By Dr Pavan Asalapuram
New Delhi: As technology advanced in healthcare, the development of drugs treating Tuberculosis (TB), one of the top 10 causes of increased morbidity and mortality among adults worldwide, came rapidly. The genesis of TB vaccines and drugs brought along a beacon of hope that Tuberculosis will end soon.
However, the chances of ending TB became slimmer and slimmer as more patients started becoming resistant to the drugs earlier responsible for treating them. And this led to a rise of greater concern as the aim to confine the disease remained under-served. It immediately sparked discussions, ‘Are we equipped to tackle TB drug resistance?’. The question remained there for many years till the diagnostic industry witnessed a technological revolution with advanced technology such as genotyping and molecular diagnostic facilities started providing key results in tackling TB drug resistance.
What is TB drug resistance?
Tuberculosis (TB) in its definition refers to a bacterial infection caused by Mycobacterium tuberculosis that primarily affects the lungs. Drug-resistant TB occurs when the bacteria develop mutations that render the drugs ineffective, making treatment more difficult and costly. It is often referred to as MDR-TB (Multidrug-resistant TB). According to WHO, Multidrug-resistant TB can be defined as a condition in which patients suffering from TB do not respond to the two most powerful anti-TB drugs isoniazid and rifampicin. Therefore, making it difficult to treat TB patients.
What causes TB drug resistance?
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 concern, requiring coordinated efforts across borders to effectively control the spread of the disease.
Technologies curbing TB drug resistance
Several cutting-edge technologies are currently utilized for the identification of drug-resistant tuberculosis (TB). While the traditional methods for identifying TB resistance involve testing samples using culture-based techniques, which can take several weeks to yield results, the evolution of rapid diagnostic tools, such as mfloDxTM MDR-TB enabled the detection of drug-resistant TB within hours. These advanced techniques rely on molecular-based methods that target specific genes associated with drug resistance, thereby providing accurate and reliable results in a short period.
Moreover, the integration of artificial intelligence (AI) and machine learning algorithms has further improved the accuracy of TB drug resistance identification. These techniques enable the analysis of large data sets and the identification of patterns that would be difficult for humans to detect. Additionally, AI-based algorithms can predict potential drug-resistant TB strains by analyzing genetic sequences and predicting mutations that may lead to resistance. Therefore, the use of technological advancements in the diagnosis of drug-resistant TB has greatly enhanced the speed, accuracy, and effectiveness of TB management and control, leading to better health outcomes for patients.
Can we end TB by 2025?
With an aim to end TB from the root level, the government of India is consistently working on National TB Elimination Program along with other initiatives. The emergence of TB drug resistance is a multifaceted issue which requires a comprehensive approach to accomplish the desired goals. Besides improvement in healthcare systems and infrastructure, the mission to eliminate TB by 2025 requires increased investment in TB research and development. Undoubtedly, the rise of technological advancements in the field of medicine such as mfloDxTM MDR-TB has significantly contributed to the identification of drug-resistant tuberculosis (TB) strains. The road to ending TB requires effective TB management for better health outcomes for patients.
By Dr Pavan Asalapuram, Co-Founder, Empe Diagnostics
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