Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Abstract

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

Keywords: PPPM; artificial intelligence; treatment failure; tuberculosis; whole genome sequencing.

Figure 1

Potential applications of AI in preventing TB and mitigating the risk of treatment failure.

Figure 2

Key applications of M. tuberculosis WGS in PPPM include characterization of complete resistance profile to reach the highest treatment efficacy, determination of transmission chains and outbreaks to prevent the spread of TB, and distinguishing the cause of TB recurrence to guide TB control and treatment.