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Updated: Jul 10, 2026
Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
Published on: February 3, 2026
Investigating the anticancer activity of eravacycline in pancreatic cancer via target-based deep learning and
Adi Jabarin1, Guy Shtar2, Valeria Feinshtein1
1Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel.
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.
Insights
Eravacycline shows promise against pancreatic cancer by targeting DNA polymerase kappa (POLK) and mutant p53. This FDA-approved antibacterial reduced tumor volume in preclinical models, offering a new avenue for pancreatic ductal adenocarcinoma treatment.
Area of Science:
- Oncology
- Computational Biology
- Drug Discovery
Background:
- Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with few treatment options.
- Eravacycline, an antibacterial, was computationally identified as a potential anticancer agent.
Purpose of the Study:
- Investigate eravacycline's anticancer activity in PDAC using a deep learning framework.
- Identify potential molecular targets and mechanisms of action for eravacycline.
Main Methods:
- Developed a two-phase in silico yeast-based prediction model.
- Conducted in vitro and in vivo experimental validation.
- Utilized a subcutaneous xenograft model for efficacy testing.
Main Results:
- DNA polymerase kappa (POLK) and mutant p53 identified as top targets.
- Eravacycline reduced mutant p53 and POLK protein levels in PDAC models.
- Eravacycline treatment decreased tumor volume by 76% in a xenograft model.
Conclusions:
- Eravacycline demonstrates significant anticancer activity in preclinical PDAC models.
- POLK is a potential therapeutic target in mutant p53 PDAC.
- An integrated computational-experimental approach can accelerate oncology drug repurposing.
