Tuberculosis:
Tuberculosis (TB) is the world’s most lethal infectious disease with around 1.6 million people dying from it each year. It is caused by isolates of Mycobacterium tuberculosis complex (MTBC) which most often develop within the lungs, but it can affect any part of the body.
In 2021, about 1/4 of the world’s population has latent TB and an estimated 10.6 million people developed the disease worldwide.
Treatment of active tuberculosis involves the uptake of antibiotics
combination for several months. This therapy will be effective only if the MTBC
strains infecting the patient haven’t developed a resistance to the drugs
constituting the patient’s regimen.
Tuberculosis drug-resistance:
Drug resistance mainly emerges when anti-TB drugs are used
inappropriately, such as prescription issues or inadequate length of treatment.
The World Health Organization (WHO) estimated that about 450,000 new cases of multidrug-resistant TB appeared in 2021 (MDR/RR-TB, multi-drug resistant or rifampicin-resistant tuberculosis), of which ~6% were extensive drug resistant TB (XDR-TB, additional resistance to at least one additional Group A drug).
To treat patients effectively and limit further emergence of TB drug resistance, rapid and precise resistance diagnosis is crucial.
Deeplex® Myc-TB
The all-in-one solution for characterizing Mycobacterium tuberculosis
Deeplex® Myc-TB, developed by GenoScreen is an innovative and integrated culture-free solution, which can provide a diagnosis on a strain’s resistance or susceptibility to 15 anti-TB drugs in less than 48 hours. The assay is based on targeted deep sequencing and automated data analysis via the Deeplex® Myc-TB secure web application.
Deeplex® Myc-TB detects the TB heteroresistance down to 3%, and also identifies Non-Tuberculous Mycobacteria (NTM) as well as TB lineages and spoligotypes.
This innovative test guides the therapeutic management of TB patients in an effective way, by giving a rapid and extensive prediction of genetic resistance to anti-TB drugs.
The Deeplex® Myc-TB Kit includes:
- A Deeplex® Master Mix: a single 24-plex PCR mix for culture-free amplification of 18 M. tuberculosis drug resistance-associated gene targets combined with targets for mycobacterial species identification and MTBC strain genotyping.
- An internal control for in-sample quality control,
- A positive control for test validation,
- An activation code to access the Deeplex® Myc-TB web application.
Download the technical note
15
Anti-TB drugs screened
48h
from clinical samples to results
3%
heteroresistance detection sensitivity
>100
mycobacterial species detectable
45
countries have implemented this solution
References
2021
Targeted next generation sequencing directly from sputum for comprehensive genetic information on drug resistant Mycobacterium tuberculosis,
Kambli P et al. – Tuberculosis
2021
Rapid genomic first- and second-line drug resistance prediction from clinical Mycobacterium tuberculosis specimens using Deeplex®-Myc,
Feuerriegel S et al. – Eur Respir J.
2020
Zoonotic tuberculosis in humans assessed by next-generation sequencing: an 18-month nationwide study in Lebanon,
El Achkar S et al. – Eur Respir J.
2020
A sister lineage of the Mycobacterium tuberculosis complex discovered in the African Great Lakes region,
Ngabonziza J.C.S et al. – Nat Commun.
2020
Deep amplicon sequencing for culture-free prediction of susceptibility or resistance to 13 anti-tuberculous drugs,
Jouet A et al. – Eur Respir J.
2019
How well do routine molecular diagnostics detect rifampin heteroresistance in Mycobacterium tuberculosis?,
Kamela C.S. Ng et al. – J Clin Microbiol.
2019
Drug-Resistant Tuberculosis, Lebanon, 2016 – 2017,
El Achkar S et al. – Emerg Infect Dis.
2018
Outbreak of multidrug-resistant tuberculosis in South Africa undetected by WHO-endorsed commercial tests: an observational study,
Makhado N.A. et al. – Lancet Infect Dis.
2017
Culture and Next-generation sequencing-based drug susceptibility testing unveil high levels of drug-resistant-TB in Djibouti: results from the first national survey,
Tagliani, E. et al. – Sci Rep.
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