Investigating the anticancer activity of eravacycline in pancreatic cancer via target-based deep learning and experimental validation.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with limited therapeutic options. In this study, we introduce a target-based deep learning framework to investigate the anticancer activity of eravacycline (Erav), a United States Food and Drug Administration (FDA)-approved antibacterial agent previously identified in our work as a potential anticancer candidate through computational screening. We developed a novel two-phase in silico yeast-based prediction model to explore potential mechanisms of action, followed by in vitro and in vivo experimental validation. DNA polymerase kappa (POLK) and mutant p53 emerged as the top-ranked candidate targets. In the studied mutant p53 PDAC model, Erav treatment significantly reduced mutant p53 protein levels and was associated with marked downregulation of POLK protein expression. POLK is a previously underexplored DNA polymerase that has been reported to be overexpressed in multiple cancer types. In a subcutaneous xenograft model, Erav treatment resulted in a 76% reduction in tumor volume. Our findings demonstrate an association between Erav treatment and reduced POLK protein expression in the studied mutant p53 PDAC model, supporting POLK as a prioritized candidate for further investigation and providing preliminary mechanistic insight into Erav activity. This integrative computational-experimental pipeline offers a robust strategy for accelerating drug repurposing in oncology.