AI-Powered Insights into Drug Resistance in Gastric Cancer: A Path Toward Precision Therapy

Abstract

Context: Gastric cancer (GC) is a major global health burden, with drug resistance representing a critical barrier to effective treatment. Understanding the mechanisms underlying drug resistance and leveraging advanced technologies, such as artificial intelligence (AI), are essential for developing innovative therapeutic strategies. Evidence Acquisition: This review systematically examines the primary mechanisms of drug resistance in GC, organized into eight categories: Reduced drug uptake, enhanced drug efflux, impaired pro-drug activation or increased inactivation, molecular target alterations, enhanced DNA damage repair, imbalance in apoptotic regulation, tumor microenvironment modifications, and phenotypic changes. Additionally, the role of AI in addressing these challenges is explored, with a focus on omics-driven insights, pathway analysis, biomarker discovery, and modeling drug-response relationships. Results: The review highlights the transformative potential of AI in advancing precision therapy for GC. Key applications include therapeutic stratification, optimization of drug combinations, adaptive therapy design, and integration with clinical workflows. Challenges such as data quality, model interpretability, and the need for interdisciplinary collaboration are identified, along with strategies to address these barriers. Future directions emphasize the development of explainable AI models, integration of multi-omics and real-time patient data, and AI-driven drug discovery targeting resistance pathways. Conclusions: By bridging research and clinical practice, AI offers a promising path to more effective, personalized, and adaptive therapeutic strategies for GC. Overcoming existing challenges and leveraging AI's potential can significantly improve treatment outcomes and address the pressing issue of drug resistance in GC.