JOURNAL ARTICLE
In vitro susceptibility testing of tetracycline‐class antibiotics against slowly growing non‐tuberculous mycobacteria.
Published In: Clinical & Experimental Pharmacology & Physiology, 2023, v. 50, n. 7. P. 604 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Li, Anqi; Tan, Zhili; He, Siyuan; Chu, Haiqing 3 of 3
Abstract
Non‐tuberculous mycobacterial infections are gradually increasing worldwide, with slow‐growing mycobacteria such as Mycobacterium avium, Mycobacterium intracellulare and Mycobacterium kansasii accounting for the majority of cases. The use of tetracyclines has received renewed attention in recent years, and this study was designed to investigate the antibacterial activity of omadacycline, eravacycline, tigecycline, sarecycline, minocycline and doxycycline against M. avium, M. intracellulare and M. kansasii. Susceptibility testing of six tetracyclines was conducted against M. avium, M. intracellulare and M. kansasii isolates, and all the clinical isolates were collected from January 2012 to December 2018. All six agents exhibited poor antibacterial activity against slowly growing mycobacteria (SGM) isolates of three subspecies with MIC50 and MIC90 ≥8 μg/mL. M. intracellulare and M. kansasii had lower resistance rates to omadacycline than the other five drugs. The severe resistance of SGM to tetracycline suggests that developing tetracycline‐class antibiotics needs to overcome existing resistance mechanisms. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Clinical & Experimental Pharmacology & Physiology. 2023/07, Vol. 50, Issue 7, p604
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0305-1870
- DOI:10.1111/1440-1681.13777
- Accession Number:164093373
- Copyright Statement:Copyright of Clinical & Experimental Pharmacology & Physiology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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