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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Research Article

Structure-activity Relationship Study on Therapeutically Relevant EGFR Double Mutant Inhibitors

Author(s): Shehnaz Fatima and Subhash M. Agarwal*

Volume 16, Issue 1, 2020

Page: [52 - 62] Pages: 11

DOI: 10.2174/1573406415666190206204853

Price: $65

Abstract

Background: EGFR is a clinically approved drug target in cancer. The first generation tyrosine kinase inhibitors targeting L858R mutated EGFR are routinely used to treat non-small cell lung cancer (NSCLC). However, the presence of a secondary mutation (T790M) tenders these inhibitors ineffective and thus results in the relapse of the disease.

Objective: New reversible inhibitors are required, which act against T790M/L858R (TMLR) double mutants and overcome resistance.

Method: In the present study, various Fragment based QSAR (G-QSAR) models along with interaction terms have been studied for amino-pyrimidine derivatives having biological activity against TMLR mutant enzyme.

Results: The G-QSAR models developed using partial least squares regression via stepwise forward- backward variable selection technique showed the best results. The model showed a high correlation coefficient (r² = 0.86), cross-validation coefficient (q² = 0.81) and predicted correlation (predicted r² = 0.62), which indicated that the model is robust and predictive. Based on the model, it was revealed that at R1 position increasing saturated carbon (number of –CH atom connected with 3 single bonds i.e. SsssCHcount) and retention index (chi3) is desired for the enhancement of bioactivity. Additionally, at the R2 position, increasing lipophilic character (slogp) and at site R3, the polarizability of compound need to be increased for better inhibitory activity. We further studied the contribution of interactions among significant descriptors in enhancing the activity of the compounds. It revealed that the presence of Sum((R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC) are the most significantly influencing descriptors. We further compared the variation in the most and least active compounds which established that retention of the above properties is essential for imparting significant inhibitory activity to these molecules.

Conclusion: The study provides site specific information wherein chemical group variation influences the inhibitory potency of TMLR amino-pyrimidine inhibitors, which can be used for designing new molecules with the desired activity.

Keywords: EGFR, double mutant inhibitor, fragment based QSAR, descriptors, interaction descriptors, reversible inhibitors, amino-pyrimidine.

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[1]
Nagai, H.; Kim, Y.H. Cancer prevention from the perspective of global cancer burden patterns. J. Thorac. Dis., 2017, 9(3), 448-451.
[http://dx.doi.org/10.21037/jtd.2017.02.75] [PMID: 28449441]
[2]
Blass, B.E. Editorial for cancer virtual issue. ACS Med. Chem. Lett., 2017, 8(12), 1205-1207.
[http://dx.doi.org/10.1021/acsmedchemlett.7b00472] [PMID: 29259733]
[3]
Díaz-Serrano, A.; Gella, P.; Jiménez, E.; Zugazagoitia, J.; Paz-Ares Rodríguez, L. Targeting EGFR in lung cancer: current standards and developments. Drugs, 2018, 78(9), 893-911.
[http://dx.doi.org/10.1007/s40265-018-0916-4] [PMID: 29915896]
[4]
Gately, K.; O’Flaherty, J.; Cappuzzo, F.; Pirker, R.; Kerr, K.; O’Byrne, K. The role of the molecular footprint of EGFR in tailoring treatment decisions in NSCLC. J. Clin. Pathol., 2012, 65(1), 1-7.
[http://dx.doi.org/10.1136/jclinpath-2011-200275] [PMID: 22039281]
[5]
Thomas, A.; Liu, S.V.; Subramaniam, D.S.; Giaccone, G. Refining the treatment of NSCLC according to histological and molecular subtypes. Nat. Rev. Clin. Oncol., 2015, 12(9), 511-526.
[http://dx.doi.org/10.1038/nrclinonc.2015.90] [PMID: 25963091]
[6]
Yadav, I.S.; Singh, H.; Khan, M.I.; Chaudhury, A.; Raghava, G.P.S.; Agarwal, S.M. EGFRIndb: epidermal growth factor receptor inhibitor database. Anticancer. Agents Med. Chem., 2014, 14(7), 928-935.
[http://dx.doi.org/10.2174/1871520614666140323203140] [PMID: 24661111]
[7]
Raghav, D.; Sharma, V.; Agarwal, S.M. Structural investigation of deleterious non-synonymous SNPs of EGFR gene. Interdiscip. Sci., 2013, 5(1), 60-68.
[http://dx.doi.org/10.1007/s12539-013-0149-x] [PMID: 23605641]
[8]
Zhou, W.; Ercan, D.; Chen, L.; Yun, C.H.; Li, D.; Capelletti, M.; Cortot, A.B.; Chirieac, L.; Iacob, R.E.; Padera, R.; Engen, J.R.; Wong, K.K.; Eck, M.J.; Gray, N.S.; Jänne, P.A. Novel mutant-selective EGFR kinase inhibitors against EGFR T790M. Nature, 2009, 462(7276), 1070-1074.
[http://dx.doi.org/10.1038/nature08622] [PMID: 20033049]
[9]
Yun, C.H.; Mengwasser, K.E.; Toms, A.V.; Woo, M.S.; Greulich, H.; Wong, K.K.; Meyerson, M.; Eck, M.J. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc. Natl. Acad. Sci. USA, 2008, 105(6), 2070-2075.
[http://dx.doi.org/10.1073/pnas.0709662105] [PMID: 18227510]
[10]
O’Kane, G.M.; Barnes, T.A.; Leighl, N.B. Resistance to epidermal growth factor receptor tyrosine kinase inhibitors, T790M, and clinical trials. Curr. Oncol.,, 2018, 25(1)(Suppl. 1 ), S28-S37.
[http://dx.doi.org/10.3747/co.25.3796] [PMID: 29910645]
[11]
Tan, C.S.; Cho, B.C.; Soo, R.A. Next-generation epidermal growth factor receptor tyrosine kinase inhibitors in epidermal growth factor receptor -mutant non-small cell lung cancer. Lung Cancer, 2016, 93, 59-68.
[http://dx.doi.org/10.1016/j.lungcan.2016.01.003] [PMID: 26898616]
[12]
Engel, J.; Lategahn, J.; Rauh, D. Hope and disappointment: Covalent inhibitors to overcome drug resistance in non-small cell lung cancer. ACS Med. Chem. Lett., 2015, 7(1), 2-5.
[http://dx.doi.org/10.1021/acsmedchemlett.5b00475] [PMID: 26819655]
[13]
Sullivan, I.; Planchard, D. Next-generation egfr tyrosine kinase inhibitors for treating EGFR-mutant lung cancer beyond first line. Front. Med. (Lausanne), 2017, 3, 76.
[http://dx.doi.org/10.3389/fmed.2016.00076] [PMID: 28149837]
[14]
Agarwal, S.M.; Pal, D.; Gupta, M.; Saini, R. Insight into discovery of next generation reversible tmlr inhibitors targeting EGFR activating and drug resistant T790M mutants. Curr. Cancer Drug Targets, 2017, 17(7), 617-636.
[http://dx.doi.org/10.2174/1568009617666170330112842] [PMID: 28359250]
[15]
Lee, H.J.; Schaefer, G.; Heffron, T.P.; Shao, L.; Ye, X.; Sideris, S.; Malek, S.; Chan, E.; Merchant, M.; La, H.; Ubhayakar, S.; Yauch, R.L.; Pirazzoli, V.; Politi, K.; Settleman, J. Noncovalent wild-type-sparing inhibitors of EGFR T790M. Cancer Discov., 2013, 3(2), 168-181.
[http://dx.doi.org/10.1158/2159-8290.CD-12-0357] [PMID: 23229345]
[16]
Yadav, I.S.; Nandekar, P.P.; Srivastavaa, S.; Sangamwar, A.; Chaudhury, A.; Agarwal, S.M. Ensemble docking and molecular dynamics identify knoevenagel curcumin derivatives with potent anti-EGFR activity. Gene, 2014, 539(1), 82-90.
[http://dx.doi.org/10.1016/j.gene.2014.01.056] [PMID: 24491504]
[17]
Sharma, V.K.; Nandekar, P.P.; Sangamwar, A.; Pérez-Sánchez, H.; Agarwal, S.M. Structure guided design and binding analysis of EGFR inhibiting analogues of erlotinib and AEE788 using ensemble docking, molecular dynamics and MM-GBSA. RSC Advances, 2016, 6, 65725-65735.
[http://dx.doi.org/10.1039/C6RA08517B]
[18]
Chan, B.K.; Hanan, E.J.; Bowman, K.K.; Bryan, M.C.; Burdick, D.; Chan, E.; Chen, Y.; Clausen, S.; Dela Vega, T.; Dotson, J.; Eigenbrot, C.; Elliott, R.L.; Heald, R.A.; Jackson, P.S.; Knight, J.D.; La, H.; Lainchbury, M.D.; Malek, S.; Purkey, H.E.; Schaefer, G.; Schmidt, S.; Seward, E.M.; Sideris, S.; Shao, L.; Wang, S.; Yeap, S.K.; Yen, I.; Yu, C.; Heffron, T.P. Discovery of a noncovalent, mutant-selective epidermal growth factor receptor inhibitor. J. Med. Chem., 2016, 59(19), 9080-9093.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00995] [PMID: 27564586]
[19]
Chauhan, J.S.; Dhanda, S.K.; Singla, D.; Agarwal, S.M.; Raghava, G.P.S. Open source drug discovery consortium. QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyri-midines based inhibitors against wild and mutant EGFR. PLoS One, 2014, 9(7)e101079
[http://dx.doi.org/10.1371/journal.pone.0101079] [PMID: 24992720]
[20]
Singh, H.; Singh, S.; Singla, D.; Agarwal, S.M.; Raghava, G.P.S. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol. Direct, 2015, 10, 10.
[http://dx.doi.org/10.1186/s13062-015-0046-9] [PMID: 25880749]
[21]
Zhao, M.; Wang, L.; Zheng, L.; Zhang, M.; Qiu, C.; Zhang, Y.; Du, D.; Niu, B. 2D-QSAR and 3D-QSAR analyses for EGFR inhibitors. BioMed Res. Int., 2017, 20174649191
[http://dx.doi.org/10.1155/2017/4649191] [PMID: 28630865]
[22]
Pourbasheer, E.; Aalizadeh, R.; Shiri, H.M.; Banaei, A.; Ganjali, M.R. 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA. Curr. Comput. Aided. Drug Des, 2015, 11(4), 292-303.
[http://dx.doi.org/10.2174/1573409912666151106120058] [PMID: 26548551]
[23]
Dhiman, K.; Agarwal, S.M. NPred: QSAR Classification model for identifying plant based naturally occurring anti-cancerous inhibitors. RSC Advances, 2016, 6, 49395-49400.
[http://dx.doi.org/10.1039/C6RA02772E]
[24]
Ajmani, S.; Jadhav, K.; Kulkarni, S.A. Group-Based QSAR (G-QSAR): Mitigating interpretation challenges in QSAR. QSAR Comb. Sci., 2009, 28(1), 36-51.
[http://dx.doi.org/10.1002/qsar.200810063]
[25]
Ajmani, S.; Agrawal, A.; Kulkarni, S.A. A comprehensive structure-activity analysis of protein kinase B-alpha (Akt1) inhibitors. J. Mol. Graph. Model., 2010, 28(7), 683-694.
[http://dx.doi.org/10.1016/j.jmgm.2010.01.007] [PMID: 20153226]
[26]
Fatima, S.; Gupta, P.; Agarwal, S.M. Insight into structural requirements of antiamoebic flavonoids: 3D-QSAR and G-QSAR studies. Chem. Biol. Drug Des., 2018, 92(4), 1743-1749.
[http://dx.doi.org/10.1111/cbdd.13343] [PMID: 29808545]
[27]
Hanan, E.J.; Eigenbrot, C.; Bryan, M.C.; Burdick, D.J.; Chan, B.K.; Chen, Y.; Dotson, J.; Heald, R.A.; Jackson, P.S.; La, H. Discovery of selective and non-covalent diaminopyrimidine based inhibitors of EGFR containing the T790M resistance mutation. J. Med. Chem., 2014, 57, 10176-10191.
[http://dx.doi.org/10.1021/jm501578n] [PMID: 25383627]
[28]
Heald, R.; Bowman, K.K.; Bryan, M.C.; Burdick, D.; Chan, B.; Chan, E.; Chen, Y.; Clausen, S.; Dominguez-Fernandez, B.; Eigenbrot, C.; Elliott, R.; Hanan, E.J.; Jackson, P.; Knight, J.; La, H.; Lainchbury, M.; Malek, S.; Mann, S.; Merchant, M.; Mortara, K.; Purkey, H.; Schaefer, G.; Schmidt, S.; Seward, E.; Sideris, S.; Shao, L.; Wang, S.; Yeap, K.; Yen, I.; Yu, C.; Heffron, T.P. Noncovalent mutant selective epidermal growth factor receptor inhibitors: A lead optimization case study. J. Med. Chem., 2015, 58(22), 8877-8895.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01412] [PMID: 26455919]
[29]
VLifeMDS: Molecular Design Suite, Version 4.6. . 2017.www.vlife-sciences.com
[30]
Baumann, K. An alignment-independent versatile structure descriptor for QSAR and QSPR based on the distribution of molecular features. J. Chem. Inf. Comput. Sci., 2002, 42(1), 26-35.
[http://dx.doi.org/10.1021/ci990070t] [PMID: 11855963]
[31]
Golbraikh, A.; Tropsha, A. QSAR modeling using chirality descriptors derived from molecular topology. J. Chem. Inf. Comput. Sci., 2003, 43(1), 144-154.
[http://dx.doi.org/10.1021/ci025516b] [PMID: 12546547]
[32]
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]
[33]
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]
[34]
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[http://dx.doi.org/10.1002/minf.201000061] [PMID: 27463326]
[35]
Manral, L.; Kumar Gupta, P.; Ganesan, K.; Chandra Malhotra, R. Gas chromatographic retention indices of fentanyl and analogues. J. Chromatogr. Sci., 2008, 46(6), 551-555.
[http://dx.doi.org/10.1093/chromsci/46.6.551] [PMID: 18647479]

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