Molecular Docking and ADMET properties of Phytochemicals of plant Berberis brandisiana and Berberis lambertii on Leishmaniasis:
An In-silico Analysis
Shalini Shinde1*, Sakshi Chougule1, Sonali Kolekar1, Purva Kesarkar1, Akash Thombre2,
Aniket Thanekar1, Dhanraj Jadge1
1Womens College of Pharmacy, Peth-Vadgaon 416 112, India.
2Ashokrao Mane Institute of Pharmacy, Ambap 416 112, India.
*Corresponding Author E-mail: ms.shalinishinde177@gmail.com
ABSTRACT:
Parasitic infections pose a significant global health challenge, particularly in economically disadvantaged regions. Leishmaniasis, caused by Leishmania parasites, remains a major concern due to its severe clinical manifestations and limited treatment options. This study explores the inhibition of cytochrome P450 enzyme sterol 14α-demethylase (CYP51) as a potential therapeutic strategy against Leishmania species. The Berberis brandisiana and Berberis lambertii plants have the potential to treat various diseases. It is also known to be enriched with multiple phytoconstituents that were subjected to molecular docking against CYP51. A molecular docking approach was utilized to screen natural phytochemicals for their inhibitory potential against CYP51. The binding interactions of 13 phytochemical compounds were analyzed using PyRx software, with Isoboldine and Oxycanthine showing the highest binding affinities. ADMET predictions confirmed their drug-like properties, indicating favorable pharmacokinetics and minimal toxicity. The findings highlight promising lead compounds for further development of anti-leishmanial drugs. Future research should focus on the experimental validation and optimization of these compounds for clinical applications.
KEYWORDS: Leishmaniasis, Cytochrome P450, Molecular docking, Phytochemicals, CYP51 inhibitors.
INTRODUCTION
Millions of people are being infected, and thousands are killed every year due to parasitic infection. Parasites are held in check by the host's immune system and are only symptomatic when the host becomes immune-compromised. Healthcare practitioners have a tremendous dilemma as the prevalence of parasitic infections rises significantly.1
The infections are distributed with high prevalence in poor and socio-economically deprived communities in the tropics and subtropics. Around 25% of the world population is infected with parasitic infections.2
Leishmaniasis is a disease caused by different species of parasites of the genus Leishmania, with female sandflies as the transmission vector. The parasitic protozoa are usually transmitted between vertebrate hosts by the bite of blood sucking female phlebotomies sand flies. According to WHO, leishmaniasis is one of the seven most topical diseases, and it represents a serious world health problem that presents a broad spectrum of clinical manifestations with a potentially fatal outcome. It is found in about 89 countries. It is endemic in Asia, Africa, the Americas, and the Mediterranean region. Between 12 and 15 million people in the world are infected, and 350 million are at risk of acquiring the disease.3
A crucial enzyme in the manufacture of ergosterols, which is one of the essential components of parasite cell membranes. Cell membrane sterols regulate membrane fluidity and contribute to the organization of membrane domains. unlike mammalian cells, but similar to Leishmania parasite cell membranes, contain ergosterol and ergosterol-like sterols rather than cholesterol.4 Lanosterol 14-demethylase is the target of one of the most significant and extensively researched mechanisms of resistance to ergosterol synthesis. Sterol 14alpha-demethylase (CYP51, LdBPK_111100.1) catalyzes the removal of a 14alpha-methyl group from lanosterol.5 Cytochrome P450s (CYP450s) are hemoproteins catalyzing diverse biochemical reactions important for the synthesis of ergosterol.6 cytochrome P450 (CYP450) enzyme sterol 14α-demethylase (CYP51), leading to disruption of the conversion of lanosterol to ergosterol.7 The antiparasitic drugs, which are widely used in medicine as preventive or therapeutic measures for infections or disorders brought on by parasites, target the cytochrome P450 enzyme and lanosterol 14-demethylase (LDM). This prevents the formation of parasite cell walls, which ultimately restricts the proliferation of the parasites.8,9
The Berberis brandisiana and Berberis lambertii plants have the potential to treat various diseases. It is also known to be enriched with multiple phytoconstituents having therapeutic potential. Phytochemicals often work by regulating molecular pathways that have been related to parasite infection. Finding antileishmaniasis medications from natural plants is still one of the most effective ways, according to the study.10,11
The clinical diagnosis of the disease is challenging due to the various manifestations and lesions that may appear depending on the spectrum and type of the disease. Some scientific advances have been achieved in the treatment, diagnosis, and prevention of leishmaniasis in the last 10 years, and the costs of several key medicines have been reduced. The absence of vaccines has led to attention being focused on the identification of novel targets and the development of an alternative drug. Therefore, the development of novel agents against these parasites is extremely significant.12 Therefore, currently, the identification of different Leishmania species is mostly inferred from the clinical and epidemiological background 10. Progress in the diagnosis and treatment of Leishmaniosis relies directly on the development of new technological tools and advancements that enable the creation and improvement of parasite identification and typing programs.13 The objectives of this study were to: identify an appropriate target protein for the discovery of drugs against leishmaniasis by the system biology approach, and identify potential lead compounds, Food and Drug Administration (FDA) compounds, for inhibition of the target enzyme by the docking method for studying protein-ligand binding interactions.14 A computer method that is essential in the development of new drugs is molecular docking. Through an examination of their potential conformations and interactions, it entails predicting the optimal orientation and binding affinity of a tiny molecule (ligand) to a target protein (receptor).15 The parasite Leishmaniadonovani causes the neglected tropical disease VL, and molecular docking has been proven to be a useful technique for locating prospective therapeutic targets, creating new medications, and improving already-effective therapies.16
MATERIALS AND METHODS:
Protein Preparation:
The Protein Data Bank (https://www.rcsb.org/) provided the structures of human sterol CYTP450 and 14α-demethylase (CYP51) in complex with the substrate lanosterol (PDB IDs: 7SV2 and 6UEZ). These structures were downloaded in PDB format and analyzed using BIOVIA Discovery Studio Visualizer 2021 v21.1.0.20298. During the analysis, polar hydrogens were added, and water molecules and heteroatoms were removed.
Ligand curation and preparation:
Literature and review articles provide valuable insights into phytochemicals that have been previously reported to exhibit anti-cancer properties. The 3D chemical structures of 13 phytochemical compounds, in SDF format, were obtained from the NCBI PubChem database (pubchem.ncbi.nlm.nih.gov) for analysis against CYTP450 and 14α-demethylase (CYP51). To streamline the integration into PyRx software, the protein and corresponding ligands were combined into a single SDF file using the Open Babel program (openbabel.org). The ligand models were prepared using the Vina Wizard program, and the final file was converted to the PDB format for further processing.
Active Site Preparation:
The prediction of the active site for proteins 7SV2 and 6UEZ was expected to be found in existing literature, Discovery Studio, and the PDB. To ensure proper coverage of the target protein's binding site in the PyRx software grid box configuration, it is essential to select the correct predicted amino acid residue. Further analysis revealed that the determined center point for the study was located at X: -22.8087, Y: -55.7715, Z: 0.1212, with grid box dimensions of 25 Ĺ in the X, Y, and Z directions.
Molecular Docking Studies:
Molecular docking simulations were performed using PyRx software version 0.8, which is designed for high-throughput virtual screening of compounds against protein targets. The binding energy of the compounds, measured in kcal/mol, was analyzed to identify those most likely to form strong interactions with the protein. In this study, the 3D SDF-formatted ligands, which had been generated and compressed, were loaded into PyRx via the built-in OpenBabel graphical user interface. Energy minimization was conducted using the Universal Force Field (UFF) with the conjugate gradient method, applying a total of 200 steps. The update process was stopped when the energy difference was below 1 kcal/mol after 1 step. Subsequently, the ligands were converted into AutoDock-compatible pdbqt format to optimize energy consumption. The protein was also converted into pdbqt format before being loaded into PyRx for docking. The docking simulations were carried out with an exhaustiveness setting of 8. The ligand with the most negative binding energy, indicating the strongest binding affinity, was identified. Finally, the interactions of the best docking poses were visualized using BIOVIA Discovery Studio.
Theoretical Prediction of ADMET Parameters:
The top-ranked compounds from the docking simulation were exported in SMILES format and analyzed using SwissADME and the pkCMS web server to predict toxicity and bioavailability, including evaluation based on Lipinski's Rule of 5. SwissADME and pkCMS are free online tools designed to predict pharmacokinetics, drug-likeness, and medicinal chemistry properties of small molecules (http://biosig.unimelb.edu.au/pkcsm/prediction) (Daina et al., 2017; Pires et al., 2015). The ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters selected for this study were evaluated based on their significance and typical ranges.
RESULT AND DISCUSSION:
To identify potential inhibitors of CYTP450 and 14α-demethylase (CYP51) from natural sources, molecular docking simulations were performed. The results indicate that several natural compounds exhibit superior binding energies compared to commonly used antiparasitic drugs. A table summarizing the binding energies of 13 compounds in comparison to traditional medications is provided. The plant-based chemicals, identified through resources like IMPPAT, Wikipedia, and other related documents, revealed promising results. Specifically, Isoboldine and Oxycanthine, found in Berberis brandisiana and Berberis lambertii, demonstrated the most favorable binding energies, with values of -10.1 kcal/mol and -10 kcal/mol, respectively. The study also presented 2D and 3D structural representations of Isoboldine and Oxycanthine interacting with lanosterol (PDB IDs: 7SV2 and 6UEZ). These structures highlighted the binding of both compounds to the ATP-binding site of the enzymes, providing insights into their potential as effective inhibitors. Additionally, the ligand-protein interactions for other phytochemicals like berbamine, berberine, oxyberberine, and oxycanthine were also found to be significant. The figures (1 to 5) illustrate these interactions, showing that these phytochemicals exhibit higher binding affinity compared to traditional antiparasitic agents.
Table 1: Docking and Interactions of berberies brandisania against Cytochrome P450s
|
Sr. No |
Pubchem ID |
Binding Affinity (kcal/mol) |
Interaction Residue |
Type of Interaction |
|
1 |
439653 |
-8 |
METB:381, LEUB:134, ILEB:488, LEUB:310 |
Van der waals |
|
TYRB:145 |
Conventional hydrogen bond |
|||
|
ILEB:379, PHEB:139, THRB:135 |
Carbon hydrogen bond |
|||
|
TYRB:131, PHEB:234 |
Pi-Pi stacked |
|||
|
METB:487, METB:378 |
Alkyl |
|||
|
METB:380, ILEB:377 |
Pi-Alkyl |
|||
|
2 |
2353 |
-9.1 |
ALAB:311, ILEB:488, THRB:135, ILEB:377, METB:487, TYRB:145, LEUB:310, METB:381 |
Van der waals |
|
PHEB:139, PHEB:274 |
Pi-Pi T stacked |
|||
|
PHEB:152, LEUB:134 |
Pi-Pi T shaped |
|||
|
LEUB:159 |
Alkyl |
|||
|
ALAB:144 |
Pi-Alkyl |
|||
|
3 |
133323 |
-10.1 |
ASPB:287 |
Conventional hydrogen bond |
|
ASPB:287 |
Pi-anion |
|||
|
LYSB:277, ALA:444 |
Alkyl |
|||
|
LYSA:436 |
Pi-Alkyl |
|||
|
4 |
19009 |
-8.4 |
METB:487, ILEB:488, LEUB:310, THRB:315, ALAB:311, THRB:135, TYRB:145 |
Van der waals |
|
PHEB:234, PHEB:139 |
Pi-Pi T stacked |
|||
|
ALAB:144, LEUB:159 |
Pi-Pi T shaped |
|||
|
PHEB:152, ILEB:377 |
Alkyl |
|||
|
LEUB:134, METB:381, TYRB:131 |
Pi-Alkyl |
|||
|
5 |
89048 |
-5.5 |
THRB:318, PHEA:195, ILEA:488, THRA:490, THRA:486, ASNA:483, THRA:485 |
Van der waals |
|
ALAA:314 |
Carbon hydrogen bond |
|||
|
ALAA:228, ALAA:231 |
Pi-Alkyl |
Table 4: Docking and Interactions of berberies lamberti against Cytochrome P51
|
Sr. No |
Pubchem ID |
Binding Affinity (kcal/mol) |
Interaction Residue |
Type of Interaction |
|
1 |
10217 |
-7.1 |
ARGA:501, ARGA:468, |
Carbon Hydrogen Bond |
|
GLYA:176 |
Pi-Anion |
|||
|
LYSA:180 |
Alkyl |
|||
|
LEUA:134 |
Pi Alkyl |
|||
|
2 |
442333 |
-10 |
ASPB:287,TYRA 456, ASNA:453 |
Carbon Hydrogen Bond |
|
LEUB:286, ALAA:444, GLYA:443, VALA:440, PROA:441, THRSB:289, ASPA:364, GLYB:293 |
Van der waals |
|||
|
PROB:295 |
Alkyl |
|||
|
LYSA:368, TYRA:439 |
Pi-Alkyl |
|||
|
GLUA:452 |
Pi-Anion |
|||
|
3 |
11066 |
-9.3 |
TYRA:145, THRA:135, LEUA:134, META:351, META:487, ILEA:488, THRA:315, ALAA:311 |
Van der waals |
|
PHEA:234 |
Pi Alkyl Stacked |
|||
|
PHEA:139 |
Pi-Pi T-shaped |
|||
|
LEUA:159, PHEA:152 |
Alkyl |
|||
|
ALAA:144 |
Pi Alkyl |
|||
|
4 |
275182 |
-8.8 |
ASPB:287, ALAA:432, TYRA:439, SERA:433, THRB:289, PROB:295, ASNA:453 |
Van der waals |
|
GLUA:435, LYSA:436, GLUA:452, GLYB:293, LYSA:368 |
Carbon Hydrogen Bond |
|||
|
GLUA:452 |
Pi- Anion |
|||
|
5 |
19009 |
-8.5 |
LEUB:310, THRB:315, ALAB:311, THR B:135, TYR B:145 |
Van der waals |
|
PHEB:234 |
Pi-Pi Stacked |
|||
|
PHEB:139 |
Pi-Pi T-Shaped |
|||
|
METB:487, LEUB:159, ALAB:144, PHEB:152 |
Alkyl |
|||
|
ILEB:488, ILEB:377, LEUB:134, METB:381, TYRB:131 |
Pi Alkyl |
|||
|
6 |
2353 |
-7.2 |
ASNA:430, TYRA:426, GLYB:293, LYSB:291, SERA:433 |
Van der waals |
|
THRB:289 |
Conventional hydrogen bond |
|||
|
TYRA:354 |
Carbon hydrogen bond |
|||
|
ASPA:364 |
Pi- Cation |
|||
|
LYSA:368 |
Pi- Anion |
|||
|
LYSA:358 |
Alkyl |
|||
|
7 |
72310 |
-7.8 |
GLUA:435, LEUB:282, ILEB:281, THRB:284, GLNB:283, ILEB:278, LYSB:277, PHEA:437, GLUA:452,TYRA:439 |
Van der waals |
|
ASPB:280, GLYA:443, VALA:440 |
Carbon hydrogen bond |
|||
|
ASPB:287 |
Pi-Anion |
|||
|
LYSA:436 |
Pi-sigma |
|||
|
PROA:441, PHEA:442 |
Alkyl |
|||
|
ALAA:444 |
Pi Alkyl |
|||
|
8 |
72323 |
-7.2 |
GLNB:283, ILEB:433, GLYA:445, GLYA:443, SERA:433, TYRA:439, LYSA:364 |
Van der waals |
|
ASPB:287 |
Pi-Anion |
|||
|
ALAA:444, PHEA:437 |
Alkyl |
|||
|
LYSB:277, LYSA:436, ALAA:432 |
Pi-Alkyl |
Figure 1. 2D and 3D interaction of Isoboldine
Figure 1. 2D and 3D interaction of Isoboldine
Figure 3. 2D and 3D interaction of Berbamine
Table 2: ADMET properties of phytochemicals by PkCSM
|
S. NO. |
Pub Chem Id |
Absorption |
Distribution |
Metabolism |
Excre tion |
Toxi city |
||||||||||||
|
Intestin- al Absorp tion (Human) |
P-Glyco-protein Sub-strate |
P-Glyco-protein Sub-strate I |
P-Glyco-protein Sub-strate II |
VDss (Human |
BBB Perme-ability |
CNS Perme-ability |
Substrate |
Inhibitors |
Total Clear-ance |
AMES Toxi city |
||||||||
|
CYP |
||||||||||||||||||
|
2D6 |
3A4 |
1A2 |
2C19 |
2C9 |
2D6 |
3A4 |
||||||||||||
|
Numeric (% absorb ed) |
Cate-gorial (Yes/ No) |
Cate-gorial (Yes/ No) |
Cate-gorial (Yes/ No) |
Numeric (log Lkg-1) |
Numeric (log BB) |
Numeric (log PS) |
Categorial (Yes/No) |
Numeric (log mL min-1kg-1) |
Cate gorial (Yes/ No) |
|||||||||
|
1 |
439653 |
91.276 |
Yes |
No |
Yes |
0.778 |
-0.502 |
-2.244 |
Yes |
Yes |
Yes |
No |
No |
No |
No |
1.04 |
No |
|
|
2 |
2353 |
97.147 |
Yes |
No |
Yes |
0.58 |
0.198 |
-1.543 |
No |
Yes |
Yes |
No |
No |
Yes |
Yes |
1.27 |
Yes |
|
|
3 |
133323 |
92.618 |
Yes |
No |
Yes |
0.999 |
-0.438 |
-2.127 |
No |
Yes |
Yes |
No |
No |
No |
No |
1.021 |
No |
|
|
4 |
19009 |
97.084 |
Yes |
Yes |
Yes |
0.641 |
-0.112 |
-1.535 |
No |
Yes |
Yes |
No |
No |
Yes |
No |
1.246 |
Yes |
|
|
5 |
89048 |
84.665 |
No |
No |
No |
-0.14 |
-0.182 |
-2.377 |
No |
No |
No |
No |
No |
No |
No |
0.471 |
No |
|
|
6 |
10217 |
95.29 |
Yes |
Yes |
No |
0.383 |
0.099 |
-1.908 |
No |
Yes |
Yes |
Yes |
Yes |
No |
No |
0.28 |
N0 |
|
|
7 |
442333 |
73.629 |
Yes |
No |
No |
0.011 |
-1.191 |
-1.633 |
No |
No |
Yes |
No |
No |
No |
No |
-109.869 |
Yes |
|
|
8 |
11066 |
83.475 |
No |
No |
No |
0.011 |
0.59 |
-1.362 |
No |
No |
Yes |
No |
No |
No |
No |
0.076 |
Yes |
|
|
9 |
275182 |
90.769 |
Yes |
Yes |
Yes |
-0.842 |
-0923 |
-2.323 |
No |
Yes |
No |
No |
No |
No |
No |
0.693 |
Yes |
|
|
10 |
19009 |
97.084 |
Yes |
Yes |
Yes |
0.641 |
-0.112 |
-1.535 |
No |
Yes |
Yes |
No |
No |
Yes |
No |
1.246 |
Yes |
|
|
11 |
2353 |
97.147 |
Yes |
No |
Yes |
0.58 |
0.198 |
-1.543 |
No |
Yes |
Yes |
No |
No |
Yes |
Yes |
1.27 |
Yes |
|
|
12 |
72310 |
86.755 |
No |
No |
No |
0.011 |
-0.03 |
-1.429 |
No |
No |
Yes |
No |
No |
No |
No |
-27.101 |
Yes |
|
|
13 |
72323 |
97.084 |
Yes |
Yes |
Yes |
0.641 |
-0.112 |
-1.535 |
No |
Yes |
Yes |
No |
No |
Yes |
No |
1.246 |
Yes |
|
Table 3: Drug-Likeness properties of phytochemicals by SwissADME
|
Sr no |
PubChem ID |
MW (g/mol) |
mLogP |
HBA |
HBD |
MR |
TPSA |
nRot |
Lipinski's Rule (Ro5) |
Veber's Rule |
Ghose's Rule |
Egan's Rule |
Muegge's Rule |
|
1 |
439653 |
329.39 |
1.75 |
5 |
2 |
97.01 |
62.16 Ų |
4 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
2 |
2353 |
336.36 |
2.19 |
4 |
0 |
97.87 |
40.80 Ų |
0 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
3 |
133323 |
327.37 |
1.75 |
5 |
2 |
96.00 |
62.16 Ų |
2 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
4 |
19009 |
352.40 |
2.01 |
4 |
0 |
101.80 |
40.80 Ų |
4 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
5 |
89048 |
207.23 |
0.85 |
3 |
1 |
59.62 |
49.77 Ų |
1 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
6 |
10217 |
337.37 |
2.30 |
4 |
0 |
98.18 |
40.16 Ų |
2 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
7 |
442333 |
608.72 |
3.55 |
8 |
1 |
181.60 |
72.86 Ų |
3 |
Yes |
yes |
no |
yes |
No |
|
8 |
11066 |
351.35 |
2.38 |
5 |
0 |
96.81 |
58.92 Ų |
2 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
9 |
275182 |
608.72 |
3.55 |
8 |
1 |
181.60 |
72.86 Ų |
3 |
Yes |
Yes |
No |
Yes |
No |
|
10 |
19009 |
352.40 |
2.01 |
4 |
0 |
101.80 |
40.80 Ų |
4 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
11 |
2353 |
336.36 |
2.19 |
4 |
0 |
94.87 |
40.80 Ų |
2 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
12 |
72310 |
338.38 |
1.78 |
4 |
1 |
97.33 |
51.80 Ų |
3 |
Yes |
Yes |
Yes |
Yes |
Yes |
|
13 |
72323 |
338.38 |
1.78 |
4 |
1 |
97.33 |
51.80 Ų |
3 |
Yes |
Yes |
Yes |
Yes |
Yes |
CONCLUSION:
Using molecular docking and computational approaches, this study successfully identified potential inhibitors targeting cytochrome P450 enzyme sterol 14α-demethylase (CYP51) in Leishmania species. The screening of 13 phytochemical compounds revealed that Isoboldine and Oxycanthine, derived from Berberis brandisiana and Berberis lambertii, exhibited the highest binding affinities. The ADMET analysis confirmed their favorable pharmacokinetic properties, suggesting strong potential as lead compounds for drug development. These findings support using natural compounds in the design of novel anti-leishmanial therapies. However, further in vitro and in vivo validation is necessary to confirm their efficacy and safety. The study underscores the importance of computational drug discovery techniques in identifying new therapeutic agents against parasitic infections, particularly leishmaniasis.
REFERENCES:
1. Zothantluanga JH, Paul A, Umar AK, Chetia D. Drug Repurposing and Computational Drug Discovery for Parasitic Diseases and Neglected Tropical Diseases (NTDs). In Drug Repurposing and Computational Drug Discovery. 2023 Oct 27 (pp. 77-109). Apple Academic Press.
2. Norhayati M, Fatmah MS, Yusof S, Edariah AB. Intestinal parasitic infections in man: a review. Medical Journal of Malaysia. 2003 Jun 1; 58(2): 296-305.
3. Ready PD. Epidemiology of visceral leishmaniasis. Clinical Epidemiology. 2014 May 3:147-54.
4. S. Emami et al. Synthesis, in vitro antifungal activity and in silico study of 3-(1,2,4-triazol-1-yl)flavanones. Eur J Med Chem(2013).
5. M. Fakhar et al. Visceral leishmaniosis (kala-azar) (2014)
6. A. Shokri et al.In vitro antileishmanial activity of novel azoles (3- imidazolylflavanones) against promastigote and amastigote stages of Leishmania major. Acta Trop. 2017.
7. McCall LI, El Aroussi A, Choi JY, Vieira DF, De Muylder G, Johnston JB, Chen S, Kellar D, Siqueira-Neto JL, Roush WR, Podust LM. Targeting ergosterol biosynthesis in Leishmaniadonovani: essentiality of sterol 14alpha-demethylase. PLoS Neglected Tropical Diseases. 2015 Mar 13; 9(3): e0003588.
8. Monk BC, Sagatova AA, Hosseini P, Ruma YN, Wilson RK, Keniya MV. Fungal Lanosterol 14αdemethylase: A target for next-generation antifungal design. BiochimBiophysActa Proteins Proteom. 2020 Mar; 1868(3): 140206.
9. Rani N, Kumar P, Singh R, Sharma A. Molecular docking evaluation of imidazole analogues as potent Candida albicans 14α-demethylase inhibitors. Current Computer-Aided Drug Design. 2015 Mar 1; 11(1): 8- 20.
10. Mehdi S, Mehmood MH, Ahmed MG, Ashfaq UA. Antidiabetic activity of Berberis brandisiana is possibly mediated through modulation of insulin signaling pathway, inflammatory cytokines and adipocytokines in high fat diet and streptozotocin-administered rats. Frontiers in Pharmacology. 2023 Apr 6; 14: 1085013.
11. https://en.wikipedia.org/wiki/Berberis_lambertii.
12. Akhoundi, M.; Downing, T.; Votýpka, J.; Kuhls, K.; Lukeš, J.; Cannet, A.; Ravel, C.; Marty, P.; Delaunay, P.; Kasbari, M.; et al. Leishmania infections: Molecular targets and diagnosis. Mol. Asp. Med. 2017; 57: 1–29.
13. Lee, H.; Baek, K.H.; Phan, T.N.; Park, I.S.; Lee, S.; Kim, J.; No, J.H. Discovery of Leishmania donovani topoisomerase IB selective inhibitors by targeting protein-protein interactions between the large and small subunits. Biochem. Biophys. Res. Commun. 2021; 569: 193–198.
14. vanGriensven, J.; Balasegaram, M.; Meheus, F.; Alvar, J.; Lynen, L.; Boelaert, M. Combination therapy for visceral leishmaniasis. Lancet Infect. Dis. 2010; 10: 184– 194. DOI: 10.1016/S1473-3099(10)70011.
15. Yadav, R.; Pandey, A.; Awasthi, N.; Shukla, A. Molecular Docking Studies of Enzyme Binding Drugs on Family of Cytochrome P450. Adv. Sci. Eng. Med. 2020; 12: 83–87.
16. Rashidi, S.; Fernández-Rubio, C.; Manzano-Román, R.; Mansouri, R.; Shafiei, R.; Ali-Hassanzadeh, M.; Barazesh, A.; Karimazar, M.; Hatam, G.; Nguewa, P. Potential therapeutic targets shared between leishmaniasis and cancer. Parasitology. 2021; 148: 655–671.
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Received on 23.04.2025 Revised on 25.06.2025 Accepted on 13.08.2025 Published on 02.01.2026 Available online from January 05, 2026 Asian J. Res. Pharm. Sci. 2026; 16(1):25-30. DOI: 10.52711/2231-5659.2026.00005 ©Asian Pharma Press All Right Reserved
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