Enhancing Computational Biology-Based Drug Repurposing Approaches for Tuberculosis Treatment: A Case Study of Federal University, Lokoja, Kogi State
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Background of the Study
Tuberculosis (TB) remains one of the most prevalent and deadly infectious diseases worldwide, with Nigeria ranking among the top 30 high-burden countries. Traditional treatments for TB have faced numerous challenges, including the development of drug resistance and the lengthy duration of treatment. Drug repurposing, which involves identifying existing drugs that may be effective for new therapeutic indications, has emerged as a promising strategy to accelerate the discovery of new treatments. Computational biology offers powerful tools to predict potential drug repurposing candidates by analyzing large datasets of molecular interactions, protein structures, and genetic pathways. Federal University, Lokoja, Kogi State, can play a key role in enhancing computational biology-based drug repurposing approaches for TB treatment by applying advanced computational models to identify novel drug candidates for TB, particularly in the context of drug-resistant strains.
Statement of the Problem
Despite the availability of anti-TB drugs, the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis presents a significant public health challenge. The lengthy treatment times for TB, coupled with the side effects of existing drugs, make the search for new treatments imperative. While drug repurposing offers a potential solution, the process of identifying suitable candidates is time-consuming and resource-intensive. Computational biology can accelerate this process by using in silico models to screen vast libraries of existing drugs for potential efficacy against TB. However, there is a gap in the application of these computational approaches specifically for TB drug repurposing in Nigeria.
Objectives of the Study
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To apply computational biology techniques in identifying potential drug repurposing candidates for tuberculosis treatment.
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To develop a computational model for screening existing drugs for effectiveness against multidrug-resistant tuberculosis strains.
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To evaluate the potential of drug repurposing as a strategy for addressing the TB crisis in Nigeria.
Research Questions
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How can computational biology techniques assist in identifying existing drugs for repurposing against tuberculosis?
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What are the key molecular targets for drug repurposing in multidrug-resistant tuberculosis strains?
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How effective are computational models in predicting the efficacy of drug candidates for TB treatment?
Significance of the Study
This study will contribute to the identification of novel drug candidates for tuberculosis treatment through computational biology-based drug repurposing approaches. The findings will help address the growing problem of drug-resistant TB in Nigeria and other high-burden countries, potentially leading to faster, more effective treatments for TB.
Scope and Limitations of the Study
The study will focus on the use of computational biology techniques for drug repurposing in tuberculosis treatment at Federal University, Lokoja, Kogi State. Limitations include the availability of molecular data on TB strains and the complexity of validating computational predictions in laboratory settings.
Definitions of Terms
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Drug Repurposing: The process of finding new therapeutic uses for existing drugs.
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Computational Biology: The application of computational techniques to the analysis and interpretation of biological data, including drug discovery.
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Multidrug-Resistant Tuberculosis (MDR-TB): A form of tuberculosis that is resistant to at least two of the most effective anti-TB drugs.
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