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Exploring the chemical space of aromatase inhibitors

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Abstract

Aromatase, a rate-limiting enzyme catalyzing the conversion of androgen to estrogen, is overexpressed in human breast cancer tissue. Aromatase inhibitors (AIs) have been used for the treatment of estrogen-dependent breast cancer in post-menopausal women by blocking the biosynthesis of estrogen. The undesirable side effects in current AIs have called for continued pursuit for novel candidates with aromatase inhibitory properties. This study explores the chemical space of all known AIs as a function of their physicochemical properties by means of univariate (i.e., statistical and histogram analysis) and multivariate (i.e., decision tree and principal component analysis) approaches in order to understand the origins of aromatase inhibitory activity. Such a non-redundant set of AIs spans a total of 973 compounds encompassing both steroidal and non-steroidal inhibitors. Substructure analysis of the molecular fragments provided pertinent information on the structural features important for ligands providing high and low aromatase inhibition. Analyses were performed on data sets stratified according to their structural scaffolds (i.e., steroids and non-steroids) and bioactivities (i.e., actives and inactives). These analyses have uncover a set of rules characteristic to active and inactive AIs as well as revealing the constituents giving rise to potent aromatase inhibition.

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References

  1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61:69–90. doi:10.3322/caac.20107

    Article  PubMed  Google Scholar 

  2. Miller WR (2003) Aromatase inhibitors: mechanism of action and role in the treatment of breast cancer. Semin Oncol 30:3–11

    Article  PubMed  CAS  Google Scholar 

  3. Jordan VC (2004) Selective estrogen receptor modulation: concept and consequences in cancer. Cancer Cell 5:207–213. doi:10.1016/S1535-6108(04)00059-5

    Article  PubMed  CAS  Google Scholar 

  4. Fisher B, Costantino JP, Redmond CK, Fisher ER, Wickerham DL, Cronin WM (1994) Endometrial cancer in tamoxifen-treated breast cancer patients: findings from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14. J Natl Cancer Inst 86:527–537. doi:10.1093/jnci/86.7.527

    Article  PubMed  CAS  Google Scholar 

  5. Ghosh D, Griswold J, Erman M, Pangborn W (2009) Structural basis for androgen specificity and oestrogen synthesis in human aromatase. Nature 457:219–223. doi:10.1038/nature07614

    Article  PubMed  CAS  Google Scholar 

  6. 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 55:8464–8476. doi:10.1021/jm300930n

    Article  PubMed  CAS  Google Scholar 

  7. Simpson ER, Clyne C, Rubin G, Boon WC, Robertson K, Britt K, Speed C, Jones M (2002) Aromatase—a brief overview. Annu Rev Physiol 64:93–127. doi:10.1146/annurev.physiol.64.081601.142703

    Article  PubMed  CAS  Google Scholar 

  8. Burstein HJ, Prestrud AA, Seidenfeld J, Anderson H, Buchholz TA, Davidson NE, Gelmon KE, Giordano SH, Hudis CA, Malin J, Mamounas EP, Rowden D, Solky AJ, Sowers MR, Stearns V, Winer EP, Somerfield MR, Griggs JJ (2010) American Society of Clinical Oncology clinical practice guideline: update on adjuvant endocrine therapy for women with hormone receptor-positive breast cancer. J Clin Oncol 28:3784–3796. doi:10.1200/jco.2009.26.3756

    Article  PubMed  Google Scholar 

  9. Ponzone R, Mininanni P, Cassina E, Pastorino F, Sismondi P (2008) Aromatase inhibitors for breast cancer: different structures, same effects? Endocr Relat Cancer 15:27–36. doi:10.1677/erc-07-0249

    Article  PubMed  CAS  Google Scholar 

  10. Lønning PE (2004) Aromatase inhibitors in breast cancer. Endocr Relat Cancer 11:179–189

    Article  PubMed  Google Scholar 

  11. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  PubMed  CAS  Google Scholar 

  12. VIDA (2013) Version 4.2.1. OpenEye Scientific Software, Santa Fe, NM

  13. Babel (2013) Version 3.3. OpenEye Scientific Software, Santa Fe, NM

  14. Isarankura-Na-Ayudhya C, Nantasenamat C, Buraparuangsang P, Piacham T, Ye L, Bülow L, Prachayasittikul V (2008) Computational insights on sulfonamide imprinted polymers. Molecules 13:3077–3091. doi:10.3390/molecules13123077

    Article  PubMed  CAS  Google Scholar 

  15. Suksrichavalit T, Prachayasittikul S, Piacham T, Isarankura-Na-Ayudhya C, Nantasenamat C, Prachayasittikul V (2008) Copper complexes of nicotinic–aromatic carboxylic acids as superoxide dismutase mimetics. Molecules 13:3040–3056. doi:10.3390/molecules13123040

    Article  PubMed  CAS  Google Scholar 

  16. Suksrichavalit T, Prachayasittikul S, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2009) Copper complexes of pyridine derivatives with superoxide scavenging and antimicrobial activities. Eur J Med Chem 44:3259–3265. doi:10.1016/j.ejmech.2009.03.033

    Article  PubMed  CAS  Google Scholar 

  17. Prachayasittikul V, Isarankura-Na-Ayudhya C, Tantimongcolwat T, Nantasenamat C, Galla HJ (2007) EDTA-induced membrane fluidization and destabilization: biophysical studies on artificial lipid membranes. Acta Biochim Biophys Sin 39:901–913. doi:10.1111/j.1745-7270.2007.00350.x

    Article  PubMed  Google Scholar 

  18. Prachayasittikul S, Wongsawatkul O, Worachartcheewan A, Nantasenamat C, Ruchirawat S, Prachayasittikul V (2010) Elucidating the structure–activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives. Molecules 15:198–214. doi:10.3390/molecules15010198

    Article  PubMed  CAS  Google Scholar 

  19. Nantasenamat C, Li H, Isarankura-Na-Ayudhya C, Prachayasittikul V (2012) Exploring the physicochemical properties of templates from molecular imprinting literature using interactive text mining approach. Chemometr Intell Lab Syst 116:128–136. doi:10.1016/j.chemolab.2012.05.006

    Article  CAS  Google Scholar 

  20. Piacham T, Isarankura-Na-Ayudhya C, Nantasenamat C, Yainoy S, Ye L, Bülow L, Prachayasittikul V (2006) Metalloantibiotic Mn(II)–bacitracin complex mimicking manganese superoxide dismutase. Biochem Biophys Res Commun 341:925–930. doi:10.1016/j.bbrc.2006.01.045

    Article  PubMed  CAS  Google Scholar 

  21. Piacham T, Nantasenamat C, Suksrichavalit T, Puttipanyalears C, Pissawong T, Maneewas S, Isarankura-Na-Ayudhya C, Prachayasittikul V (2009) Synthesis and theoretical study of molecularly imprinted nanospheres for recognition of tocopherols. Molecules 14:2985–3002. doi:10.3390/molecules14082985

    Article  PubMed  CAS  Google Scholar 

  22. Mandi P, Nantasenamat C, Srungboonmee K, Isarankura-Na-Ayudhya C, Prachayasittikul V (2012) QSAR study of anti-prion activity of 2-aminothiazoles. EXCLI J 11:453–467

    Google Scholar 

  23. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2008) Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. J Mol Graph Model 27:188–196. doi:10.1016/j.jmgm.2008.04.005

    Article  PubMed  CAS  Google Scholar 

  24. Nantasenamat C, Piacham T, Tantimongcolwat T, Naenna T, Isarankura-Na-Ayudhya C, Prachayasittikul V (2008) QSAR model of the quorum-quenching N-acyl-homoserine lactone lactonase activity. J Biol Syst 16:279–293. doi:10.1142/S021833900800254X

    Article  CAS  Google Scholar 

  25. Pingaew R, Tongraung P, Worachartcheewan A, Nantasenamat C, Prachayasittikul S, Ruchirawat S, Prachayasittikul V (2012) Cytotoxicity and QSAR study of (thio)ureas derived from phenylalkylamines and pyridylalkylamines. Med Chem Res. doi:10.1007/s00044-012-0402-6

    Google Scholar 

  26. Thippakorn C, Suksrichavalit T, Nantasenamat C, Tantimongcolwat T, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2009) Modeling the LPS neutralization activity of anti-endotoxins. Molecules 14:1869–1888. doi:10.3390/molecules14051869

    Article  PubMed  CAS  Google Scholar 

  27. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul S, Prachayasittikul V (2011) Predicting the free radical scavenging activity of curcumin derivatives. Chemometr Intell Lab Syst 109:207–216. doi:10.1016/j.chemolab.2011.09.010

    Article  CAS  Google Scholar 

  28. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013) Predicting antimicrobial activities of benzimidazole derivatives. Med Chem Res. doi:10.1007/s00044-013-0539-y

    Google Scholar 

  29. Worachartcheewan A, Nantasenamat C, Naenna T, Isarankura-Na-Ayudhya C, Prachayasittikul V (2009) Modeling the activity of furin inhibitors using artificial neural network. Eur J Med Chem 44:1664–1673. doi:10.1016/j.ejmech.2008.09.028

    Article  PubMed  CAS  Google Scholar 

  30. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2007) Quantitative structure–imprinting factor relationship of molecularly imprinted polymers. Biosens Bioelectron 22:3309–3317. doi:10.1016/j.bios.2007.01.017

    Article  PubMed  CAS  Google Scholar 

  31. Nantasenamat C, Isarankura-Na-Ayudhya C, Tansila N, Naenna T, Prachayasittikul V (2007) Prediction of GFP spectral properties using artificial neural network. J Comput Chem 28:1275–1289. doi:10.1002/jcc.20656

    Article  PubMed  CAS  Google Scholar 

  32. Nantasenamat C, Naenna T, Prachayasittikul V (2005) Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J Comput Aided Mol Des 19:509–524. doi:10.1016/S1535-6108(04)00059-5

    Article  PubMed  CAS  Google Scholar 

  33. Nantasenamat C, Srungboonmee K, Jamsak S, Tansila N, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013) Quantitative structure–property relationship study of spectral properties of green fluorescent protein with support vector machine. Chemometr Intell Lab Syst 120:42–52. doi:10.1016/j.chemolab.2012.11.003

    Article  CAS  Google Scholar 

  34. Worachartcheewan A, Dansethakul P, Nantasenamat C, Pidetcha P, Prachayasittikul V (2012) Determining the optimal cutoff points for waist circumference and body mass index for identification of metabolic abnormalities and metabolic syndrome in urban Thai population. Diabetes Res Clin Pract 98:e16–e21. doi:10.1016/j.diabres.2012.09.018

    Article  PubMed  Google Scholar 

  35. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, Revision A.02. Gaussian, Inc., Wallingford

  36. DRAGON for Windows (Software for Molecular Descriptor Calculations) (2007) Version 5.5. Talete srl, Milano, Italy

  37. The Unscrambler (2005) Version 9.5. Camo Process AS, Oslo, Norway

  38. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington

    Google Scholar 

  39. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Pidetcha P, Prachayasittikul V (2010) Identification of metabolic syndrome using decision tree analysis. Diabetes Res Clin Pract 90:e15–18. doi:10.1016/j.diabres.2010.06.009

    Article  PubMed  Google Scholar 

  40. JChem (2012) Version 5.10. ChemAxon Ltd., Hungary

  41. Li SF, He HD, Parthiban LJ, Yin HQ, Serajuddin ATM (2005) IV–IVC considerations in the development of immediate-release oral dosage form. J Pharm Sci 21. doi:10.1002/jps.20378

  42. Strazielle N, Ghersi-Egea JF (2005) Factors affecting deivery of antiviral drugs to the brain. Rev Med Virol 15:105–133. doi:10.1002/rmv.454

    Article  PubMed  CAS  Google Scholar 

  43. Bulat FA, Chamorro E, Fuentalba P, Toro-Labbe A (2004) Condensation of frontier molecular orbital fukui functions. J Phys Chem A 108:342–349. doi:10.1021/jp036416r

    Article  CAS  Google Scholar 

  44. Aihara J (1999) Reduced HOMO–LUMO gap as an index of kinetic stability for polycyclic aromatic hydrocarbons. J Phys Chem A 103:7487–7495. doi:10.1021/jp990092i

    Article  CAS  Google Scholar 

  45. Esbensen KH, Guyot D, Westad F, Houmoller LP (2004) Multivariate data analysis—in practice. CAMO Process AS, Esbjerg

    Google Scholar 

  46. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2:37–52. doi:10.1016/0169-7439(87)80084-9

    Article  CAS  Google Scholar 

  47. Suenderhauf C, Hammann F, Huwyler J (2012) Computational prediction of blood–brain barrier permeability using decision tree induction. Molecules 17:10429–10445. doi:10.3390/molecules170910429

    Article  PubMed  CAS  Google Scholar 

  48. Yang XG, Chen D, Wang M, Xue Y, Chen YZ (2009) Prediction of antibacterial compounds by machine learning approaches. J Comput Chem 30:1202–1211. doi:10.1002/jcc.21148

    Google Scholar 

  49. DeSimone RW, Currie KS, Mitchell SA, Darrow JW, Pippin DA (2007) Privileged structures: applications in drug discovery. Comb Chem High Throughput Screen 7:473–493. doi:10.2174/1386207043328544

    Article  Google Scholar 

  50. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11:494–5025. doi:10.1016/j.cbpa.2007.08.033

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The Goal-Oriented Research Grant of Mahidol University to C.N. is gratefully acknowledged for financial support of this research. H.L., P.M., and T.M. are grateful for research assistantship supported by Grants from Mahidol University. This project was also supported in part by the Office of the Higher Education Commission and Mahidol University under the National Research Universities Initiative.

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Authors and Affiliations

  1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok,  10700, Thailand

    Chanin Nantasenamat, Hao Li, Prasit Mandi, Apilak Worachartcheewan & Teerawat Monnor

  2. Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok,  10700, Thailand

    Chanin Nantasenamat, Prasit Mandi, Apilak Worachartcheewan, Chartchalerm Isarankura-Na-Ayudhya & Virapong Prachayasittikul

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  1. Chanin Nantasenamat
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Correspondence to Chanin Nantasenamat.

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Nantasenamat, C., Li, H., Mandi, P. et al. Exploring the chemical space of aromatase inhibitors. Mol Divers 17, 661–677 (2013). https://doi.org/10.1007/s11030-013-9462-x

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