In-Silico QSAR Studies of Some Pyrazolone Compounds



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Submitted Aug 30, 2023
Published Oct 18, 2023
10.24018/ejchem.2023.4.4.148

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In-silico studies are fascinating the workers/scientists working in the field of drugs designing. The present paper includes QSAR studies related to some novel substituted pyrazolone derivatives. A series of seven pyrazolone compounds is taken for these studies. All the compounds were evaluated for antimicrobial activity against six different microbes viz. bacterial and fungal microbes. Their reported antimicrobial activities were used for Quantitative Structure Activity Relationship (QSAR) studies. The correlation between different computed molecular descriptor of the compounds with their reported biological activities has also been studied and reported in the paper.

Keywords: Antimicrobial activities Pyrazolones QSAR studies

Introduction

It is one of the key objectives of organic and medicinal chemistry that workers working in these branches are extensively and actively involved in the designing and synthesis of molecule that possess potent therapeutic values and use. The rapid development of resistance towardsany present antimicrobial drugs puts a serious challenge before scientific community. Consequently, there is utmost requirement to develop new antimicrobial agents with potent activity against resistant microorganism [1]–[10].

Pyrazolone derivatives have a long history of application in pharmaceutical industry. Due to their wide range of biological activity, pyrazolones have received a considerable interest in the field of drug discovery and therefore, these ring compounds have been a relevant synthetic target in pharmaceutical industry. In fact, these heterocyclic compounds have the core structure of a number of drugs.

QSAR is one of the methods used to correlate the biological property of molecule with molecular descriptor derived from chemical structure. It is a mathematical model of a statistically validated correlation between the chemical structure and their activities [11], [12].

Keeping the above facts in mind and in continuation to the efforts in the study of novel compounds for antimicrobial infection, the author is hereby reporting the in-silico semi-empirical quantum chemical based QSAR studies of pyrazolone compounds in this present paper.

Materials And Methods

Seven pyrazolone derivatives, mentioned in Table I, have been identified and their antimicrobial activities against six different bacterial and fungal microbes have been recorded [13]. The reported antimicrobial activities of these pyrazolones derivatives against different bacterial and fungal microbes which are used for further studies are adopted as such from the literature [13] where the authors have compared these with standard viz. Chloramphenicol (for bacterial stains) and Fluconazole (for fungal stains). In this work, the reported activities are adopted for further studies as such. These activities are mentioned in Table I. The structures of these compounds (A–G) are given in the Fig. 1.

Compound Name compd. Molecular formula E. coli S. aureus B. anthrecis P. aeruginosa C. albicanes A. niger
A 3-methyl-5-oxo-N-phenyl-4,5-dihydro-1H-pyrazole-1-carbothioamide C11H11N3OS 17 19 15 16 17 16
B 3-methyl-5-oxo-N-(2-methylphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3OS 18 16 13 18 15 18
C 3-methyl-5-oxo-N-(3-methylphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3OS 18 16 14 16 17 18
D 3-methyl-5-oxo-N-(4-methylphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3OS 14 16 12 17 13 15
E N-(2-methyoxyphenyl)-3-methyl-5-oxo-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3O2S 15 17 15 15 16 17
F N-(3-methyoxyphenyl)-3-methyl-5-oxo-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3O2S 17 16 13 18 18 19
G N-(4-methyoxyphenyl)-3-methyl-5-oxo-4,5-dihydro-1H-pyrazole-1-carbothioamide C12H13N3O2S 19 19 13 20 15 14
Table I. Antimicrobial Activity Data in MIC (µg/ml) for the Compounds under Study

Fig. 1. Ball & stick structures of compounds under study.

Computational Details

AM1, PM3, MNDO and ZINDO Hamiltonia were used for studies of these compounds to develop one dimensional and later three descriptors 3D-QSAR equations. The structures of compounds were drawn using a professional version of HYPERCHEM software 8.0. The different descriptors studied were Surface Area (SAA), Surface Area Grid (SAG), Volume (VOL), Hydration Energy (HE), Refractivity (REF), Polarizability (POL), Total Energy (TE), Electronic Energy (EE), Heat of Formation (HF), Dipole Moment (DM) and Zero Point Energy (ZPE). All the computations using above mentioned software were done with the aid of Pentium core-2 duo machine with the following configuration

Intel ® core TM 2 Duo CPU

T5450@1.66 GHz

982 MHz 896 MB RAM

150 GB HDD

Windows-Microsoft windows XP software as an operating system were used to perform regression analyses to get QSAR equations. The statistical calculations were done with the help of MS EXCEL software [14].

Results and Discussion

The results are obtained for different descriptors after computing as mentioned in the experimental section above. Out of these results, the selected descriptors are used which provide the nearly perfect QSAR equations. The p(MIC) and Computed p(MIC) values on the basis of these framed QSAR equations are mentioned in the Tables II–V.

E. coli S. aureus B. anthrecis P. aeruginosa C. albicanes A. niger
p (MIC) C P (MIC) P(MIC) CP(MIC) P(MIC) C P(MIC) P(MIC) c p(MIC) p(MIC) C P (MIC) p(Mic) c p (MIC)
−1.23045 −1.31811 −1.27875 −1.27512 −1.17609 −1.18026 −1.20412 −0.69287 −1.23045 −1.23728 −1.20412 −1.21792
−1.25527 −1.31816 −1.20412 −1.19992 −1.11394 −1.12531 −1.25527 −0.61671 −1.17609 −1.19854 −1.25527 −1.23516
−1.25527 −1.31717 −1.20412 −1.21347 −1.14613 −1.11983 −1.20412 −0.71754 −1.23045 −1.17974 −1.25527 −1.20843
−1.14613 −1.31619 −1.20412 −1.20215 −1.07918 −1.09053 −1.23045 −0.91478 −1.11394 −1.13626 −1.17609 −1.19696
−1.17609 −1.31785 −1.23045 −1.25182 −1.17609 −1.18169 −1.17609 −0.58928 −1.20412 −1.21814 −1.23045 −1.22975
−1.23045 −1.31767 −1.23045 −1.24347 −1.17609 −1.17243 −1.25527 −0.62031 −1.25527 −1.20765 −1.27875 −1.23293
−1.27875 −1.3178 −1.27875 −1.24491 −1.17609 −1.17359 −1.30103 −0.62469 −1.17609 −1.2087 −1.14613 −1.23045
Table II. p(MIC) and Computed p(MIC) on the Basis of Framed QSAR Equations Using AM1 Methods
E. coli S. aureus B. anthrecis P. aeruginosa C. albicanes A. niger
p (MIC) C P (MIC) P (MIC) C P (MIC) P(MIC) C P (MIC) P(MIC) C P (MIC) p(MIC) C P (MIC) p(Mic) C P (MIC)
−1.23045 −1.96554 −1.27875 −1.27876 −1.17609 −0.32346 −1.20412 −1.26911 −1.23045 −1.23046 −1.20412 −1.20501
−1.25527 −2.02622 −1.20412 −1.20721 −1.11394 −0.19879 −1.25527 −1.31315 −1.17609 −1.19924 −1.25527 −1.23535
−1.25527 −1.97328 −1.20412 −1.2038 −1.14613 −0.1822 −1.20412 −1.30382 −1.23045 −1.17074 −1.25527 −1.22541
−1.14613 −1.90583 −1.20412 −1.20139 −1.07918 −0.17044 −1.23045 −1.26899 −1.11394 −1.15052 −1.17609 −1.19472
−1.17609 −1.94751 −1.23045 −1.24614 −1.17609 −0.79562 −1.17609 −1.28845 −1.20412 −1.20831 −1.23045 −1.25052
−1.23045 −1.99739 −1.23045 −1.24662 −1.17609 −0.79797 −1.25527 −1.32976 −1.25527 −1.21235 −1.27875 −1.2107
−1.27875 −1.98096 −1.27875 −1.24692 −1.17609 −0.79943 −1.30103 −1.31068 −1.17609 −1.21485 −1.14613 −1.21582
Table III. p(MIC) and Computed p(MIC) on the Basis of Framed QSAR Equations Using PM3 Methods
E. coli S. aureus B. anthrecis P. aeruginosa C. albicanes A. niger
p (MIC) C P (MIC) p(MIC) C P (MIC) P(MIC) C P (MIC) P(MIC) C P (MIC) p(MIC) C P (MIC) p(Mic) C P (MIC)
−1.23045 −1.21546 −1.27875 −1.27898 −1.17609 −1.17608 −1.20412 −1.20797 −1.23045 −1.23045 −1.20412 −1.21249
−1.25527 −1.26776 −1.20412 −1.2043 −1.11394 −1.11905 −1.25527 −1.25556 −1.17609 −1.19445 −1.25527 −1.24515
−1.25527 −1.20913 −1.20412 −1.20432 −1.14613 −1.1109 −1.20412 −1.22876 −1.23045 −1.16589 −1.25527 −1.23144
−1.14613 −1.18663 −1.20412 −1.20432 −1.07918 −1.10926 −1.23045 −1.20703 −1.11394 −1.16015 −1.17609 −1.16732
−1.17609 −1.20002 −1.23045 −1.24683 −1.17609 −1.17415 −1.17609 −1.21845 −1.20412 −1.20508 −1.23045 −1.25245
−1.23045 −1.25631 −1.23045 −1.24682 −1.17609 −1.17803 −1.25527 −1.27448 −1.25527 −1.21866 −1.27875 −1.21367
−1.27875 −1.2258 −1.27875 −1.24682 −1.17609 −1.17606 −1.30103 −1.24312 −1.17609 −1.21175 −1.14613 −1.20331
Table IV. p(MIC) and Computed p(MIC) on the Basis of Framed QSAR Equations Using MNDO Methods
E. coli S. aureus B. anthrecis P. aeruginosa C. albicanes A. niger
p (MIC) C P (MIC) p(MIC) C P (MIC) P(MIC) C P (MIC) P(MIC) C P (MIC) p(MIC) C P (MIC) p(Mic) C P (MIC)
−1.23045 −1.22116 −1.27875 −1.28834 −1.17609 −1.17663 −1.20412 −1.18354 −1.23045 −1.23024 −1.20412 −1.08387
−1.25527 −1.21228 −1.20412 −1.20774 −1.11394 −1.11076 −1.25527 −1.22779 −1.17609 −1.18385 −1.25527 −1.18217
−1.25527 −1.22864 −1.20412 −1.22728 −1.14613 −1.11711 −1.20412 −1.23624 −1.23045 −1.16445 −1.25527 −1.23823
−1.14613 −1.22323 −1.20412 −1.21824 −1.07918 −1.11067 −1.23045 −1.24457 −1.11394 −1.17343 −1.17609 −1.23931
−1.17609 −1.22769 −1.23045 −1.2667 −1.17609 −1.1848 −1.17609 −1.2273 −1.20412 −1.20557 −1.23045 −1.31815
−1.23045 −1.22027 −1.23045 −1.24473 −1.17609 −1.16689 −1.25527 −1.25142 −1.25527 −1.22744 −1.27875 −1.34077
−1.27875 −1.23819 −1.27875 −1.26859 −1.17609 −1.17616 −1.30103 −1.25743 −1.17609 −1.20374 −1.14613 −1.39615
Table V. p(MIC) and Computed p(MIC) on the Basis of Framed QSAR Equations Using ZINDO Methods

The QSAR equations that are obtained after computation as explained in the above section are 3D-QSAR equations. These equations for different pathogens using different methods correlating different descriptors are mentioned below:

QSAR Equations Applying AM1 Method

AM1/ E. coli

p(MIC) = 0.024476974 (Hyd E) + 0.001535544 (Vol) − 0.002059239 (SAG) − 1.28758951

n = 7

AM1/ P. aeruginosa

p(MIC) = 0.063609 (logP) − 0.01189 (Hyd E) + 0.001282 (SAG) − 1.98186

n = 7;

AM1/ A. niger

p(MIC) = 0.012788 (Hyd E) + 0.002262 (SAG) − 0.0013 (Vol) − 1.23802

n = 7;

AM1/ C. albican

p(MIC) = 0.024755 (Hyd E) + 0.008081 (logP) − 0.002486 (ZPE) − 1.45128

n = 7;

AM1/ B. antherus

p(MIC) = 0.015109 (Hyd E) + 0.042314 (logP) − 0.002744 (ZPE) − 1.53894

n = 7;

AM1/ S. aureus

p(MIC) = 0.041091 (log P) + 0.00339 (ZPE) − 0.002406 (Hyd E) − 1.78223

n = 7;

QSAR Equations Applying PM3 Method

PM3/ E. coli

p(MIC) = 0.068015 (Hyd E) − 0.13352 (logP) − 0.00142 (Vol) − 0.3731211

n = 7;

PM3/ P.aeruginosa

p(MIC) = 0.036733 (Hyd E) − 0.00134 (Vol) − 0.08456 (logP) + 069417

n = 7;

PM3/ A. niger

p(MIC) = 0.010543 (Hyd E) + 0.006917 (SAG) − 0.0042 (Vol) − 1.23791

n = 7;

PM3/ C. albican

p(MIC) = 0.019254 (Hyd E) + 0.018832 (logP) + 0.017841 (Pol) − 1.61703

n = 7;

PM3/ B. antherus

p(MIC) = 0.011206 (Hyd E) + 0.053223 (logP) − 0.02936 (Pol) − 1.80016

n = 7;

PM3/ S. aureus

p(MIC) = 0.0023 (Hyd E) + 0.52942 (logP) + 0.034585 (Pol) − 2.252248

n = 7;

QSAR Equations Applying MNDO Method

MNDO/ E. coli

p(MIC) = 0.041759 (Hyd E) − 0.00086 (Vol) − 0.08402 (logP) − 0.25849

n = 7;

MNDO/ P.aeruginosa

p(MIC) = 0.037992 (Hyd E) − 0.08924 (logP) − 0.00131(Vol) + 0.062422

n = 7;

MNDO/ A. niger

p(MIC) = 0.01879 (Hyd E) + 0.004444 (SAG) − 0.00236 (Vol) − 1.4261

n = 7;

MNDO/ C. albican

p(MIC) = 0.011706 (Hyd E) + 0.030577 (logP) + 0.023333 (Pol) − 1.83279

n = 7;

MNDO/ B. antherus

p(MIC) = 0.065228 (logP) + 0.003341 (Hyd E) − 0.027155 (Pol) − 1.97735

n = 7;

MNDO/ S. aureus

p(MIC) = 0.056513 (logP) − 5.7 × 10−5 (Hyd E) + 0.03567 (Pol) − 2.29989

n = 7

QSAR Equations Applying ZINDO Method

ZINDO/ E. coli

p(MIC) = 0.000667 (Hyd E) − 0.003653 (log P) + 0.000254 (SAG) − 1.33166

n = 7;

ZINDO/ P.aeruginosa

p(MIC) = 0.002923 (Hyd E) − 0.02142 (logP) − 0.000281 (Vol) − 1.00658

n = 7;

ZINDO/ A. niger

p(MIC) = −0.0063 (Hyd E) + 0.001001 (REF) − 0.00463 (ZPE) − 0.14658

n = 7;

ZINDO/ C. albican

p(MIC) = 0.065576 (logP) − 0.00016 (SAA) + 0.059963 (Pol) − 2.91461

n = 7;

ZINDO/ B. antherus

p(MIC) = 0.061931 (logP) + 0.00011 (SAA) − 0.0008 (Hyd E) − 1.26357

n = 7;

ZINDO/ S. aureus

p(MIC) = 0.00016 (SAA) + 0.037396 (logP) + 2.17 × 10−5 (Vol) − 1.33564

n = 7;

The correlation coefficient (cc) for these 3D-QSAR equations ranges from 0.670 to 0.999. This shows the nearly perfect correlation between parameters under study. Similarly, standard error (SE) for the above 3D-QSAR equations ranges from 0.020 to 0.060 which is indicative of nearly perfect equations which are selected and presented above from these studies. Some of the correlation diagrams between observed and computed values of p (MIC) on the basis of above computed 3D-QSAR equations are shown in the Figs. 2A–2F below.

Fig. 2. (A) Correlation graph between p(MIC) and computed p(MIC).

Fig. 2. (B) Correlation graph between p(MIC) and computed p(MIC).

Fig. 2. (C) Correlation graph between p(MIC) and computed p(MIC).

Fig. 2. (D) Correlation graph between p(MIC) and computed p(MIC).

Fig. 2. (E) Correlation graph between p(MIC) and computed p(MIC).

Fig. 2. (F) Correlation graph between p(MIC) and computed p(MIC).

Conclusions

In-silico studies are studies of interest among workers. Scientists are using different methods and software for these studies [14]–[28]. Studies present in this paper are In-silico studies using different semi-empirical methods viz. AM1, PM3, MNDO and ZINDO are somehow useful, reasonable and less time consuming in establishing the QSAR models for the pyrazolone compounds taken for studies for which results are reported in this paper. Among the methods employed, AM1 method comparatively provides reasonably good results.

References

  1. Mariappan G, Saha BP, Sutharson L, Singh A, Garg S, Pandey L, et al. The diverse pharmacological importance of pyrazolone derivatives. J Pharma Res. 2010;3:2856–9.
     Google Scholar
  2. Micha L. Hypersenstivity to pyrazolones. Thorax. 2000;55:572–4.
     Google Scholar
  3. Halen PK, Yadav MR, Murumkar P, Giridhar R. Prodrug designing of NSAIDs. Mini Rev Med Chem. 2009;9:124–39.
     Google Scholar
  4. Agrawal RK, Arora K. Synthesis and characterization of dioxouranium (VI) complexes of pyrazolone ligands. Polish J Chem. 1993;67:25.
     Google Scholar
  5. Agrawal RK, Arora K. Synthesis and characterization of thorium (IV) adducts of some pyrazolone ligands. Synth and React and Met-Org Chem. 1993;23:653.
     Google Scholar
  6. Tong A, Akama Y, Tanaka S. Reversed–phase high performance liquid chromatography of aluminium (III) and indium (III) with 1-phenyl-3- methyl-4-benzoyl-5-pyrazolone. J Chromatogr. 1989;478:408–14.
     Google Scholar
  7. Chauhan PMS, Singh S, Chatterjee RK. Antifilarial profile of substituted pyrazoles: a new class of antifilarial agents. Indian J Chem Sect B. 1993;32:858–61.
     Google Scholar
  8. Ziarati A, Safaei Ghomi A, Rohani S. Sonochamically synthesis of pyrazolone using reusable catalyst CuI nanoparticles that was prepared by sonication. Ultrason Sonochem. 2013;20:1069–75.
     Google Scholar
  9. Vijesh AM, Isloor AM, Prabhu V, Ahmed S, Malladi S. Synthesis of some new pyrazolone derivatives as potent antimicrobial agents. Eur J Med Chem. 2010;45:5460–4.
     Google Scholar
  10. Khan R, UddinMI, Alam MS, Hossain MM, Islam MR. Synthesis and preliminary evaluation of brominated 5-methyl-2,4-digydropyrazol-3-one and its derivatives as cytotoxic agents. Bangladesh J Pharmacol. 2008;3:27–35.
     Google Scholar
  11. Arora K. 3D-QSAR studies of some pharmacological important compounds. Int J Pharm Bio Sci. 2014;5(1):571–9.
     Google Scholar
  12. Arora K, Nathani V. In vitro antimicrobial studies of some pyrazolones and their SAR studies. Asian J Chemistry. 2012;24(12):5803–5.
     Google Scholar
  13. Gupta P, Gupta JK, Bansal S, Halve AK. Synthesis and in-vitro antimicrobial studies of some new pyrazolones. Int. J Curr Pharma Rese. 2015;7:25–9.
     Google Scholar
  14. Arora K, Sharma AK, Parmar A. Comparitive studies of experimental and simulated IR spectra of some selected flavonoid compounds. Eur J Advan Chemis Rese. 2022;3(3):25–31.
     Google Scholar
  15. Daoui O, El Mouhi R, Barghady F, MkhayarK, Elkhattabi S, Chtita S, et al. 3D-QSARmodeling, molecular docking and drug-like properties investigations of novel heterocyclic compounds derived from magnolia officinalis as hit compounds against NSCLC. Moroccan J Chemi. 2022;10:34498. doi: 10.48317/IMIST.PRSM/morjchem-v10i4.34498.
     Google Scholar
  16. Raina K, Bouachrine M, Ouammou AA. Combined 3D-QSAR and molecular docking analysis of styrylquinoline derivatives as potent anti-cancer agents. Physical Chemistry Research. 2022;10:345. doi: 10.22036/PCR.2021.304969.1967.
     Google Scholar
  17. Gopinath P, Kathiravan MK. Molecular field-based QSAR studies and docking analysis of mercaptoquinazolinone benzene sulponamide derivatives against hCA XII. Rasayan J Chem. 2022;15:686–99. doi: 10.31788/RJC.2022.1516767.
     Google Scholar
  18. Shyanfar S, Shyanfar A. Comparison of various methods for validity evaluation of QSAR models. BMC Chemist. 2022;1:1–9. doi: 10.1186/s13065-022-00856-4.
     Google Scholar
  19. Rhabori S, El Aissouq A, Chtita S, Khalil F. In silico drug discovery of new anti-breast cancer inhibitors based on 3D-QSAR, molecular docking and ADMET investigation. Sci Forum, Published: 01 November 2022 by MDPI in 8th International Electronic Conference on Medicinal Chemistry session Emerging technologies in drug discovery, Online conference, 2022.
     Google Scholar
  20. Ghaleb A, Aouidate A, El Ayouchia HB, Aarjane M, Anane H, Stiriba SE. In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening. J Biomole Struct Dynam. 2022;40:143–53. doi: 10.1080/07391102.2020.1808530.
     Google Scholar
  21. DengR,HeW,GuoH, Su Z, WuW, Wu Z. In silico design of RORγ inverse agonists based on 3D-QSAR and molecular docking. New J Chemis. 2022;46:8464–77.
     Google Scholar
  22. Mustapha Abdullahi AB, Uzairu A, Shallangwa GA, Mamza PA, Ibrahim MT. In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions. Heliyon. 2022;e10101:1–21.
     Google Scholar
  23. Pingaew R, Prachayasittikul V, Worachartcheewan A, Thongnum A, Prachayasittikul S, Ruchirawat S, et al. Anticancer activity and QSAR study of sulfur-containing thiourea and sulfonamide derivatives. Heliyon. 2022;8:e10067.
     Google Scholar
  24. KassarMJ, EzzatMO. Theoretical calculations andmolecular design of novel quinoline derivatives as antibacterial drugs. Egyptian J Chemsitry. 2023;66:119–28. doi: 10.21608/EJCHEM.2022.151949.6577.
     Google Scholar
  25. El Reda M, Khalil El K, Ayoub K, Larbi El M, Mohammed AA, Tahar L, et al. 3D-QSAR, ADMET, and molecular docking studies for designing new 1,3,5-triazine derivatives as anticancer agents. Egypt J Chem. 2022;65:9–18. doi: 10.21608/EJCHEM.2022.76000.3715.
     Google Scholar
  26. Amal OAI, Ahmed MM, Mohamed MT, Hussain T, Aboubakr HA. Synthesis and in silico docking study of some new quinazolin-2,4-diones targeting COVID-19 (SARS-Cov-2) main protease: a search for AntiCovid19 drug candidates. Egypt J Chem. 2022;65:1553–60. doi: 10.21608/EJCHEM.2022.117407.5296.
     Google Scholar
  27. Bansal P, Tuli HS, Saini AK, Saini RV, Dhama K, Mohapatra RK, et al. In Silico Targeting of influenza virus haemagglutinin receptor protein using Diosmetin, Tangeritin, and Anthocyanidins as potential drugs. J Experiment Biolo Agricul Sci. 2022;10:995–1002. doi: 10.18006/2022.10(5).995.1002.
     Google Scholar
  28. Radha M, Jyothsana M, Eswaramoorthy K, Jyotsna A, Suganya J, Priya R, et al. In silico QSAR studies on Indian medicinal plant of Coriandrum sativam (Coriander) and Centella asiatica(Gotu Kola) as fungal inhibitors Int. Int J Adv Res Biol Sci. 2022;9(5):Special Issue 1:173–84. doi: 10.22192/ijarbs.
     Google Scholar