NETWORK PHARMACOLOGY-GUIDED TARGET IDENTIFICATION OF CADAMBINE AND DERIVATIVES FROM NEOLAMARCKIA CADAMBA AGAINST ALZHEIMER’S DISEASE WITH IN SILICO ADMET PROFILING

Authors

  • KAVITHA M Department of Pharmacology, School of Pharmaceutical Sciences, Vels Institute of Sciences, Technology and Advanced Studies, Chennai, Tamil Nadu, India.
  • RONALD DARWIN C Department of Pharmacology, School of Pharmaceutical Sciences, Vels Institute of Sciences, Technology and Advanced Studies, Chennai, Tamil Nadu, India. https://orcid.org/0000-0002-2731-8196

DOI:

https://doi.org/10.22159/ajpcr.2026v19i4.57815

Keywords:

Neolamarckia cadamba,, Cadambine, Alzheimer’s disease, Network pharmacology, Protein–protein interaction, Cytoscape, SwissADME, ProTox-3.0, ADMET, Blood–brain barrier

Abstract

Objectives: To identify Alzheimer’s disease (AD)-relevant molecular targets of cadambine and its derivatives from Neolamarckia cadamba using a predictive network pharmacology framework and to evaluate their drug-likeness, permeability, and safety through in silico ADMET profiling.

Methods: Disease genes were obtained from GeneCards, whereas compound targets for cadambine, 3-dihydrocadambine, and 3β-isodihydrocadambine were predicted through SwissTargetPrediction. Overlapping targets were analyzed using STRING and Cytoscape for protein–protein interactions and node centrality. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment provided functional insights. SwissADME and ProTox 3.0 assessed physiochemical, permeability, and toxicity profiles. In recent years, network pharmacology has matured into a robust, systems-level methodology that integrates polypharmacology, systems biology, bioinformatics, and network modeling to elucidate complex disease mechanisms and multitarget drug actions. Contemporary studies published in 2024–2025 demonstrate the application of network pharmacology to AD contexts, combining target prediction with gene-disease association data and pathway enrichment to reveal multi-pathway therapeutic hypotheses and actionable biological insights.

Results: Key hub targets included caspase-3, epidermal growth factor receptor, peroxisome proliferator-activated receptor gamma, matrix metalloproteinase-9/matrix metalloproteinase-2, solute carrier family 2 member 1, and adenosine receptors, linked to apoptosis, neuroinflammation, and metabolism. GO/KEGG analysis revealed neuronal membrane localization and G protein-coupled receptor signaling. ADMET predicted low blood– brain barrier permeability, low acute toxicity (LD₅₀ ~3000 mg/kg), and possible immuno- and respiratory toxicity. In vitro assays demonstrated dose-dependent enhancement of antioxidant enzyme activities, supporting a general neuroprotective phenotype.

Conclusion: Cadambine and its derivatives exhibit multitarget potential relevant to AD through network-level modulation of oxidative stress, inflammatory, and signaling pathways. These findings support further target-specific validation and formulation-based strategies to improve central nervous system delivery.

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References

1. World Health Organization. Dementia. World Health Organization. Available from: https://www.who.int/news-room/fact-sheets/detail/ dementia [Last accessed on 2025 Oct 29].

2. Querfurth HW, LaFerla FM. Alzheimer’s disease. N Engl J Med. 2010 Jan 28;362(4):329-44. doi: 10.1056/nejmra0909142, PMID 20107219

3. Hopkins AL. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol. 2008 Nov;4(11):682-90. doi: 10.1038/ nchembio.118, PMID 18936753

4. Sindhu TJ, James JP, Fathima CZ, Vasudevan R, Alegaon SG. Focused insights into Alzheimer’s treatment strategies: Pharmacophore modelling, DFT studies, MD simulations and SH-SY5Y neuroprotection of Naringin. Int J Appl Pharm. 2025;17(6):485-98.

5. Pandey A, Negi PS. Traditional uses, phytochemistry and pharmacological properties of Neolamarckia cadamba: A review. J Ethnopharmacol. 2016 Apr;181:118-35. doi: 10.1016/j. jep.2016.01.036, PMID 26821190

6. George J, Dhavan P, Jadhav B, Meshram G, Patil V. FT-IR coupled secondary metabolites profiling and biological activities of Neolamarckia cadamba leaves. Nat Resour Hum Health. 2022 Oct 30;3(1):94-100. doi: 10.53365/nrfhh/148092

7. Dubey A, Nayak S, Goupale DC. Anthocephalus cadamba: A review. Pharmacogn J. 2011 Jan;2(18):71-6. doi: 10.1016/S0975- 3575(11)80029-5.

8. Pardridge WM. The blood-brain barrier: Bottleneck in brain drug development. Neurorx. 2005 Jan;2(1):3-14. doi: 10.1602/neurorx.2.1.3, PMID 15717053

9. Li S, Zhang B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin J Nat Med. 2013 Mar;11(2):110-20. doi: 10.1016/S1875-5364(13)60037-0, PMID 23787177

10. Ellman GL, Courtney KD, Andres V Jr., Feather-stone RM. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem Pharmacol. 1961 Jul;7(2):88-95. doi: 10.1016/0006- 2952(61)90145-9, PMID 13726518

11. Dringen R. Metabolism and functions of glutathione in brain. Prog Neurobiol. 2000 Dec;62(6):649-71. doi: 10.1016/S0301- 0082(99)00060-X

12. McCord JM, Fridovich I. Superoxide dismutase: The first twenty years (1968-1988). Free Radic Biol Med. 1988;5(5-6):363-9. doi: 10.1016/0891-5849(88)90109-8, PMID 2855736

13. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016 Jun;54(1):1.30.1- 33. doi: 10.1002/cpbi.5, PMID 27322403

14. Daina A, Michielin O, Zoete V. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019 Jul 2;47(W1):W357-64. doi: 10.1093/nar/gkz382, PMID 31106366

15. Venny 2.1.0. Available from: https://bioinfogp.cnb.csic.es/tools/venny [Last accessed on 2025 Oct 29].

16. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-

Cepas J. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019 Jan 8;47(D1):D607-13. doi: 10.1093/nar/gky1131, PMID 30476243

17. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov;13(11):2498-504. doi: 10.1101/gr.1239303, PMID 14597658

18. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM. Gene Ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000 May;25(1):25-9. doi: 10.1038/75556, PMID 10802651

19. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27-30. doi: 10.1093/ nar/28.1.27, PMID 10592173

20. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57(1):289-300. doi: 10.1111/j.2517-6161.1995.tb02031.x

21. Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017 Mar 3;7(1):42717. doi: 10.1038/srep42717, PMID 28256516

22. Daina A, Zoete V. A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016 Jun 6;11(11):1117-21. doi: 10.1002/cmdc.201600182, PMID 27218427

23. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018 Jul 2;46(W1):W257-63. doi: 10.1093/nar/gky318, PMID 29718510

24. Marklund S, Marklund G. Involvement of the superoxide anion radical in the autoxidation of pyrogallol and a convenient assay for superoxide dismutase. Eur J Biochem. 1974 Sep;47(3):469-74. doi: 10.1111/j.1432- 1033.1974.tb03714.x, PMID 4215654

25. Carlberg I, Mannervik B. Glutathione reductase. Methods Enzymol. 1985;113:484-90. doi: 10.1016/s0076-6879(85)13062-4, PMID 3003504

26. Zhang R, Zhu X, Bai H, Ning K. Network pharmacology databases for traditional Chinese medicine: Review and assessment. Front Pharmacol. 2019 Feb 21;10:123. doi: 10.3389/fphar.2019.00123, PMID 30846939

27. Aoyama K, Nakaki T. Glutathione in cellular redox homeostasis: Association with the excitatory amino acid Carrier 1 (EAAC1). Molecules. 2015 May 14;20(5):8742-58. doi: 10.3390/ molecules20058742, PMID 26007177

28. Perry JJ, Shin DS, Getzoff ED, Tainer JA. The structural biochemistry of the superoxide dismutases. Biochim Biophys Acta. 2010 Feb;1804(2):245-62. doi: 10.1016/j.bbapap.2009.11.004, PMID 19914407

29. Hengartner MO. The biochemistry of apoptosis. Nature. 2000 Oct;407(6805):770-6. doi: 10.1038/35037710, PMID 11048727

30. Dwevedi A, Sharma K, Sharma YK. Cadamba: A miraculous tree having enormous pharmacological implications. Pharmacogn Rev. 2015;9(18):107-13. doi: 10.4103/0973-7847.162110, PMID 26392707

31. Misrani A, Tabassum S, Yang L. Mitochondrial dysfunction and oxidative stress in Alzheimer’s disease. Front Aging Neurosci. 2021 Feb 18;13:617588. doi: 10.3389/fnagi.2021.617588, PMID 33679375

32. Jurcău MC, Andronie-Cioara FL, Jurcău A, Marcu F, Ţiț DM, Pașcalău N. The link between oxidative stress, mitochondrial dysfunction and neuroinflammation in the pathophysiology of Alzheimer’s disease: Therapeutic implications and future perspectives. Antioxidants (Basel). 2022 Oct 31;11(11):2167. doi: 10.3390/ antiox11112167, PMID 36358538

33. Song D, Hao J, Fan D. Biological properties and clinical applications of berberine. Front Med. 2020 Oct;14(5):564-82. doi: 10.1007/s11684- 019-0724-6, PMID 32335802

34. Stone TW, Ceruti S, Abbracchio MP. Adenosine receptors and neurological disease: Neuroprotection and neurodegeneration. Handb Exp Pharmacol 2009;193:535-87. doi: 10.1007/978-3-540-89615-9_17

35. Birks JS, Harvey RJ. Donepezil for dementia due to Alzheimer’s disease. Cochrane Database Syst Rev. 2018 Jun 18;6(6):CD001190. doi: 10.1002/14651858.CD001190.pub3, PMID 29923184

36. Pohanka M. Cholinesterases, a target of pharmacology and toxicology. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2011 Sep 1;155(3):219-29. doi: 10.5507/bp.2011.036, PMID 22286807

37. Butterfield DA, Swomley AM, Sultana R. Amyloid β- peptide (1-42)-induced oxidative stress in Alzheimer disease: Importance in disease pathogenesis and progression. Antioxid Redox Signal. 2013 Sep 10;19(8):823-35.

38. Heneka MT, Fink A, Doblhammer G. Effect of pioglitazone medication on the incidence of dementia. Ann Neurol. 2015 Aug;78(2):284-94. doi: 10.1002/ana.24439, PMID 25974006

39. Spagnuolo C, Napolitano M, Tedesco I, Moccia S, Milito A, Russo GL. Neuroprotective role of natural polyphenols. Curr Top Med Chem. 2016 Apr 29;16(17):1943-50. doi: 10.2174/156802661666616020412 2449, PMID 26845551

40. Winiarska-Mieczan A, Kwiecień M, Jachimowicz- Rogowska K, Donaldson J, Tomaszewska E, Baranowska-Wójcik E. Anti-inflammatory, antioxidant, and neuroprotective effects of polyphenols-polyphenols as an element of diet therapy in depressive disorders. Int J Mol Sci. 2023 Jan 23;24(3):2258.

41. Chen J, Jiang QD, Chai YP, Zhang H, Peng P, Yang XX. Natural terpenes as penetration enhancers for transdermal drug delivery. Molecules. 2016 Dec 11;21(12):1709. doi: 10.3390/molecules21121709, PMID 27973428

42. Podolak I, Galanty A, Sobolewska D. Saponins as cytotoxic agents: A review. Phytochem Rev. 2010 Sep;9(3):425-74. doi: 10.1007/s11101- 010-9183-z, PMID 20835386

43. Martin YC. A bioavailability score. J Med Chem. 2005 May 1;48(9):3164- 70. doi: 10.1021/jm0492002, PMID 15857122

Published

07-04-2026

How to Cite

KAVITHA M, and RONALD DARWIN C. “NETWORK PHARMACOLOGY-GUIDED TARGET IDENTIFICATION OF CADAMBINE AND DERIVATIVES FROM NEOLAMARCKIA CADAMBA AGAINST ALZHEIMER’S DISEASE WITH IN SILICO ADMET PROFILING”. Asian Journal of Pharmaceutical and Clinical Research, vol. 19, no. 4, Apr. 2026, pp. 144-57, doi:10.22159/ajpcr.2026v19i4.57815.

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