Computational Metabolomics: A Framework for the Million Metabolome

Chem Res Toxicol. 2016 Dec 19;29(12):1956-1975. doi: 10.1021/acs.chemrestox.6b00179. Epub 2016 Oct 12.


"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

Publication types

  • Review
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis
  • Computational Biology*
  • Environmental Exposure
  • Environmental Monitoring
  • Humans
  • Metabolomics*