QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models

  • Silvina E. FioressiEmail author
  • Daniel E. Bacelo
  • Pablo R. DuchowiczEmail author
Original Research


Many compounds have been proposed and tested as human epidermal growth factor receptor (EGFR) inhibitors for cancer treatment. Recently, new survival mechanisms of cancer cells have been discovered with the consequent resistance to therapy, which makes it necessary to search for new anticancer drugs. Here we perform a quantitative structure-activity relationship (QSAR) study on 290 compounds reported in the literature as EGFR inhibitors to analyze the molecular properties that may influence their activity. A large number of nonconformational descriptors (17,974) were explored including molecular descriptors, flexible molecular descriptors, and combination of both. To avoid ambiguities derived from the existence of several conformational states, only constitutional and topological molecular descriptors have been considered. The models were validated through Y-randomization, cross-validation, and mean absolute error criteria. A simple model involving flexible descriptors shows the best predictive performance and suggests that the presence of multiple aromatic rings and amino groups in a compound structure may increase its EGFR inhibitory activity.


Cancer EGFR QSAR Tyrosine kinase protein HER1 Drug design 



Epidermal growth factor receptor


Quantitative structure-activity relationship


United State Food and Drug Administration


The inhibitory activity was expressed as the concentration of the test compound that inhibited the activity of EGFR by 50%


The logarithmic molar IC50 values


Molecular-input line-entry system


Structural representation


Hydrogen-suppressed graph


Hydrogen-filled graph


Graph of atomic orbitals


Structural attributes


Defined flexible descriptor


Correlation weights


Monte Carlo simulation


Threshold value


Balanced subsets method (BSM)


k-means cluster analysis


Replacement method


Multivariable linear regression


Leave-one-out cross-validation


Loo variance


Mean absolute error


Applicability domain


Calculated leverage value


Warning leverage value


Standard deviation in the validation set


Fisher parameter


Number of outlier compounds in the training set



We are grateful for financial support provided by the National Research Council of Argentina (CONICET) project PIP11220130100311 and to the Ministerio de Ciencia, Tecnología e Innovación Productiva for access to electronic library facilities. SEF, DEB, and PRD are members of the scientific researcher career of CONICET.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

44_2019_2437_MOESM1_ESM.pdf (838 kb)
Supplementary Information


  1. Arteaga CL, Engelman JA (2014) ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell 25:282–303CrossRefGoogle Scholar
  2. Barber TD, Vogelstein B, Kinzler KW, Velculescu VE (2004) Somatic mutations of EGFR in colorectal cancers and glioblastomas. N Engl J Med 351:2883CrossRefGoogle Scholar
  3. Bathini R, Sivan SK, Fatima S, Manga V (2016) Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR inhibitors. J Chem Sci 128:1163–1173CrossRefGoogle Scholar
  4. Cai X, Zhai H-X, Wang J, Forrester J, Qu H, Yin L, Lai C-J, Bao R, Qian C (2010) Discovery of 7-(4-(3-ethynylphenylamino)-7-methoxyquinazolin-6-yloxy)-N-hydroxyheptanamide (CUDC-101) as a potent multi-acting HDAC, EGFR, and HER2 inhibitor for the treatment of cancer. J Med Chem 53:2000–2009CrossRefGoogle Scholar
  5. Chauhan J, Dhanda S, Singla D (2014) The open source drug discovery; Agarwal, SM; Raghava, GPS QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR. PLoS ONE 9:e101079CrossRefGoogle Scholar
  6. Curran MP (2010) Lapatinib: in postmenopausal women with hormone receptor-positive, HER2-positive metastatic breast cancer. Drugs 70:1411–1422CrossRefGoogle Scholar
  7. Dassault Systèmes Biovia (2017) Discovery Studio Modeling Environment. Accessed 28 July 2018.
  8. Duchowicz PR, Castro EA, Fernández FM (2006) Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies. MATCH Commun Math Comput Chem 55:179–192Google Scholar
  9. Duchowicz PR, Comelli NC, Ortiz EV, Castro EA (2012) QSAR study for carcinogenicity in a large set of organic compounds. Curr Drug Saf 7:282–288CrossRefGoogle Scholar
  10. Duchowicz PR, Fioressi SE, Castro E, Wróbel K, Ibezim NE, Bacelo DE (2017) Conformation‐independent QSAR study on human epidermal growth factor receptor‐2 (HER2) inhibitors. ChemistrySelect 2:3725–3731CrossRefGoogle Scholar
  11. Eriksson L, Jaworska J, Worth AP, Cronin MT, Mcdowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Perspect 111:1361CrossRefGoogle Scholar
  12. Faghih-Mirzaei E, Sabouri S, Zeidabadinejad L, Abdolahramazani S, Abaszadeh M, Khodadadi A, Shamsadinipour M, Jafari M, Pirhadi S (2019) Metronidazole aryloxy, carboxy and azole derivatives: synthesis, anti-tumor activity, QSAR, molecular docking and dynamics studies. Bioorg Med Chem 27:305–314CrossRefGoogle Scholar
  13. Fink BE, Vite GD, Mastalerz H, Kadow JF, Kim S-H, Leavitt KJ, Du K, Crews D, Mitt T, Wong TW (2005) New dual inhibitors of EGFR and HER2 protein tyrosine kinases. Bioorg Med Chem Lett 15:4774–4779CrossRefGoogle Scholar
  14. Fink BE, Norris D, Mastalerz H, Chen P, Goyal B, Zhao Y, Kim S-H, Vite GD, Lee FY, Zhang H (2011) Novel pyrrolo [2, 1-f][1, 2, 4] triazin-4-amines: Dual inhibitors of EGFR and HER2 protein tyrosine kinases. Bioorg Med Chem Lett 21:781–785CrossRefGoogle Scholar
  15. Gaber AA, Bayoumi AH, El-Morsy AM, Sherbiny FF, Mehany AB, Eissa IH (2018) Design, synthesis and anticancer evaluation of 1H-pyrazolo [3, 4-d] pyrimidine derivatives as potent EGFRWT and EGFRT790M inhibitors and apoptosis inducers. Bioorg Chem 80:375–395CrossRefGoogle Scholar
  16. Gadaleta D, Mangiatordi GF, Catto M, Carotti A, Nicolotti O (2016) Applicability domain for QSAR models: where theory meets reality. Int J Quant Struct-Prop Relat 1:45–63Google Scholar
  17. Gazit A, Osherov N, Posner I, Yaish P, Poradosu E, Gilon C, Levitzki A (1991) Tyrphostins. II. heterocyclic and. alpha.-substituted benzylidenemalononitrile tyrphostins as potent inhibitors of EGF receptor and ErbB2/neu tyrosine kinases. J Med Chem 34:1896–1907CrossRefGoogle Scholar
  18. Gazit A, Osherov N, Posner I, Bar-Sinai A, Gilon C, Levitzki A (1993) Tyrphostins. 3. Structure-activity relationship studies of alpha-substituted benzylidenemalononitrile 5-S-aryltyrphostins. J Med Chem 36:3556–3564CrossRefGoogle Scholar
  19. Golbraikh A, Tropsha A (2002) Beware ofq2! J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  20. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  21. Gupta A, Bhunia S, Balaramnavar V, Saxena A (2011) Pharmacophore modelling, molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase inhibitors. SAR QSAR Environ Res 22:239–263CrossRefGoogle Scholar
  22. Hong H, Xie Q, Ge W, Qian F, Fang H, Shi L, Su Z, Perkins R, Tong W (2008) Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337–1344CrossRefGoogle Scholar
  23. Jutten B, Keulers TG, Schaaf MB, Savelkouls K, Theys J, Span PN, Vooijs MA, Bussink J, Rouschop KM (2013) EGFR overexpressing cells and tumors are dependent on autophagy for growth and survival. Radiother Oncol 108:479–483CrossRefGoogle Scholar
  24. Kalyankrishna S, Grandis JR (2006) Epidermal growth factor receptor biology in head and neck cancer. J Clin Oncol 24:2666–2672CrossRefGoogle Scholar
  25. Kroep JR, Linn SC, Boven E, Bloemendal HJ, Baas J, Mandjes IA, Van Den Bosch J, Smit WM, De Graaf H, Schroder CP, Vermeulen GJ, Hop WC, Nortier JW (2010) Lapatinib: clinical benefit in patients with HER 2-positive advanced breast cancer. Neth J Med 68:371–376Google Scholar
  26. Lemmon MA, Schlessinger J (2010) Cell signaling by receptor tyrosine kinases. Cell 141:1117–1134CrossRefGoogle Scholar
  27. Levitzki A, Gazit A (1995) Tyrosine kinase inhibition: an approach to drug development. Science 267:1782–1788CrossRefGoogle Scholar
  28. Li H-Q, Yan T, Yang Y, Shi L, Zhou C-F, Zhu H-L (2010) Synthesis and structure–activity relationships of N-benzyl-N-(X-2-hydroxybenzyl)-N′-phenylureas and thioureas as antitumor agents. Bioorg Med Chem 18:305–313CrossRefGoogle Scholar
  29. Liang K, Esteva FJ, Albarracin C, Stemke-Hale K, Lu Y, Bianchini G, Yang CY, Li Y, Li X, Chen CT, Mills GB, Hortobagyi GN, Mendelsohn J, Hung MC, Fan Z (2010) Recombinant human erythropoietin antagonizes trastuzumab treatment of breast cancer cells via Jak2-mediated Src activation and PTEN inactivation. Cancer Cell 18:423–435CrossRefGoogle Scholar
  30. Lv P-C, Zhou C-F, Chen J, Liu P-G, Wang K-R, Mao W-J, Li H-Q, Yang Y, Xiong J, Zhu H-L (2010) Design, synthesis and biological evaluation of thiazolidinone derivatives as potential EGFR and HER-2 kinase inhibitors. Bioorg Med Chem 18:314–319CrossRefGoogle Scholar
  31. Marzaro G, Chilin A, Guiotto A, Uriarte E, Brun P, Castagliuolo I, Tonus F, González-Díaz H (2011) Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 46:2185–2192CrossRefGoogle Scholar
  32. Mastalerz H, Chang M, Chen P, Dextraze P, Fink BE, Gavai A, Goyal B, Han WC, Johnson W, Langley D, Lee FY, Marathe P, Mathur A, Oppenheimer S, Ruediger E, Tarrant J, Tokarski JS, Vite GD, Vyas DM, Wong H, Wong TW, Zhang H, Zhang G (2007) New C-5 substituted pyrrolotriazine dual inhibitors of EGFR and HER2 protein tyrosine kinases. Bioorg Med Chem Lett 17:2036–2042CrossRefGoogle Scholar
  33. Mastalerz H, Chang M, Chen P, Fink BE, Gavai A, Han W-C, Johnson W, Langley D, Lee FY, Leavitt K (2007) 5-((4-Aminopiperidin-1-yl) methyl) pyrrolotriazine dual inhibitors of EGFR and HER2 protein tyrosine kinases. Bioorg Med Chem Lett 17:4947–4954CrossRefGoogle Scholar
  34. Mastalerz H, Chang M, Gavai A, Johnson W, Langley D, Lee FY, Marathe P, Mathur A, Oppenheimer S, Tarrant J, Tokarski JS, Vite GD, Vyas DM, Wong H, Wong TW, Zhang H, Zhang G (2007) Novel C-5 aminomethyl pyrrolotriazine dual inhibitors of EGFR and HER2 protein tyrosine kinases. Bioorg Med Chem Lett 17:2828–2833CrossRefGoogle Scholar
  35. Mok TS, Wu Y-L, Thongprasert S, Yang C-H, Chu D-T, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y (2009) Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947–957CrossRefGoogle Scholar
  36. Nandi S, Bagchi MC (2010) 3D-QSAR and molecular docking studies of 4-anilinoquinazoline derivatives: a rational approach to anticancer drug design. Mol Diversity 14:27–38CrossRefGoogle Scholar
  37. Noolvi MN, Patel HM (2010) 3d QSAR studies on a series of quinazoline derrivatives as tyrosine kinase (egfr) inhibitor: the k-nearest neighbor molecular field analysis approach. J Basic Clin Pharm 1:153Google Scholar
  38. Orman JS, Perry CM (2007) Trastuzumab. Drugs 67:2781–2789CrossRefGoogle Scholar
  39. Pohlmann PR, Mayer IA, Mernaugh R (2009) Resistance to trastuzumab in breast cancer. Clin Cancer Res 15:7479–7491CrossRefGoogle Scholar
  40. Rojas C, Tripaldi P, Duchowicz PR (2016) A new QSPR study on relative sweetness. Int J Quant Struct-Prop Relat 1:78–93Google Scholar
  41. Roy K (2007) On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opin Drug Discov 2:1567–1577CrossRefGoogle Scholar
  42. Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29CrossRefGoogle Scholar
  43. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33CrossRefGoogle Scholar
  44. Ruslin R, Amelia R, Yamin Y, Megantara S, Wu C, Arba M (2019) 3D-QSAR, molecular docking, and dynamics simulation of quinazoline–phosphoramidate mustard conjugates as EGFR inhibitor. J Appl Pharm Sci 9:089–097CrossRefGoogle Scholar
  45. Schlessinger J (2000) Cell signaling by receptor tyrosine kinases. Cell 103:211–225CrossRefGoogle Scholar
  46. Shinde MG, Modi SJ, Kulkarni VM (2017) QSAR and molecular docking of phthalazine derivatives as epidermal growth factor receptor (EGFR) inhibitors. J Appl Pharm Sci 7:181–191Google Scholar
  47. Sigismund S, Avanzato D, Lanzetti L (2018) Emerging functions of the EGFR in cancer. Mol Oncol 12:3–20CrossRefGoogle Scholar
  48. Singh H, Singh S, Singla D, Agarwal SM, Raghava GP (2015) QSAR based model for discriminating EGFR inhibitors and non-inhibitors using random forest. Biol Direct 10:10CrossRefGoogle Scholar
  49. Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GP (2016) Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer 16:77CrossRefGoogle Scholar
  50. Sun X-Q, Chen L, Li Y-Z, Li W-H, Liu G-X, Tu Y-Q, Tang Y (2014) Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors. Acta Pharm Sin 35:301CrossRefGoogle Scholar
  51. Talevi A, Bellera CL, Di Ianni M, Duchowicz PR, Bruno-Blanch LE, Castro EA (2012) An integrated drug development approach applying topological descriptors. Curr Comput Aided Drug Des 8:172–181CrossRefGoogle Scholar
  52. Tan X, Lambert PF, Rapraeger AC, Anderson RA (2016) Stress-induced EGFR trafficking: mechanisms, functions, and therapeutic implications. Trends Cell Biol 26:352–366CrossRefGoogle Scholar
  53. The Mathworks I (2018) MATLAB 7.0 and Statistics Toolbox 7.1. Accessed 29 Mar 2019
  54. Toropova A, Toropov A, Martyanov S, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna. Chemom Intell Lab Syst 110:177–181CrossRefGoogle Scholar
  55. U.S. Environmental Protection Agency (2016) Estimation Programs Interface Suite. Accessed 6 June 2019
  56. Ueno NT, Zhang D (2011) Targeting EGFR in triple negative breast cancer. J Cancer 2:324CrossRefGoogle Scholar
  57. Verma RP, Hansch C (2005) An approach toward the problem of outliers in QSAR. Bioorg Med Chem 13:4597–4621CrossRefGoogle Scholar
  58. Walker F, Abramowitz L, Benabderrahmane D, Duval X, Descatoire V, Hénin D, Lehy T, Aparicio T (2009) Growth factor receptor expression in anal squamous lesions: modifications associated with oncogenic human papillomavirus and human immunodeficiency virus. Hum Pathol 40:1517–1527CrossRefGoogle Scholar
  59. Wold S, Eriksson L, Clementi S (1995) Statistical validation of QSAR results. In: van de Waterbeemd H (ed) Chemometric methods in molecular design. Wiley-VCH, Weinheim, p 309–338Google Scholar
  60. Wu P, Nielsen TE, Clausen MH (2016) Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov Today 21:5–10CrossRefGoogle Scholar
  61. Xu G, Abad MC, Connolly PJ, Neeper MP, Struble GT, Springer BA, Emanuel SL, Pandey N, Gruninger RH, Adams M (2008) 4-Amino-6-arylamino-pyrimidine-5-carbaldehyde hydrazones as potent ErbB-2/EGFR dual kinase inhibitors. Bioorg Med Chem Lett 18:4615–4619CrossRefGoogle Scholar
  62. Xu G, Searle LL, Hughes TV, Beck AK, Connolly PJ, Abad MC, Neeper MP, Struble GT, Springer BA, Emanuel SL (2008) Discovery of novel 4-amino-6-arylaminopyrimidine-5-carbaldehyde oximes as dual inhibitors of EGFR and ErbB-2 protein tyrosine kinases. Bioorg Med Chem Lett 18:3495–3499CrossRefGoogle Scholar
  63. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Facultad de Ciencias Exactas y NaturalesUniversidad de BelgranoBuenos AiresArgentina
  2. 2.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113y 64La PlataArgentina

Personalised recommendations