The 6-pack provides information on health hazards from short-term exposure to a test substance. It is a battery of in vivo tests that evaluate 1) acute systemic toxicity by different routes of exposure (i.e., oral, inhalation and dermal); 2) skin and eye irritation/corrosion; 3) dermal sensitization. It is used in the assessment of many products and there is a great deal of focus on the development of non-animal alternatives to the 6-pack, including in silico models.

We have been working for a long time on the development of a comprehensive in silico assessment of the endpoints from the 6-pack. To date, we have published our approaches to predicting sensitization1 and acute toxicity2. These papers, and others, explain the methodologies and they outline protocols to perform such assessments and expert review3. We have also been collaborating within cross-industry groups to establish whether these methods are fit-for-purpose. For example, in a recent assessment of acute toxicity prediction results across different industrial sectors, we showed that 95% of acute toxicity in silico assessments (i.e., predictions of GHS category) were either correctly predicted or predicted in a more conservative category.

We are also in the process of generating further publications supporting the acceptance of these approaches including a description of our skin and eye irritation/corrosion models and fit-for-purpose assessments of other endpoints related to the 6-pack studies. There is also an opportunity to share knowledge derived from proprietary databases, without sharing any information on confidential chemicals or studies, based on the SAR fingerprinting methodology4.

If you are interested in collaborating on non-animal approaches, and more specifically in silico methods, for the replacement to the 6-pack, please get in touch (Glenn Myatt;


1 Johnson, C., et al., (2020) Skin sensitization in silico protocol. Regul. Toxicol. Pharmacol. 116, 104688.

2 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.

3 Johnson, C., et al. (2021) “Evaluating confidence in toxicity assessments based on experimental data and in silico predictions.” Computational Toxicology: 100204.

4 Ahlberg, E., et al. (2016) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity. Regul. Toxicol. Pharmacol. 77, 1–12.

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.