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ELIXIR and Toxicology: A Community in Development
Citation:
Martens, M., R. Stierum, E. Schymanski, C. Evelo, R. Aalizadeh, H. Aladjov, K. Arturi, K. Audouze, P. Babica, K. Berka, J. Bessems, L. Blaha, E. Bolton, M. Cases, D. Damalas, K. Dave, M. Dilger, T. Exner, D. Geerke, R. Grafstrom, A. Gray, J. Hancock, H. Hollert, N. Jeliazkova, D. Jennen, F. Jourdan, P. Kahlem, J. Klanova, J. Kleinjans, T. Kondic, B. Kone, I. Lynch, U. Maran, S. Cuesta, H. Menager, S. Neumann, P. Nymark, H. Oberacher, N. Ramirez, S. Remy, P. Rocca-Serra, R. Salek, B. Sallach, S. Sansone, F. Sanz, H. Sarimveis, S. Sarntivijai, T. Schulze, J. Slobodnik, O. Spjuth, J. Tedds, N. Thomaidis, R. Weber, G. van Westen, AND C. Wheelock. ELIXIR and Toxicology: A Community in Development. F1000 Research. Faculty of 1000, London, Uk, (10):1129, (2023). https://doi.org/10.12688/f1000research.74502.2
Impact/Purpose:
Toxicology as a field tries to understand the negative consequences that may arise from the interactions of chemicals with living organisms. This ELIXIR (European life-sciences Infrastructure for biological Information) Community will concentrate on the focus areas of ELIXIR, including the protection of human, animal, and environmental health. There are several chemical and biological “interoperability” issues key to the toxicology field that translate into data interoperability issues. These include the connection between the action and activity of a particular chemical compound to its effective amount available at a biological target (the link between toxicodynamics and toxicokinetics). Typically, this is also a link between biological data analysis (including large-scale multi-omics) and kinetic modelling. Other examples include interactions between a compound and its target (a protein, nucleic sequence, or membrane structure for instance). This is primarily based on the interplay between chemistry (the chemical structure and, for example, its related properties in terms of functional groups, charge, shape, and related binding affinity) and biochemistry (like biomolecular 3D structures). Also, mixture toxicity needs to be considered as combinations of chemicals with synergistic or antagonistic behaviour, or a combination thereof. While chemicals with similar modes of action may act in terms of concentration addition, those with different modes may rather act according to independent action. Substances with low toxicity may interact in concentration addition rather than as excess toxicity drivers of one compound. Often there is a need to translate the knowledge about one compound into knowledge about other compounds, where approaches like quantitative structure-activity relationships (QSARs) help to elucidate this knowledge and predict required property and toxicity, and in more general when “read-across approaches” come into play that are based on chemical data only, biological data only, or are hybrid. These again need detailed information about the relationships between related chemical compounds, specific properties, and toxicological endpoints and, when considering chemical-biological interactions, details regarding both the chemical structures and adequate descriptions of the biological targets. Since toxicological endpoints can be represented in a myriad of ways, the toxicological effect data are often scattered over multiple repositories and databases hosting different types of data; i.e., chemical structures, toxicity data (in vivo and in vitro), biological target details, and omics data. This is not a problem, per se, but the separation and segregation make the data difficult to find and connect. Currently, many of these deposition databases (where datasets can be archived) do not provide adequate descriptions regarding typical toxicological study designs and parameters, quality control, data acceptance criteria, or even clear identification of the compounds tested. This all adds to the need for an adequate FAIRification process, to make toxicology data more Findable, Accessible, Interoperable and Reusable (FAIR).
Description:
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
URLs/Downloads:
DOI: ELIXIR and Toxicology: A Community in Development
https://pubmed.ncbi.nlm.nih.gov/37842337/