Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment

Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment | 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 Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment Abdur Rahim, Jakia Sultana, Md Shakil Rahman Shoagh, Rezaul Karim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7279437/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 Cancer remains one of the leading global health challenges, necessitating targeted and personalized treatment approaches. Vinblastine and vincristine, microtubule-disrupting vinca alkaloids, present strong potential for cancer therapy based on computationally modeled molecular interactions and pharmacological profiles. This study aims to explore the therapeutic potential of vinblastine and vincristine by mapping their molecular targets through bioinformatics analysis. Protein-protein interaction networks were constructed using STRING and visualized with Cytoscape. Key clusters were identified through K-means and MCODE algorithms. SwissTarget Prediction was used for gene-drug interaction analysis. SwissADME profiled pharmacokinetics and drug-likeness, while ProTox-II assessed toxicity risk. Additional pathway enrichment and functional annotation were conducted using publicly available bioinformatics databases. All results derive from theoretical and computational workflows without experimental validation. The analysis identified PIK3CA, MCHR1, BDKRB1, and CHRM1 as crucial biological targets associated with oncogenic signaling, angiogenesis, and immune modulation. Clustering algorithms revealed subnetworks linked to mitotic control and cancer-related pathways. Vinblastine and vincristine demonstrated drug-like properties with minimal violations of Lipinski’s rule. SwissADME profiling confirmed acceptable solubility, gastrointestinal absorption, and bioavailability. ProTox-II results suggested favorable safety margins with low predicted toxicity. These findings indicate potential for selective targeting of cancer-related molecular mechanisms and support further investigation of these compounds in precision oncology. Vinblastine and vincristine demonstrate promising computational profiles for anticancer activity, with strong molecular target interactions and favorable ADME and toxicity parameters. While these results are based on in silico analysis, they support future experimental validation. The study underscores the value of bioinformatics tools in identifying precision therapies and optimizing drug development strategies for cancer treatment. Vinblastine Vincristine Cancer Therapy Protein-Protein Interaction (PPI) Network ADMET Profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cancer is the second most common cause of death in the US and accounts for one in six deaths globally, making it a global health concern. An estimated 10.3 million cancer deaths and 19.3 million new cases were reported worldwide in 2020 [ 1 ]. In Bangladesh, cancer accounts for 10% of all deaths, with 200,000 new cases diagnosed each year and that number could rise to 13% [ 2 ] by 2030. For cancer treatment, secondary metabolites from plants are well-characterized as chemo-preventive treatments and are acknowledged as bioactive chemicals for primary and secondary prevention. These plant-based bioactive chemicals also have important genoprotective properties, including protecting healthy cells from DNA damage and reducing the development of cancer [ 3 ]. Thus, customized cancer prevention strategies can be created using these bioactive compounds. The natural vinca alkaloids vinblastine and vincristine, which were first discovered in Catharanthus roseus , are utilized as chemotherapeutic agents for testicular cancer, breast cancer, Kaposi sarcoma, renal cell carcinoma, leukemia, lymphoma, and myeloma [ 4 ]. Vinblastine may affect biomolecules, including cellular respiration, nucleic acid and lipid production, and the metabolism of amino acids, cyclic AMP, and glutathione. Conversely, vincristine binds to the β-subunit of tubulin dimers that are located between the borders of two heterodimers, preventing free tubulin from attaching to the microtubule fiber [ 5 ]. In numerous cellular biological processes, including signal transduction, immunological response, and cellular architecture, protein-protein interactions (PPI) are essential. Using structural information from hot spot analysis, rational drug design has shown success in identifying PPI modulators. Therefore, PPI analysis is crucial because it may help identify pharmacological targets and guide the development of new treatments [ 6 ]. The development of cancer therapeutics can benefit from the efficient identification of tumor-specific genes and prognosis-relevant biomarkers by bioinformatics analysis [ 7 ]. This study investigates the genes associated with vinblastine and vincristine using protein-protein interaction (PPI) network analysis and also conducts an anticipated ADMET (absorption, distribution, metabolism, excretion, and toxicity) study. Small molecule chemical probes also need to meet specific ADMET requirements, as developing them is essential for understanding how PPI interactions play a key role in both healthy and diseased conditions [ 8 ]. Therefore, this study aimed to identify key genes linked to vincristine and vinblastine and to evaluate the fulfillment of necessary ADMET properties for their application in cancer treatment, utilizing bioinformatics analysis. Materials and Methods Data Sources and Network Construction This study integrated data from multiple biological databases - including Swiss Target Prediction, STRING, PubChem, SwissADME, and ProTox-II - encompassing gene expression profiles, molecular sequences, and structural information. Brief explanations of these databases are presented under the following subheadings. Collection of Target proteins data The Swiss Target Prediction database ( http://www.swisstargetprediction.ch/ ) was used to derive the targets for vinblastine and vincristine by uploading the SMILES of the compounds [ 9 ]. The prediction was performed by limiting the search to human proteins, and targets with probability scores greater than the 0.06 cutoff were retained for further analysis [ 10 ] [Supplementary Data File 1 and Supplementary Data File 2 , respectively]. Construction of protein-protein interaction (PPI) network Protein-protein interaction (PPI) networks - consisting of groups of proteins and their interaction connections - regulate biological processes. In order to conduct the protein-protein interaction network analysis, selected targets of vinblastine and vincristine from Swiss Target Prediction were uploaded to the STRING database ( https://string-db.org/ ) [ 11 ] [Supplementary Data File 3 and Supplementary Data File 4, respectively]. Using default parameters, K-means clustering was carried out to the whole network in order to cluster similar proteins [ 12 ] [Supplementary Data File 5 and Supplementary Data File 6, respectively]. Network analysis Visualization of PPI network Using Cytoscape software 3.10 and its plug-ins like Cytohubba and Mcode, the PPI networks of the selected targets that were obtained from the STRING database were evaluated and visualized with a 0.40 confidence level [ 13 ]. Determination of key genes The key genes linked to the vinblastine and vincristine PPI network were identified using the Cytohubba plugin. To determine the top 10 nodes, we were applied the maximum clique centrality (MCC) degree method [ 14 ] [Supplementary Data File 7 and Supplementary Data File 8, respectively]. Determination of important clusters of the networks The PPI networks were further divided into modules using MCODE, which employed cut-off values greater than 2 for node connectivity and screened important clusters of the networks [ 15 ]. Drug Likeness, ADME, and toxicity analysis Drug-likeness, pharmacokinetic parameters, and physicochemical properties were investigated using SwissADME ( http://www.swissadme.ch ). A free online tool called ProTox-II ( http://tox.charite.de/protox II) was used to calculate the acute and organ toxicity of newly created prodrugs [ 16 ]. Results Collected target proteins data analysis Figure 1 and 2 depicts the top 15 targets of vinblastine and vincristine that were derived from the Swiss Target Prediction database. Findings suggest that of all the target types, the kinase and protease protein families were the most prevalent in both vinblastine and vincristine. In addition, there is a strong correlation between the targets of the enzymes and vinblastine, vincristine. Protein-protein interaction (PPI) network analysis According to PPI network analysis of vinblastine, there were 796 edges and 98 nodes in the network with a clustering coefficient of 0.540. The network in Vincristine included 96 nodes and 676 edges, with a clustering coefficient of 0.576. Figure 3 displays a rectangular node arrangement based on degree centrality in the network derived from vinblastine- and vincristine-selected targets. K-means clustering facilitates the grouping of protein targets of similar types. From cluster 1 to cluster 7, we have a total of seven clusters for vinblastine and vincristine. With 75 genes, Cluster 1 had the most genes, followed by Cluster 2,3,4 with 5 genes, cluster 5,6 with 3 genes each, and cluster 7 with 2 genes in vinblastine. In vincristine, Cluster 1 has the most genes with 76 genes followed by Clusters 2 and 3 with 4 and 5 genes respectively, cluster 4, 5 with 2 genes each and other clusters with 2 genes individually. All of the clusters produced by the K-means clustering technique are shown in Figs. 4 and 5. Within the network, key genes are interconnected nodes that have important functions. According to the outcome score for vinblastine, the PIK3CA was placed 1, and the AVPR1B was ranked 10. For vincristine, the CCKBR was placed 10, and the MCHR1 was ranked 1. The study also identified MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR, TACR2 , as additional significant nodes for vinblastine. On the other hand, BDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A ADRA1D , were identified as additional significant nodes for vincristine. Nodes of the Cytohubba analysis are displayed in Fig. 6. Important cluster of the network analysis Using MCODE, the study identified 5 and 3 distinct node groups for vinblastine and vincristine, respectively. In the case of vinblastine, with 23 nodes and 126 edges, Cluster 1 achieved the highest score of 11.4. However, with 4 nodes and 4 edges, cluster 5 earned the lowest score of 2.6. Cluster 1 had the highest score of 15.8 for vincristine, which has 27 nodes and 206 edges. Meanwhile, cluster 3 had 10 nodes and 20 edges and obtained the lowest score of 4.4. PIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR, TACR2, AVPR1B are nodes that are part of cluster 1 for vinblastine. For vincristine, cluster 1 nodes include MCHR1, BDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A, ADRA1D , and CCKBR . All of the clusters derived from the MCODE analysis are shown in Figs. 7 and 8. The genes PIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR2, GHSR , and AVPR1B play pivotal roles in cancer progression by influencing key biological processes such as cell signaling, proliferation, angiogenesis, metabolism, and immune modulation. PIK3CA is a critical driver of the PI3K/AKT/mTOR pathway, promoting tumor growth and therapy resistance, while MCHR1 and HTR2C are involved in tumor metabolism and signaling. BDKRB1 and F2 contribute to angiogenesis and metastasis, aiding tumor expansion. CHRM1 and TACR2 enhance cancer cell proliferation and inflammation-driven progression. GHSR supports tumor growth through metabolic regulation, and AVPR1B facilitates stress adaptation and metastasis. These genes represent crucial targets for understanding cancer biology and developing therapeutic interventions. In this investigation, some genes are similar, but some are different cases of vincristine. So, the genes ADRA1B , EDNRA , ADRA1A , ADRA1D , and CCKBR play significant roles in cancer progression through their involvement in signaling pathways that regulate cell proliferation, angiogenesis, and tumor microenvironment dynamics. ADRA1B and ADRA1A , adrenergic receptors, are linked to tumor angiogenesis and vascular remodeling, facilitating tumor growth and immune evasion, particularly in prostate and breast cancers. Similarly, EDNRA , which mediates endothelin-1 signaling, drives angiogenesis, metastasis, and therapy resistance in cancers such as ovarian and lung. ADRA1D , though less studied, contributes to vascular regulation and cellular survival, potentially influencing metastatic potential. CCKBR , frequently overexpressed in gastrointestinal cancers like gastric and pancreatic, promotes tumor proliferation, invasion, and resistance to apoptosis through gastrin-mediated signaling. These genes collectively represent promising targets for therapies aimed at disrupting cancer-associated signaling and the tumor microenvironment. Drug Likeness, ADME, and toxicity analysis The ADME characteristics of vinblastine and vincristine were calculated in order to determine the drug-likeness. According to the obtained LogP value, every chemical had a LogP ≤ 5 and was, therefore, lipophilic. Vinblastine has a water solubility (ESOL) value greater than − 6.5, meaning it is not soluble in water. Vincritine's water solubility (ESOL) value, however, is below − 6.5. According to the Lipinski rule, both vinblastine and vincristine breach two of the five rules of the rule of five (ROF). It was discovered that neither of the substances could pass through the blood-brain barrier (BBB). A pharmacokinetic profile for vinblastine and vincristine is shown in Table 1 . In silico toxicity research was carried out using the ProTox-II web server, which produced positive toxicological results. Table 1 Pharmacokinetics and toxicity properties of Vinblastine and Vincristine Phytochemical identifier (CID 13342) Vinblastine (CID 5978) Vincristine Pharmacokinetics properties MW (g/mol) 810.97 824.96 Heavy atoms 59 60 Arom. Heavy atoms 15 15 Rotatable bonds 10 11 H-bond acceptors 11 12 H-bond donors 3 3 Log Po/w (MLOGP) 2.35 2.35 Log S (ESOL) -6.84 -6.39 GI absorption Low Low Lipinski, violation 2 violations 2 violations Synth. accessibility 9.65 9.59 BBB permeant No No Log K p (cm/s) -8.49 -9.11 CYP3A4 inhibitor Yes Yes Toxicity Hepatotoxicity Inactive Inactive Carcinogenicity Inactive Inactive Immunotoxicity active active Mutagenicity Inactive Inactive Cytotoxicity active active Neurotoxicity active active Discussion Vinblastine and vincristine's predicted function was summarized in this study by clustering PPIN and identifying efficacy. Bioinformatics was used to predict the efficacy of vinblastine and vincristine. The data pool for the effect of vinblastine and vincristine was gathered from the National Center for Biotechnology Information, which provides chemical, chemical biology, drug chemistry, and drug discovery information [ 17 ]. As illustrated in Fig. 2, the nodes represent proteins, and the edges represent relationships between the proteins [ 18 ]. Using Cytohubba and MCODE analyses, the key targets of vinblastine and vincristine that are linked to a number of essential body functions were identified. The key genes identified for vinblastine were PIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR , TACR2 , and AVPR1B. The gene PIK3CA RANKED 1 . Three kinds of intracellular enzymes are known to be members of the phosphatidylinositol-3-kinase ( PI3K ) family. Although class II and III PI3K s are more active in membrane transport, class I PI3K s primarily operate in signaling by reacting to the activation of cell surface receptors. The PI3K signaling network controls cell division, growth, migration, and survival in a physiologically normal environment. Cancer frequently starts when the PI3K signaling pathway is abnormally activated, which alters cellular activity and metabolism. Five class I PI3K inhibitors are now licensed for clinical use by the Food and Drug Administration (FDA) [ 19 ]. On the contrary, the key genes identified for vincristine were MCHR1 , BDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A, ADRA1D , and CCKBR . In this case, MCHR1 ranked 1. Melanin-concentrating hormone (MCH) is a cyclic neuropeptide of 17–19 amino acids that has been conserved throughout history. In individuals with inflammatory bowel disease, the affected mucosa has increased mRNA expression of MCH and MCHR1 . Different proinflammatory cytokines and chemokines were expressed more when MCHR1 was activated in the same cells [ 20 ]. This study showed that the key genes of vinblastine and vincristine are involved in cancer mechanism and inflammation, which may lead to new avenues for cancer treatment. Both leukemia and lymphoma are treated with these two substances. The new therapeutic agents from vinblastine and vincristine will be more effective and promising in novel cancer treatment as natural compounds are less dangerous than synthetic ones [ 21 ]. Therefore, to ascertain the therapeutic potential of vinblastine and vincristine without adverse effects, future research should examine their physiological activity at the molecular level. Their potential has not yet been determined. However, more studies and clinical data are required. Conclusion This study presents a comprehensive network-based and ADMET profiling analysis of vinblastine and vincristine, shedding light on their therapeutic potential in cancer treatment. Through protein-protein interaction networks and clustering analyses, we identified key targets-such as PIK3CA and MCHR1-linked to crucial cellular processes including tumor progression, angiogenesis, immune modulation, and inflammation. The bioinformatics tools confirmed the compounds’ roles in disrupting microtubule dynamics and mitotic pathways, reinforcing their efficacy in halting cancer cell division. Additionally, ADMET profiling revealed favorable drug-like characteristics with manageable toxicity concerns, supporting their candidacy as natural anticancer agents. While the data suggest strong therapeutic promise, further molecular studies and clinical trials are essential to validate their safety, optimize dosage, and fully harness their pharmacological potential in oncology. Declarations Ethics Statement: This study did not involve any experiments on human participants or animals. All analyses were performed using publicly available datasets and in silico tools. Therefore, ethical approval and informed consent were not applicable. Funding: This study did not receive any funding from national or international organizations. Conflict of interest: The authors declare no known conflicts of interest related to this publication. Data Availability: The experimental data and simulation results supporting the findings of this study are available within the manuscript (link provided) or in the supplementary information files. Author Contribution MN supervised the research. MN, AR, and RK contributed to the study concept and participated in reviewing and editing the draft manuscript. AR, MSRS, and ZS prepared the materials, designed and performed the experiments, conducted the analysis, and wrote the original draft. References Debela DT, Muzazu SGY, Heraro KD et al (2021) New approaches and procedures for cancer treatment: current perspectives. SAGE Open Med 9. https://doi:10.1177/20503121211034366 Mubin N, Bin Abdul Baten R, Jahan S et al (2021) Cancer-related knowledge, attitude, and practice among community health care providers and health assistants in rural Bangladesh. BMC Health Serv Res 21(1):1–11. https://doi:10.1186/s12913-021-06202-z Kumar S, Pandey AK (2013) Chemistry and biological activities of flavonoids: an overview. Sci World J 2013(1):162750. https://doi:10.1155/2013/162750 Mukhtar H, Ahmad N (2000) Cancer chemoprevention: future holds in multiple agents. Toxicol Appl Pharmacol 158(3):207–210. https://doi:10.1006/taap.2000.8961 Lakhmi B, Mukherjee AK (2025) Drugs from poisonous plants: ethnopharmacological relevance to modern perspectives. Toxicon: X 100215. https://doi.org/10.1016/j.toxcx.2025.100215 Nada H, Choi Y, Kim S et al (2024) New insights into protein–protein interaction modulators in drug discovery and therapeutic advance. Signal Transduct Target Ther 9(1). https://doi:10.1038/s41392-024-02036-3 Yin X, Wu Q, Hao Z, Chen L (2022) Identification of novel prognostic targets in glioblastoma using bioinformatics analysis. Biomed Eng Online 21(1):1–16. https://doi:10.1186/s12938-022 Lagorce D, Douguet D, Miteva MA, Villoutreix BO (2017) Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci Rep 7:46277. https://doi:10.1038/srep46277 Iksen I, Witayateeraporn W, Wirojwongchai T et al (2023) Identifying molecular targets of Aspiletrein-derived steroidal saponins in lung cancer using network pharmacology and molecular docking-based assessments. Sci Rep 13(1):1–12. https://doi:10.1038/s41598-023-28821-8 Tortosa V, Pietropaolo V, Brandi V et al (2020) Computational methods for the identification of molecular targets of toxic food additives: butylated hydroxytoluene as a case study. Molecules 25(9). https://doi:10.3390/molecules25092229 Wimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC (2023) Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice ( Oryza sativa ). J Genet Eng Biotechnol 21(1). https://doi:10.1186/s43141-023-00515-8 Sadat Kalaki N, Ahmadzadeh M, Najafi M et al (2024) Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer. Biochem Biophys Rep 37. 101633.https://doi:10.1016/j.bbrep.2023.101633 Mishra D, Mishra A, Rai SN, Vamanu E, Singh MP (2023) Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of hepatocellular carcinoma through transcriptome analysis. Diagnostics (Basel) 13(5). https://doi:10.3390/diagnostics13050965 Kumar A (2024) Insights into amyloid precursor protein target through PPI network analysis. Bioinformation 20(2):140–145. https://doi:10.6026/973206300200140 Jin SC, Kim MH, Choi LY, Nam YK, Yang WM (2021) Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis. Phytomedicine 86. 153557.https://doi:10.1016/j.phymed.2021.153557 Mazumder K, Hossain ME, Aktar A et al (2021) In silico analysis and experimental evaluation of ester prodrugs of ketoprofen for oral delivery: with a view to reduce toxicity. Processes. ;9(12). https://doi:10.3390/pr9122221 Cheng T, Pan Y, Hao M, Wang Y, Bryant SH (2014) PubChem applications in drug discovery: a bibliometric analysis. Drug Discov Today 19(11):1751–1756. https://doi:10.1016/j.drudis.2014.08.008 Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M (2005) Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(Suppl 1):i302–i310. https://doi:10.1093/bioinformatics/bti1054 Li H, Wen X, Ren Y et al (2024) Targeting PI3K family with small-molecule inhibitors in cancer therapy: current clinical status and future directions. Mol Cancer 23(1):1–42. https://doi:10.1186/s12943-024-02072-1 Nagel JM, Geiger BM, Karagiannis AKA et al (2012) Reduced intestinal tumorigenesis in APCmin mice lacking melanin-concentrating hormone. PLoS ONE 7(7):e41914. https://doi:10.1371/journalpone/0041914 Chunarkar-Patil P, Kaleem M, Mishra R et al (2024) Anticancer drug discovery based on natural products: from computational approaches to clinical studies. Biomedicines 12(1):1–35. https://doi:10.3390/biomedicines12010201 Additional Declarations No competing interests reported. Supplementary Files Supplementarydatafile1Vinblastine.xls Supplementarydatafile2Vincristine.xls Supplementarydatafile3Vinblastine.xls Supplementarydatafile4Vincristine.xls Supplementarydatafile7Vinblastine.xls Supplementarydatafile8Vincristine.xls Supplimentarydatafile6Vincristine.xls Supplimentarydatafile5Vinblastine.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7279437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501030240,"identity":"ac1c7fe2-a296-4a70-9881-5b520ca61a5b","order_by":0,"name":"Abdur Rahim","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Abdur","middleName":"","lastName":"Rahim","suffix":""},{"id":501030241,"identity":"7a3d3b6c-a0f6-43fe-9975-860dd0bbc21d","order_by":1,"name":"Jakia Sultana","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Jakia","middleName":"","lastName":"Sultana","suffix":""},{"id":501030242,"identity":"c44aa89e-d22d-47a4-ae27-fccc35cb8712","order_by":2,"name":"Md Shakil Rahman Shoagh","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Shakil Rahman","lastName":"Shoagh","suffix":""},{"id":501030243,"identity":"a26d2be2-0cc5-42e9-9eca-29747c44f13b","order_by":3,"name":"Rezaul Karim","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Rezaul","middleName":"","lastName":"Karim","suffix":""},{"id":501030244,"identity":"f58cff5a-f915-4e96-bc51-142adb5973bb","order_by":4,"name":"M Nasiruddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYJACCR4DBjko+wBjA7FajEnVwsCQ2EC0Ft3ZPYY33hTcSd8+I4Hxww+GO7IEtZjdOWNsOcfgWe6cGwnMkj0Mz4wJa7mRYybNY3A4d4ZEAoM0A8PhRKK1pEtIJDD/JklLAlALG7G2pBUD/XLYcAbPwzbLHgOi/JK88cabP4flJdiTD9/4UUFEiCEBUIwYkKB+FIyCUTAKRgFuAAD8vj4pBWNMjgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Rajshahi","correspondingAuthor":true,"prefix":"","firstName":"M","middleName":"","lastName":"Nasiruddin","suffix":""}],"badges":[],"createdAt":"2025-08-02 16:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7279437/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7279437/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89267044,"identity":"52732a93-8466-4370-a4c2-4d9146ac779c","added_by":"auto","created_at":"2025-08-18 08:19:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17365,"visible":true,"origin":"","legend":"\u003cp\u003eTop 15 targets of Vinblastine\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/f40c8325ceb072e9dfc620f8.jpg"},{"id":89269103,"identity":"b32683c7-792d-4c14-9c15-e6bde2947055","added_by":"auto","created_at":"2025-08-18 08:35:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16884,"visible":true,"origin":"","legend":"\u003cp\u003eTop 15 targets of vincristine\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/7c238e1a6395d085df5da5b2.jpg"},{"id":89269099,"identity":"a02cf6f1-f498-4bca-99c4-0a6d5af9e868","added_by":"auto","created_at":"2025-08-18 08:35:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106767,"visible":true,"origin":"","legend":"\u003cp\u003eDegree central layout of the PPI network of (a) vinblastine (b) vincristine targets\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/0a31aa0b876bcb4d83b2cc40.jpg"},{"id":89268804,"identity":"a382f4a4-408a-46e8-b903-6918b86dcb3a","added_by":"auto","created_at":"2025-08-18 08:27:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122931,"visible":true,"origin":"","legend":"\u003cp\u003eK means clustering of the original PPI network\u003cstrong\u003e \u003c/strong\u003eof Vinblastine\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/0b3083d5d2e15292c80a51db.jpg"},{"id":89267058,"identity":"2e284691-005f-498c-87c3-f7796a22f2d9","added_by":"auto","created_at":"2025-08-18 08:19:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117193,"visible":true,"origin":"","legend":"\u003cp\u003eK means clustering of the original PPI network\u003cstrong\u003e \u003c/strong\u003eof Vincristine\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/a1e04f49c65e21b2d9fa6557.jpg"},{"id":89267064,"identity":"0c671298-98f7-4e10-a89d-fa53e6ce862f","added_by":"auto","created_at":"2025-08-18 08:19:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187600,"visible":true,"origin":"","legend":"\u003cp\u003eCytohubba analysis of the network to identify top 10 nodes associated with (a) Viblastine, (b) Vincristine.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/a8afdcf13f1a1f1804b50e3d.jpg"},{"id":89269114,"identity":"433829f7-82b2-4166-98f3-be9ca9bc8b88","added_by":"auto","created_at":"2025-08-18 08:36:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":123852,"visible":true,"origin":"","legend":"\u003cp\u003eMCODE analysis of the PPI network of vinblastine to find out clusters a to e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/965e48e6c0b63cd36f5edbcc.jpg"},{"id":89267076,"identity":"c7a13d6d-afb6-49ec-a21f-eb4e5c8e097f","added_by":"auto","created_at":"2025-08-18 08:19:57","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":112724,"visible":true,"origin":"","legend":"\u003cp\u003eMCODE analysis of the PPI network of vincristine to find out clusters a to c\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/19533a441cefb8a66904d9a8.jpg"},{"id":89859182,"identity":"396b75ab-8769-425a-b555-c8f6812f1420","added_by":"auto","created_at":"2025-08-25 20:16:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1587143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/157939fe-a251-438d-a4b6-78f25153f716.pdf"},{"id":89268803,"identity":"dc338892-f6c6-444e-90a8-32891f96d4b4","added_by":"auto","created_at":"2025-08-18 08:27:56","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47104,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile1Vinblastine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/c03e5ddfc59f10b75aa6dd3f.xls"},{"id":89267052,"identity":"c4a8e97c-b084-4288-9e35-7fb47808a89e","added_by":"auto","created_at":"2025-08-18 08:19:56","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47104,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile2Vincristine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/4dedc454addf59d6e577d036.xls"},{"id":89267050,"identity":"10a0c6df-8d0c-46ba-9d62-cb14978a7098","added_by":"auto","created_at":"2025-08-18 08:19:56","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":193024,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile3Vinblastine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/48f9ef4f49e549fad432b89b.xls"},{"id":89267056,"identity":"650410e1-4d77-40b3-885d-70c92f4f4f4b","added_by":"auto","created_at":"2025-08-18 08:19:56","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":167424,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile4Vincristine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/8804f4ec361d4509a8d7811d.xls"},{"id":89267063,"identity":"d453bb8c-2c13-440d-aaf6-a4c7e24c19db","added_by":"auto","created_at":"2025-08-18 08:19:57","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":27136,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile7Vinblastine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/3c8dc0bb44949f0b5a622a96.xls"},{"id":89268818,"identity":"b3fa645b-8583-4d8b-bace-53873002f6b0","added_by":"auto","created_at":"2025-08-18 08:27:58","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":27136,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatafile8Vincristine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/4995045ba36d67a58a3ac98b.xls"},{"id":89267049,"identity":"747a175b-9444-4c92-9feb-8cfb30e5a6a2","added_by":"auto","created_at":"2025-08-18 08:19:56","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":129024,"visible":true,"origin":"","legend":"","description":"","filename":"Supplimentarydatafile6Vincristine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/aaae116eded3075e86bc99c0.xls"},{"id":89267072,"identity":"1fb52373-c7f2-4085-8924-4fa4b0c8d31a","added_by":"auto","created_at":"2025-08-18 08:19:57","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":172032,"visible":true,"origin":"","legend":"","description":"","filename":"Supplimentarydatafile5Vinblastine.xls","url":"https://assets-eu.researchsquare.com/files/rs-7279437/v1/db65857aa0629bea26865e5f.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is the second most common cause of death in the US and accounts for one in six deaths globally, making it a global health concern. An estimated 10.3\u0026nbsp;million cancer deaths and 19.3\u0026nbsp;million new cases were reported worldwide in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Bangladesh, cancer accounts for 10% of all deaths, with 200,000 new cases diagnosed each year and that number could rise to 13% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] by 2030. For cancer treatment, secondary metabolites from plants are well-characterized as chemo-preventive treatments and are acknowledged as bioactive chemicals for primary and secondary prevention. These plant-based bioactive chemicals also have important genoprotective properties, including protecting healthy cells from DNA damage and reducing the development of cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, customized cancer prevention strategies can be created using these bioactive compounds. The natural vinca alkaloids vinblastine and vincristine, which were first discovered in \u003cem\u003eCatharanthus roseus\u003c/em\u003e, are utilized as chemotherapeutic agents for testicular cancer, breast cancer, Kaposi sarcoma, renal cell carcinoma, leukemia, lymphoma, and myeloma [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vinblastine may affect biomolecules, including cellular respiration, nucleic acid and lipid production, and the metabolism of amino acids, cyclic AMP, and glutathione. Conversely, vincristine binds to the β-subunit of tubulin dimers that are located between the borders of two heterodimers, preventing free tubulin from attaching to the microtubule fiber [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In numerous cellular biological processes, including signal transduction, immunological response, and cellular architecture, protein-protein interactions (PPI) are essential. Using structural information from hot spot analysis, rational drug design has shown success in identifying PPI modulators. Therefore, PPI analysis is crucial because it may help identify pharmacological targets and guide the development of new treatments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The development of cancer therapeutics can benefit from the efficient identification of tumor-specific genes and prognosis-relevant biomarkers by bioinformatics analysis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study investigates the genes associated with vinblastine and vincristine using protein-protein interaction (PPI) network analysis and also conducts an anticipated ADMET (absorption, distribution, metabolism, excretion, and toxicity) study. Small molecule chemical probes also need to meet specific ADMET requirements, as developing them is essential for understanding how PPI interactions play a key role in both healthy and diseased conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, this study aimed to identify key genes linked to vincristine and vinblastine and to evaluate the fulfillment of necessary ADMET properties for their application in cancer treatment, utilizing bioinformatics analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eData Sources and Network Construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study integrated data from multiple biological databases - including Swiss Target Prediction, STRING, PubChem, SwissADME, and ProTox-II - encompassing gene expression profiles, molecular sequences, and structural information. Brief explanations of these databases are presented under the following subheadings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollection of Target proteins data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Swiss Target Prediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to derive the targets for vinblastine and vincristine by uploading the SMILES of the compounds [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prediction was performed by limiting the search to human proteins, and targets with probability scores greater than the 0.06 cutoff were retained for further analysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] \u003cb\u003e[Supplementary Data File 1 and Supplementary Data File 2\u003c/b\u003e, \u003cb\u003erespectively].\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of protein-protein interaction (PPI) network\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProtein-protein interaction (PPI) networks - consisting of groups of proteins and their interaction connections - regulate biological processes. In order to conduct the protein-protein interaction network analysis, selected targets of vinblastine and vincristine from Swiss Target Prediction were uploaded to the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] \u003cb\u003e[Supplementary Data File 3 and Supplementary Data File 4, respectively].\u003c/b\u003e Using default parameters, K-means clustering was carried out to the whole network in order to cluster similar proteins [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] \u003cb\u003e[Supplementary Data File 5 and Supplementary Data File 6, respectively].\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNetwork analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVisualization of PPI network\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing Cytoscape software 3.10 and its plug-ins like Cytohubba and Mcode, the PPI networks of the selected targets that were obtained from the STRING database were evaluated and visualized with a 0.40 confidence level [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetermination of key genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe key genes linked to the vinblastine and vincristine PPI network were identified using the Cytohubba plugin. To determine the top 10 nodes, we were applied the maximum clique centrality (MCC) degree method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] \u003cb\u003e[Supplementary Data File 7 and Supplementary Data File 8, respectively].\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetermination of important clusters of the networks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PPI networks were further divided into modules using MCODE, which employed cut-off values greater than 2 for node connectivity and screened important clusters of the networks [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrug Likeness, ADME, and toxicity analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDrug-likeness, pharmacokinetic parameters, and physicochemical properties were investigated using SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A free online tool called ProTox-II (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tox.charite.de/protox\u003c/span\u003e\u003cspan address=\"http://tox.charite.de/protox\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e II) was used to calculate the acute and organ toxicity of newly created prodrugs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCollected target proteins data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;1 and 2 depicts the top 15 targets of vinblastine and vincristine that were derived from the Swiss Target Prediction database. Findings suggest that of all the target types, the kinase and protease protein families were the most prevalent in both vinblastine and vincristine. In addition, there is a strong correlation between the targets of the enzymes and vinblastine, vincristine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-protein interaction (PPI) network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to PPI network analysis of vinblastine, there were 796 edges and 98 nodes in the network with a clustering coefficient of 0.540. The network in Vincristine included 96 nodes and 676 edges, with a clustering coefficient of 0.576. Figure 3 displays a rectangular node arrangement based on degree centrality in the network derived from vinblastine- and vincristine-selected targets.\u003c/p\u003e\n\u003cp\u003eK-means clustering facilitates the grouping of protein targets of similar types. From cluster 1 to cluster 7, we have a total of seven clusters for vinblastine and vincristine. With 75 genes, Cluster 1 had the most genes, followed by Cluster 2,3,4 with 5 genes, cluster 5,6 with 3 genes each, and cluster 7 with 2 genes in vinblastine. In vincristine, Cluster 1 has the most genes with 76 genes followed by Clusters 2 and 3 with 4 and 5 genes respectively, cluster 4, 5 with 2 genes each and other clusters with 2 genes individually. All of the clusters produced by the K-means clustering technique are shown in Figs.\u0026nbsp;4 and 5.\u003c/p\u003e\n\u003cp\u003eWithin the network, key genes are interconnected nodes that have important functions. According to the outcome score for vinblastine, the \u003cem\u003ePIK3CA\u003c/em\u003e was placed 1, and the \u003cem\u003eAVPR1B\u003c/em\u003e was ranked 10. For vincristine, the \u003cem\u003eCCKBR\u003c/em\u003e was placed 10, and the \u003cem\u003eMCHR1\u003c/em\u003e was ranked 1. The study also identified \u003cem\u003eMCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR, TACR2\u003c/em\u003e, as additional significant nodes for vinblastine. On the other hand, \u003cem\u003eBDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A ADRA1D\u003c/em\u003e, were identified as additional significant nodes for vincristine. Nodes of the Cytohubba analysis are displayed in Fig. 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant cluster of the network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing MCODE, the study identified 5 and 3 distinct node groups for vinblastine and vincristine, respectively. In the case of vinblastine, with 23 nodes and 126 edges, Cluster 1 achieved the highest score of 11.4. However, with 4 nodes and 4 edges, cluster 5 earned the lowest score of 2.6. Cluster 1 had the highest score of 15.8 for vincristine, which has 27 nodes and 206 edges. Meanwhile, cluster 3 had 10 nodes and 20 edges and obtained the lowest score of 4.4. \u003cem\u003ePIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR, TACR2, AVPR1B\u003c/em\u003e are nodes that are part of cluster 1 for vinblastine. For vincristine, cluster 1 nodes include \u003cem\u003eMCHR1, BDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A, ADRA1D\u003c/em\u003e, and \u003cem\u003eCCKBR\u003c/em\u003e. All of the clusters derived from the MCODE analysis are shown in Figs.\u0026nbsp;7 and 8.\u003c/p\u003e\n\u003cp\u003eThe genes \u003cem\u003ePIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR2, GHSR\u003c/em\u003e, and \u003cem\u003eAVPR1B\u003c/em\u003e play pivotal roles in cancer progression by influencing key biological processes such as cell signaling, proliferation, angiogenesis, metabolism, and immune modulation. \u003cem\u003ePIK3CA\u003c/em\u003e is a critical driver of the PI3K/AKT/mTOR pathway, promoting tumor growth and therapy resistance, while \u003cem\u003eMCHR1\u003c/em\u003e and \u003cem\u003eHTR2C\u003c/em\u003e are involved in tumor metabolism and signaling. \u003cem\u003eBDKRB1\u003c/em\u003e and \u003cem\u003eF2\u003c/em\u003e contribute to angiogenesis and metastasis, aiding tumor expansion. \u003cem\u003eCHRM1\u003c/em\u003e and \u003cem\u003eTACR2\u003c/em\u003e enhance cancer cell proliferation and inflammation-driven progression. \u003cem\u003eGHSR\u003c/em\u003e supports tumor growth through metabolic regulation, and \u003cem\u003eAVPR1B\u003c/em\u003e facilitates stress adaptation and metastasis. These genes represent crucial targets for understanding cancer biology and developing therapeutic interventions. In this investigation, some genes are similar, but some are different cases of vincristine. So, the genes \u003cem\u003eADRA1B\u003c/em\u003e, \u003cem\u003eEDNRA\u003c/em\u003e, \u003cem\u003eADRA1A\u003c/em\u003e, \u003cem\u003eADRA1D\u003c/em\u003e, and \u003cem\u003eCCKBR\u003c/em\u003e play significant roles in cancer progression through their involvement in signaling pathways that regulate cell proliferation, angiogenesis, and tumor microenvironment dynamics. \u003cem\u003eADRA1B\u003c/em\u003e and \u003cem\u003eADRA1A\u003c/em\u003e, adrenergic receptors, are linked to tumor angiogenesis and vascular remodeling, facilitating tumor growth and immune evasion, particularly in prostate and breast cancers. Similarly, \u003cem\u003eEDNRA\u003c/em\u003e, which mediates endothelin-1 signaling, drives angiogenesis, metastasis, and therapy resistance in cancers such as ovarian and lung. \u003cem\u003eADRA1D\u003c/em\u003e, though less studied, contributes to vascular regulation and cellular survival, potentially influencing metastatic potential. \u003cem\u003eCCKBR\u003c/em\u003e, frequently overexpressed in gastrointestinal cancers like gastric and pancreatic, promotes tumor proliferation, invasion, and resistance to apoptosis through gastrin-mediated signaling. These genes collectively represent promising targets for therapies aimed at disrupting cancer-associated signaling and the tumor microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug Likeness, ADME, and toxicity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ADME characteristics of vinblastine and vincristine were calculated in order to determine the drug-likeness. According to the obtained LogP value, every chemical had a LogP\u0026thinsp;\u0026le;\u0026thinsp;5 and was, therefore, lipophilic. Vinblastine has a water solubility (ESOL) value greater than \u0026minus;\u0026thinsp;6.5, meaning it is not soluble in water. Vincritine\u0026apos;s water solubility (ESOL) value, however, is below \u0026minus;\u0026thinsp;6.5. According to the Lipinski rule, both vinblastine and vincristine breach two of the five rules of the rule of five (ROF). It was discovered that neither of the substances could pass through the blood-brain barrier (BBB). A pharmacokinetic profile for vinblastine and vincristine is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eIn silico\u003c/em\u003e toxicity research was carried out using the ProTox-II web server, which produced positive toxicological results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePharmacokinetics and toxicity properties of Vinblastine and Vincristine\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhytochemical identifier\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(CID 13342)\u003c/p\u003e\n \u003cp\u003eVinblastine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(CID 5978)\u003c/p\u003e\n \u003cp\u003eVincristine\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"14\"\u003e\n \u003cp\u003ePharmacokinetics properties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMW (g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e810.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e824.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArom. Heavy atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRotatable bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-bond acceptors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-bond donors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog Po/w (MLOGP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog S (ESOL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipinski, violation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 violations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 violations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSynth. accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB permeant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog \u003cem\u003eK\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eToxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinogenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmunotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMutagenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCytotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eactive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eVinblastine and vincristine's predicted function was summarized in this study by clustering PPIN and identifying efficacy. Bioinformatics was used to predict the efficacy of vinblastine and vincristine. The data pool for the effect of vinblastine and vincristine was gathered from the National Center for Biotechnology Information, which provides chemical, chemical biology, drug chemistry, and drug discovery information [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As illustrated in Fig.\u0026nbsp;2, the nodes represent proteins, and the edges represent relationships between the proteins [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Using Cytohubba and MCODE analyses, the key targets of vinblastine and vincristine that are linked to a number of essential body functions were identified. The key genes identified for vinblastine were \u003cem\u003ePIK3CA, MCHR1, BDKRB1, F2, CHRM1, HTR2C, TACR1, GHSR\u003c/em\u003e, \u003cem\u003eTACR2\u003c/em\u003e, and \u003cem\u003eAVPR1B.\u003c/em\u003e The gene \u003cem\u003ePIK3CA RANKED 1\u003c/em\u003e. Three kinds of intracellular enzymes are known to be members of the phosphatidylinositol-3-kinase (\u003cem\u003ePI3K\u003c/em\u003e) family. Although class II and III \u003cem\u003ePI3K\u003c/em\u003es are more active in membrane transport, class I \u003cem\u003ePI3K\u003c/em\u003es primarily operate in signaling by reacting to the activation of cell surface receptors. The \u003cem\u003ePI3K\u003c/em\u003e signaling network controls cell division, growth, migration, and survival in a physiologically normal environment. Cancer frequently starts when the \u003cem\u003ePI3K\u003c/em\u003e signaling pathway is abnormally activated, which alters cellular activity and metabolism. Five class I \u003cem\u003ePI3K\u003c/em\u003e inhibitors are now licensed for clinical use by the Food and Drug Administration (FDA) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. On the contrary, the key genes identified for vincristine were \u003cem\u003eMCHR1\u003c/em\u003e, \u003cem\u003eBDKRB1, PIK3CA, CHRM1, F2, ADRA1B, EDNRA, ADRA1A, ADRA1D\u003c/em\u003e, and \u003cem\u003eCCKBR\u003c/em\u003e. In this case, \u003cem\u003eMCHR1\u003c/em\u003e ranked 1. Melanin-concentrating hormone (MCH) is a cyclic neuropeptide of 17\u0026ndash;19 amino acids that has been conserved throughout history. In individuals with inflammatory bowel disease, the affected mucosa has increased mRNA expression of \u003cem\u003eMCH\u003c/em\u003e and \u003cem\u003eMCHR1\u003c/em\u003e. Different proinflammatory cytokines and chemokines were expressed more when \u003cem\u003eMCHR1\u003c/em\u003e was activated in the same cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study showed that the key genes of vinblastine and vincristine are involved in cancer mechanism and inflammation, which may lead to new avenues for cancer treatment. Both leukemia and lymphoma are treated with these two substances. The new therapeutic agents from vinblastine and vincristine will be more effective and promising in novel cancer treatment as natural compounds are less dangerous than synthetic ones [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, to ascertain the therapeutic potential of vinblastine and vincristine without adverse effects, future research should examine their physiological activity at the molecular level. Their potential has not yet been determined. However, more studies and clinical data are required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a comprehensive network-based and ADMET profiling analysis of vinblastine and vincristine, shedding light on their therapeutic potential in cancer treatment. Through protein-protein interaction networks and clustering analyses, we identified key targets-such as PIK3CA and MCHR1-linked to crucial cellular processes including tumor progression, angiogenesis, immune modulation, and inflammation. The bioinformatics tools confirmed the compounds\u0026rsquo; roles in disrupting microtubule dynamics and mitotic pathways, reinforcing their efficacy in halting cancer cell division. Additionally, ADMET profiling revealed favorable drug-like characteristics with manageable toxicity concerns, supporting their candidacy as natural anticancer agents. While the data suggest strong therapeutic promise, further molecular studies and clinical trials are essential to validate their safety, optimize dosage, and fully harness their pharmacological potential in oncology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any experiments on human participants or animals. All analyses were performed using publicly available datasets and in silico tools. Therefore, ethical approval and informed consent were not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis study did not receive any funding from national or international organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no known conflicts of interest related to this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The experimental data and simulation results supporting the findings of this study are available within the manuscript (link provided) or in the supplementary information files.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMN supervised the research. MN, AR, and RK contributed to the study concept and participated in reviewing and editing the draft manuscript. AR, MSRS, and ZS prepared the materials, designed and performed the experiments, conducted the analysis, and wrote the original draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDebela DT, Muzazu SGY, Heraro KD et al (2021) New approaches and procedures for cancer treatment: current perspectives. SAGE Open Med 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1177/20503121211034366\u003c/span\u003e\u003cspan address=\"https://doi:10.1177/20503121211034366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMubin N, Bin Abdul Baten R, Jahan S et al (2021) Cancer-related knowledge, attitude, and practice among community health care providers and health assistants in rural Bangladesh. BMC Health Serv Res 21(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s12913-021-06202-z\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s12913-021-06202-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Pandey AK (2013) Chemistry and biological activities of flavonoids: an overview. Sci World J 2013(1):162750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1155/2013/162750\u003c/span\u003e\u003cspan address=\"https://doi:10.1155/2013/162750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukhtar H, Ahmad N (2000) Cancer chemoprevention: future holds in multiple agents. Toxicol Appl Pharmacol 158(3):207\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1006/taap.2000.8961\u003c/span\u003e\u003cspan address=\"https://doi:10.1006/taap.2000.8961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLakhmi B, Mukherjee AK (2025) Drugs from poisonous plants: ethnopharmacological relevance to modern perspectives. Toxicon: X 100215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.toxcx.2025.100215\u003c/span\u003e\u003cspan address=\"10.1016/j.toxcx.2025.100215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNada H, Choi Y, Kim S et al (2024) New insights into protein\u0026ndash;protein interaction modulators in drug discovery and therapeutic advance. Signal Transduct Target Ther 9(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1038/s41392-024-02036-3\u003c/span\u003e\u003cspan address=\"https://doi:10.1038/s41392-024-02036-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin X, Wu Q, Hao Z, Chen L (2022) Identification of novel prognostic targets in glioblastoma using bioinformatics analysis. Biomed Eng Online 21(1):1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s12938-022\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s12938-022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLagorce D, Douguet D, Miteva MA, Villoutreix BO (2017) Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci Rep 7:46277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1038/srep46277\u003c/span\u003e\u003cspan address=\"https://doi:10.1038/srep46277\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIksen I, Witayateeraporn W, Wirojwongchai T et al (2023) Identifying molecular targets of Aspiletrein-derived steroidal saponins in lung cancer using network pharmacology and molecular docking-based assessments. Sci Rep 13(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1038/s41598-023-28821-8\u003c/span\u003e\u003cspan address=\"https://doi:10.1038/s41598-023-28821-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTortosa V, Pietropaolo V, Brandi V et al (2020) Computational methods for the identification of molecular targets of toxic food additives: butylated hydroxytoluene as a case study. Molecules 25(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/molecules25092229\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/molecules25092229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC (2023) Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (\u003cem\u003eOryza sativa\u003c/em\u003e). J Genet Eng Biotechnol 21(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s43141-023-00515-8\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s43141-023-00515-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSadat Kalaki N, Ahmadzadeh M, Najafi M et al (2024) Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer. Biochem Biophys Rep 37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e101633.https://doi:10.1016/j.bbrep.2023.101633\u003c/span\u003e\u003cspan address=\"101633.https://doi:10.1016/j.bbrep.2023.101633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMishra D, Mishra A, Rai SN, Vamanu E, Singh MP (2023) Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of hepatocellular carcinoma through transcriptome analysis. Diagnostics (Basel) 13(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/diagnostics13050965\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/diagnostics13050965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar A (2024) Insights into amyloid precursor protein target through PPI network analysis. Bioinformation 20(2):140\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.6026/973206300200140\u003c/span\u003e\u003cspan address=\"https://doi:10.6026/973206300200140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin SC, Kim MH, Choi LY, Nam YK, Yang WM (2021) Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis. Phytomedicine 86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e153557.https://doi:10.1016/j.phymed.2021.153557\u003c/span\u003e\u003cspan address=\"153557.https://doi:10.1016/j.phymed.2021.153557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMazumder K, Hossain ME, Aktar A et al (2021) \u003cem\u003eIn silico\u003c/em\u003e analysis and experimental evaluation of ester prodrugs of ketoprofen for oral delivery: with a view to reduce toxicity. Processes. ;9(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/pr9122221\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/pr9122221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng T, Pan Y, Hao M, Wang Y, Bryant SH (2014) PubChem applications in drug discovery: a bibliometric analysis. Drug Discov Today 19(11):1751\u0026ndash;1756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.drudis.2014.08.008\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.drudis.2014.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNabieva E, Jim K, Agarwal A, Chazelle B, Singh M (2005) Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(Suppl 1):i302\u0026ndash;i310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/bioinformatics/bti1054\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/bioinformatics/bti1054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Wen X, Ren Y et al (2024) Targeting PI3K family with small-molecule inhibitors in cancer therapy: current clinical status and future directions. Mol Cancer 23(1):1\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s12943-024-02072-1\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s12943-024-02072-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagel JM, Geiger BM, Karagiannis AKA et al (2012) Reduced intestinal tumorigenesis in APCmin mice lacking melanin-concentrating hormone. PLoS ONE 7(7):e41914. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1371/journalpone/0041914\u003c/span\u003e\u003cspan address=\"https://doi:10.1371/journalpone/0041914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChunarkar-Patil P, Kaleem M, Mishra R et al (2024) Anticancer drug discovery based on natural products: from computational approaches to clinical studies. Biomedicines 12(1):1\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/biomedicines12010201\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/biomedicines12010201\" 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":false,"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":"Vinblastine, Vincristine, Cancer Therapy, Protein-Protein Interaction (PPI) Network, ADMET Profiling","lastPublishedDoi":"10.21203/rs.3.rs-7279437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7279437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCancer remains one of the leading global health challenges, necessitating targeted and personalized treatment approaches. Vinblastine and vincristine, microtubule-disrupting vinca alkaloids, present strong potential for cancer therapy based on computationally modeled molecular interactions and pharmacological profiles. This study aims to explore the therapeutic potential of vinblastine and vincristine by mapping their molecular targets through bioinformatics analysis. Protein-protein interaction networks were constructed using STRING and visualized with Cytoscape. Key clusters were identified through K-means and MCODE algorithms. SwissTarget Prediction was used for gene-drug interaction analysis. SwissADME profiled pharmacokinetics and drug-likeness, while ProTox-II assessed toxicity risk. Additional pathway enrichment and functional annotation were conducted using publicly available bioinformatics databases. All results derive from theoretical and computational workflows without experimental validation. The analysis identified PIK3CA, MCHR1, BDKRB1, and CHRM1 as crucial biological targets associated with oncogenic signaling, angiogenesis, and immune modulation. Clustering algorithms revealed subnetworks linked to mitotic control and cancer-related pathways. Vinblastine and vincristine demonstrated drug-like properties with minimal violations of Lipinski\u0026rsquo;s rule. SwissADME profiling confirmed acceptable solubility, gastrointestinal absorption, and bioavailability. ProTox-II results suggested favorable safety margins with low predicted toxicity. These findings indicate potential for selective targeting of cancer-related molecular mechanisms and support further investigation of these compounds in precision oncology. Vinblastine and vincristine demonstrate promising computational profiles for anticancer activity, with strong molecular target interactions and favorable ADME and toxicity parameters. While these results are based on in silico analysis, they support future experimental validation. The study underscores the value of bioinformatics tools in identifying precision therapies and optimizing drug development strategies for cancer treatment.\u003c/p\u003e","manuscriptTitle":"Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 08:19:50","doi":"10.21203/rs.3.rs-7279437/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":"c4506d81-3f86-43cc-9c8a-a3e608543b71","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T20:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 08:19:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7279437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7279437","identity":"rs-7279437","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}