Read-across is an example of a flexible in silico toxicology methodology whereby a toxicity prediction for a chemical (often referred to as the target compound) is made using experimental data on one or more similar analogs (often referred to as source compound(s)). This is a lengthy process highly reliant on an expert review of the information to identify and justify the analogs selected and the prediction made.

Read-across is used in many current and emerging applications, including supporting the assessment of repeated dose toxicity of chemicals or N-nitrosamines carcinogenicity potency categories.

At the heart of any read-across is the need to identify suitable analogs. This is where comprehensive high-quality structure-searchable databases are critical, containing both public and proprietary experimental data. Global similarity is often used to search such databases by identifying and prioritizing chemical analogs based on their whole molecule similarity. However, in many instances it is important to retrieve analogs that have a local similarity around the structural class that is most likely associated with the toxicological endpoint being assessed. This may also consider the structural environment responsible for any activation or deactivation of the specific toxicity of interest. The use of structural alerts or QSAR models may support this assessment and justification, when run against both the target and source compounds(s). Actually, mechanistic similarity can be in general explored by running alert profilers, such as reaction domain or bioactivation profilers, across both the target and source chemicals to identify similarities and differences. In this way, reactivity of the source and target chemicals is compared in terms of structural alerts or reactive features. Indeed, justifying the selected analogs based on common biological properties or mechanisms, and toxicity profiles is key in a read-across framework. Further analysis of the physico-chemical properties is also prudent.

The read-across process can be assisted and simplified by the availability of both high-quality structure-searchable databases coupled with user-friendly read-across tools that are aligned with regulatory expectations, including appropriate documentation of the results. We are actively developing such methodologies and technologies to support further acceptance of read-across. If you are interested in collaborating in this area, please get in touch (Glenn Myatt; glenn.myatt@instem.com).

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.