ACS Publications. Most Trusted. Most Cited. Most Read
My Activity

Quantitative Binding Models for CYP2C9 Based on Benzbromarone Analogues

View Author Information
School of Molecular Biosciences, Washington State University, Pullman, Washington 99164, and Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630
Cite this: Biochemistry 2004, 43, 22, 6948–6958
Publication Date (Web):May 13, 2004
Copyright © 2004 American Chemical Society

    Article Views





    Other access options
    Supporting Info (1)»


    The cytochrome P450 (CYP) isoforms involved in xenobiotic metabolism are enzymes whose substrate selectivity remains difficult to predict due to wide specificity and dynamic protein−substrate interactions. To uncover the determinants of specificity for cytochrome CYP2C9, a novel library of benzbromarone (bzbr) inhibitors was used to reevaluate its pharmacophore. CoMSIA was used with the bzbr ligands to generate both quantitative binding models and three-dimensional contour plots that pinpoint predicted interactions that are important for binding to 2C9. Since this class of compounds is more potent than any other toward 2C9, the small molecule properties deemed most ideal by the software were used to address protein−ligand interactions using new mutagenesis and structural data. Nine new bzbr analogues provide evidence that specific electrostatic and hydrophobic interactions contribute the most to 2C9's specificity. Three of the new analogues are better isosteres of bzbr that contain bulky groups adjacent to the phenol and have increased pKa values. These ligands test the hypothesis that anionic substrates bind with higher affinity to 2C9. Since they have higher affinity than the previous nonacidic analogues, the importance of bulky groups on the phenol ring appears to have been underestimated. CoMSIA models predict that these bulky groups are favorable for their hydrophobicity, while a negative charge is favored at the ketone oxygen rather than the phenol oxygen. The overlap of this ketone with electronegative groups of other 2C9 substrates suggests they act as key positive charge acceptors.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.


    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

     This work was supported by National Institutes of Health Grants GM032165 and ES009122.

     School of Molecular Biosciences.


     Department of Chemistry.


    In papers with more than one author, the asterisk indicates the name of the author to whom inquiries about the paper should be addressed.

    Supporting Information Available

    Jump To

    An example of (S)-warfarin inhibition data fit to the competitive inhibition model. This material is available free of charge via the Internet at

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system:

    Cited By

    This article is cited by 29 publications.

    1. Keiko Maekawa, Motoyasu Adachi, Yumiko Matsuzawa, Qinghai Zhang, Ryota Kuroki, Yoshiro Saito, and Manish B. Shah . Structural Basis of Single-Nucleotide Polymorphisms in Cytochrome P450 2C9. Biochemistry 2017, 56 (41) , 5476-5480.
    2. Matthew A. Lardy, Laurie LeBrun, Drew Bullard, Charles Kissinger, and Alberto Gobbi . Building a Three-Dimensional Model of CYP2C9 Inhibition Using the Autocorrelator: An Autonomous Model Generator. Journal of Chemical Information and Modeling 2012, 52 (5) , 1328-1336.
    3. Johannes Kirchmair, Mark J. Williamson, Jonathan D. Tyzack, Lu Tan, Peter J. Bond, Andreas Bender, and Robert C. Glen . Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms. Journal of Chemical Information and Modeling 2012, 52 (3) , 617-648.
    4. Kazuya Yasuo, Noriyuki Yamaotsu, Hiroaki Gouda, Hideki Tsujishita and Shuichi Hirono . Structure-Based CoMFA As a Predictive Model - CYP2C9 Inhibitors As a Test Case. Journal of Chemical Information and Modeling 2009, 49 (4) , 853-864.
    5. Chi-Chi Peng, Jonathan L. Cape, Tom Rushmore, Gregory J. Crouch and Jeffrey P. Jones . Cytochrome P450 2C9 Type II Binding Studies on Quinoline-4-Carboxamide Analogues. Journal of Medicinal Chemistry 2008, 51 (24) , 8000-8011.
    6. Matthew G. Hudelson, Nikhil S. Ketkar, Lawrence B. Holder, Timothy J. Carlson, Chi-Chi Peng, Benjamin J. Waldher and Jeffrey P. Jones . High Confidence Predictions of Drug−Drug Interactions: Predicting Affinities for Cytochrome P450 2C9 with Multiple Computational Methods. Journal of Medicinal Chemistry 2008, 51 (3) , 648-654.
    7. Matthew G. McDonald and Allan E. Rettie. Sequential Metabolism and Bioactivation of the Hepatotoxin Benzbromarone: Formation of Glutathione Adducts From a Catechol Intermediate. Chemical Research in Toxicology 2007, 20 (12) , 1833-1842.
    8. A.A. Heller, S.Y. Lockwood, T.M. Janes, D.M. Spence. Technologies for Measuring Pharmacokinetic Profiles. Annual Review of Analytical Chemistry 2018, 11 (1) , 79-100.
    9. Avanthika Venkatachalam, Abhinav Parashar, Kelath Murali Manoj. Functioning of drug-metabolizing microsomal cytochrome P450s: In silico probing of proteins suggests that the distal heme ‘active site’ pocket plays a relatively ‘passive role’ in some enzyme-substrate interactions. In Silico Pharmacology 2016, 4 (1)
    10. K. Wang, H. Wang, Y. Peng, J. Zheng. Identification of Epoxide-Derived Metabolite(s) of Benzbromarone. Drug Metabolism and Disposition 2016, 44 (4) , 607-615.
    11. Oraphan Phuangsawai, Supa Hannongbua, Mathew Paul Gleeson. Quantitative Structure–Activity Relationship (QSAR) Methods for the Prediction of Substrates, Inhibitors, and Inducers of Metabolic Enzymes. 2014, 319-350.
    12. Abhinav Parashar, Sudeep Kumar Gade, Mahesh Potnuru, Nandita Madhavan, Kelath Murali Manoj, . The Curious Case of Benzbromarone: Insight into Super-Inhibition of Cytochrome P450. PLoS ONE 2014, 9 (3) , e89967.
    13. Samuel A. Siles, Anand Srinivasan, Christopher G. Pierce, José L. Lopez-Ribot, Anand K. Ramasubramanian. High-Throughput Screening of a Collection of Known Pharmacologically Active Small Compounds for Identification of Candida albicans Biofilm Inhibitors. Antimicrobial Agents and Chemotherapy 2013, 57 (8) , 3681-3687.
    14. Gang Xie, Chi C Wong, Ka‐Wing Cheng, Liqun Huang, Panayiotis P Constantinides, Basil Rigas. Regioselective oxidation of phospho‐NSAIDs by human cytochrome P450 and flavin monooxygenase isoforms: implications for their pharmacokinetic properties and safety. British Journal of Pharmacology 2012, 167 (1) , 222-232.
    15. Jayalakshmi Sridhar, Jiawang Liu, Maryam Foroozesh, Cheryl L. Klein Stevens. Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies. Molecules 2012, 17 (8) , 9283-9305.
    16. Rongwei Shi, Yin Wang, Xiaolei Zhu, Xiaohua Lu. Exploration of the binding of curcumin analogues to human P450 2C9 based on docking and molecular dynamics simulation. Journal of Molecular Modeling 2012, 18 (6) , 2599-2611.
    17. A. V. Veselovsky, B. N. Sobolev, M. S. Zharkova, A. I. Archakov. Computer-based substrate specificity prediction for cytochrome P450. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry 2010, 4 (1) , 75-81.
    18. A.V. Veselovsky, B.N. Sobolev, M.S. Zharkova, A.I. Archakov. Computer-based substrate specifity prediction for cytochrome P450. Biomeditsinskaya Khimiya 2010, 56 (1) , 90-100.
    19. Eva Stjernschantz, Nico PE Vermeulen, Chris Oostenbrink. Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450. Expert Opinion on Drug Metabolism & Toxicology 2008, 4 (5) , 513-527.
    20. C.W. Murray, M.J. Hartshorn. New Applications for Structure-Based Drug Design. 2007, 775-806.
    21. Larry J. Jolivette, Sean Ekins. Methods for Predicting Human Drug Metabolism. 2007, 131-176.
    22. Thomas Fox, Jan M. Kriegl. Chapter 5 Linear Quantitative Structure–Activity Relationships for the Interaction of Small Molecules with Human Cytochrome P450 Isoenzymes. 2007, 63-81.
    23. Jan L Wahlstrom, Dan A Rock, J Greg Slatter, Larry C Wienkers. Advances in predicting CYP-mediated drug interactions in the drug discovery setting. Expert Opinion on Drug Discovery 2006, 1 (7) , 677-691.
    24. Vikas Kumar, Chuck W. Locuson, Yuk Y. Sham, Timothy S. Tracy. Amiodarone Analog-Dependent Effects on CYP2C9-Mediated Metabolism and Kinetic Profiles. Drug Metabolism and Disposition 2006, 34 (10) , 1688-1696.
    25. Matthew G. Hudelson, Jeffrey P. Jones. Line-Walking Method for Predicting the Inhibition of P450 Drug Metabolism. Journal of Medicinal Chemistry 2006, 49 (14) , 4367-4373.
    26. Dermot F. McGinnity, James Tucker, Steve Trigg, Robert J. Riley. PREDICTION OF CYP2C9-MEDIATED DRUG-DRUG INTERACTIONS: A COMPARISON USING DATA FROM RECOMBINANT ENZYMES AND HUMAN HEPATOCYTES. Drug Metabolism and Disposition 2005, 33 (11) , 1700-1707.
    27. Allan E. Rettie, Jeffrey P. Jones. CLINICAL AND TOXICOLOGICAL RELEVANCE OF CYP2C9: Drug-Drug Interactions and Pharmacogenetics. Annual Review of Pharmacology and Toxicology 2005, 45 (1) , 477-494.
    28. Matthew A. Hummel, Charles W. Locuson, Peter M. Gannett, Dan A. Rock, Carrie M. Mosher, Allan E. Rettie, Timothy S. Tracy. CYP2C9 Genotype-Dependent Effects on in Vitro Drug-Drug Interactions: Switching of Benzbromarone Effect from Inhibition to Activation in the CYP2C9.3 Variant. Molecular Pharmacology 2005, 68 (3) , 644-651.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
    Click to create an ACS ID

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Your Mendeley pairing has expired. Please reconnect