Advertisement

Chemical Papers

, Volume 72, Issue 11, pp 2833–2847 | Cite as

Structural basis of pyrazolopyrimidine derivatives as CAMKIIδ kinase inhibitors: insights from 3D QSAR, docking studies and in silico ADMET evaluation

  • Adnane AouidateEmail author
  • Adib Ghaleb
  • Mounir Ghamali
  • Samir Chtita
  • Abdellah Ousaa
  • M’barek Choukrad
  • Abdelouahid Sbai
  • Mohammed Bouachrine
  • Tahar Lakhlifi
Original Paper
  • 163 Downloads

Abstract

Ca2+/calmodulin-dependent protein kinase II (CAMKIIδ) belongs to the serine/threonine kinase family, which is involved in a broad range of cellular events in cell survival and proliferation as well as a number of other signal transduction pathways. Thus, it is regarded a promising target for treatment of cancers. In the present paper, a three-dimensional quantitative structure–activity relationship and molecular docking were applied to investigate a series of new CAMKIIδ inhibitors of pyrazolopyrimidine derivatives. The determination coefficient (R2) and leave-one-out cross-validation coefficient (Q2) of CoMSIA model are 0.676 and 0.956, respectively. The predictive ability of this model was evaluated by the external validation using a test set of eight compounds with a predicted determination coefficient \(R^{ 2}_{\text{test}}\) of 0.80, besides the mean absolute error of the test set was 0.328 log units. Docking results are in concordance with CoMSIA contour maps, gave the information for interactive mode exploration. Based on those satisfactory results, newly designed molecules were predicted with highly potent CAMKIIδ inhibitory activity, additionally, they have showed promising results in the preliminary in silico ADMET evaluations. This study could expand our understanding of pyrazolopyrimidine derivatives as inhibitors of CAMKIIδ and would be of great help in lead optimization for early drug discovery of highly potent CAMKIIδ inhibitors.

Keywords

3D-QSAR Molecular docking CAMKIIδ Drug design Pyrazolopyrimidine In silico ADMET 

Notes

Acknowledgement

We are grateful to the “Association Marocaine des Chimistes Théoriciens” (AMCT) and “Moroccan Centre of Scientific and Technique research” (CNRST) for their pertinent help concerning the programs.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

References

  1. AbdulHameed MDM, Hamza A, Liu J, Zhan C-G (2008) Combined 3D-QSAR modeling and molecular docking study on indolinone derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1. J Chem Inf Model 48:1760–1772.  https://doi.org/10.1021/ci800147v CrossRefPubMedGoogle Scholar
  2. Aouidate A, Ghaleb A, Ghamali M, Chtita S, Choukrad M, Sbai A, Bouachrine M, Lakhlifi T (2017) Combined 3D-QSAR and molecular docking study on 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] quinazoline series compounds to understand the binding mechanism of DHFR inhibitors. J Mol Struct 1139:319–327.  https://doi.org/10.1016/j.molstruc.2017.03.039 CrossRefGoogle Scholar
  3. Baroni M, Clementi S, Cruciani G, Costantino G, Riganelli D, Oberrauch E (1992) Predictive ability of regression models. Part II: selection of the best predictive PLS model. J Chemom 6:347–356CrossRefGoogle Scholar
  4. Britschgi A, Bill A, Brinkhaus H, Rothwell C, Clay I, Duss S, Rebhan M, Raman P, Guy CT, Wetzel K, George E, Popa MO, Lilley S, Choudhury H, Gosling M, Wang L, Fitzgerald S, Borawski J, Baffoe J, Labow M, Gaither LA, Bentires-Alj M (2013) Calcium-activated chloride channel ANO1 promotes breast cancer progression by activating EGFR and CAMK signaling. Proc Natl Acad Sci 110:E1026–E1034.  https://doi.org/10.1073/pnas.1217072110 CrossRefPubMedGoogle Scholar
  5. Chai S, Xu X, Wang Y, Zhou Y, Zhang C, Yang Y, Yang Y, Xu H, Xu R, Wang K (2015) Ca2+/calmodulin-dependent protein kinase IIγ enhances stem-like traits and tumorigenicity of lung cancer cells. Oncotarget 6:16069–16083.  https://doi.org/10.18632/oncotarget.3866 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Clark M, Cramer RD, Van Opdenbosch N (1989) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:982–1012.  https://doi.org/10.1002/jcc.540100804 CrossRefGoogle Scholar
  7. Cruciani G, Baroni M, Clementi S, Costantino G, Riganelli D, Skagerberg B (1992) Predictive ability of regression models. Part I: standard deviation of prediction errors (SDEP). J Chemom 6:335–346.  https://doi.org/10.1002/cem.1180060604 CrossRefGoogle Scholar
  8. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717.  https://doi.org/10.1038/srep42717 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 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 14:35–55CrossRefPubMedGoogle Scholar
  10. Dassault Systèmes BIOVIA (2016) Discovery studio modeling environment, release 2017, San Diego: Dassault Systèmes. [WWW document], 2016. http://accelrys.com/products/collaborative-science/biovia-discovery-studio/. Accessed 25 Feb 17
  11. Discovery D (2004) Preclinical drug developmentGoogle Scholar
  12. Egan WJ, Merz KM, Baldwin JJ (2000) Prediction of drug absorption using multivariate statistics. J Med Chem 43:3867–3877CrossRefPubMedGoogle Scholar
  13. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276.  https://doi.org/10.1016/S1093-3263(01)00123-1 CrossRefPubMedGoogle Scholar
  14. Gu Y, Chen T, Meng Z, Gan Y, Xu X, Lou G, Li H, Gan X, Zhou H, Tang J, Xu G, Huang L, Zhang X, Fang Y, Wang K, Dc W (2013) The natural product berbamine CaMKII, a critical regulator of CML stem/progenitor cells, is a target of the natural product berbamine. Blood 120:4829–4839.  https://doi.org/10.1182/blood-2012-06-434894 CrossRefGoogle Scholar
  15. Gupta P, Garg P, Roy N (2012) Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach. Chem Biol Drug Des 79:835–849.  https://doi.org/10.1111/j.1747-0285.2012.01326.x CrossRefPubMedGoogle Scholar
  16. Hudmon A, Schulman H (2002) Structure-function of the multifunctional Ca2+/calmodulin-dependent protein kinase II. Biochem J 364:593–611.  https://doi.org/10.1042/BJ20020228 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46:499–511.  https://doi.org/10.1021/jm020406h CrossRefPubMedGoogle Scholar
  18. Jain AN (2007) Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des 21:281–306.  https://doi.org/10.1007/s10822-007-9114-2 CrossRefPubMedGoogle Scholar
  19. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146.  https://doi.org/10.1021/jm00050a010 CrossRefGoogle Scholar
  20. Koltun DO, Parkhill EQ, Kalla R, Perry TD, Elzein E, Li X, Simonovich SP, Ziebenhaus C, Hansen TR, Marchand B, Hung WK, Lagpacan L, Hung M, Aoyama G, Murray BP, Perry JK, Somoza JR, Armando G, Pagratis N, Zablocki JA (2017) Discovery of potent and selective inhibitors of calmodulin-dependent kinase II (CaMKII) Department of Structural Chemistry. Bioorg Med Chem Lett.  https://doi.org/10.1016/j.bmcl.2017.10.040 CrossRefPubMedGoogle Scholar
  21. Kubinyi H (2003) Comparative molecular field analysis (CoMFA). Handb Chemoinform.  https://doi.org/10.1002/9783527618279.ch44d CrossRefGoogle Scholar
  22. Levy DE, Wang DX, Lu Q, Chen Z, Perumattam J, Xu YJ, Higaki J, Dong H, Liclican A, Laney M, Mavunkel B, Dugar S (2008a) Aryl-indolyl maleimides as inhibitors of CaMKIIδ. Part 2: SAR of the amine tether. Bioorg Med Chem Lett 18:2395–2398.  https://doi.org/10.1016/j.bmcl.2008.02.058 CrossRefPubMedGoogle Scholar
  23. Levy DE, Wang DX, Lu Q, Chen Z, Perumattam J, Xu YJ, Liclican A, Higaki J, Dong H, Laney M, Mavunkel B, Dugar S (2008b) Aryl-indolyl maleimides as inhibitors of CaMKIIδ. Part 1: SAR of the aryl region. Bioorg Med Chem Lett 18:2390–2394.  https://doi.org/10.1016/j.bmcl.2008.02.059 CrossRefPubMedGoogle Scholar
  24. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341.  https://doi.org/10.1016/j.ddtec.2004.11.007 CrossRefPubMedGoogle Scholar
  25. Lu Q, Chen Z, Perumattam J, Wang DX, Liang W, Xu YJ, Do S, Bonaga L, Higaki J, Dong H, Liclican A, Sideris S, Laney M, Dugar S, Mavunkel B, Levy DE (2008) Aryl-indolyl maleimides as inhibitors of CaMKIIδ. Part 3: importance of the indole orientation. Bioorg Med Chem Lett 18:2399–2403.  https://doi.org/10.1016/j.bmcl.2008.02.057 CrossRefPubMedGoogle Scholar
  26. Mavunkel B, Xu YJ, Goyal B, Lim D, Lu Q, Chen Z, Wang DX, Higaki J, Chakraborty I, Liclican A, Sideris S, Laney M, Delling U, Catalano R, Higgins LS, Wang H, Wang J, Feng Y, Dugar S, Levy DE (2008) Pyrimidine-based inhibitors of CaMKIIδ. Bioorg Med Chem Lett 18:2404–2408.  https://doi.org/10.1016/j.bmcl.2008.02.056 CrossRefPubMedGoogle Scholar
  27. Pellicena P, Schulman H (2014) CaMKII inhibitors: from research tools to therapeutic agents. Front Pharmacol 5:1–10.  https://doi.org/10.3389/fphar.2014.00021 CrossRefGoogle Scholar
  28. Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072.  https://doi.org/10.1021/acs.jmedchem.5b00104 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Pourbasheer E, Aalizadeh R (2016) 3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods. SAR QSAR Environ Res 27:385–407.  https://doi.org/10.1080/1062936X.2016.1184713 CrossRefPubMedGoogle Scholar
  30. Pourbasheer E, Amanlou M (2014) 3D-QSAR analysis of anti-cancer agents by CoMFA and CoMSIA. Med Chem Res 23:800–809.  https://doi.org/10.1007/s00044-013-0676-3 CrossRefGoogle Scholar
  31. Pourbasheer E, Aalizadeh R, Ebadi A, Ganjali M (2015a) 3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA. Comb Chem High Throughput Screen 18:751–766.  https://doi.org/10.2174/1386207318666150803141738 CrossRefPubMedGoogle Scholar
  32. Pourbasheer E, Aalizadeh R, Shiri HM, Banaei A, Ganjali MR (2015b) 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA. Curr Comput Aided Drug Des 11:292–303CrossRefPubMedGoogle Scholar
  33. Pourbasheer E, Shokouhi Tabar S, Masand VH, Aalizadeh R, Ganjali MR (2015c) 3D-QSAR and docking studies on adenosine A 2A receptor antagonists by the CoMFA method. SAR QSAR Environ Res 26:461–477.  https://doi.org/10.1080/1062936X.2015.1049666 CrossRefPubMedGoogle Scholar
  34. Purcell WP, Singer JA (1967) A brief review and table of semiempirical parameters used in the Hueckel molecular orbital method. J Chem Eng Data 12:235–246.  https://doi.org/10.1021/je60033a020 CrossRefGoogle Scholar
  35. 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.  https://doi.org/10.1016/j.chemolab.2016.01.008 CrossRefGoogle Scholar
  36. Rücker C, Rücker G, Meringer M (2007) Y-randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357.  https://doi.org/10.1021/ci700157b CrossRefPubMedGoogle Scholar
  37. 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.  https://doi.org/10.1002/qsar.200390007 CrossRefGoogle Scholar
  38. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461.  https://doi.org/10.1002/jcc.21334 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Wang J, Kollman PA, Kuntz ID (1999) Flexible ligand docking: a multistep strategy approach. Proteins 36:1–19CrossRefPubMedGoogle Scholar
  40. Wang C, Li N, Liu X, Zheng Y, Cao X (2008) A novel endogenous human CaMKII inhibitory protein suppresses tumor growth by inducing cell cycle arrest via p27 stabilization. J Biol Chem 283:11565–11574.  https://doi.org/10.1074/jbc.M800436200 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Wang T, Guo S, Liu Z, Wu L, Li M, Yang J, Chen R, Liu X, Xu H, Cai S, Chen H, Li W, Xu S, Wang L, Hu Z, Zhuang Q, Wang L, Wu K, Liu J, Ye Z, Ji J-Y, Wang C, Chen K (2014) CAMK2N1 inhibits prostate cancer progression through androgen receptor-dependent signaling. Oncotarget 5:10293–10306.  https://doi.org/10.18632/oncotarget.2511 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Wold S (1991) Validation of QSAR’s. Quant Struct Relationsh 10:191–193.  https://doi.org/10.1002/qsar.19910100302 CrossRefGoogle Scholar
  43. Xing L, Rai B, Lunney EA (2014) Scaffold mining of kinase hinge binders in crystal structure database. J Comput Aided Mol Des 28:13–23.  https://doi.org/10.1007/s10822-013-9700-4 CrossRefPubMedGoogle Scholar
  44. Xu D, Li L, Zhou D, Liu D, Hudmon A, Meroueh SO (2017) Structure-based target-specific screening leads to small-molecule CaMKII inhibitors. ChemMedChem 12:660–677.  https://doi.org/10.1002/cmdc.201600636 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Zheng J, Xiao G, Guo J, Zheng Y, Gao H, Zhao S, Zhang K, Sun P (2011) Exploring QSARs for 5-lipoxygenase (5-LO) inhibitory activity of 2-substituted 5-hydroxyindole-3-carboxylates by CoMFA and CoMSIA. Chem Biol Drug Des 78:314–321.  https://doi.org/10.1111/j.1747-0285.2011.01146.x CrossRefPubMedGoogle Scholar

Copyright information

© Institute of Chemistry, Slovak Academy of Sciences 2018

Authors and Affiliations

  • Adnane Aouidate
    • 1
    Email author
  • Adib Ghaleb
    • 1
  • Mounir Ghamali
    • 1
  • Samir Chtita
    • 1
  • Abdellah Ousaa
    • 1
  • M’barek Choukrad
    • 1
  • Abdelouahid Sbai
    • 1
  • Mohammed Bouachrine
    • 2
  • Tahar Lakhlifi
    • 1
  1. 1.MCNSL, School of SciencesMoulay Ismail UniversityMeknesMorocco
  2. 2.High School of TechnologyMoulay Ismail UniversityMeknesMorocco

Personalised recommendations