In Silico Modelling of Pesticide Aquatic Toxicity
Apstrakt
Human activities have introduced tens of thousands of chemicals into water systems around the world which has significantly impacted water quality and aquatic ecosystems. The aim of this study was to develop an in silico QSAR model, capable of predicting the aquatic toxicity of pesticides in terms of a lethal dose (LD50) for fish without requiring the use of in vivo testing. A large data set of 230 diverse pesticides, including fungicides, herbicides and insecticides, with experimentally measured LD50 values was used to develop a predictive QSAR model. Each pesticide molecule was described using 62 calculated molecular descriptors. These descriptors were then related to the LD50 values via an Artificial Neural Network. Sensitivity analysis was used to select descriptors that best describe the model. The developed model included 13 molecular descriptors related to lipophilicity, hydrogen binding and polarity. Note the value of the predictive squared correlation coefficient (q(2)) for th...e final model was 0.748, demonstrating the model's predictability. In the domain of QSAR studies, a q(2) value above 0.5 renders a model to be predictive. The model could therefore be used to accurately screen a wide range of compounds without the need for actual compound synthesis and to prioritize potentially toxic compounds for further testing.
Ključne reči:
ANNs / aquatic toxicity / LD50 / pesticides / QSARsIzvor:
Combinatorial Chemistry & High Throughput Screening, 2014, 17, 9, 808-818Izdavač:
- Bentham Science Publ Ltd, Sharjah
DOI: 10.2174/1386207317666141021110738
ISSN: 1386-2073
PubMed: 25335880
WoS: 000345279500010
Scopus: 2-s2.0-84929088735
Institucija/grupa
PharmacyTY - JOUR AU - Agatonović-Kuštrin, Snežana AU - Morton, David W. AU - Ražić, Slavica PY - 2014 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2137 AB - Human activities have introduced tens of thousands of chemicals into water systems around the world which has significantly impacted water quality and aquatic ecosystems. The aim of this study was to develop an in silico QSAR model, capable of predicting the aquatic toxicity of pesticides in terms of a lethal dose (LD50) for fish without requiring the use of in vivo testing. A large data set of 230 diverse pesticides, including fungicides, herbicides and insecticides, with experimentally measured LD50 values was used to develop a predictive QSAR model. Each pesticide molecule was described using 62 calculated molecular descriptors. These descriptors were then related to the LD50 values via an Artificial Neural Network. Sensitivity analysis was used to select descriptors that best describe the model. The developed model included 13 molecular descriptors related to lipophilicity, hydrogen binding and polarity. Note the value of the predictive squared correlation coefficient (q(2)) for the final model was 0.748, demonstrating the model's predictability. In the domain of QSAR studies, a q(2) value above 0.5 renders a model to be predictive. The model could therefore be used to accurately screen a wide range of compounds without the need for actual compound synthesis and to prioritize potentially toxic compounds for further testing. PB - Bentham Science Publ Ltd, Sharjah T2 - Combinatorial Chemistry & High Throughput Screening T1 - In Silico Modelling of Pesticide Aquatic Toxicity VL - 17 IS - 9 SP - 808 EP - 818 DO - 10.2174/1386207317666141021110738 ER -
@article{ author = "Agatonović-Kuštrin, Snežana and Morton, David W. and Ražić, Slavica", year = "2014", abstract = "Human activities have introduced tens of thousands of chemicals into water systems around the world which has significantly impacted water quality and aquatic ecosystems. The aim of this study was to develop an in silico QSAR model, capable of predicting the aquatic toxicity of pesticides in terms of a lethal dose (LD50) for fish without requiring the use of in vivo testing. A large data set of 230 diverse pesticides, including fungicides, herbicides and insecticides, with experimentally measured LD50 values was used to develop a predictive QSAR model. Each pesticide molecule was described using 62 calculated molecular descriptors. These descriptors were then related to the LD50 values via an Artificial Neural Network. Sensitivity analysis was used to select descriptors that best describe the model. The developed model included 13 molecular descriptors related to lipophilicity, hydrogen binding and polarity. Note the value of the predictive squared correlation coefficient (q(2)) for the final model was 0.748, demonstrating the model's predictability. In the domain of QSAR studies, a q(2) value above 0.5 renders a model to be predictive. The model could therefore be used to accurately screen a wide range of compounds without the need for actual compound synthesis and to prioritize potentially toxic compounds for further testing.", publisher = "Bentham Science Publ Ltd, Sharjah", journal = "Combinatorial Chemistry & High Throughput Screening", title = "In Silico Modelling of Pesticide Aquatic Toxicity", volume = "17", number = "9", pages = "808-818", doi = "10.2174/1386207317666141021110738" }
Agatonović-Kuštrin, S., Morton, D. W.,& Ražić, S.. (2014). In Silico Modelling of Pesticide Aquatic Toxicity. in Combinatorial Chemistry & High Throughput Screening Bentham Science Publ Ltd, Sharjah., 17(9), 808-818. https://doi.org/10.2174/1386207317666141021110738
Agatonović-Kuštrin S, Morton DW, Ražić S. In Silico Modelling of Pesticide Aquatic Toxicity. in Combinatorial Chemistry & High Throughput Screening. 2014;17(9):808-818. doi:10.2174/1386207317666141021110738 .
Agatonović-Kuštrin, Snežana, Morton, David W., Ražić, Slavica, "In Silico Modelling of Pesticide Aquatic Toxicity" in Combinatorial Chemistry & High Throughput Screening, 17, no. 9 (2014):808-818, https://doi.org/10.2174/1386207317666141021110738 . .