoodTox database. Environmental Toxicology and Pharmacology, 53, 158-163. https://doi.org/10.1016/j.etap.2017.05.011
• Toropov, A.A., Toropova, A.P., Benfenati, E. (2020). QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations". Aquatic Toxicology, 227, 105589. https://doi.org/10.1016/j.aquatox.2020.105589
• Yu, X. (2020a). Prediction of chemical toxicity to Tetrahymena pyriformis with four descriptor models. Ecotoxicology and Environmental Safety, 190, 110146. https://doi.org/10.1016/j.ecoenv.2019.110146
• Yu, X. (2020b). Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata. Aquatic Toxicology, 224, 105496. https://doi.org/10.1016/j.aquatox.2020.105496
• Yu, X. (2021). Support vector machine-based model for toxicity of organic compounds against fish. Regulatory Toxicology and Pharmacology, 123, 104942. https://doi.org/10.1016/j.yrtph.2021.104942
• Yu, X., Zeng, Q. (2022). Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes. Aquatic Toxicology, 251, 106265. https://doi.org/10.1016/j.aquatox.2022.106265
• Yu, X. (2023). Global classification models for predicting acute toxicity of chemicals towards Daphnia magna. Environmental Research, 238, 117239. https://doi.org/10.1016/j.envres.2023.117239