Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data

Abstract

Background: Hepatotoxicity accounts for a substantial number of drugs being withdrawn from the market. Using traditional animal models to detect hepatotoxicity is expensive and time-consuming. Alternative in vitro methods, in particular cell-based high-throughput screening (HTS) studies, have provided the research community with a large amount of data from toxicity assays. Among the various assays used to screen potential toxicants is the antioxidant response element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress and was used to test > 10,000 compounds from the Tox21 program.

Objective: The ARE-bla computational model and HTS data from a big data source (PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxicity data.

Methods: Quantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when no ARE-bla data were available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (> 10 million data points). By ranking the in vitro-in vivo correlations (IVIVCs), the most relevant bioassay(s) related to hepatotoxicity were identified.

Results: The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Potential toxicophores for well-known toxicants were created by identifying chemical features that existed only in compounds with high IVIVCs.

Conclusion: Profiling chemical IVIVCs created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources.

Citation: Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ Health Perspect 124:634-641; http://dx.doi.org/10.1289/ehp.1509763.

Figure 1

The workflow for profiling liver toxicants consists of three major stages: (1) automated biological response profiling, (2) quantitative structure–activity relationship (QSAR) modeling of quantitative high-throughput screening antioxidant response element beta lactamase reporter gene (qHTS ARE-bla) activation, and (3) chemical IVIVC evaluation. In the columns (Liver Damage, 1, 2, 3, “…”, n, ARE-bla), actives are red and “1;” inactives are blue and “0;” and inconclusive or untested are white and empty.

Figure 2

Chemical space plot of (A) the modeling set (actives = red, inactives = purple) versus left out compounds (yellow) and (B) the modeling set versus the FDA liver damage compounds (green) using the top three principal components generated using 186 MOE 2-D descriptors.

Figure 3

The IVIVC between selected assays and liver damage was evaluated by classifying responses as true positive (TP), true negative (TN), false positive (FP), or false negative (FN) for a χ ² (α = 0.05) or correct classification rate (CCR) test. (A) The biological response profile (red = active or toxic, blue = inactive or non-toxic, yellow = inconclusive or untested) of liver damage compounds represented in the heat map using the top four assays (AIDs 686978, 743067, 743140, and 743202). Individual assays show weak IVIVC, but the combined responses of the assays using threshold RA > 0.25 as active resulted in a statistically significant IVIVC (χ ² p-value = 0.000292). (B) The IVIVC between experimental quantitative high-throughput screening antioxidant respionse element beta lactamase reporter gene (qHTS ARE-bla) activation and liver damage and the QSAR predictions for each liver damage compound, for subsets of overlapping compounds with potential toxicophores A (left) and B (right).

Figure 4

The potential liver toxicity mechanism of compounds such as oxyphenbutazone (CID 4641) and 5-fluorouracil (CID 3385), which contain either of the proposed toxicophores A or B, can generate reactive oxygen species. These types of stimuli activate the antioxidant response element signaling pathway (ARE) (AID 743202) and the peroxisome proliferator-activated receptor gamma (PPARγ) signaling pathway (AID 743140), inhibit the human tyrosyl-DNA phosphodiesterase 1 (TDP1) signaling pathway (686978), or disrupt the thyroid receptor (TR) signaling pathway (AID 743067).