QSAR analyses

Computerized models for predicting toxicological and ecotoxicological properties

QSAR* models may identify chemical hazards and improve the safe use of drugs and other chemicals.  Laboratory testing may be avoided by using QSAR models to predict chemical behaviours directly from chemical structure and simulating adverse effects in cells, tissues, lab animals and the environment. QSAR models are applied in many disciplines including risk assessment, classification and labelling, toxicity and ecotoxicity prediction, regulatory decisions, and development of new chemicals and optimization of existing products.

Benefits of QSAR
QSAR models are non-testing alternatives to animal testing and their primary benefits are reduced time for experimentation as well as minimized cost. QSAR methods may replace further animal tests by using already existing experimental data when possible. They generate data significantly faster and cheaper, and prediction can be carried out for a broad range of substances including pharmaceuticals, biocides, pesticides, cosmetics and other chemicals.  

Who needs QSAR?
Any company developing new chemicals or wishing to optimize its existing products may benefit from QSAR services.

QSAR models may serve as a cheap and faster alternative to generate/interpret data and fill the data gaps for companies getting ready for the next REACH registration.

Furthermore, companies manufacturing cosmetics or pharmaceuticals can benefit from QSAR services when generating data on the toxicological and ecotoxicological properties of their products without carrying out animal studies.

DHI uses QSAR models to predict a large variety of toxicological and ecotoxicological endpoints, including:

  • Aquatic toxicity
  • Terrestrial toxicity
  • Bioaccumulation
  • Skin irritation and sensitization
  • Eye irritation/corrosion
  • Carcinogenicity/mutagenicity
  • Genotoxicity and developmental toxicity
  • Reproductive Toxicity

* QSAR (Quantitative Structure-Activity Relationship)

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Brian Svend Nielsen