By Dr Pavan Asalapuram
New Delhi: As technology advanced in health care, the development of drugs for the treatment of tuberculosis (TB), one of the top 10 causes of increased morbidity and mortality among adults worldwide. world, it came quickly. The genesis of TB vaccines and drugs brought with it a beacon of hope that TB will soon end.
However, the chances of ending TB became increasingly slim as more patients began to become resistant to the drugs that previously treated them. And this caused increased concern, as the goal of containing the disease continued to be neglected. It immediately sparked discussions, ‘Are we equipped to address resistance to tuberculosis drugs?’. The question remained there for many years until the diagnostic industry witnessed a technological revolution with advanced technology such as genotyping and molecular diagnosis facilities began to provide key results to address resistance to tuberculosis drugs.
What is TB drug resistance?
Tuberculosis (TB) in its definition refers to a bacterial infection caused by Mycobacterium tuberculosis that mainly affects the lungs. Drug-resistant TB occurs when bacteria develop mutations that make drugs ineffective, making treatment more difficult and expensive. It is often known as MDR-TB (multi-resistant TB). According to the WHO, multidrug-resistant TB can be defined as a condition in which TB patients do not respond to the two most potent anti-TB drugs, isoniazid and rifampicin. Thus, making it difficult to treat tuberculosis patients.
What causes resistance to TB drugs?
Factors that contribute to the development and spread of drug-resistant TB include inadequate treatment, suboptimal drug regimens, poor patient adherence, and lack of access to effective drugs. Furthermore, the global nature of TB and the ease of travel have made drug-resistant TB a global public health problem, requiring coordinated efforts across borders to effectively control the spread of the disease.
Technologies that curb resistance to TB drugs
Several cutting-edge technologies are currently used for the identification of drug-resistant tuberculosis (TB). While traditional methods of identifying TB resistance involve testing samples using culture-based techniques, which can take several weeks to return results, the evolution of rapid diagnostic tools such as mfloDxTM MDR-TB enabled the detection of drug-resistant tuberculosis in a matter of hours. These advanced techniques are based on molecular methods that target specific genes associated with drug resistance, providing accurate and reliable results in a short period of time.
Furthermore, the integration of artificial intelligence (AI) and machine learning Algorithms have further improved the accuracy of identifying TB drug resistance. These techniques allow the analysis of large data sets and the identification of patterns that would be difficult for humans to detect. Furthermore, AI-based algorithms can predict potential drug-resistant TB strains by analyzing gene sequences and predicting mutations that can lead to resistance. Therefore, the use of technological advances in the diagnosis of drug-resistant TB has greatly improved the speed, accuracy, and efficacy of tuberculosis management and control, leading to better health outcomes for patients.
Can we end tuberculosis by 2025?
With the aim of ending TB at the root, the Indian government is constantly working on the National TB Elimination Program along with other initiatives. The emergence of TB drug resistance is a multifaceted problem that requires a comprehensive approach to achieve the desired goals. In addition to improving healthcare systems and infrastructure, the mission to eliminate TB by 2025 requires increased investment in TB research and development. Undoubtedly, the rise of technological advances in the medical field, such as mfloDxTM MDR-TB, has contributed significantly to the identification of drug-resistant tuberculosis (TB) strains. The path to ending TB requires tuberculosis management for better health outcomes for patients.
By Dr. Pavan Asalapuram, Co-Founder, Empe Diagnostics
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