Kortagere et al. docking score and an average hybrid score for each crystal structure and used these as cutoff scores for the GoldScore- and hybrid score– based classifications, respectively (Table 1). Predictions based on different docking methods were assessed using a standard set of statistical indicators to evaluate different ligand and docking classification approaches: sensitivity, specificity, overall prediction accu- racy, and Matthews correlation coefficient (Kortagere et al. 2009). We generated 2D schematic representations of the ligands in the binding site using the LIGX option in MOE (Chemical Computing Group, Montreal, Quebec, Canada). Machine learning with 2D descrip­ tors. We generated a Bayesian classification model using Discovery Studio 2.1 with the Laplacian-corrected Bayesian classifier and molecular descriptors, as previously described for 115 steroidal compounds (namely, androstanes, estratrienes, pregnanes, and bile salts), with hPXR activation determined by a luciferase-based reporter assay (Ekins et al. 2009). Compounds with Bayesian scores above –5.792 were classed as PXR agonists (Ekins et al. 2009). Reporter gene assay with HepG2 cells. The 13 top-scoring compounds and 2 low-scoring compounds predicted as nonagonists by GoldScore were selected for in vitro testing in the HepG2 human liver cell line. We deter- mined hPXR activation in HepG2 cells using a luciferase-based reporter assay, as previously described (Ekins et al. 2008b; Krasowski et al. 2005a). Ligands that activated hPXR were classified as strong [median effective concen- tration (EC50) < 10 µM], medium (EC50 = 11–50 µM), or weak (EC50 > 50 µM, but still able to activate with at least 10% of the efficacy of 10 µM rifampicin) agonists (Ekins et al. 2008b). Reporter gene assay with DPX-2 cells. The tissue culture protocols were performed in a sterile laminar flow hood, and all incubations were carried out at 37°C and 5% CO2. When DPX-2 cells were approximately 50–70% confluent, medium was aspirated and cells rinsed with 5 mL phosphate-buffered saline (PBS). The PBS was replaced with 2 mL trypsin/EDTA and incubated for 5 min. Two milliliters medium was added, and the entire mixture was transferred to a centrifuge tube. Cells were centrifuged at 900 rpm for 3 min and resuspended in 5 mL culturing medium; 100 µL cell suspension (correspond- ing to 25,000 cells) was added to each well of a 96-well plate, using an eight-channel Precision XS microplate sample processor, and incu- bated overnight. Drug stock solutions (50 mM for each compound) were prepared in DMSO and diluted to 11-point 1:3 compound titra- tions (50–0 µM) using the Precision XS to serially transfer 75 µL of diluted compounds 1414 into 150 µL Puracyp dosing media. The final DMSO concentration was maintained at 0.1% in all dilutions. Medium from DPX-2 cells in 96-well plates was aspirated using an EL406 liquid handler (BioTek); plates were then placed in the Precision XS, and 100 µL dosing medium containing the appropriate concentration of agonist/antagonist was trans- ferred. Each condition was repeated in qua- druplicate. After 24 hr, the cell medium was aspirated and cells were redosed as described above. After 48 hr, the cell viability (CellTiter- Fluor) and reporter assay (Bright-Glo) was carried out according to the protocol provided by Promega (Madison, WI). Log-normalized drug concentrations were fitted to dose–response curves for each com- pound tested [relative luminescence units (RLU)], normalized to control (DMSO- treated) cells (agonist mode) and as a percent- age of RLU observed with 10 µM rifampicin (antagonist mode). Curves were fitted using a nonlinear regression model [variable slope (four parameters) equation] (GraphPad Prism, version 4.0a; GraphPad Software Inc., La Jolla, CA). In this assay, the standard agonist compound rifampicin had a mean EC50 of 1.99 µM. Results Sampling ToxCast compounds as potential PXR agonists using docking. In the present study we used molecular docking coupled with hybrid scoring strategies to select compounds for in vitro testing based on predicted hPXR activity. Our initial selection of 15 ToxCast compounds was supplemented with 13 com- pounds from a previous study (Lemaire et al. 2006) that were also included in the ToxCast data set (Table 2). Using the docking con- sensus classification, we correctly classified 15 of 28 (55.6%) molecules from this sample ToxCast data set. Of these 28 compounds, 12 were classified as agonists and 16 as non- agonists, based on the actual ToxCast data. Docking-based classification using GoldScore correctly predicted 8 of 12 agonists and 7 of 16 nonagonists. Using the hybrid scoring scheme, we correctly predicted 7 of 12 ago- nists and 8 of 16 nonagonists. Mancozeb was classified as a nonagonist in all docking models and was found to be a nonagonist in experimental studies, whereas butafenacil, per- methrin, and β-cyfluthrin were all classified as agonists in both the docking and experimental methods based on our HepG2 data and the NCGC ToxCast data (Judson et al. 2010) [Table 2 and Supplemental Material, Figure 1 (doi:10.1289/ehp.1001930)]. Foramsulfuron and bensulfuron methyl were both weak ago- nists in our hPXR luciferase assay in HepG2 cells but were classed as nonagonists based on the U.S. EPA NCGC data generated using the DPX-2 cell line (Judson et al. 2010), a derivative of HepG2. These compounds may be hPXR agonists but have only weak activity at high concentrations. With high concentrations of compounds, hPXR activa- tion may not be detected in some cell lines because of cellular toxicity or limited solubil- ity of the compound(s). Similarly, using the hPXR DPX-2 agonist assay, we found gener- ally good agreement with the hPXR HepG2 data, although Z,E-fenpyroximate appeared to be a weak hPXR agonist; the cell viability of this compound suggests it is also cytotoxic [median inhibitory concentration (IC50), 0.04; Table 2]. The standard agonist compound rifampi- cin had a mean EC50 of 1.99 µM in DPX-2 cells, which is lower than reported previously (Ekins et al. 2007) and closer to those reported in other cell assay systems in HepG2 [EC50, 400 nM (Hurst and Waxman 2004)] and CV-1 cells [EC50, 700–852 nM (Chrencik et al. 2005); EC50, 710 nM (Moore et al. 2000)]. The NCGC data (Judson et al. 2010) were from DPX-2 cells, which we would expect to yield results similar to our own using DPX-2 cells, but not to our data using HepG2 cells. For example, fenarimol was classified as a nonagonist based on the docking and hybrid scores, whereas it is an agonist accord- ing to the NCGC data (Judson et al. 2010) and our Bayesian classification. In the pres- ent study, the docking scores for this com- pound were very close to the cutoff scores and hence could not be effectively predicted. This is a limitation of classifying compounds based on docking scores. Hybrid scores, which are designed based on similarity-weighted dock- ing scores, can resolve these limitations only when the test compound has high similarity to the cocrystallized ligand. Compounds such as fenbuconazole and difenzoquat metilsulfate were classified as agonists based on the high GoldScores but were classified as nonagonists in the hybrid scoring scheme because of their low similarity to their respective cocrystalized ligands. NCGC experimental data classified both these compounds as nonagonists (Judson et al. 2010). Comparing NCGC and docking data for all ToxCast compounds. Our results with the sample data set suggest that computational docking methods can be used as an effective strategy in prioritizing future ToxCast com- pounds for in vitro testing as PXR agonists before actual testing. To test this hypothe- sis, we docked all the compounds from the ToxCast database to all five hPXR crystal structures and scored the complexes using GoldScore and hybrid scoring functions. Based on the in vitro NCGC ToxCast data (Judson et al. 2010), 65 compounds have been classified as agonists of hPXR and 246 as nonagonists (U.S. EPA 2010). In the pres- ent study, GoldScore-based classification ovlume 118 | number 10 | October 2010 • Environmental Health Perspectives