INTEGRATING STRUCTURE AND LIGAND-BASED APPROACHES FOR MODELLING THE HISTONE DEACETYLASE INHIBITION ACTIVITY OF HYDROXAMIC ACID DERIVATIVES

Authors

  • Hai Pham-the Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13–15 Le Thanh Tong, Hoan Kiem, Hanoi, Vietnam. http://orcid.org/0000-0002-3826-1279
  • Huong Le-thi-thu Department of Pharmacy and Pharmacology of Traditional Materials, School of Medicine and Pharmacy, Vietnam National University, Hanoi 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam. http://orcid.org/0000-0003-4531-7223

DOI:

https://doi.org/10.22159/ajpcr.2018.v11i2.22995

Keywords:

Docking, Quantitative structure-activity relationship, Histone deacetylase, Rational drug design, Hydroxamic acid

Abstract

 

 Objective: Structure and ligand-based drug design approaches have be been integrated to accurately predict the inhibition activity of hydroxamic acid (HA) derivatives against the histone deacetylase-2 enzyme (HDAC2).

Methods: The active conformations†of the ligands in the binding site of the enzyme were determined by docking assays. More than 1000 0–3 dimensional molecular descriptors included in Dragon package were calculated and utilized for developing quantitative structure-activity relationship (QSAR) models through a multiple linear regression approach coupled with the genetic algorithm (GA-MLR).

Results: The final model obtained showed suitable robustness and stability, with low correlation between descriptors and good predictive power. QSAR model was then used for screening bioactivity from a series of 36 novel HAs and found five candidates with very good bioactivity (half maximal inhibitory concentration<0.1 μM). Docking experiment revealed the binding mode of these compounds into the active site of HDAC2. Drug-likeness and toxicity profiles of the compounds were checked through chemoinformatics tools.

Conclusion: The results from this study can lead to rational design and synthesis of highly selective and potent HDAC2 inhibitors.

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References

Gupta SP, Sharma A. The chemistry of hydroxamic acids. In: Gupta SP, editor. Hydroxamic Acids: A Unique Family of Chemicals with Multiple Biological Activities. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. p. 1-17.

Politzer P, Murray JS. Some intrinsic features of hydroxylamines, oximes and hydroxamic acids: Integration of theory and experiment. PATAI’S Chemistry of Functional Groups.Chichester, West Sussex PO19 8SQ, UK: John Wiley & Sons, Ltd; 2009.

Thaler F, Patil VM, Gupta SP. Hydroxamic acids as histone deacetylase inhibitors. In: Gupta SP, editor. Hydroxamic Acids: A Unique Family of Chemicals with Multiple Biological Activities. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. p. 99-151.

Haberland M, Montgomery RL, Olson EN. The many roles of histone deacetylases in development and physiology: Implications for disease and therapy. Nat Rev Genet 2009;10:32-42.

Islam T. Crucial challenges in epigenetic cancer therapeutic strategy yet to be resolved. Int J Pharm Pharm Sci 2016;7:326-31.

Falkenberg KJ, Johnstone RW. Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders. Nat Rev Drug Discov 2014;13:673-91.

Mottamal M, Zheng S, Huang TL, Wang G. Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents. Molecules 2015;20:3898-941.

Whitehead L, Dobler MR, Radetich B, Zhu Y, Atadja PW, Claiborne T, et al. Human HDAC isoform selectivity achieved via exploitation of the acetate release channel with structurally unique small molecule inhibitors. Bioorg Med Chem 2011;19:4626-34.

Pham-The H, Casañola-Martin G, Diéguez-Santana K, Nguyen-Hai N,Ngoc NT, Vu-Duc L, et al. Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries. SAR QSAR Environ Res 2017;28:199-220.

Humberto GD. Editorial (Hot Topic: Bioinformatics and Quantitative Structure-Property Relationship (QSPR) Models). Curr Bioinform 2013;8:387-9.

Xie A, Liao C, Li Z, Ning Z, Hu W, Lu X, et al. Quantitative structure-activity relationship study of histone deacetylase inhibitors. Curr Med Chem Anticancer Agents 2004;4:273-99.

Guo Y, Xiao J, Guo Z, Chu F, Cheng Y, Wu S, et al. Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses. Bioorg Med Chem 2005;13:5424-34.

Juvale DC, Kulkarni VV, Deokar HS, Wagh NK, Padhye SB, Kulkarni VM, et al 3D-QSAR of histone deacetylase inhibitors: Hydroxamate analogues. Org Biomol Chem 2006;4:2858-68.

Katritzky AR, Slavov SH, Dobchev DA, Karelson M. Comparison between 2D and 3D-QSAR approaches to correlate inhibitor activity for a series of indole amide hydroxamic acids. QSAR Comb Sci 2007;26:333-45.

Ragno R, Simeoni S, Valente S, Massa S, Mai A. 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds. J Chem Inf Model 2006;46:1420-30.

Chen YD, Jiang YJ, Zhou JW, Yu QS, You QD. Identification of ligand features essential for HDACs inhibitors by pharmacophore modeling. J Mol Graph Model 2008;26:1160-8.

Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Kollias G, Igglessi-Markopoulou O, et al. Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors. Mol Divers 2009;13:301-11.

Pontiki E, Hadjipavlou-Litina D. Histone deacetylase inhibitors (HDACIs). Structure-activity relationships: History and new QSAR perspectives. Med Res Rev 2012;32:1-165.

Xiang Y, Hou Z, Zhang Z. Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors. Chem Biol Drug Des 2012;79:760-70.

Sharma MC, Sharma S. Molecular modeling study of uracil-based hydroxamic acids-containing histone deacetylase inhibitors. Arab J Chem 2015;[Article In Press].

Bieliauskas AV, Pflum MK. Isoform-selective histone deacetylase inhibitors. Chem Soc Rev 2008;37:1402-13.

Rajaganapathy K, Sathiyasundar R, Ramesh GK, Kalaichelvan VK. Designing of anti-cancerous histone deacetylase inhibitors through mimicking of protein-protein interfaces. Int J Pharm Pharm Sci. 2014;6:208-12.

Lauffer BE, Mintzer R, Fong R, Mukund S, Tam C, Zilberleyb I, et al. Histone deacetylase (HDAC) inhibitor kinetic rate constants correlate with cellular histone acetylation but not transcription and cell viability. J Biol Chem 2013;288:26926-43.

Mai DD, Phuong DP, Kim OD, Khac VT, Hyunggu H, Woo HP, et al. Exploration of novel 5′(7′)-substituted-2′-oxospiro[1,3]dioxolane-2,3′-indoline-based N-hydroxypropenamides as histone deacetylase inhibitors and antitumor agents. Arab J Chem 2015;10:465-72.

Chun PS, Kim WH, Kim JS, Kang JA, Lee HJ, Park JY, et al. Synthesis and importance of bulky aromatic cap of novel SAHA analogs for HDAC inhibition and anticancer activity. Bull Korean Chem Soc 2011;32:1891-6.

Dung do TM, Dung PT, Oanh DT, Hai PT, Huong le TT, Loi VD, et al. Novel 3-substituted-2-oxoindoline-based N-hydroxypropenamides as histone deacetylase inhibitors and antitumor agents. Med Chem 2015;11:725-35.

Lan HT, Mai DD, Phuong DP, Thanh HP, Khac VT, Hyunggu H, et al. Novel 2-oxoindoline-based hydroxamic acids: Synthesis, cytotoxicity, and inhibition of histone deacetylation. Tetrahedron Lett 2015;56:6425-9.

Lai MJ, Huang HL, Pan SL, Liu YM, Peng CY, Lee HY, et al. Synthesis and biological evaluation of 1-arylsulfonyl-5-(N-hydroxyacrylamide)indoles as potent histone deacetylase inhibitors with antitumor activity in vivo. J Med Chem 2012;55:3777-91.

Ito A, Kawaguchi Y, Lai CH, Kovacs JJ, Higashimoto Y, Appella E, et al. MDM2–HDAC1-mediated deacetylation of p53 is required for its degradation. EMBO J 2002;21:6236-45.

Marvin Sketch. 6.5.1 ed. LLC 14th Floor, Cambridge Innovation Center, One Broadway Cambridge, MA 02142: Chem Axon; 2016.

Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular

docking to drug discovery. Curr Top Med Chem 2008;8:1555-72.

Wu R, Lu Z, Cao Z, Zhang Y. Zinc chelation with hydroxamate in histone deacetylases modulated by water access to the linker binding channel. J Am Chem Soc 2011;133:6110-3.

Abagyan RA, Totrov MM, Kuznetsov DA. ICM–a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 1994;15:488-506.

Pottel J, Therrien E, Gleason JL, Moitessier N. Docking ligands into flexible and solvated macromolecules 6. Development and application to the docking of HDACs and other zinc metalloenzymes inhibitors. J Chem Inf Model 2014;54:254-65.

Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC. Accurate calculation of the absolute free energy of binding for drug molecules. Chem Sci 2016;7:207-18.

Huong TT, Dung DT, Huan NV, Cuong LV, Hai PT, Huong LT, et al. Novel N-hydroxybenzamides incorporating 2-oxoindoline with unexpected potent histone deacetylase inhibitory effects and antitumor cytotoxicity. Bioorg Chem 2017;71:160-9.

Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF chimera – a visualization system for exploratory research and analysis. J Comput Chem 2004;25:1605-12.

Milano Chemometrics and QSAR Research Group. DRAGON for Windows (Software for Molecular Descriptor Calculator). 6.0 ed. Milano, Italy: Talete srl.

Todeschini R, Ballabio D, Consonni V, Mauri A, Pavan M. Mobydigs: Computer Software. 1.1 ed. New York: WILEY – VCH; 2009.

González MP, Terán C, Saíz-Urra L, Teijeira M. Variable selection methods in QSAR: An overview. Curr Top Med Chem 2008;8:1606-27.

Hachamovitch R, Shufelt C. Statistical analysis of medical data. Part III: Multivariable analysis. J Nucl Cardiol 2000;7:484-95.

Todeschini R, Consonni V, Mauri A, Pavan M. Detecting bad†regression models: Multicriteria fitness functions in regression analysis. Anal Chim Acta 2004;515:199-208.

Cruz-Monteagudo M, Borges F, Perez González M, Cordeiro MN. Computational modeling tools for the design of potent antimalarial bisbenzamidines: Overcoming the antimalarial potential of pentamidine. Bioorg Med Chem 2007;15:5322-39.

Atkinson AC. Plots, Transformations and Regression. Oxford: Clarendon Press; 1985.

Gramatica P. Principles of QSAR models validation: Internal and external. QSAR Comb Sci 2007;26:694-701.

Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012;52:3099-105.

Cruciani G, Pastor M, Guba W. VolSurf: A new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci 2000;11 Suppl 2:S29-39.

Gohlke H, Hendlich M, Klebe G. Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 2000;295:337-56.

Lombardi PM, Cole KE, Dowling DP, Christianson DW. Structure, mechanism, and inhibition of histone deacetylases and related metalloenzymes. Curr Opin Struct Biol 2011;21:735-43.

Austin PC, Steyerberg EW. The number of subjects per variable required in linear regression analyses. J Clin Epidemiol 2015;68:627-36.

Izenman AJ. Model Assessment and selection in multiple regression. In: Izenman AJ, editor. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. New York, NY: Springer New York; 2008. p. 107-58.

Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform 2010;29:476-88.

Rücker C, Rücker G, Meringer M. Y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 2007;47:2345-57.

Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics. Mannhold R, Kubinyi H, Folkers G, editors. Weinheim, Germany: Wiley VCH Verlag GmbH & Co.; 2009.

Pham-The H, González-Ãlvarez I, Bermejo M, Garrigues T, Le-Thi-Thu H, Cabrera-Pérez MÃ, et al. The use of rule-based and QSPR approaches in ADME profiling: A Case study on caco-2 permeability. Mol Inform 2013;32:459-79.

Seelig A, Blatter XL, Wohnsland F. Substrate recognition by P-glycoprotein and the multidrug resistance-associated protein MRP1: A comparison. Int J Clin Pharmacol Ther 2000;38:111-21.

Teague SJ, Davis AM, Leeson PD, Oprea T. The design of leadlike combinatorial libraries. Angew Chem Int Ed Engl 1999;38:3743-8.

Published

01-02-2018

How to Cite

Pham-the, H., and H. Le-thi-thu. “INTEGRATING STRUCTURE AND LIGAND-BASED APPROACHES FOR MODELLING THE HISTONE DEACETYLASE INHIBITION ACTIVITY OF HYDROXAMIC ACID DERIVATIVES”. Asian Journal of Pharmaceutical and Clinical Research, vol. 11, no. 2, Feb. 2018, pp. 198-06, doi:10.22159/ajpcr.2018.v11i2.22995.

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