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Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish.

Lombardo A, Roncaglioni A, Boriani E, Milan C, Benfenati E - Chem Cent J (2010)

Bottom Line: The performance of the model was compared with BCFBAF v3.00.For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model.The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Chemistry and Environmental Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.

ABSTRACT

Background: Bioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish.

Results: To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R² and Q² were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals).

Conclusions: The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

No MeSH data available.


BCFBAF v3.00 performance. Comparison of the experimental logBCF values and the predicted ones using the BCFBAF v3.00 model for the ionic training, non-ionic training, validation and external sets.
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Figure 3: BCFBAF v3.00 performance. Comparison of the experimental logBCF values and the predicted ones using the BCFBAF v3.00 model for the ionic training, non-ionic training, validation and external sets.

Mentions: Figure 3 shows the performance of BAFBCF v3.00 model reporting the results for the compounds used by the developers in their validation and training sets. BCFBAF v3.00 split chemicals in ionic and non-ionic, so we left this information. The developers did not use the compounds in the external during the model development. This external set is of 82 compounds and many of the compounds we took from Arnot were already present in the BAFBCF v3.00 training set. Compared to the 450 compounds in the BAFBCF v3.00 training set, the number of compounds (82) in this external validation set amount to 18%.


Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish.

Lombardo A, Roncaglioni A, Boriani E, Milan C, Benfenati E - Chem Cent J (2010)

BCFBAF v3.00 performance. Comparison of the experimental logBCF values and the predicted ones using the BCFBAF v3.00 model for the ionic training, non-ionic training, validation and external sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2913328&req=5

Figure 3: BCFBAF v3.00 performance. Comparison of the experimental logBCF values and the predicted ones using the BCFBAF v3.00 model for the ionic training, non-ionic training, validation and external sets.
Mentions: Figure 3 shows the performance of BAFBCF v3.00 model reporting the results for the compounds used by the developers in their validation and training sets. BCFBAF v3.00 split chemicals in ionic and non-ionic, so we left this information. The developers did not use the compounds in the external during the model development. This external set is of 82 compounds and many of the compounds we took from Arnot were already present in the BAFBCF v3.00 training set. Compared to the 450 compounds in the BAFBCF v3.00 training set, the number of compounds (82) in this external validation set amount to 18%.

Bottom Line: The performance of the model was compared with BCFBAF v3.00.For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model.The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Chemistry and Environmental Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.

ABSTRACT

Background: Bioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish.

Results: To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R² and Q² were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals).

Conclusions: The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

No MeSH data available.