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Bhhatarai, B.; Gramatica, P. Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse. Mol. Diversity 2010, 15, 467–476.

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Bhhatarai, B.; Gramatica, P. Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse. Mol. Diversity 2010, 15, 467–476.

QDB archive DOI: 10.15152/QDB.170   DOWNLOAD

QsarDB content

Property pLD50mouse: Mouse oral toxicity as log(LD50) [-log(mmol/kg)]

Compounds: 58 | Models: 1 | Predictions: 1

Eq1: model for 58 polyfluorinated compounds for mouse oral toxicity

Regression model (regression)

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Name Type n

R2

σ

train training 58 0.759 0.396

Property pLD50rat: Rat oral toxicity as log(1/LD50) [-log(mmol/kg)]

Compounds: 50 | Models: 1 | Predictions: 1

Eq2: model for 50 polyflourinated compounds for rat oral toxicity

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

train training 50 0.883 0.422

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Title: Bhhatarai, B.; Gramatica, P. Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse. Mol. Diversity 2010, 15, 467–476.
Abstract: Quantitative structure-activity relationship (QSAR) analyses were performed using the LD 50 oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD 50 oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC 50 rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.
URI: http://hdl.handle.net/10967/170
http://dx.doi.org/10.15152/QDB.170
Date: 2015-09-25


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    Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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