Medicinal Chemistry Research

, Volume 24, Issue 5, pp 1901–1915 | Cite as

Pharmacophore modeling, virtual screening, and 3D-QSAR studies on a series of non-steroidal aromatase inhibitors

  • Huiding XieEmail author
  • Kaixiong Qiu
  • Xiaoguang XieEmail author
Original Research


Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent non-steroidal aromatase inhibitors (NSAIs) with lower side effects and overcome cellular resistance, Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database was used to derive 3D pharmacophore models. The obtained best pharmacophore model contains one acceptor atom, one donor atom, and two hydrophobes, which was used in effective alignment of dataset. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 84 structurally diverse NSAIs to build 3D-QSAR models based on both pharmacophore and docking alignments. The CoMFA and CoMSIA models based on the pharmacophore alignment show better statistical results (CoMFA: q 2 = 0.634, r ncv 2  = 0.986, r pred 2  = 0.737; CoMSIA: q 2 = 0.668, r ncv 2  = 0.926, r pred 2  = 0.708). This 3D-QSAR approach provides significant insights that can be used to develop novel and potent NSAIs. In addition, the best pharmacophore model was used as a 3D query for virtual screening against NCI2000 database. The hit compounds were further filtered by docking, and their biological activities were predicted by the CoMFA and CoMSIA models, and six structurally diverse compounds with good predicted pIC50 values were obtained, which are expected to design novel NSAIs with new skeletons.


Non-steroidal aromatase inhibitors Pharmacophore 3D-QSAR CoMFA CoMSIA Virtual screening 



This work was financially supported by the Science and Technology Planning Project of Yunnan Province (No. 2011FZ096).


  1. Andrade CH, Salum LB, Pasqualoto KFM, Ferreira EI, Andricopulo AD (2008) Three-dimensional quantitative structure-activity relationships for a large series of potent antitubercular agents. Lett Drug Des Discov 5:377–387CrossRefGoogle Scholar
  2. Bhatt HG, Patel PK (2012) Pharmacophore modeling, virtual screening and 3D-QSAR studies of 5-tetrahydroquinolinylidine aminoguanidine derivatives as sodium hydrogen exchanger inhibitors. Bioorg Med Chem Lett 22:3758–3765CrossRefPubMedGoogle Scholar
  3. Bonfield K, Amato E, Bankemper T, Agard H, Steller J, Keeler JM, Roy D, McCallum A, Paula S, Ma L (2012) Development of a new class of aromatase inhibitors: Design, synthesis and inhibitory activity of 3-phenylchroman-4-one (isoflavanone) derivatives. Bioorg Med Chem 20:2603–2613CrossRefPubMedCentralPubMedGoogle Scholar
  4. Brueggemeier RW, Hackett JC, Diaz-Cruz ES (2005) Aromatase inhibitors in the treatment of breast cancer. Endocr Rev 26:331–345CrossRefPubMedGoogle Scholar
  5. Caballero J (2010) 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds. J Mol Graph Model 29:363–371CrossRefPubMedGoogle Scholar
  6. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefPubMedGoogle Scholar
  7. Dorfman RJ, Smith KM, Masek BB, Clark RD (2008) A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers. J Comput Aided Mol Des 22:681–691CrossRefPubMedGoogle Scholar
  8. Dutta U, Pant K (2008) Aromatase inhibitors: past, present and future in breast cancer therapy. Med Oncol 25:113–124CrossRefPubMedGoogle Scholar
  9. Ghosh D, Lo J, Morton D, Valette D, Xi J, Griswold J, Hubbell S, Egbuta C, Jiang W, An J, Davies HM (2012) Novel Aromatase Inhibitors by Structure-Guided Design. J Med Chem 55:8464–8476CrossRefPubMedCentralPubMedGoogle Scholar
  10. Hong Y, Rashid R, Chen S (2011) Binding features of steroidal and nonsteroidal inhibitors. Steroids 76:802–806CrossRefPubMedCentralPubMedGoogle Scholar
  11. Honorio KM, Garratt RC, Polikatpov I, Andricopulo AD (2007) 3D QSAR comparative molecular field analysis on nonsteroidal farnesoid X receptor activators. J Mol Graph Model 25:921–927CrossRefPubMedGoogle Scholar
  12. Hu RJ, Barbault F, Delamar M, Zhang RS (2009) Receptor- and ligand-based 3D-QSAR study for a series of non-nucleoside HIV-1 reverse transcriptase inhibitors. Bioorg Med Chem 17:2400–2409CrossRefPubMedGoogle Scholar
  13. Hu QZ, Yin L, Hartmann RW (2012) Selective dual inhibitors of CYP19 and CYP11B2: targeting cardiovascular diseases hiding in the shadow of breast cancer. J Med Chem 55:7080–7089CrossRefPubMedGoogle Scholar
  14. Jordan VC, Brodie AMH (2007) Development and evolution of therapies targeted to the estrogen receptor for the treatment and prevention of breast cancer. Steroids 72:7–25CrossRefPubMedCentralPubMedGoogle Scholar
  15. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146CrossRefPubMedGoogle Scholar
  16. Kothandan G, Madhavan T, Gadhe CG, Cho SJ (2013) A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach. Med Chem Res 22:1773–1787CrossRefGoogle Scholar
  17. Perez EA (2006) Appraising adjuvant aromatase inhibitor therapy. Oncologist 11:1058–1069CrossRefPubMedGoogle Scholar
  18. Recanatini M, Cavalli A, Valenti P (2002) Nonsteroidal aromatase inhibitors: recent advances. Med Res Rev 22:282–304CrossRefPubMedGoogle Scholar
  19. Richmond NJ, Abrams CA, Wolohan PRN, Abrahamian E, Willett P, Clark RD (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20:567–587CrossRefPubMedGoogle Scholar
  20. Salum LD, Polikarpov I, Andricopulo AD (2007) Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies. J Mol Graph Model 26:434–442CrossRefGoogle Scholar
  21. Seralini GE, Moslemi S (2001) Aromatase inhibitors: past, present and future. Mol Cell Endocrinol 178:117–131CrossRefPubMedGoogle Scholar
  22. Shepphird JK, Clark RD (2006) A marriage made in torsional space: using GALAHAD models to drive pharmacophore multiplet searches. J Comput Aided Mol Des 20:763–771CrossRefPubMedGoogle Scholar
  23. Sun B, Hoshino J, Jermihov K, Marler L, Pezzuto JM, Mesecar AD, Cushman M (2010) Design, synthesis, and biological evaluation of resveratrol analogues as aromatase and quinone reductase 2 inhibitors for chemoprevention of cancer. Bioorg Med Chem 18:5352–5366CrossRefPubMedCentralPubMedGoogle Scholar
  24. Wang JN, Wang FF, Xiao ZT, Sheng GW, Li Y, Wang YH (2012) Molecular simulation of a series of benzothiazole PI3 K alpha inhibitors: probing the relationship between structural features, anti-tumor potency and selectivity. J Mol Model 18:2943–2958CrossRefPubMedGoogle Scholar
  25. Winer EP, Hudis C, Burstein HJ et al (2005) American society of clinical oncology technology assessment on the use of aromatase inhibitors as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer: status report 2004. J Clin Oncol 23:619–629CrossRefPubMedGoogle Scholar
  26. Yin L, Hu QZ, Hartmann RW (2013) Tetrahydropyrroloquinolinone type dual inhibitors of aromatase/aldosterone synthase as a novel strategy for breast cancer patients with elevated cardiovascular risks. J Med Chem 56:460–470CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of ChemistryYunnan UniversityKunmingPeople’s Republic of China
  2. 2.Department of Chemistry, School of Pharmaceutical Science & Yunnan Key Laboratory of Pharmacology for Natural ProductsKunming Medical UniversityKunmingPeople’s Republic of China

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