Definition
Developmental toxicity includes any effect interfering with normal development, both before and after birth. (more...)
Information requirement
The standard data requirements for developmental toxicity under the REACH Regulations are as follows:
- A reproduction/developmental toxicity screening test (OECD TGs 421 or 422), usually required for substances produced or imported in quantities between 10 and 100 tons/year (according to Annex VIII of the REACH legislation).
- A prenatal developmental toxicity study (EU B.31, OECD TG 414) in one species, usually required for substances produced or imported in quantities of ≥100 tons/year (according to Annex IX and X of the REACH legislation). A study in a second species may be considered necessary at these levels of production/importation.
Testing of developmental effects
The golden standard test under REACH for developmental toxicity is OECD 414, which recommends the use of 80 adult animals (rats or rabbits) for this study. (more...)
Hazard assessment
Developmental toxicity 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 developmental toxicity
The dataset extracted from Arena et al. (2004) includes 292 compounds divided into a training and a test set.
Chemical compounds were categorized into toxicant or non toxicant according to FDA risk factors.
| FDA classes | Definition | CAESAR Binary class |
|---|---|---|
| Category A | Negative human studies | Non developmental toxicant |
| Category B |
Negative animal studies &
No human studies executed OR Positive animal studies & Negative human studies |
|
| Category C | Postive animal studies &
No human studies executed OR No studies at all |
Developmental toxicant |
| Category D | Postive human studies | |
| Category X | Animal OR human studies show abnormalities AND/OR Evidence of foetal risk based on human experience |
The chemical descriptors have been calculated in collaboration with Dr Todd Martin, US EPA with a public US EPA program .
Several models have been developed: in one case (model A) the software used for the model is WEKA (Waikato Environment for Knowledge Analysis), an open source workbench. In this case we used 13 chemical descriptors.
The algorithm used for modelling is Random Forest that constructs a "forest" of random "trees".
A second model (model B) was developed using Adaptive Fuzzy Partition (AFP) – AFP was used to develop classification models implementing a fuzzy partition algorithm. It models relations between molecular descriptors and chemical activities by dynamically dividing the descriptor space into a set of fuzzy partitioned subspaces. The aim of this algorithm is to select the descriptor and the cut position that allow to get the maximal difference between the two fuzzy rule scores generated by the new subspaces. The score is determined by the weighted average of the chemical activity values in an active subspace A and in its neighbouring subspaces.
In this case we used 6 chemical descriptors. For the feature selection, a hybrid selection algorithm (HSA), which combines the genetic algorithm (GA) concepts and a stepwise regression, was used to select the best descriptors for classifying developmental toxicity dataset.
Below there are the results for the two models on developmental toxicity dataset.
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.










