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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Research on the Anti-tumor Activity of a Novel Aminopeptidase Inhibitor Based on 3D QSAR Model

Author(s): Liqiang Meng*, Yanhong Ou-Yang, Fuyin Lv, Jiarong Song and Jianxin Yao

Volume 19, Issue 9, 2022

Published on: 25 March, 2022

Page: [811 - 822] Pages: 12

DOI: 10.2174/1570180819666220210101641

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Abstract

Background: Aminopeptidase N (APN) is a type II transmembrane zinc ion-dependent metalloprotease. It is closely related to many processes of tumor occurrence and development, such as the formation of new blood vessels and tumor metastasis. Recent studies have shown that APN is a member of the family of surface markers of liver cancer stem cells. Therefore, APN small molecule inhibitors may have multiple compound functions, exerting multiple anti-tumor effects at multiple stages of cancer occurrence and development.

Methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) approaches were employed in the study.

Results: Both internal and external cross-validations were conducted to obtain high predictive and satisfactory CoMFA model (q2 = 0.627, r2 = 0.995, SEE = 0.043) and CoMSIA model (q2 = 0.575, r2 = 0.998, SEE = 0.031) values. The statistical results obtained from CoMFA and CoMSIA models were found to be credible and having remarkable predictive power.

Conclusion: The results of 3D-QSAR are reliable and significant with high predictive (q2) ability, and a lower value of the standard error of estimation indicates a good correlation between predicted and observed activity. All these results have revealed many useful structural insights to improve the activity of the newly designed APN small molecule inhibitors.

Keywords: 3D-QSAR, CoMFA, CoMSIA, PLS, APN, small molecule inhibitors.

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[1]
Li, H.; Li, Y.; Yao, Q.; Fan, J.; Sun, W.; Long, S.; Shao, K.; Du, J.; Wang, J.; Peng, X. In situ imaging of aminopeptidase N activity in hepatocellular carcinoma: A migration model for tumour using an activatable two-photon NIR fluorescent probe. Chem. Sci. (Camb.), 2018, 10(6), 1619-1625.
[http://dx.doi.org/10.1039/C8SC04685A ] [PMID: 30842824]
[2]
Kis, A.; Dénes, N.; Szabó, J.P.; Arató, V.; Beke, L.; Matolay, O.; Enyedi, K.N.; Méhes, G. Mező G.; Bai, P.; Kertész, I.; Trencsényi, G. In vivo molecular imaging of the efficacy of aminopeptidase N (APN/CD13) receptor inhibitor treatment on experimental tumors using 68Ga-NODAGA-c(NGR) peptide. BioMed Res. Int., 2021, 2021(3), 6642973.
[http://dx.doi.org/10.1155/2021/6642973 ] [PMID: 33778075]
[3]
Laszczyk, M.N. Pentacyclic triterpenes of the lupane, oleanane and ursane group as tools in cancer therapy. Planta Med., 2009, 75(15), 1549-1560.
[http://dx.doi.org/10.1055/s-0029-1186102 ] [PMID: 19742422]
[4]
Wang, Q.; Holst, J. L-type amino acid transport and cancer: Targeting the mTORC1 pathway to inhibit neoplasia. Am. J. Cancer Res., 2015, 5(4), 1281-1294.
[PMID: 26101697]
[5]
Friedman, D.I.; Amidon, G.L. Oral absorption of peptides: Influence of pH and inhibitors on the intestinal hydrolysis of leu-enkephalin and analogues. Pharm. Res., 1991, 8(1), 93-96.
[http://dx.doi.org/10.1023/A:1015842609565 ] [PMID: 2014216]
[6]
Sun, Z.P.; Wang, X.J.; Fang, C.Y.; Dai, G.; Xu, W.F. Aminopeptidase: A therapeutic target in human liver cancer stem cells. J. Int. Pharm. Res., 2014, 41(5), 522-527.
[7]
Santiago, C.; Mudgal, G.; Reguera, J.; Recacha, R.; Albrecht, S.; Enjuanes, L.; Casasnovas, J.M. Allosteric inhibition of aminopeptidase N functions related to tumor growth and virus infection. Sci. Rep., 2017, 7(46045), 46045.
[http://dx.doi.org/10.1038/srep46045 ] [PMID: 28393915]
[8]
Kang, J.M.; Ju, J.W.; Kim, J.Y.; Ju, H.L.; Lee, J.; Lee, K.H.; Lee, W.J.; Sohn, W.M.; Kim, T.S.; Na, B.K. Expression and biochemical characterization of a type I methionine aminopeptidase of Plasmodium vivax. Protein Expr. Purif., 2015, 108, 48-53.
[http://dx.doi.org/10.1016/j.pep.2015.01.003 ] [PMID: 25595410]
[9]
Seo, Y.; Lee, S.B.; Kim, Y.G. Stereoselective synthesis of novel bestatin analogs. J. Org. Chem., 2018, 26(1), 111-115.
[10]
Wang, T.; Wu, M.B.; Lin, J.P.; Yang, L.R. Quantitative structure-activity relationship: Promising advances in drug discovery platforms. Expert Opin. Drug Discov., 2015, 10(12), 1283-1300.
[http://dx.doi.org/10.1517/17460441.2015.1083006 ] [PMID: 26358617]
[11]
Wang, Z.Z.; Yang, J.; Sun, X.D.; Ma, C.Y.; Gao, Q.B.; Ding, L.; Liu, H.M. Probing the binding mechanism of substituted pyridine derivatives as effective and selective lysine-specific demethylase 1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations. J. Biomol. Struct. Dyn., 2019, 37(13), 3482-3495.
[http://dx.doi.org/10.1080/07391102.2018.1518158 ] [PMID: 30175693]
[12]
Vaidya, A.; Jain, A.K.; Kumar, B.; Sastry, G.N.; Agrawal, R.K. Comfa, comsia, knn mfa and docking studies of 1,2,4-oxadiazole derivatives as potent caspase-3 activators. Arab. J. Chem., 2017, 10, 3936-3946.
[http://dx.doi.org/10.1016/j.arabjc.2014.05.034]
[13]
Cao, J.Y. Design, synthesis and anti-tumor activity of a novel aminopeptidase N (APN/CD13) inhibitors, Shan Dong University: Shan Dong. 2018.
[14]
Rastija, V.; Brlas, D.; Masand, K. Application a new pymol plugin in quantitative structure-toxicity relationship study of pesticides. WSEAS Trans. Biol. Biomed., 2017, 14, 105-111.
[15]
Liu, H.C.; Tang, S.Z.; Lu, S.; Ran, T.; Wang, J.; Zhang, Y.M.; Xu, A.Y.; Lu, T.; Chen, Y.D. Studies on [5,6]-fused bicyclic scaffolds derivatives as potent dual B-Raf/KDR Inhibitors using docking and 3D-QSAR approaches. Int. J. Mol. Sci., 2015, 16(10), 24451-24474.
[http://dx.doi.org/10.3390/ijms161024451 ] [PMID: 26501259]
[16]
Meng, L.Q.; Sun, L.Q.; Yan, C.Q. 3D-QSAR studies of tricyclic compounds as porcupine inhibitors for wnt pathway inhibitory activity. Lett. Drug Des. Discov., 2018, 15(7), 721-732.
[http://dx.doi.org/10.2174/1570180814666171002162025]
[17]
Shinkai-Ouchi, F.; Koyama, S.; Ono, Y.; Hata, S.; Ojima, K.; Shindo, M.; duVerle, D.; Ueno, M.; Kitamura, F.; Doi, N.; Takigawa, I.; Mamitsuka, H.; Sorimachi, H. Predictions of cleavability of calpain proteolysis by quantitative structure-activity relationship analysis using newly determined cleavage sites and catalytic efficiencies of an oligopeptide array. Mol. Cell. Proteomics, 2016, 15(4), 1262-1280.
[http://dx.doi.org/10.1074/mcp.M115.053413 ] [PMID: 26796116]
[18]
Dowlati Beirami, A.; Hajimahdi, Z.; Zarghi, A. Docking-based 3D-QSAR (CoMFA, CoMFA-RG, CoMSIA) study on hydroquinoline and thiazinan-4-one derivatives as selective COX-2 inhibitors. J. Biomol. Struct. Dyn., 2019, 37(11), 2999-3006.
[http://dx.doi.org/10.1080/07391102.2018.1502687 ] [PMID: 30035675]
[19]
Tong, J.; Lei, S.; Qin, S.; Wang, Y. QSAR studies of TIBO derivatives as HIV-1 reverse transcriptase inhibitors using HQSAR, CoMFA and CoMSIA. J. Mol. Struct., 2018, 1168, 56-64.
[http://dx.doi.org/10.1016/j.molstruc.2018.05.005]
[20]
Ra, A.; Eh, B.; Fh, C.; Ta, B. Investigation of pyrimidine analogues as xanthine oxidase inhibitors to treat of hyperuricemia and gout through combined qsar techniques, molecular docking and molecular dynamics simulations-science direct. J. Taiwan Inst. Chem. Eng., 2020, 113, 72-100.
[http://dx.doi.org/10.1016/j.jtice.2020.08.028]
[21]
Liu, H.C.; Tang, S.Z.; Lu, S.; Ran, T.; Wang, J.; Zhang, Y.M.; Xu, A.Y.; Lu, T.; Chen, Y.D. Studies on [5,6]-fused bicyclic scaffolds derivatives as potent dual b-rafv600e/kdr inhibitors using docking and 3d-qsar approaches. Int. J. Mol. Sci., 2015, 16(10), 24451-24474.
[http://dx.doi.org/10.3390/ijms161024451 ] [PMID: 26501259]
[22]
Srinivas, N. Tripuraneni, Afzal M.; Azam, A combination of pharmacophore modeling, atom-based 3d-qsar, molecular docking and molecular dynamics simulation studies on pde4 enzyme inhibitors. J. Biomol. Struct. Dyn., 2016, 34, 2481-2492.
[http://dx.doi.org/10.1080/07391102.2015.1119732 ] [PMID: 26587754]
[23]
Vehtari, A.; Gelman, A.; Gabry, J. Practical bayesian model evaluation using leave-one-out cross-validation and waic. Stat. Comput., 2015, 27, 1-20.
[24]
Yang, L.Z.; Liu, M. A double-activity (green algae toxicity and bacterial genotoxicity) 3d-qsar model based on the comprehensive index method and its application in fluoroquinolones’ modification. Int. J. Environ. Res. Public Health, 2020, 17(3), 942-956.
[http://dx.doi.org/10.3390/ijerph17030942 ] [PMID: 32028728]
[25]
Rondla, R.; Rao, L.P.; Ramatenki, V.; Vadija, R.; Mukkera, T.; Potlapally, S.R. Azolium analogues as cdk4 inhibitors: Pharmacophore modeling, 3D QSAR study and new lead drug discovery. J. Mol. Struct., 2017, 1134, 482-491.
[http://dx.doi.org/10.1016/j.molstruc.2016.12.106]
[26]
Chavda, J.; Bhatt, H. 3D-QSAR (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as b-raf inhibitors for the treatment of melanoma cancer. Struct. Chem., 2019, 30, 2093-2107.
[http://dx.doi.org/10.1007/s11224-019-01334-9]
[27]
Tahir, M.H.; Mubashir, T.; Shah, T.; Mahmood, A. Impact of electron-withdrawing and electron-donating substituents on the electrochemical and charge transport properties of indacenodithiophene-based small molecule acceptors for organic solar cells. J. Phys. Org. Chem., 2019, 32(3), 3909-3915.
[http://dx.doi.org/10.1002/poc.3909]
[28]
Bhatt, H.G.; Patel, P.K. Pharmacophore modeling, virtual screening and 3D-QSAR studies of 5-tetrahydroquinolinylidine aminoguanidine derivatives as sodium hydrogen exchanger inhibitors. Bioorg. Med. Chem. Lett., 2012, 22(11), 3758-3765.
[http://dx.doi.org/10.1016/j.bmcl.2012.04.012 ] [PMID: 22546667]

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