Anti-Lung Cancer Agent Discovery and Development: A Review of Cross-Platform Correlation Across in silico, Network Pharmacology, in vitro, and in vivo Assessments.

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/AKT-centered 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.