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JRC contributes to the development of computational models of acute intravenous toxicity of organic chemicals for mice

News details
Feb 07, 2012

QSAR analysis of 68 monofunctional chemical compounds

The determination of acute toxicity in mammals, particularly rodents, is a key consideration in the toxicological evaluation of chemicals. The application of approaches that avoid the use of animals, including computational ('in silico') methods such as quantitative structure-activity relationship (QSAR) models, is encouraged by EU legislation such as REACH (the EU regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals). QSAR models can be used to obtain information on the properties and activities of substances from chemical structure alone, and may therefore be suitable for filling data gaps in the safety assessment of chemicals.

This study, co-authored by a scientist from the Joint Research Centre, Institute for Health and Consumer Protection, evaluated the contribution of various functional groups of organics compounds in acute intravenous toxicity in mice, and generated corresponding linear and non-linear QSAR models.

Preliminary consideration of data for these chemical compounds showed the need to consider non-linear relationships in addition to linear ones. Linear and non-linear QSAR models were therefore constructed and analysed for several classes of organic chemical compounds.

Read the paper:

O. A. Raevsky, E. A. Liplavskaya, A. V. Yarkov, O. E. Raevskaya, and A. P. Worth, "Linear and Nonlinear QSAR Models of Acute Intravenous Toxicity of Organic Chemicals for Mice" (2011), Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry 5, 213–225. doi: 10.1134/S1990750811030103

A quantitative structure-activity relationship (QSAR) of a series of chemicals is an attempt to form a mathematic quantitative relationship between the biological effects (i.e. the activity) and the chemistry (i.e. the structure) of each of these chemicals. The variations in the properties of the compounds can thus be correlated with changes in their molecular features, characterized by the so-called 'molecular descriptors'. Based on these mathematic relationships, "rules" are established than can be used to evaluate and predict the activity of new compounds (to develop 'predictive models').
The early approaches to QSARs assumed that the activity of the chemical entity varies linearly with the 'descriptor' values that affect it: however, this is usually not the case. 'Non-linear' relationships were then also taken into account (in which there isn't a proportional increase and/or decrease between the independent and dependent variables), and 'non-linear' approaches were developed to correlate descriptors and outcomes.