Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays

Abstract

Genotoxicity is a critical component of a comprehensive toxicological profile. The Tox21 Program used five quantitative high-throughput screening (qHTS) assays measuring some aspect of DNA damage/repair to provide information on the genotoxic potential of over 10 000 compounds. Included were assays detecting activation of p53, increases in the DNA repair protein ATAD5, phosphorylation of H2AX, and enhanced cytotoxicity in DT40 cells deficient in DNA-repair proteins REV3 or KU70/RAD54. Each assay measures a distinct component of the DNA damage response signaling network; >70% of active compounds were detected in only one of the five assays. When qHTS results were compared with results from three standard genotoxicity assays (bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus), a maximum of 40% of known, direct-acting genotoxicants were active in one or more of the qHTS genotoxicity assays, indicating low sensitivity. This suggests that these qHTS assays cannot in their current form be used to replace traditional genotoxicity assays. However, despite the low sensitivity, ranking chemicals by potency of response in the qHTS assays revealed an enrichment for genotoxicants up to 12-fold compared with random selection, when allowing a 1% false positive rate. This finding indicates these qHTS assays can be used to prioritize chemicals for further investigation, allowing resources to focus on compounds most likely to induce genotoxic effects. To refine this prioritization process, models for predicting the genotoxicity potential of chemicals that were active in Tox21 genotoxicity assays were constructed using all Tox21 assay data, yielding a prediction accuracy up to 0.83. Data from qHTS assays related to stress-response pathway signaling (including genotoxicity) were the most informative for model construction. By using the results from qHTS genotoxicity assays, predictions from models based on qHTS data, and predictions from commercial bacterial mutagenicity QSAR models, we prioritized Tox21 chemicals for genotoxicity characterization.

Figure 1:

DNA repair pathway coverage provided by the five Tox21 genotoxicity assays

Figure 2:

Activity type and flag type distribution for the five Tox21 genotoxicity assay endpoints. a) Active, inactive, and inconclusive rate percentage. b) Flag type percentage.

Figure 3:

Overlap among active compounds for each Tox21 genotoxicity assay endpoint. a) Degree of overlap based on grouping by similar molecular targets of functions. b) Degree of overlap among varying activity patterns among all 5 assays; black dots denote activity pattern across assays, the height of the red bar/gray triangle represents the number of positive/negative predictions by the in silico BM model, and the width of the black bar on the left side of the figure (with yellow text) represents the number of active chemicals in each of the 5 assays.

Figure 4:

Predictivity of Tox21 genotoxicity assay endpoints for traditional genotoxicity endpoints. a) Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) based on contingency tables; the blue/magenta colors represent the 50^th percentile from the bootstrap statistics and the gray color represents the 95% of confidence interval (dot: average; range: maximum and minimum). b) ROC enrichment allowing a 1% false positive rate; the black color presents the 50^th percentile from the bootstrap statistics and the gray color represents the 95% of confidence interval (point: average; range: maximum and minimum); the red line indicates performance expected by random guess.

Figure 5:

Genotoxicity prediction based on Tox21 qHTS data. a) model performance. Dot: average performance; range: 95% confidence interval; blue: models were constructed using all available traditional genotoxicity data and Tox21 qHTS data; black: models were constructed based on chemicals with available traditional genotoxicity data and also active in one of the Tox21 genotoxicity assay endpoints; red: models were constructed based on chemicals with available traditional genotoxicity data, where calls were reported with doses comparable to qHTS concentration range (positive) or beyond the range (negative), and also active in one of the Tox21 genotoxicity assay endpoints; b) coefficients in the best predictive models. Error bars represent the 95% confidence interval.

Figure 6:

Chemical prioritization for genotoxicity potential. A flowchart to put chemicals into bins based on in silico BM predictions and to arrange them into tiers based on the data availability in the BM endpoint (either +S9 or −S9) and the in vivo micronucleus endpoint. Tier 1: no data for either of the endpoints; Tier 2: data available for one of the endpoints; Tier 3: data available for both endpoints.

Figure 7:

Top 10 chemicals in the first tier of the prioritization scheme (presented using ToxPi software). The eight pies represent a chemical’s activities in Tox21 genotoxicity assays (Ku70/Rad54, Rev3, ATAD5, γH2AX, p53) and its probability for genotoxicity (htsBM, htsCA, htsMN) based on linear regression models using Tox21 qHTS data.