Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using Machine Learning-Assisted QSAR Modeling and Virtual Reverse Pharmacology approach

Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using Machine Learning-Assisted QSAR Modeling and Virtual Reverse Pharmacology approach | 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 Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using Machine Learning-Assisted QSAR Modeling and Virtual Reverse Pharmacology approach Manisha Shah, Sivakumar Arumugam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4371326/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Introduction Cardiovascular disease is a chronic inflammatory disease with several categories of risk factors that impart a high mortality rate. Despite TNF-alpha being a prominent pro-inflammatory cytokine associated with chronic inflammation within cardiovascular disease, the adverse effects of current TNF-alpha based medications prompt an urgent need to identify efficient inhibitors as alternatives. This study not only explores the quantitative structural activity relationship (QSAR) of TNF-alpha inhibitors but also identifies potential drug targets to treat cardiovascular disease. Materials and Methods A GitHub Repository-based pipeline was used to curate data from the ChEMBL database. This was followed by pre-processing to exclude remove TNF-alpha inhibitors with missing bioactivity values and identify significant properties of molecules using exploratory data analysis (EDA). The extracted molecules were subjected to PubChem (PC) and SubStructure (SS) fingerprint descriptors, and a QSAR-based Random Forest model (QSAR-RF) was generated using the WEKA tool. QSAR-RF was validated using FDA drugs and molecules from PubChem and ZINC databases and used to predict the pIC50 value of the molecules selected from the docking study followed by molecular dynamic simulation with a time step of 100ns. Through virtual reverse pharmacology, we determined the main drug targets for the top four hit compounds obtained via molecular docking study. Our analysis included an integrated bioinformatics approach to pinpoint potential drug targets, as well as a PPI network to investigate critical targets. To further elucidate the findings, we utilized g:Profiler for GO and KEGG pathway analysis, ultimately identifying the most relevant cardiovascular disease-related pathway for the hub genes involved. Results A unique pipeline was used to create QSAR-RF a machine-learning model that identifies TNF-alpha inhibitors based on molecular features. It distinctly used PC and SS fingerprints, which show strong correlation coefficients of 0.993 and 0.992 respectively, with 0.607 and 0.716 as the respective 10-fold cross-validation scores. The VIP method extracts important features for each model. The QSAR-RF model was built using SS-fingerprints, and validated by docking study and small molecule bioactivity prediction. Irinotecan showed strong binding to TNF-alpha, with three important inhibitory features identified using a comprehensive variance importance plot (VIP). MD simulation confirmed the structural stability of the Irinotecan-TNF-alpha complex. For, the reverse network pharmacology approach, we identified four scaffolds namely, Tirilazad, Irinotecan, Diosgenin, and Gitogenin with higher binding scores. As a result, a total of 289 potential drug targets were identified for cardiovascular diseases (CVD). PPI network analysis identified EGRF, HSP900A1, STAT3, SRC, AKT1, MDM2, and other possible CVD targets. The treatment of CVD using four different scaffold drug targets was found to involve in oxidative stress, smooth muscle proliferation, organonitrogen compound, and multiple pathways such as PI3K-AKT signaling, lipid and atherosclerosis, among others. Conclusion In conclusion, Our study applies a ligand-based drug design approach to generate a SubStructure-based QSAR-RF prediction model to unravel the structural inhibitory feature of TNF-alpha inhibitors. And also identified multiple targets to treat CVD through a reverse network pharmacology approach. QSAR TNF-alpha inhibitor Machine Learning TNF-alpha Autoimmune Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2024 Editor assigned by journal 06 May, 2024 Submission checks completed at journal 06 May, 2024 First submitted to journal 05 May, 2024 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-4371326","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299698723,"identity":"185502c4-82db-46c6-9a21-ceeb73541fd9","order_by":0,"name":"Manisha Shah","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Manisha","middleName":"","lastName":"Shah","suffix":""},{"id":299698724,"identity":"b2cdeec4-c153-4f15-83a0-77ab532b8b00","order_by":1,"name":"Sivakumar Arumugam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZhBxAMr5wCABYSQQq4VxBoOEBGEtDEhamHkYYNbgAfLtPIYfGM4cjuaffYDxs22bRR0D++EHDA934NZicJjHWILhxuHcGecSmKVz24AO40kzYEg8g0cLM1uCBMOHw7kNQEXSOWdAfslhYEhsw+OwZrbkHyAt888wMP+2AGnhf4NfC8Nh5mNgh204w8AmzVAB1CJBwBYDoBYLhjPpuRvPMLZZ9lRISLZJPDM4gNdh/QebbzAcs86dd4b58I0fBnX8/PzJDx/+xOcwIGD+A6YYG8AUGwMiNYyCUTAKRsEoIBMAAC5SSU07QrixAAAAAElFTkSuQmCC","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Sivakumar","middleName":"","lastName":"Arumugam","suffix":""}],"badges":[],"createdAt":"2024-05-05 10:41:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4371326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4371326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56374208,"identity":"352fef2d-a3a0-49fc-979d-d6e2db10f07d","added_by":"auto","created_at":"2024-05-13 10:47:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1644678,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4371326/v1_covered_6f10ea29-1218-44dc-b322-46f405dceef1.pdf"},{"id":56373869,"identity":"610f9c7d-1e37-4f35-8633-e9265969c6da","added_by":"auto","created_at":"2024-05-13 10:39:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":796766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4371326/v1/ba422947ec2a12912a25455b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using Machine Learning-Assisted QSAR Modeling and Virtual Reverse Pharmacology approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"QSAR, TNF-alpha inhibitor, Machine Learning, TNF-alpha, Autoimmune","lastPublishedDoi":"10.21203/rs.3.rs-4371326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4371326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eCardiovascular disease is a chronic inflammatory disease with several categories of risk factors that impart a high mortality rate. Despite TNF-alpha being a prominent pro-inflammatory cytokine associated with chronic inflammation within cardiovascular disease, the adverse effects of current TNF-alpha based medications prompt an urgent need to identify efficient inhibitors as alternatives. This study not only explores the quantitative structural activity relationship (QSAR) of TNF-alpha inhibitors but also identifies potential drug targets to treat cardiovascular disease.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eA GitHub Repository-based pipeline was used to curate data from the ChEMBL database. This was followed by pre-processing to exclude remove TNF-alpha inhibitors with missing bioactivity values and identify significant properties of molecules using exploratory data analysis (EDA). The extracted molecules were subjected to PubChem (PC) and SubStructure (SS) fingerprint descriptors, and a QSAR-based Random Forest model (QSAR-RF) was generated using the WEKA tool. QSAR-RF was validated using FDA drugs and molecules from PubChem and ZINC databases and used to predict the pIC50 value of the molecules selected from the docking study followed by molecular dynamic simulation with a time step of 100ns. Through virtual reverse pharmacology, we determined the main drug targets for the top four hit compounds obtained via molecular docking study. Our analysis included an integrated bioinformatics approach to pinpoint potential drug targets, as well as a PPI network to investigate critical targets. To further elucidate the findings, we utilized g:Profiler for GO and KEGG pathway analysis, ultimately identifying the most relevant cardiovascular disease-related pathway for the hub genes involved.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA unique pipeline was used to create QSAR-RF a machine-learning model that identifies TNF-alpha inhibitors based on molecular features. It distinctly used PC and SS fingerprints, which show strong correlation coefficients of 0.993 and 0.992 respectively, with 0.607 and 0.716 as the respective 10-fold cross-validation scores. The VIP method extracts important features for each model. The QSAR-RF model was built using SS-fingerprints, and validated by docking study and small molecule bioactivity prediction. Irinotecan showed strong binding to TNF-alpha, with three important inhibitory features identified using a comprehensive variance importance plot (VIP). MD simulation confirmed the structural stability of the Irinotecan-TNF-alpha complex. For, the reverse network pharmacology approach, we identified four scaffolds namely, Tirilazad, Irinotecan, Diosgenin, and Gitogenin with higher binding scores. As a result, a total of 289 potential drug targets were identified for cardiovascular diseases (CVD). PPI network analysis identified EGRF, HSP900A1, STAT3, SRC, AKT1, MDM2, and other possible CVD targets. The treatment of CVD using four different scaffold drug targets was found to involve in oxidative stress, smooth muscle proliferation, organonitrogen compound, and multiple pathways such as PI3K-AKT signaling, lipid and atherosclerosis, among others.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, Our study applies a ligand-based drug design approach to generate a SubStructure-based QSAR-RF prediction model to unravel the structural inhibitory feature of TNF-alpha inhibitors. And also identified multiple targets to treat CVD through a reverse network pharmacology approach.\u003c/p\u003e","manuscriptTitle":"Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using Machine Learning-Assisted QSAR Modeling and Virtual Reverse Pharmacology approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 10:39:25","doi":"10.21203/rs.3.rs-4371326/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-07T11:54:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-07T02:37:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-06T06:15:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2024-05-05T10:39:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"53c242d5-a254-49dd-9b31-1ee3206e681b","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-19T07:41:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 10:39:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4371326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4371326","identity":"rs-4371326","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}