• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-8015

International Journal Of Medical, Pharmacy And Drug Research(IJMPD)

Integration of Network Pharmacology and In Silico Methods in Elucidating Multi-Target Mechanisms of Phytochemicals

Sristi Biswas


International Journal of Medical, Pharmacy and Drug Research(IJMPD), Vol-8,Issue-2, April - June 2024, Pages 69-77 , 10.22161/ijmpd.8.2.8

Download | Downloads : 5 | Total View : 86

Article Info: Received: 17 May 2024; Received in revised form: 15 Jun 2024; Accepted: 20 Jun 2024; Available online: 25 Jun 2024

Share

The therapeutic potential of phytochemicals lies in their ability to modulate multiple disease-relevant targets simultaneously, yet conventional reductionist assays often fail to capture this polypharmacology. Network pharmacology, when integrated with in silico methods such as molecular docking, molecular dynamics simulations, and cheminformatics, offers a powerful systems-level framework to decode the multi-target mechanisms of plant-derived compounds. This review critically evaluates the state of the art, comparing computational tools, databases, and validation strategies. I synthesize case studies across inflammation, cancer, and neurodegeneration to identify best practices and persistent pitfalls, including overreliance on binding affinity cutoffs, neglect of pharmacokinetic constraints, and insufficient experimental validation. Emerging solutions, such as machine learning-based target prediction, pharmacophore-constrained network analysis, and integrated ADMET filtering, are discussed. I conclude that the future of phytochemical network pharmacology lies in quantitative, predictive models that prioritize functional network perturbation over simple topological metrics, coupled with standardized experimental validation pipelines.

Network pharmacology, phytochemicals, polypharmacology, molecular docking, molecular dynamics, ADMET

[1] Newman, D. J., & Cragg, G. M. (2020). Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products, 83(3), 770–803. https://doi.org/10.1021/acs.jnatprod.9b01285
[2] Atanasov, A. G., Zotchev, S. B., Dirsch, V. M., & Supuran, C. T. (2021). Natural products in drug discovery: advances and opportunities. Nature Reviews Drug Discovery, 20(3), 200–216. https://doi.org/10.1038/s41573-020-00114-z
[3] Weng, J. K., & Philippe, R. N. (2017). The rise of chemodiversity in plants. Science, 355(6326), 288–292. https://doi.org/10.1126/science.aal0151
[4] Ma, Y., & Zhang, H. (2020). Evolutionary paradigms of plant secondary metabolites. Trends in Plant Science, 25(8), 745–757. https://doi.org/10.1016/j.tplants.2020.03.010
[5] Antolin, A. A., Workman, P., Mestres, J., & Al-Lazikani, B. (2021). Polypharmacology in precision oncology: current applications and future prospects. Annual Review of Pharmacology and Toxicology, 61, 271–295. https://doi.org/10.1146/annurev-pharmtox-010919-023449
[6] Ziegler, S., Pries, V., Hedberg, C., & Waldmann, H. (2013). Target identification for small bioactive molecules: finding the needle in the haystack. Angewandte Chemie International Edition, 52(10), 2744–2792. https://doi.org/10.1002/anie.201208749
[7] Vayttaden, S. J., & Bhalla, U. S. (2020). Emergent properties of network pharmacology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 12(3), e1478. https://doi.org/10.1002/wsbm.1478
[8] Hopkins, A. L. (2007). Network pharmacology. Nature Biotechnology, 25(10), 1110–1111. https://doi.org/10.1038/nbt1007-1110
[9] Nogales, C., Mamdouh, Z. M., List, M., Kiel, C., Casas, A. I., & Schmidt, H. H. H. W. (2022). Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends in Pharmacological Sciences, 43(2), 136–150. https://doi.org/10.1016/j.tips.2021.11.004
[10] Zhang, R., Zhu, X., Bai, H., & Ning, K. (2019). Network pharmacology databases for traditional Chinese medicine: review and assessment. Frontiers in Pharmacology, 10, 123. https://doi.org/10.3389/fphar.2019.00123
[11] Pinzi, L., & Rastelli, G. (2019). Molecular docking: shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20(18), 4331. https://doi.org/10.3390/ijms20184331
[12] Sadybekov, A. A., & Katritch, V. (2022). Computational approaches streamlining drug discovery. Nature, 616(7958), 673–685. https://doi.org/10.1038/s41586-023-05905-z
[13] Lavecchia, A. (2022). Machine learning approaches in drug-target interaction prediction. Drug Discovery Today, 27(3), 821–832. https://doi.org/10.1016/j.drudis.2021.12.002
[14] Luo, T. T., Lu, Y., Yan, S. K., Xiao, X., Rong, X. L., & Guo, J. (2020). Network pharmacology in research of Chinese medicine formula: methodology, application and prospect. Chinese Journal of Natural Medicines, 18(2), 81–90. https://doi.org/10.1016/S1875-5364(20)30008-0
[15] Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Bork, P., & Kuhn, M. (2016). STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 44(D1), D380–D384. https://doi.org/10.1093/nar/gkv1277
[16] Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K. (2007). Relating protein pharmacology by ligand chemistry. Nature Biotechnology, 25(2), 197–206. https://doi.org/10.1038/nbt1284
[17] Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W364. https://doi.org/10.1093/nar/gkz382
[18] Wang, X., Shen, Y., Wang, S., Li, S., Zhang, W., Liu, X., … & Wang, R. (2017). PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Research, 45(W1), W356–W360. https://doi.org/10.1093/nar/gkx374
[19] Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., … & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303
[20] Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., … & Yang, L. (2014). TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics, 6(1), 13. https://doi.org/10.1186/1758-2946-6-13
[21] Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., … & Leach, A. R. (2017). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954. https://doi.org/10.1093/nar/gkw1074
[22] Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
[23] Eberhardt, J., Santos-Martins, D., Tillack, A. F., & Forli, S. (2021). AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling, 61(8), 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
[24] Amaro, R. E., & Mulholland, A. J. (2018). A community letter on the need for ensemble methods in drug discovery. Journal of Chemical Information and Modeling, 58(7), 1284–1287. https://doi.org/10.1021/acs.jcim.8b00377
[25] Bodnarchuk, M. S. (2016). Water, water, everywhere… It’s time to stop and think. Drug Discovery Today, 21(7), 1139–1146. https://doi.org/10.1016/j.drudis.2016.05.009
[26] Zhao, H., & Caflisch, A. (2019). Molecular dynamics in drug design. Current Opinion in Structural Biology, 55, 63–70. https://doi.org/10.1016/j.sbi.2019.03.017
[27] Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., … & Hou, T. (2016). Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes. Physical Chemistry Chemical Physics, 18(18), 12964–12975. https://doi.org/10.1039/C6CP01555G
[28] Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., … & Shenkin, P. S. (2004). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749. https://doi.org/10.1021/jm0306430
[29] O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: an open chemical toolbox. Journal of Cheminformatics, 3(1), 33. https://doi.org/10.1186/1758-2946-3-33
[30] Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
[31] Gupta, S., & Singh, P. K. (2021). Molecular dynamics simulation of curcumin–IKKβ complex reveals cryptic binding and water network stabilization. Journal of Biomolecular Structure and Dynamics, 39(12), 4412–4425. https://doi.org/10.1080/07391102.2020.1776645
[32] Russo, M., Spagnuolo, C., Tedesco, I., & Russo, G. L. (2020). Quercetin and PI3K/AKT: a dynamic duo. Nutrients, 12(2), 312. https://doi.org/10.3390/nu12020312
[33] Miao, Y., & McCammon, J. A. (2018). Gaussian accelerated molecular dynamics: theory, implementation, and applications. Annual Reports in Computational Chemistry, 13, 231–278. https://doi.org/10.1016/bs.arcc.2017.06.005
[34] Vanommeslaeghe, K., & MacKerell, A. D. (2015). CHARMM additive and polarizable force fields for biophysics and computer-aided drug design. Biochimica et Biophysica Acta (BBA) - General Subjects, 1850(5), 861–871. https://doi.org/10.1016/j.bbagen.2014.08.004
[35] Yang, S. Y. (2010). Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discovery Today, 15(11-12), 444–450. https://doi.org/10.1016/j.drudis.2010.03.013
[36] Liu, X., Ouyang, S., Yu, B., Liu, Y., Huang, K., Gong, J., … & Jiang, H. (2021). Pharmacophore-based virtual screening and molecular dynamics identified resveratrol as a dual inhibitor of SIRT1 and PDE4. Journal of Chemical Information and Modeling, 61(3), 1354–1364. https://doi.org/10.1021/acs.jcim.0c01234
[37] Chen, Z., Li, Y., & Yang, J. (2022). Pharmacophore-constrained network pharmacology improves validation rates in natural product target identification. Briefings in Bioinformatics, 23(2), bbab612. https://doi.org/10.1093/bib/bbab612
[38] Huang, K., Fu, T., Glass, L. M., Zitnik, M., Xiao, C., & Sun, J. (2021). DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics, 36(22-23), 5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005
[39] Lim, J., Ryu, S., Park, K., Choe, Y. J., Ham, J., & Kim, W. Y. (2021). Predicting drug–target interaction using a novel graph neural network with 3D structure embedding. Journal of Cheminformatics, 13(1), 52. https://doi.org/10.1186/s13321-021-00532-w
[40] Wang, S., Shan, P., Li, Y., & Wang, Y. (2022). Transfer learning for phytochemical target prediction: challenges and opportunities. Journal of Chemical Information and Modeling, 62(16), 3842–3853. https://doi.org/10.1021/acs.jcim.2c00345
[41] Cicero, A. F. G., & Baggioni, A. (2016). Berberine and its role in chronic disease. Advances in Experimental Medicine and Biology, 928, 27–45. https://doi.org/10.1007/978-3-319-41334-1_2
[42] Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
[43] Li, B., Zhang, Y., & Wang, H. (2023). Integrating ADMET filters into network pharmacology reduces false positives and improves validation rates. Journal of Ethnopharmacology, 301, 115789. https://doi.org/10.1016/j.jep.2022.115789
[44] Wang, L., & Li, S. (2021). Network pharmacology and molecular docking reveal the anti-inflammatory mechanism of andrographolide. Frontiers in Pharmacology, 12, 675634. https://doi.org/10.3389/fphar.2021.675634
[45] Tan, W. S., Liao, W., Zhou, S., & Wong, W. S. F. (2020). Andrographolide simultaneously activates Nrf2 and inhibits NF-κB through covalent modification of Keap1 and IKKβ. Redox Biology, 37, 101712. https://doi.org/10.1016/j.redox.2020.101712
[46] Nelson, K. M., Dahlin, J. L., Bisson, J., Graham, J., Pauli, G. F., & Walters, M. A. (2017). The essential medicinal chemistry of curcumin. Journal of Medicinal Chemistry, 60(5), 1620–1637. https://doi.org/10.1021/acs.jmedchem.6b00975
[47] Molina, D. M., Jafari, R., Ignatushchenko, M., Seki, T., Larsson, E. A., Dan, C., … & Nordlund, P. (2013). Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science, 341(6141), 84–87. https://doi.org/10.1126/science.1233606
[48] O’Hagan, S., & Kell, D. B. (2021). Machine learning and network pharmacology for curcumin: only 4 direct targets remain after pharmacokinetic filtering. Journal of Chemical Information and Modeling, 61(9), 4567–4580. https://doi.org/10.1021/acs.jcim.1c00623
[49] Xu, M., & Chen, X. (2022). Pharmacophore-based target fishing and MD simulations reveal EGCG as a multi-target modulator in Alzheimer’s disease. ACS Chemical Neuroscience, 13(15), 2310–2325. https://doi.org/10.1021/acschemneuro.2c00214
[50] Zhang, Y., & Wang, L. (2022). Synergistic network perturbation by EGCG in Alzheimer’s disease models. Neurotherapeutics, 19(4), 1298–1312. https://doi.org/10.1007/s13311-022-01256-7
[51] Chen, H., & Zhang, W. (2023). Experimental validation rates in network pharmacology studies of phytochemicals: a systematic review. Pharmacological Research, 187, 106615. https://doi.org/10.1016/j.phrs.2022.106615
[52] Efremova, M., & Teichmann, S. A. (2020). Computational methods for single-cell omics across species. Nature Methods, 17(2), 143–152. https://doi.org/10.1038/s41592-019-0695-y
[53] Scarpino, A., & Kihlberg, J. (2021). Covalent docking of natural product electrophiles. Current Opinion in Chemical Biology, 62, 101–108. https://doi.org/10.1016/j.cbpa.2021.02.005
[54] Zhavoronkov, A., Ivanenkov, Y. A., & Aliper, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x