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CORAL: model for no observed adverse effect level (NOAEL).

Toropov AA, Toropova AP, Pizzo F, Lombardo A, Gadaleta D, Benfenati E - Mol. Divers. (2015)

Bottom Line: Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task.The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed.The probabilistic definition for the domain of applicability is suggested.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20159, Milan, Italy, andrey.toropov@marionegri.it.

ABSTRACT
The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure-activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.

No MeSH data available.


Related in: MedlinePlus

Correlations between the Split Defect and the root-mean-square error (RMSE) for the external validation set (for 12 random splits)
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Fig4: Correlations between the Split Defect and the root-mean-square error (RMSE) for the external validation set (for 12 random splits)

Mentions: The additional analysis of twelve splits (including three represented by models which are calculated with Eqs. 7–9; these are the splits 1, 2, and 3; Table 5 contains the data) gives possibility to study the criterion calculated with Eq. 6. Figure 3 represents the correlation between Split Defect calculated with Eq. 6 and the determination coefficient between experimental and predicted lgNOAEL for the validation set (12 random splits). Figure 4 represents the correlation between Split Defect calculated with Eq. 6 and the root-mean-square error for the validation set (twelve random splits). Unexpectedly, the increase of the Split Defect is accompanied by increase of the determination coefficient and by decrease for root-mean-square error for the external validation set. Thus, very likely, these correlations can be useful criteria to compare different splits into the training set and test set.Fig. 3


CORAL: model for no observed adverse effect level (NOAEL).

Toropov AA, Toropova AP, Pizzo F, Lombardo A, Gadaleta D, Benfenati E - Mol. Divers. (2015)

Correlations between the Split Defect and the root-mean-square error (RMSE) for the external validation set (for 12 random splits)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4487358&req=5

Fig4: Correlations between the Split Defect and the root-mean-square error (RMSE) for the external validation set (for 12 random splits)
Mentions: The additional analysis of twelve splits (including three represented by models which are calculated with Eqs. 7–9; these are the splits 1, 2, and 3; Table 5 contains the data) gives possibility to study the criterion calculated with Eq. 6. Figure 3 represents the correlation between Split Defect calculated with Eq. 6 and the determination coefficient between experimental and predicted lgNOAEL for the validation set (12 random splits). Figure 4 represents the correlation between Split Defect calculated with Eq. 6 and the root-mean-square error for the validation set (twelve random splits). Unexpectedly, the increase of the Split Defect is accompanied by increase of the determination coefficient and by decrease for root-mean-square error for the external validation set. Thus, very likely, these correlations can be useful criteria to compare different splits into the training set and test set.Fig. 3

Bottom Line: Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task.The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed.The probabilistic definition for the domain of applicability is suggested.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20159, Milan, Italy, andrey.toropov@marionegri.it.

ABSTRACT
The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure-activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.

No MeSH data available.


Related in: MedlinePlus