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PubMed2026

Immunoinformatics Approach for Optimization of Targeted Vaccine Design: New Paradigm in Clinical Trials and Healthcare Management.

INTRODUCTION: The immunoinformatics approach combines bioinformatics and computational tools, offering a revolutionary method for improving vaccine development by analyzing immune responses at the molecular level. Immunoinformatics enables the creation of customized vaccines designed for specific infections or cancer cells. OBJECTIVE: The primary objective of immunoinformatics is to enhance the vaccine development process by predicting and boosting the body's immune response. It aims to identify potential immunogenic epitopes and biomarkers that are important for creating vaccines with greater specificity and efficacy, especially when dealing with large-scale data. METHODS: Immunoinformatics utilizes a combination of proteomic, genomic, and epigenomic data, as well as machine learning algorithms and artificial intelligence techniques. These tools predict how various immunological components, e.g., T-cell and B-cell epitopes, interact with the immune system. This approach allows researchers to avoid traditional trial-and-error methods, enabling the efficient identification of potential vaccine candidates. Additionally, personalized vaccines can be developed by considering individual genetic and immunological characteristics. RESULTS: The use of immunoinformatics techniques accelerates the screening of vaccine candidates, enhances patient stratification, and optimizes formulations for clinical trials. This approach has been shown to improve vaccine safety, efficacy, and development speed. It also holds promise for managing healthcare on a large scale by producing vaccines tailored to specific populations, thereby improving the overall effectiveness of vaccination programs. CONCLUSION: Immunoinformatics represents a transformative approach to vaccine research, improving clinical trial efficiency and enabling the development of more reliable, flexible, and personalized vaccines. This approach has the potential to significantly enhance global healthcare outcomes by accelerating the vaccine development process and optimizing vaccination strategies.

Immunoinformatics↗

PubMed2025

Multiple Epigenetic Mechanisms Functionally Cooperate to Silence Expression of Somatostatin Receptor Type 2 in Pancreatic Neuroendocrine Tumors.

Pancreatic neuroendocrine tumors (PNETs) are a rare and understudied set of cancers, with increasing incidence. Neuroendocrine tumors are unique in the fact that they express high levels of the somatostatin receptor type 2 (SSTR2), which represents a target for both tumor imaging and therapeutics. PNET grade inversely correlates with SSTR2 tumor staining and higher tumor grade is associated with poor patient prognosis. With no known mutations, SSTR2 expression is believed to be lost through aberrant epigenetic mechanisms. Enhanced knowledge of the epigenetic biology and players controlling SSTR2 expression may allow for identification of novel PNET imaging and treatment modalities. Through in-depth studies, we found that the specific de novo DNA methyltransferase (DNMT), DNMT3B, is responsible for SSTR2 gene CpG methylation and silencing. Using DNMT3B as a starting point, along with the concept of functional crosstalk between various epigenetic mechanisms, we further discovered that Polycomb Repressor Complexes 1 and 2 (PRC1 and PRC2) play important roles in silencing SSTR2. Moreover, we found several histone lysine demethylases, enzymes that remove activating histone H3K4 methylation marks, to be critical for silencing expression of SSTR2. We additionally identified several chromatin remodeling enzymes/complexes as cellular factors that negatively regulate SSTR2 expression. Finally, using the HiBiT luminescent reporter system, we exploited functional chemo-genomic screens to further expand our knowledge of SSTR2 epigenetic control. These screens both reinforced several of our initial findings and helped to identify additional silencing mechanism potentially regulating SSTR2 expression. A commonality in our findings point to the presence, or necessity, of Class I HDACs in nearly all the epigenetic silencing mechanisms characterized. Overall, our work demonstrates that SSTR2 gene expression is likely silenced through various dynamic and interconnected epigenetic events, resulting in a compacted, transcriptionally repressed chromatin environment. Our study offers novel potential therapeutic targets and combinations to best increase expression of SSTR2, which are currently being tested in pre-clinical studies from our group, with the goal of future clinical trials aimed at increasing SSTR2 expression in high-grade, SSTR2-low NET patients.

Journal Article↗