Advances in Herbal Research
Advances in Herbal Research | online ISSN 2209-1890
45
Citations
269.8k
Views
95
Articles
Volume 9 Number 1 2026
REVIEWS (Open Access)
Network-Driven Drug Repurposing and Natural Products in Cancer: A Systematic Review of Validation and Reproducibility
Shakharia Islam Rabby 1, Shahariar Hossain 1, Fazle Rabbi 1, Md. Fariduzzaman 1, Nayem Khan 1, Mehebin Khan Sumaiya 1, Meherab Hasan Rakib 1, Mir Nasir Ahmed Shawn 1
Advances in Herbal Research 9 (1) 1-20 https://doi.org/10.25163/ahi.9110946
Submitted: 22 July 2026 Revised: 17 September 2026 Accepted: 28 September 2026 Published: 20 September 2026
Abstract
Cancer remains one of the most stubborn problems in modern medicine, and network pharmacology has, over the last decade or so, become one of the more promising ways of thinking about it differently — not gene by gene, but as a system of interacting modules that a single compound might perturb in several places at once. This review asks a fairly simple question that turns out to be surprisingly hard to answer well: when a network analysis prioritizes a natural product or an already-approved drug as a candidate cancer therapy, how much should we actually believe it? We set out, through a protocol-led systematic search of MEDLINE/PubMed, Embase, Scopus, Web of Science, and the Cochrane Library (2015 to the final September 2026 search date), to identify, appraise, and synthesize studies in which a biological network materially shaped the choice of candidate, rather than simply illustrating a decision already made. Two reviewers will screen and extract data independently, appraise computational studies against a purpose-built instrument informed by PROBAST, and apply design-specific tools to animal and clinical components; pooling will occur only where studies are genuinely comparable. Verified illustrative studies already show that network approaches can surface noncanonical kinase dependencies, metabolic vulnerabilities, subtype-specific repositioning candidates, and biologically active natural compounds — yet the wider literature still leans heavily on docking and enrichment analyses that are, frankly, presented as validation more often than they deserve to be. Our conclusion, tentative as any conclusion about an unfinished evidence base must be, is that network-driven prioritization is a genuinely useful hypothesis-generating tool, not proof of efficacy, and that its translational credibility should be judged candidate by candidate against data provenance, independent validation, target engagement, realistic exposure, and staged experimental confirmation.
Keywords: Cancer; network medicine; network pharmacology; systems pharmacology; drug repurposing; natural products; multi-omics; knowledge graph; precision oncology; systematic review.
References
Ahmed, F., Yang, Y. J., Samantasinghar, A., et al. (2023). Network-based drug repurposing for HPV-associated cervical cancer. Computational and Structural Biotechnology Journal, 21, 5186–5200. https://doi.org/10.1016/j.csbj.2023.10.038
Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68. https://doi.org/10.1038/nrg2918
Campbell, M., McKenzie, J. E., Sowden, A., et al. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, 368, l6890. https://doi.org/10.1136/bmj.l6890
Cavalcante, B. R. R., Freitas, R. D., Siquara da Rocha, L. O., et al. (2024). In silico approaches for drug repurposing in oncology: A scoping review. Frontiers in Pharmacology, 15, 1400029. https://doi.org/10.3389/fphar.2024.1400029
Cheng, F., Desai, R. J., Handy, D. E., et al. (2018). Network-based approach to prediction and population-based validation of in silico drug repurposing. Nature Communications, 9(1), 2691. https://doi.org/10.1038/s41467-018-05116-5
Cheng, F., Lu, W., Liu, C., et al. (2019). A genome-wide positioning systems network algorithm for in silico drug repurposing. Nature Communications, 10(1), 3476. https://doi.org/10.1038/s41467-019-10744-6
Dorel, M., Barillot, E., Zinovyev, A., & Kuperstein, I. (2015). Network-based approaches for drug response prediction and targeted therapy development in cancer. Biochemical and Biophysical Research Communications, 464(2), 386–391. https://doi.org/10.1016/j.bbrc.2015.06.094
Fan, X., Yan, Q., Xu, W., et al. (2025). Network pharmacology with experimental validation unveils the anti-colorectal cancer mechanism of Xianlian Jiedu decoction. Journal of Ethnopharmacology, 342, 119361. https://doi.org/10.1016/j.jep.2025.119361
Fang, J., Liu, C., Wang, Q., Lin, P., & Cheng, F. (2018). In silico polypharmacology of natural products. Briefings in Bioinformatics, 19(6), 1153–1171. https://doi.org/10.1093/bib/bbx045
Federico, A., Fratello, M., Scala, G., et al. (2022). Integrated network pharmacology approach for drug combination discovery: A multi-cancer case study. Cancers, 14(8), 2043. https://doi.org/10.3390/cancers14082043
Firoozbakht, F., Rezaeian, I., Rueda, L., & Ngom, A. (2022). Computationally repurposing drugs for breast cancer subtypes using a network-based approach. BMC Bioinformatics, 23(1), 143. https://doi.org/10.1186/s12859-022-04662-6
Fiscon, G., Conte, F., Farina, L., & Paci, P. (2022). A comparison of network-based methods for drug repurposing along with an application to human complex diseases. International Journal of Molecular Sciences, 23(7), 3703. https://doi.org/10.3390/ijms23073703
Guney, E., Menche, J., Vidal, M., & Barabási, A. L. (2016). Network-based in silico drug efficacy screening. Nature Communications, 7, 10331. https://doi.org/10.1038/ncomms10331
Guyatt, G. H., Oxman, A. D., Vist, G. E., et al. (2008). GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926. https://doi.org/10.1136/bmj.39489.470347.AD
Hooijmans, C. R., Rovers, M. M., de Vries, R. B., et al. (2014). SYRCLE’s risk of bias tool for animal studies. BMC Medical Research Methodology, 14, 43. https://doi.org/10.1186/1471-2288-14-43
Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690. https://doi.org/10.1038/nchembio.118
Hu, S., Li, S., Xu, Y., et al. (2024). The antitumor effects of herbal medicine Triphala on oral cancer by inactivating PI3K/Akt signaling pathway: Network pharmacology, docking, and experimental validation. Phytomedicine, 128, 155488. https://doi.org/10.1016/j.phymed.2024.155488
Iqbal, J. (2025). National cancer registries in Bangladesh and social determinants of health: Challenging cancer disparities. Cancer Control, 32, 10732748251323757. https://doi.org/10.1177/10732748251323757
Kuenzi, B. M., Remsing Rix, L. L., Stewart, P. A., et al. (2017). Polypharmacology-based ceritinib repurposing using integrated functional proteomics. Nature Chemical Biology, 13(12), 1222–1231. https://doi.org/10.1038/nchembio.2489
Lee, L. M., Christodoulou, E. G., Shyamsunder, P., et al. (2022). A novel network pharmacology approach for leukaemia differentiation therapy using Mogrify. Oncogene, 41(48), 5160–5175. https://doi.org/10.1038/s41388-022-02505-5
Li, J. X., Li, R. Z., Sun, A., et al. (2021). Metabolomics and integrated network pharmacology reveal Tricin as the active anti-cancer component of Weijing decoction. Pharmacological Research, 171, 105574. https://doi.org/10.1016/j.phrs.2021.105574
Lin, F., Zhang, G., Yang, X., et al. (2023). Network pharmacology and experimental validation of Wei-Tong-Xin against colorectal cancer through PI3K/AKT signaling. Journal of Ethnopharmacology, 303, 115933. https://doi.org/10.1016/j.jep.2022.115933
McGowan, J., Sampson, M., Salzwedel, D. M., et al. (2016). PRESS Peer Review of Electronic Search Strategies: 2015 guideline statement. Journal of Clinical Epidemiology, 75, 40–46. https://doi.org/10.1016/j.jclinepi.2016.01.021
Ng, I. I., Zhang, J., Tian, T., et al. (2024). Network-based screening identifies sitagliptin as an antitumor drug targeting dendritic cells. Journal for ImmunoTherapy of Cancer, 12(3), e008254. https://doi.org/10.1136/jitc-2023-008254
Nogales, C., Mamdouh, Z. M., List, M., et al. (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
Noor, F., Tahir Ul Qamar, M., Ashfaq, U. A., et al. (2022). Network pharmacology approach for medicinal plants: Review and assessment. Pharmaceuticals, 15(5), 572. https://doi.org/10.3390/ph15050572
Pacheco, M. P., Bintener, T., Ternes, D., et al. (2019). Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine, 43, 98–106. https://doi.org/10.1016/j.ebiom.2019.04.046
Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Palve, V., Liao, Y., Remsing Rix, L. L., & Rix, U. (2021). Turning liabilities into opportunities: Off-target based drug repurposing in cancer. Seminars in Cancer Biology, 68, 209–229. https://doi.org/10.1016/j.semcancer.2020.02.003
Panossian, A. (2025). Trends and pitfalls in the progress of network pharmacology research on natural products. Pharmaceuticals, 18(4), 538. https://doi.org/10.3390/ph18040538
Percie du Sert, N., Hurst, V., Ahluwalia, A., et al. (2020). The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biology, 18(7), e3000410. https://doi.org/10.1371/journal.pbio.3000410
Pramesh, C. S., Badwe, R. A., Bhoo-Pathy, N., et al. (2022). Priorities for cancer research in low- and middle-income countries: A global perspective. Nature Medicine, 28(4), 649–657. https://doi.org/10.1038/s41591-022-01738-x
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., et al. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Journal of the Medical Library Association, 109(2), 174–200. https://doi.org/10.5195/jmla.2021.962
Schaduangrat, N., Lampa, S., Simeon, S., et al. (2020). Towards reproducible computational drug discovery. Journal of Cheminformatics, 12, 9. https://doi.org/10.1186/s13321-020-0408-x
Seo, E. J., Dawood, M., Hult, A. K., Olsson, M. L., & Efferth, T. (2021). Network pharmacology of triptolide in cancer cells: Implications for transcription factor binding. Investigational New Drugs, 39(6), 1523–1537. https://doi.org/10.1007/s10637-021-01137-y
Sterne, J. A., Hernán, M. A., Reeves, B. C., et al. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 355, i4919. https://doi.org/10.1136/bmj.i4919
Sterne, J. A. C., Savovic, J., Page, M. J., et al. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. https://doi.org/10.1136/bmj.l4898
Sung, H., Filho, A. M., Laversanne, M., et al. (2026). Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries. CA: A Cancer Journal for Clinicians, 76(4), e70090. https://doi.org/10.3322/caac.70090
Tan, A., Huang, H., Zhang, P., & Li, S. (2019). Network-based cancer precision medicine: A new emerging paradigm. Cancer Letters, 458, 39–45. https://doi.org/10.1016/j.canlet.2019.05.015
Tian, S., Xu, M., Geng, X., et al. (2025). Network medicine-based strategy identifies maprotiline as a repurposable drug by inhibiting PD-L1 expression via targeting SPOP in cancer. Advanced Science, 12(1), e2410285. https://doi.org/10.1002/advs.202410285
Turanli, B., Altay, O., Borén, J., et al. (2021). Systems biology based drug repositioning for development of cancer therapy. Seminars in Cancer Biology, 68, 47–58. https://doi.org/10.1016/j.semcancer.2019.09.020
Weich, A., Berges, J., Flamann, C., et al. (2025). Repurposed clindamycin suppresses pyroptosis in tumor-associated macrophages through inhibition of caspase-1. Journal of Experimental & Clinical Cancer Research, 44(1), 225. https://doi.org/10.1186/s13046-025-03478-5
Wolff, R. F., Moons, K. G. M., Riley, R. D., et al. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376
World Health Organization. (2020). Cancer Bangladesh 2020 country profile. WHO.
Yang, J., Qin, L., Zhou, S., et al. (2024). Network pharmacology, molecular docking and experimental study of cepharanthine in nasopharyngeal carcinoma. Journal of Ethnopharmacology, 323, 117667. https://doi.org/10.1016/j.jep.2023.117667
Yu, F., Tan, W., Chen, Z., et al. (2022). Nitidine chloride induces caspase 3/GSDME-dependent pyroptosis by inhibiting PI3K/Akt pathway in lung cancer. Chinese Medicine, 17(1), 115. https://doi.org/10.1186/s13020-022-00671-y
Yuan, M., Shong, K., Li, X., et al. (2022). A gene co-expression network-based drug repositioning approach identifies candidates for treatment of hepatocellular carcinoma. Cancers, 14(6), 1573. https://doi.org/10.3390/cancers14061573
Zeng, X., Zhu, S., Liu, X., et al. (2019). deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics, 35(24), 5191–5198. https://doi.org/10.1093/bioinformatics/btz418
Zhang, B., Fu, D., Wang, X., & Hu, X. (2025). Network-based analysis and experimental validation of natural compounds from Yinchen Wuling San for acute myeloid leukemia. Frontiers in Pharmacology, 16, 1591164. https://doi.org/10.3389/fphar.2025.1591164
Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457–i466. https://doi.org/10.1093/bioinformatics/bty294