Instem will be starting a new working group on the application of In Silico models to support read-across for the derivation of a point of departure for data poor substances. Read-across is being increasingly adopted to support a wide variety of applications including the assessment of non-genotoxic impurities, E&L (extractables & leachables), NIAS (non-intentionally added substances), occupational safety, to name a few.
In a recent publication, we showed how a combination of read-across alongside computational models was successfully used as part of a regulatory submission to assess a degradant within a drug product.1 An additional example was presented at this year’s SOT meeting discussing how an expert review of In Silico results for acute toxicity can be supported using read-across approaches.2
Despite many read-across publications, there is a still a need for a transparent and easy-to-adopt framework. Such a framework should be accepted by regulators and industry alike, as well as tailored to the specific application areas. It is important to address the justification of a suitable analog(s) (often referred to as the source chemicals) taking into consideration the general structural similarity, the similarity of the physico-chemical properties, at the same time as the biological similarity using either experimental data and/or In Silico predictions of biological properties or mechanistic profiles. This information needs to be assessed in a consistent and defendable manner, based on the weight of the evidence.
Furthermore, such a framework needs to address how you can read-across available experimental data of the chemical analogs on to the test (or target) chemical. One of the major elements here is the availability, relevance and reliability of the experimental data on any identified analogs, that needs to be evaluated in light of the application context.
This initiative is being led by Dr. Arianna Bassan and is focusing on specific read-across applications and case studies, thus building on the earlier published framework and examples.
Would you like to contribute to this activity or like to learn more about this new collaborative working group? If so, please get in touch with Glenn Myatt, PhD, firstname.lastname@example.org
- A. Bassan et al., In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity, Comput. Toxicol. 20 (2021) 100187. https://doi.org/10.1016/j.comtox.2021.100187
- G. Myatt et al. Predicting the Acute Toxicity 6-Pack to Support Health, Safety, and Environmental Product Stewardship (3671/ P156), Poster presented at the Society of Toxicology 2023 annual meeting