نوع مقاله : مقاله پژوهشی کامل
نویسندگان
بخش شیمی، دانشگاه پیام نور، تهران، ایران
چکیده
یک مدل ارتباط کمی ساختار- فعالیت برای پیشبینی فعالیت آنتاگونیستی مشتقات بنزیل تترازول بهعنوان گیرنده هیستامین ایجاد شدهاست. انواع مختلف توصیفکنندههای مولکولی برای نشاندادن جنبههای مختلف ساختارهای مولکولی استفاده شده است. در این روش، تمامی مجموعه دادههای ترکیبات به دو بخش آموزشی و آزمایشی تقسیم شدهاند. مدل ارتباط بین توصیفکنندههای مولکولی و فعالیت بیولوژیکی مولکولها با استفاده از رگرسیون خطی چندگانه، و با استفاده از روش گام به گام و ژنتیک الگوریتم ایجاد شده است. مقایسه نتایج حاصل شده، نشاندهنده برتری روش ژنتیک الگوریتم - رگرسیون خطی چندگانه نسیت به روش گام به گام رگرسیون خطی چندگانه میباشد. مدل ارتباط کمی ساختار فعالیت با استفاده از روش ارزیابی متقاطع یک نمونه خارج از رده و آماره فیشر، دسته آزمون خارجی (N=64, R2=0.808, F= 30.806, Q2adj=0.782, Q2LOO=0.751, Q2LGO=0.669) و روش بهم ریختگی تصادفی تأیید شد. در نتیجه، مدل ارتباط کمی ساختار- فعالیت ایجاد شده میتواند به عنوان ابزار ارزشمند برای طراحی ساختارهای مشابه آنتاگونیستهای جدید گیرنده هیستامین مورد استفاده قرار گیرد.
کلیدواژه‌ها
[1] Y.w. Zhang, S. Liu, X. Zhang, W.B. Li, Y. Chen, X. Huang, L. Sun, W. Luo, W.J. Netzer, R. Threadgill, G. Wiegand, R. Wang, S.N. Cohen, P. Greengard, F.F. Liao, L. Li and H. Xu, A Functional Mouse Retroposed Gene Rps23r1 Reduces Alzheimer's β-Amyloid Levels and Tau Phosphorylation, Neuron. 64 (2009) 328-340.
[2] H.O. Tayeb, H.D. Yang, B.H. Price and F.I. Tarazi, Pharmacotherapies for Alzheimer's disease: beyond cholinesterase inhibitors, Pharmacol. Ther. 134 (2012) 8-25.
[3] D. Cvetkovic-Dozic, M. Skender-Gazibara and S. Dozic, Neuropathological hallmarks of Alzheimers disease, Arch. Oncol. 9 (2001) 195-199.
[4] S.D. Bembenek, J.M. Keith, M.A. Letavic, R. Apodaca, A.J. Barbier, L. Dvorak, L. Aluisio, K.L. Miller, T.W. Lovenberg and N.I. Carruthers, Lead identification of acetylchol-inesterase inhibitors–histamine H3 receptor antagonists from molecular modeling, Bioorg. Med. Chem.Lett. 16 (2008) 2968-2973.
[5] R. Nirogi, V. Kandikere, G. Bhyrapuneni, N. Muddana, R. Saralaya, R.K. Ponnamaneni and A.K. Manoharan, In vivo receptor occupancy assay of histamine H< sub> 3</sub> receptor antagonist in rats using non-radiolabeled tracer, J. Pharmacol. Toxicol. Methods. 65 (2012) 115-121.
[6] R.R. Dandu, J.A. Gruner, J.R. Mathiasen, L.D. Aimone, G. Hostetler, C. Benfield, R.J. Bendesky, V.R. Marcy, R. Raddatz and R.L. Hudkins, Synthesis and evaluation of pyridazinone-phenethylamine derivatives as selective and orally bioavailable histamine H3 receptor antagonists with robust wake-promoting activity, Bioorg. Med. Chem. Lett. 21 (2011) 6362-6365.
[7] O. Roche, M. Nettekoven, W. Vifian and R.M.R. Sarmiento, Refinement of histamine H3 ligands pharmacophore model leads to a new class of potent and selective naphthalene inverse agonists, Bioorg. Med. Chem. Lett. 18 (2008) 4377-4379.
[8] R.M. Rodríguez Sarmiento, M.H. Nettekoven, S. Taylor, J.M. Plancher and H. Richter, O. Roche, Selective naphthalene H3 receptor inverse agonists with reduced potential to induce phospholipidosis and their quinoline analogs, Bioorg. Med. Chem. Lett. 19 (2009) 4495-4500.
[9] R.O. Millán-Guerrero, L.M. Baltazar-Rodríguez, M.I. Cárdenas-Rojas, M. Ramírez-Flores, S. Isais-Millán, I. Delgado-Enciso, R. Caballero-Hoyos and B. Trujillo-Hernández, A280V polymorphism in the histamine H3 receptor as a risk factor for migraine, Arch. Med. Res. 42 (2011) 44-47.
[10] Y. Mitobe, S. Ito, T. Mizutani, T. Nagase, N. Sato and S. Tokita, Development of a selective and potent radioactive ligand for histamine H3 receptors: A compound potentially useful for receptor occupancy studies, Bioorg. Med. Chem. Lett, 19 (2009) 4075-4078.
[11] E.P. Reza Aalizadeh and M.R. Ganjali, Analysis of B-RafV600E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies, Mol. Divers 19 (2015) 915-930.
[12] E. Pourbasheer, S. Riahi, M.R. Ganjali and P. Norouzi, QSAR study of C allosteric binding site of HCV NS5B polymerase inhibitors by support vector machine, Mol. Divers. 15 (2011) 645-653.
[13] E. Pourbasheer, R. Aalizadeh, H. Shiri, A. Banaei and M. Ganjali, 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA, Curr. Comput Aided. Drug. Des. (2015).
[14] E. Pourbasheer, R. Bazl and M. Amanlou, Molecular docking and 3D-QSAR studies on the MAPKAP-K2 inhibitors, Med. Chem. Res. 23 (2014) 2252-2263.
[15] E. Pourbasheer and M. Amanlou, 3D-QSAR analysis of anti-cancer agents by CoMFA and CoMSIA, Med. Chem. Res. 23 (2014) 800-809.
[16] A. Beheshti, S. Riahi and M.R. Ganjali, Quantitative structure–property relationship study on first reduction and oxidation potentials of donor-substituted phenylquino-linylethynes and phenylisoquinolinylethynes: Quantum chemical investigation, Electrochim. Acta. 54 (2009) 5368-5375.
[17] Y.M. Alvarez-Ginarte, R. Crespo-Otero, Y. Marrero-Ponce, P. Noheda-Marin, J.M. Garcia de la Vega, L.A. Montero-Cabrera, J.A. Ruiz García, J.A. Caldera-Luzardo and Y.J. Alvarado, Chemometric and chemoin-formatic analyses of anabolic and androgenic activities of testosterone and dihydrotest-osterone analogues, Bioorg. Med. Chem.16 (2008) 6448-6459.
[18] J. Caballero, M. Fernández, M. Saavedra and F.D. González-Nilo, 2D Autocorrelation, CoMFA, and CoMSIA modeling of protein tyrosine kinases’ inhibition by substituted pyrido [2, 3- d] pyrimidine derivatives, Bioorg. Med. Chem. 16 (2008) 810-821.
[19] M. Fatemi and E. Baher, Quantitative structure–property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere, SAR. QSAR. Environmen. Res. 20 (2009) 77-90.
[20] E. Pourbasheer, R. Aalizadeh, M.R. Ganjali, P. Norouzi, J. Shadmanesh and C. Methenitis, QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA-MLR), Med. Chem. Res. 23 (2014) 2264-2276.
[21] E. Pourbasheer, R. Aalizadeh, M.R. Ganjali, P. Norouzi and A. Banaei, QSAR study of mGlu5 inhibitors by genetic algorithm-multiple linear regressions, Med. Chem. Res. 23 (2014) 3082-3091.
[22] M. Nekoei, M. Salimi, M. Dolatabadi and M. Mohammadhosseini, Prediction of antileukemia activity of berbamine derivatives by genetic algorithm–multiple linear regression, Monatsh. Chem. 142 (2011) 943-948.
[23] A.J. Davenport, C.C. Stimson, M. Corsi, D. Vaidya, E. Glenn, T.D. Jones, S. Bailey, M.J. Gemkow, U. Fritz and D.J. Hallett, Discovery of substituted benzyl tetrazoles as histamine H3 receptor antagonists, Bioorg. Med. Chem.Lett. 20 (2010) 5165-5169.
[24] M. Nekoei, M. Mohammadhosseini and E. Pourbasheer, QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach, Med. Chem. Res. (2015) 1-10.
[25] L. Firoozpour, K. Sadatnezhad, S. Dehghani, E. Pourbasheer, A. Foroumadi, A. Shafiee and M. Amanlou, An efficient piecewise linear model for predicting activity of caspase-3 inhibitors, DARU, J. Pharmaceut. Sci. 20 (2012) 31-36.
[26] E. Polak and G. Ribiere, Note sur la convergence de méthodes de directions conjuguées." ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique 3 (1969) 35-43.
[27] S. Riahi, M.R. Ganjali, E. Pourbasheer, F. Divsar, P. Norouzi and M. Chaloosi, Development and validation of a rapid chemometrics-assisted spectro-photometry and liquid chromatography methods for the simultaneous determination of the phenylalanine, tryptophan and tyrosine in the pharmaceutical products, Curr. Pharm. Anal. 4 (2008) 231-237.
[28] Z. Bayat and S. Vahdani, A simple and readily integratable approach to toxicity prediction for some of the anti cancer drugs, J. Chem. Pharm. Res. 3 (2011) 93-102.
[29] S. Vahdani and Z. Bayat, A Quantitative Structure-Activity Relationship (QSAR) Study of Anti-cancer Drugs, Der. Chemica. Sinica. 2 (2011) 235-243.
[30] A.S. Om, J.C. Ryu and J.H. Kim, Quantitative structure-activity relationships for radical scavenging activities of flavonoid compounds by GA-MLR technique, Mol. Cell. Toxicol. 4 (2008) 170-176.
[31] M. Adimi, M. Salimi, M. Nekoei, E. Pourbasheer and A. Beheshti, A quantitative structure activity relationship study on histamine receptor antagonists using the genetic algorithm multi parameter linear regression method, J. Serb. Chem. Soc. 77 (2012) 639–650.
[32] J. Li, B. Lei, H. Liu, S. Li, X. Yao, M. Liu and P. Gramatica, QSAR study of malonyl‐CoA decarboxylase inhibitors using GA‐MLR and a new strategy of consensus modeling, J. comput. chem. 29 (2008) 2636-2647.
[33] H. Kušić, B. Rasulev, D. Leszczynska, J. Leszczynski and N. Koprivanac, Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: A QSAR study, Chemosphere. 75 (2009) 1128-1134.
[34] M. Jalali‐Heravi and M. Asadollahi‐Baboli, QSAR Analysis of Platelet‐derived Growth Inhibitors Using GA‐ANN and Shuffling Crossvalidation, QSAR. Comb. Sci. 27 (2008) 750-757.
)