Computational Drug Repurposing of H1-Antihistamines for Inhibiting Tumour Progression in Pancreatic Cancer: An In Silico Evaluation
Abstract
Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive malignancies, characterized by poor prognosis, early metastasis, and limited therapeutic options. Drug repurposing has emerged as an attractive strategy for identifying novel anticancer agents by utilizing the established safety profiles of approved drugs. In this study, FDA-approved H1-antihistamines were evaluated as potential therapeutic candidates against PAAD using an integrated computational approach. Methods: Differentially expressed genes (DEGs) associated with PAAD were identified from publicly available transcriptomic datasets obtained from TCGA and GEO. Functional enrichment, protein–protein interaction (PPI) network construction, and hub gene analysis were performed to identify key molecular targets. Selected FDA-approved H1-antihistamines were screened against hub proteins (ACTB, ITGB1, CDC42, CDK1, and CTNNB1) using molecular docking. The top-ranked compounds were further evaluated for drug-likeness, pharmacokinetic properties, and toxicity using SwissADME, Molinspiration, OSIRIS, and pkCSM. Results: Bioinformatics analysis identified 2,000 significant DEGs and five major hub proteins involved in cell cycle regulation, cytoskeletal organization, and tumour progression. Molecular docking demonstrated that Rupatadine, Mizolastine, and Ebastine exhibited the strongest binding affinities toward the selected targets, with Ebastine showing the highest affinity for CDK1 (−10.1 kcal/mol). ADME analysis indicated favourable oral bioavailability and acceptable drug-likeness, while toxicity predictions suggested that Rupatadine possessed the most balanced safety profile. Conclusion: The findings suggest that second-generation H1-antihistamines, particularly Rupatadine, represent promising candidates for repurposing as potential therapeutics for pancreatic adenocarcinoma. Further in vitro and in vivo studies are required to validate these computational predictions and assess their clinical applicability.