We are happy to have completed, after many years of work, a full battery of in silico models to evaluate acute toxicity. These models predict acute lethality by three routes of exposure (oral, dermal, inhalation), as well as skin and eye irritation/corrosion, and skin sensitization. They aim to support a variety of applications, including health, safety, and environmental product stewardship.

The underlying databases have been constructed to support the development of the computational models and include 175,885 chemicals with lethality data, 3,775 chemicals with skin irritation/corrosion data, 6,619 chemicals with eye irritation/corrosion data, and 2,188 chemicals with skin sensitization data. In addition, overall grades have been assigned based on the reported studies for each chemical aligned with international standards such as the Globally Harmonized System of Classification and Labeling of Chemicals (GHS)1 or the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC)2.

It is currently considered best practice to use multiple computational methodologies3 – expert rule-based and statistically-based. Hence, both methodologies have been made available in this battery to predict the six different graded endpoints (i.e., GHS or ECETOC classifications). Many of the expert-rule based methods incorporate knowledge from the literature, such as the BfR rules for skin and eye irritation/corrosion4.

The models support a thorough expert review by providing access to the information behind the prediction, such as chemical analogs and, whenever possible, mechanistic information detailed as part of the expert rules. In addition, the predictions are fully integrated with the new read-across tool5 which might be used to complement the predictions.

In a recent study based on proprietary data, the initial version of the rat oral acute models was able to predict 95% of the chemicals as either correct or in a more conservative category6.

We would like to thank all our colleagues in India, Italy, UK and USA for all their hard work making this important milestone in in silico toxicology possible.

If you would like to discuss these new models, please contact me (Glenn Myatt; glenn.myatt@instem.com).


  1. Globally Harmonized System of Classification and Labelling of Chemicals (GHS) (“The Purple Book”), United Nations, 2005 First Revised Edition, available at https://unece.org/ghs-rev1-2005
  2. ECETOC 2008. Potency Values the Local Lymph Node Assay: Application to Classification, Labelling and Risk Assessment ECETOC Document No. 46 Brussels, December 2008 https://www.ecetoc.org/wp-content/uploads/2014/08/DOC-0461.pdf
  3. Myatt GJ, et al. (2018) In silico toxicology protocols. Regul Toxicol Pharmacol 96:1–17 https://doi.org/10.1016/j.yrtph.2018.04.014
  4. Tsakovska, I., et al., 2007. Evaluation of SARs for the prediction of eye irritation/corrosion potential–structural inclusion rules in the BfR decision support system†. SAR and QSAR in Environmental Research 18, 221–235. https://doi.org/10.1080/10629360701304063
  5. https://insilicoinsider.blog/2022/10/06/new-read-across-tool/
  6. Bercu, J., et al., 2021. A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling. Regul. Toxicol. Pharmacol. 120, 104843. https://doi.org/10.1016/j.yrtph.2020.104843

Published by Glenn Myatt

Glenn J. Myatt is the co-founder of Leadscope and currently Vice President, Informatics of Instem with over 25 years’ experience in computational chemistry/toxicology. He holds a Bachelor of Science degree in Computing, a Master of Science degree in Artificial Intelligence and a Ph.D. in Chemoinformatics. He has published 34 papers, 10 book chapters and three books.