Quantitative structure–activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout <i>Onchorhyncus mykiss</i>

Bermúdez-Saldaña, José María; Cronin, Mark T.D.

doi:10.1002/ps.1233

Cited by 26 publications

(13 citation statements)

References 30 publications

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“…For instance, Cronin, Schultz and co-workers developed a series of QSARs for electrophilic toxicants where the electrophilic nature of the compounds was accounted for by molecular orbital properties; [40][41][42] Bermudez-Saldana and Cronin developed QSARs for the toxicity of organophosphates to fish by including specific terms for the organophosphate group. 43 In some circumstances, given the difficulty of obtaining reliable data sets, read-across may become a practical alternative to QSAR. 44 In this context read-across is suitable where a small number of similar compounds are available, with data for at least one of them.…”

Section: (Q)sars For Acute Toxicitymentioning

confidence: 99%

(Q)SARs to predict environmental toxicities: current status and future needs

Cronin

2017

Environ. Sci.: Processes Impacts

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The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

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Section: (Q)sars For Acute Toxicitymentioning

confidence: 99%

(Q)SARs to predict environmental toxicities: current status and future needs

Cronin

2017

Environ. Sci.: Processes Impacts

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“…Traditional QSAR models on pesticides are built mainly based on lethal concentration 50% (LC50) 8) or physico-chemical data [9][10][11] , which are then used to construct a regression model. However, no attempt has yet been made to predict QSAR models utilizing health-based guidance such as ADI due to uncertainty factors.…”

Section: -25)mentioning

confidence: 99%

Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?

Kim

2012

Korean Journal of Environmental Health Sciences

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SObjectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water.Methods: A subset of calculated descriptors was selected from Dragon ® software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results:The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory.Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

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“…Promotion of genetically modified crops for the fulfillment of food and crop demand of increasing population around the globe by the agrochemical companies are thoroughly changing the face of agriculture in recent era . But limited extension of agricultural lands still encourages the indiscriminate applications of agrochemicals for plant protection and increasing production yields .…”

Section: Introductionmentioning

confidence: 99%

Acute Rat and Mouse Oral Toxicity Determination of Anticholinesterase Inhibitor Carbamate Pesticides: A QSTR Approach

Roy

Banjare

Verma

et al. 2019

Molecular Informatics

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The toxic potentials of carbamates to human and non target organisms are of public concern in relation to society and ecosystem for their unregulated and indiscriminate use. No computational study was found on rat and mouse oral toxicity for carbamate pesticides. In this context, carbamate pesticides were collected from ChemIDplus databases for the modeling study. A series of local QSTR model for both rat and mouse oral toxicity of carbamate derivatives were developed according to OECD principle from 2D descriptors by using Genetic Algorithm (GA) as feature selection chemometric tools using QSARINS software. All the models indicate the importance of auto correlation descriptors related to charge, I-State, atom type E state for fragment À OÀ in relation to acute mammalian toxicity. Reliability of predictions of the models was verified by applicability domain (AD) and prediction reliability index. Finally developed models were applied to unknown carbamate pesticides to evaluate their predictions and AD. The toxic nature of the prioritized compounds with structural alerts were commented in a consensus way. Additional toxicity-toxicity relationship studies (QTTR) between these two responses with similar findings promoted further application of QTTR models in absence of one response. These findings may help the scientific community in prioritizing potentially hazardous pesticides of carbamate and related classes.

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Quantitative structure–activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss

Cited by 26 publications

References 30 publications

(Q)SARs to predict environmental toxicities: current status and future needs

(Q)SARs to predict environmental toxicities: current status and future needs

Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?

Acute Rat and Mouse Oral Toxicity Determination of Anticholinesterase Inhibitor Carbamate Pesticides: A QSTR Approach

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