Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches

Md Sarowre Hossen; Abdullah Al Nafiz; Shakil Hossen; Md Shahidul Islam; Safia Iqbal; Md Rezaul Karim

doi:doi:10.11648/j.bs.20261203.11

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| Peer-Reviewed

Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches

Md Sarowre Hossen

, Abdullah Al Nafiz

, Shakil Hossen

, Md Shahidul Islam

, Safia Iqbal

, Md Rezaul Karim^*

Published in Biomedical Sciences (Volume 12, Issue 3)

Received: 5 August 2026 Accepted: 20 August 2026 Published: 22 September 2026

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Abstract

Alzheimer's disease (AD) is a neurological disorder characterized by progressive memory loss, cognitive decline, and neuronal degeneration. The pathophysiology of Alzheimer's disease (AD) involves amyloid-beta buildup, tau hyperphosphorylation, oxidative stress, making the development of effective therapies challenging. This highlights the need for multi-target neuroprotective medicines. As a result, it is very important to quickly find natural bioactive compounds. Withania somnifera (Ashwagandha), has exhibited notable antioxidant, anti-inflammatory, and neuroprotective effects. We collected 65 phytochemicals and examined 49 of them based on ADMET characteristics. We collected gene targets for these chemicals (8,964) and AD-related genes (15,293) from separate databases. A comparison investigation revealed 920 overlapping genes. We detected 13 hub genes using Cytoscape for topological analysis: TP53, SRC, AKT1, HSP90AA1, EGFR, IL6, MAPK1, MAPK3, JUN, ESR1, BCL2, HSP90AB1, and GNAI1. After then, STRING was used to create networks of protein-protein interactions (PPIs). Network pharmacology demonstrated that the 13 hub genes are significantly associated with the regulation of apoptosis, MAPK signaling, PI3K-AKT signaling, and neuroinflammation. Docking studies revealed that significant phytochemicals like as Somniferine, Withaferin A, and Withanolide O have considerable binding affinity to hub proteins including AKT1, JUN, and HSP90AA1. This research on integrative network pharmacology and molecular docking demonstrates the potential application of W. Somnifera phytochemicals for the treatment of Alzheimer's disease.

Published in	Biomedical Sciences (Volume 12, Issue 3)
DOI	10.11648/j.bs.20261203.11
Page(s)	44-57
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Alzheimer’s Disease, Withania somnifera, Network Pharmacology, Molecular Docking, Hub Genes, Neuroprotection

References

[1]	Qian, Q. and W. L. Luo, A network pharmacology method explores the molecular mechanism of Coptis chinensis for the treatment of Alzheimer’s disease. Medicine, 2024. 103(5): p. e37103. https://doi.org/10.1097/MD.0000000000037103
[2]	Hampel, H., et al., The Amyloid-β Pathway in Alzheimer’s Disease. Molecular Psychiatry, 2021. 26(10): p. 5481–5503. https://doi.org/10.1038/s41380-021-01249-0
[3]	Azargoonjahromi, A., The duality of amyloid-β: its role in normal and Alzheimer’s disease states. Molecular Brain, 2024. 17(1): p. 44. https://doi.org/10.1186/s13041-024-01118-1
[4]	Kamat, P. K., et al., Mechanism of Oxidative Stress and Synapse Dysfunction in the Pathogenesis of Alzheimer’s Disease: Understanding the Therapeutics Strategies. Molecular Neurobiology, 2016. 53(1): p. 648–661. https://doi.org/10.1007/s12035-014-9053-6
[5]	Calero, M., et al., Additional mechanisms conferring genetic susceptibility to Alzheimer’s disease. Frontiers in Cellular Neuroscience, 2015. Volume 9 - 2015. https://doi.org/10.3389/fncel.2015.00138
[6]	John, A. and P. H. Reddy, Synaptic basis of Alzheimer’s disease: Focus on synaptic amyloid beta, P-tau and mitochondria. Ageing Research Reviews, 2021. 65: p. 101208. https://doi.org/10.1016/j.arr.2020.101208
[7]	Sharma, V. K., et al., Apoptotic Pathways and Alzheimer’s Disease: Probing Therapeutic Potential. Neurochemical Research, 2021. 46(12): p. 3103–3122. https://doi.org/10.1007/s11064-021-03418-7
[8]	Bhat, B. A., et al., Natural Therapeutics in Aid of Treating Alzheimer’s Disease: A Green Gateway Toward Ending Quest for Treating Neurological Disorders. Frontiers in Neuroscience, 2022. Volume 16 - 2022. https://doi.org/10.3389/fnins.2022.884345
[9]	Singh, D. D., D. K. Yadav, and D. Shin, Antioxidant Natural Compounds Integrated with Targeted Protein Degradation: A Multi-Modal Strategy for Alzheimer's Disease Therapy. Antioxidants (Basel), 2025. 14(12). https://doi.org/10.3390/antiox14121426
[10]	Gupta, M. and G. Kaur, Withania somnifera (L.) Dunal ameliorates neurodegeneration and cognitive impairments associated with systemic inflammation. BMC Complementary and Alternative Medicine, 2019. 19(1): p. 217. https://doi.org/10.1186/s12906-019-2635-0
[11]	Pahal, S., et al., Network pharmacological evaluation of Withania somnifera bioactive phytochemicals for identifying novel potential inhibitors against neurodegenerative disorder. Journal of Biomolecular Structure and Dynamics, 2022. 40(21): p. 10887–10898. https://doi.org/10.1080/07391102.2021.1951355
[12]	Tancreda, G., S. Ravera, and I. Panfoli, Preclinical Evidence of Withania somnifera and Cordyceps spp.: Neuroprotective Properties for the Management of Alzheimer’s Disease. International Journal of Molecular Sciences, 2025. 26(11): p. 5403. https://doi.org/10.3390/ijms26115403
[13]	Tai, J., et al., Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions. Evidence-Based Complementary and Alternative Medicine, 2019. 2019(1): p. 7631365. https://doi.org/10.1155/2019/7631365
[14]	Wu, X., et al., A network pharmacology approach to identify the mechanisms and molecular targets of curcumin against Alzheimer disease. Medicine, 2022. 101(34): p. e30194. https://doi.org/10.1097/MD.0000000000030194
[15]	Chowdhury, M. R., et al., Exploring the therapeutic potential of marine actinomycetes: a systems biology-based approach for Alzheimer’s disease treatment. Discover Molecules, 2024. 1(1): p. 4. https://doi.org/10.1007/s44345-024-00004-6
[16]	Patil, N., et al., Network pharmacology-based approach to elucidate the pharmacologic mechanisms of natural compounds from Dictyostelium discoideum for Alzheimer's disease treatment. Heliyon, 2024. 10(8). https://doi.org/10.1016/j.heliyon.2024.e28852
[17]	Chi, L.-M., X. Wang, and G.-X. Nan, In silico analyses for molecular genetic mechanism and candidate genes in patients with Alzheimer’s disease. Acta Neurologica Belgica, 2016. 116(4): p. 543–547. https://doi.org/10.1007/s13760-016-0613-6
[18]	Karbalaei, R., et al., Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterol Hepatol Bed Bench, 2018. 11(1): p. 27–33.
[19]	Sabarathinam, S., Unraveling the therapeutic potential of quercetin and quercetin-3-O-glucuronide in Alzheimer's disease through network pharmacology, molecular docking, and dynamic simulations. Scientific Reports, 2024. 14(1): p. 14852. https://doi.org/10.1038/s41598-024-61779-9
[20]	Wee, J. J. and S. Kumar, Prediction of hub genes of Alzheimer's disease using a protein interaction network and functional enrichment analysis. Genomics Inform, 2020. 18(4): p. e39. https://doi.org/10.5808/GI.2020.18.4.e39
[21]	Kanehisa, M. and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 2000. 28(1): p. 27–30. https://doi.org/10.1093/nar/28.1.27
[22]	Kim, S., et al., PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research, 2020. 49(D1): p. D1388–D1395. https://doi.org/10.1093/nar/gkaa971
[23]	Degtyarenko, K., et al., ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Research, 2007. 36(suppl_1): p. D344–D350. https://doi.org/10.1093/nar/gkm791
[24]	Mohanraj, K., et al., IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics. Scientific Reports, 2018. 8(1): p. 4329. https://doi.org/10.1038/s41598-018-22631-z
[25]	Ge, S. X., D. Jung, and R. Yao, ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics, 2019. 36(8): p. 2628–2629. https://doi.org/10.1093/bioinformatics/btz931
[26]	Karim, M. R., et al., A Network Pharmacology and Molecular-Docking-Based Approach to Identify the Probable Targets of Short-Chain Fatty-Acid-Producing Microbial Metabolites against Kidney Cancer and Inflammation. Biomolecules, 2023. 13(11): p. 1678. https://doi.org/10.3390/biom13111678
[27]	Mi, H., et al., Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nature Protocols, 2019. 14(3): p. 703–721. https://doi.org/10.1038/s41596-019-0128-8
[28]	Aljasir, M. A., An Integrated Network Biology and Molecular Dynamics Approach Identifies CD44 as a Promising Therapeutic Target in Multiple Sclerosis. Pharmaceuticals, 2026. 19(2): p. 254. https://doi.org/10.3390/ph19020254
[29]	Zhang, S., et al., Integrated Analysis of Immune Infiltration and Hub Pyroptosis-Related Genes for Multiple Sclerosis. Journal of Inflammation Research, 2023. 16(null): p. 4043–4059. https://doi.org/10.2147/JIR.S422189
[30]	Daina, A., O. Michielin, and V. Zoete, SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 2017. 7(1): p. 42717. https://doi.org/10.1038/srep42717
[31]	Xiong, G., et al., ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 2021. 49(W1): p. W5–W14. https://doi.org/10.1093/nar/gkab255
[32]	Pires, D. E. V., T. L. Blundell, and D. B. Ascher, pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. Journal of Medicinal Chemistry, 2015. 58(9): p. 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
[33]	Banerjee, P., et al., ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 2018. 46(W1): p. W257–W263. https://doi.org/10.1093/nar/gky318
[34]	Benet, L. Z., et al., BDDCS, the Rule of 5 and drugability. Advanced Drug Delivery Reviews, 2016. 101: p. 89–98. https://doi.org/10.1016/j.addr.2016.05.007
[35]	Acoba, D. and A. Reznichenko, Kidney mRNA-protein expression correlation: what can we learn from the Human Protein Atlas? Journal of Nephrology, 2024. 38(1): p. 135–141. https://doi.org/10.1007/s40620-024-02126-z
[36]	Pallesen, J., et al., Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen. Proceedings of the National Academy of Sciences, 2017. 114(35): p. E7348–E7357. https://doi.org/10.1073/pnas.1707304114
[37]	Zhang, J., et al., A bioinformatics investigation into molecular mechanism of Yinzhihuang granules for treating hepatitis B by network pharmacology and molecular docking verification. Scientific Reports, 2020. 10(1): p. 11448. https://doi.org/10.1038/s41598-020-68224-7
[38]	Tian, W., et al., CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, 2018. 46(W1): p. W363–W367. https://doi.org/10.1093/nar/gky473
[39]	Kemmish, H., M. Fasnacht, and L. Yan, Fully automated antibody structure prediction using BIOVIA tools: Validation study. PLOS ONE, 2017. 12(5): p. e0177923. https://doi.org/10.1371/journal.pone.0177923
[40]	Dallakyan, S. and A. J. Olson, Small-Molecule Library Screening by Docking with PyRx, in Chemical Biology: Methods and Protocols, J. E. Hempel, C. H. Williams, and C. C. Hong, Editors. 2015, Springer New York: New York, NY. p. 243–250. https://doi.org/10.1007/978-1-4939-2269-7_19
[41]	Zhou, G., et al., NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research, 2019. 47(W1): p. W234–W241. https://doi.org/10.1093/nar/gkz240
[42]	Breijyeh, Z. and R. Karaman Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules, 2020. 25, 5789. https://doi.org/10.3390/molecules25245789
[43]	Singh, N., et al., An overview on ashwagandha: a Rasayana (rejuvenator) of Ayurveda. Afr J Tradit Complement Altern Med, 2011. 8(5 Suppl): p. 208–13. https://doi.org/10.4314/ajtcam.v8i5S.9
[44]	Gfeller, D., et al., SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Research, 2014. 42(W1): p. W32–W38. https://doi.org/10.1093/nar/gku293
[45]	Sachdeva, P. and F. Ahmad, In silico Characterization of Predominant Genes Involved in Early Onset of Alzheimer's Disease. The Journal of Neurobehavioral Sciences, 2021. 8(3): p. 179–190. https://doi.org/10.4103/jnbs.jnbs_34_21
[46]	Szklarczyk, D., et al., STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 2018. 47(D1): p. D607–D613. https://doi.org/10.1093/nar/gky1131
[47]	Chin, C.-H., et al., cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 2014. 8(4): p. S11. https://doi.org/10.1186/1752-0509-8-S4-S11
[48]	Ashburner, M., et al., Gene Ontology: tool for the unification of biology. Nature Genetics, 2000. 25(1): p. 25–29. https://doi.org/10.1038/75556
[49]	Du, J., et al., A decision analysis model for KEGG pathway analysis. BMC Bioinformatics, 2016. 17(1): p. 407. https://doi.org/10.1186/s12859-016-1285-1
[50]	da Silva, C. H. T. P., I. Carvalho, and C. A. Taft, Virtual Screening, Molecular Interaction Field, Molecular Dynamics, Docking, Density Functional, and ADMET Properties of Novel AChE Inhibitors in Alzheimer's Disease. Journal of Biomolecular Structure and Dynamics, 2007. 24(6): p. 515–523. https://doi.org/10.1080/07391102.2007.10507140
[51]	Pradeepkiran, J. A., S. B. Sainath, and K. V. L. Shrikanya, Chapter 5 - In silico validation and ADMET analysis for the best lead molecules, in Brucella Melitensis, J. A. Pradeepkiran and S. B. Sainath, Editors. 2021, Academic Press. p. 133–176. https://doi.org/10.1016/B978-0-323-85681-2.00008-2
[52]	Zhong, G., et al., Mechanism of angiotensin-converting enzyme inhibitors in the treatment of dilated cardiomyopathy based on a protein interaction network and molecular docking. Cardiovasc Diagn Ther, 2023. 13(3): p. 534–549. https://doi.org/10.21037/cdt-23-112

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Hossen, M. S., Nafiz, A. A., Hossen, S., Islam, M. S., Iqbal, S., et al. (2026). Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomedical Sciences, 12(3), 44-57. https://doi.org/10.11648/j.bs.20261203.11

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ACS Style

Hossen, M. S.; Nafiz, A. A.; Hossen, S.; Islam, M. S.; Iqbal, S., et al. Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomed. Sci. 2026, 12(3), 44-57. doi: 10.11648/j.bs.20261203.11

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AMA Style

Hossen MS, Nafiz AA, Hossen S, Islam MS, Iqbal S, et al. Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomed Sci. 2026;12(3):44-57. doi: 10.11648/j.bs.20261203.11

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@article{10.11648/j.bs.20261203.11,
  author = {Md Sarowre Hossen and Abdullah Al Nafiz and Shakil Hossen and Md Shahidul Islam and Safia Iqbal and Md Rezaul Karim},
  title = {Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches},
  journal = {Biomedical Sciences},
  volume = {12},
  number = {3},
  pages = {44-57},
  doi = {10.11648/j.bs.20261203.11},
  url = {https://doi.org/10.11648/j.bs.20261203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bs.20261203.11},
  abstract = {Alzheimer's disease (AD) is a neurological disorder characterized by progressive memory loss, cognitive decline, and neuronal degeneration. The pathophysiology of Alzheimer's disease (AD) involves amyloid-beta buildup, tau hyperphosphorylation, oxidative stress, making the development of effective therapies challenging. This highlights the need for multi-target neuroprotective medicines. As a result, it is very important to quickly find natural bioactive compounds. Withania somnifera (Ashwagandha), has exhibited notable antioxidant, anti-inflammatory, and neuroprotective effects. We collected 65 phytochemicals and examined 49 of them based on ADMET characteristics. We collected gene targets for these chemicals (8,964) and AD-related genes (15,293) from separate databases. A comparison investigation revealed 920 overlapping genes. We detected 13 hub genes using Cytoscape for topological analysis: TP53, SRC, AKT1, HSP90AA1, EGFR, IL6, MAPK1, MAPK3, JUN, ESR1, BCL2, HSP90AB1, and GNAI1. After then, STRING was used to create networks of protein-protein interactions (PPIs). Network pharmacology demonstrated that the 13 hub genes are significantly associated with the regulation of apoptosis, MAPK signaling, PI3K-AKT signaling, and neuroinflammation. Docking studies revealed that significant phytochemicals like as Somniferine, Withaferin A, and Withanolide O have considerable binding affinity to hub proteins including AKT1, JUN, and HSP90AA1. This research on integrative network pharmacology and molecular docking demonstrates the potential application of W. Somnifera phytochemicals for the treatment of Alzheimer's disease.},
 year = {2026}
}

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TY - JOUR
T1 - Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches
AU - Md Sarowre Hossen
AU - Abdullah Al Nafiz
AU - Shakil Hossen
AU - Md Shahidul Islam
AU - Safia Iqbal
AU - Md Rezaul Karim
Y1 - 2026/09/22
PY - 2026
N1 - https://doi.org/10.11648/j.bs.20261203.11
DO - 10.11648/j.bs.20261203.11
T2 - Biomedical Sciences
JF - Biomedical Sciences
JO - Biomedical Sciences
SP - 44
EP - 57
PB - Science Publishing Group
SN - 2575-3932
UR - https://doi.org/10.11648/j.bs.20261203.11
AB - Alzheimer's disease (AD) is a neurological disorder characterized by progressive memory loss, cognitive decline, and neuronal degeneration. The pathophysiology of Alzheimer's disease (AD) involves amyloid-beta buildup, tau hyperphosphorylation, oxidative stress, making the development of effective therapies challenging. This highlights the need for multi-target neuroprotective medicines. As a result, it is very important to quickly find natural bioactive compounds. Withania somnifera (Ashwagandha), has exhibited notable antioxidant, anti-inflammatory, and neuroprotective effects. We collected 65 phytochemicals and examined 49 of them based on ADMET characteristics. We collected gene targets for these chemicals (8,964) and AD-related genes (15,293) from separate databases. A comparison investigation revealed 920 overlapping genes. We detected 13 hub genes using Cytoscape for topological analysis: TP53, SRC, AKT1, HSP90AA1, EGFR, IL6, MAPK1, MAPK3, JUN, ESR1, BCL2, HSP90AB1, and GNAI1. After then, STRING was used to create networks of protein-protein interactions (PPIs). Network pharmacology demonstrated that the 13 hub genes are significantly associated with the regulation of apoptosis, MAPK signaling, PI3K-AKT signaling, and neuroinflammation. Docking studies revealed that significant phytochemicals like as Somniferine, Withaferin A, and Withanolide O have considerable binding affinity to hub proteins including AKT1, JUN, and HSP90AA1. This research on integrative network pharmacology and molecular docking demonstrates the potential application of W. Somnifera phytochemicals for the treatment of Alzheimer's disease.
VL - 12
IS - 3
ER -

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Author Information

Md Sarowre Hossen

Department of Biotechnology and Genetic Engineering, Islamic University Bangladesh, Kushtia, Bangladesh;Lab of Oriental Medicine and Bioinformatics, Islamic University Bangladesh, Kushtia, Bangladesh

Contact Email

http://orcid.org/0009-0003-4978-5961
Abdullah Al Nafiz

Department of Biotechnology and Genetic Engineering, Islamic University Bangladesh, Kushtia, Bangladesh;Lab of Oriental Medicine and Bioinformatics, Islamic University Bangladesh, Kushtia, Bangladesh

Contact Email

http://orcid.org/0009-0003-7238-8438
Shakil Hossen

Department of Biotechnology and Genetic Engineering, Islamic University Bangladesh, Kushtia, Bangladesh;Lab of Oriental Medicine and Bioinformatics, Islamic University Bangladesh, Kushtia, Bangladesh

Contact Email

http://orcid.org/0009-0003-2895-5327
Md Shahidul Islam

Department of Microbiology, Rabindra Maitree University, Kushtia, Bangladesh

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http://orcid.org/0009-0007-7572-7174
Safia Iqbal

Lab of Oriental Medicine and Bioinformatics, Islamic University Bangladesh, Kushtia, Bangladesh;Department of Microbiology, Rabindra Maitree University, Kushtia, Bangladesh

Contact Email

http://orcid.org/0000-0001-8128-5607
Md Rezaul Karim

Department of Biotechnology and Genetic Engineering, Islamic University Bangladesh, Kushtia, Bangladesh;Lab of Oriental Medicine and Bioinformatics, Islamic University Bangladesh, Kushtia, Bangladesh

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http://orcid.org/0000-0002-7385-5443

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APA Style

Hossen, M. S., Nafiz, A. A., Hossen, S., Islam, M. S., Iqbal, S., et al. (2026). Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomedical Sciences, 12(3), 44-57. https://doi.org/10.11648/j.bs.20261203.11

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ACS Style

Hossen, M. S.; Nafiz, A. A.; Hossen, S.; Islam, M. S.; Iqbal, S., et al. Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomed. Sci. 2026, 12(3), 44-57. doi: 10.11648/j.bs.20261203.11

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AMA Style

Hossen MS, Nafiz AA, Hossen S, Islam MS, Iqbal S, et al. Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches. Biomed Sci. 2026;12(3):44-57. doi: 10.11648/j.bs.20261203.11

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@article{10.11648/j.bs.20261203.11,
  author = {Md Sarowre Hossen and Abdullah Al Nafiz and Shakil Hossen and Md Shahidul Islam and Safia Iqbal and Md Rezaul Karim},
  title = {Uncovering Gene Targets Modulated by Withania somnifera Phytochemicals for Alzheimer’s Disease Intervention Using Network Pharmacology Approaches},
  journal = {Biomedical Sciences},
  volume = {12},
  number = {3},
  pages = {44-57},
  doi = {10.11648/j.bs.20261203.11},
  url = {https://doi.org/10.11648/j.bs.20261203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bs.20261203.11},
  abstract = {Alzheimer's disease (AD) is a neurological disorder characterized by progressive memory loss, cognitive decline, and neuronal degeneration. The pathophysiology of Alzheimer's disease (AD) involves amyloid-beta buildup, tau hyperphosphorylation, oxidative stress, making the development of effective therapies challenging. This highlights the need for multi-target neuroprotective medicines. As a result, it is very important to quickly find natural bioactive compounds. Withania somnifera (Ashwagandha), has exhibited notable antioxidant, anti-inflammatory, and neuroprotective effects. We collected 65 phytochemicals and examined 49 of them based on ADMET characteristics. We collected gene targets for these chemicals (8,964) and AD-related genes (15,293) from separate databases. A comparison investigation revealed 920 overlapping genes. We detected 13 hub genes using Cytoscape for topological analysis: TP53, SRC, AKT1, HSP90AA1, EGFR, IL6, MAPK1, MAPK3, JUN, ESR1, BCL2, HSP90AB1, and GNAI1. After then, STRING was used to create networks of protein-protein interactions (PPIs). Network pharmacology demonstrated that the 13 hub genes are significantly associated with the regulation of apoptosis, MAPK signaling, PI3K-AKT signaling, and neuroinflammation. Docking studies revealed that significant phytochemicals like as Somniferine, Withaferin A, and Withanolide O have considerable binding affinity to hub proteins including AKT1, JUN, and HSP90AA1. This research on integrative network pharmacology and molecular docking demonstrates the potential application of W. Somnifera phytochemicals for the treatment of Alzheimer's disease.},
 year = {2026}
}

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