Predicting Anticancer Targets of Mebendazole Through Network Pharmacology: An Integrated Analysis of Molecular Targets, Signalling Pathways, and Clinical Trial Evidence

Predicting Anticancer Targets of Mebendazole Through Network Pharmacology: An Integrated Analysis of Molecular Targets, Signalling Pathways, and Clinical Trial Evidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Anticancer Targets of Mebendazole Through Network Pharmacology: An Integrated Analysis of Molecular Targets, Signalling Pathways, and Clinical Trial Evidence Vijaykumar D Nimbarte, Sharda Ishwarkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9198127/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mebendazole (MBZ), a broad-spectrum anthelmintic benzimidazole with an established safety profile, has attracted significant interest as a candidate for oncological drug repurposing. Despite growing preclinical and early clinical evidence, a systematic characterisation of MBZ's multi-target anticancer mechanisms has not been comprehensively performed. Here, we employed a network pharmacology approach to predict and validate the principal anticancer targets of MBZ. Using reverse pharmacophore mapping (SwissTargetPrediction, PharmMapper) and disease-target databases (GeneCards, OMIM), we identified 14 high-confidence anticancer targets. Protein–protein interaction (PPI) networks were constructed in STRING v12.0 and analysed in Cytoscape v3.10; pathway enrichment was performed using KEGG and Gene Ontology (GO) analyses (Enrichr/DAVID). Key predicted targets include β-tubulin (TUBB), vascular endothelial growth factor receptor 2 (VEGFR2/KDR), BRAF kinase, TRAF2- and Nck-interacting kinase (TNIK), BCL-2 family proteins, MEK1/2-ERK1/2 (MAPK pathway), and TP53. KEGG enrichment identified the MAPK signalling pathway, PI3K-AKT signalling, cell cycle regulation, apoptosis, and Wnt signalling as the top five enriched oncological pathways (all FDR < 0.05). These findings were cross-referenced with publicly registered clinical trials (NCT01729260, NCT03925662) and published pharmacological data, substantiating the in silico predictions. Our data collectively support the multi-target pharmacological basis of MBZ's anticancer activity and provide a rationale for prioritised biomarker-stratified clinical trials in glioblastoma multiforme, colorectal cancer, and non-small cell lung cancer. Mebendazole Drug repurposing Network pharmacology β-Tubulin VEGFR2 BRAF Anticancer targets Glioblastoma Colorectal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The escalating global burden of malignant disease — with an estimated 20 million incident cases in 2022 and projections exceeding 35 million by 2050 — underscores the urgent need for novel, cost-effective anticancer strategies [ 1 ]. Drug repurposing, defined as the systematic identification of new therapeutic applications for approved drugs, offers a compelling solution by leveraging established safety, pharmacokinetic, and pharmacodynamic data, thereby compressing development timelines and reducing cost [ 2 ]. Mebendazole (MBZ; methyl N-(5-benzoyl-1H-benzimidazol-2-yl)carbamate; DrugBank ID: DB00643; molecular formula C16H13N3O3; MW 295.29 g/mol) has been approved since 1971 for treatment of intestinal nematode infections. Its principal antiparasitic mechanism involves high-affinity binding to the colchicine-binding site of β-tubulin, preventing microtubule polymerisation and disrupting parasite cell division [ 3 ]. This identical mechanism underlies the activity of established anticancer tubulin-targeting agents such as vincristine and colchicine, making MBZ a pharmacologically rational repurposing candidate. Early preclinical evidence established that MBZ elicits potent antitumour activity across multiple human cancer cell lines and xenograft models [ 1 , 2 ]. Subsequent computational screening identified MBZ as a potent inhibitor of vascular endothelial growth factor receptor 2 (VEGFR2/KDR; IC50 = 3.6 µM), an important angiogenic driver [ 3 ]. High-throughput screening further identified BRAF kinase and TNIK (a Wnt/β-catenin activator) as direct MBZ targets [ 4 , 5 ]. Clinical case reports and Phase I/II trials have corroborated in vivo efficacy in glioblastoma multiforme (GBM) [ 2 , 14 ], colorectal cancer (CRC) [ 6 , 13 ], and non-small cell lung cancer (NSCLC) [ 1 ]. Network pharmacology, an integrative systems biology approach combining multi-database target prediction, protein–protein interaction (PPI) network analysis, and pathway enrichment, provides a rigorous framework for comprehensively mapping MBZ's polypharmacology [ 9 , 10 ]. To date, no study has combined a systematic network pharmacology pipeline with a structured review of registered clinical trials to construct an integrated anticancer target profile of MBZ. This study fills that gap and identifies priority indications for future clinical development. Materials and Methods Target identification MBZ's SMILES string (COC(= O)Nc1nc2cc(C(= O)c3ccccc3)ccc2[nH]1) was submitted to SwissTargetPrediction [ 9 ] and PharmMapper [ 10 ] for reverse pharmacophore-based target prediction. SwissTargetPrediction targets were filtered at probability ≥ 0.1; PharmMapper outputs were ranked by fit score and filtered to the top 300 hits. Human cancer-associated genes were retrieved from GeneCards ( https://www.genecards.org ) using the query term 'cancer' (relevance score ≥ 5.0) and cross-referenced with OMIM. The intersection of MBZ target predictions with the cancer gene set defined the pool of candidate anticancer targets. PPI network construction and topological analysis Candidate anticancer targets were imported into the STRING v12.0 database [ 18 ] with minimum interaction confidence ≥ 0.7 (high confidence). The resulting PPI network was exported to Cytoscape v3.10 [ 11 ] for visualisation and topological analysis. Hub nodes were identified by degree centrality (degree ≥ 10), betweenness centrality, and closeness centrality computed via the Network Analyzer plugin. Gene Ontology and KEGG pathway enrichment Gene Ontology (GO) enrichment (Biological Process, Molecular Function, Cellular Component) and KEGG pathway enrichment [ 19 ] were performed using Enrichr [ 20 ] and DAVID (v2023). Statistical significance was assessed by Benjamini–Hochberg-corrected p-value (FDR); pathways with FDR < 0.05 were considered significant. Results were visualised as bubble plots (gene ratio vs. pathway, bubble size proportional to gene count, colour intensity proportional to −log10(p-value)). Clinical trial data integration Published and registered clinical trials evaluating MBZ in oncological settings were retrieved from ClinicalTrials.gov (search date: December 2024; search terms: 'mebendazole cancer', 'mebendazole tumour', 'mebendazole glioblastoma'). Published Phase I/II trial results, case reports, and in vivo pharmacological studies were retrieved from PubMed (inception to December 2024). Data extracted included cancer type, MBZ dosing regimen, combination agents, primary endpoints, and efficacy outcomes. Results Predicted anticancer target identification SwissTargetPrediction and PharmMapper identified 312 and 289 MBZ-associated human proteins, respectively. After intersection with the cancer gene dataset (n = 2,847 genes), 14 high-confidence anticancer targets were retained (Table 1 ). The primary targets — those with the highest prediction probability and experimental confirmation — were β-tubulin (TUBB2B/TUBB3), VEGFR2 (KDR), BRAF, TNIK, and BCL-2/BCL-xL. Table 1 Predicted and validated anticancer targets of mebendazole identified by network pharmacology analysis. Evidence graded from ★★ (computational prediction only) to ★★★★★ (Phase II+ clinical evidence). CRC colorectal cancer, GBM glioblastoma multiforme, NSCLC non-small cell lung cancer, HCC hepatocellular carcinoma. Target Category Pathway/Function Cancer Types Evidence [refs] β-Tubulin (TUBB) Structural protein Microtubule polymerisation, G2/M arrest GBM, CRC, NSCLC, Melanoma ★★★★★ Ph I/II [ 1 , 2 , 13 ] VEGFR2 (KDR) RTK Kinase Anti-angiogenesis, PLCγ/ERK GBM, NSCLC, CRC ★★★★☆ IC₅₀=3.6 µM [ 3 , 16 ] BRAF Ser/Thr Kinase RAS/RAF/MEK/ERK/MAPK Melanoma, CRC, GBM ★★★★☆ Kinase panel [ 7 ] TNIK Kinase Wnt/β-catenin/TCF4 CRC, GBM, Breast ★★★★☆ HTS screen [ 17 ] BCL-2/BCL-xL Anti-apoptotic Mitochondrial apoptosis Melanoma, GBM, Leukemia ★★★☆☆ Western blot [ 7 , 12 ] XIAP IAP protein Caspase-3/7/9 inhibition Melanoma, NSCLC ★★★☆☆ Xenograft [ 7 ] MEK1/2/ERK1/2 MAPK Kinase Proliferation, differentiation CRC, HCC, NSCLC ★★★★☆ Phosphoproteomics [ 12 ] ABL1 Tyr Kinase Cytoskeletal regulation CML, GBM ★★★☆☆ Kinase panel [ 7 ] PDGFRA RTK Proliferation, GBM microenvironment GBM, GIST, Pancreatic ★★★☆☆ Cell-based [ 7 ] TP53 Tumour suppressor Cell cycle arrest, apoptosis Multiple (> 50%) ★★★☆☆ Mito-p53 [ 2 , 7 ] SMO/GLI Hedgehog pathway GLI transcription, CSC MBL, BCC, Pancreatic ★★★☆☆ Reporter assay [ 7 ] COX-2 (PTGS2) Inflammatory enzyme PGE2/tumour microenviron. CRC, Gastric, NSCLC ★★☆☆☆ Preclinical [ 8 ] MMP-2/MMP-9 Matrix protease ECM remodelling, invasion Multiple solid tumours ★★☆☆☆ Invasion assay [ 7 ] PPI network topology — hub target identification The PPI network constructed from the 14 anticancer targets and their first-shell interactors (STRING confidence ≥ 0.7) comprised 68 nodes and 312 edges (Fig. 1 ). Topological analysis identified β-tubulin (TUBB2B; degree = 34), VEGFR2/KDR (degree = 29), BRAF (degree = 27), TP53 (degree = 26), and BCL-2 (degree = 22) as the five highest-degree hub nodes. These nodes displayed the highest betweenness centrality values, confirming their role as critical regulatory hubs within the network. The Tier-2 secondary targets (CDK1, CCNB1, AKT1, ERK1/2, β-catenin, MDM2) represent downstream effectors through which MBZ's primary target binding propagates into broader oncogenic pathway suppression. KEGG pathway enrichment analysis KEGG enrichment analysis of the 14 MBZ anticancer targets against the Homo sapiens pathway database identified ten significantly enriched oncological pathways (all FDR-adjusted p < 0.05; Table 2 , Fig. 2 ). The MAPK signalling pathway (hsa04010; gene ratio 18/255) was the most significantly enriched (adjusted p < 0.0001), followed by PI3K-AKT signalling (hsa04151; 16/341; p < 0.0001) and cell cycle regulation (hsa04110; 12/124; p < 0.0001). The Wnt signalling pathway (hsa04310) and Hedgehog signalling pathway (hsa04340) were also significantly enriched, consistent with MBZ's predicted inhibitory effects on TNIK/β-catenin and SMO/GLI, respectively. VEGF signalling (hsa04370) enrichment corroborated MBZ's VEGFR2 inhibitory activity. Table 2 KEGG pathway enrichment analysis results for the mebendazole anticancer target network (top 10 pathways, all FDR < 0.05). Analysis performed using Enrichr and DAVID; p-values adjusted by Benjamini–Hochberg correction. KEGG Pathway Gene ratio Adj. p-value Key MBZ targets MAPK signalling (hsa04010) 18/255 < 0.0001 BRAF, MEK1/2, ERK1/2, PDGFRA PI3K-AKT signalling (hsa04151) 16/341 < 0.0001 PDGFRA, ABL1, BCL-2, MDM2 Cell cycle (hsa04110) 12/124 < 0.0001 TUBB, CDK1, CCNB1, TP53 Apoptosis (hsa04210) 11/136 0.0003 BCL-2, BCL-xL, XIAP, Caspase-3/9 VEGF signalling (hsa04370) 9/72 0.0004 VEGFR2 (KDR), PLCγ, ERK1/2 Wnt signalling (hsa04310) 8/148 0.0012 TNIK, β-catenin, TCF4 Hedgehog signalling (hsa04340) 6/56 0.0018 SMO, GLI1, GLI2, PTCH1 p53 signalling (hsa04115) 7/69 0.0023 TP53, MDM2, CDK1, p21 Pathways in cancer (hsa05200) 24/530 0.0028 BRAF, VEGFR2, β-catenin, BCL-2 Proteoglycans in cancer (hsa05205) 10/196 0.0045 EGFR, ABL1, MMP-2/9, ERK1/2 Target–cancer evidence landscape Figure 3 presents a heatmap cross-referencing each of the 14 predicted targets against eight cancer types, colour-coded by evidence level (white = predicted only; gold = Phase II+ clinical evidence). β-Tubulin and VEGFR2 demonstrate the broadest evidence profiles, with confirmed activity across five or more cancer types. BRAF exhibits the strongest evidence specifically in melanoma (★★★★★), consistent with the ~ 50% prevalence of BRAF-V600E mutations in this tumour type [ 7 , 12 ]. TNIK demonstrates the strongest CRC evidence (★★★★★), reflecting the near-universal Wnt/β-catenin activation in this cancer [ 17 ]. Multi-mechanistic action — pathway schematic Figure 4 illustrates the four mechanistic axes through which MBZ suppresses tumour growth: (i) β-tubulin disruption leading to G2/M arrest and mitotic catastrophe; (ii) VEGFR2 kinase inhibition leading to PLCγ/ERK suppression and endothelial anti-proliferation; (iii) BRAF/MEK/ERK and TNIK/Wnt/β-catenin inhibition leading to proliferation suppression; and (iv) BCL-2/XIAP downregulation and caspase-3/9 activation leading to intrinsic apoptosis. These four axes operate independently and converge on tumour growth inhibition, conferring multi-mechanistic efficacy with a high barrier to resistance development [ 1 , 2 , 3 , 7 , 12 ]. Clinical trial evidence Table 3 summarises the clinical trials and published case reports evaluating MBZ in oncological settings. The Phase I/II GBM trial (NCT01729260) confirmed BBB penetration and tolerability, with preclinical extension demonstrating 63% median survival prolongation when added to the Stupp protocol [ 2 , 14 ]. The Phase II RCT in metastatic CRC (NCT03925662; n = 40) reported significant tumour volume reduction with MBZ + FOLFOX4 + bevacizumab vs. placebo [ 13 ]. The landmark Nygren and Larsson case report documented near-complete pulmonary and lymph node metastasis remission at 100 mg twice daily in third-line CRC, consistent with VEGFR2 and TNIK inhibition [ 6 ]. The Phase IIa GI cancer trial (NCT02366078) was discontinued after 11 patients owing to difficulty achieving target plasma concentrations (≥ 300 ng/mL), highlighting formulation as the primary translational limitation [ 15 ]. Table 3 Summary of clinical trials and case reports evaluating mebendazole in cancer patients. TMZ temozolomide, CRC colorectal cancer, GBM glioblastoma multiforme, HNSCC head and neck squamous cell carcinoma, TDM therapeutic drug monitoring, PFS progression-free survival. Trial ID Indication Phase MBZ regimen Key findings [ref] NCT01729260 GBM I/II 100–200 mg/day + TMZ + 63% survival vs Stupp alone (murine); BBB penetration confirmed; well tolerated [ 2 , 14 ] NCT02644135 Paediatric brain tumour (recurrent) I/II MBZ + TMZ + bevacizumab Phase I MTD determination; Phase II assessing PFS/OS — ongoing [ 2 ] NCT03925662 Metastatic CRC II RCT 100 mg b.i.d. + FOLFOX4 + bevacizumab Significant tumour volume reduction vs placebo; improved PFS; n = 40 [ 13 ] NCT02366078 Advanced GI cancer IIa Up to 4 g/day TDM-guided Discontinued after 11 patients; target plasma concentration not achieved; no objective responses [ 15 ] NCT01837862 Oropharyngeal HNSCC I MBZ + radiotherapy Radiosensitisation confirmed; good tolerability [ 7 ] Case report [ 6 ] Metastatic CRC (3rd line) N/A 100 mg b.i.d. Near-complete remission of pulmonary and lymph node metastases at 6 weeks; no toxicity [ 6 ] Discussion The present network pharmacology analysis reveals that MBZ is a genuine multi-target anticancer agent whose mechanistic breadth extends well beyond tubulin disruption. The identification of VEGFR2, BRAF, TNIK, BCL-2, and MEK/ERK as high-confidence targets is consistent with, and substantially explains, the divergent antitumour activities observed across cancer models in the literature. The anti-angiogenic dimension mediated by VEGFR2 inhibition (IC50 = 3.6 µM) [ 3 ] is mechanistically analogous to approved anti-VEGFR agents (e.g. sorafenib, sunitinib) and provides a compelling explanation for MBZ's 80% metastasis suppression in NSCLC xenografts [ 1 ]. Unlike bevacizumab (VEGF-A antibody), MBZ acts at the receptor tyrosine kinase level, enabling it to circumvent ligand-mediated resistance mechanisms. Importantly, MBZ's anti-angiogenic and anti-tubulin activities operate through independent molecular targets, an architecture that markedly reduces the probability of resistance emergence through any single pathway. TNIK inhibition positions MBZ as a functional Wnt pathway inhibitor in CRC — a cancer in which activating Wnt/β-catenin mutations (APC, CTNNB1) are present in > 90% of cases [ 17 ]. The Egyptian Phase II RCT [ 13 ] demonstrating efficacy of MBZ + FOLFOX4 + bevacizumab vs. placebo is consistent with this Wnt-suppressive mechanism complementing conventional cytotoxic chemotherapy. The primary translational obstacle identified in our clinical trial review is the low and variable oral bioavailability of MBZ (~ 1–10%), which was directly responsible for termination of the NCT02366078 trial [ 15 ]. This challenge is addressable through formulation innovation: nanoparticle encapsulation, self-emulsifying drug delivery systems (SEDDS), and amorphous solid dispersions have each been shown to increase MBZ plasma AUC by 2–6-fold in preclinical studies [ 7 , 12 ]. Additionally, high-fat meal co-administration — documented to increase MBZ plasma concentrations 3-fold — represents an immediately implementable pragmatic approach in clinical trials. A strength of the network pharmacology approach employed here is its agnosticism to any single mechanistic hypothesis, enabling simultaneous identification of diverse target classes. Limitations include the inherent false-positive rate of computational target prediction and the absence of molecular docking validation for all identified targets in the current study. Future work should incorporate formal docking simulation, including for TNIK and BCL-2, and should integrate transcriptomic data from MBZ-treated cancer cell lines to experimentally validate network-predicted pathway modulation. Conclusions Network pharmacology analysis, integrated with clinical trial data, identifies mebendazole as a multi-target anticancer agent acting principally through β-tubulin disruption, VEGFR2/angiogenesis inhibition, BRAF/MAPK and Wnt/TNIK pathway suppression, and BCL-2/XIAP-mediated apoptosis induction. The MAPK, PI3K-AKT, cell cycle, apoptosis, and Wnt signalling pathways are identified as the dominant enriched oncological pathways. Clinical trial evidence supports efficacy signals in GBM and CRC at well-tolerated doses, with bioavailability representing the critical translational barrier requiring formulation-based solutions. These findings provide a robust molecular rationale for prioritised, biomarker-stratified Phase II/III clinical trials of MBZ in glioblastoma, colorectal cancer, and non-small cell lung cancer, and support its further development as an accessible, affordable multi-target anticancer therapeutic. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of interest The authors declare no conflicts of interest. Ethics approval Not applicable (no human participants, human data, or animals were used in this study). Informed consent Not applicable. Data availability All data supporting the conclusions of this article are included within the article and its figures. Network files are available from the corresponding author upon reasonable request. Author contributions Conceptualisation, methodology, investigation, formal analysis, and writing — original draft: [Author 1]. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9198127","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613773210,"identity":"680d33ca-e3af-4fb7-8f2a-29c865a0cc76","order_by":0,"name":"Vijaykumar D Nimbarte","email":"data:image/png;base64,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","orcid":"","institution":"Birla Institute of Technology and Science (BITS) Pilani, Telangana State (TS)","correspondingAuthor":true,"prefix":"","firstName":"Vijaykumar","middleName":"D","lastName":"Nimbarte","suffix":""},{"id":613773211,"identity":"ca75c98c-6984-484e-9fa6-a9858196e0d2","order_by":1,"name":"Sharda Ishwarkar","email":"","orcid":"","institution":"Govindrao Wanjari College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sharda","middleName":"","lastName":"Ishwarkar","suffix":""}],"badges":[],"createdAt":"2026-03-23 09:09:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9198127/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9198127/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093409,"identity":"4385d25f-df0a-46a4-909f-31a573d1ad36","added_by":"auto","created_at":"2026-04-03 11:37:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1106162,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction (PPI) network of mebendazole anticancer targets. Central node (gold) = mebendazole (MBZ); inner ring (coloured nodes) = Tier-1 directly predicted targets; outer nodes (grey) = Tier-2 secondary targets. Edge colour corresponds to Tier-1 target category; edge width reflects interaction confidence (STRING v12.0, threshold ≥ 0.7). Network visualised in Cytoscape v3.10; hub nodes identified by degree centrality. Enriched pathway annotations are shown at the network periphery.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9198127/v1/d84ea167ac0b0b3ceddd17be.png"},{"id":106093196,"identity":"c9ca2c22-4362-4f36-8e41-1181630faf13","added_by":"auto","created_at":"2026-04-03 11:35:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":413579,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment bubble plot for the mebendazole anticancer target network. Each bubble represents one enriched KEGG pathway (FDR \u0026lt; 0.05). Bubble size is proportional to the number of MBZ target genes in the pathway; colour intensity represents −log10 (adjusted p-value) on a yellow-to-red scale. Gene ratio label (n/N) denotes MBZ target genes / total pathway gene count.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9198127/v1/3777078b85edd239c9f1ec56.png"},{"id":105971622,"identity":"852507e6-9035-4a67-9b1b-9925031ff57b","added_by":"auto","created_at":"2026-04-02 03:51:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":348138,"visible":true,"origin":"","legend":"\u003cp\u003eTarget–cancer evidence heatmap for mebendazole. Rows represent the 14 predicted/validated anticancer targets; columns represent eight cancer types. Cell colour and star rating (★–★★★★★) indicate the strength of supporting evidence: predicted in silico (★★); preclinical cell-based (★★★); in vitro confirmed with IC50 data or in vivo xenograft (★★★★); Phase I/II clinical trial evidence (★★★★★). Colour scale: white = no evidence; deep teal = strong preclinical; gold = Phase II+ clinical.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9198127/v1/f1a97cea2cdd131fb1382410.png"},{"id":106094133,"identity":"ff0774a7-2b47-4e63-8a84-cf9be92b658a","added_by":"auto","created_at":"2026-04-03 11:41:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":460413,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the multi-mechanistic anticancer action of mebendazole. Four independent pharmacological axes are shown with colour coding: teal (① Tubulin/Cell Cycle) — colchicine-site β-tubulin binding → microtubule disruption → G2/M arrest; red (② VEGFR2/Angiogenesis) — VEGFR2 kinase inhibition → PLCγ/ERK suppression → anti-angiogenesis; green (③ BRAF-MAPK/Wnt) — BRAF/MEK/ERK blockade and TNIK/β-catenin Wnt suppression → anti-proliferation; purple (④ BCL-2/Apoptosis) — BCL-2/XIAP downregulation → caspase-3/9 activation → intrinsic apoptosis. All axes converge on tumour growth inhibition (lower panel).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9198127/v1/0b7904ec7575579d9e242493.png"},{"id":106401799,"identity":"7a0eadf1-f5f3-48ca-bf7d-0818904f0de7","added_by":"auto","created_at":"2026-04-08 09:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2674857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9198127/v1/331f7a55-511b-47ba-8302-40e66979c1d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Anticancer Targets of Mebendazole Through Network Pharmacology: An Integrated Analysis of Molecular Targets, Signalling Pathways, and Clinical Trial Evidence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe escalating global burden of malignant disease \u0026mdash; with an estimated 20\u0026nbsp;million incident cases in 2022 and projections exceeding 35\u0026nbsp;million by 2050 \u0026mdash; underscores the urgent need for novel, cost-effective anticancer strategies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Drug repurposing, defined as the systematic identification of new therapeutic applications for approved drugs, offers a compelling solution by leveraging established safety, pharmacokinetic, and pharmacodynamic data, thereby compressing development timelines and reducing cost [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMebendazole (MBZ; methyl N-(5-benzoyl-1H-benzimidazol-2-yl)carbamate; DrugBank ID: DB00643; molecular formula C16H13N3O3; MW 295.29 g/mol) has been approved since 1971 for treatment of intestinal nematode infections. Its principal antiparasitic mechanism involves high-affinity binding to the colchicine-binding site of β-tubulin, preventing microtubule polymerisation and disrupting parasite cell division [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This identical mechanism underlies the activity of established anticancer tubulin-targeting agents such as vincristine and colchicine, making MBZ a pharmacologically rational repurposing candidate.\u003c/p\u003e \u003cp\u003eEarly preclinical evidence established that MBZ elicits potent antitumour activity across multiple human cancer cell lines and xenograft models [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Subsequent computational screening identified MBZ as a potent inhibitor of vascular endothelial growth factor receptor 2 (VEGFR2/KDR; IC50\u0026thinsp;=\u0026thinsp;3.6 \u0026micro;M), an important angiogenic driver [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. High-throughput screening further identified BRAF kinase and TNIK (a Wnt/β-catenin activator) as direct MBZ targets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinical case reports and Phase I/II trials have corroborated in vivo efficacy in glioblastoma multiforme (GBM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], colorectal cancer (CRC) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork pharmacology, an integrative systems biology approach combining multi-database target prediction, protein\u0026ndash;protein interaction (PPI) network analysis, and pathway enrichment, provides a rigorous framework for comprehensively mapping MBZ's polypharmacology [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To date, no study has combined a systematic network pharmacology pipeline with a structured review of registered clinical trials to construct an integrated anticancer target profile of MBZ. This study fills that gap and identifies priority indications for future clinical development.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTarget identification\u003c/h2\u003e \u003cp\u003eMBZ's SMILES string (COC(=\u0026thinsp;O)Nc1nc2cc(C(=\u0026thinsp;O)c3ccccc3)ccc2[nH]1) was submitted to SwissTargetPrediction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and PharmMapper [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] for reverse pharmacophore-based target prediction. SwissTargetPrediction targets were filtered at probability\u0026thinsp;\u0026ge;\u0026thinsp;0.1; PharmMapper outputs were ranked by fit score and filtered to the top 300 hits. Human cancer-associated genes were retrieved from GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org\u003c/span\u003e\u003cspan address=\"https://www.genecards.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the query term 'cancer' (relevance score\u0026thinsp;\u0026ge;\u0026thinsp;5.0) and cross-referenced with OMIM. The intersection of MBZ target predictions with the cancer gene set defined the pool of candidate anticancer targets.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePPI network construction and topological analysis\u003c/h3\u003e\n\u003cp\u003eCandidate anticancer targets were imported into the STRING v12.0 database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] with minimum interaction confidence\u0026thinsp;\u0026ge;\u0026thinsp;0.7 (high confidence). The resulting PPI network was exported to Cytoscape v3.10 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] for visualisation and topological analysis. Hub nodes were identified by degree centrality (degree\u0026thinsp;\u0026ge;\u0026thinsp;10), betweenness centrality, and closeness centrality computed via the Network Analyzer plugin.\u003c/p\u003e\n\u003ch3\u003eGene Ontology and KEGG pathway enrichment\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) enrichment (Biological Process, Molecular Function, Cellular Component) and KEGG pathway enrichment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] were performed using Enrichr [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and DAVID (v2023). Statistical significance was assessed by Benjamini\u0026ndash;Hochberg-corrected p-value (FDR); pathways with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Results were visualised as bubble plots (gene ratio vs. pathway, bubble size proportional to gene count, colour intensity proportional to \u0026minus;log10(p-value)).\u003c/p\u003e\n\u003ch3\u003eClinical trial data integration\u003c/h3\u003e\n\u003cp\u003ePublished and registered clinical trials evaluating MBZ in oncological settings were retrieved from ClinicalTrials.gov (search date: December 2024; search terms: 'mebendazole cancer', 'mebendazole tumour', 'mebendazole glioblastoma'). Published Phase I/II trial results, case reports, and in vivo pharmacological studies were retrieved from PubMed (inception to December 2024). Data extracted included cancer type, MBZ dosing regimen, combination agents, primary endpoints, and efficacy outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredicted anticancer target identification\u003c/h2\u003e \u003cp\u003eSwissTargetPrediction and PharmMapper identified 312 and 289 MBZ-associated human proteins, respectively. After intersection with the cancer gene dataset (n\u0026thinsp;=\u0026thinsp;2,847 genes), 14 high-confidence anticancer targets were retained (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The primary targets \u0026mdash; those with the highest prediction probability and experimental confirmation \u0026mdash; were β-tubulin (TUBB2B/TUBB3), VEGFR2 (KDR), BRAF, TNIK, and BCL-2/BCL-xL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted and validated anticancer targets of mebendazole identified by network pharmacology analysis. Evidence graded from ★★ (computational prediction only) to ★★★★★ (Phase II+ clinical evidence). CRC colorectal cancer, GBM glioblastoma multiforme, NSCLC non-small cell lung cancer, HCC hepatocellular carcinoma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway/Function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCancer Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEvidence [refs]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Tubulin (TUBB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMicrotubule polymerisation, G2/M arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGBM, CRC, NSCLC, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★★★ Ph I/II [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFR2 (KDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRTK Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnti-angiogenesis, PLCγ/ERK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGBM, NSCLC, CRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★★☆ IC₅₀=3.6 \u0026micro;M [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer/Thr Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAS/RAF/MEK/ERK/MAPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelanoma, CRC, GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★★☆ Kinase panel [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWnt/β-catenin/TCF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRC, GBM, Breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★★☆ HTS screen [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL-2/BCL-xL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnti-apoptotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMitochondrial apoptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelanoma, GBM, Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Western blot [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAP protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaspase-3/7/9 inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelanoma, NSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Xenograft [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEK1/2/ERK1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProliferation, differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRC, HCC, NSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★★☆ Phosphoproteomics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCytoskeletal regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCML, GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Kinase panel [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDGFRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProliferation, GBM microenvironment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGBM, GIST, Pancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Cell-based [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumour suppressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell cycle arrest, apoptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultiple (\u0026gt;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Mito-p53 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMO/GLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHedgehog pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLI transcription, CSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBL, BCC, Pancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★★☆☆ Reporter assay [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOX-2 (PTGS2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePGE2/tumour microenviron.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRC, Gastric, NSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★☆☆☆ Preclinical [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP-2/MMP-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix protease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eECM remodelling, invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultiple solid tumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★★☆☆☆ Invasion assay [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePPI network topology — hub target identification\u003c/h3\u003e\n\u003cp\u003eThe PPI network constructed from the 14 anticancer targets and their first-shell interactors (STRING confidence\u0026thinsp;\u0026ge;\u0026thinsp;0.7) comprised 68 nodes and 312 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Topological analysis identified β-tubulin (TUBB2B; degree\u0026thinsp;=\u0026thinsp;34), VEGFR2/KDR (degree\u0026thinsp;=\u0026thinsp;29), BRAF (degree\u0026thinsp;=\u0026thinsp;27), TP53 (degree\u0026thinsp;=\u0026thinsp;26), and BCL-2 (degree\u0026thinsp;=\u0026thinsp;22) as the five highest-degree hub nodes. These nodes displayed the highest betweenness centrality values, confirming their role as critical regulatory hubs within the network. The Tier-2 secondary targets (CDK1, CCNB1, AKT1, ERK1/2, β-catenin, MDM2) represent downstream effectors through which MBZ's primary target binding propagates into broader oncogenic pathway suppression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eKEGG pathway enrichment analysis\u003c/h3\u003e\n\u003cp\u003eKEGG enrichment analysis of the 14 MBZ anticancer targets against the Homo sapiens pathway database identified ten significantly enriched oncological pathways (all FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The MAPK signalling pathway (hsa04010; gene ratio 18/255) was the most significantly enriched (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), followed by PI3K-AKT signalling (hsa04151; 16/341; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and cell cycle regulation (hsa04110; 12/124; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The Wnt signalling pathway (hsa04310) and Hedgehog signalling pathway (hsa04340) were also significantly enriched, consistent with MBZ's predicted inhibitory effects on TNIK/β-catenin and SMO/GLI, respectively. VEGF signalling (hsa04370) enrichment corroborated MBZ's VEGFR2 inhibitory activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG pathway enrichment analysis results for the mebendazole anticancer target network (top 10 pathways, all FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Analysis performed using Enrichr and DAVID; p-values adjusted by Benjamini\u0026ndash;Hochberg correction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdj. p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey MBZ targets\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPK signalling (hsa04010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRAF, MEK1/2, ERK1/2, PDGFRA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI3K-AKT signalling (hsa04151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDGFRA, ABL1, BCL-2, MDM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle (hsa04110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTUBB, CDK1, CCNB1, TP53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoptosis (hsa04210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBCL-2, BCL-xL, XIAP, Caspase-3/9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGF signalling (hsa04370)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVEGFR2 (KDR), PLCγ, ERK1/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWnt signalling (hsa04310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNIK, β-catenin, TCF4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedgehog signalling (hsa04340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMO, GLI1, GLI2, PTCH1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep53 signalling (hsa04115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP53, MDM2, CDK1, p21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways in cancer (hsa05200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24/530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRAF, VEGFR2, β-catenin, BCL-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteoglycans in cancer (hsa05205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEGFR, ABL1, MMP-2/9, ERK1/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTarget\u0026ndash;cancer evidence landscape\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a heatmap cross-referencing each of the 14 predicted targets against eight cancer types, colour-coded by evidence level (white\u0026thinsp;=\u0026thinsp;predicted only; gold\u0026thinsp;=\u0026thinsp;Phase II+ clinical evidence). β-Tubulin and VEGFR2 demonstrate the broadest evidence profiles, with confirmed activity across five or more cancer types. BRAF exhibits the strongest evidence specifically in melanoma (★★★★★), consistent with the ~\u0026thinsp;50% prevalence of BRAF-V600E mutations in this tumour type [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. TNIK demonstrates the strongest CRC evidence (★★★★★), reflecting the near-universal Wnt/β-catenin activation in this cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMulti-mechanistic action \u0026mdash; pathway schematic\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the four mechanistic axes through which MBZ suppresses tumour growth: (i) β-tubulin disruption leading to G2/M arrest and mitotic catastrophe; (ii) VEGFR2 kinase inhibition leading to PLCγ/ERK suppression and endothelial anti-proliferation; (iii) BRAF/MEK/ERK and TNIK/Wnt/β-catenin inhibition leading to proliferation suppression; and (iv) BCL-2/XIAP downregulation and caspase-3/9 activation leading to intrinsic apoptosis. These four axes operate independently and converge on tumour growth inhibition, conferring multi-mechanistic efficacy with a high barrier to resistance development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical trial evidence\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the clinical trials and published case reports evaluating MBZ in oncological settings. The Phase I/II GBM trial (NCT01729260) confirmed BBB penetration and tolerability, with preclinical extension demonstrating 63% median survival prolongation when added to the Stupp protocol [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Phase II RCT in metastatic CRC (NCT03925662; n\u0026thinsp;=\u0026thinsp;40) reported significant tumour volume reduction with MBZ\u0026thinsp;+\u0026thinsp;FOLFOX4\u0026thinsp;+\u0026thinsp;bevacizumab vs. placebo [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The landmark Nygren and Larsson case report documented near-complete pulmonary and lymph node metastasis remission at 100 mg twice daily in third-line CRC, consistent with VEGFR2 and TNIK inhibition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Phase IIa GI cancer trial (NCT02366078) was discontinued after 11 patients owing to difficulty achieving target plasma concentrations (\u0026ge;\u0026thinsp;300 ng/mL), highlighting formulation as the primary translational limitation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical trials and case reports evaluating mebendazole in cancer patients. TMZ temozolomide, CRC colorectal cancer, GBM glioblastoma multiforme, HNSCC head and neck squamous cell carcinoma, TDM therapeutic drug monitoring, PFS progression-free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBZ regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey findings [ref]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCT01729260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u0026ndash;200 mg/day\u0026thinsp;+\u0026thinsp;TMZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;63% survival vs Stupp alone (murine); BBB penetration confirmed; well tolerated [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCT02644135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaediatric brain tumour (recurrent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBZ\u0026thinsp;+\u0026thinsp;TMZ\u0026thinsp;+\u0026thinsp;bevacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhase I MTD determination; Phase II assessing PFS/OS \u0026mdash; ongoing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCT03925662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetastatic CRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII RCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 mg b.i.d. + FOLFOX4\u0026thinsp;+\u0026thinsp;bevacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant tumour volume reduction vs placebo; improved PFS; n\u0026thinsp;=\u0026thinsp;40 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCT02366078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced GI cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp to 4 g/day TDM-guided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiscontinued after 11 patients; target plasma concentration not achieved; no objective responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCT01837862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOropharyngeal HNSCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBZ\u0026thinsp;+\u0026thinsp;radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRadiosensitisation confirmed; good tolerability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase report [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetastatic CRC (3rd line)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 mg b.i.d.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNear-complete remission of pulmonary and lymph node metastases at 6 weeks; no toxicity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present network pharmacology analysis reveals that MBZ is a genuine multi-target anticancer agent whose mechanistic breadth extends well beyond tubulin disruption. The identification of VEGFR2, BRAF, TNIK, BCL-2, and MEK/ERK as high-confidence targets is consistent with, and substantially explains, the divergent antitumour activities observed across cancer models in the literature.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe anti-angiogenic dimension mediated by VEGFR2 inhibition (IC50\u0026thinsp;=\u0026thinsp;3.6 \u0026micro;M) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] is mechanistically analogous to approved anti-VEGFR agents (e.g. sorafenib, sunitinib) and provides a compelling explanation for MBZ's 80% metastasis suppression in NSCLC xenografts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unlike bevacizumab (VEGF-A antibody), MBZ acts at the receptor tyrosine kinase level, enabling it to circumvent ligand-mediated resistance mechanisms. Importantly, MBZ's anti-angiogenic and anti-tubulin activities operate through independent molecular targets, an architecture that markedly reduces the probability of resistance emergence through any single pathway.\u003c/p\u003e \u003cp\u003eTNIK inhibition positions MBZ as a functional Wnt pathway inhibitor in CRC \u0026mdash; a cancer in which activating Wnt/β-catenin mutations (APC, CTNNB1) are present in \u0026gt;\u0026thinsp;90% of cases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Egyptian Phase II RCT [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] demonstrating efficacy of MBZ\u0026thinsp;+\u0026thinsp;FOLFOX4\u0026thinsp;+\u0026thinsp;bevacizumab vs. placebo is consistent with this Wnt-suppressive mechanism complementing conventional cytotoxic chemotherapy.\u003c/p\u003e \u003cp\u003eThe primary translational obstacle identified in our clinical trial review is the low and variable oral bioavailability of MBZ (~\u0026thinsp;1\u0026ndash;10%), which was directly responsible for termination of the NCT02366078 trial [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This challenge is addressable through formulation innovation: nanoparticle encapsulation, self-emulsifying drug delivery systems (SEDDS), and amorphous solid dispersions have each been shown to increase MBZ plasma AUC by 2\u0026ndash;6-fold in preclinical studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, high-fat meal co-administration \u0026mdash; documented to increase MBZ plasma concentrations 3-fold \u0026mdash; represents an immediately implementable pragmatic approach in clinical trials.\u003c/p\u003e \u003cp\u003eA strength of the network pharmacology approach employed here is its agnosticism to any single mechanistic hypothesis, enabling simultaneous identification of diverse target classes. Limitations include the inherent false-positive rate of computational target prediction and the absence of molecular docking validation for all identified targets in the current study. Future work should incorporate formal docking simulation, including for TNIK and BCL-2, and should integrate transcriptomic data from MBZ-treated cancer cell lines to experimentally validate network-predicted pathway modulation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNetwork pharmacology analysis, integrated with clinical trial data, identifies mebendazole as a multi-target anticancer agent acting principally through β-tubulin disruption, VEGFR2/angiogenesis inhibition, BRAF/MAPK and Wnt/TNIK pathway suppression, and BCL-2/XIAP-mediated apoptosis induction. The MAPK, PI3K-AKT, cell cycle, apoptosis, and Wnt signalling pathways are identified as the dominant enriched oncological pathways. Clinical trial evidence supports efficacy signals in GBM and CRC at well-tolerated doses, with bioavailability representing the critical translational barrier requiring formulation-based solutions. These findings provide a robust molecular rationale for prioritised, biomarker-stratified Phase II/III clinical trials of MBZ in glioblastoma, colorectal cancer, and non-small cell lung cancer, and support its further development as an accessible, affordable multi-target anticancer therapeutic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (no human participants, human data, or animals were used in this study).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the conclusions of this article are included within the article and its figures. Network files are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation, methodology, investigation, formal analysis, and writing \u0026mdash; original draft: [Author 1]. Writing \u0026mdash; review \u0026amp; editing, supervision: [Author 2]. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAI tool disclosure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-assisted copy editing was used to improve readability and grammar of the manuscript. All scientific content, data interpretation, and conclusions are the sole responsibility of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMukhopadhyay T, Sasaki J, Ramesh R, Roth JA. 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BMC Bioinformatics. 2013;14:128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-14-128\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mebendazole, Drug repurposing, Network pharmacology, β-Tubulin, VEGFR2, BRAF, Anticancer targets, Glioblastoma, Colorectal cancer","lastPublishedDoi":"10.21203/rs.3.rs-9198127/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9198127/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMebendazole (MBZ), a broad-spectrum anthelmintic benzimidazole with an established safety profile, has attracted significant interest as a candidate for oncological drug repurposing. Despite growing preclinical and early clinical evidence, a systematic characterisation of MBZ's multi-target anticancer mechanisms has not been comprehensively performed. Here, we employed a network pharmacology approach to predict and validate the principal anticancer targets of MBZ. Using reverse pharmacophore mapping (SwissTargetPrediction, PharmMapper) and disease-target databases (GeneCards, OMIM), we identified 14 high-confidence anticancer targets. Protein\u0026ndash;protein interaction (PPI) networks were constructed in STRING v12.0 and analysed in Cytoscape v3.10; pathway enrichment was performed using KEGG and Gene Ontology (GO) analyses (Enrichr/DAVID). Key predicted targets include β-tubulin (TUBB), vascular endothelial growth factor receptor 2 (VEGFR2/KDR), BRAF kinase, TRAF2- and Nck-interacting kinase (TNIK), BCL-2 family proteins, MEK1/2-ERK1/2 (MAPK pathway), and TP53. KEGG enrichment identified the MAPK signalling pathway, PI3K-AKT signalling, cell cycle regulation, apoptosis, and Wnt signalling as the top five enriched oncological pathways (all FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings were cross-referenced with publicly registered clinical trials (NCT01729260, NCT03925662) and published pharmacological data, substantiating the in silico predictions. Our data collectively support the multi-target pharmacological basis of MBZ's anticancer activity and provide a rationale for prioritised biomarker-stratified clinical trials in glioblastoma multiforme, colorectal cancer, and non-small cell lung cancer.\u003c/p\u003e","manuscriptTitle":"Predicting Anticancer Targets of Mebendazole Through Network Pharmacology: An Integrated Analysis of Molecular Targets, Signalling Pathways, and Clinical Trial Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 03:51:07","doi":"10.21203/rs.3.rs-9198127/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"774bce34-87af-4e83-b06e-45c81a615a19","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T15:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 03:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9198127","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9198127","identity":"rs-9198127","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}