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Papa, E.; Kovarich, S.; Gramatica, P. Development, Validation and Inspection of the Applicability Domain of QSPR Models for Physicochemical Properties of Polybrominated Diphenyl Ethers. QSAR Comb. Sci. 2009, 28, 790–796.

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Papa, E.; Kovarich, S.; Gramatica, P. Development, Validation and Inspection of the Applicability Domain of QSPR Models for Physicochemical Properties of Polybrominated Diphenyl Ethers. QSAR Comb. Sci. 2009, 28, 790–796.

QDB archive DOI: 10.15152/QDB.146   DOWNLOAD

QsarDB content

Property LogH: Henry's law constant as logH [Pa.m3/mol] i

Compounds: 7 | Models: 1 | Predictions: 3

Tab2.M1: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 7 0.968 0.123
Testing set (inside of the AD) testing 128 N/A N/A
Testing set (outside of the AD) testing 85 N/A N/A

Property TM: Melting point [°C]

Compounds: 25 | Models: 1 | Predictions: 3

Tab2.M2: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 25 0.844 19.312
Testing set (inside of the AD) testing 189 N/A N/A
Testing set (outside of the AD) testing 6 N/A N/A

Property LogPL: Subcooled liquid vapour pressure as logPL [Pa] i

Compounds: 34 | Models: 1 | Predictions: 3

Tab2.M3: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 34 0.987 0.158
Testing set (inside of the AD) testing 168 N/A N/A
Testing set (outside of the AD) testing 18 N/A N/A

Property LogSw: Water solubility as logSw [mol/L] i

Compounds: 12 | Models: 1 | Predictions: 3

Tab2.M4: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 12 0.918 0.229
Testing set (inside of the AD) testing 201 N/A N/A
Testing set (outside of the AD) testing 7 N/A N/A

Property LogKoa: Octanol-air partition coefficient as logKoa

Compounds: 30 | Models: 1 | Predictions: 3

Tab2.M5: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 30 0.974 0.231
Testing set (inside of the AD) testing 169 N/A N/A
Testing set (outside of the AD) testing 21 N/A N/A

Property LogKow: Octanol-water partition coefficient as logKow

Compounds: 20 | Models: 1 | Predictions: 3

Tab2.M6: Model for polybrominated diphenyl ethers

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 20 0.965 0.194
Testing set (inside of the AD) testing 186 N/A N/A
Testing set (outside of the AD) testing 14 N/A N/A

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Title: Papa, E.; Kovarich, S.; Gramatica, P. Development, Validation and Inspection of the Applicability Domain of QSPR Models for Physicochemical Properties of Polybrominated Diphenyl Ethers. QSAR Comb. Sci. 2009, 28, 790–796.
Abstract: Polybrominated diphenyl ethers (PBDEs) are a group of brominated flame retardants (BFRs), which were widely used in a variety of consumer products. Because of evidences of toxicity effects on different organisms and humans, as well as the ubiquitary profile of these compounds, PBDEs are considered an emerging group of toxic and persistent organic pollutants. However, due to the small amount of experimental data available, still little is known about the properties of most of these chemicals. In this study several physicochemical properties, experimentally available for few PBDE congeners and hexabromobenzene (HBB), were investigated through a modelling approach based on quantitative structure – property relationships (QSPR). The OLS regression models, based on theoretical molecular descriptors, are calculated for Henry's law constant, melting point, subcooled liquid vapor pressure, water solubility, octanol-air partition coefficient, and octanol-water partition coefficient. These models can be useful to predict the big amount of missing data and to plan safer alternatives to dangerous BFRs. The innovative aspect of the proposed models, compared to those already published in the literature, is their development according to the OECD principles for regulatory acceptability of QSARs. This includes the validation for predictivity (both by internal and external statistical validation) and the inspection of the applicability domain.
URI: http://hdl.handle.net/10967/146
http://dx.doi.org/10.15152/QDB.146
Date: 2015-04-01


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

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