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5D-QSAR studies of 1H-pyrazole derivatives as EGFR inhibitors

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Abstract

Epidermal growth factor receptor (EGFR) is highlighted as a target for anticancer treatment. Several EGFR inhibitors were approved in cancer treatment. Comparatively, 5D-QSAR is a new methodology which considers an ensemble of different induced-fit models. Based on 1H-pyrazole derivatives as EGFR inhibitors, a 5D-QSAR was studied in which the method of quasi-atomistic receptor surface modeling was used. The presented QSAR model showed contributions of the hydrogen bond acceptor, and hydrophobic and salt bridge fields to the activity. The QSAR model was statistically validated and also externally validated applying 19 compounds (test set) which were not included in the model generation process. The scramble tests were performed to further verify the robustness. Apart from exploration of the binding of 1H-pyrazole derivatives to the EGFR, the 5D-QSAR model can be helpful to design of new EGFR inhibitors.

Graphical Abstract

The five-dimensional quantitative structure–activity relationship (5D-QSAR) of 1H-pyrazole derivatives as EGFR inhibitors with quasi-atomistic receptor surface modeling approach is described.

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Data availability

All data concerning the results of this study is shown in this manuscript, and raw material may be sent on request.

Code availability

All the software used in the study are available online for use. No additional coding is done.

References

  1. He Y, Harrington BS, Hooper JD (2016) New crossroads for potential therapeutic intervention in cancer-intersections between CDCP1, EGFR family members and downstream signaling pathways. Oncoscience 3:5–8

    Article  Google Scholar 

  2. Yarden Y, Sliwkowski MX (2001) Untangling the ErbB signalling network. Nat rev Mol Cell Bio 2:127–137

    Article  CAS  Google Scholar 

  3. Hsu JL, Hung MC (2016) The role of HER2, EGFR, and other receptor tyrosine kinases in breast cancer. Cancer Metastasis Rev 35:575–588

    Article  CAS  Google Scholar 

  4. Huo LF, Wang YN, Xia WY, Hsu SC, Lai CC, Li LY, Chang WC, Wang Y, Hsu MC, Yu YL (2010) RNA helicase A is a DNA-binding partner for EGFR-mediated transcriptional activation in the nucleus. Proc Natl Acad ScI USA 107:16125–16130

    Article  CAS  Google Scholar 

  5. Xu MJ, Johnson DE, Grandis JR (2017) EGFR-targeted therapies in the post-genomic era. Cancer Metastasis Rev 36:463–473

    Article  Google Scholar 

  6. Hossam M, Lasheen DS, Abouzid KAM (2016) Covalent EGFR inhibitors: binding mechanisms, synthetic approaches, and clinical profiles. Arch Pharm 349:573–593

    Article  CAS  Google Scholar 

  7. Liu F, Tang B, Liu H, Li L, Liu G, Cheng Y, Xu Y, Chen WW, Huang Y (2016) 4-Anilinoquinazoline derivatives with epidermal growth factor receptor inhibitor activity. Anti-Cancer Agents in Med Chem 16:1652–1664

    Article  CAS  Google Scholar 

  8. Burotto M, Chiou VL, Lee JM, Kohn EC (2014) The MAPK pathway across different malignancies: a new perspective. Cancer 120:3446–3456

    Article  CAS  Google Scholar 

  9. Kubinyi H (1997) QSAR and 3D QSAR in drug design Part 2: applications and problems. Drug Discov Today 2:538–546

    Article  CAS  Google Scholar 

  10. Kubinyi H (1997) QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discov Today 2:457–467

    Article  CAS  Google Scholar 

  11. Vedani A, Dobler M (2002) Multidimensional QSAR: moving from three- to five-dimensional concepts. Quant Struct-Act Relat 21:382–390

    Article  CAS  Google Scholar 

  12. Roy K, Das RN (2014) A review on principles, theory and practices of 2D-QSAR. Curr Drug Metab 15:346–379

    Article  CAS  Google Scholar 

  13. Damale MG, Harke SN, Kalam Khan FA, Shinde DB, Sangshetti JN (2014) Recent advances in multidimensional QSAR (4D–6D): a critical review. Mini Rev Med Chem 14:35–55

    Article  CAS  Google Scholar 

  14. Vedani A, Dobler M, Lill MA (2005) Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J Med Chem 48:3700–3703

    Article  CAS  Google Scholar 

  15. Oberdorf C, Schmidt TJ, Wunsch B (2010) 5D-QSAR for spirocyclic sigma1 receptor ligands by Quasar receptor surface modeling. Eur J Med Chem 45:3116–3124

    Article  CAS  Google Scholar 

  16. Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assesment. Newer QSAR techniques. Academic Press, Elsevier, pp 319–356

    Google Scholar 

  17. Lv PC, Li HQ, Sun J, Zhou Y, Zhu HL (2010) Synthesis and biological evaluation of pyrazole derivatives containing thiourea skeleton as anticancer agents. Biorg Med Chem 18:4606–4614

    Article  CAS  Google Scholar 

  18. Hassinen T, Perkyl M (2001) New energy terms for reduced protein models implemented in an off-lattice force field. J Comput Chem 22:1229–1242

    Article  CAS  Google Scholar 

  19. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  Google Scholar 

  20. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718

    Article  Google Scholar 

  21. Patil R, Sawant S (2015) Molecular dynamics guided receptor independent 4D QSAR studies of substituted coumarins as anticancer agents. Curr Comput Aided Drug Des 11:39–50

    Article  CAS  Google Scholar 

  22. Berendsen HJC, Postma JV, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690

    Article  CAS  Google Scholar 

  23. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N⋅log (N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  24. Vedani A, Dobler M. User and reference manual Quasar 5.0. Biograf Laboratory 3R, Basel, Switzerland,

  25. Pourbasheer E, Aalizadeh R, Shiri HM, Banaei A, Ganjali MR (2015) 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase by COMFA and COMSIA. Curr Comput Aided Drug Des 11:292–303

    Article  CAS  Google Scholar 

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Funding

The project was supported by the Undergraduate Innovation and Entrepreneurship Foundation (2021CX242, 2020CX289), the Nanchang University Teaching Reform Foundation (NCUJGLX-19–130, NCUJGLX-19–124), and the Jiangxi Province Science Foundation (20171BAB205104).

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Authors and Affiliations

  1. Department of Medicinal Chemistry, School of Pharmaceutical Science, Nanchang University, Nanchang, 330006, China

    Daogang Qin, Tiansheng Zhao, Biying Cai, Bowen Yang & Guogang Tu

  2. Jiangxi Provincial Drug Administration, Nanchang, 330006, China

    Xiaoqi Zeng

Authors
  1. Daogang Qin
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  2. Xiaoqi Zeng
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  3. Tiansheng Zhao
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  4. Biying Cai
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  5. Bowen Yang
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  6. Guogang Tu
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Contributions

DQ: methods, writing and result analysis. XZ: problem selection, writing and result analysis. TZ: data analysis. BC: data analysis. BY: data analysis. GT: methods, project management, result analysis, manuscript editing.

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Correspondence to Guogang Tu.

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Qin, D., Zeng, X., Zhao, T. et al. 5D-QSAR studies of 1H-pyrazole derivatives as EGFR inhibitors. J Mol Model 28, 379 (2022). https://doi.org/10.1007/s00894-022-05370-x

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