High clustering rate and genotypic drug-susceptibility screening for the newly recommended anti-tuberculosis drugs among global extensively drug-resistant Mycobacterium tuberculosis isolates

Molecular mechanisms of resistance and treatment efficacy of clofazimine and bedaquiline against Mycobacterium tuberculosis

Clofazimine (CFZ) and bedaquiline (BDQ) are currently used for the treatment of multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb) strains. In recent years, adding CFZ and BDQ to tuberculosis (TB) drug regimens against MDR Mtb strains has significantly improved treatment results, but these improvements are threatened by the emergence of MDR and extensively drug-resistant (XDR) Mtb strains. Recently, CFZ and BDQ have attracted much attention for their strong clinical efficacy, although very little is known about the mechanisms of action, drug susceptibility test (DST), resistance mechanisms, cross-resistance, and pharmacokinetics of these two drugs. In this current review, we provide recent updates on the mechanisms of action, DST, associated mutations with individual resistance and cross-resistance, clinical efficacy, and pharmacokinetics of CFZ and BDQ against Mtb strains. Presently, known mechanisms of resistance for CFZ and/or BDQ include mutations within the Rv0678, pepQ, Rv1979c, and atpE genes. The cross-resistance between CFZ and BDQ may reduce available MDR-/XDR-TB treatment options. The use of CFZ and BDQ for treatment in the setting of limited DST could allow further spread of drug resistance. The DST and resistance knowledge are urgently needed where CFZ and BDQ resistance do emerge. Therefore, an in-depth understanding of clinical efficacy, DST, cross-resistance, and pharmacokinetics for CFZ and BDQ against Mtb can provide new ideas for improving treatment outcomes, reducing mortality, preventing drug resistance, and TB transmission. Along with this, it will also help to develop rapid molecular diagnostic tools as well as novel therapeutic drugs for TB.

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis

The detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.

Clinical implications of molecular drug resistance testing for Mycobacterium tuberculosis: a 2023 TBnet/RESIST-TB consensus statement

Drug-resistant tuberculosis is a substantial health-care concern worldwide. Despite culture-based methods being considered the gold standard for drug susceptibility testing, molecular methods provide rapid information about the Mycobacterium tuberculosis mutations associated with resistance to anti-tuberculosis drugs. This consensus document was developed on the basis of a comprehensive literature search, by the TBnet and RESIST-TB networks, about reporting standards for the clinical use of molecular drug susceptibility testing. Review and the search for evidence included hand-searching journals and searching electronic databases. The panel identified studies that linked mutations in genomic regions of M tuberculosis with treatment outcome data. Implementation of molecular testing for the prediction of drug resistance in M tuberculosis is key. Detection of mutations in clinical isolates has implications for the clinical management of patients with multidrug-resistant or rifampicin-resistant tuberculosis, especially in situations when phenotypic drug susceptibility testing is not available. A multidisciplinary team including clinicians, microbiologists, and laboratory scientists reached a consensus on key questions relevant to molecular prediction of drug susceptibility or resistance to M tuberculosis, and their implications for clinical practice. This consensus document should help clinicians in the management of patients with tuberculosis, providing guidance for the design of treatment regimens and optimising outcomes.

Global trends in the incidence rates of MDR and XDR tuberculosis: Findings from the global burden of disease study 2019

Purpose: The study aimed to quantify the global trends of the incidence rates of multidrug-resistant (MDR) tuberculosis (MDR-TB) and extensively drug-resistant (XDR) tuberculosis (XDR-TB). Methods: Cases, age-standardized rates (ASRs), and incidence rates of MDR-TB and XDR-TB during 2010-2019 were obtained from the Global Burden of Disease Study 2019. The incidence trends of MDR-TB and XDR-TB were evaluated using the estimated annual percentage changes (EAPCs) in ASRs. The relationships among the ASRs of MDR-TB and XDR-TB, the MDR rate, the XDR rate, and socio-demographic index (SDI) were assessed using locally weighted regression and Pearson's correlation coefficient. Results: The global ASR of MDR-TB on average decreased by 1.36% (EAPC = -1.36, 95% confidence interval [CI] = -2.19 to -0.52) per year whereas that of XDR-TB was stable (EAPC = 0.69, 95% CI = -0.15-1.54) during 2010-2019. The incidence trends of MDR-TB in most regions and countries were decreasing, but those of XDR-TB were increasing. People aged 35-44 and 55-64 years had the highest incidence rates for MDR-TB and XDR-TB. The MDR and XDR rates both peaked in those aged 35-44 years. Areas with higher SDI tended to have lower ASRs of MDR-TB (p < 0.001, ρ = -0.43). Conclusion: The current achievements for the incidence trends of MDR-TB and XDR-TB are insufficient. More strategies and tools need to be developed to further curb MDR-TB and XDR-TB, especially in high-risk areas and age groups, and in low SDI regions.