Intertek’s Computational Predictive Toxicology Modeling services support the safety assessment of novel molecules in the absence of experimental safety data.

Computational Predictive Toxicology Modeling based on (Quantitative) Structure-Activity Relationships [(Q)SAR] and metabolic modeling have emerged as fundamental tools in the preliminary safety screening of novel molecules, such as plant extracts, fermentation products, or fungal secondary metabolites that are intended for use in food.

In the process of developing new molecules that are intended for use in food or in human sensory trials, such as ‘sip & spit’ and consumption studies, generating preliminary safety data for novel substances can be a costly and time-consuming process due to the large number of candidate substances. 

Intertek’s chemical toxicology experts have extensive experience in using computational predictive toxicology modeling tools to screen   novel substances for safety concerns. This is a valuable tool in streamlining the research and development process for novel ingredients by targeting those with safer molecular profiles.  

Intertek offers the scientific expertise necessary to provide reliable advice on the predicted safety of novel substances.

Our Computational Predictive Toxicology Modeling Services include the following:

  • Predicting the metabolic pathway and potential metabolites of novel molecules using predictive software programs;
  • Assessing the safety of novel substances and their potential metabolites using industry-recognized computer programs:
    • To identify structural alerts,
    • To predict safety of novel molecules base on key toxicity endpoints, such as mutagenicity, genotoxicity, and carcinogenicity using QSAR validated software (e.g., OECD QSAR Toolbox);
  • Predicting the ecotoxicity of novel substances. 
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