Future Prospects for Using Clinical Phenotypes in Tuberculosis Precision Medicine—An Approach for Clinical Management
Clinical Infectious Diseases·
- DOI
- 10.1093/cid/ciaf663
- PMID
- 41537599
- PMCID
- PMC13189667
- OpenAlex
- W7124421029
- Study type
- Journal article
- Publisher
- Oxford University Press (OUP)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Shorter treatment regimens that prevent drug resistance development and achieve relapse-free cure would reduce treatment burden and improve tuberculosis control, particularly in high-endemic settings where many proposed monitoring tools are suitable.
Structured evidence summary
Research question
The article examines how clinical phenotype characterization can enable precision medicine approaches to develop shorter tuberculosis treatment regimens.
Study design
This is a narrative review or perspective article discussing methodological approaches rather than reporting primary research data.
Population and setting
The discussion focuses on tuberculosis patients in general, with explicit mention that proposed tools are suitable for high-endemic settings.
Main findings
Clinical phenotype includes patient characteristics, radiological disease extent, mycobacterial burden, drug susceptibility, and host response. Advances in omics, precision dosing, and artificial intelligence algorithms require adaptation and validation in clinical trials. Integrated monitoring using bacterial load quantification, transcriptomic and epigenetic biosignatures, radiological scoring, and symptom assessment can enable prompt drug adjustments to reduce bacterial load, prevent resistance, and achieve relapse-free cure with shorter treatment.
Public-health relevance
Shorter treatment regimens that prevent drug resistance development and achieve relapse-free cure would reduce treatment burden and improve tuberculosis control, particularly in high-endemic settings where many proposed monitoring tools are suitable.
Important limitations
This summary relies on the supplied single-article abstract and metadata. The abstract does not report primary data, specify which tools have been validated, quantify expected treatment duration reductions, or discuss implementation barriers. The original paper is required for decision-grade interpretation.
GIDS interpretation
This article was identified through classifier links for Tuberculosis, Antimicrobial Resistance, and Treatment, indicating its relevance to precision medicine approaches in TB management. The content addresses methodological frameworks rather than outbreak signals or emerging resistance patterns.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 7 auditable classifier relationships to diseases, places, topics, and study design.