Omics Data in the Qualitative and Quantitative Characterization of the Mode of Action in Support of IRIS Assessments
Knowledge and information generated using new tools/methods collectively called "Omics" technologies could have a profound effect on qualitative and quantitative characterizations of human health risk assessments.
The suffix "Omics" is a descriptor used for a series of emerging disciplines with a comprehensive approach to quantitative characterizations of selective components of biological systems (i.e. cellular genes, proteins and metabolites, namely genomics, proteomics and metabolomics). These data are being used to better characterize the mode of action (MOA), which is a description of the key events from exposure to a toxin to an adverse outcome. Advances in the ability to detect differential changes in genes, proteins and metabolites, at many levels of organization (from cell to whole organism), support detection of toxicant signature profiles of exposure/effect/susceptibility within and across species, and the development of sensitive and specific biomarkers for precursor events in the progression from health to disease.
New informatics, analytical methods, and organizational databases are being developed in parallel to integrate the vast amount of biological data and turn it into useful information. Given the complexity of biological systems, the broader scope of an "Omic" perspective is needed to better characterize the MOA. Generally, an MOA is first characterized qualitatively, that is, a text or graphical description of the key biological events from exposure to an adverse effect ( e.g., kidney proximal tubular damage cadmium exposure). A quantitative characterization of the MOA follows, and is essential to biologically based computer models used in predictive risk assessments. Presented here is an example of a qualitative description of the toxicokinetic and toxicodynamic events in the MOA for cadmium toxicity, as well as the key steps where there is a paucity of information. "Omics" technologies that might fill important data gaps are discussed within the context of the qualitative scheme, aimed at progressing towards a quantitative characterization of the MOA to support the development of biologically based mechanistic models and a greatly improved predictive capability for risk assessment.