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Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation

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Abstract

Pharmacophore modelling studies have been performed for a series of 2,4-disubstituted-pyrimidines derivatives as EGFR L858R/T790M tyrosine kinase inhibitors. The high scoring AARR.15 hypothesis was selected as the best pharmacophore model with the highest survival score of 3.436 having two hydrogen bond acceptors and two aromatic ring features. Pharmacophore-based virtual screening followed by structure-based yielded the six molecules (ZINC17013227, ZINC17013215, ZINC9573324, ZINC9573445, ZINC24023331 and ZINC17013503) from the ZINC database with significant in silico predicted activity and strong binding affinity towords the EGFR L858R/T790M tyrosine kinase. In silico toxicity and cytochrome profiling indicates that all the 06 virtually screened compounds were substrate/inhibitors of the CYP-3A4 metabolizing enzyme and were non-carcinogenic and devoid of Ames mutagenesis. Density functional theory (DFT) and molecular dynamic (MD) simulation further validated the obtained hits.

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References

  • Ahmad I, Shaikh M, Surana S, Ghosh A, Patel H (2020) p38α MAP kinase inhibitors to overcome EGFR tertiary C797S point mutation associated with osimertinib in non-small cell lung cancer (NSCLC): emergence of fourth-generation EGFR inhibitor. J Biomol Struct Dyn 1–14

  • Ahmad I, Kumar D, Patel H (2021a) Computational investigation of phytochemicals from Withania somnifera (Indian ginseng/ashwagandha) as plausible inhibitors of GluN2B-containing NMDA receptors. J Biomol Struct Dyn 10:1–13

    Google Scholar 

  • Ahmad I, Jadhav H, Shinde Y, Jagtap V, Girase R, Patel H (2021b) Optimizing Bedaquiline for cardiotoxicity by structure based virtual screening, DFT analysis and molecular dynamic simulation studies to identify selective MDR-TB inhibitors. In Silico Pharmacol 23(9):23

    Article  Google Scholar 

  • Amala M, Rajamanikandan S, Prabhu D, Surekha K, Jeyakanthan J (2019) Identification of anti-filarial leads against aspartate semialdehyde dehydrogenase of Wolbachia endosymbiont of Brugia malayi: combined molecular docking and molecular dynamics approaches. J Biomol Struct Dyn 37(2):394–410

    Article  CAS  PubMed  Google Scholar 

  • Benet LZ, Hosey CM, Ursu O, Oprea TI (2016) BDDCS, the rule of 5 and drugability. Adv Drug Deliv Rev 101:89–98

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bhadoriya KS, Sharma MC, Jain SV (2015) Pharmacophore modeling and atom-based 3D-QSAR studies on amino derivatives of indole as potent isoprenylcysteine carboxyl methyltransferase (Icmt) inhibitors. J Mol 1081:466–476

    CAS  Google Scholar 

  • Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113(18):2110–2142

    Article  CAS  Google Scholar 

  • Bonomi P (2003) Erlotinib: a new therapeutic approach for non-small cell lung cancer. Expert Opin Inv Drug 12:1395–1401

    Article  CAS  Google Scholar 

  • Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: SC'06: Proceedings of the (2006), ACM/IEEE Conference on Supercomputing, pp 43–43

  • Chabon JJ, Simmons AD, Lovejoy AF, Esfahani MS, Newman AM, Haringsma HJ, Kurtz DM, Stehr H, Scherer F, Karlovich CA, Harding TC, Durkin KA, Otterson GA, Purcell WT, Camidge DR, Goldman JW, Sequist LV, Piotrowska Z, Wakelee HA, Neal JW, Alizadeh AA, Diehn M (2016) Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. Nat Commun 7:11815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chan S, Han K, Qu R, Tong L, Li Y, Zhang Z, Cheng H, Lu X, Patterson A, Smaill J, Ren X (2015) 2, 4-Diarylamino-pyrimidines as kinase inhibitors co-targeting IGF1R and EGFRL858R/T790M. Bioorg Med Chem Lett 25:4277–4281

    Article  CAS  PubMed  Google Scholar 

  • Chaudhari P, Bari S (2016) in silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening. Mol Divers 20:41–53

    Article  CAS  PubMed  Google Scholar 

  • Chekkara R, Kandakatla N, Gorla VR, Tenkayala SR, Susithra E (2017) Theoretical studies on benzimidazole and imidazo [1, 2-a] pyridine derivatives as Polo-like kinase 1 (Plk1) inhibitors: pharmacophore modeling, atom-based 3D-QSAR and molecular docking approach. J Saudi Chem Soc 21:S311–S321

    Article  CAS  Google Scholar 

  • Chen F, Liu H, Sun H, Pan P, Li Y, Li D, Hou T (2016) Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys Chem Chem Phys 18:22129–22139

    Article  CAS  PubMed  Google Scholar 

  • Chen L, Fu W, Zheng L, Liu Z, Liang G (2017) Recent progress of small-molecule epidermal growth factor receptor (EGFR) inhibitors against C797S resistance in non-small-cell lung cancer: miniperspective. J Med Chem 61:4290–4300

    Article  PubMed  CAS  Google Scholar 

  • Chinnasamy S, Selvaraj G, Kaushik AC, Kaliamurthi S, Chandrabose S, Singh SK, Thirugnanasambandam R, Gu K, Wei DQ (2019) Molecular docking and molecular dynamics simulation studies to identify potent AURKA inhibitors: Assessing the performance of density functional theory, MM-GBSA and mass action kinetics calculations. J Biomol Struct Dyn 38:1–11

    Google Scholar 

  • Cho K, Joannopoulos JD, Kleinman L (1993) Constant-temperature molecular dynamics with momentum conservation. Phys Rev E 47(5):3145

    Article  CAS  Google Scholar 

  • Choubey SK, Jeyaraman J (2016) A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D-QSAR analysis, molecular docking, density functional and molecular dynamics simulation study. J Mol Graph Model 70:54–69

    Article  CAS  PubMed  Google Scholar 

  • Chow E, Rendleman CA, Bowers KJ, Dror RO, Hughes DH, Gullingsrud J, Sacerdoti FD (2008) Desmond performance on a cluster of multicore processors. DE Shaw Research Technical Report DESRES. TR 01. https://deshawresearch.com

  • Cohen MH, Williams GA, Sridhara R, Chen G, Pazdur R (2003) FDA drug approval summary: gefitinib (ZD1839)(Iressa) tablets. Clin Oncol 8:303–306

    CAS  Google Scholar 

  • Deniz U, Ozkirimli E, Ulgen KO (2016) A systematic methodology for large scale compound screening: a case study on the discovery of novel S1PL inhibitors. J Mol Graph Mode 63:110–124

    Article  CAS  Google Scholar 

  • Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006a) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol 20:647–671

    Article  CAS  Google Scholar 

  • Dixon SL, Smondyrev AM, Rao SN (2006b) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67:370–372

    Article  CAS  PubMed  Google Scholar 

  • Doak BC, Kihlberg J (2017) Drug discovery beyond the rule of 5-Opportunities and challenges. Expert Opin Drug Discov 12:115–119

    Article  PubMed  Google Scholar 

  • Dong X, Zheng W (2008) A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. Curr Chem Genom 2:29

    Article  CAS  Google Scholar 

  • Elokely KM, Doerksen RJ (2013) Docking challenge: protein sampling and molecular docking performance. J Chem Inf Model 53:1934–1945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Engelman JA, Jänne PA (2008) Mechanisms of acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors in non–small cell lung cancer. Clin Cancer Res 14:2895–2899

    Article  PubMed  Google Scholar 

  • Evans DJ, Holian BL (1985) The nose–hoover thermostat. J Chem Phys 83:4069–4074

    Article  CAS  Google Scholar 

  • Golbraikh A, Tropsha A (2002) Beware of q2? J Mol Graph Model 20:269–276

    Article  CAS  PubMed  Google Scholar 

  • Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253

    Article  CAS  PubMed  Google Scholar 

  • Goss G, Tsai CM, Shepherd FA, Bazhenova L, Lee JS, Chang GC, Crino L, Satouchi M, Chu Q, Hida T, Han JY (2016) Osimertinib for pretreated EGFR Thr790Met-positive advanced non-small-cell lung cancer (AURA2): a multicentre, open-label, single-arm, phase 2 study. Lancet Oncol 17:1643–1652

    Article  CAS  PubMed  Google Scholar 

  • Hang H, Ma G, Zhu Y, Zeng L, Ahmad A, Wang C, Pang B, Fang H, Zhao L, Hao Q (2018) Active-site conformational fluctuations promote the enzymatic activity of NDM-1. Antimicrob Agents Chemother 62:e01579–18 

  • Hirsch FR, Varella-Garcia M, Bunn PA, Di Maria MV, Veve R, Bremnes RM, Barón AE, Zeng C, Franklin WA (2003) Epidermal growth factor receptor in non–smcall-cell lung carcinomas: correlation between gene copy number and protein expression and impact on prognosis. Int J Clin Oncol 21:3798–3807

    Article  CAS  Google Scholar 

  • http://dude.docking.org/targets/egfr

  • https://www.drugs.com/history/tagrisso.html

  • https://www.rcsb.org/structure/2JIU

  • https://www.rcsb.org/structure/4ZAU

  • Jafari F, Nowroozi A, Shahlaei M (2018) Discovery of novel glucagon receptor antagonists using combined pharmacophore modeling and docking. Iran J Pharm Res: IJPR 17:1263

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jänne PA, Yang JCH, Kim DW, Planchard D, Ohe Y, Ramalingam SS, Ahn MJ, Kim SW, Su WC, Horn L, Haggstrom D (2015) AZD9291 in EGFR inhibitor–resistant non–small-cell lung cancer. N Engl J Med 372:1689–1699

    Article  PubMed  Google Scholar 

  • Jordaan MA, Ebenezer O, Damoyi N, Shapi M (2020) Virtual screening, molecular docking studies and DFT calculations of FDA approved compounds similar to the non-nucleoside reverse transcriptase inhibitor (NNRTI) efavirenz. Heliyon 6(8):e04642

    Article  PubMed  PubMed Central  Google Scholar 

  • Kandakatla N, Ramakrishnan G, Karthikeyan J, Chekkara R (2014) Pharmacophore modeling, atom based 3D-QSAR and docking studies of chalcone derivatives as tubulin inhibitors. Orient J Chem 30:1083–1098

    Article  CAS  Google Scholar 

  • Kausar S, Falcao AO (2018) An automated framework for QSAR model building. J Cheminformatics 10:1

    Article  Google Scholar 

  • Kawahara A, Yamamoto C, Nakashima K, Azuma K, Hattori S, Kashihara M, Aizawa H, Basaki Y, Kuwano M, Kage M, Mitsudomi T (2010) Molecular diagnosis of activating EGFR mutations in non–small cell lung cancer using mutation-specific antibodies for immunohistochemical analysis. Clin Cancer Res 16:3163–3170

    Article  CAS  PubMed  Google Scholar 

  • Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov 2:436–444

    Google Scholar 

  • Khadikar PV, Karmarkar S, Agrawa VK (2001) A novel PI index and its applications to QSPR/QSAR studies. J Chem Inf Comput Sci 41:934–949

    Article  CAS  PubMed  Google Scholar 

  • Khan MF, Verma G, Akhtar W, Shaquiquzzaman M, Akhter M, Rizvi MA, Alam MM (2019) Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1, 3, 4-thiadiazole amides and sulfonamides as antitubulin agents. Arab J Chem 12:5000–5018

    Article  CAS  Google Scholar 

  • Kim Y, Ko J, Cui Z, Abolhoda A, Ahn JS, Ou SH, Ahn MJ, Park K (2012) The EGFR T790M mutation in acquired resistance to an irreversible second-generation EGFR inhibitor. Mol Cancer Ther 11:784–791

    Article  CAS  PubMed  Google Scholar 

  • Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, Johnson BE, Eck MJ, Tenen DG, Halmos B (2005) EGFR mutation and resistance of non–small-cell lung cancer to gefitinib. N Engl J Med 352:786–792

    Article  CAS  PubMed  Google Scholar 

  • Kumar V, Elizabeth Sobhia M (2012) Implication of crystal water molecules in inhibitor binding at ALR2 active site. Comput Math Method Med. https://doi.org/10.1155/2012/541594

    Article  Google Scholar 

  • Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  • Lu X, Yu L, Zhang Z, Ren X, Smaill JB, Ding K (2018) Targeting EGFRL858R/T790M and EGFRL858R/T790M/C797S resistance mutations in NSCLC: Current developments in medicinal chemistry. Med Res Rev 38:1550–1581

    Article  CAS  PubMed  Google Scholar 

  • Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 49:4805–4808

    Article  CAS  PubMed  Google Scholar 

  • Molina JR, Yang P, Cassivi SD, Schild SE, Adjei AA (2008) Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc 83:584–594

    Article  PubMed  Google Scholar 

  • Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Noolvi MN, Patel HM (2013) A comparative QSAR analysis and molecular docking studies of quinazoline derivatives as tyrosine kinase (EGFR) inhibitors: a rational approach to anticancer drug design. J Saudi Chem Soc 17:361–379

    Article  CAS  Google Scholar 

  • Ohsaki YO, Tanno SA, Fujita Y, Toyoshima E, Fujiuchi S, Nishigaki Y, Ishida S, Nagase A, Miyokawa N, Hirata S, Kikuchi K (2000) Epidermal growth factor receptor expression correlates with poor prognosis in non-small cell lung cancer patients with p53 overexpression. Curr Oncol Rep 7:603–610

    CAS  Google Scholar 

  • Pan Y, Wang Y, Bryant SH (2013) Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin-4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) inhibitors. J Chem Inf Model 53:938–947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Panwar U, Singh SK (2020) Atom-based 3D-QSAR, molecular docking, DFT, and simulation studies of acylhydrazone, hydrazine, and diazene derivatives as IN-LEDGF/p75 inhibitors. J Struct Chem 32:1–16

    Google Scholar 

  • Pao W, Miller VA, Politi KA, Riely GJ, Somwar R, Zakowski MF, Kris MG, Varmus H (2005) Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med 2:e73

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Patel H, Pawara R, Ansari A, Surana S (2017) Recent updates on third generation EGFR inhibitors and emergence of fourth generation EGFR inhibitors to combat C797S resistance. Eur J Med Chem 142:32–47

    Article  CAS  PubMed  Google Scholar 

  • Patel H, Ansari A, Pawara R, Ansari I, Jadhav H, Surana S (2018a) Design and synthesis of novel 2, 4-disubstituted aminopyrimidines: reversible non-covalent T790M EGFR inhibitors. J Recept Signal Transduct Res 38(5–6):393–412

    Article  CAS  PubMed  Google Scholar 

  • Patel H, Pawara R, Surana S (2018b) In-silico evidences for binding of Glucokinase activators to EGFR C797S to overcome EGFR resistance obstacle with mutant-selective allosteric inhibition. Comput Biol Chem 74:167–189

    Article  CAS  PubMed  Google Scholar 

  • Patel H, Dhangar K, Sonawane Y, Surana S, Karpoormath R, Thapliyal N, Shaikh M, Noolvi M, Jagtap R (2018c) In search of selective 11β-HSD type 1 inhibitors without nephrotoxicity: an approach to resolve the metabolic syndrome by virtual based screening. Arab J Chem 11:221–232

    Article  CAS  Google Scholar 

  • Patel S, Modi P, Chhabria M (2018d) Rational approach to identify newer caspase-1 inhibitors using pharmacophore based virtual screening, docking and molecular dynamic simulation studies. J Mol Graph Model 8:106–115

    Article  CAS  Google Scholar 

  • Patel H, Ahmad I, Jadhav H, Pawara R, Lokwani D, Surana S (2020a) Investigating the impact of different acrylamide (electrophilic warhead) on osimertinib’s pharmacological spectrum by molecular mechanic and quantum mechanic approach. Comb Chem High Throughput Screen. https://doi.org/10.2174/1386207323666201204125524 

    Article  Google Scholar 

  • Patel HM, Shaikh M, Ahmad I, Lokwani D, Surana (2020b) BREED based de novo hybridization approach: generating novel T790M/C797S-EGFR tyrosine kinase inhibitors to overcome the problem of mutation and resistance in non-small cell lung cancer (NSCLC). J Biomol Struct Dyn 39:1–19

    CAS  Google Scholar 

  • Patel HM, Ahmad I, Pawara R, Shaikh M, Surana SJ (2020c) In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation. J Biomol Struct Dyn 39:1–15

    CAS  Google Scholar 

  • Prabhu K, Manoj Kumar M, Gopalakrishnan VK (2014) Pharmacophore modeling and QSAR study of Thieno [3, 2-b] pyrimidine analogs as VEGFR-2 inhibitors. Int J Pharm Pharm Sci 6:200–207

    Google Scholar 

  • QikProp (2010) version 9.0, Schrodinger, LLC. New York, NY

  • Raghu R, Devaraji V, Leena K, Riyaz SD, Baby Rani P, Kumar Naik P, Dubey PK, Velmurugan D, Vijayalakshmi M (2014) Virtual screening and discovery of novel aurora kinase inhibitors. Curr Top Med Chem 14:2006–2019

    Article  CAS  PubMed  Google Scholar 

  • Roy K, Kar S, Ambure P (2015) on a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab 145:22–29

    Article  CAS  Google Scholar 

  • Rydberg P, Gloriam DE, Zaretzki J, Breneman C, Olsen L (2010) SMARTCyp: a 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS Med Chem Lett 1(3):96–100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rydberg P, Rostkowski M, Gloriam DE, Olsen L (2013) The contribution of atom accessibility to site of metabolism models for cytochromes P450. Mol Pharm 10:1216–1223

    Article  CAS  PubMed  Google Scholar 

  • Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234

    Article  PubMed  CAS  Google Scholar 

  • Shaikh M, Shinde Y, Pawara R, Noolvi M, Surana S, Ahmad I, Patel H (2021) Emerging approaches to overcome acquired drug resistance obstacles to osimertinib in non-small-cell lung cancer. J Med Chem. https://doi.org/10.1021/acs.jmedchem.1c00876

    Article  PubMed  Google Scholar 

  • Sharma SV, Bell DW, Settleman J, Haber DA (2007) Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer 7:169–181

    Article  CAS  PubMed  Google Scholar 

  • Sharma V, Kumar H, Wakode S (2016) Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors. RSC Adv 6:75805–75819

    Article  CAS  Google Scholar 

  • Shen M, Béguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A (2004) Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. J Med Chem 47:2356–2364

    Article  CAS  PubMed  Google Scholar 

  • Shinoda W, Mikami M (2003) Rigid-body dynamics in the isothermal-isobaric ensemble: a test on the accuracy and computational efficiency. J Comput Chem 24:920–930

    Article  CAS  PubMed  Google Scholar 

  • Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6:1509–1519

    Article  CAS  PubMed  Google Scholar 

  • Singh KD, Karthikeyan M, Kirubakaran P, Nagamani S (2011) Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. J Mol Graph Model 30:186–197

    Article  CAS  Google Scholar 

  • Song Z, Ge Y, Wang C, Huang S, Shu X, Liu K, Zhou Y, Ma X (2016) Challenges and perspectives on the development of small-molecule EGFR inhibitors against T790M-mediated resistance in non-small-cell lung cancer: miniperspective. J Med Chem 59:6580–6594

    Article  CAS  PubMed  Google Scholar 

  • Tawari NR, Bag S, Degani MS (2008) Pharmacophore mapping of a series of pyrrolopyrimidines, indolopyrimidines and their congeners as multidrug-resistance-associated protein (MRP1) modulators. J Mol 14:911–921

    CAS  Google Scholar 

  • Teli MK, Rajanikant GK (2012) Pharmacophore generation and atom-based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors. Org Med Chem Lett 2:25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Thress KS, Paweletz CP, Felip E, Cho BC, Stetson D, Dougherty B, Lai Z, Markovets A, Vivancos A, Kuang Y, Ercan D (2015) Acquired EGFR C797S mutation mediates resistance to AZD9291 in non–small cell lung cancer harboring EGFR T790M. Nat Med 21:560–562

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Qsar Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  • Ugale VG, Bari SB (2016) Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling, virtual screening and molecular docking studies. Sar Qsar Environ Res 27(2):125–145

    Article  CAS  PubMed  Google Scholar 

  • Ugale VG, Patel HM, Surana SJ (2017) Molecular modeling studies of quinoline derivatives as VEGFR-2 tyrosine kinase inhibitors using pharmacophore based 3D QSAR and docking approach. Arab J Chem 10:S1980–S2003

    Article  CAS  Google Scholar 

  • Vansteenkiste JF, Schildermans RH (2005) The future of adjuvant chemotherapy for resected non-small cell lung cancer. Expert Rev Anticancer Ther 5(1):165–175

    Article  CAS  PubMed  Google Scholar 

  • Verma G, Khan MF, Akhtar W, Alam MM, Akhter M, Alam O, Hasan SM, Shaquiquzzaman M (2019) Pharmacophore modeling, 3D-QSAR, docking and ADME prediction of quinazoline based EGFR inhibitors. Arab J Chem 12:4815–4839

    Article  CAS  Google Scholar 

  • Wang J, Li Y, Yang Y, Zhang J, Du J, Zhang S, Yang L (2015) Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor. RSC Adv 5:78278–78298

    Article  CAS  Google Scholar 

  • Yun CH, Mengwasser KE, Toms AV, Woo MS, Greulich H, Wong KK, Meyerson M, Eck MJ (2008) The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc Natl Acad Sci 105:2070–2075

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou W, Ercan D, Chen L, Yun CH, Li D, Capelletti M, Cortot AB, Chirieac L, Iacob RE, Padera R, Engen JR (2009) Novel mutant-selective EGFR kinase inhibitors against EGFR T790M. Nature 462:1070–1074

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank “Indian Council of Medical Research (ICMR), Govt. of India” (Grant No. ISRM/12(11)/2019) for funding the project.

Funding

The authors would like to thank ‘Indian Council of Medical Research (ICMR) Ministry of Health and Family Welfare, Department of Health Research Govt. of India’ (Grant No. ISRM/12(11)/2019) for funding the project.

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  1. Division of Computer-Aided Drug Design, Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, District Dhule, Shirpur, Maharashtra, 425 405, India

    Rahul Pawara, Iqrar Ahmad, Sanjay Surana & Harun Patel

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  1. Rahul Pawara
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RP, IA and HP was involved in the idea generation and performing the computational chemistry work. SS have contributed for the manuscript writing and grammatical check.

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Pawara, R., Ahmad, I., Surana, S. et al. Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation. In Silico Pharmacol. 9, 54 (2021). https://doi.org/10.1007/s40203-021-00113-x

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