Design and Prediction of ADME/Tox Properties of Novel Magnolol Derivatives
as Anticancer Agents for NSCLC Using 3D-QSAR, Molecular Docking,
MOLCAD and MM-GBSA Studies

Ossama      Daoui; Souad      Elkhattabi; Samir      Chtita
doi:10.2174/1570180819666220510141710
Abstract

Introduction: In this work, we used several molecular modeling techniques to design new molecules for the treatment of non-small cell lung cancer (NSCLC).
Methods: For this purpose, we applied 3D-QSAR, molecular docking, MOLCAD, ADMET, and MMGBSA studies to a series of 51 natural derivatives of magnolol.
Results: The developed models showed excellent statistical results (R² = 0.90; Q² = 0.672; R²pred = 0.86) for CoMFA and (R² = 0.82; Q² = 0.58; R² _pred = 0.78) CoMSIA. The design of eleven new molecules was based on predictions derived from the 3D-QSAR model contour maps, molecular docking and MolCAD analyses. In silico drug-like and ADMET properties studies led to the selection of four new molecules designed as potential agents for NSCLC therapy. Molecular docking and MM-GBSA simulations of proposed structures with EGFR-TKD (PDB code: 1M17) showed that ligands X10 and 30 attained better stability in the 1M17 protein pocket compared to the Erlotinib ligand used as a reference.
Conclusion: Incorporating all the molecular modelling techniques used in this work is conducive to the design of new molecules derived from the 3-(4-aminobipyridin-1-yl)methyl structure of magnolol, a candidate for drug design for the treatment of non-small cell lung cancer. Therefore, the molecular structures (X10 and 30) can be proposed as a key to designing new drugs against NSCLC.
Keywords: Magnolol, 3D-QSAR, NSCLC, molecular docking, MOLCAD, MM-GBSA.
« Previous Next »
Graphical Abstract

[1]
Brahmer, J.R.; Govindan, R.; Anders, R.A.; Antonia, S.J.; Sagorsky, S.; Davies, M.J.; Dubinett, S.M.; Ferris, A.; Gandhi, L.; Garon, E.B.; Hellmann, M.D.; Hirsch, F.R.; Malik, S.; Neal, J.W.; Papadimitrakopoulou, V.A.; Rimm, D.L.; Schwartz, L.H.; Sepesi, B.; Yeap, B.Y.; Rizvi, N.A.; Herbst, R.S. The society for immunotherapy of cancer consensus statement on immunotherapy for the treatment of non-small cell lung cancer (NSCLC). J. Immunother. Cancer,  2018, 6(1), 75.
 [http://dx.doi.org/10.1186/s40425-018-0382-2] [PMID:  30012210]

[2]
Benbrahim, Z.; Antonia, T.; Mellas, N. EGFR mutation frequency in Middle East and African non-small cell lung cancer patients: A systematic review and meta-analysis. BMC Cancer,  2018, 18(1), 891.
 [http://dx.doi.org/10.1186/s12885-018-4774-y] [PMID:  30217176]

[3]
Zago, G.; Muller, M.; van den Heuvel, M.; Baas, P. New targeted treatments for non-small-cell lung cancer - role of nivolumab. Biologics,  2016, 10, 103-117.
 [PMID:  27536062]

[4]
Chan, B.A.; Coward, J.I.G. Chemotherapy advances in small-cell lung cancer. J. Thorac. Dis.,  2013, 5(Suppl. 5), S565-S578.
 [http://dx.doi.org/10.3978/j.issn.2072-1439.2013.07.43] [PMID:  24163749]

[5]
Fennell, D.A.; Summers, Y.; Cadranel, J.; Benepal, T.; Christoph, D.C.; Lal, R.; Das, M.; Maxwell, F.; Visseren-Grul, C.; Ferry, D. Cisplatin in the modern era: The backbone of first-line chemotherapy for non-small cell lung cancer. Cancer Treat. Rev.,  2016, 44, 42-50.
 [http://dx.doi.org/10.1016/j.ctrv.2016.01.003] [PMID:  26866673]

[6]
Chan, B.A.; Hughes, B.G. Targeted therapy for non-small cell lung cancer: Current standards and the promise of the future. Transl. Lung Cancer Res.,  2015, 4(1), 36-54.
 [PMID:  25806345]

[7]
Porta, R.; Sánchez-Torres, J.M.; Paz-Ares, L.; Massutí, B.; Reguart, N.; Mayo, C.; Lianes, P.; Queralt, C.; Guillem, V.; Salinas, P.; Catot, S.; Isla, D.; Pradas, A.; Gúrpide, A.; de Castro, J.; Polo, E.; Puig, T.; Tarón, M.; Colomer, R.; Rosell, R. Brain metastases from lung cancer responding to erlotinib: The importance of EGFR mutation. Eur. Respir. J.,  2011, 37(3), 624-631.
 [http://dx.doi.org/10.1183/09031936.00195609] [PMID:  20595147]

[8]
Wei, Y.; Zou, Z.; Becker, N.; Anderson, M.; Sumpter, R.; Xiao, G.; Kinch, L.; Koduru, P.; Christudass, C.S.; Veltri, R.W.; Grishin, N.V.; Peyton, M.; Minna, J.; Bhagat, G.; Levine, B. EGFR-mediated Beclin 1 phosphorylation in autophagy suppression, tumor progression, and tumor chemoresistance. Cell,  2013, 154(6), 1269-1284.
 [http://dx.doi.org/10.1016/j.cell.2013.08.015] [PMID:  24034250]

[9]
Dang, A.; Dang, S.; Vallish, B.N. Efficacy and Safety of EGFR Inhibitors in the Treatment of EGFRPositive NSCLC Patients: A Meta-Analysis. Rev. Recent Clin. Trials,  2021, 16(2), 193-201.
 [http://dx.doi.org/10.2174/1574887115999201103200248] [PMID:  33155914]

[10]
Stamos, J.; Sliwkowski, M.X.; Eigenbrot, C. Structure of the epidermal growth factor receptor kinase domain alone and in complex with a 4-anilinoquinazoline inhibitor. J. Biol. Chem.,  2002, 277(48), 46265-46272.
 [http://dx.doi.org/10.1074/jbc.M207135200] [PMID:  12196540]

[11]
Emam, A.M.; Dahal, A.; Singh, S.S.; Tosso, R.D.; Ibrahim, S.M.; El-Sadek, M.; Jois, S.D.; Enriz, R.D.; Kothayer, H. Quinazoline-tethered hydrazone: A versatile scaffold toward dual anti-TB and EGFR inhibition activities in NSCLC. Arch. Pharm. (Weinheim),  2021, 354(12), e2100281.
 [http://dx.doi.org/10.1002/ardp.202100281] [PMID:  34585758]

[12]
Shaikh, G.M.; Murahari, M.; Thakur, S.; Kumar, M.S.; Yc, M. Studies on ligand-based pharmacophore modeling approach in identifying potent future EGFR inhibitors. J. Mol. Graph. Model.,  2022, 112, 108114.
 [http://dx.doi.org/10.1016/j.jmgm.2021.108114] [PMID:  34979367]

[13]
Kiriwan, D.; Seetaha, S.; Jiwacharoenchai, N.; Tabtimmai, L.; Sousa, S. F.; Songtawee, N.; Choowongkomon, K. Identification of tripeptides against tyrosine kinase domain of EGFR for lung cancer cell inhibition by in silico and in vitro studies. Chem. Biol. Drug Des, 
 [http://dx.doi.org/10.1111/cbdd.14010]

[14]
Townsend, M.H.; Anderson, M.D.; Weagel, E.G.; Velazquez, E.J.; Weber, K.S.; Robison, R.A.; O’Neill, K.L. Non-small-cell lung cancer cell lines A549 and NCI-H460 express hypoxanthine guanine phosphoribosyltransferase on the plasma membrane. OncoTargets Ther.,  2017, 10, 1921-1932.
 [http://dx.doi.org/10.2147/OTT.S128416] [PMID:  28408844]

[15]
Cheng, W.; Liang, C.; Xu, L.; Liu, G.; Gao, N.; Tao, W.; Luo, L.; Zuo, Y.; Wang, X.; Zhang, X.; Zeng, X.; Mei, L. TPGS-functionalized polydopamine-modified mesoporous silica as drug nanocarriers for enhanced lung cancer chemotherapy against multidrug resistance. Small,  2017, 13(29), 1700623.
 [http://dx.doi.org/10.1002/smll.201700623] [PMID:  28594473]

[16]
Liang, C.; Wang, H.; Zhang, M.; Cheng, W.; Li, Z.; Nie, J.; Liu, G.; Lian, D.; Xie, Z.; Huang, L.; Zeng, X. Self-controlled release of Oxaliplatin prodrug from d-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) functionalized mesoporous silica nanoparticles for cancer therapy. J. Colloid Interface Sci.,  2018, 525, 1-10.
 [http://dx.doi.org/10.1016/j.jcis.2018.04.058] [PMID:  29679795]

[17]
Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J. Nat. Prod.,  2020, 83(3), 770-803.
 [http://dx.doi.org/10.1021/acs.jnatprod.9b01285] [PMID:  32162523]

[18]
Ahmad, R.; Khan, M.A.; Srivastava, A.N.; Gupta, A.; Srivastava, A.; Jafri, T.R.; Siddiqui, Z.; Chaubey, S.; Khan, T.; Srivastava, A.K. Anticancer potential of dietary natural products: A comprehensive review. Anticancer. Agents Med. Chem.,  2020, 20(2), 122-236.
 [http://dx.doi.org/10.2174/1871520619666191015103712] [PMID:  31749433]

[19]
Lee, Y-J.; Lee, Y.M.; Lee, C-K.; Jung, J.K.; Han, S.B.; Hong, J.T. Therapeutic applications of compounds in the Magnolia family. Pharmacol. Ther.,  2011, 130(2), 157-176.
 [http://dx.doi.org/10.1016/j.pharmthera.2011.01.010] [PMID:  21277893]

[20]
Xu, H.L.; Tang, W.; Du, G.H.; Kokudo, N. Targeting apoptosis pathways in cancer with magnolol and honokiol, bioactive constituents of the bark of Magnolia officinalis. Drug Discov. Ther.,  2011, 5(5), 202-210.
 [http://dx.doi.org/10.5582/ddt.2011.v5.5.202] [PMID:  22466367]

[21]
Fu, Y.; Liu, B.; Zhang, N.; Liu, Z.; Liang, D.; Li, F.; Cao, Y.; Feng, X.; Zhang, X.; Yang, Z. Magnolol inhibits lipopolysaccharide-induced inflammatory response by interfering with TLR4 mediated NF-κB and MAPKs signaling pathways. J. Ethnopharmacol.,  2013, 145(1), 193-199.
 [http://dx.doi.org/10.1016/j.jep.2012.10.051] [PMID:  23127653]

[22]
Shen, J-L.; Man, K-M.; Huang, P-H.; Chen, W-C.; Chen, D-C.; Cheng, Y-W.; Liu, P-L.; Chou, M-C.; Chen, Y-H. Honokiol and magnolol as multifunctional antioxidative molecules for dermatologic disorders. Molecules,  2010, 15(9), 6452-6465.
 [http://dx.doi.org/10.3390/molecules15096452] [PMID:  20877235]

[23]
Hu, H.; Wang, Z.; Hua, W.; You, Y.; Zou, L. Effect of chemical profiling change of processed Magnolia officinalis on the pharmacokinetic profiling of Honokiol and Magnolol in rats. J. Chromatogr. Sci.,  2016, 54(7), 1201-1212.
 [http://dx.doi.org/10.1093/chromsci/bmw052] [PMID:  27107095]

[24]
Zhao, M.; Zheng, Y-H.; Zhao, Q-Y.; Zheng, W.; Yang, J-H.; Pei, H-Y.; Liu, L.; Liu, K-J.; Xue, L-L.; Deng, D-X.; Wang, L.; Ma, X.; Fu, S.H.; Peng, A.H.; Tang, M.H.; Luo, Y.Z.; Ye, H.Y.; Chen, L.J. Synthesis and evaluation of new compounds bearing 3-(4-aminopiperidin-1-yl)methyl magnolol scaffold as anticancer agents for the treatment of non-small cell lung cancer via targeting autophagy. Eur. J. Med. Chem.,  2021, 209, 112922.
 [http://dx.doi.org/10.1016/j.ejmech.2020.112922] [PMID:  33069436]

[25]
Chtita, S.; Aouidate, A.; Belhassan, A.; Ousaa, A.; Taourati, A.I.; Elidrissi, B.; Ghamali, M.; Bouachrine, M.; Lakhlifi, T. QSAR Study of N-substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods. New J. Chem.,  2020, 44(5), 1747-1760.
 [http://dx.doi.org/10.1039/C9NJ04909F]

[26]
Chtita, S.; Belhassan, A.; Bakhouch, M.; Taourati, A.I.; Aouidate, A.; Belaidi, S.; Moutaabbid, M.; Belaaouad, S.; Bouachrine, M.; Lakhlifi, T. QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods. Chemom. Intell. Lab. Syst.,  2021, 210, 104266.
 [http://dx.doi.org/10.1016/j.chemolab.2021.104266] [PMID:  33558778]

[27]
Peter, S.C.; Dhanjal, J.K.; Malik, V.; Radhakrishnan, N.; Jayakanthan, M.; Sundar, D.; Sundar, D.; Jayakanthan, M. >Encyclopedia of Bioinformatics and Computational Biology; Ranganathan Grib-Skov, M.; Nakai, K; Schönbach, C., Ed.; , 2018, pp. 661-676.

[28]
Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design-a review. Curr. Top. Med. Chem.,  2010, 10(1), 95-115.
 [http://dx.doi.org/10.2174/156802610790232260] [PMID:  19929826]

[29]
Attoui, A.; Sobhi, W.; Hammoudi, N.E.H.; Benguerba, Y. Fragment-based drug design of antitumoral molecules polo-like kinase 1 inhibitors: In-silico approach. Lett. Drug Des. Discov.,  2021, 18(8), 779-794.
 [http://dx.doi.org/10.2174/1570180818999201230195526]

[30]
Kasmi, R.; Elmchichi, L.; Aissouq, A.E.; Bouachrine, M.; Ouammou, A. In silico drug design: Development of new pyrimidine-based benzothiazole derivatives, selective for CDK2. Lett. Drug Des. Discov.,  2021, 18(10), 961-975.
 [http://dx.doi.org/10.2174/1570180818666210421134819]

[31]
Bank, R.P.D. RCSB PDB - 1M17: Epidermal growth factor receptor tyrosine kinase domain with 4-anilinoquinazoline inhibitor erlotinib.  Available from: https://www.rcsb.org/structure/1M17 (Accessed on 2021 -05 -07).

[32]
Choudhary, M.I.; Shaikh, M.; Tul-Wahab, A.; Ur-Rahman, A. In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PLoS One,  2020, 15(7), e0235030.
 [http://dx.doi.org/10.1371/journal.pone.0235030] [PMID:  32706783]

[33]
Jin, Z.; Wang, Y.; Yu, X-F.; Tan, Q-Q.; Liang, S-S.; Li, T.; Zhang, H.; Shaw, P-C.; Wang, J.; Hu, C. Structure-based virtual screening of influenza virus RNA polymerase inhibitors from natural compounds: Molecular dynamics simulation and MM-GBSA calculation. Comput. Biol. Chem.,  2020, 85, 107241.
 [http://dx.doi.org/10.1016/j.compbiolchem.2020.107241] [PMID:  32120300]

[34]
van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug Discov.,  2003, 2(3), 192-204.
 [http://dx.doi.org/10.1038/nrd1032] [PMID:  12612645]

[35]
James, J.P.; Kumar, P.; Kumar, A.; Bhat, K.I.; Shastry, C.S. In silico anticancer evaluation, molecular docking and pharmacophore modeling of flavonoids against various cancer targets. Lett. Drug Des. Discov.,  2020, 17(12), 1485-1501.
 [http://dx.doi.org/10.2174/1570180817999200730164222]

[36]
Clark, M.; Cramer, R.D.; Van Opdenbosch, N. Validation of the general purpose tripos 5.2 force field. J. Comput. Chem.,  1989, 10(8), 982-1012.
 [http://dx.doi.org/10.1002/jcc.540100804]

[37]
Aparoy, P.; Suresh, G.K.; Kumar Reddy, K.; Reddanna, P. CoMFA and CoMSIA studies on 5-hydroxyindole-3-carboxylate derivatives as 5-lipoxygenase inhibitors: Generation of homology model and docking studies. Bioorg. Med. Chem. Lett.,  2011, 21(1), 456-462.
 [http://dx.doi.org/10.1016/j.bmcl.2010.10.119] [PMID:  21084193]

[38]
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc.,  1988, 110(18), 5959-5967.
 [http://dx.doi.org/10.1021/ja00226a005] [PMID:  22148765]

[39]
Borisa, A.; Bhatt, H. 3D-QSAR (CoMFA, CoMFA-RG, CoMSIA) and molecular docking study of thienopyrimidine and thienopyridine derivatives to explore structural requirements for aurora-B kinase inhibition. Eur. J. Pharm. Sci.,  2015, 79, 1-12.
 [http://dx.doi.org/10.1016/j.ejps.2015.08.017] [PMID:  26343315]

[40]
Fu, L.; Chen, Y.; Xu, C.; Wu, T.; Guo, H.; Lin, Z.; Wang, R.; Shu, M. 3D-QSAR, HQSAR, molecular docking, and new compound design study of 1, 3, 6-trisubstituted 1, 4-Diazepan-7-Ones as human KLK7 inhibitors. Med. Chem. Res.,  2020, 29(6), 1012-1029.
 [http://dx.doi.org/10.1007/s00044-020-02542-3]

[41]
Zhao, X.; Chen, M.; Huang, B.; Ji, H.; Yuan, M. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment. Int. J. Mol. Sci.,  2011, 12(10), 7022-7037.
 [http://dx.doi.org/10.3390/ijms12107022] [PMID:  22072933]

[42]
Shah, B.M.; Modi, P.; Trivedi, P. Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors. J. Biomol. Struct. Dyn.,  2021, 39(6), 2021-2043.
 [http://dx.doi.org/10.1080/07391102.2020.1750485] [PMID:  32242496]

[43]
Madhavi, M.; Venkatesh, N.; Parthasarathy, T. 3D QSAR pharmacophore based virtual screening, ADME analysis and estimation of MM/GBSA binding free energies of azoles as a potential inhibitor of DprE1 for mycobacterium tuberculosis. 2021.

[44]
Chalkha, M.; Akhazzane, M.; Moussaid, F.Z.; Daoui, O.; Nakkabi, A.; Bakhouch, M.; Chtita, S.; Elkhattabi, S.; Housseini, A.I.; Yazidi, M.E. Design, synthesis, characterization, in vitro screening, molecular docking, 3D-QSAR, and ADME-Tox investigations of novel pyrazole derivatives as antimicrobial agents. New J. Chem., 2021.
 [http://dx.doi.org/10.1039/D1NJ05621B]

[45]
Ouassaf, M.; Belaidi, S.; Khamouli, S.; Belaidi, H.; Chtita, S. Combined 3D-QSAR and molecular docking analysis of thienopyrimidine derivatives as Staphylococcus aureus inhibitors. Acta Chim. Slov.,  2021, 68(2), 289-303.
 [http://dx.doi.org/10.17344/acsi.2020.5985] [PMID:  34738130]

[46]
Aouidate, A.; Ghaleb, A.; Ghamali, M.; Chtita, S.; Ousaa, A.; Sbai, A.; Bouachrine, M.; Lakhlifi, T. Molecular Docking and 3D-QSAR studies on 7-azaindole derivatives as inhibitors of Trk A: A strategic design in novel anticancer agents. Lett. Drug Des. Discov.,  2018, 15(11), 1211-1223.
 [http://dx.doi.org/10.2174/1570180815666171229151138]

[47]
Xue, C.X.; Cui, S.Y.; Liu, M.C.; Hu, Z.D.; Fan, B.T. 3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA. Eur. J. Med. Chem.,  2004, 39(9), 745-753.
 [http://dx.doi.org/10.1016/j.ejmech.2004.05.009] [PMID:  15337287]

[48]
Zhu, W.; Chen, G.; Hu, L.; Luo, X.; Gui, C.; Luo, C.; Puah, C.M.; Chen, K.; Jiang, H. QSAR analyses on ginkgolides and their analogues using CoMFA, CoMSIA, and HQSAR. Bioorg. Med. Chem.,  2005, 13(2), 313-322.
 [http://dx.doi.org/10.1016/j.bmc.2004.10.027] [PMID:  15598554]

[49]
Cramer, R.D., III; Bunce, J.D.; Patterson, D.E.; Frank, I.E. Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR Studies. Quant. Struct.-. Act. Relatsh,  1988, 7(1), 18-25.
 [http://dx.doi.org/10.1002/qsar.19880070105]

[50]
Wang, Y-L.; Wang, F.; Shi, X-X.; Jia, C-Y.; Wu, F-X.; Hao, G-F.; Yang, G-F. Cloud 3D-QSAR: A web tool for the development of quantitative structure–activity relationship models in drug discovery. Brief. Bioinform.,  2021, 22(4), bbaa276.

[51]
Golbraikh, A.; Tropsha, A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol. Divers.,  2002, 5(4), 231-243.
 [http://dx.doi.org/10.1023/A:1021372108686] [PMID:  12549674]

[52]
Rücker, C.; Rücker, G.; Meringer, M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model.,  2007, 47(6), 2345-2357.
 [http://dx.doi.org/10.1021/ci700157b] [PMID:  17880194]

[53]
Kumar, P.; Kumar, A. Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method. J. Biomol. Struct. Dyn.,  2020, 38(11), 3296-3306.
 [http://dx.doi.org/10.1080/07391102.2019.1656109] [PMID:  31411551]

[54]
Kumar, P.; Kumar, A. Monte Carlo Method Based QSAR studies of mer kinase inhibitors in compliance with OECD principles. Drug Res. (Stuttg.),  2018, 68(4), 189-195.
 [http://dx.doi.org/10.1055/s-0043-119288] [PMID:  28992659]

[55]
Belhassan, A.; Chtita, S.; Lakhlifi, T.; Bouachrine, M. QSPR study of the retention/release property of odorant molecules in pectin gels using statistical methods. J. Taibah Univ. Sci.,  2017, 11(6), 1030-1046.
 [http://dx.doi.org/10.1016/j.jtusci.2017.05.004]

[56]
Netzeva, T.I.; Worth, A.; Aldenberg, T.; Benigni, R.; Cronin, M.T.; Gramatica, P.; Jaworska, J.S.; Kahn, S.; Klopman, G.; Marchant, C.A.; Myatt, G.; Nikolova-Jeliazkova, N.; Patlewicz, G.Y.; Perkins, R.; Roberts, D.; Schultz, T.; Stanton, D.W.; van de Sandt, J.J.; Tong, W.; Veith, G.; Yang, C. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim.,  2005, 33(2), 155-173.
 [http://dx.doi.org/10.1177/026119290503300209] [PMID:  16180989]

[57]
Lin, K.; Cheng, J.; Yang, T.; Li, Y.; Zhu, B. EGFR-TKI down-regulates PD-L1 in EGFR mutant NSCLC through inhibiting NF-κ. B. Biochem. Biophys. Res. Commun.,  2015, 463(1-2), 95-101.
 [http://dx.doi.org/10.1016/j.bbrc.2015.05.030] [PMID:  25998384]

[58]
Visualizer, D.S.V. 16.1. 0; AccelrysInc: San Diego , 2016. 

[59]
Seeliger, D.; de Groot, B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des.,  2010, 24(5), 417-422.
 [http://dx.doi.org/10.1007/s10822-010-9352-6] [PMID:  20401516]

[60]
MGLTools 1.5.6 RC3 Release Announcement — MGLTools.  Available from: http://mgltools.scripps.edu/News/mgltools-1-5-6-release-announcement (Accessed 2021 -05 -07).

[61]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem.,  1998, 19(14), 1639-1662.
 [http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B]

[62]
Daoui, O.; Elkhattabi, S.; Chtita, S.; Elkhalabi, R.; Zgou, H.; Benjelloun, A.T. QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase. Heliyon,  2021, 7(7), e07463.
 [http://dx.doi.org/10.1016/j.heliyon.2021.e07463] [PMID:  34296007]

[63]
Fischer, A.; Smieško, M.; Sellner, M.; Lill, M.A. Decision making in structure-based drug discovery: Visual inspection of docking results. J. Med. Chem.,  2021, 64(5), 2489-2500.
 [http://dx.doi.org/10.1021/acs.jmedchem.0c02227] [PMID:  33617246]

[64]
Liu, F-F.; Wang, T.; Dong, X-Y.; Sun, Y. Rational design of affinity peptide ligand by flexible docking simulation. J. Chromatogr. A,  2007, 1146(1), 41-50.
 [http://dx.doi.org/10.1016/j.chroma.2007.01.130] [PMID:  17298835]

[65]
Jain, A.N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem.,  2003, 46(4), 499-511.
 [http://dx.doi.org/10.1021/jm020406h] [PMID:  12570372]

[66]
Spitzer, R.; Jain, A.N. Surflex-Dock: Docking benchmarks and real-world application. J. Comput. Aided Mol. Des.,  2012, 26(6), 687-699.
 [http://dx.doi.org/10.1007/s10822-011-9533-y] [PMID:  22569590]

[67]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep.,  2017, 7(1), 42717.
 [http://dx.doi.org/10.1038/srep42717] [PMID:  28256516]

[68]
Pires, D.E.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem.,  2015, 58(9), 4066-4072.
 [http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID:  25860834]

[69]
Release, S. Prime; Schrödinger, LLC, 2020, pp. 2020-2023.

[70]
Rajagopal, K.; Varakumar, P.; Aparna, B.; Byran, G.; Jupudi, S. Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies. J. Biomol. Struct. Dyn.,  2021, 39(15), 5551-5562.
 [http://dx.doi.org/10.1080/07391102.2020.1798285] [PMID:  32720578]

[71]
Cao, H.; Zhang, H.; Zheng, X.; Gao, D. 3D QSAR studies on a series of potent and high selective inhibitors for three kinases of RTK family. J. Mol. Graph. Model.,  2007, 26(1), 236-245.
 [http://dx.doi.org/10.1016/j.jmgm.2006.12.001] [PMID:  17293140]

[72]
Buolamwini, J.K.; Assefa, H. CoMFA and CoMSIA 3D QSAR and docking studies on conformationally-restrained cinnamoyl HIV-1 integrase inhibitors: Exploration of a binding mode at the active site. J. Med. Chem.,  2002, 45(4), 841-852.
 [http://dx.doi.org/10.1021/jm010399h] [PMID:  11831895]

[73]
Zentrum für Bioinformatik: Universität Hamburg - Proteins Plus Server.  Available from: https://proteins.plus/ (Accessed on 2021-05-07).

[74]
Shepherd, F.A.; Rodrigues Pereira, J.; Ciuleanu, T.; Tan, E.H.; Hirsh, V.; Thongprasert, S.; Campos, D.; Maoleekoonpiroj, S.; Smylie, M.; Martins, R.; van Kooten, M.; Dediu, M.; Findlay, B.; Tu, D.; Johnston, D.; Bezjak, A.; Clark, G.; Santabárbara, P.; Seymour, L. Erlotinib in previously treated non-small-cell lung cancer. N. Engl. J. Med.,  2005, 353(2), 123-132.
 [http://dx.doi.org/10.1056/NEJMoa050753] [PMID:  16014882]

[75]
Lipinski, C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol.,  2004, 1(4), 337-341.
 [http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID:  24981612]

[76]
Egan, W.J.; Merz, K.M., Jr; Baldwin, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem.,  2000, 43(21), 3867-3877.
 [http://dx.doi.org/10.1021/jm000292e] [PMID:  11052792]

[77]
Veber, D.F.; Johnson, S.R.; Cheng, H-Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem.,  2002, 45(12), 2615-2623.
 [http://dx.doi.org/10.1021/jm020017n] [PMID:  12036371]

[78]
Faidallah, H. M.; Al-Mohammadi, M. M.; Alamry, K. A.; Khan, K. A. Synthesis and biological evaluation of fluoropyrazolesulfonylurea and thiourea derivatives as possible antidiabetic agents. J. Enzyme Inhib. Med. Chem,  2016, 31(sup1), 157-163.
 [http://dx.doi.org/10.1080/14756366.2016.1180594]

[79]
Fukunishi, Y.; Kurosawa, T.; Mikami, Y.; Nakamura, H. Prediction of synthetic accessibility based on commercially available compound databases. J. Chem. Inf. Model.,  2014, 54(12), 3259-3267.
 [http://dx.doi.org/10.1021/ci500568d] [PMID:  25420000]

[80]
Kalantzi, L.; Goumas, K.; Kalioras, V.; Abrahamsson, B.; Dressman, J.B.; Reppas, C. Characterization of the human upper gastrointestinal contents under conditions simulating bioavailability/bioequivalence studies. Pharm. Res.,  2006, 23(1), 165-176.
 [http://dx.doi.org/10.1007/s11095-005-8476-1] [PMID:  16308672]

[81]
König, J.; Müller, F.; Fromm, M.F. Transporters and drug-drug interactions: Important determinants of drug disposition and effects. Pharmacol. Rev.,  2013, 65(3), 944-966.
 [http://dx.doi.org/10.1124/pr.113.007518] [PMID:  23686349]

[82]
Fromm, M.F. Importance of P-glycoprotein at blood-tissue barriers. Trends Pharmacol. Sci.,  2004, 25(8), 423-429.
 [http://dx.doi.org/10.1016/j.tips.2004.06.002] [PMID:  15276711]

[83]
Han, Y.; Zhang, J.; Hu, C.Q.; Zhang, X.; Ma, B.; Zhang, P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front. Pharmacol.,  2019, 10, 434.
 [http://dx.doi.org/10.3389/fphar.2019.00434] [PMID:  31068821]

[84]
Lynch, T.; Price, A. The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am. Fam. Physician,  2007, 76(3), 391-396.
 [PMID:  17708140]

[85]
Zanger, U.M.; Schwab, M. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther.,  2013, 138(1), 103-141.
 [http://dx.doi.org/10.1016/j.pharmthera.2012.12.007] [PMID:  23333322]

[86]
Stead, A.G.; Hasselblad, V.; Creason, J.P.; Claxton, L. Modeling the Ames test. Mutat. Res.,  1981, 85(1), 13-27.
 [http://dx.doi.org/10.1016/0165-1161(81)90282-X] [PMID:  7010142]
Rights & Permissions Print Cite
Article Metrics
60
3
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1570180819666220510141710	Print ISSN 1570-1808
Publisher Name Bentham Science Publisher	Online ISSN 1875-628X
Letters in Drug Design & Discovery

Design and Prediction of ADME/Tox Properties of Novel Magnolol Derivatives as Anticancer Agents for NSCLC Using 3D-QSAR, Molecular Docking, MOLCAD and MM-GBSA Studies

Abstract

Graphical Abstract

Related Journals

Related Books

Related Articles
