Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

Basant, Nikita; Singh, Kunwar P.

doi:10.1016/j.chemosphere.2015.06.063

Cited by 36 publications

(22 citation statements)

References 60 publications

(47 reference statements)

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“…A direct comparison of the current study (global model) with recently published studies is inappropriate because various prediction methodologies were used in those studies . However, a simple comparison of model statistics suggests that the accuracy level of the global model is quite good and within the acceptable range.…”

Section: Resultsmentioning

confidence: 94%

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An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals

Önlü

Saçan

2016

Enviro Toxic and Chemistry

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The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC.

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Section: Resultsmentioning

confidence: 94%

“…Figure 7 displays the high prediction capability and broad structural coverage (84%). It is worth noting that [14][15][16]. However, a simple comparison of model statistics suggests that the accuracy level of the global model is quite good and within the acceptable range.…”

Section: Resultsmentioning

confidence: 98%

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals

Önlü

Saçan

2016

Enviro Toxic and Chemistry

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“…The mayfly has also been used as a biological indicator of the well-being of a stream in the Niger Delta (Nigeria), whose abundance has been characterised by the Shannon-Weaver diversity index [99]. Basant et al [100] commented that work based on these models had focused on a single species and that for a thorough safety inspection of an ecosystem, models should include other species in different trophic levels so that their responses can be assessed, estimated or predicted. (In principle, the authors of the present work tend to agree, but suggest that the single-species application can be reserved for endangered species such as listed bees and exotic birds.)…”

Section: Ecotoxicological Modelsmentioning

confidence: 99%

Quantification of Neonicotinoid Pesticides in Six Cultivable Fish Species from the River Owena in Nigeria and a Template for Food Safety Assessment

Adegun

Akinnifesi

Ololade

et al. 2020

Water

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The Owena River Basin in Nigeria is an area of agricultural importance for the production of cocoa. To optimise crop yield, the cocoa trees require spraying with neonicotinoid insecticides (Imidacloprid, Thiacloprid Acetamiprid and Thiamethoxam). It is proposed that rainwater runoff from the treated area may pollute the Owena River and that these pesticides may thereby enter the human food chain via six species of fish (Clariasgariepinus, Clariasanguillaris, Sarotherodongalilaeus, Parachannaobscura, Oreochromisniloticus and Gymnarchusniloticus) which are cultured in the river mostly for local consumption. This work aims to establish a working method to quantify the likely levels of the insecticides in the six species of fish, firstly by undertaking a laboratory-based study employing the QuEChERS method to extract the four neonicotinoids from fish purchased in marketplace in the UK, spiked with known quantities of the pesticide and using liquid chromatography coupled with tandem mass spectrometry (LC-MS-MS) as the detection method; secondly, by using these samples to optimise the detection method for very low levels of pesticides, then applying the optimised techniques to the analysis of three of each six species of fish taken from the Owena River. A significant benefit of this combined technique is that only small samples of fish are required. Success with this part of the study showed that very low concentrations of the insecticides could be detected in fish muscle. The third aim is to apply a simple quantitative risk assessment model using the data sets obtained, together with information about daily diet, human body weight and recommended safety limits of pesticides in food to illustrate how human health may be affected by the consumption of these fish. The multiple determinations of neonicotinoids in edible fishes in Nigeria are pioneer research and fill a gap in addressing the relationship between waterborne pesticides and food quality in the country. Fundamentally, this work is an exercise to demonstrate the applicability of the aforementioned instrumental method of analysis to fish muscle, which requires only a small sample size of fish; a large number of fish is not required for a proof of concept, in this case. Although not a monitoring programme for the whole Owena River Basin ecosystem per se, this work successfully demonstrates the technical feasibility of a system of chemical analysis and establishes the foundation for ecological surveys in the immediate future. Parameters involving exposures to xenobiotics in ecotoxicological modelling can now be expressed in terms of both mass and molar concentrations of a chemical in animal tissues if so desired.

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“…Quantitative structure–activity relationship (QSAR) models played a significant role in multi‐disciplinary fields such as pesticides (Zou et al, ), drug design (Yousefinejad & Hemmateenejad ), environmental toxicology (Altenburger et al, ; Basant et al, ), and medicine (Roy et al, ). Since the development of QSAR approaches, various methods have significantly contributed in drug design such as principal component analysis (PCA) (Abdi & Williams, ), partial least squares (PLS) (Abdi, ), stepwise multiple line regression (SW‐MLR) (Kokaly & Clark ), and support vector machine (SVM) (Miao et al, ).…”

Section: Introductionmentioning

confidence: 99%

The activity prediction of indole inhibitors against HCV NS5B polymerase

Muhire

Zhai

et al. 2019

Chem Biol Drug Des

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Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus.Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC 50 ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q 2 ) for the training set and the R-squared of prediction (R 2 p ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique. K E Y W O R D Sbiological activity prediction model, hepatitis C virus, NS5B polymerase inhibitors, quantitative structure-activity relationship, Tchebichef image moments

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Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

Cited by 36 publications

References 60 publications

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals

Quantification of Neonicotinoid Pesticides in Six Cultivable Fish Species from the River Owena in Nigeria and a Template for Food Safety Assessment

The activity prediction of indole inhibitors against HCV NS5B polymerase

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