ReviewDrug design, development and therapy2026
Peptides in the Diagnosis and Treatment of Pancreatic Cancer and Other Pancreatic Diseases from Basic Research to Clinical Translation.
Review in Drug design, development and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
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Abstract
Peptides have emerged as pivotal agents in biomedicine due to their exceptional physicochemical properties, including high binding affinity, minimal immunogenicity, and precise target specificity. This review critically examines recent advancements in peptide-based strategies for pancreatic disorders, encompassing inflammatory conditions (e.g. pancreatitis), metabolic dysfunctions (e.g. diabetes), and notably, pancreatic cancer. We delineate the evolution of peptide therapeutics, emphasizing rational drug design approaches such as backbone cyclization and N-methylation to enhance metabolic stability, complemented by computational methodologies like molecular docking and AI-driven affinity maturation to optimize target engagement. The discussion highlights key innovations, including peptide probes for early diagnostic detection and peptide-drug conjugates for targeted intervention, while evaluating their efficacy in preclinical models and assessing their biosafety profiles. Furthermore, we survey current clinical trials aimed at translating these engineered peptides into clinical applications. Concluding with a perspective on precision medicine, we outline future trajectories necessitating advanced AI-integrated design frameworks and robust clinical validation to accelerate the bench-to-bedside translation of peptide technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.