Using Transcriptomics and Other High-throughput Technologies to Fill Toxicity Data Gaps

toxicogenomics concept represented by wooden letter tiles

by Patrick McMullen

ScitoVation has a long history of innovation and application of high-content data streams in toxicology. Omics technologies, screening platforms, and high-content imaging can all be used to generate information that can augment our understanding of chemical toxicity. Toxicogenomics, the application of whole-genome transcriptomics to toxicology questions, has been a fast-developing tool for us and our clients. We have found several uses for toxicogenomics, ranging from mode of action characterization to regulatory applications. Here, we highlight a couple of key applications that are emerging opportunities in risk assessment.

Biological read across

Read-across—the use of structure-based tools to draw analogies between the toxicity of similar chemistries—is used regularly in risk assessments and many registration applications. The principle 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). 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.

Figure 1. Comparing bioactivity signatures of compounds can augment classical read across approaches.

Point of departure analysis

Chemicals with different potencies lead to apical effects at different exposure levels (i.e., have different points of departure, POD). It has been similarly established that compounds with lower points of departure also induce transcriptomic changes at lower concentrations. This observation has led to the concept of the transcriptomic point of departure as a proxy for compound toxicity. The impact of a compound on the transcriptome can be quantitatively determined and used to compare potency (Figure 1B). We are using this concept with clients in applications related to compound screening and down-selection. There are also potential applications in product stewardship, such as alternatives assessment.

An emerging opportunity

Perfluorinated alkyl substances (PFAS) are useful compounds. They feature carbon-fluorine chemistry which has unique properties with applications in fire safety, lubrication, and surface chemistry. However, PFAS are under scrutiny due to their potential impact on human health, and several compounds of concern have been linked to toxicological effects. PFAS are a diverse class, and it is unclear whether all fluorinated chemistries share the bioactivity and long half-lives of compounds like perfluorooctanoic acid. We need to develop a system for evaluating the potential impact of alternative PFAS chemistries. Recently, scientists at Health Canada, NIEHS, and University of Ottawa published a pair of manuscripts leading the way for such a framework. Authors Steve Fergusson and Ella Atlas recently presented their findings in the ScitoVation Webinar Series. 

The project used transcriptomics to measure the response of hepatocyte spheroids to 23 different PFAS. They were able to ascertain the sensitivity of the biological response to each compound by considering the dose response. Interestingly, the distribution of effective concentration spanned approximately two orders of magnitude for the 15 compounds that triggered a response. An additional 8 compounds did not drive a robust response at the highest concentration tested, indicating that they have less hazard potential than the others. This approach suggests significant differences among PFAS and can provide the basis for grouping of PFAS into distinct categories based on their potential effects.

ScitoVation’s expertise in transcriptomics and history with PFAS toxicity modeling make us well suited to work with stakeholders to leverage the work done by Drs. Fergusson, Atlas, and colleagues as a reference database against which to assess bioactivity of similar compounds. If you would like to discuss further, please contact Patrick McMullen (pmcmullen@scitovation.com).