Chromatographic evaluation of the toxicity in fish of pesticides

Bermúdez-Saldaña, José María; Escuder-Gilabert, Laura; Medina-Hernández, M.J.; Villanueva-Camañas, R.M.; Sagrado, S.

doi:10.1016/j.jchromb.2004.10.007

Cited by 36 publications

(18 citation statements)

References 29 publications

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“…These programmes are integrated in the EPI Suite software (developed by Syracuse Research Corporation for the US Enviromental Protection Agency). Table 2 summarizes previously developed and validated QRAR models based on BMC retention in standard analytical columns to estimate passive transport of xenobiotics through different biological barriers-gastrointestinal tract (Molero-Monfort et al, 2001); blood-brain barrier (Escuder-Gilabert et al, 2004) and skin (Martínez-Pla et al, 2003)-as well as developed and validated QRAR models for predicting the ecotoxicological parameters-lethal concentration to rainbow trout (96 h test) for compounds with non-polar narcosis mechanism of toxic action (Bermúdez-Saldaña et al, 2005a) and the bioconcentration factor in fish for R53-compounds according to the Directive 93/21/ EEC of the Commission of the European Communities (Commission of the European Communities, 1993;Bermúdez-Saldaña et al, 2005b).…”

Section: Datamentioning

confidence: 99%

“…polar narcosis) show equal or higher toxicity than the predicted by these relationships. In a previous paper, our research group proposed an alternative baseline toxicity based on the retention in BMC to estimate LC 50 values to rainbow trout (96 h test; see Table 2), which showed better descriptive and predictive abilities than the corresponding one based on log P (Bermúdez-Saldaña et al, 2005a). Table 3 shows the predicted LC 50 values for organochlorines from BMC data together with the pLC 50 median experimental values reported in bibliography (ECOTOX database, http://www.epa.gov/ecotox) and Fig.…”

Section: Ecotoxicological Profile Estimation Of Organochlorine Compoundsmentioning

confidence: 99%

“…1(B, C), the validation plots obtained using the baseline toxicity based on log P values (Bermúdez-Saldaña et al, 2005a), and ECOSAR estimations are included. As can be observed, there is a better agreement between experimental pLC 50 values and predictions obtained using BMC QRAR models.…”

Section: Ecotoxicological Profile Estimation Of Organochlorine Compoundsmentioning

confidence: 99%

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Permeability and toxicological profile estimation of organochlorine compounds by biopartitioning micellar chromatography

Escuder-Gilabert

Villanueva-Camañas

Sagrado

et al. 2008

Biomedical Chromatography

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This paper points out the usefulness of biopartitioning micellar chromatography (BMC) as a high-throughput primary screening tool providing key information about the oral absorption, skin permeability (K(p)), brain-blood distribution coefficient (BB) and ecotoxicological parameters such as median lethal concentration (LC(50)) and bioconcentration factors of 15 organochloride compounds. The retention data of compounds in BMC conditions were interpolated in previously developed quantitative-retention activity relationships by our research group. Results show that the compounds studied readily cross the intestinal barrier (oral absorption > 90%) and the blood-brain barrier (log BB > 0.4). In addition, the organochlorines DDE, chlorobenzene, 1,3-dichlorobenzene and 1,2-dichlorobenzene are the compounds which can more quickly cross the skin barrier (log K(p ) > -0.74 cm/h). From a ecotoxicological point of view, it can be concluded that the most retained compounds, DDE, DDD, hexachlorobenzene and dicofol, are the most toxic and bioacumulative.

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Section: Datamentioning

confidence: 99%

Section: Ecotoxicological Profile Estimation Of Organochlorine Compoundsmentioning

confidence: 99%

Section: Ecotoxicological Profile Estimation Of Organochlorine Compoundsmentioning

confidence: 99%

See 1 more Smart Citation

Permeability and toxicological profile estimation of organochlorine compounds by biopartitioning micellar chromatography

Escuder-Gilabert

Villanueva-Camañas

Sagrado

et al. 2008

Biomedical Chromatography

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show abstract

“…Furthermore, test data may be inadequate, inappropriate or incomplete for many chemical substances. Human and material resources are lacking in order to obtain even basic experimental information on environmental fate and effects for all these chemicals [4]. An attractive alternative is the development of a methodology that enables predictions of effects to be made directly from chemical structure.…”

Section: Introductionmentioning

confidence: 99%

Exploring Predictive QSAR Models Using Quantum Topological Molecular Similarity (QTMS) Descriptors for Toxicity of Nitroaromatics to Saccharomyces cerevisiae

Roy

Popelier²

2008

QSAR Comb. Sci.

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In view of the widespread industrial use of nitroaromatics and their consequent ecotoxicological hazard potential, we constructed predictive Quantitative Structure -Activity Relationship (QSAR) models for the toxicity of nitroaromatics to the ecologically important species Saccharomyces cerevisiae. We used Quantum Topological Molecular Similarity (QTMS) descriptors along with electrophilicity index (E LUMO ) and lipid water partition coefficient (log K ow ) as predictor variables. The QTMS descriptors were calculated at B3LYP/6-311 þ G(2d,p) level of theory. QTMS descriptors were employed to complement the deficiency of E LUMO in setting up predictive QSAR models from the view point of external validation. The dataset was divided into a training set (18 compounds) and test set (six compounds) in a ratio of three to one. Partial Least Square (PLS) models were developed based on the training set compounds. The predictive capacity of the models was assessed by the test compounds. The models were also validated by a randomisation test and leave-one-seventh-out crossvalidation test. The results suggest that Bond Critical Point (BCP) descriptors can develop predictive QSAR models for nitroaromatic toxicity to Saccharomyces cerevisiae when used along with E LUMO and log K ow . The diagnostic potential of QTMS descriptors could also reveal the importance of the nitro group for nitroaromatic toxicity.

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“…The application of chromatographic parameters to QSAR gives rise to a new field, quantitative retention-activity relationship (QRAR). [10][11][12][13] The predictive and interpretative capability of QRAR is supported by the fact that under adequate experimental conditions the solute partitioning into chromatographic system can emulate the solute partitioning into lipid bilayers of biological membranes. Successful applications of immobilized artificial membrane (IAM) [14][15][16][17] and biopartitioning micellar chromatography (BMC) 18,19 to describe skin permeability to construct QRAR models were established.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Retention‐Activity Relationship Studies by Liposome Electrokinetic Chromatography to Predict Skin Permeability

et al. 2008

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Liposome electrokinetic chromatography (LEKC) provides a simple and facile approach for drug membrane interactions using liposome as a pseudostationary phase. This study evaluated the potential of LEKC for high-throughput skin permeability profiled as an in vitro technique. A quantitative retention-activity relationship (QRAR) model for the estimation of skin permeability was proposed. For the 16 structurally diverse chemicals, lg k correlated well with permeability values (R 2 ＝0.886). The predictive ability of the model was evaluated by cross-validation. The result was compared to traditional quantitative structure-activity relationship, QSAR, models using some molecular descriptors and physicochemical parameters. Interestingly, a single LEKC retention parameter was capable of describing the skin permeability, while three variables in QSAR were needed to achieve a similar correlation (R 2 ＝0.704). The QRAR models developed in this paper may be a useful method to screening new chemicals and in the early stage of development and selection of chemicals.

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Chromatographic evaluation of the toxicity in fish of pesticides

Cited by 36 publications

References 29 publications

Permeability and toxicological profile estimation of organochlorine compounds by biopartitioning micellar chromatography

Permeability and toxicological profile estimation of organochlorine compounds by biopartitioning micellar chromatography

Exploring Predictive QSAR Models Using Quantum Topological Molecular Similarity (QTMS) Descriptors for Toxicity of Nitroaromatics to Saccharomyces cerevisiae

Quantitative Retention‐Activity Relationship Studies by Liposome Electrokinetic Chromatography to Predict Skin Permeability

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