Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer

Biomedicine and Pharmacotherapy

Volumn 71, Issue , 2015, Pages 37-45

  Subhani, Syed ^a   Jayaraman, Archana ^b   Jamil, Kaiser ^a,b  

^a Bhagwan Mahavir Medical Research Centre   (India)

^b JNT University Anantapur   (India)

Author keywords

Binding affinity; Chemotherapeutics; Homology modelling; MDR1; Non small cell lung cancer

Indexed keywords

CARBOPLATIN; CISPLATIN; DOCETAXEL; GEMCITABINE; MULTIDRUG RESISTANCE PROTEIN 1; PACLITAXEL; ABCB1 PROTEIN, HUMAN; ANTINEOPLASTIC AGENT; DEOXYCYTIDINE; MULTIDRUG RESISTANCE PROTEIN; TAXOID;

ARTICLE; BINDING AFFINITY; CANCER PATIENT; DRUG PROTEIN BINDING; DRUG TARGETING; HYDROGEN BOND; INDUCED FIT DOCKING; MOLECULAR DOCKING; MOUSE; NON SMALL CELL LUNG CANCER; NONHUMAN; PRIORITY JOURNAL; SEQUENCE HOMOLOGY; AMINO ACID SEQUENCE; ANALOGS AND DERIVATIVES; ANIMAL; BINDING SITE; CARCINOMA, NON-SMALL-CELL LUNG; CHEMISTRY; HUMAN; LUNG NEOPLASMS; MOLECULAR GENETICS; SEQUENCE ALIGNMENT; STRUCTURAL HOMOLOGY; THERMODYNAMICS;

AMINO ACID SEQUENCE; ANIMALS; ANTINEOPLASTIC AGENTS; ANTINEOPLASTIC COMBINED CHEMOTHERAPY PROTOCOLS; BINDING SITES; CARBOPLATIN; CARCINOMA, NON-SMALL-CELL LUNG; CISPLATIN; DEOXYCYTIDINE; HUMANS; LUNG NEOPLASMS; MICE; MOLECULAR DOCKING SIMULATION; MOLECULAR SEQUENCE DATA; P-GLYCOPROTEINS; PACLITAXEL; SEQUENCE ALIGNMENT; STRUCTURAL HOMOLOGY, PROTEIN; TAXOIDS; THERMODYNAMICS;

EID: 84928939485     PISSN: 07533322     EISSN: 19506007     Source Type: Journal    
DOI: 10.1016/j.biopha.2015.02.009     Document Type: Article

References (42)

1. Glavinas H., Krajcsi P., Cserepes J., Sarkadi B. The role of ABC transporters in drug resistance, metabolism and toxicity. Curr Drug Deliv 2004, 1:27-42.
2. Loscher W., Potschka H. Drug resistance in brain diseases and the role of drug efflux transporters. Nat Rev Neurosci 2005, 6:591-602.
3. Dean M., Rzhetsky A., Allikmets R. The human ATP-binding cassette (ABC) transporter superfamily. Genome Res 2001, 11:1156-1166.
4. Sarkadi B., Homolya L., Szakács G., Váradi A. Human multidrug resistance ABCB and ABCG transporters: participation in a chemoimmunity defense system. Physiol Rev 2006, 86:1179-1236.
5. Ambudkar S.V., Kimchi-Sarfaty C., Sauna Z.E., Gottesman M.M. P-glycoprotein: from genomics to mechanism. Oncogene 2003, 22:7468-7485.
6. Fletcher J.I., Haber M., Henderson M.J., Norris M.D. ABC transporters in cancer: more than just drug efflux pumps. Nat Rev Cancer 2010, 10:147-156.
7. Polli J.W., Wring S.A., Humphreys J.E., Huang L., Morgan J.B., Webster L.O., et al. Rational use of in vitro P-glycoprotein assays in drug discovery. Int J Clin Pharmacol Ther 2001, 299:620-628.
8. Van de Waterbeemd H., Gifford E. ADMET in silico modelling: towards prediction paradise?. Nat Rev Drug Discov 2003, 2:192-204.
9. Ekins S., Ecker G.F., Chiba P., Swaan P.W. Future directions for drug transporter modelling. Xenobiotica 2007, 37:1152-1170.
10. Hou T.J., Xu X.J. Recent development and application of virtual screening in drug discovery: an overview. Curr Pharm Des 2004, 10:1011-1033.
11. Chen L., Li Y., Zhao Q., Peng H., Hou T. ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive Bayesian classification techniques. Mol Pharm 2011, 8:889-900.
12. Crivori P., Reinach B., Pezzetta D., Poggesi I. Computational models for identifying potential P-glycoprotein substrates and inhibitors. Mol Pharm 2006, 3:33-44.
13. Ekins S., Kim R.B., Leake B.F., Dantzig A.H., Schuetz E.G., Lan L.B., et al. Application of three-dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol 2002, 61:974-981.
14. Gombar V.K., Polli J.W., Humphreys J.E., Wring S.A., Serabjit-Singh C.S. Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model. J Pharm Sci 2004, 93:957-968.
15. Langer T., Eder M., Hoffmann R.D., Chiba P., Ecker G.F. Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model. Arch Pharm 2004, 337:317-327.
16. Li W.X., Li L., Eksterowicz J., Ling X.B., Cardozo M. Significance analysis and multiple pharmacophore models for differentiating P-glycoprotein substrates. J Chem Inf Model 2007, 47:2429-2438.
17. de Cerqueira Lima P., Golbraikh A., Oloff S., Xiao Y., Tropsha A. Combinatorial QSAR modeling of P-glycoprotein substrates. J Chem Inf Model 2006, 46:1245-1254.
18. Pajeva I.K., Wiese M. Pharmacophore model of drugs involved in P-glycoprotein multidrug resistance: explanation of structural variety (Hypothesis). J Med Chem 2002, 45:5671-5686.
19. Penzotti J.E., Lamb M.L., Evensen E., Grootenhuis P.D. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem 2002, 45:1737-1740.
20. Schmid D., Ecker G., Kopp S., Hitzler M., Chiba P. Structure-activity relationship studies of propafenone analogs based on P-glycoprotein ATPase activity measurements. Biochem Pharmacol 1999, 58:1447-1456.
21. Sun H.M. A naive Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J Med Chem 2005, 48:4031-4039.
22. Wang Y.H., Li Y., Yang S.L., Yang L. An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network. J Comput Aided Mol Des 2005, 19:137-147.
23. Wang Y.H., Li Y., Yang S.L., Yang L. Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach. J Chem Inf Model 2005, 45:750-757.
24. Jianhua W., Xin L., Cheng W., Xie Q., Liu Y., Zhao C. Quantitative structure activity relationship (QSAR) approach to multiple drug resistance (MDR) modulators based on combined hybrid system. QSAR Combinatorial Sci 2009, 28:969-978.
25. Xue Y., Yap C.W., Sun L.Z., Cao Z.W., Wang J.F., Chen Y.Z. Prediction of P-glycoprotein substrates by a support vector machine approach. J Chem Inf Comput Sci 2004, 44:1497-1505.
26. Cabrera M.A., González I., Fernández C., Navarro C., Bermejo M. A topological substructural approach for the prediction of P-glycoprotein substrates. J Pharm Sci 2006, 95:589-606.
27. Cianchetta G., Singleton R.W., Zhang M., Wildgoose M., Giesing D., Fravolini A., et al. A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. J Med Chem 2005, 48:2927-2935.
28. Ekins S., Kim R.B., Leake B.F., Dantzig A.H., Schuetz E.G., Lan L.B., et al. Three-dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein. Mol Pharm 2002, 61:964-973.
29. Huang J., Ma G., Muhammad I., Cheng Y. Identifying P-glycoprotein substrates using a support vector machine optimized by a particle swarm. J Chem Inf Model 2007, 47:1638-1647.
30. Pajeva I.K., Globisch C., Wiese M. Combined pharmacophore modeling, docking, and 3D QSAR studies of ABCB1 and ABCC1 transporter inhibitors. Chemmedchem 2009, 4:1883-1896.
31. Wang Z., Chen Y., Liang H., Bender A., Glen R.C., Yan A. P-glycoprotein substrate models using Support Vector Machines based on a comprehensive data set. J Chem Inf Model 2011, 51:1447-1456.
32. Ha S.N., Hochman J., Sheridan R.P. Mini review on molecular modeling of P-glycoprotein (Pgp). Curr Top Med Chem 2007, 7:1525-1529.
33. Demel M.A., Schwaha R., Krämer O., Ettmayer P., Haaksma E.E., Ecker G.F. In silico prediction of substrate properties for ABC-multidrug transporters. Expert Opin Drug Metab Toxicol 2008, 4:1167-1180.
34. Ecker G.F., Stockner T., Chiba P. Computational models for prediction of interactions with ABC-transporters. Drug Discovery Today 2008, 13:311-317.
35. Demel M.A., Krämer O., Ettmayer P., Haaksma E.E., Ecker G.F. Predicting ligand interactions with ABC transporters in ADME. Chem Biodivers 2009, 6:1960-1969.
36. Seeger M.A., van Veen H.W. Molecular basis of multidrug transport by ABC transporters. Biochim Biophys Acta 2009, 1794:725-737.
37. Wise J.G. Catalytic transitions in the human MDR1 P-glycoprotein drug binding sites. Biochemistry 2012, 51:5125-5141.
38. Palestro P.H., Gavernet L., Estiu G.L., Bruno Blanch L.E. Docking applied to the prediction of the affinity of compounds to P-glycoprotein. Biomed Res Int 2014, 2014:358425. 10.1155/2014/358425.
39. Horn L., Pao W., Johnson D.H. Neoplasms of the lung. Harrison's Principles of Internal Medicine 2011, vol. 1:737-753. McGraw-Hill, New York. 18th ed.
40. Douillard J.Y., Lerouge D., Monnier A., Bennouna J., Haller A.M., Sun X.S., et al. Combined paclitaxel and gemcitabine as first-line treatment in metastatic non-small cell lung cancer: a multicentre phase II study. Br J Cancer 2001, 84:1179-1184.
41. Kosmidis P. Paclitaxel/carboplatin vs paclitaxel/gemcitabine in advanced non-small-cell lung cancer 2015, Retrieved from: http://www.cancernetwork.com/review-article/paclitaxelcarboplatin-vs-paclitaxelgemcitabine-advanced-non%E2%80%93small-cell-lung-cancer.
42. Khodadad K., Khosravi A., Esfahani-Monfared Z., Karimi S., Seifi S. Comparing docetaxel plus cisplatin with paclitaxel plus carboplatin in chemotherapy-naïve patients with advanced non-small-cell lung cancer: a single institute study, Iran. J Pharm Res 2014, 13:575-581.