Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay

Benchmark dose (BMD) modeling estimates the dose of a
chemical
that causes a perturbation from baseline. Transcriptional BMDs have
been shown to be relatively consistent with apical end point BMDs,
opening the door to using molecular BMDs to derive human health-based
guidance values for chemical exposure. Metabolomics measures the responses
of small-molecule endogenous metabolites to chemical exposure, complementing
transcriptomics by characterizing downstream molecular phenotypes
that are more closely associated with apical end points. The aim of
this study was to apply BMD modeling to in vivo metabolomics data,
to compare metabolic BMDs to both transcriptional and apical end point
BMDs. This builds upon our previous application of transcriptomics
and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP),
applying metabolomics to the same archived tissues. Specifically,
liver from rats exposed to five doses of TPhP was investigated using
liquid chromatography–mass spectrometry and 1H nuclear
magnetic resonance spectroscopy-based metabolomics. Following the
application of BMDExpress2 software, 2903 endogenous metabolic features
yielded viable dose-response models, confirming a perturbation to
the liver metabolome. Metabolic BMD estimates were similarly sensitive
to transcriptional BMDs, and more sensitive than both clinical chemistry
and apical end point BMDs. Pathway analysis of the multiomics data
sets revealed a major effect of TPhP exposure on cholesterol (and
downstream) pathways, consistent with clinical chemistry measurements.
Additionally, the transcriptomics data indicated that TPhP activated
xenobiotic metabolism pathways, which was confirmed by using the underexploited
capability of metabolomics to detect xenobiotic-related compounds.
Eleven biotransformation products of TPhP were discovered, and their
levels were highly correlated with multiple xenobiotic metabolism
genes. This work provides a case study showing how metabolomics and
transcriptomics can estimate mechanistically anchored points-of-departure.
Furthermore, the study demonstrates how metabolomics can also discover
biotransformation products, which could be of value within a regulatory
setting, for example, as an enhancement of OECD Test Guideline 417
(toxicokinetics).