Identification and drug susceptibility testing of the subspecies of Mycobacterium avium complex clinical isolates in mainland China

Evaluating amikacin minimum inhibitory concentration in trailing growth for Mycobacterium avium complex

BACKGROUND

Amikacin is a first-line drug that must be evaluated when performing an antimycobacterial susceptibility test (AST) for Mycobacterium avium complex (MAC). However, the presence of sporadic trailing growth in MAC makes determining the precise point for reading its minimal inhibitory concentration (MIC) challenging.

METHODS

Susceptibility was re-tested for 134 MAC clinical isolates using the Sensititre SLOMYCOI panel, the rrs gene was sequenced, and amikacin exposure history was investigated. The MIC50, MIC90, and the epidemiological cut-off value (ECOFF) were calculated using the EUCAST method.

RESULTS

After re-testing and ignoring trailing growth, of the 22 M. intracellulare isolates originally classified as resistant to amikacin according to the CLSI guideline, 10 strains were reclassified as intermediate and four as susceptible. Similarly, from the seven resistant M. avium strains, one was reclassified as intermediate and four as susceptible. No rrs gene mutations were detected in any isolates, including resistant strains. When ignoring trailing growth, the calculated MIC50, MIC90, and ECOFF values closely aligned with the EUCAST MIC distribution.

CONCLUSION

To maintain the current CLSI breakpoint, trailing growth should be ignored when reading the amikacin MIC of MAC. To read the MIC at complete bacterial inhibition, the CLSI breakpoint needs to be raised.

In vitro susceptibility patterns for slowly growing non-tuberculous mycobacteria in the USA from 2018 to 2022

BACKGROUND

Treatment of slowly growing non-tuberculous mycobacteria (SGM) is challenging. In vitro antimicrobial susceptibility testing (AST) is needed to optimize a multidrug regimen but requires weeks to result. Aggregated AST patterns, or an antibiogram, of SGM would be helpful to providers.

OBJECTIVES

We aggregated and analysed human SGM isolates sent to our laboratory from across the USA between 2018 and 2022 to describe their in vitro susceptibility patterns and construct an antibiogram.

METHODS

SGM isolates' species/subspecies and mutations in rrs or rrl were identified by a line probe assay. AST was done primarily by broth microdilution and interpreted using the latest CLSI guideline. Mutational and AST results for SGM with ≥15 isolates were collated and analysed with descriptive statistics.

RESULTS

There were 32 different species/subspecies of SGM from 10 131 isolates between January 2018 and December 2022 from across the USA, 80% of which were from organisms in Mycobacterium avium complex (MAC). Most specimens were sputum and came from Florida (2892). MAC ranged from 94% to 100% susceptible to clarithromycin, 64% to 91% to amikacin, 2% to 31% to linezolid, and 4% to 41% to moxifloxacin. Non-MAC SGM ranged from 82% to 100% susceptible to clarithromycin, 49% to 100% to amikacin, and 76% to 100% to rifabutin, but susceptibilities to other antimicrobials varied widely. WT rrs and rrl predicted >96% of phenotypic non-resistance to amikacin and clarithromycin, respectively, whereas mutant genotypes predicted >90% of phenotypic resistance.

CONCLUSIONS

Most SGM are likely to be susceptible to clarithromycin and amikacin, complementing their treatment guidance by mycobacterial experts. Molecular identification of resistant genotypes is accurate and helpful. This antibiogram for SGM will help providers.

Treatment of non-tuberculosis mycobacteria skin infections

Non-tuberculosis mycobacteria (NTM) skin infections have become increasingly prevalent in recent years, presenting a unique challenge in clinical management. This review explored the complexities of NTM infections localized to the superficial tissues and provided valuable insights into the optimal therapeutic strategies. The antibiotic selection should base on NTM species and their susceptibility profiles. It is recommended to adopt a comprehensive approach that considers the unique characteristics of superficial tissues to improve treatment effectiveness and reduce the incidence of adverse reactions, infection recurrence, and treatment failure. Infection control measures, patient education, and close monitoring should complement the treatment strategies to achieve favorable outcomes in managing NTM skin infections. Further efforts are warranted to elucidate factors and mechanisms contributing to treatment resistance and relapse. Future research should focus on exploring novel treatment options, innovative drug development/delivery platforms, and precise methodologies for determining therapeutic duration. Longitudinal studies are also needed to assess the long-term safety profiles of the integrated approaches.

Variability of Mycobacterium avium Complex Isolates Drug Susceptibility Testing by Broth Microdilution

Non-tuberculous mycobacteria are widely distributed in environments and are capable of infecting humans, particularly those with a compromised immune system. The most prevalent species that cause nontuberculous mycobacterial lung diseases are slow-growing bacteria from the Mycobacterium avium complex (MAC), mainly M. avium or M. intracellulare. The key treatment of MAC infections includes macrolides, ethambutol, and rifampicin; however, the therapy outcomes are unsatisfactory. Phenotypic drug susceptibility testing is a conditional recommendation prior to treatment, and critical concentrations for clarithromycin, amikacin, moxifloxacin, and linezolid have been established. In this review, data from studies on the determination of MIC of clinical isolates using the broth microdilution method were summarized. A significant variation in the MIC distributions from different studies was found. The main reasons could impact the findings: insufficient reproducibility of the phenotypic testing and variation in species lineages identified in different laboratories, which could have various intrinsic susceptibility to drugs. For most of the drugs analyzed, the MICs are too high, which could undermine the treatment efficiency. Further improvement of treatment outcomes demands the validation of microbiological resistance criteria together with the identification of molecular mechanisms of resistance.