Exploring Diagnostic Methods for Drug-Resistant Tuberculosis

In high-burden settings, tuberculosis (TB) diagnosis often relies on sputum smear microscopy, a method with limited sensitivity, particularly among HIV-infected patients. Traditional culture-based methods, though accurate, are slow and expensive, making them impractical for resource-limited areas. The introduction of Xpert MTB/RIF revolutionized TB diagnosis by providing rapid, automated detection of TB and rifampicin resistance using real-time PCR. This cartridge-based system is user-friendly, allowing relatively unskilled healthcare workers to obtain results in under two hours. Recognizing its potential, the World Health Organization (WHO) recommended Xpert as the initial diagnostic tool for suspected multidrug-resistant TB (MDR-TB) and HIV-associated TB cases in 2010. Its implementation has been particularly impactful in southern Africa, improving the detection of smear-negative TB cases and enabling early treatment initiation.[3]

Despite its advantages, Xpert alone is insufficient to drastically reduce TB incidence in the long term. The persistence of a large pool of latently infected individuals and the lower transmission likelihood of smear-negative cases mean that TB remains a significant public health challenge even after decades of Xpert use. Furthermore, its widespread adoption places additional demands on healthcare systems, increasing the need for first-line TB treatment, HIV management, and second-line therapies for drug-resistant cases. While Xpert has enhanced TB control efforts, reducing the global burden of TB requires a comprehensive approach, including preventive strategies, improved treatment regimens, and strengthened healthcare infrastructure.[3]

MTB drug resistance is categorized into intrinsic (cell wall permeability, efflux pumps, metabolism) and acquired mechanisms (gene mutations like inhA and rrs). Capreomycin (CPM) resistance stems from tlyA and rrs mutations, altering ribosomal methylation and drug targets.[1]

The Roche™ solid ratio method, though cost-effective, requires long culture times, while the nitrate reductase test offers faster MTB sensitivity assessment. Line probe assay (LPA) detects rifampicin resistance with high accuracy and is WHO-approved for MDR-TB diagnosis. Digital PCR enhances heterologous resistance detection, and next-generation sequencing (NGS) enables high-throughput pathogen analysis but may overlook resistance data.[1]

Emerging technologies continue to refine Mycobacterium tuberculosis (MTB) drug resistance testing, balancing speed, accuracy, and accessibility. Laboratories are well-equipped to test resistance to established drugs like fluoroquinolones (FQs) and second-line injectables (SLIDs) but face challenges with novel drugs like bedaquiline (BDQ). Phenotypic drug susceptibility tests (pDSTs) remain the gold standard, classifying bacteria as resistant based on growth in drug-containing media. While accurate, pDSTs are slow, requiring up to six weeks for results. Automated systems like BACTEC MGIT 960 and Sensititre streamline pDST by detecting mycobacterial growth through fluorescence or microdilution, respectively. Colorimetric assays such as the Alamar Blue Assay and Thin-Layer Agar (TLA) method provide low-cost alternatives, particularly for low-resource settings.[2]

Molecular drug susceptibility tests (DSTs) offer faster results by detecting resistance-associated gene mutations, bypassing the need for culture. PCR-based assays like Xpert MTB/RIF and Xpert MTB/XDR, endorsed by WHO, enable rapid detection of MTB and rifampicin resistance within hours. Line probe assays (LPAs) such as GenoType MTBDRplus and MTBDRsl extend detection to isoniazid and second-line drugs, aiding multidrug-resistant tuberculosis (MDR-TB) diagnosis. Whole-genome sequencing (WGS) provides the most comprehensive resistance profiling, analyzing known and emerging mutations, though its complexity and cost limit widespread use. Platforms like Illumina and MinION offer sequencing flexibility, with MinION being particularly cost-effective and portable.[2]

Advanced methodologies like MALDI-TOF MS and QuantaMatrix Multiplexed Assay Platform (QMAP) refine resistance detection by analyzing bacterial protein profiles or magnetic microparticles. These approaches, alongside bioinformatics tools like TBProfiler and Resistance Sniffer, enhance accuracy while reducing turnaround times. Despite technological advancements, traditional phenotypic testing remains irreplaceable for confirming drug efficacy. Integrating molecular, sequencing, and phenotypic methods ensures a robust framework for MTB resistance surveillance, enabling tailored treatment strategies and reducing transmission risks.[2]

Nanopore-targeted sequencing improves pneumonia pathogen detection. Gene-core technology identifies rpoB, katG, and inhA mutations. Rifampicin (RIF) inhibits MTB transcription, but rpoB mutations drive resistance, with metabolic changes offering new drug targets. Spectral analysis aids MTB detection. MALDI-TOF MS, first used in 2004, provides rapid, accurate, high-throughput MTB identification and resistance screening.[1]

Phenotypic drug sensitivity testing is accurate but slow, while molecular methods are fast but struggle with heterogeneous resistance and silent mutations. Emerging technologies show promise but face challenges like false positives and incomplete reverse transcription.[1]

References:

1. Xiong, X.S., Zhang, X.D., Yan, J.W., Huang, T.T., Liu, Z.Z., Li, Z.K., Wang, L. and Li, F., 2024. Identification of Mycobacterium tuberculosis resistance to common antibiotics: an overview of current methods and techniques. Infection and Drug Resistance, pp.1491-1506.

2. Sanchini, A., Lanni, A., Giannoni, F. and Mustazzolu, A., 2024. Exploring Diagnostic Methods for Drug-Resistant Tuberculosis: A Comprehensive Overview. Tuberculosis, p.102522.

3. Menzies, N.A., Cohen, T., Lin, H.H., Murray, M. and Salomon, J.A., 2012. Population health impact and cost-effectiveness of tuberculosis diagnosis with Xpert MTB/RIF: a dynamic simulation and economic evaluation. PLoS medicine, 9(11), p.e1001347.