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Combinatorial Chemistry & High Throughput Screening

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ISSN (Online): 1875-5402

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Research Article

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Author(s): Neda Ahmadinejad and Fatemeh Shafiei*

Volume 22, Issue 6, 2019

Page: [387 - 399] Pages: 13

DOI: 10.2174/1386207322666190708112251

Price: $65

Abstract

Aim and Objective: A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods: A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results: QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion: The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

Keywords: Camptothecin (CPT) derivatives, QSAR, polarizability, GA-MLR, molecular descriptors, Leave-One-Out Cross- Validation, molar refractivity, van der Waals surface, van der Waals volume.

« Previous Next »
[1]
Wall, M.E.; Wani, M.C.; Cook, C.E.; Palmer, K.H.; McPhail, A.T.; Sim, G.A. Plant antitumor agents. I. the isolation and structure of camptothecin, a novel alkaloidal leukemia and tumor inhibitor from Camptotheca acuminate. J. Am. Chem. Soc., 1966, 88(16), 3888-3890.
[http://dx.doi.org/10.1021/ja00968a057]
[2]
Kim, D.K.; Lee, N. Recent advances in topoisomerase I-targeting agents, camptothecin analogues. Mini Rev. Med. Chem., 2002, 2(6), 611-619.
[http://dx.doi.org/10.2174/1389557023405530] [PMID: 12370044]
[3]
Thomas, C.J.; Rahier, N.J.; Hecht, S.M. Camptothecin: Current perspectives. Bioorg. Med. Chem., 2004, 12(7), 1585-1604.
[http://dx.doi.org/10.1016/j.bmc.2003.11.036] [PMID: 15028252]
[4]
Pommier, Y. Topoisomerase I inhibitors: camptothecins and beyond. Nat. Rev. Cancer, 2006, 6(10), 789-802.
[http://dx.doi.org/10.1038/nrc1977] [PMID: 16990856]
[5]
Hautefaye, P.; Cimetière, B.; Pierré, A.; Léonce, S.; Hickman, J.; Laine, W.; Bailly, C.; Lavielle, G. Synthesis and pharmacological evaluation of novel non-lactone analogues of camptothecin. Bioorg. Med. Chem. Lett., 2003, 13(16), 2731-2735.
[http://dx.doi.org/10.1016/S0960-894X(03)00534-1] [PMID: 12873503]
[6]
Hsiang, Y.H.; Hertzberg, R.; Hecht, S.; Liu, L.F. Camptothecin induces protein-linked DNA breaks via mammalian DNA topoisomerase I. J. Biol. Chem., 1985, 260(27), 14873-14878.
[PMID: 2997227]
[7]
Zhang, L.; Ma, D.; Zhang, Y.; He, W.; Yang, J.; Li, C.; Jiang, H. Characterization of DNA topoisomerase-1 in Spodoptera exigua for toxicity evaluation of camptothecin and hydoxy-camptothecin. PLoS One, 2013, 8(2), e56458-e56458.
[http://dx.doi.org/10.1371/journal.pone.0056458] [PMID: 23451051]
[8]
Hansch, C.; Verma, R.P. 20-(S)-camptothecin analogues as DNA topoisomerase I inhibitors: A QSAR study. ChemMedChem, 2007, 2(12), 1807-1813.
[http://dx.doi.org/10.1002/cmdc.200700138] [PMID: 17886246]
[9]
Hsiang, Y.H.; Liu, L.F.; Wall, M.E.; Wani, M.C.; Nicholas, A.W.; Manikumar, G.; Kirschenbaum, S.; Silber, R.; Potmesil, M. DNA topoisomerase I-mediated DNA cleavage and cytotoxicity of camptothecin analogues. Cancer Res., 1989, 49(16), 4385-4389.
[PMID: 2545341]
[10]
Hansch, C.; Muir, R.M.; Fujita, T.; Maloney, P.P.; Geiger, F.; Streich, M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. J. Am. Chem. Soc., 1963, 8(5), 2817-2824.
[http://dx.doi.org/10.1021/ja00901a033]
[11]
Hansch, C.; Leo, A.; Hoekman, D. Exploring QSAR, v.1 Fundamentals and applications in chemistry and biology; v.2 Hydrophobic, electronic, and steric constants. American Chemical Society, Washington, DC, , 1995.
[12]
Supuran, C.T.; Scozzafava, A.; Briganti, F.; Clare, B.W. Protease inhibitors: Synthesis and QSAR study of novel classes of nonbasic thrombin inhibitors incorporating sulfonylguanidine and O-methylsulfonylisourea moieties at P1. J. Med. Chem., 2000, 43(9), 1793-1806.
[http://dx.doi.org/10.1021/jm9903693] [PMID: 10794696]
[13]
Pourbasheer, E.; Aalizadeh, R.; Ganjali, M.R.; Norouzi, P. QSAR study of IKKβ inhibitors by the genetic algorithm: Multiple linear regressions. Med. Chem. Res., 2014, 23(1), 57-66.
[http://dx.doi.org/10.1007/s00044-013-0611-7]
[14]
Singh, S.; Das, S.; Pandey, A.; Paliwal, S.; Singh, R. Quantitative structure activity relationship studies of topoisomerase i inhibitors as potent antibreast cancer agents. J. Chem., 2013, 2013, 1-9.
[http://dx.doi.org/10.1155/2013/849793]
[15]
Ikediobi, O.N.; Davies, H.; Bignell, G.; Edkins, S.; Stevens, C.; O’Meara, S.; Santarius, T.; Avis, T.; Barthorpe, S.; Brackenbury, L.; Buck, G.; Butler, A.; Clements, J.; Cole, J.; Dicks, E.; Forbes, S.; Gray, K.; Halliday, K.; Harrison, R.; Hills, K.; Hinton, J.; Hunter, C.; Jenkinson, A.; Jones, D.; Kosmidou, V.; Lugg, R.; Menzies, A.; Mironenko, T.; Parker, A.; Perry, J.; Raine, K.; Richardson, D.; Shepherd, R.; Small, A.; Smith, R.; Solomon, H.; Stephens, P.; Teague, J.; Tofts, C.; Varian, J.; Webb, T.; West, S.; Widaa, S.; Yates, A.; Reinhold, W.; Weinstein, J.N.; Stratton, M.R.; Futreal, P.A.; Wooster, R. Mutation analysis of 24 known cancer genes in the NCI-60 cell line set. Mol. Cancer Ther., 2006, 5(11), 2606-2612.
[http://dx.doi.org/10.1158/1535-7163.MCT-06-0433] [PMID: 17088437]
[16]
Sampath, P.; Amundson, E.; Wall, M.E.; Tyler, B.M.; Wani, M.C.; Alderson, L.M.; Colvin, M.; Brem, H.; Weingart, J.D. Camptothecin analogs in malignant gliomas: Comparative analysis and characterization. J. Neurosurg., 2003, 98(3), 570-577.
[http://dx.doi.org/10.3171/jns.2003.98.3.0570] [PMID: 12650430]
[17]
Janát-Amsbury, M.M.; Yockman, J.W.; Lee, M.; Kern, S.; Furgeson, D.Y.; Bikram, M.; Kim, S.W. Combination of local, nonviral IL12 gene therapy and systemic paclitaxel treatment in a metastatic breast cancer model. Mol. Ther., 2004, 9(6), 829-836.
[http://dx.doi.org/10.1016/j.ymthe.2004.03.015] [PMID: 15194049]
[18]
Arthur, D.E.; Uzairu, A.; Mamza, P.; Abechi, S. Quantitative structure–activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines. J. Adv. Res., 2016, 7(5), 823-837.
[http://dx.doi.org/10.1016/j.jare.2016.03.010]
[19]
Venditto, V.J.; Simanek, E.E. Cancer therapies utilizing the camptothecins: A review of the in vivo literature. Mol. Pharm., 2010, 7(2), 307-349.
[http://dx.doi.org/10.1021/mp900243b] [PMID: 20108971]
[20]
Fereidoonnezhad, M.; Faghih, Z.; Mojaddami, A.; Rezaei, Z.; Sakhteman, A. A comparative QSAR analysis, molecular docking and PLIF studies of some N-arylphenyl-2, 2-dichloroacetamide analogues as anticancer agents. Iran. J. Pharm. Res., 2017, 16(3), 981-998.
[PMID: 29535790]
[21]
Li, F.; Jiang, T.; Li, Q.; Ling, X. Camptothecin (CPT) and its derivatives are known to target topoisomerase I (Top1) as their mechanism of action: did we miss something in CPT analogue molecular targets for treating human disease such as cancer? Am. J. Cancer Res., 2017, 7(12), 2350-2394.
[PMID: 29312794]
[22]
Lundberg, B.B. Biologically active camptothecin derivatives for incorporation into liposome bilayers and lipid emulsions. Anticancer Drug Des., 1998, 13(5), 453-461.
[PMID: 9702210]
[23]
Sultana, N. Clinically useful anticancer, antitumor, and antiwrinkle agent, ursolic acid and related derivatives as medicinally important natural product. J. Enzyme Inhib. Med. Chem., 2011, 26(5), 616-642.
[http://dx.doi.org/10.3109/14756366.2010.546793] [PMID: 21417964]
[24]
Arthur, D.E.; Uzairu, A.; Mamza, P.; Abechi, E.; Shallangwa, G. QSAR modelling of some anticancer PGI50 activity on HL-60 cell lines. A. J. Ph. Sci., 2016, 3(1), 4-9.
[25]
Fan, Y.; Shi, L.M.; Kohn, K.W.; Pommier, Y.; Weinstein, J.N. Quantitative structure-antitumor activity relationships of camptothecin analogues: Cluster analysis and genetic algorithm-based studies. J. Med. Chem., 2001, 44(20), 3254-3263.
[http://dx.doi.org/10.1021/jm0005151] [PMID: 11563924]
[26]
Lu, A.J.; Zhang, Z.S.; Zheng, M.Y.; Zou, H.J.; Luo, X.M.; Jiang, H.L. 3D-QSAR study of 20 (S)-camptothecin analogs. Acta Pharmacol. Sin., 2007, 28(2), 307-314.
[http://dx.doi.org/10.1111/j.1745-7254.2007.00477.x] [PMID: 17241535]
[27]
Hussain, I.; Bania, K.K.; Gour, N.K.; Deka, R.C. Application of physicochemical and DFT based descriptors for QSAR study of camptothecin derivatives. ChemistrySelect, 2016, 1(15), 4973-4978.
[http://dx.doi.org/10.1002/slct.201600609]
[28]
Padrón, J.A.; Carrasco, R.; Pellón, R.F. Molecular descriptor based on a molar refractivity partition using Randic-type graph-theoretical invariant. J. Pharm. Pharm. Sci., 2002, 5(3), 258-266.
[PMID: 12553894]
[29]
Hansch, C.; Kurup, A. QSAR of chemical polarizability and nerve toxicity. 2. J. Chem. Inf. Comput. Sci., 2003, 43(5), 1647-1651.
[http://dx.doi.org/10.1021/ci030289e] [PMID: 14502499]
[30]
Gabr, A.; Kuin, A.; Aalders, M.; El-Gawly, H.; Smets, L.A. Cellular pharmacokinetics and cytotoxicity of camptothecin and topotecan at normal and acidic pH. Cancer Res., 1997, 57(21), 4811-4816.
[PMID: 9354443]
[31]
Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Pople, J.A. Gaussian, Inc., Wallingford CT , 2009.
[32]
Roothaan, C.C.J. New developments in molecular orbital theory. Rev. Mod. Phys., 1951, 23(2), 69-89.
[http://dx.doi.org/10.1103/RevModPhys.23.69]
[33]
Binkley, J.S.; Pople, J.A.; Hehre, W.J. Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements. J. Am. Chem. Soc., 1980, 102(3), 939-947.
[http://dx.doi.org/10.1021/ja00523a008]
[34]
Dohoo, I.R.; Ducrot, C.; Fourichon, C.; Donald, A.; Hurnik, D. An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies. Prev. Vet. Med., 1997, 29(3), 221-239.
[http://dx.doi.org/10.1016/S0167-5877(96)01074-4] [PMID: 9234406]
[35]
González, M.P.; Terán, C.; Saíz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem., 2008, 8(18), 1606-1627.
[http://dx.doi.org/10.2174/156802608786786552] [PMID: 19075770]
[36]
Lučić, B.; Trinajstić, N. New developments in QSPR/QSAR modeling based on topological indices. SAR QSAR Environ. Res., 1997, 7, 45-62.
[http://dx.doi.org/10.1080/10629369708039124]
[37]
Kapur, G.S.; Ecker, A.; Meusinger, R. Establishing quantitative structure-property relationships (QSPR) of diesel samples by proton-NMR & multiple linear regression(MLR) analysis, ‎. Energy Fuels, 2001, 15, 943-948.
[http://dx.doi.org/10.1021/ef010021u]
[38]
Yin, C.; Liu, X.; Guo, W.; Lin, T.; Wang, X.; Wang, L. Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors. Water Res., 2002, 36(12), 2975-2982.
[http://dx.doi.org/10.1016/S0043-1354(01)00532-2] [PMID: 12171394]
[39]
Gramatica, P.; Pilutti, P.; Papa, E. Ranking of volatile organic compounds for tropospheric degradability by oxidants: A QSPR approach. SAR QSAR Environ. Res., 2002, 13(7-8), 743-753.
[http://dx.doi.org/10.1080/1062936021000043472] [PMID: 12570050]
[40]
Diudea, M.V. QSPR/QSAR studies for molecular descriptors Ed Nova Science Hunting don, New York. , 2000.
[41]
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[http://dx.doi.org/10.1016/S1093-3263(01)00123-1] [PMID: 11858635]
[42]
Lavine, B.K.; Davidson, C.E.; Breneman, C.; Katt, W.; Sundling, C.M. Electronic van der Waals surface property descriptors and genetic algorithms for developing structure-activity correlations in olfactory databases. J. Chem. Inf. Comput. Sci., 2003, 43(6), 1890-1905.
[http://dx.doi.org/10.1021/ci030016j] [PMID: 14632438]
[43]
Moorthy, N.S.H.N.; Ramos, M.J.; Fernandes, P.A. Analysis of van der Waals surface area properties for human ether-a-go-go-related gene blocking activity: computational study on structurally diverse compounds. SAR QSAR Environ. Res., 2012, 23(5-6), 521-536.
[http://dx.doi.org/10.1080/1062936X.2012.666264] [PMID: 22452318]
[44]
Ciubotariu, D.; Medeleanu, M.; Vlaia, V.; Olariu, T.; Ciubotariu, C.; Dragos, D.; Corina, S. Molecular van der Waals space and topological indices from the distance matrix. Molecules, 2004, 9(12), 1053-1078.
[http://dx.doi.org/10.3390/91201053] [PMID: 18007504]
[45]
Bondi, A. van der Waals volumes and radii. J. Phys. Chem., 1964, 68(3), 441-451.
[http://dx.doi.org/10.1021/j100785a001]
[46]
Batsanov, S.S. Van der Waals radii of elements. Inorg. Chem., 2001, 37(9), 871-885.
[47]
Almi, Z. Structure activity relationships, QSAR modeling and drug-like calculations of TP inhibition of 1,3,4- oxadiazoline-2-thione derivatives. Int. Lett. Chem. Phys. Astron, 2014, 37, 113-124.
[48]
Srivastava, A.K.; Shukla, N.; Pandey, A.; Srivastava, A. QSAR based modeling on a series of α-hydroxy amides as a novel class of bradykinin B1 selective antagonists. J. Saudi Chem. Soc., 2011, 15(3), 215-220.
[http://dx.doi.org/10.1016/j.jscs.2010.09.001]
[49]
Verma, R.P.; Kurup, A.; Hansch, C. On the role of polarizability in QSAR. Bioorg. Med. Chem., 2005, 13(1), 237-255.
[http://dx.doi.org/10.1016/j.bmc.2004.09.039] [PMID: 15582468]
[50]
https://chem.nlm.nih.gov/chemidplus/ (Accessed on Sep 2019).`.
[51]
Kawczak, P.; Belka, M.; Slawinski, J.; Baczek, T. QSRR evaluation of the new anticancer sulfonamides in view of the cis-trans isomerism. Curr. Pharm. Anal., 2018, 14(1), 35-40.
[52]
Rouhollahi, A.; Ghasemi, J.B.; Babaee, E.; Ouammou, A. Quantitative structure activity relationship modeling of environmentally important diphenyl ether herbicides using MLR and PLS. Curr. Anal. Chem., 2010, 6(1), 3-10.
[http://dx.doi.org/10.2174/157341110790069583]
[53]
Weisberg, S. Applied Linear Regression, 3rd ed; John & Sonc, Inc.: Hoboken, 2005.
[http://dx.doi.org/10.1002/0471704091]
[54]
Chatterje, S.; Hadi, A.S. Regression Analysis by Example; 4th ed.,John Wiley & Sonc, Inc.: Hoboken,. , 2006.
[http://dx.doi.org/10.1002/0470055464]
[55]
Depiereux, E.; Vincke, G.; Dehertogh, B. Biostatistics, 2005.
[56]
Gramatica, P. On the Development and Validation of QSAR Models. In: Computational Toxicology; Reisfeld, B., Mayeno, A.N., Ed., Springer: Science+Business Media, LLC,. , 2013; Vol. II, pp. 499-529.
[57]
Craney, T.A.; Surles, J.G. Model-dependent variance inflation factor cutoff values. Qual. Eng., 2002, 14, 391-403.
[http://dx.doi.org/10.1081/QEN-120001878]
[58]
Pourbasheer, E.; Ahmadpour, S.; Zare-Dorabei, R.; Nekoei, M.M. Quantitative structure activity relationship study of p38a MAP kinase inhibitors. Arab. J. Chem., 2017, 10(1), 33-34.
[http://dx.doi.org/10.1016/j.arabjc.2013.05.009]
[59]
Chatterjee, S.; Simonoff, J.S. Handbook of regression analysis; John Wiley & Sons, 2013, Vol. 5, .
[60]
Hateka, N.R. Tests for Detecting Autocorrelation. Principles of Econometrics: An Introduction (Using R); SAGE Publications, 2010, pp. 379-382.
[http://dx.doi.org/10.4135/9781446270110]
[61]
Benigni, R.; Bossa, C. Predictivity of QSAR. J. Chem. Inf. Model., 2008, 48(5), 971-980.
[http://dx.doi.org/10.1021/ci8000088] [PMID: 18426198]
[62]
Stone, M. Cross-validatory choice and assessment of statistical predictions. J. Stat. Soc. B., 1974, 36, 111-147.
[http://dx.doi.org/10.1111/j.2517-6161.1974.tb00994.x]
[63]
Cramer, R.D., III; Bunce, J.D.; Patterson, D.E. Cross validation, Bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant. Struct. Act. Relat., 1988, 7, 18-25.
[http://dx.doi.org/10.1002/qsar.19880070105]
[64]
Kleinbaum, D.G. Applied regression analysis and other multivariable methods; Brooks/Cole: Belmont, CA, Australia, 2008.
[65]
Tarpey, T. A note on the prediction sum of squares statistic for restricted least squares. Am. Stat., 2000, 54(2), 116-118.
[66]
Broto, P.; Moreau, G.; Vandicke, C. Molecular Structures: Perception, Autocorrelation Descriptor and SAR Studies. Autocorrelation Descriptor in the QSAR study of two non-narcotic analgesic series. Eur. J. Med. Chem., 1984, 19, 79-84.
[67]
Broto, P.; Devillers, J. Autocorrelation of Properties Distributed on Molecular Graphs. In: Practical Applications Of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology; Karcher, W.; Devillers, J., Eds.; Kluwer Academic Publishers: Dordrecht, 1990; pp. 105-127.
[68]
Ponzoni, I.; Sebastián-Pérez, V.; Requena-Triguero, C.; Roca, C.; Martínez, M.J.; Cravero, F.; Díaz, M.F.; Páez, J.A.; Arrayás, R.G.; Adrio, J.; Campillo, N.E. Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery. Sci. Rep., 2017, 7(1), 2403-2412.
[http://dx.doi.org/10.1038/s41598-017-02114-3] [PMID: 28546583]
[69]
Hu, B.; Kuang, K.Z.; Feng, S.Y.; Wang, D.; He, S.B.; Xin, Kong De. Supplementary Materials: Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape similarity profile based on PDB ligands as molecular descriptor. Molecules, 2016, 21(11), 1554-1565.
[http://dx.doi.org/10.3390/molecules21111554] [PMID: 27869685]

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