Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression
Copyright © 2024 Jain, Tailang, Chandrasekaran, Khazaleh, Thangavel, Makeen, Albratty, Najmi, Alhazmi, Zoghebi, Alagusundaram and Jain..
Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Frontiers in pharmacology - 15(2024) vom: 20., Seite 1290398 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jain, Nem Kumar [VerfasserIn] |
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| Links: |
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| Themen: |
African traditional medicine |
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| Anmerkungen: |
Date Revised 21.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fphar.2024.1290398 |
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| Förderinstitution / Projekttitel: |
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NLM369950240 |
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| 245 | 1 | 0 | |a Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression |
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| 520 | |a Copyright © 2024 Jain, Tailang, Chandrasekaran, Khazaleh, Thangavel, Makeen, Albratty, Najmi, Alhazmi, Zoghebi, Alagusundaram and Jain. | ||
| 520 | |a Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a African traditional medicine | |
| 650 | 4 | |a Alchornea laxiflora | |
| 650 | 4 | |a depression | |
| 650 | 4 | |a ethnomedicine | |
| 650 | 4 | |a in silico | |
| 650 | 4 | |a molecular docking | |
| 650 | 4 | |a network pharmacology | |
| 700 | 1 | |a Tailang, Mukul |e verfasserin |4 aut | |
| 700 | 1 | |a Chandrasekaran, Balakumar |e verfasserin |4 aut | |
| 700 | 1 | |a Khazaleh, Nasha't |e verfasserin |4 aut | |
| 700 | 1 | |a Thangavel, Neelaveni |e verfasserin |4 aut | |
| 700 | 1 | |a Makeen, Hafiz A |e verfasserin |4 aut | |
| 700 | 1 | |a Albratty, Mohammed |e verfasserin |4 aut | |
| 700 | 1 | |a Najmi, Asim |e verfasserin |4 aut | |
| 700 | 1 | |a Alhazmi, Hassan Ahmad |e verfasserin |4 aut | |
| 700 | 1 | |a Zoghebi, Khalid |e verfasserin |4 aut | |
| 700 | 1 | |a Alagusundaram, M |e verfasserin |4 aut | |
| 700 | 1 | |a Jain, Hemant Kumar |e verfasserin |4 aut | |
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