Development of Predictive Antioxidant Models for 1,3,4-Oxadiazoles by Quantitative Structure Activity Relationship

Year 2019, , 103 - 114, 15.06.2019

Ikechukwu Alisi , Adamu Uzairu Stephen Eyije Abechi , Suleiman Ola Idris

https://doi.org/10.18596/jotcsa.406207

Cited By: 4

Abstract

The
free radical scavenging properties of 1,3,4-oxadiazoles have been explored by
the application of quantitative structure activity relationship (QSAR) studies.
The entire data set of the oxadiazole derivatives were minimized and
subsequently optimized at the density functional theory (DFT) level in combination
with the Becke's three-parameter Lee-Yang-Parr hybrid functional (B3LYP) hybrid
functional and 6-311G* basis set. Kennard
Stone algorithm was employed in data division into training and test
sets. The training set were employed in QSAR model development by genetic
function algorithm (GFA), while the test set were used to validate the
developed models. The applicability domain of the developed model was accessed
by the leverage approach. The varation inflation factor, degree of contribution
and mean effect of each descriptor were calculated.  Quantum chemical and molecular descriptors
were generated for each molecule in the data set. Five predictive models that
met all the requirements for acceptability with good validation results were
developed. The best of the five models gave the following validation results:



















,

 

,

 and ^c

 

,  rmsep

 . The QSAR analysis revealed that the sum of
e-state descriptors of strength for potential hydrogen bonds of path length 9 (SHBint9)
and topological radius (topoRadius) are the most crucial descriptors that influence
the free radical scavenging activities of
1,3,4-oxadiazole derivatives.

Keywords

Antioxidants, 1 3 4-OXadiazoles, Descriptors, Model validation, QSAR

References

• 1. Brewer MS. Natural Antioxidants: Sources, Compounds, Mechanisms of Action, and Potential Applications. Compr. Rev. Food Sci. Food Saf., 2011 June;10: 221-247. DOI: 10.1111/j.1541-4337.2011.00156.x
• 2. Valko M, Leibfritz D, Moncol J, Cronin MTD, Mazur M, Telser J. Free radicals and antioxidants in normal physiological functions and human disease. Int. J. Biochem. Cell Biol. 2007; 39(1):44-84.
• 3. Kotaiah Y, Harikrishna N, Nagaraju K, Venkata RC. Synthesis and antioxidant activity of 1,3,4-oxadiazole tagged thieno[2,3-d]pyrimidine derivatives. Eur J Med Chem. 2012 Dec; 58:340-5. Doi: 10.1016/j.ejmech.2012.10.007
• 4. Zhang Y, Zou B, Chen Z, Pan Y, Wang H, Liang H, Yi X. Synthesis and antioxidant activities of novel 4-Schiff base-7-benzyloxy-coumarin derivatives. Bioorg Med Chem Lett. 2011 Nov;21(22):6811-5. Doi: 10.1016/j.bmcl.2011.09.029
• 5. Kang TS, Jo HO, Park WK, Kim JP, Konishi Y, Kong JY, Park NS, Jung YS. Synthesis and antioxidant activities of 3,5-dialkoxy-4-hydroxycinnamamides. Bioorg Med Chem Lett. 2008 Mar; 18(5):1663-7. doi: 10.1016/j.bmcl.2008.01.061
• 6. Ma L, Xiao Y, Li C, Xie ZL, Li DD, Wang YT, Ma HT, Zhu HL, Wang MH, Ye YH. Synthesis and antioxidant activity of novel Mannich base of 1,3,4-oxadiazole derivatives possessing 1,4-benzodioxan. Bioorg Med Chem. 2013 Nov; 21(21):6763-70. Doi: 10.1016/j.bmc.2013.08.002
• 7. Lu J, Li C, Chai YF, Yang DY, Sun CR. The antioxidant effect of imine resveratrol analogues. Bioorg Med Chem Lett. 2012 Sep; 22(17):5744-7. doi: 10.1016/j.bmcl.2012.06.026
• 8. Molyneux, P. The use of the stable free radical diphenylpicrylhydrazyl (DPPH) for estimating antioxidant activity. Songklanakarin J. Sci. Technol., 2004, 26(2): 211-219.
• 9. Hamid AA, Aiyelaagbe OO, Usman LA, Ameen OM, Lawal A. Antioxidants: Its medicinal and pharmacological applications. Afr. J. Pure Appl. Chem. 2010 Aug; 4(8), 142-151.
• 10 Mohana KN, Kumar CBP. Synthesis and Antioxidant Activity of 2-Amino-5-methylthiazol Derivatives Containing 1,3,4-Oxadiazole-2-thiol Moiety. ISRN Org Chem. 2013 Aug; 2013: 620718. doi: 10.1155/2013/620718
• 11. Aanandhi MV, Mansoori MH, Shanmugapriya S, George S, Shanmugasundaram P. Synthesis and In- vitro antioxidant activity of substituted Pyridinyl 1, 3, 4 oxadiazole derivatives. Res. J. Pharm., Biol. Chem. Sci., 2010 Oct-Dec;1(4), 1083-1090.
• 12. Ogadimma AI, Adamu U. Quantitative Structure Activity Relationship Analysis of Selected Chalcone Derivatives as Mycobacterium tuberculosis Inhibitors. OALib. 2016 Mar; 3: e2432 doi: http://dx.doi.org/10.4236/oalib.1102432.
• 13. Sanmati KJ, Achal M. 3D QSAR Analysis on Isatin Derivatives as Carboxyl Esterase Inhibitors Using K-Nearest Neighbor Molecular Field Analysis. J Theor Comput Sci. 2015 May; 2:124. Doi:10.4172/2376-130X.1000124
• 14. Aicha K, Belaidi S, Lanez T. Computational Study of Structure-Property Relationships for 1,2,4-Oxadiazole-5-Amine Derivatives. Quantum Matter. 2016 Feb; 5(1): pp. 45-52(8). DOI:10.1166/qm.2016.1253
• 15. Yokoi T, Nakagawa Y, Miyagawa H. Quantitative structure-activity relationship of substituted imidazothiadiazoles for their binding against the ecdysone receptor of Sf-9 cells. Bioorg. Med. Chem. Lett., 2017 Oct; 27(23):5305-5309. DOI: 10.1016/j.bmcl.2017.10.013
• 16. Alisi IO, Uzairu A, Abechi SE, Idris SO, Quantitative structure activity relationship analysis of coumarins as free radical scavengers by genetic function algorithm. Phys. Chem. Res. 2018 Jan; 6(1):208-222. DOI:10.22036/pcr.2017.95755.1409
• 17. Li Z, Wan H, Shi Y. Ouyang ,P.,. Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch. J Chem Inf Comput Sci. 2004 Sep-Oct; 44(5): 1886–1890. DOI:10.1021/ci049794h
• 18. Shao Y, Molnar LF, Jung Y, Kussmann J, Ochsenfeld C, Brown ST, Gilbert ATB, Slipchenko LV, Levchenko SV, O’Neill DP, DiStasio RA, Lochan RC, Wang T, Beran GJO, Besley NA, Herbert JM, Lin CY, Van Voorhis T, Chien SH, Sodt A, Steele RP, Rassolov VA, Maslen PE, Korambath PP, Adamson RD, Austin B, Baker J, Byrd EFC, Dachsel H, Doerksen RH, Dreuw A, Dunietz BD, Dutoi AD, Furlani TR, Gwaltney SR, Heyden A, Hirata S, Hsu CP, Kedziora G, Khalliulin RZ, Klunzinger P, Lee AM, Lee MS, Liang WZ, Lotan I, Nair N, Peters B, Proynov EI, Pieniazek PA, Rhee YM, Ritchie J, Rosta E, Sherril CD, Simmonett AC, Subotnik JE, Woodcock III HL, Zhang W, Bell AT, Chakraborty AK, Chipman DM, Keil FJ, Warshel A, Hehre WJ, Schaefer HF, Kong J, Krylov AI, Gill PMW, Head-Gordon M. Advances in methods and algorithms in modern quantum chemistry program package. Phys. Chem. Chem. Phys., 2006 June; 8(27): 2006, pages 3172-3191. Doi.org/10.1039/B517914A
• 19. Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B Condens. Matter, 1988 Jan; 37(2): 785-789. DOI:https://doi.org/10.1103/PhysRevB.37.785
• 20. Yap C. W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011 May; 32(7): 1466–1474. Doi: 10.1002/jcc.21707
• 21. Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R. Comb. Chem. High Throughput Screening. 2014 June; 136: 147-154. DOI: 10.1016/j.chemolab.2014.05.010
• 22. Ambure P, Aher RB, Gajewicz A, Puzyn T. NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modelling. Chemom. Intell. Lab. Syst. 2015 Oct; 147: 1-13. doi.org/10.1016/j.chemolab.2015.07.007
• 23. Brignole M, Auricchio A, Baron-Esquivias G, Bordachar P, Boriani G, Breithardt OA, Cleland J, Deharo JC, Delgado V, Elliott PM, Gorenek B, Israel CW, etal., 2013 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy: the Task Force on cardiac pacing and resynchronization therapy of the European Society of Cardiology (ESC). Developed in collaboration with the European Heart Rhythm Association (EHRA). Eur Heart J. 2013 Aug;34(29):2281-329. doi: 10.1093/eurheartj/eht150.
• 24. Golbraikh A1, Tropsha A. Beware of q2! J Mol Graph Model. 2002 Jan;20(4):269-76. Doi.org/10.1016/S1093-3263(01)00123-1
• 25. Todd MM, Harten P, Douglas MY, Muratov EN, Golbraikh A, Zhu H, Tropsha A. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? J. Chem. Inf. Model. 2012 Oct; 52 (10): 2570–2578. DOI: 10.1021/ci300338w
• 26. Mitra I, Saha A, Roy K. Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols. Sci Pharm. 2011 Jan-Mar; 79(1):31-57. Doi: 10.3797/scipharm.1011-02.
• 27. Das ND, Roy K. Development of classification and regression models for Vibrio fischeri toxicity of ionic liquids: Green solvents for the future. Toxicol. Res. 2012 July; 1: 186-195. DOI:10.1039/C2TX20020A
• 28. Tropsha A, Gramatica P, Gombar, VK. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 2003 April; 22(1), 69-77. DOI: 10.1002/qsar.200390007
• 29. Kar S, Roy K. Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs. Indian J Biochem Biophys. 2011 Apr;48(2):111-22.
• 30. Roy PP, Roy K. On some aspects of variable selection for partial least squares regression models QSAR Comb. Sci. 2008 March; 27(3): 302-313. Doi:10.1002/qsar.200710043
• 31. Roy K, Mitra I. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen. 2011 Jul;14(6):450-74. DOI:10.2174/138620711795767893
• 32. Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN. Some case studies on application of "r(m)2" metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J Comput Chem. 2013 May 5;34(12):1071-82. doi: 10.1002/jcc.23231
• 33. Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Inf. 2010 July; 29(6-7): 476–488. DOI: 10.1002/minf.201000061
• 34. Roy K, Kar S, Das RN. Statistical Methods in QSAR/QSPR. Springer Briefs in Molecular Science. 2015. p. 37-59.
• 35. Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz GY, Perkins R, Roberts D, Schultz T, Stanton DW, van de Sandt JJ, Tong W, Veith G, Yang C. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim. 2005 Apr;33(2):155-73.
• 36. Gramatica P, Giani E, Papa E. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction. J. Mol. Graphics Modell. 2006 Aug; 25(6):755-766 DOI: 10.1016/j.jmgm.2006.06.005
• 37. P. Gramatica, Chemiometric methods and theoretical mo¬lecular descriptors in predictive QSAR modeling of the environ¬mental behavior of organic pollutants. In: T. Puzyn et al. (eds.), Recent Advances in QSAR Studies, (Dordrecht, Hei¬delberg, London, 2010), pp. 327-366.
• 38. Sharma BK, Singh P. Chemometric Descriptor Based QSAR Rationales for the MMP-13 Inhibition Activity of Non-Zinc-Chelating Compounds. Med chem. 2013 April; 3:168-178. Doi:10.4172/2161-0444.1000134
• 39. Mitra I, Saha A, Roy K. Chemometric modeling of free radical scavenging activity of flavone derivatives. Eur J Med Chem. 2010 Nov;45(11):5071-9. Doi: 10.1016/j.ejmech.2010.08.016
• 40. Veerasamy R, Rajak H, Abhishek J, Sivadasan S, Varghese CP, Agrawal RK. Validation of QSAR Models - Strategies and Importance. Int. J. Drug Des. Discovery 2011 July – September; 2(3): 511-519.
• 41. Saaidpour S. Quantitative Modeling for Prediction of Critical Temperature of Refrigerant Compounds. Phys. Chem. Res. 2016 March; 4(1): 61-71. DOI: 10.22036/pcr.2016.11759
• 42. Baumann K. Chance Correlation in Variable Subset Regression: Influence of the Objective Function, the Selection Mechanism, and Ensemble Averaging. QSAR Comb. Sci. 2005 November; 24(9):1033–1046. DOI: 10.1002/qsar.200530134.