TY - JOUR
T1 - Comprehensive ecotoxicological assessment of pesticides on multiple avian species
T2 - Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across
AU - Das, Shubha
AU - Samal, Abhisek
AU - Kumar, Ankur
AU - Ghosh, Vinayak
AU - Kar, Supratik
AU - Ojha, Probir Kumar
N1 - Publisher Copyright:
© 2024 The Institution of Chemical Engineers
PY - 2024/8
Y1 - 2024/8
N2 - The rapid increase in the use of pesticides is driven by the growing demand in the agricultural sector. However, the widespread application of these pesticides and their inherent toxicity have significant repercussions on the ecosystem, particularly impacting animal and bird species. In this present study, we have developed four 2D quantitative structure-toxicity relationships (QSTRs) models for four different avian species using the largest number of available experimental data points to date employing the partial least squares (PLS) algorithm. Furthermore, we have also performed the read-across algorithm to improve the test set results. Based on the information derived from the models, it was found that hydrophilic characteristics, the presence of molecular branching and thio imide groups impact negatively to the pesticide toxicity, while the presence of phosphate group, presence of halogens viz. chlorine and bromine atoms, presence of hetero atoms, high molecular weight, presence of bridgehead atoms, presence of secondary aliphatic amide and fragments like RCONHR escalates avian toxicity. The developed QSTR models were further employed to predict the Pesticide Properties DataBase (PPDB) for all four avian species as a measure of data gap-filling and risk assessment. Thus, the developed models can be utilized for eco-toxicological data-gap filling, prediction of toxicity of untested pesticides as well as the development of novel and safe environmental-friendly pesticides.
AB - The rapid increase in the use of pesticides is driven by the growing demand in the agricultural sector. However, the widespread application of these pesticides and their inherent toxicity have significant repercussions on the ecosystem, particularly impacting animal and bird species. In this present study, we have developed four 2D quantitative structure-toxicity relationships (QSTRs) models for four different avian species using the largest number of available experimental data points to date employing the partial least squares (PLS) algorithm. Furthermore, we have also performed the read-across algorithm to improve the test set results. Based on the information derived from the models, it was found that hydrophilic characteristics, the presence of molecular branching and thio imide groups impact negatively to the pesticide toxicity, while the presence of phosphate group, presence of halogens viz. chlorine and bromine atoms, presence of hetero atoms, high molecular weight, presence of bridgehead atoms, presence of secondary aliphatic amide and fragments like RCONHR escalates avian toxicity. The developed QSTR models were further employed to predict the Pesticide Properties DataBase (PPDB) for all four avian species as a measure of data gap-filling and risk assessment. Thus, the developed models can be utilized for eco-toxicological data-gap filling, prediction of toxicity of untested pesticides as well as the development of novel and safe environmental-friendly pesticides.
KW - 2D-QSTR
KW - Acute toxicity
KW - Avian species
KW - Pesticides
KW - Read-across
UR - http://www.scopus.com/inward/record.url?scp=85194081738&partnerID=8YFLogxK
U2 - 10.1016/j.psep.2024.05.095
DO - 10.1016/j.psep.2024.05.095
M3 - Article
AN - SCOPUS:85194081738
SN - 0957-5820
VL - 188
SP - 39
EP - 52
JO - Process Safety and Environmental Protection
JF - Process Safety and Environmental Protection
ER -