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This review synthesizes evidence from eleven primary studies that tested anti–lung cancer agents across computational, cellular, and organism-level platforms. Included studies combined in silico target prediction and/or molecular docking and network pharmacology with coordinated in vitro cytotoxicity and in vivo efficacy evaluation. Findings show computational and network analyses converge on oncogenic signaling hubs, especially PI3K/AKT-centered pathways, corroborated by selective antiproliferative cellular activity and tumor growth suppression in animal models with acceptable safety. Robust performance emerges when molecular interactions, network engagement, cellular responses, and in vivo outcomes are integrated coherently.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/anti-lung-cancer-agent-discovery-and-development-a-review-of-cross-platform-correlation-across-in-silico-network-pharmacology-in-vitro-and-in-vivo-assessments/361446/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/anti-lung-cancer-agent-discovery-and-development-a-review-of-cross-platform-correlation-across-in-silico-network-pharmacology-in-vitro-and-in-vivo-assessments/361446.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this review?","Question",{"text":112,"@type":113},"To systematically assess whether anti–lung cancer agents show consistent results when evaluated across computational, cellular, and in vivo platforms within an integrated development continuum.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which computational methods were required in the eligible studies?",{"text":117,"@type":113},"Studies had to report either in silico target prediction/molecular docking or network pharmacology analysis, or both.",{"name":119,"@type":110,"acceptedAnswer":120},"What biological signaling hubs were consistently highlighted across the analyzed studies?",{"text":121,"@type":113},"Computational and network approaches converged on key oncogenic signaling hubs, particularly PI3K/AKT-centered pathways and receptor tyrosine kinase-related signaling.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},361446,1790777739,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Drug Design, Development and Therapy  \nOpen Access Full Text Article REVIEW  \nAnti-Lung Cancer Agent Discovery and Development: A Review of Cross-Platform Correlation Across in silico, Network Pharmacology, in vitro, and in vivo Assessments  \nSandra Megantara 1 , Rohani Rohani 2 , M Rahmatullah Zakaria 3 , M Theodorik S. Biu4 , Ezatul Ezleen Kamarulzaman 5 , 6 , Agus Rusdin 7  \n1Department of Analytical Pharmacy and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran (UNPAD), Bandung, Indonesia; 2Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran (UNPAD), Bandung, Indonesia; 3Technology Development Division, Research & Development Pharma, PT. Kalbe Farma, Bekasi, Indonesia; 4Police Medical and Health Center (Pusdokkes), Indonesian National Police, Jakarta, Indonesia; 5Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia; 6Department of Pharmaceutical Science, Faculty of Pharmacy, Universitas Airlangga (UNAIR), Surabaya, Indonesia; 7Department of Pharmacy, Jakarta Global University, Depok, Indonesia  \nCorrespondence: Sandra Megantara; Agus Rusdin, Email [s.megantara@unpad.ac.id](s.megantara@unpad.ac.id); [agusrusdin@jgu.ac.id](agusrusdin@jgu.ac.id)  \n\n| Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the urgent need for more effective and clinically translatable therapeutic agents. In recent years, drug discovery strategies integrating computational modeling with experimental validation have gained increasing attention; however, their predictive consistency across biological scales remains a critical challenge. This review systematically synthesises evidence from eleven primary studies that evaluated anti–lung cancer agents across computational, cellular, and organism-level platforms within a single developmental continuum. Eligible studies were required to report either in silico target prediction/molecular docking or network pharmacology analysis (or both), in direct conjunction with in vitro cytotoxic evaluation and in vivo efficacy assessment. Relevant studies were identified using a structured Boolean keyword strategy integrating computational, cellular, animal-based, and lung cancer–specific terms. Across the analyzed studies, computational and network approaches consistently converged on key oncogenic signaling hubs, particularly PI3K/AKTcentered pathways and receptor tyrosine kinases. These predictions were corroborated by cellular assays demonstrating selective antiproliferative activity in lung cancer cell lines and subsequently validated in animal models showing significant tumor growth suppression with acceptable safety profiles. Notably, agents optimized through targeted delivery systems, formulation strategies, or rational combination therapies exhibited the most consistent cross-platform performance. Collectively, the findings highlight that robust anti–lung cancer efficacy is most reliably achieved when molecular interaction data, systems-level network engagement, cellular responses, and in vivo outcomes are coherently integrated. This integrated evaluation framework provides a rational and predictive foundation for future anti–lung cancer drug discovery and development.\u003Cbr>Keywords: anticancer, cancer, clinical trial, in silico, in vitro, in vivo |\n| --- |\n| Introduction\u003Cbr>Lung cancer remains the leading cause of cancer-related mortality worldwide, accounting for a substantial proportion of global cancer deaths each year. Despite advances in screening, molecular diagnostics, and targeted therapeutics, the overall prognosis for lung cancer patients particularly those with advanced-stage disease remains poor. Non-small cell lung cancer (NSCLC), which constitutes most lung cancer cases, is characterized by pronounced molecular heterogeneity, complex oncogenic signaling networks, and dynamic tumor–microenvironment interactions ","cbCaikPrm5Huxcmy","https://ap.wps.com/l/cbCaikPrm5Huxcmy","pdf",3549469,24,"English","# Introduction\n# Drug discovery strategies and cross-scale predictive consistency\n# Study selection and evaluation framework\n# Cross-platform findings and mechanistic hubs\n# Translational implications for future anti–lung cancer development","[{\"question\":\"What is the main goal of this review?\",\"answer\":\"To systematically assess whether anti–lung cancer agents show consistent results when evaluated across computational, cellular, and in vivo platforms within an integrated development continuum.\"},{\"question\":\"Which computational methods were required in the eligible studies?\",\"answer\":\"Studies had to report either in silico target prediction/molecular docking or network pharmacology analysis, or both.\"},{\"question\":\"What biological signaling hubs were consistently highlighted across the analyzed studies?\",\"answer\":\"Computational and network approaches converged on key oncogenic signaling hubs, particularly PI3K/AKT-centered pathways and receptor tyrosine kinase-related signaling.\"}]","Anti-Lung Cancer Agent Discovery and Development: A Review of Cross-Platform Correlation Across in silico, Network Pharmacology, in vitro, and in vivo Assessments | PDF",1790144353]