Read Across Fills Toxicological Data Gaps, Simplifying The Chain of Assumptions Between a Compound Structure and Possible Toxicity

The application of whole transcriptome gene expression data and cellular ontologies, gene sets and other cellular functional data associated with specific genes has grown out of the modern ‘omics big data era (whole organism genomics, proteomics, and metabolomics).  The observation that changes in gene expression upon exposure to a chemical occur well in advance of observable phenotypic changes allows transcriptomic data alone to inform potential cellular MoA and possible adverse outcomes. 

“Read across” is the process of filling toxicology data gaps using information about compounds with similar structure. It is common to use structure-based approaches in risk assessments and registration applications. The principal of read across is that compounds with similar structural features will have similar biological activity, and therefore will elicit similar toxicity. A biological read across, by contrast, directly measures biological activity driven by exposure. Compounds with similar bioactivity profiles are more likely to evoke similar apical effects (Figure 1A).

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Most commonly, we use toxicogenomics based on exposures performed in one or more in vitro cell systems to evaluate bioactivity of relevant compounds. A typical experiment would involve both data-rich source compounds and one or more data-poor target compounds. The advantage of a biological read across is that it simplifies the chain of assumptions between a compound’s structure and its possible toxicity.