Use of an Adverse Outcome Pathway for Hepatic Steatosis

Use of an Adverse Outcome Pathway for Hepatic Steatosis to Build Chemical Groups

PRESENTING AUTHOR: 

Mark D Nelms

INSTITUTION / COMPANY : 

Oak Ridge Institute for Science Education

AUTHOR(S): 

Mark D Nelms, Claire L Mellor, Michelle M Angrish, Brian N Chorley, Steven J Enoch, Judith M Madden, Jane Ellen Simmons, Mark TD Cronin, Stephen W Edwards

REFERENCES: 

Integrated Systems Toxicology Division, NHEERL, US EPA, RTP, NC, USA

School of Pharmacy and Biomolecular Science, Liverpool John Moores University, Liverpool, United Kingdom

ABSTRACT CONTENT / DETAILS: 

The Adverse Outcome Pathway (AOP) framework systematically documents the mechanisms underlying effects of chemicals starting with the initial interaction of chemicals with the biological system, i.e. the molecular initiating event (MIE). Chemical activity in assays designed to monitor the MIE can inform chemical grouping by identifying chemicals with the ability to interact with a common biological (macro-)molecule. The establishment of such groups will aid both toxicological evaluation and risk assessment of chemical mixtures.

In this study, we developed mechanism-based chemical groups by combining high-throughput toxicity data (HTT) and structural alerts based on an AOP network for hepatic steatosis (Angrish et al 2016). HTT data from ToxCast were extracted for nine nuclear receptors (NRs) that have been seen to be associated with steatosis: AHR, ER, FXR, GR, LXR, PPAR, PXR, RAR, RXR (Mellor et al 2016). Using these data, we identified chemicals that were active in at least one out of the suite of assays for each individual NR (assay suites ranged from 2 to 20). The number of chemicals active in at least one assay varied across the nine NRs with LXR having the fewest (20 actives) and ER the most (1501 actives). Each chemical list was taken in turn and profiled against 214 previously developed structural alerts relating to NR initiation of steatosis (Mellor et al 2016). Additionally, each chemical list was utilised to generate chemical categories based upon structural similarity.

Alerts for AHR, ER, PPAR, and GR identified between 35 and 60% of chemicals active for a specific NR as having the ability to bind to the specific NR. Alerts for RAR, RXR, and LXR only identified between 0 and 5% of active chemicals highlighting the need to expand the chemical space of alerts for these NRs. Meanwhile, alerts for FXR and PXR identified 10 and 18% of active chemicals, respectively. Upon examination of the chemical groups we were able to identify refinements to existing alerts and identify potential new alerts for some of the NRs. This study shows the value of combining information garnered from suites of toxicological assays with chemical structure information held in structure-activity relationships such as structural alerts to identify chemical groupings.

This abstract does not reflect the views or policies of the EPA.