Computational discovery of new PXR agonists correctly predicted 48 agonists and 120 non- agonists, whereas the hybrid scoring scheme correctly predicted 45 agonists and 137 non- agonists [Supplemental Material, Table 2 (doi:10.1289/ehp.1001930)]. The rest of the compounds were either false positives or false negatives. The sensitivity of scoring schemes for correctly predicting hPXR agonists with the NCGC data (Judson et al. 2010) was 73.8% and 69.2% for GoldScore and hybrid score classification schemes, respectively. Predictions using the steroidal Bayesian hPXR model. The hPXR agonist predictions using the Bayesian model based on steroidal compounds (Ekins et al. 2009) classified all compounds other than mancozeb as agonists, based on the specified activity cutoff (Table 2). Testing for hPXR antagonists and allo­ steric antagonists. We evaluated whether some of the molecules selected from ToxCast by docking (mesosulfuron-methyl, bensulfuron methyl, esfenvalerate, Z,E-fenpyroximate, α-cypermethrin, β-cyfluthrin, and permethrin) were also potential PXR antagonists or allos- teric antagonists (Ekins et al. 2007, 2008a) in the presence of rifampicin. Our results for these compounds tested with the DPX-2 cell assay suggest that they were not antagonists or allosteric antagonists (data not shown). Discussion Recent studies have showed the complexity and challenges of producing predictive computa- tional models for hPXR (Ekins et al. 2009). In the present study we used docking to analyze the ToxCast compounds and select molecules to validate the approach of identifying com- pounds of interest from large data sets without experimentally screening the whole data set. Subsequently, the release of the NCGC data in which the whole data set was experimen- tally screened provided additional data to com- pare with our own laboratory hPXR activation data and our computational predictions in five crystal structures. An advantage of using these different cell systems is that some are more sensitive than others and thus may identify additional compounds as agonists compared with using a single cell type (Table 2). For example, diethylhexyl phthalate, esfenvalerate, α-cypermethrin, β-cyfluthrin, and permethrin were more active in HepG2 cells compared with NCGC DPX-2 cells (Table 2). Fenarinol, imazalil, alachlor, and fipronil were classed as active in NCGC DPX-2 cells but not in HeLa cells [as reported previously by Lemaire et al. (2006)], whereas fenbuconazole, and pro- chloraz were more active in HeLa cells than DPX-2 cells (Table 2) (Lemaire et al. 2006). Possible reasons for these differences include non–liver-type cells possibly lacking transporters (or expressing different ones) that can influence drug entry/efflux or may show differential toxicity. This is a problem with conjugated steroids and bile salts. We are not sure if this is the case for the pesticides and industrial chemicals studied here. HeLa cells [used by Lemaire et al. (2006)], HepG2 cells, and hepatocytes are markedly different in terms of their cells of origin (HeLa and HepG2 are essentially cancer cell lines). The HeLa cell line may have different levels of corepressors and coactivators that affect the function of PXR differently than in liver cell lines. The transactivation of reporters varies in both cell lines to a degree. For example, HNF4α-mediated effects on PXR are greater in HeLa cells than in HepG2 cells, suggest- ing weaker PXR transactivation profiles in HeLa versus hepatic-derived cell lines (Tirona et al. 2003). One could perhaps use primary human hepatocytes for such studies, but they have the limitations of cost, limited supply, and variability. It is also possible that cell lines differentially metabolize the test compounds, which could also affect PXR activation results. In the pressent study (and for the NCGC data), the metabolism of the compound in Table 2. Summary of predicted and experimental data for sample ToxCast data set compounds. Docking classificationa Bayesian score (classification) HepG2 hPXR EC50 (µM)c X X 1.8 (S) > 50 (W) 89.4 (W) 1.5 (S) X 6 (S) 1.6 (S) – 2.5 (S) 5.4 (S) Efficacy relative to 10 µM rifampicinc 0.63 0.18 0.64 0.53 0.54 0.54 0.53 Cell viability IC50 (µM)c X X X 0.04 X > 100 X DPX-2 hPXR EC50 (µM)c X X 8.94 (S) 32.74 (M) 0.88 (S) 18.2 (M) 29.09 (M) N A A N A A A A A A A A A A A N A A A N A N N N N N N N –7.253 (N) –1.589 (A) 1.943 (A) 1.868 (A) –1.165 (A) –1.653 (A) 0.601 (A) 5.796 (A) 2.613 (A) 3.317 (A) 5.346 (A) –1.998 (A) 5.346 (A) 4.83 (A) –1.942 (A) 4.791 (A) 3.475 (A) 5.390 (A) 2.705 (A) 3.466 (A) 4.663 (A) 7.842 (A) –0.563 (A) 4.357 (A) –2.825 (A) –0.033 (A) 2.879 (A) 1.265 (A) U.S. EPA ToxCast (NCGC) hPXR DPX-2 classification [EC50 (µM)]b N N A (20.75) N A (1.57) N N A (26.98) N N A (18.3) N A (19.7) A (20.26) N A (20.29) A (36.81) N N A (36.54) A (5.49) A (15.35) N N N A (12.55) N N Compound Mancozeb Mesosulfuron-methyl Diethylhexyl phthalate Methyl hydrogen phthalate Bensulide Foramsulfuron Bensulfuron methyl Esfenvalerate Z,E-fenpyroximate Butafenacil α-Cypermethrin Triflusulfuron methyl β-Cyfluthrin Permethrin Oxasulfuron Fenarimold Propiconazoled Fenbuconazoled Prochlorazd Imazalild Oxadiazond Alachlord 2,4-Dd Diurond Atrazined Fipronild Thiabendazoled Carbaryld Abbreviations: 2,4-D, 2,4-dichlorophenoxyacetic acid; A, agonist; M, medium agonist; N, nonagonist; S, strong agonist; W, weak agonist; X, no activity measurable. Values with Bayesian scores greater than –5.792 were classed as PXR agonists based on the model output (Ekins et al. 2009). Docking classification was performed using GoldScore, with cutoff values listed in Table 1. Agonists were classified based on the following criteria used in a previous study (Ekins et al. 2008b): S, EC50 < 10 µM; M, EC50 11–50 µM; W, EC50 > 50 µM (but with activation at least 10% that of 10 µM rifampicin). aBased on data provided in Supplemental Material, Table 1 (doi:10.1289/ehp.1001930). bFor NCGC data (Judson et al. 2010), the cutoff for activity was 200 µM. cAssays performed in the present study. dComponds with previously published data generated in HeLa cells (Lemaire et al. (2006). Environmental Health Perspectives • volume 118 | number 10 | October 2010 1415