Unraveling new avenues in pancreatic cancer treatment: A comprehensive exploration of drug repurposing using transcriptomic data.

Abstract

Pancreatic cancer, a malignancy notorious for its late-stage diagnosis and low patient survival rates, remains a formidable global health challenge. The currently available FDA-approved treatments for pancreatic cancer, notably chemotherapeutic agents, exhibit suboptimal efficacy, often accompanied by concerns regarding toxicity. Given the intricate nature of pancreatic cancer pathogenesis and the time-intensive nature of in silico drug discovery approaches, drug repurposing emerges as a compelling strategy to expedite the development of novel therapeutic interventions. In our study, we harnessed transcriptomic data from an exhaustive exploration of four diverse databases, ensuring a rigorous and unbiased analysis of differentially expressed genes, with a particular focus on upregulated genes associated with pancreatic cancer. Leveraging these pancreatic cancer-associated host protein targets, we employed a battery of cutting-edge bioinformatics tools, including Cytoscape STRING, GeneMANIA, Connectivity Map, and NetworkAnalyst, to identify potential small molecule drug candidates and elucidate their interactions. Subsequently, we conducted meticulous docking and redocking simulations for the selected drug-protein target pairs. This rigorous computational approach culminated in the identification of two promising broad-spectrum drug candidates against four pivotal host genes implicated in pancreatic cancer. Our findings strongly advocate for further investigation and preclinical validation of these candidates. Specifically, we propose prioritizing Dasatinib for evaluation against MMP3, MMP9, and EGFR due to their remarkable binding affinities, as well as Pioglitazone against MMP3, MMP2 and MMP9. These discoveries hold great promise in advancing the therapeutic landscape for pancreatic cancer, offering new avenues for improving patient outcomes.