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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

Toxicological information

Carcinogenicity

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Administrative data

Description of key information

The conclusions were derived hased on the read across study and the QSAR predictions.

Key value for chemical safety assessment

Justification for classification or non-classification

Despite the QSAR Toolbox identification in the target chemical of the potentially hazardous carbamate group, the read across approach could be applied. The experimental test result of the source chemical was read-across to L-Valine, N-[(1,1-dimethylethoxy)carbonyl]-, 2-[(2-amino-1,6-dihydro-6-oxo-9H-purin-9-yl)methoxy]ethyl ester, concluding that L-Valine, N-[(1,1-dimethylethoxy)carbonyl]-, 2-[(2-amino-1,6-dihydro-6-oxo-9H-purin-9-yl)methoxy]ethyl ester is NOT CARCINOGEN. This conclusion was further confirmed by the QSAR predictions: the L-Valine, N-[(1,1-dimethylethoxy)carbonyl]-, 2-[(2-amino-1,6-dihydro-6-oxo-9H-purin-9-yl)methoxy]ethyl ester, was in fact, predicted negative for carcinogenicity on rat, both male and female. In addition, no alerts for non genotoxic carcinogenicity were identified and the only identified alert for genotoxic carcinogenicity was the alkyl carbamate, already commented for not being likely to cause genotoxic activity because of the presence of the protective tert-butyl group. Thus, it is finally concluded that L-Valine, N-[(1,1-dimethylethoxy)carbonyl]-, 2-[(2-amino-1,6-dihydro-6-oxo-9H-purin-9-yl)methoxy]ethyl ester is NOT CARCINOGENIC.

Additional information

Different in silico approaches were applied, including the read-across and QSAR models. In addition, different QSAR tools were used, when possible, in order to apply a consensus approach to enhance the reliability of the predictions. In fact, when multiple models and multiple approaches are combined in a consensus analysis, more accurate predictions can be achieved. In general, the consensus approach was performed according to the precautionary principle and weighting QSAR results more than the read-across results, since they are considered more reliable and scientifically robust.