ReviewThe Lancet. Microbe2024
Developing biomarker assays to accelerate tuberculosis drug development: defining target product profiles.
Review in The Lancet. Microbe, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Blood transcriptomic signatures predict poor outcomes in drug-susceptible pulmonary tuberculosis in Brazil.American journal of respiratory and critical care medicine · 2026Article
- Article
- Simultaneous measurement of 16S-rRNA and pre-16S-rRNA as a strategy to monitor clinical tuberculosis.Frontiers in antibiotics · 2026Article
- Clinical evaluation of the diagnostic performances and treatment monitoring of the new PATHFAST TB LAM Ag assay in sputum specimens of patients with tuberculosis or with nontuberculous mycobacteria pulmonary disease.Journal of clinical microbiology · 2025Article
- Blood transcriptomic signatures predict poor treatment outcomes in drug-susceptible pulmonary TB in Brazil.medRxiv : the preprint server for health sciences · 2025Article
- Stool-Based Molecular Tuberculosis Treatment Monitoring: A Faster Means for Detecting Persistent Mycobacteria Compared to Phenotypic Culture.Open forum infectious diseases · 2025Article
- Strategies for shortening tuberculosis therapy.Nature medicine · 2025Review
- PROLONG: penalized regression for outcome guided longitudinal omics analysis with network and group constraints.Bioinformatics (Oxford, England) · 2025Article
- Inclusion of patient-centered, non-microbiological endpoints and biomarkers in tuberculosis drug trials.Frontiers in antibiotics · 2025Review
- Quantification of bactericidal activity using the PATHFAST TB LAM Ag assay during the first 14 days of pulmonary tuberculosis treatment.Frontiers in antibiotics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Drug development for tuberculosis is hindered by the methodological limitations in the definitions of patient outcomes, particularly the slow organism growth and difficulty in obtaining suitable and representative samples throughout the treatment. We developed target product profiles for biomarker assays suitable for early-phase and late-phase clinical drug trials by consulting subject-matter experts on the desirable performance and operational characteristics of such assays for monitoring of tuberculosis treatment in drug trials. Minimal and optimal criteria were defined for scope, intended use, pricing, performance, and operational characteristics of the biomarkers. Early-stage trial assays should accurately quantify the number of viable bacilli, whereas late-stage trial assays should match the number, predict relapse-free cure, and replace culture conversion endpoints. The operational criteria reflect the infrastructure and resources available for drug trials. The effective tools should define the sterilising activity of the drug and lower the probability of treatment failure or relapse in people with tuberculosis. The target product profiles outlined in this Review should guide and de-risk the development of biomarker-based assays suitable for phase 2 and 3 clinical drug trials.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.