Prediction of toxicological endpoints by QSAR modeling
Commonly used acronym: QSAR
Scope of the method
- Animal health
- Human health
- Basic Research
- Education and training
- Regulatory use - Routine production
- Translational - Applied Research
- In silico
- Animal derived cells / tissues / organs
- predictive modeling
- multivariate analyses
- in silico analysis
- molecular descriptors
Quantitative Structure Activity Relationship modeling is generally used to construct models in which molecular descriptors of chemical compounds are used to predict endpoints/activities of interest. Commercial packages are available that can be implemented, but new models can be constructed if sufficient data are available.
No lab equipment is needed, the methodology aims to use existing data (in vitro, in vivo) to make predictive models.
- Still in development
- History of use
- Published in peer reviewed journal
Pros, cons & Future potential
Depending on the strength of the developed models for a specific endpoint, animal experiments can be avoided and new chemicals (within the application domain) can be predicted for the specific endpoint.
Good quality and sufficiently large datasets (containing sufficient chemicals and well performed experiments/measurements) need to be available to start modeling efforts for new endpoints.
Existing models can be improved by adding new experimental datasets.
References, associated documents and other information
- Tuenter E, Creylman J, Verheyen G, Pieters L, Van Miert S. (2019) Development of a classification model for the antigenotoxic activity of flavonoids. Bioorganic Chemistry 98. Doi: 10.1016/j.bioorg.2020.103705
- Van Miert S, Verheyen GR, Creylman J. (2019) Mining a Nanoparticle Dataset, Compiled Within the MODENA-COST Action. International Journal of Quantitative Structure-Property Relationships Vol 4 (1): 1-17. DOI: 10.4018/IJQSPR.2019010101
- Verheyen GR, Van Deun K, Van Miert S. (2017) Testing the mutagenicity potential of chemicals. Journal of Genetics and Genome Research, 4:029. DOI:10.23937/2378-3648/1410029
- Verheyen GR, Braeken E, Van Deun K, Van Miert S. (2017) Evaluation of in silico tools to predict the skin sensitisation potential of chemicals. SAR and QSAR in Environmental Research, 28: 59-73
- Verheyen GR, Braeken E, Van Deun K, Van Miert S. (2017) Evaluation of existing (Q)SAR models for skin and eye irritation and corrosion to use for REACH registration. Toxicology Letters, 265: 47-52
Contact personGeert Verheyen
OrganisationsThomas More University of Applied Sciences