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Knowledge Discovery and Drug-Repurposing Framework for Pancreatic Ductal Adenocarcinoma: Molecular Networking and
Tarik Corbo1, Elisabeth Pimpisa Graarud2, Mathilde Resell2
1Laboratory for Bioinformatics and Biostatistics, University of Sarajevo - Institute for Genetic Engineering and Biotechnology, Sarajevo, Bosnia and Herzegovina.
Abstract:
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, driven by profound molecular heterogeneity and resistance to current therapy. To support systematic target identification, we established a proteomics-anchored knowledge discovery framework integrating cross-model proteomics harmonization, network topology, high-confidence structural modeling, and large-scale in silico docking. From 1,975 proteins consistently detected across murine and human PDAC models, 32 immunohistochemically confirmed candidates were prioritized for structure-based screening against 7,509 clinically characterized compounds. Blind docking, refined pose sampling, ligand-efficiency scoring, and ADME filtering identified EIF2A, STAM, ANXA2, and AHNAK2 as robustly druggable targets. These proteins exhibited high-affinity interactions with zavegepant (a clinically approved CGRP receptor antagonist), omilancor, bemcentinib, conivaptan, and APTO-253. Docking validation (RMSD 1.98 to 2.56 Å) confirmed methodological reliability, and network analyses placed the 4 proteins within modules linked to endosomal/membrane trafficking and invasive phenotypes. Survival analyses in 176 PDAC patients further supported their clinical relevance. Thus, we suggest a systems-level platform for nominating ligandable PDAC targets and clinically actionable compounds. The framework highlights opportunities for rational drug repurposing and motivates future mechanistic studies at the intersection of proteomics and structure-based screening for targets to PDAC.
Insights
This study introduces a novel framework to identify new drug targets for pancreatic cancer (PDAC). It successfully pinpointed four druggable targets and compounds for potential repurposing, offering hope for improved pancreatic cancer treatments.
Area of Science:
- Oncology
- Proteomics
- Computational Biology
Background:
- Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with significant molecular heterogeneity and therapeutic resistance.
- Effective treatment strategies are limited due to complex disease biology.
Purpose of the Study:
- To establish a proteomics-anchored knowledge discovery framework for identifying druggable targets in PDAC.
- To nominate clinically actionable compounds for PDAC treatment through structure-based screening.
Main Methods:
- Integrated cross-model proteomics harmonization, network topology, structural modeling, and in silico docking.
- Prioritized 32 protein candidates from 1,975 consistently detected proteins across human and murine PDAC models.
- Screened prioritized candidates against 7,509 compounds using blind docking, pose sampling, ligand-efficiency scoring, and ADME filtering.
Main Results:
- Identified EIF2A, STAM, ANXA2, and AHNAK2 as robustly druggable PDAC targets.
- Found high-affinity interactions between these targets and compounds including zavegepant, omilancor, bemcentinib, conivaptan, and APTO-253.
- Validated docking methodology and linked the four targets to endosomal/membrane trafficking and invasive phenotypes relevant to PDAC patient survival.
Conclusions:
- Developed a systems-level platform for nominating ligandable PDAC targets and actionable compounds.
- Highlighted opportunities for rational drug repurposing in PDAC.
- Motivated future mechanistic studies combining proteomics and structure-based screening for PDAC targets.
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