Integrative Bacterial Network Analysis and Molecular Docking of Vitex negundo Bioactives for Targeted Acne Therapy in the Community | Community Acquired Infection

Integrative Bacterial Network Analysis and Molecular Docking of Vitex negundo Bioactives for Targeted Acne Therapy in the Community

Authors

  • Amruta Balekundri Food and Micronutrient analysis Lab JNMC Campus, KLE Academy of Higher Education and Research Belagavi 590010 Karnataka, India and KLE College of Pharmacy, KLE Academy of Higher Education and Research Belagavi 590010 Karnataka, India
  • Eknath D. Ahire MET’s Institute of Pharmacy, BKC, Affiliated to Savitribai Phule Pune University, Adgaon Nashik, Maharashtra, India- 422003

DOI:

https://doi.org/10.54844/cai.2024.0836

Keywords:

Acne vulgaris, Vitex negundo, Network pharmacology, Molecular docking, Bacterial targets, AutoDock, Acne therapy, Integrative analysis

Abstract

Background: Acne vulgaris is a common inflammatory skin condition caused by bacterial infections. Herbal remedies have gained attention for their potential in treating acne. This study investigates the antibacterial potential of Vitex negundo using network pharmacology and molecular docking techniques to identify key targets and interactions relevant to acne therapy.

Methods: Targets related to acne vulgaris were identified through open databases and literature review. Protein-protein interaction data were obtained from Metascape and STRING databases, and network construction was performed using Cytoscape. Molecular docking was conducted with AutoDock to assess the binding affinity between Vitex negundo bioactives and bacterial targets associated with acne.

Results: The network analysis revealed several key targets with high interaction degrees. Molecular docking showed strong binding affinities for selected bioactives with bacterial targets, indicating their potential role in inhibiting acne-related bacterial growth.

Conclusion: Vitex negundo bioactives demonstrate significant antibacterial potential, making them promising candidates for targeted acne therapy. 

Author Biography

Eknath D. Ahire, MET’s Institute of Pharmacy, BKC, Affiliated to Savitribai Phule Pune University, Adgaon Nashik, Maharashtra, India- 422003

Prof. Eknath D. Ahire Currently working as an Assistant Professor in the Department of Pharmaceutics at MET’s, Institute of Pharmacy, Bhujbal Knowledge City, Adgoan, Nashik Affiliated to Savitribai Phule Pune University, Pune. He has obtained his graduation (B. Pharm) degree from Savitribai Phule Pune University. He obtained his master’s degree (M. S. Pharm) in Pharmaceutics from the National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A). He was recognized as NFST Ph.D. scholar by govt. of India. 

He has a total of 6 + years of experience in academics and industry as Formulation Scientist II at Torrent Research Centre – Gandhinagar, Gujrat. He is working as a Reviewer for several reputed international journals like “Pharmaceutical Development and technology” (Taylor and Francis), “International Journal of Applied Nanotechnology Research (IJANR) (IGI Global), and many more.  He is an Editorial Advisory Board Member at Acta Scientific, an Open Access Journal. He has more than 20 Publications (Research, Review, and Book chapters) published in different National and International reputed indexed journals with one textbook. He has participated and attended several National and International conferences and workshops. He has worked on Nanotechnology-based Drug Delivery Systems and Novel Drug Delivery Systems.

 

 

Downloads

Published

2025-08-28

How to Cite

1.
Balekundri A, Ahire ED. Integrative Bacterial Network Analysis and Molecular Docking of Vitex negundo Bioactives for Targeted Acne Therapy in the Community . Community Acquir Infect. 2025;12. doi:10.54844/cai.2024.0836

Issue

Vol. 12 (2025)

Section

Original Articles

Downloads

Download data is not yet available.
ORIGINAL ARTICLE

Integrative bacterial network analysis and molecular docking of Vitex negundo bioactives for targeted acne therapy


Amruta Balekundri1,2, Eknath D. Ahire3,4,*

1Food and Micronutrient analysis Lab JNMC Campus, KLE Academy of Higher Education and Research, Belagavi 590010, Karnataka, India

2KLE College of Pharmacy, KLE Academy of Higher Education and Research, Belagavi 590010, Karnataka, India

3Department of Pharmaceutics, MET's Institute of Pharmacy, Savitribai Phule Pune University, Nashik 422003, Maharashtra, India

4Department of Quality Assurance, MET's Institute of Pharmacy, Savitribai Phule Pune University, Nashik 422003, Maharashtra, India


*Corresponding Author:

Eknath D. Ahire, Department of Pharmaceutics and Department of Quality Assurance, Savitribai Phule Pune University, Nashik 422003, Maharashtra, India. E-mail: eknathahire05@gmail.com; https://orcid.org/0000-0001-6542-884X


Received: 19 December 2024 Revised: 23 March 2025 Accepted: 10 April 2025


ABSTRACT

Background: Acne vulgaris is a common inflammatory skin condition caused by bacterial infections. Herbal remedies have gained attention for their potential in treating acne. This study investigated the antibacterial potential of Vitex negundo. Network pharmacology and molecular docking techniques were used to identify key targets and interactions relevant to acne therapy. Methods: Targets related to acne vulgaris were identified through open databases and literature reviews. Protein–protein interaction data were obtained from the Metascape and STRING databases, and network construction was performed using Cytoscape. Molecular docking was conducted using AutoDock to assess the binding affinity between Vitex negundo bioactives and bacterial targets associated with acne. Results: The network analysis revealed several key targets with high degrees of interaction. Molecular docking showed strong binding affinities between selected bioactives and bacterial targets, indicating their potential role in inhibiting acne-related bacterial growth. Conclusion: Vitex negundo bioactives demonstrate significant antibacterial potential, making them promising candidates for targeted acne therapy.

Key words: acne vulgaris, Vitex negundo, network pharmacology, molecular docking, bacterial targets, AutoDock, acne therapy, integrative analysis

INTRODUCTION

Herbal medicines are extensively used worldwide, with nearly 80% of the global population relying on traditional remedies. Despite their effectiveness, these systems often lack proper documentation. Integrating modern tools such as network pharmacology can bridge this gap, providing a scientific framework that can be used to validate and enhance traditional knowledge, paving the way for innovative and evidence-based therapeutic applications.[1,2]

Vitex negundo Linn., commonly known as “nirgundi” in Sanskrit, is a medicinal shrub belonging to the Verbenaceae family, with widespread distribution in tropical and temperate regions, particularly South Asia. Recognized in traditional medicine for its therapeutic potential, this plant is used to treat ailments such as asthma, jaundice, migraines, and wounds.[3] Its bioactive compounds, including polyphenols, terpenoids, and alkaloids, exhibit antioxidant, antimicrobial, anti-inflammatory, and anthelmintic properties. With its rich phytochemical profile, including p-hydroxybenzoic acid and β-sitosterol, Vitex negundo is a promising candidate for pharmaceutical applications, offering an affordable and accessible alternative in primary healthcare systems.[4–6]

Cutibacterium acnes is a key pathogen in acne, and its treatment typically involves oral antibiotics and topical formulations. However, the growing issue of antibiotic resistance poses significant challenges to conventional treatment strategies. Herbal medicines and their advanced formulations constitute viable alternatives, minimizing side effects while providing therapeutic efficacy.[7–9] Leveraging network pharmacology, a cutting-edge tool for elucidating drug mechanisms and multitarget interactions, this study explores the bioactive compounds of Vitex negundo and their molecular targets. Utilizing network pharmacology and molecular docking, the study seeks to predict and validate the therapeutic potential of Vitex negundo in combating acne.[10–13]

Acne vulgaris is a widespread dermatological condition for which few satisfactory treatments are available. Although conventional therapies such as benzoyl peroxide effectively suppress C. acnes, alternative approaches are increasingly being explored due to concerns about irritation and resistance. Among emerging diagnostic techniques, digital fluorescence photography (FP) has been identified as a fast, non-invasive tool for assessing acne and other skin conditions.[14,15] FP utilizes the autofluorescence properties of the skin, which can be excited by UV light and detected using specialized imaging systems. It is particularly useful in cosmetic and skin care research for evaluating pathological skin states such as acne and psoriasis. Recent advancements in FP technology, especially the integration of AI, have enhanced its potential for automated analysis and more precise diagnostics.[16,17] Additionally, new therapeutic approaches, such as Trachyspermum ammi (ajwain) essential oil, have shown promise in acne treatment, demonstrating significant reductions in lesion counts, sebum levels, and skin erythema. Given the rapid evolution of FP technology and AI-driven analysis, further research is needed to expand its applications in dermatology and skin care. This article explores the latest developments in acne diagnostics and treatment, emphasizing innovative imaging techniques and emerging natural therapies.[18–20]

Studies have explored the therapeutic potential of Vitex negundo in addressing various ailments, including asthma, jaundice, migraines, and wounds. Its bioactive compounds, such as polyphenols, terpenoids, and alkaloids, demonstrate potent antioxidant, antimicrobial, and anti-inflammatory properties. While network pharmacology and docking studies have been conducted on Vitex negundo, no literature exists on its bacterial network pharmacology.[21–23] This study investigates its bacterial network pharmacology and molecular interactions with acne-causing bacteria.

MATERIALS AND METHODS

Data mining and target identification

The bioactive compounds of Vitex negundo were mined from reliable phytochemical databases, including Dr. Duke's Phytochemical Database (https://phytochem.nal.usda.gov/phytochem/search) and the IMPPAT database (https://cb.imsc.res.in/imppat/), complemented by relevant literature reviews. The molecular targets of Vitex negundo phytochemicals were identified using the SwissTargetPrediction database (http://www.swisstargetprediction.ch/). Additionally, disease-specific targets for acne were retrieved from the GeneCards database (https://www.genecards.org/) and the Therapeutic Target Database (TTD; http://db.idrblab.net/ttd/). Common targets between the plant bioactives and acne-associated genes were identified using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/).[24–26]

Network construction

A drug-disease-target interaction network was constructed using Cytoscape (v3.9.1, https://cytoscape.org/). Unique shapes and colors were employed to visually distinguish the drug, disease, and target node.[27,28]

Drug profile analysis

The drug-likeness properties of the phytochemicals were evaluated using Molsoft (https://molsoft.com/mprop/) based on Lipinski's rule of five. The ADMET profiles of the compounds were predicted using ADMETlab 3.0 (https://admetlab3.scbdd.com/server/screening).[29,30]

Gene ontology (GO) and pathway enrichment analysis

GO and pathway enrichment analyses for the identified targets were performed using Metascape (v3.5.20230501, https://metascape.org/gp/index.html#/main/step1) to determine the molecular mechanisms underlying the therapeutic effects of Vitex negundo.[31,32]

Protein–protein interaction (PPI) analysis

PPIs among the identified targets were analyzed using the STRING database (https://string-db.org/), providing insights into functional interactions and biological pathways.[33,34]

Molecular docking

The top five targets with the highest degree of connectivity from the constructed network were selected for molecular docking studies. Ligand structures of Vitex negundo bioactives were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and protein target structures were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/). Discovery Studio Visualizer 2021 was utilized for structure extraction and visualization, while PyRx (https://pyrx.sourceforge.io/) facilitated multiple ligand docking.[23,35,36]

RESULTS

Phytocompounds and target identification

A total of 230 phytocompounds of Vitex negundo were identified. Of these, several compounds were found to interact with the disease targets associated with acne-causing bacteria. A total of 123 targets with a probability value greater than 0.3 were identified using the SwissTargetPrediction database. Common potential targets between the plant and the bacteria were plotted using Venny 2.1. This analysis led to the identification of 28 key targets, which were further explored. The Venn plot of the common targets is presented in Figure 1. Table 1 comprising of the phytocompounds characters from the Vitex negundo.

Figure 1

Figure 1. Venn plot for the identification of potential targets.

Table 1: Phytocompounds characters from the Vitex negundo
Compound name Molecular formula Molecular weight (kDa) NHBA NHBD MlogP DLS
Kaempferol C15 H10 O6 286.05 6 4 1.61 0.50
Myricetin C15 H10 O8 318.04 6 8 0.97 -0.24
Quercetin C15 H10 O7 302.04 7 5 1.19 0.52
Luteolin C15 H10 O6 286.05 6 4 2.78 0.38
Palmitic acid C16 H32 O2 256.24 2 1 6.64 -0.54
Pentadecanoic acid C15 H30 O2 242.22 2 1 6.13 -0.54
Stearic acid C18 H36 O2 284.27 2 1 7.65 -0.54
Lauric acid C12 H24 O2 200.18 2 1 4.62 -0.54
Arachidic acid C20 H40 O2 312.30 2 1 8.66 -0.54
Myristic acid C14 H28 O2 228.21 2 1 5.63 -0.54
beta-D-Glucose C6 H12 O6 180.06 6 5 -3.02 -0.12
Oleic acid C18 H34 O2 282.26 2 1 7.11 -0.30
Linoleic acid C18 H32 O2 280.24 2 1 6.60 -0.30
Linolenic acid C18 H30 O2 278.22 2 1 5.88 0.09
NHBA, number of hydrogen bond acceptors; NHBD, number of hydrogen bond donors; MlogP, Moriguchi octanol-water partition coefficient; DLS, dynamic light scattering.

Network construction

The connections between the bioactive compounds of Vitex negundo and target genes were determined using the SwissTargetPrediction database, leveraging chemical structure similarity to predict molecular targets. Acne-related genes were sourced from GeneCards and TTD, with common targets identified using Venny 2.1, forming the foundation for network construction in Cytoscape. The network, built for key Vitex negundo compounds-including kaempferol, myricetin, quercetin, peroxisome proliferator-activated receptor alpha (PPARA), apigenin, and luteolin-highlighted kaempferol as the most interconnected node (degree = 18), followed by myricetin (degree = 15), quercetin (degree = 14), PPARA (degree = 11), and apigenin and luteolin (degree = 10 each). These degree values suggest their potential significance in acne treatment. Figure 2 illustrates the network, using distinct colors and shapes to enhance clarity and interpretation.

Figure 2

Figure 2. Network of drug-targets-disease. TNKS, Tankyrase; PIM1, Proto-Oncogene, Serine/Threonine Kinase; PPARG, Peroxisome Proliferator-Activated Receptor Gamma; EGFR, Epidermal Growth Factor Receptor; ESR1, Estrogen Receptor 1; PTK2, Protein Tyrosine Kinase 2; F2, Coagulation Factor II (Thrombin); MMP13, Matrix Metallopeptidase 13; SRC, SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase; SYK, Spleen Tyrosine Kinase; MMP9, Matrix Metallopeptidase 9; ALOX5, Arachidonate 5-Lipoxygenase; XDH, Xanthine Dehydrogenase; MMP2, Matrix Metallopeptidase 2; CA3, Carbonic Anhydrase 3; CYP19A1, Cytochrome P450 Family 19 Subfamily A Member 1; PPARA, Peroxisome Proliferator-Activated Receptor Alpha; IGF1R, Insulin-like Growth Factor 1 Receptor; IL2, Interleukin 2; AHR, Aryl Hydrocarbon Receptor; TNF, Tumor Necrosis Factor; CNR2, Cannabinoid Receptor 2; TYR, Tyrosinase; CYP17A1, Cytochrome P450 Family 17 Subfamily A Member 1; NR1H3, Nuclear Receptor Subfamily 1 Group H Member 3; PTGS2, Prostaglandin-Endoperoxide Synthase 2; SHBG, Sex Hormone-Binding Globulin; MPO, Myeloperoxidase.

GO, pathway enrichment analysis, and protein interactions

In terms of biological processes (BP; Figure 3), the bubble plot highlights key processes with low false discovery rate (FDR) values, including inflammation regulation, lipid metabolism, and oxidative stress reduction, suggesting that Vitex negundo helps reduce cytokine release, balance hormonal signaling (e.g., estrogen pathways), and neutralize reactive oxygen species. In addition, regarding signaling pathways (Kyoto Encyclopedia of Genes and Genomes [KEGG]; Table 2, Figure 4), the heat map produced based on the pathway enrichment analysis shows that Vitex negundo combats Propionibacterium acnes infections by modulating inflammatory responses, regulating lipid metabolism, and mitigating oxidative stress, all of which are crucial for acne management. Finally, in terms of metabolic processes (Reactome; Figure 5), the PPI network highlights key targets such as TNF, MMPs, and ESR1, indicating that Vitex negundo may modulate inflammatory cytokines, regulate matrix degradation enzymes, and influence hormonal pathways to prevent acne-related inflammation and skin damage.

Figure 3

Figure 3. Heat map produced based on the path enrichment analysis. GO, gene ontology.

Figure 4

Figure 4. Biological process (gene ontology enrichment). FDR, false discovery rate.

Figure 5

Figure 5. Network of protein–protein interactions.

Table 2: Enrichment analysis of proteins involved in acne infection
Pathway Pathway description Gene count False discovery rate Matching proteins
hsa04915 Estrogen signaling pathway 5 0.0000125 MMP2, EGFR, MMP9, SRC, ESR1
hsa04370 VEGF signaling pathway 3 0.0008700 PTK2, PTGS2, SRC
hsa04510 Focal adhesion 4 0.0012000 EGFR, PTK2, SRC, IGF1R
hsa05163 Human cytomegalovirus infection 4 0.0014000 EGFR, PTK2, PTGS2, SRC
hsa04657 IL-17 signaling pathway 3 0.0020000 MMP13, PTGS2, MMP9
hsa04625 C-type lectin receptor signaling pathway 3 0.0024000 PTGS2, SRC, SYK
hsa04670 Leukocyte transendothelial migration 3 0.0030000 MMP2, PTK2, MMP9
hsa04151 PI3K-Akt signaling pathway 4 0.0055000 EGFR, PTK2, SYK, IGF1R
hsa04921 Oxytocin signaling pathway 3 0.0059000 EGFR, PTGS2, SRC
hsa05167 Kaposi sarcoma-associated herpesvirus infection 3 0.0107000 PTGS2, SRC, SYK
hsa04015 Rap1 signaling pathway 3 0.0127000 EGFR, SRC, IGF1R
hsa00590 Arachidonic acid metabolism 2 0.0193000 PTGS2, ALOX5
hsa04144 Endocytosis 3 0.0193000 EGFR, SRC, IGF1R
hsa04664 Fc epsilon RI signaling pathway 2 0.0211000 ALOX5, SYK
hsa04917 Prolactin signaling pathway 2 0.0211000 SRC, ESR1
hsa05120 Epithelial cell signaling in Helicobacter pylori infection 2 0.0211000 EGFR, SRC
hsa05100 Bacterial invasion of epithelial cells 2 0.0217000 PTK2, SRC
hsa04064 NF-κB signaling pathway 2 0.0353000 PTGS2, SYK
hsa04066 HIF-1 signaling pathway 2 0.0353000 EGFR, IGF1R
hsa04668 TNF signaling pathway 2 0.0388000 PTGS2, MMP9
hsa05135 Yersinia infection 2 0.0461000 PTK2, SRC
hsa04068 FoxO signaling pathway 2 0.0466000 EGFR, IGF1R
MMP, matrix metallopeptidase; EGFR, epidermal growth factor receptor; SRC, SRC proto-oncogene (non-receptor tyrosine kinase); ESR1, estrogen receptor 1; PTK2, protein tyrosine kinase 2; FAK, focal adhesion kinase; PTGS2, prostaglandin-endoperoxide synthase 2; COX-2, cyclooxygenase-2; IGF1R, insulin-like growth factor 1 receptor; SYK, spleen tyrosine kinase; ALOX5, arachidonate 5-lipoxygenase.

Molecular docking

The highest binding affinity was associated with kaempferol, myricetin, quercetin, and luteolin. These ligands, along with PPARA and apigenin, were further docked using PyRx software based on their associations found in the constructed network. The best five conformations were shown by TNKS_Luteoline (Figure 6), MPO_Myrectin, MMP13_Myrectin, MMP13_ Kaempferol, and MPO_Quercetin, with binding energies of -10.1, -9.5, -9.5, -9.4, and -9.2 kcal/mol, respectively.

Figure 6

Figure 6. Molecular docking interactions of TNKS with luteolin. (A) 3D binding pose of luteolin in the TNKS active site, showing key hydrogen bonds and hydrophobic interactions. (B) 2D interaction map highlighting bonding interactions between luteolin and TNKS residues, including hydrogen bonds and π-π stacking.

DISCUSSION

Approximately 80% of the world's population depends on herbal remedies for disease prevention and treatment. In this study, Vitex negundo was analyzed for its potential use against acne vulgaris using a network pharmacology approach. A total of 230 phytocompounds were identified, of which 28 showed interactions with acne-related bacterial targets.[37,38] The network analysis identified kaempferol, myricetin, and quercetin as key compounds, with kaempferol having the highest edge count. These compounds likely modulate inflammatory responses, lipid metabolism, and oxidative stress pathways, as highlighted in enrichment analyses.[39] PPI analysis further identified targets such as TNF, MMPs, and ESR1, emphasizing the potential of Vitex negundo in regulating cytokine release, enzyme activity, and hormonal balance. The docking results revealed that TNKS_Luteolin exhibited the highest binding affinity (-10.1 kcal/mol), followed by MPO_Myrectin, MMP13_Myrectin, MMP13_Kaempferol, and MPO_Quercetin.[40] The strong binding energies indicate a favorable interaction between these ligands and their respective targets, suggesting their potential as promising candidates for further investigation in drug development. The significant binding affinity of luteolin and myricetin highlights their role in stabilizing protein-ligand complexes, supporting their therapeutic relevance.[41,42]

CONCLUSION

This study identified 230 phytocompounds from Vitex negundo and explored their interactions with acne-related targets. A total of 123 targets were identified, with 28 common targets between the plant and P. acnes. Network analysis highlighted key compounds such as kaempferol, myricetin, and quercetin. GO and pathway enrichment revealed that Vitex negundo modulates inflammation, lipid metabolism, and oxidative stress, targeting key proteins such as TNF, MMPs, and ESR1. The docking results showed that TNKS_Luteolin exhibited the highest binding affinity of -10.1 kcal/mol, indicating its strong interaction with the target protein. These findings suggest that Vitex negundo has potential as a natural anti-acne therapy, and further investigations are needed for formulation development.

DECLARATIONS

Acknowledgement

We would like to thank the KLE Academy of Higher Education and Research, Karnataka, India and the MET Institute of Pharmacy, BKC, affiliated with Savitribai Phule Pune University, Nashik, India, for their constant support and for providing all the facilities required to conduct this research.

Author contributions

Balekundri A: Writing and revision of the first draft. Ahire ED: English editing and proofreading. Both authors agreed on the final version and submitted the article.

Source of funding

No funding was received.

Ethical approval

Not applicable.

Informed consent

Not applicable.

Conflict of interest

Eknath D. Ahire is an editorial board member of the journal. The article was subject to the journal's standard procedures, with peer review handled independently of this editor and his research group.

Use of large language models, AI and machine learning tools

None declared.

Data availability statement

Supplementary data will be made available upon request.

REFERENCES

  1. Lee WY, Lee CY, Kim YS, Kim CE. The methodological trends of traditional herbal medicine employing network pharmacology. Biomolecules. 2019;9(8):362.    DOI: 10.3390/biom9080362    PMID: 31412658
  2. Balekundri A, Mannur V. Quality control of the traditional herbs and herbal products: a review. Future J Pharm Sci. 2020;6(1):67.
  3. Koirala N, Dhakal C, Munankarmi NN, et al. Vitex negundo Linn: phytochemical composition, nutritional analysis, and antioxidant and antimicrobial activity. Cell Mol Biol (Noisy-le-grand). 2020;66(4):1–7.    PMID: 32583767
  4. Dharmasiri MG, Jayakody JC, Galhena G, Liyanage SP, Ratnasooriya WD. Anti-inflammatory and analgesic activities of mature fresh leaves of Vitex negundo. J Ethnopharmacol. 2003;87(2/3):199–206.    DOI: 10.1016/s0378-8741(03)00159-4    PMID: 12860308
  5. Lad H, Joshi A, Dixit D, Sharma H, Bhatnagar D. Antioxidant, genoprotective and immunomodulatory potential of Vitex negundo leaves in experimental arthritis. Orient Pharm Exp Med. 2016;16(3):217–224.
  6. Zheng CJ, Li HQ, Ren SC, et al. Phytochemical and pharmacological profile of Vitex negundo. Phytother Res. 2015;29(5):633–647.    DOI: 10.1002/ptr.5303    PMID: 25641408
  7. Zaenglein AL. Acne vulgaris. N Engl J Med. 2018;379(14):1343–1352. [LinkOut]
  8. Dreno B, Martin R, Moyal D, Henley JB, Khammari A, Seité S. Skin microbiome and acne vulgaris: Staphylococcus, a new actor in acne. Exp Dermatol. 2017;26(9):798–803.    DOI: 10.1111/exd.13296    PMID: 28094874
  9. Walsh TR, Efthimiou J, Dréno B. Systematic review of antibiotic resistance in acne: an increasing topical and oral threat. Lancet Infect Dis. 2016;16(3):e23–e33.    DOI: 10.1016/S1473-3099(15)00527-7    PMID: 26852728
  10. Kulkarni RR, Virkar AD, D’Mello P. Antioxidant and antiinflammatory activity of Vitex negundo. Indian J Pharm Sci. 2008;70(6):838–840.    DOI: 10.4103/0250-474X.49140    PMID: 21369459
  11. Singh Y, Mishra P, Kannojia P. Morphology, phytochemistry and pharmacological activity of Vitex negundo: an overview. J Drug Delivery Ther. 2020;10(3–s):280–285.
  12. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682–690.    DOI: 10.1038/nchembio.118    PMID: 18936753
  13. Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235–242.    DOI: 10.1093/nar/28.1.235    PMID: 38298012
  14. Dréno B, Thiboutot D, Gollnick H, et al. Large-scale worldwide observational study of adherence with acne therapy. Int J Dermatol. 2010;49(4):448–456.    DOI: 10.1111/j.1365-4632.2010.04416.x    PMID: 20465705
  15. Zaenglein AL, Pathy AL, Schlosser BJ, et al. Guidelines of care for the management of acne vulgaris. J Am Acad Dermatol. 2016;74(5):945–973.e33.    DOI: 10.1016/j.jaad.2015.12.037    PMID: 26897386
  16. Bergman H, Tsai KY, Seo S-J, Kvedar JC, Watson AJ. Remote assessment of acne: the use of acne grading tools to evaluate digital skin images. Telemed J E Health. 2009;15(5):426–430.    DOI: 10.1089/tmj.2008.0128    PMID: 19548822
  17. Thiboutot D, Gollnick H, Bettoli V, et al. New insights into the management of acne: an update from the global alliance to improve outcomes in acne group. J Am Acad Dermatol. 2009;60(5 Suppl):S1–S50.    DOI: 10.1016/j.jaad.2009.01.019    PMID: 19376456
  18. Moein MR, Zomorodian K, Pakshir K, Yavari F, Motamedi M, Zarshenas MM. Trachyspermum ammi (L.) sprague: chemical composition of essential oil and antimicrobial activities of respective fractions. J Evid Based Complementary Altern Med. 2015;20(1):50–56.    DOI: 10.1177/2156587214553302    PMID: 25305209
  19. Li A, Fang R, Sun Q. Artificial intelligence for grading in acne vulgaris: current situation and prospect. J Cosmet Dermatol. 2022;21(2):865–866.    DOI: 10.1111/jocd.14599    PMID: 34859571
  20. Pagnoni A, Kligman AM, Kollias N, Goldberg S, Stoudemayer T. Digital fluorescence photography can assess the suppressive effect of benzoyl peroxide on Propionibacterium acnes. J Am Acad Dermatol. 1999;41(5 Pt 1):710–716.    DOI: 10.1016/s0190-9622(99)70005-8    PMID: 10534632
  21. Chekanov K, Danko D, Tlyachev T, Kiselev K, Hagens R, Georgievskaya A. State-of-the-art in skin fluorescent photography for cosmetic and skincare research: from molecular spectra to AI image analysis. Life (Basel). 2024;14(10):1271.    DOI: 10.3390/life14101271    PMID: 39459571
  22. Talebi Z, Kord Afshari G, Ahmad Nasrollahi S, et al. Potential of Trachyspermum ammi (ajwain) gel for treatment of facial acne vulgaris: a pilot study with skin biophysical profile assessment and red fluorescence photography. Res J Pharmacognosy. 2020;7(2):61-69.
  23. Ahire ED, Balekundri A. Bacterial network construction and molecular docking approach to study interaction of Myristica fragrans on Acne infections. Community Acquir Infect. 2023;10.
  24. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–D1082.    DOI: 10.1093/nar/gkx1037    PMID: 29126136
  25. Mohanraj K, Karthikeyan BS, Vivek-Ananth RP, et al. IMPPAT: a curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics. Sci Rep. 2018;8(1):4329.    DOI: 10.1038/s41598-018-22631-z    PMID: 29531263
  26. Stelzer G, Rosen N, Plaschkes I, et al.. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1–1.30.33.    DOI: 10.1002/cpbi.5    PMID: 27322403
  27. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504.    DOI: 10.1101/gr.1239303    PMID: 14597658
  28. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–432.    DOI: 10.1093/bioinformatics/btq675    PMID: 21149340
  29. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1/2/3):3–26.    DOI: 10.1016/s0169-409x(00)00129-0    PMID: 11259830
  30. Xiong G, Wu Z, Yi J, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49(W1):W5–W14.    DOI: 10.1093/nar/gkab255    PMID: 33893803
  31. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.    DOI: 10.1038/s41467-019-09234-6    PMID: 30944313
  32. Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–29.
  33. Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–612.    DOI: 10.1093/nar/gkaa1074    PMID: 33237311
  34. von Mering C, Huynen M, Jaeggi D, et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258–261.    DOI: 10.1093/nar/gkg034    PMID: 12519996
  35. Kim S, Chen J, Cheng T, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(d1):D1373–1380.    DOI: 10.1093/nar/gkac956    PMID: 36305812
  36. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–461.    DOI: 10.1002/jcc.21334    PMID: 19499576
  37. Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod. 2007;70(3):461–477.    DOI: 10.1021/np068054v    PMID: 17309302
  38. Vivek-Ananth RP, Mohanraj K, Sahoo AK, Samal A. IMPPAT 2.0: an enhanced and expanded phytochemical atlas of Indian medicinal plants. ACS Omega. 2023;8(9):8827–8845.    DOI: 10.1021/acsomega.3c00156    PMID: 36910986
  39. Muhammad J, Khan A, Ali A, et al. Network pharmacology: exploring the resources and methodologies. Curr Top Med Chem. 2018;18(12):949–964.    DOI: 10.2174/1568026618666180330141351    PMID: 29600765
  40. Uppal K, Ma C, Go YM, Jones DP, Wren J. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics. 2018;34(4):701–702.    DOI: 10.1093/bioinformatics/btx656    PMID: 29069296
  41. Jensen LJ, Kuhn M, Stark M, et al. STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37(Database issue):D412–D416.    DOI: 10.1093/nar/gkn760    PMID: 18940858
  42. Arcon JP, Modenutti CP, Avendaño D, et al. AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions. Bioinformatics. 2019;35(19):3836–3838.    DOI: 10.1093/bioinformatics/btz152    PMID: 30825370

License

Copyright (c) 2025 Amruta Balekundri, Eknath D. Ahire

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC
Themes by Openjournaltheme.com