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Combination of Computational New Approach Methods for Enhancing Evidence of Biological Pathway Conservation Across Species
Citation:
Schumann, P., C. Rivetti, J. Houghton, M. Blanco-Rubio, B. Campos, G. Hodges, AND C. Lalone. Combination of Computational New Approach Methods for Enhancing Evidence of Biological Pathway Conservation Across Species. SETAC, Dublin, IRELAND, April 30 - May 04, 2023. https://doi.org/10.23645/epacomptox.22589047
Impact/Purpose:
There are thousands of new chemicals marketed each year. Many of these chemicals will eventually end up in the environment in some form. This a problem because the rate at which these chemicals can be adequately tested for environmental safety is outpaced by the rate at which they are being produced. Currently, testing a chemical for environmental safety is dependent on the use of animals, which is time-consuming and ethically challenging. Therefore, there is a need to develop ways of assessing chemical safety that are non-animal based. As technology and computational power continues to advance, it is more possible to use computational methods to help assess the environmental safety of a chemical. This work demonstrates one aspect of improving computational methods by making use of biological pathway information. A biological pathway describes a series of actions that molecules (for example, proteins) take to lead to certain changes in an organism, such as photosynthesis, which allows for energy production in plants. Biological pathways can also be used to help describe adverse changes in an organism, which is the case when there is an exposure to a toxic chemical. This work shows that the different proteins of a biological pathway can be used to help make predictions of what organisms are the most likely to be impacted by exposure to a particular chemical. This is done by combining two different computational tools, Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) and Genes to Pathways – Species Conservation Analysis (G2P-SCAN). By improving predictions of what organisms are the most likely impacted by a chemical, the ways in which those chemicals are used can also be improved to ultimately help protect the health of the environment.
Description:
There is a need to develop computational new approach methods (NAMs) for chemical safety assessment to help keep pace with the rate at which new chemicals are being added to the market. The ability to better predict which chemicals are of the most concern for environmental safety is dependent on the ability to extrapolate chemical effects across many species. This work investigated the combination of two computational NAMs to support cross-species predictions of chemical susceptibility, the US EPA Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool [1] and the Unilever Genes to Pathways – Species Conservation Analysis (G2P-SCAN) tool (publication in progress). Both tools rely on existing biological information to help support predictions of chemical toxicity and biological pathway conservation across species. Using case examples (i.e., peroxisome proliferator activated receptor alpha (PPARA), estrogen receptor 1 (ESR1), and Gamma-Aminobutyric Acid Type A Receptor Subunit Alpha (GABRA1)), the utility and challenges of this combined computational approach (see Figure 1) were demonstrated. Overall, the weight of evidence to support cross-species susceptibility predictions was enhanced by using biological pathway information. Through comparisons of relevant molecular and functional data gleaned from Adverse Outcome Pathways (AOPs) to mapped biological pathways obtained through G2P-SCAN, it was possible to gain a toxicological context for various chemical-protein interactions. Therefore, the information gained through this approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility and potentially by expanding the biologically plausible taxonomic domain of applicability (tDOA) of relevant AOPs.
URLs/Downloads:
DOI: Combination of Computational New Approach Methods for Enhancing Evidence of Biological Pathway Conservation Across Species
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