Quantitative Structure-Activity Relationships of 1.2.3 Triazole Derivatives as Aromatase Inhibition Activity

Year 2020, Volume: 4 Issue: 1, 1 - 11, 15.06.2020

Mebarka Ouassaf Salah Belaıdı , İmane Benbrahim Houmam Belaidi Samir Chtita

https://doi.org/10.33435/tcandtc.545369

Cited By: 3

Abstract

Aromatase is an estrogen biosynthesis enzyme belonging to the cytochrome P450 family that catalyzes the rate-limiting step of converting androgens to estrogens. As it is pertinent toward tumor cell growth promotion aromatase is a lucrative therapeutic target for breast cancer. In the pursuit of robust aromatase inhibitors, a set of thirty 1-substituted mono- and bis-benzonitrile or phenyl analogs of 1.2.3-triazole letrozole were employed in quantitative structure activity relationship (QSAR) study using multiple linear regression (MLR).The results demonstrated good predictive ability for the MLR model. After dividing the dataset into training and test set. The models were statistically robust internally (R2 = 0.982) and the model predictability was tested by several parameters, including the external criteria (R2pred = 0.851. CCC= 0.946). Insights gained from the present study are anticipated to provide pertinent information contributing to the origins of aromatase inhibitory activity and therefore aid in our on-going quest for aromatase inhibitors with robust properties.

Keywords

1.2.3-triazole, Aromatase inhibitors, QSAR, MLR

References

• [1] M. P. Leze, M. Le Borgne, P.Pinson, A. Palusczak, M. Duflos, G. LeBaut,and R. W. Hartmann,2- and 3-[(aryl)(azolyl)methyl]indoles as potential non-steroidal aromatase inhibitors, Bioorg. Med. Chem. Lett. 1134(2004)549-557.
• [2] L. A. Torre, F. Islami, R. L. Siegel, E. M. Ward, et A. Jemal,Global Cancer in Women: Burden and Trends, Cancer Epidemiology and Prevention Biomarkers 26 (2017) 444 57.
• [3] P. P. Koonings, K.Campbell, D. R. J. Mishell,and D. A.Grimes,Global Cancer in Women: Burden and Trends , Obstet. Gynecol.74,(1989) 921-926.
• [4] M. J. Reed, The role of aromatase in breast tumors, Breast Cancer Res. Treat. 30, (1994) 7-17.
• [5] E. R. Simpson, M. S. Mahendroo, G. D.Means, M. W.Kilgore, M. M.Hinshelwood, S. Graham-Lorence, B.Amarneh,Y.Ito, C. R. Fisher, andM. D. Michael, Aromatase cytochrome P450, the enzyme responsible for estrogen biosynthesis, Endocr. Rev.15 (1994) 342-355.
• [6] M. A. C. Neves,T. C. P. Dinis, G.Colombo, M. L. S Melo,An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors, Eur. J. Med. Chem. 44,(2009) 4121-4127.
• [7] A.Howell, J. F. R.Robertson, I. Vergote, A review of the efficacy of anastrozole in postmenopausal women with advanced breast cancer with visceral metastases., Breast Cancer Res. Treat. 82(2003) 215-222.
• [8] D. Simpson, M. P. Curran, andC. M. Perry, Letrozole: a review of its use in postmenopausal women with breast cancer. Drugs. 64(2004) 1213-1230.
• [9] T. Fornander, A.C. Hellstrom, B.Moberger, Descriptive clinicopathologic study of 17 patients with endometrial cancer during or after adjuvant tamoxifen in early breast cancer. J. Natl.Cancer Inst. 85,(1993)1850-1855.
• [10] Jeong, H.-J., Shin, Y. G., Kim, I.-H., & Pezzuto, J. M. Inhibition of aromatase activity by flavonoids. Archives of Pharmacal Research, 22(1999), 309. 312
• [11] S. G. Agalave, S. R.Maujan,and V. S. Pore, Click Chemistry: 1,2,3-Triazoles as Pharmacophores.Chem. Asian J. 6(2011) 2696-2718.
• [12] A. D.Favia,O. Nicolotti, A.Stefanachi,F.Leonetti,and A. Carotti, Computational methods for the design of potent aromatase inhibitors, Expert Opin. Drug Discov. 8 (2013) 395-409.
• [13] C. Hansch and A. Leo, Exploring QSAR, Fundamentals and Applications in Chemistry and Biology.American Chemical Society. Washington. D. C.(1995).
• [14] A. Tropsha, Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics 29, (2010). 476–488.
• [15] S. Belaidi, A. Dibi and M. Omari, Contribution à l’étude du contrôle stéréochimique dans les macrolides à 16 chaînons par la mécanique moléculaire, J. Soc. Alg. Chim., 10(2000), 221-232,
• [16] S. Belaidi, H. Belaidi and D. Bouzidi, Computational Methods Applied in Physical-Chemistry property Relationships of Thiophene Derivatives, J. Comput. Theor. Nanosci., 12 (2015)1737-1745
• [17] C. Nantasenamat, A. Worachartcheewan, S.Prachayasittikul, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul, QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole. Eur. J. Med. Chem.69, (2013) 99-114
• [18] HyperChem (Molecular Modeling System) Hypercube, Inc., 1115 NW, 4th Street, Gainesville, FL 32601, USA (2007).
• [19] Gaussian 09, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J. A. Montgomery, J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, T. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, A. D. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski, and D. J. Fox, Gaussian Inc., Wallingford, CT (2010).
• [20] Ghose, A. K., & Crippen, G. M. Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. Journal of Chemical Information and Computer Sciences, 27(1987) 21 35.
• [21] Database,(http://www.molinspiration.com).
• [22] M. Y. Zhao, M. H. Abraham, J. Le, A. Hersey, C. N. Luscombe, G.Beck,B.Sherborne, Rate-limited steps of human oral absorption and QSAR studies. Pharm. Res. 19,(2002) 1446-1457.
• [23] P. R. Andrews,D. J.Craik,J. L. Martin, Functional group contributions to drug-receptor interactions J. Med. Chem. 27, (1984) 1648-1657.
• [24] SPSS software packages, SPSS Inc., 444 North Michigan Avenue, Suite 3000, Chicago, Illinoi, 60611, USA.
• [25] Z. Almi, S. Belaidi, L. Segueni, Structural Exploration and Quantitative Structure-Activity Relationships Properties for 1.2. 5-Oxadiazole Derivatives, Rev. Theor. Sci. 3, (2015) 264-272.
• [26] A.E Ivanescu, LiP., B. George, A.W. Brown, S.W.Keith, D. Raju, and D.B. Allison, The Importance of Prediction Model Validation and Assessment in Obesity and Nutrition Research. Int J Obes (Lond) 40 (2016) 887–894.
• [27] K. Roy, A. S.Mandal, Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. Journal of Enzyme Inhibition and Medicinal Chemistry. 23 (2008) 980–995
• [28] R. Guha,P.C.Jurs,Determining the validity of a QSAR model A Classification Approach.J. Chem. Inf. Model. 45, (2005) 65-73
• [29] E.Novellino,C.Fattorusso,G. Greco, Use of comparative molecular field and cluster analysis in series design.Pharm. Acta Helv. 70,(1995) 149-154.
• [30] N. S. Zefirov, V. A. Palyulin, QSAR for Boiling Points of “Small” Sulfides. Are the “High-Quality Structure-Property-Activity Regressions” the Real High Quality QSAR Models? J. Chem. Inf. Comput. Sci. 41 (2001) 1022–1027.
• [31] N. Frimayanti, M. L. Yam, H. B. Lee, R. Othman, S. M.Zain., N. A. Rahman, Design of new competitive dengue Ns2b/Ns3 protease inhibitors-a computational approach. International Journal of Molecular Sciences 12 (2011) 8626–8644.
• [32] A. Golbraikh, A. Tropsha, Predictive QSAR Modeling Based on Diversity Sampling of Experimental Datasets for the Training and Test Set Selection. Mol. Divers. 5 (2000) 231–243.
• [33] L. Sachs ,Applied Statistics: A Handbook of Techniques, Springer-Verlag ,BerlirdNew York,(1984).
• [34] P. P. Roy, S. Paul, I. Mitra, K. Roy, On Two Novel Parameters for Validation of Predictive QSAR Models. Molecules. 14 (2009) 1660–1701.
• [35] R. Veerasamy, H.Rajak, A. Jain, S. Sivadasan,C.P. Varghese, and R. K. Agrawal,Validation of QSAR models-strategies and importance .Int J Drug &Discov. 2(2011) 511-519.
• [36] D. L. J. Alexander. A. Tropsha, Winkler, D. A. Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. J Chem Inf Model, 55 (2015) 1316–1322.
• [37] N.Chirico and P.Gramatica, Real External Predictivity of QSAR Models. Part 2. New Intercomparable Thresholds for Different Validation Criteria and the Need for Scatter Plot Inspection. J. Chem. Inf. Model. 52, (2012) 2044-2058.
• [38] S. O. P. Kuzmanovic, D. D. Cvetkovic, D. J. Barna, QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa. Int. J. Mol. Sci. 10, (2009) 1670-1682.
• [39] M. J. Heravi , A. Kyani, Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. J. Chem. Inf. Comput. Sci. 44, (2004) 1328-1335.