Comprehensive Review on the Application of Bio-immunoinformatics in the Development of Highly Ef-fective New Candidate Vaccines against Tuberculosis

Tuberculosis (TB) remains a significant public health challenge worldwide. Currently, Bacillus Calmette-Guerin (BCG) is the only vaccine available for TB prophylaxis. However, the efficacy of the BCG vaccine against adult pulmonary TB is considered inconsistent. This condition encourages researchers to look for more effective options, such as subunit vaccines. This condition requires the development of a more effective subunit vaccine to protect active TB in productive and adult ages. There is an urgent need for more effective vaccines, as the Bacillus Calmette-Guérin (BCG) vaccine currently available has inconsistent efficacy and is only partially effective in adults. Bio-immunoinformatics, an interdisciplinary field integrating bioinformatics with immunology, offers promising strategies for vaccine development. By utilizing genomic and proteomic data from Mycobacterium tuberculosis (Mtb) genome, bio-immunoinformatics tools can precisely predict B-cell and T-cell epitopes, facilitating the design of vaccines that induce robust and long-lasting immune responses. Furthermore, integrating immunoinformatics with systems biology and machine learning enables the identification of immune escape mechanisms and variability in host responses, improving candidate selection. This review examines bio-immunoinformatics' application in identifying potential antigens, mapping epitopes, and designing highly effective TB vaccine candidates. This article highlights recent computational approaches and methodologies advancements, underscoring their pivotal role in accelerating TB vaccine research and development. Finally, we discuss the transformative potential of bio-immunoinformatics in revolutionizing TB vaccine design, ultimately contributing to more effective and widely applicable TB prevention strategies.