Research Evaluation of Computational Docking to Identify Pregnane X Receptor Agonists in the ToxCast Database Sandhya Kortagere,1 Matthew D. Krasowski,2* Erica J. Reschly,2 Madhukumar Venkatesh,3 Sridhar Mani,3 and Sean Ekins 4,5,6 1Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA; 2Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; 3Albert Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, New York, USA; 4Collaborations in Chemistry, Jenkintown, Pennsylvania, USA; 5Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland, USA; 6Department of Pharmacology, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USA Background : The pregnane X receptor (PXR) is a key transcriptional regulator of many genes [e.g., cytochrome P450s (CYP2C9, CYP3A4, CYP2B6), MDR1] involved in xenobiotic metabolism and excretion. o Bjectivs : As part of an evaluation of different approaches to predict compound affinity for nuclear hormone receptors, we used the molecular docking program GOLD and a hybrid scoring scheme based on similarity weighted GoldScores to predict potential PXR agonists in the ToxCast database of pesticides and other industrial chemicals. We present some of the limitations of different in vitro systems, as well as docking and ligand-based computational models. Methods : Each ToxCast compound was docked into the five published crystallographic structures of human PXR (hPXR), and 15 compounds were selected based on their consensus docking scores for testing. In addition, we used a Bayesian model to classify the ToxCast compounds into PXR agonists and nonagonists. hPXR activation was determined by luciferase-based reporter assays in the HepG2 and DPX-2 human liver cell lines. r esult : We tested 11 compounds, of which 6 were strong agonists and 2 had weak agonist activity. Docking results of additional compounds were compared with data reported in the literature. The prediction sensitivity of PXR agonists in our sample ToxCast data set (n = 28) using docking and the GoldScore was higher than with the hybrid score at 66.7%. The prediction sensitivity for PXR ago- nists using GoldScore for the entire ToxCast data set (n = 308) compared with data from the NIH (National Institutes of Health) Chemical Genomics Center data was 73.8%. c onclusi : Docking and the GoldScore may be useful for prioritizing large data sets prior to in vitro testing with good sensitivity across the sample and entire ToxCast data set for hPXR agonists. k ey owrds : Bayesian model, docking, GoldScore, hybrid scoring, PXR, ToxCast. Environ Health Perspect 118:1412–1417 (2010). doi:10.1289/ehp.1001930 [Online 17 June 2010] also bind outside the LBD on the PXR surface and act as allosteric antagonists (Ekins et al. 2007, 2008a). It is also difficult to computa- tionally identify antagonists that would com- pete with agonist binding in the LBD (Xue et al. 2007a). Several previous studies have constructed ligand-based computational models for hPXR agonists employing pharmacophores (Bachmann et al. 2004; Ekins and Erickson 2002; Ekins et al. 2007; Schuster and Langer 2005), quantitative structure–activity relation- ships (QSARs), and machine learning methods (Ekins et al. 2006, 2009; Jacobs 2004; Ung et al. 2007; Yasuda et al. 2008). hPXR agonist pharmacophore models have been shown to possess hydrophobic, hydrogen bond accep- tor, and hydrogen bond donor features, a finding consistent with the crystallographic structures of hPXR ligand-receptor complexes (Bachmann et al. 2004; Ekins et al. 2007; Ekins and Erickson 2002; Schuster and Langer 2005). These pharmacophore features may also relate closely to a recent analysis in which Ngan et al. (2009) used docking of small probe molecules into the LBD to identify five hot spots. As part of an ongoing analysis of NHRs Address correspondence to S. Ekins, Collaborations in Chemistry, 601 Runnymede Ave., Jenkintown, PA 19046 USA. Telephone/Fax: (215) 687-1320. E-mail: ekinssean@yahoo.com *Current address: Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA. Supplemental Material is available online (doi:10.1289/ehp.1001930 via http://dx.doi.org/). S.E. gratefully acknowledges Accelrys for making Discovery Studio available. M.D.K. was supported by National Institutes of Health grant K08-GM074238 and the Competitive Medical Research Fund from the University of Pittsburgh Medical Center. S.K is supported by American Heart Association grant 09SDG2110054. S.M. was supported by the Damon Runyon Clinical Investigator Award (to CI15-02), 1R01 CA127231-01 (National Cancer Institute), and Miriam Mandel Cancer Research Scholar Fund, Albert Einstein College of Medicine, Bronx, NY. The funders had no role in study design, data collec- tion and analysis, decision to publish, or preparation of the manuscript. S.E. is employed by Collaborations in Chemistry, Jenkintown, PA. The authors declare they have no other actual or potential competing financial interests. Received 12 January 2010; accepted 17 June 2010. ovlume 118 | number 10 | October 2010 • Environmental Health Perspectives The pregnane X receptor (PXR, also known as NR1I2, SXR, or PAR) is a nuclear hormone receptor (NHR) that regulates the transcrip- tion of genes involved in xenobiotic metabo- lism and excretion. PXR agonists include a wide range of structurally diverse endogenous and exogenous compounds such as bile acids, steroid hormones, dietary fat-soluble vita- mins, prescription medications, and herbal drugs, as well as environmental chemicals such as pesticides, estrogens, and antiestrogens (Mnif et al. 2007). PXR agonists can mediate clinically significant drug–drug interactions (Ekins et al. 2007, 2008a; Mani et al. 2009). Furthermore, PXR action in various patho- physiological states indicates that PXR agonists could variably affect human and animal health. For example PXR agonists can impact cho- lesterol metabolism and the endocrine system (Wada et al. 2009; Zhai et al. 2007) as well as potentiate the toxicity of other environmental contaminants, as reviewed recently (Biswas et al. 2009). Animal models may not reliably predict human PXR (hPXR)-related problems because of the diversity of PXRs across species (Ekins et al. 2008b; Moore et al. 2002), result- ing in differences in ligand selectivity (Tirona 1412 et al. 2004). Therefore, the identification and characterization of hPXR agonists is important to human pharmacokinetics and toxicology of environmental chemicals. Five hPXR crystal structures (1M13, 1NRL, 1SKX, 2O9I, and 2QNV) are available in the Protein Data Bank (PDB) (Chrencik et al. 2005; Teotico et al. 2008; Watkins et al. 2003a, 2003b; Xue et al. 2007a), with another structure to be deposited (Xue et al. 2007b). These structures have enabled characteriza- tion of the ligand-binding domain (LBD) and PXR–ligand interactions. The cocrystallized ligands tend to be hydrophobic, with a wide range in shape, size, and chemical composi- tion that can be accommodated in the LBD (Ekins et al. 2009). The hPXR ligand-binding pocket (LBP) is lined with 28 amino acid resi- dues: 20 hydrophobic, 4 polar, and 4 charged. Because of the large size and flexibility of the LBP, molecules can bind in multiple locations. This creates a challenge for in silico methods for predicting hPXR agonists, but many approaches have been evaluated nonetheless (Ekins et al. 2009). An added complexity to predicting whether a compound binds to PXR is the recent discovery that some molecules can