Drug repositioning for human MKN45 gastric cancer mouse model using deep learning AI and experimental validation.

BACKGROUND: Gastric cancer (GC) requires novel therapies. This study employed artificial neural networks (ANNs) to repurpose existing drugs against GC. METHODS: ANN models predicted candidates from DrugBank using pharmacogenomic descriptors. Top compounds, selected by favorable IC50 and Z-scores, were screened in vitro (cytotoxicity in AGS/MKN-45 cells; toxicity in HDFs). Leads were validated in vivo in MKN-45 xenograft mice, assessing tumor markers (Ki67, CD44) and target genes. RESULTS: Two leads, amitriptylinoxide (20313) and phytonadione (5284607), exhibited high in vitro potency and selectivity. In vivo, both significantly inhibited tumor growth, achieving final volumes of 2.0 ± 1.9 mm3 and 0.05 ± 0.02 mm3, respectively. This efficacy was comparable (0.08 ± 0.03 mm3) or superior to cisplatin. Critically, both compounds induced significant body weight gain, indicating markedly lower systemic toxicity than the weight loss observed with cisplatin. They also suppressed Ki67/CD44 expression (>50%) and stemness genes. CONCLUSION: This ANN-driven approach successfully identified amitriptylinoxide and phytonadione as potent GC drug candidates with in vivo efficacy rivaling cisplatin and a significantly improved toxicity profile.