1. INTRODUCTION:

The majority of human diseases caused by fungal pathogenic species belong to the genus namely Aspergillus, Candida, Cryptococcus, Histoplasma, Mucor, and Pneumocystis.^1,2. Among these fungal species, Candida species are the most common pathogens, and account for about 8% of nosocomial invasive infections³. A new Candida species, Candida auris, has been rapidly emerging and creating global worry due to its high fatality rate (30-60%)⁴. In immunocompromised patients, studies have revealed the rise of C. auris antifungal resistance⁴.

Centre for Disease Control and Prevention (CDC) in the USA has declared it a global threat (CDC 2019). Most of the reported cases (about 90%) of C. auris are inherently resistant to fluconazole. There have also been reports of resistance to all major classes of antifungal drugs polyenes (amphotericin B), azoles (miconazole, fluconazole), and echinocandins (caspofungin)⁶. Another issue confronting doctors and scientists around the world is the toxicity and low absorption of currently available antifungal medicines⁷. As a result, developing innovative, safe, and effective antifungal medicines against C. auris with improved pharmacodynamics and pharmacokinetics properties is critical. Recently, vaccine candidates have been designed against C. auris infections^8,9.

The UPR route is a proteostatic pathway that maintains homeostasis inside the cell due to misfolded or unfolded proteins in the endoplasmic reticulum¹⁰. In previous studies the UPR element Hac1p has been shown to play an important role in the pathogenesis of different fungi such as Cryptococcus neoformans, Aspergillus fumigatus, Candida albicans, and Candida parapsilosis ^11,12. As the UPR elements, Hac1p and Ire1p play an important role in the fungal pathogenesis they could be potential targets novel antifungal drugs. Low bioavailability, drug resistance, and toxicity are main issues with the current antifungal stewardship. In this study various phytochemicals were explored as new candidate drugs for C. auris¹³. Similar studies were shown against Trichophyton rubrum, C. neoformans and C. albicans^14–18. Compound selected in the present study like Chloroquine phosphate and Flinderole-B is an anti-malarial drug¹⁹. Similarly Mangiferin were shown to be antiviral, anti-diabetic, antimicrobial, anti-sclerotic properties²⁰^-²². Butanoic acid has shown a role in the treatment of inflammatory intestinal diseases²³. Friedelin isolated from the Azima tetracantha has antipyretic, analgesic, and anti-inflammatory²⁴. As these compounds have already shown a plethora of therapeutic effects, their antifungal potential by inhibiting UPR elements is evaluated in this study using in silico tools.

2. MATERIAL AND METHODS:

2.1. Prediction of the bioactivity, molecular properties, and the drug-likeness of the compounds:

The compounds selected in this study are listed in Table 1. The SMILES structures of the compounds were obtained from the PubChem²⁵. The SMILES structures of the compounds were used as input in Molinspiration tool to determine their bioactivity. Various molecular properties like partition coefficient (LopP), molecular weight, number of hydrogen bonds, total polar surface area (TPSA). Further these properties were evaluated by Lipinski’s rule of 5²⁶. Further, various bioactivities of the compounds such as kinase (KI), protease (PI), or enzyme (EI) inhibitors, ion-channel modulators, GPCR ligand, or nuclear receptor ligand was predicted by Molsoft webserver. For the prediction of the ADMET properties admetSAR2 webserver was used²⁷. The admetSAR2 server gives information about drug absorption, blood-brain barrier human intestinal absorption of compounds, the ability of the compounds to cross the blood-brain barrier, AMES toxicity, hepatotoxicity, carcinogenicity and several other properties of the compounds.

2.2. Structure determination and Molecular docking:

The amino acid sequences of said proteins were obtained from the Candida Genome Database²⁸. C. auris strain B8441_V2, protein sequence B9J08_001826, and B9J08_001611 were used to predict the tertiary structures of Hac1p and Ire1p respectively using I-Tasser server²⁹. Finally, the tertiary structure of the proteins were validated by generating Ramachandran plot using the Rampage webserver³⁰. Molecular docking was performed using the PatchDock server³⁰. Before docking the SDF file of the compounds was converted to a PDB file by MarvinView software. Then, the PDB file of compounds was used as ligand and the PDB files of tertiary structures of Hac1p and Ire1p were used as receptors. While docking protein-small ligand docking was selected and an RMSD value of 1.5 was used.

2.3. Molecular dynamics simulation:

To comprehend the molecular dynamics of all the top-scoring docked sets, molecular dynamics simulation was done by Amber 18 tool³¹. Molecular topographies were produced utilizing the vestibule³². With TIP3P water box and sodium particles, all the frameworks were solvated and maintained to a neutral regime. Delicate energy minimization in two stages was performed. Particle Mesh Ewald calculation for short- and long-range associations was likewise utilized. A sum of 50 ns recreation for every framework was performed³³^-³⁴.

3. RESULTS:

3.1 Prediction of the bioactivity, molecular properties, and the drug-likeness of the compoundsl:

Among all the compounds, 1,7-Dihydroxy-3-methoxyxanthone, Plumbagin, Butanoic acid, Alpha pyrone, Drummondin-E, and Gallic falls as drugs Lipinski’s rule of five (refer to nVio in Table 1). Molecules with bioactivity score above 0 are most likely to have considerable bioactivity, while -0.50 to 0 will be moderately bioactive and below -0.50 is expected to be inactive. 1,7-Dihydroxy-3-methoxyxanthone was determined to be a nuclear receptor ligand (NRL). Other abbreviations mentioned in the table are enzyme inhibitor (EI), kinase inhibitor (KI), GPCR ligand, ion channel modulator (ICM). Betulinic acid exhibited the most bioactivity. It was predicted to be GPCR ligand, EI, KI, a protease inhibitor (PI), NRL, and ICM. Drummondin-E and Mangiferin are predicted to be NRL and EI. Flinderole-B is likely to be GPCR ligand and EI. Friedelin is expected to be NRL and EI. The bioactivities of all the compounds are listed in Table 2. Finally, chloroquine phosphate, Betulinic acid, Drummondin-E, Ursolic acid, Flinderole-B, Oleanolic acid, Mangiferin, and Stigmasterol were predicted to be potential drug-like molecules based on their drug-likeness score. The drug-likeness score (DLS) of all the compounds is listed in Table 1. Compounds with a drug-likeness score between 0-2.5 are more likely to be drugs.

Table 1: Molecular properties of the compounds

S. N	Compound	LogP	TPSA	Natom	MW	nON	nOHN	nVio	nRot	Volume	DLS
1	Linoleic acid	6.86	37.3	20	280.45	2	1	1	14	312.65	-0.30
2	Butanoic acid	1	37.3	6	88.11	2	1	0	2	89.80	-1.28
3	Benz(a)Anthracene, 1,12-dimethyl	6.24	0	20	256.35	0	0	1	0	249.14	-1.33
4	Oleic acid	7.58	37.3	20	282.47	2	1	1	15	318.84	-0.30
5	Palmitic acid	7.06	37.3	18	256.43	2	1	1	14	291.42	-0.54
6	Mangiferin	-0.16	201.27	30	422.34	11	8	2	2	335.80	2.25
7	Plumbagin	1.78	54.37	14	188.18	3	1	0	0	163.16	-0.23
8	Betulinic acid	7.04	57.53	33	456.71	3	2	1	2	472.04	0.25
9	1,7-Dihyroxy-3-methoxyxanthone	4.47	79.9	24	324.33	5	2	0	3	285.87	0.01
10	Ursolic acid	6.79	57.53	33	456.71	3	2	1	1	471.49	0.66
11	Oleanolic acid	6.72	57.53	33	456.71	3	2	1	1	471.14	0.37
12	Chloroquine phosphate	5	28.16	22	319.88	3	1	1	8	313.12	1
13	Alpha pyrone	3.95	30.21	14	194.27	2	0	0	5	201.73	-0.85
14	Flinderole-B	7.58	27.2	38	508.75	4	1	2	9	516.62	0.37
15	Gallic acid	0.59	97.98	12	170.12	5	4	0	1	135.1	-0.22
16	Drummondin-E	4.68	141.36	33	498.57	8	4	0	9	463.71	0.38
17	Stigmasterol	7.87	20.23	30	412.7	1	1	1	5	450.33	0.62

Table 2. ADMET properties

S. N	Compound	GPCR	ICM	KI	NRL	PI	EI	BBB	HIA	AMES toxicity	Hepatotoxicity	Carcinogenicity
1	Linoleic acid	0.29	0.17	-0.16	0.31	0.12	0.38	+	+	Non-toxic	Non-toxic	Non-carcinogen
2	Butanoic acid	-3.34	-3.52	-3.77	-3.13	-3.32	-3.18	+	+	Non-toxic	Non-toxic	Non-carcinogen
3	Benz(a)Anthracene, 1,12-dimethyl	-0.03	-0.17	0.05	-0.01	-0.23	0.06	+	+	Toxic	Toxic	Non-carcinogen
4	Oleic acid	0.17	0.07	-0.22	0.23	0.07	0.27	+	+	Non-toxic	Non-toxic	Non-carcinogen
5	Palmitic acid	0.02	0.06	-0.33	0.08	-0.04	0.18	+	+	Non-toxic	Non-toxic	Non-carcinogen
6	Mangiferin	0.06	-0.04	0.06	0.14	-0.03	0.48	-	+	Toxic	Toxic	Non-carcinogen
7	Plumbagin	-0.84	-0.31	-0.57	-0.69	-1.00	0.02	+	+	Toxic	Toxic	Non-carcinogen
8	Betulinic acid	0.31	0.03	0.50	0.93	0.14	0.55	+	+	Non-toxic	Non-toxic	Non-carcinogen
9	1,7-Dihyroxy-3-methoxyxanthone	-0.15	-0.26	-0.15	0.40	-0.20	0.27	-	+	Toxic	Toxic	Non-carcinogen
10	Ursolic acid	0.28	-0.03	-0.5	0.89	0.23	0.69	+	+	Non-toxic	Non-toxic	Non-carcinogen
11	Oleanolic acid	0.28	-0.06	-0.4	0.77	0.15	0.65	+	+	Non-toxic	Non-toxic	Non-carcinogen
12	Chloroquine phosphate	0.32	0.32	0.38	-0.19	0.05	0.11	+	+	Toxic	Toxic	Non-carcinogen
13	Alpha pyrone	-0.91	-0.79	-1.27	-0.84	-0.67	-0.26	+	+	Non-toxic	Non-toxic	Non-carcinogen
14	Flinderole B	0.27	-0.12	0.09	-0.06	-0.08	0.17	+	+	Toxic	Toxic	Non-carcinogen
15	Gallic acid	-0.77	-0.26	-0.88	-0.52	-0.94	-0.17	-	+	Non-toxic	Non-toxic	Non-carcinogen
16	Drummondin E	0.32	0.32	0.38	-0.19	0.05	0.11	+	+	Non-toxic	Toxic	Non-carcinogen
17	Stigmasterol	0.12	-0.08	-0.48	0.74	-0.02	0.53	+	+	Non-toxic	Non-toxic	Non-carcinogen

3. 2 Prediction of ADMET properties:

After the overall analysis of ADMET properties of the compounds Flinderole B, Linoleic acid, Butanoic acid, Oleic acid, Palmitic acid, Betulinic acid, Ursolic acid, Oleanolic acid, Alpha pyrone, and Stigmasterol were found to have the ability to cross the blood-brain barrier (BBB), and could permeate and get absorbed in the human intestine (HIA). These two compounds were also found to be non-carcinogenic. The detailed ADMET properties of the other compounds are in Table 2.

3. 3 Protein structure determination and molecular docking of the Hac1 and Ire1:

The C-score of the best 3D model of Hac1p is -2.35. The C-score of the best 3D model of Ire1p is -0.84. Typically the C-score value ranges from -5 to 2 and higher C-score values imply the model of higher significance²⁹. Altogether 87.2% of the residues in Hac1 and 86.1% of Ire1 were present in favored or allowed regions of the Ramachandran plot. Based on parameters used Flinderole-B, Drummondin-E, Betulinic acid, Ursolic acid, Oleanolic acid, Stigmasterol were selected for further analysis. These compounds have good drug-likeness scores and ADMET properties. They were found to be non-carcinogenic, non-toxic, and non-hepatotoxic; and followed Lipinski’s rule of five. The ability of these compounds to interact with the Hac1p and Ire1p was determined by performing molecular docking analysis. The compounds with the highest geometric shape complementarity score and lowest atomic contact energy (ACE), when docked with the Hac1p and Ire1p, were selected³⁰. Moreover, lower atomic contact energy implies the complex is more stable and favorable due to the low desolvation energy ³⁵. Interaction between Betulinic acid and Hac1p has the lowest ACE (Table 3) suggesting the interaction between Hac1p and Betulinic acid is more stable. The docking between Ire1p and Flinderole-B has the lowest ACE (Table 3). Furthermore, the types of bonds involved in the interaction of the compounds and the proteins and the length of these bonds were determined by Ligplot analysis³⁶. With Hac1p betulinic acid formed two H-bonds at SER 210 (2A⁰) and ARG 114 (2.4A⁰) residues along with hydrophobic interactions. In the interaction of Hac1p with Stigmasterol and Drummondin-E, only hydrophobic interactions were involved. In the interaction of Hac1p with Oleanolic acid (Ser112, 2.13A⁰), Flinderole-B (Asn110, 2.31A⁰), and Ursolic acid (Ser210, 2.33A⁰) one hydrogen bond and hydrophobic interactions were involved. Drummondin E formed two H-bonds with Ire1p at Lys28 (2.48A⁰) and Gly727 (3.07A⁰) residues along with hydrophobic interactions. Flinderole B formed one H-bond with Ire1p at Ser17 (2.97A⁰) residues along with hydrophobic interactions. However, in the interaction of Ire1p with Betulinic acid, Oleanolic acid, Stigmasterol and Ursolic only hydrophobic interactions were involved.

Table 3: Docking of Hac1 and Ire1 of C. auris with compounds

S. N.	Receptor	Ligand	Score	Area	ACE (kcal/ mole)
1	Hac1p	Betulinic Acid	5194	698.00	-373.10
2	Hac1p	Drummondin-E	5726	744.20	-285.80
3	Hac1p	Flinderole-B	6726	869.10	-359.11
4	Hac1p	Oleanolic acid	5410	695.50	-267.24
5	Hac1p	Stigmasterol	5724	796.40	-328.51
6	Hac1p	Ursolic Acid	5418	734.80	-356.10
7	IRE1	Betulinic Acid	5874	728.70	-260.67
8	IRE1	Drummondin-E	6654	80.7.60	-189.74
9	IRE1	Flinderole-B	7020	987.30	-271.78
10	IRE1	Oleanolic acid	6080	738.40	-192.52
11	IRE1	Stigmasterol	6356	804.40	-217.25
12	IRE1	Ursolic Acid	5798	732.00	-249.00

3.4 Molecular Dynamics Simulation:

Among the compounds that could interact with Hac1p, Betulinic acid formed the most H-bonds and had better atomic contact energy and geometric shape complementarity score. Similarly, Drummondin-E interact with Ire1³⁷. Hence, these two molecules were selected for molecular dynamics simulation. RMSD was in range of 1 to 5 Å and RMSF was found to be less than 3.0 Å for both the docked complexes (Figure 1). The molecular dynamics simulation studies show that the interactions between the compounds and Hac1p and Ire1p are stable and have low fluctuation.

4. DISCUSSION:

C. auris infection is a rapidly emerging public health problem. The emergence of antifungal drug resistance in C. auris and the low bioavailability of the existing antifungal drugs are further exacerbating the situation ^6,7. To address this grave situation, it is important to look for novel compounds that can treat fungal infections safely and effectively. In this regard, the compounds that have the potential to target virulent proteins in fungi can be valuable. Various studies have been conducted to develop novel antifungals such as derivatives of amino-Oxadiazole and 1, 8-naphthyridine semicarbazides, and nano-micelles of miconazole nitrate^38–40. Phytochemicals can be a major source of compounds that can target the fungal UPR elements. The bioactive compounds of natural origin are also considered to have fewer side effects and have more effectiveness and safety in comparison to synthetic drugs⁴¹. Hence, phytochemicals that have already exhibited different therapeutic potential were selected in this study for their ability to target Hac1p and Ire1p of C. auris using in silico methods. Moreover, in silico studies can be a rapid and cost-effective approach to identify and refine novel drug candidates. Previously, in silico studies have been performed to identify compounds with antidiabetic activity, antifungal activity, anti-SARS-CoV-2 activity, anticancer property, anti-inflammatory property and anti-alopecia activity^42–46.

After the analysis of the molecular properties, drug-likeness, bioactivity, and ADMET properties of the compounds, Flinderole B, Drummondin E, Betulinic acid, Ursolic acid, Oleanolic acid, Stigmasterol were chosen for further studies. They showed good drug-likeness score and ADME properties. They were found to be non-carcinogenic, nontoxic, and non-hepatotoxic and followed Lipinski’s rule of five. These findings suggest that these compounds are more likely to be drug compounds. Then, the ability of these compounds to interact with the HAC1 and IRE1 proteins was determined by performing molecular docking analysis. Based on the ACE and several hydrogen bonds formed during the docking of phytochemicals with the Hac1p and Ire1p, Betulinic acid and Drummondi E were identified as potential compounds to target Hac1p and Ire1p of C. auris. Further, the molecular dynamics simulations showed the interactions between the HAC1-Betulinic acid complex and IRE1-Drummondin E complex were found to have fewer fluctuations and good stability. A similar, study has been performed to identify the phytochemicals to target different virulent proteins of C. albicans³⁷. Jha et al reported different phytochemicals that can target cell wall proteins, transcriptional regulators, and proteins necessary for biofilm formation and hypha growth of C. albicans using in silico approach³⁷. Another study has also ascertained some ligands to inhibit the CPH1-MAP kinase pathway of C. albicans⁴⁷. In another in silico study Akhtar et al predicted Drummodin-E and Flinderole-B as potential inhibitors of SARS-CoV-2 RNA dependent RNA polymerase⁴⁸

Figure 1: A. RMSD plot of HAC1-Betulinic acid and IRE1-Drummondin E complexes (In Å) B. RMSF Plot of HAC1-Betulinic acid and IRE1-Drummondin E complexes (In Å)

5. CONCLUSION:

In this study, we present small molecules that may target the UPR elements Hac1 and Ire1 proteins in C. auris. Drug likeness score and ADMET attributes were good for Flinderole-B, Drummondin-E, Betulinic acid, Ursolic acid, Oleanolic acid, and Stigmasterol. They were also determined to be non-carcinogenic, non-toxic, and non-hepatotoxic, as well as according to Lipinski's rule of five. Observations presented in the study led to the conclusion that these chemicals are more likely to be pharmaceuticals. Further, their ability to target Hac1p and Ire1p of C. auris was evaluated. Based on the analysis of molecular docking Betulinic acid showed the best potential to target Hac1p and Drummondin-E showed the best potential to target Ire1p. Molecular dynamics simulation showed the interactions between the drug and their receptor was stable. We surmise that betulinic acid and Drummondin-E could be potential inhibitors of the UPR pathway in C. auris.

6. CONFLICTS OF INTEREST:

All authors declare they do not have any competing or any conflict of interest.

7. ACKNOWLEDGEMENTS:

The lab funding from the Scientific and Engineering Research Board (SERB), Core Research Grant, towards file no. EMR/2017/002299, India is duly acknowledged.

8. REFERENCES:

1. Brown GD. Denning DW. Gow NAR. Levitz SM. Netea MG. White TC. Hidden Killers: Human fungal infections. Science Translational Medicine. 2012;4(165):165rv13. doi.org/10.1126/scitranslmed.3004404

2. Kim JY. Human fungal pathogens: Why should we learn? Journal of Microbiology. 2016;54(3):145-148. doi.org/10.1007/s12275-016-0647-8

3. Turner SA. Butler G. The Candida pathogenic species complex. Cold Spring Harbor Perspectives in Medicine. 2014;4(9):a019778. doi.org/10.1101/cshperspect.a019778

4. Chowdhary A. Sharma C. Meis JF. Candida auris : A rapidly emerging cause of hospital-acquired multidrug-resistant fungal infections globally. PLoS Pathogens. 2017; 13(5):e1006290. doi.org/10.1371/journal.ppat.1006290

5. Candida auris | Candida auris | Fungal Diseases | CDC. Accessed August 22, 2019. https://www.cdc.gov/fungal/candida-auris/index.html

6. Cortegiani A. Misseri G. Fasciana T. Giammanco A. Giarratano A. Chowdhary A. Epidemiology, clinical characteristics, resistance, and treatment of infections by Candida auris. Journal of Intensive Care. 2018;6(1):69. doi.org/10.1186/s40560-018-0342-4

7. Thammahong A. Puttikamonkul S. Perfect JR. Brennan RG. Cramer RA. Central role of the trehalose biosynthesis pathway in the pathogenesis of human fungal infections: Opportunities and Challenges for Therapeutic Development. Microbiology and Molecular Biology Reviews. 2017;81(2): e00053. doi.org/10.1128/MMBR.00053-16

8. Singh S. Uppuluri P. Mamouei Z. Alqarihi A. Elhassan A. French S. Lockhart SR. Chiller T. Edwards JE. Ibrahim AS. The NDV-3A vaccine protects mice from multidrug resistant Candida auris infection. PLoS Pathogens. 2019;15(8):e1007460. doi.org/10.1371/journal.ppat.1007460

9. Akhtar N. Joshi A. Kaushik V. Kumar M. Mannan MA. In-silico design of a multivalent epitope-based vaccine against Candida auris. Microbial Pathogenesis. 2021; 155:104879. doi: 10.1016/j.micpath.2021.104879

10. Miyazaki T. Kohno S. ER stress response mechanisms in the pathogenic yeast Candida glabrata and their roles in virulence. Virulence. 2014;5(2):365-370. doi.org/10.4161/viru.27373

11. Krishnan K. Askew DS. Endoplasmic reticulum stress and fungal pathogenesis. Fungal Biology Reviews. 2014;28(2-3):29-35. doi.org/10.1016/j.fbr.2014.07.001

12. Iracane E. Donovan PD. Ola M. Butler G. Holland LM. Identification of an exceptionally long intron in the HAC1. mSphere. 2018;3(6): e00532. doi.org/10.1128/mSphere.00532-18

13. Leitzmann C. Characteristics and health benefits of phytochemicals. Complementary Medicine Research. 2016;23(2):69-74. doi.org/10.1159/000444063

14. Akhtar N. Ayoubi R. Kour V. Gautam U. Mannan MA. Natural products, for fungal diseases management and prevention. The Natural Products Journal. 2022;12(2): e110521193310. doi.org/10.2174/2210315511666210512035847

15. Sharma K. Akhtar N. Upadhyay AK. Mannan MA. Efficacy of Trichoderma harzianum, a biocontrol agent for controlling opportunistic fungal pathogens. Journal of Pharmaceutical Sciences and Research. 2020;12(2):282-285.

16. Sarkar A. Akhtar N. Mannan MA. Antimicrobial property of cell wall lysed Chlorella, an edible alga. Research Journal of Pharmacy and Technology. 2021;14(7):3695-3699. doi.org/10.52711/0974-360X.2021.00639

17. Punasiya R. Dindorkar G. Pillai S. Antibacterial and antifungal activity of flower extract of Murraya paniculata L. Asian Journal of Research in Pharmaceutical Sciences. 2020;10(1):17-20. doi.org/10.5958/2231-5659.2020.00004.1

18. Saha D. Paul S. In vitro screening of antifungal activity of methanol extract of Plumbago indica L. against some pathogenic species of fungi. Asian Journal of Research in Pharmaceutical Sciences. 2012;2(2):55-57.

19. Singh AK. Singh A. Shaikh A. Singh R. Misra A. Chloroquine and hydroxychloroquine in the treatment of COVID-19 with or without diabetes: A systematic search and a narrative review with a special reference to India and other developing countries. Diabetes and Metabolic Syndrome: Clinical Research and Reviews. 2020;14(3):241-246. doi.org/10.1016/j.dsx.2020.03.011

20. Matkowski A. Kus P. Goralska E. Wozniak D. Mangiferin – a bioactive xanthonoid, not only from mango and not just antioxidant. Mini-Reviews in Medicinal Chemistry. 2013;13(3):439-455. doi.org/10.2174/1389557511313030011

21. Fernandez LS. Buchanan MS. Carroll AR. Feng YJ. Quinn RJ. Avery VM. Flinderoles A-C: Antimalarial bis-indole alkaloids from Flindersia species. Organic Letters. 2009;11(2):329-332. doi.org/10.1021/ol802506n

22. Jayasuriya H. Clark AM. McChesney JD. New antimicrobial filicinic acid derivatives from hypericum drummondii. Journal of Natural Products. 1991;54(5):1314-1320. doi.org/10.1021/np50077a013

23. Spina L. Cavallaro F. Fardowza NI. Lagoussis P. Bona D. Ciscato C. Rigante A. Vecchi M. Butyric acid: pharmacological aspects and routes of administration. Digestive and Liver Disease Supplements. 2007;1(1):7-11. doi.org/10.1016/S1594-5804(08)60004-2

24. Antonisamy P. Duraipandiyan V. Ignacimuthu S. Anti-inflammatory, analgesic and antipyretic effects of friedelin isolated from Azima tetracantha Lam. in mouse and rat models. Journal of Pharmacy and Pharmacology. 2011;63(8):1070-1077. doi.org/10.1111/j.2042-7158.2011. 01300.x

25. Kim S. Chen J. Cheng T. Gindulyte A. He J. He S. Li Q et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research. 2018;47(D1): D1102-D1109. doi.org/10.1093/nar/gky1033

26. Lipinski CA. Lombardo F. Dominy BW. Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews. 2001;46(1-3):3-26. doi.org/10.1016/S0169-409X (00)00129-0

27. Yang H. Lou C. Sun L. Li J. Cai Y. Wang Z. Li W. Liu G. Tang Y. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2018;35(6):1067-1069. doi.org/10.1093/bioinformatics/bty707

28. Skrzypek MS. Binkley J. Binkley G. Miyasato SR. Simison M. Sherlock G. The Candida genome database (CGD): incorporation of Assembly 22, systematic identifiers and visualization of high throughput sequencing data. Nucleic Acids Research. 2016;45(D1): D592-D596. doi.org/10.1093/nar/gkw924

29. Yang J. Yan R. Roy A. Xu D. Poisson J. Zhang Y. The I-TASSER suite: Protein structure and function prediction. Nature Methods. 2014;12(1):7-8. doi.org/10.1038/nmeth.3213

30. Schneidman-Duhovny D. Inbar Y. Nussinov R. Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Research. 2005;33(suppl_2): W363-W367. doi.org/10.1093/nar/gki481

31. Case DA. Cheatham TE. Darden T. Gohlke H. Luo R. Merz KM. Onufriev A et al. The Amber biomolecular simulation programs. Journal of Computational Chemistry. 2005;26(16):1668-1688. doi.org/10.1002/jcc.20290

32. Wang J. Wang W. Kollman PA. Case DA. Automatic atom type and bond type perception in molecular mechanical calculations. Journal of Molecular Graphics and Modelling. 2006;25(2):247-260. doi.org/10.1016/j.jmgm.2005.12.005

33. Toukmaji A. Sagui C. Board J. Darden T. Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions. Journal of Chemical Physics. 2000;113(24):10913-10927. doi.org/10.1063/1.1324708

34. Roe DR. Cheatham TE. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. Journal of Chemical Theory and Computation. 2013;9(7):3084-3095. doi.org/10.1021/ct400341p

35. Guo F. Li SC. Wang L. Zhu D. Protein-protein binding site identification by enumerating the configurations. BMC Bioinformatics. 2012;13(1):158. doi.org/10.1186/1471-2105-13-158

36. Laskowski RA. Swindells MB. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling. 2011;51(10):2778-2786. doi.org/10.1021/ci200227u

37. Jha A. Kumar A. multiple drug targeting potential of novel ligands against virulent proteins of Candida albicans. International Journal of Peptide Research and Therapeutics. 2020;26(2):921-942. doi.org/10.1007/s10989-019-09897-1

38. Domala R. Butti ARS. Eppakayla L. Study of antifungal activity of 2-Methyl-3-(5’-aryl/aryloxymethyl-1’, 3’, 4’-oxadiazol-2’-yl) amino-1, 8-naphthyridines. Research Journal of Pharmacy and Technology. 2022;15(2):757-760. doi.org/10.52711/0974-360X.2022.00126

39. Noshi SH. Basha M. Awad GEA. Elsayyad NME. Miconazole nitrate loaded Soluplus®-Pluronic® nano-micelles as promising drug delivery systems for ocular fungal infections: In vitro and in vivo considerations. Research Journal of Pharmacy and Technology. 2022;15(2):501-511. doi.org/10.52711/0974-360X.2022.00081

40. Deore SK. Surawase RK. Maru A. Formulation and evaluation of O/W nanoemulsion of ketoconazole. Research Journal of Pharmaceutical Dosage Forms and Technology. 2019;11(4):269-274. doi.org/10.5958/0975-4377.2019.00045.4

41. de Andrade Monteiro C. Ribeiro Alves dos Santos J. Phytochemicals and their antifungal potential against pathogenic yeasts. In Phytochemicals in human health. Edited by Rao V, Mans DRA and Rao L. IntechOpen, London. 2020. doi.org/10.5772/intechopen.87302

42. Parvatikar P. Saha B. Das SK. Reddy C. Bagali S. Kulkarni RV. Patil AV. Biradar MS. Das KK. Molecular docking identifies novel phytochemical inhibitors against SARS-COV-2 for Covid-19 therapy. Research Journal of Pharmacy and Technology. 2022;15(2):555-558. doi.org/10.52711/0974-360X.2022.00090

43. Sathyanarayan S. Pillai KS. Udhayakumar M. Subramaniam S. Molecular socking analysis of compounds from polyherbal powder against the proteins involved in diabetes mellitus. Research Journal of Pharmacy and Technology. 2022;15(2):859-862. doi.org/10.52711/0974-360X.2022.00143

44. Parameswari P. Devika R. In silico molecular docking studies of quercetin compound against anti-inflammatory and anticancer proteins. Research Journal of Pharmacy and Technology. 2019;12(11):5305-5309. doi.org/10.5958/0974-360X.2019.00919.3

45. Patadiya N. Vaghela V. Design, in-silico ADME study and molecular docking study of novel quinoline-4-on derivatives as factor Xa inhibitor as potential anti-coagulating agents. Asian Journal of Pharmaceutical Research. 2022;12(3):207-211. doi.org/10.52711/2231-5691.2022.00034

46. Otuokere IE. Amaku FJ. Alisa CO. In silico geometry optimization, excited – state properties of (2E)-N-hydroxy-3-[3-(phenylsulfamoyl) phenyl] prop-2-enamide (Belinostat) and its molecular docking studies with Ebola virus glycoprotein. Asian Journal of Pharmaceutical Research. 2015;5(3):131-137. doi.org/10.5958/2231-5691.2015.00020.9

47. Jha A. Vimal A. Bakht A. Kumar A. Inhibitors of CPH1-MAP kinase pathway: Ascertaining potential ligands as multi-target drug candidate in Candida albicans. International Journal of Peptide Research and Therapeutics. 2019;25(3):997-1010. doi.org/10.1007/s10989-018-9747-0

48. Akhtar N. Verma H. Silkari OM. Upadhyay AK. Kaushik V. Mannan MA. Drummondin E and Flinderole B are potential inhibitors of RNA-dependent RNA polymerase of SARS-CoV-2: an in-silico study. Biotechnologia. 2022;103(1):53-70. doi.org/10.5114/bta.2022.113915

Received on 14.03.2022 Modified on 16.09.2022

Research J. Pharm. and Tech 2023; 16(6):2867-2872.

DOI: 10.52711/0974-360X.2023.00472