What are QSAR’s and why use them?
Quantitative Structure-Activity Relationship “(Q)SAR” is the study of the correlation between chemical structure and associated (biological) activity. They are used to predict the activity of untested chemicals based on structurally related compounds of known activity. The “Q” indicates that they can be both qualitative predictive tools (i.e., structure-activity relationships (SARs)) and quantitative predictive methods (i.e., quantitative structure-activity relationships (QSARs)). Although the term (Q)SAR often refers to predictive models, most commonly computer-based models, (Q)SAR is actually inclusive of a variety of computerized and non-computerized tools and approaches.
The use of (Q)SAR assessment has become a key contributor in chemical safety evaluation, and regulatory submissions. In silico prediction models are now widely used to provide an early indication of the potential toxicity of a given chemical. Their use enables companies to reduce in vivo testing and reduce costs. Additionally, they can be used to inform and refine the overall safety risk assessment for a chemical by providing information on potential adverse health outcomes that can then either be disregarded or which may require further consideration and subsequent data generation.
QSAR models and software tools
A wide variety of publicly available and commercial computational tools have been developed that are suitable for the application of (Q)SARs as an alternative strategy to testing. All (Q)SAR systems are built on experimental toxicity data with rules derived from the data. Rules are also based on expert judgment (e.g. SARs describing reactive chemistry) and/or statistical induction (e.g. QSARs).
Examples of (Q)SAR rule-based systems include TOPKAT and Toxtree. Toxtree includes options for applying the Cramer decision tree (Cramer et al, 1978)1 for assessing chemicals into classes based on potential oral toxicity to make an estimation of the Threshold of Toxicological Concern (TTC).
Derek Nexus (developed and marketed by Lhasa Ltd http://www.lhasalimited.org) is a knowledge-based expert system created with knowledge of structure-toxicity relationships and an emphasis on understanding mechanisms of action and metabolism.
Other systems, such as TIMES and ECOSAR, are hybrids of the two.
The OECD QSAR Toolbox is a publically available application which links a number of existing public domain tools as well as a library of existing (Q)SAR models.
Regulatory uses of (Q)SAR
QSAR can be used as part of an early safety evaluation for compound development as well as in regulatory submissions in areas including;
Plant protection products (EC Regulation no. 1107/2009): Within the EFSA Guidance on the establishment of the residue definition for dietary risk assessment (2016), (Q)SAR predictions and read-across are identified as recommended approaches for the prediction of genotoxicity for metabolites where there is an absence of test data.
REACH (EC Regulation no. 1907/2006): (Q)SAR assessment can be used to fulfil REACH data requirements by informing the evaluation of existing test data, and filling gaps in the hazard data set required for the overall risk assessment, classification and labelling.
Biocidal products (EC Regulation no. 528/2012): Allows the use of non-test methods, such as (Q)SARs, if certain conditions are fulfilled. It provides an overview of the key aspects to consider when predicting properties of substances using (Q)SAR models. This year ECHA published a guidance document: How to apply ECHA’s practical guide ‘How to use and report (Q)SARs’ for the assessment of substances under BPR’.
How can we help you?
(Q)SAR assessment is an important approach for predicting the potential impact of chemicals on human health. Cambridge Environmental Assessments offer (Q)SAR assessment to help you navigate your way through any safety challenges or regulatory requirements for your materials, products or technologies.
For further advice on the above or any other chemical regulatory concern, please contact firstname.lastname@example.org .
1 Cramer, G. M., Ford, R. A., and Hall, R. L. (1978). Estimation of toxic hazard—a decision tree approach. Food Cosmet. Toxicol. 16, 255–276.
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