Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Triply deprotonated DGAILDGAILD was reacted in the gas-phase with doubly charged copper, cobalt, and iron metal complexes containing either two or three phenanthroline ligands. Reaction products result from two major pathways. The first pathway involves the transfer of an electron from the negatively charged peptide to the transition-metal complex. The other major pathway consists of the displacement of the phenanthroline ligands by the peptide resulting in the incorporation of the transition-metal into the peptide to form [M − 3H + X^II]⁻ ions, where X is Cu, Co, or Fe, respectively. The extent to which each pathway contributes is dependent on the nature of transition-metal complex. In general, bis-phen complexes result in more electron-transfer than the tris—phen complexes, while the tris—phen complexes result in more metal insertion. The metal in the complex plays a large role as well, with the Cu containing complexes giving rise to more electron transfer than the corresponding complexes of Co and Fe. The results show that a single reagent solution can be used to achieve two distinct sets of products (i.e., electron-transfer products and metal insertion products). These results constitute the demonstration of novel means for the gas-phase transformation of peptide anions from one ion type to another via ion/ion reactions using reagents formed via electrospray ionization.

Toxicokinetic (TK) assays and in vitro–in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography–mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.