Definition
Carcinogenicity
The process of carcinogenesis involves the transition of normal cells into cancer cells via a
sequence of stages that entail both genetic alterations (i.e. mutations) and non-genetic events.
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Carcinogens
Chemicals are defined as carcinogenic if they induce tumours, increase tumour incidence and/or malignancy or shorten the time to tumour occurrence.
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Information requirement
For the endpoint of carcinogenicity, standard information requirements are specifically described for substances produced or imported in quantities of ≥1000 tons/year. (more...)
Testing of carcinogenic potential
The objective of investigating the carcinogenicity of chemicals is to identify potential human carcinogens, their mode(s) of action, and their potency. The golden standard test under REACH for carcinogenicity is OECD 451. OECD guideline no. 451 recommends the use of 400 animals (rats and mice) for the studies.
Hazard assessment
Carcinogenic potential may be identified from epidemiological studies, from animal experiments and/or other appropriate means that may include (Quantitative) Structure-Activity Relationships ((Q)SAR) analyses and/or extrapolation from structurally similar substances (read-across).
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CAESAR QSAR model for Carcinogenicity
To address the carcinogenicity issue two complementary approaches were investigated:
- a regression approach
- a continuous approach
The original dataset used for the development of CAESAR model for carcinogenicity contains 805 chemicals extracted from CPDBAS with associated TD50 values for rat. In classification any compound with a finite TD50 dose was associated with the toxic class, while non positive compounds were assigned to the non toxic class. This dataset was then spit into training (n = 644) and test (n = 161) sets. In regression, only compounds with a TD50 dose were used.
Regression Model
In collaboration with the EC project CHEMPREDICT we developed quantitative structure - activity relationships (QSAR) models based on SMILES. Simplified molecular input line entry system (SMILES) has been used as elucidation of the molecular structure for QSAR to predict carcinogenicity. Using the Monte Carlo method we constructed optimal descriptors, which are a mathematical function of composition of the SMILES elements together with special codes of cycles present in molecules.
Good results have been obtained on both the training and test set, as shown below.
The graphic above show the correlation between the experimental TD50 (x-axis) and that calculated by the model (y-axis).
Classification Model
A classification model has been developed adopting the Counter-Propagation Artificial Neural Network (CP-ANN) method and a set of MDL Chemical Descriptors.
Results of the binary classification model are shown below:
Notice for the use of in silico CAESAR models addressing human toxicology related to
human toxicology (i.e.: carcinogenicity and developmental toxicity models).
Currently, the role of in silico models in these endpoints can be limited to consider them as an
ingredient in deriving a weight of evidence rather than to substitute per se existing methods.
Their utility is consequently in support to the overall assessment.
The user is also advised that, since some of the models are based on datasets focused on a limited
chemical space, particular attention should be placed for these two endpoints in the evaluation of
similar compounds already present in the studied datasets and the model's ability to correctly
predict them.










