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Implementation of targeted next-generation sequencing for the diagnosis of drug-resistant tuberculosis in low-resource settings: a programmatic model, challenges, and initial outcomes.

ABSTRACT: Targeted next-generation sequencing (tNGS) from clinical specimens has the potential to become a comprehensive tool for routine drug-resistance (DR) prediction of Mycobacterium tuberculosis complex strains (MTBC), the causative agent of tuberculosis (TB). However, TB mainly affects low- and middle-income countries, in which the implementation of new technologies have specific needs and challenges. We propose a model for programmatic implementation of tNGS in settings with no or low previous sequencing capacity/experience. We highlight the major challenges and considerations for a successful implementation. This model has been applied to build NGS capacity in Namibia, an upper middle-income country located in Southern Africa and suffering from a high-burden of TB and TB-HIV, and we describe herein the outcomes of this process.

SUBMITTER: de Araujo L 

PROVIDER: S-EPMC10478709 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Implementation of targeted next-generation sequencing for the diagnosis of drug-resistant tuberculosis in low-resource settings: a programmatic model, challenges, and initial outcomes.

de Araujo Leonardo L   Cabibbe Andrea Maurizio AM   Mhuulu Lusia L   Ruswa Nunurai N   Dreyer Viola V   Diergaardt Azaria A   Günther Gunar G   Claassens Mareli M   Gerlach Christiane C   Utpatel Christian C   Cirillo Daniela Maria DM   Nepolo Emmanuel E   Niemann Stefan S  

Frontiers in public health 20230803

Targeted next-generation sequencing (tNGS) from clinical specimens has the potential to become a comprehensive tool for routine drug-resistance (DR) prediction of <i>Mycobacterium tuberculosis</i> complex strains (MTBC), the causative agent of tuberculosis (TB). However, TB mainly affects low- and middle-income countries, in which the implementation of new technologies have specific needs and challenges. We propose a model for programmatic implementation of tNGS in settings with no or low previo  ...[more]

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