Evidence map›Paper›PMID 37732197›Full record

ArticlemedRxiv : the preprint server for health sciences2023

A Nanopore sequencing-based pharmacogenomic panel to personalize tuberculosis drug dosing.

Renu Verma, Kesia Esther da Silva, Neesha Rockwood, Roeland E Wasmann, Nombuso Yende, Taeksun Song, Eugene Kim, Paolo Denti, Robert J Wilkinson, Jason R Andrews

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 5 countries.

Renu VermaDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA.
Kesia Esther da SilvaDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA.
Neesha RockwoodCenter for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine and Dept. Medicine, University of Cape Town, Observatory 7925, South Africa.
Roeland E WasmannDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Nombuso YendeDepartment of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory 7925, South Africa.
Taeksun SongDepartment of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory 7925, South Africa.
Eugene KimDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA.
Paolo DentiDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Robert J WilkinsonCenter for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine and Dept. Medicine, University of Cape Town, Observatory 7925, South Africa.
Jason R AndrewsDivision of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA.
University of Cape Town · ZAStanford University · USManipal Academy of Higher Education · IN

Funding

Point-of-care pharmacogenomic testing to optimize isoniazid dosing for tuberculosis preventionR21AI172182 · NIAID · STANFORD UNIVERSITY · PI ANDREWS, JASON RANDOLPH, CRODA, JULIO HENRIQUE ROSA · 2022 to 2023
$422k
NIAID NIH HHS R21 AI172182Wellcome Trust CC2112
6 · The paper itself

Abstract

Rationale: Standardized dosing of anti-tubercular (TB) drugs leads to variable plasma drug levels, which are associated with adverse drug reactions, delayed treatment response, and relapse. Mutations in genes affecting drug metabolism explain considerable interindividual pharmacokinetic variability; however, pharmacogenomic (PGx) assays that predict metabolism of anti-TB drugs have been lacking. Objectives: To develop a Nanopore sequencing panel and validate its performance in active TB patients to personalize treatment dosing. Measurements and Main Results: We developed a Nanopore sequencing panel targeting 15 single nucleotide polymorphisms (SNP) in 5 genes affecting the metabolism of isoniazid (INH), rifampin (RIF), linezolid and bedaquiline. For validation, we sequenced DNA samples (n=48) from the 1000 genomes project and compared variant calling accuracy with Illumina genome sequencing. We then sequenced DNA samples from patients with active TB (n=100) from South Africa on a MinION Mk1C and evaluated the relationship between genotypes and pharmacokinetic parameters for INH and RIF. Results: The PGx panel achieved 100% concordance with Illumina sequencing in variant identification for the samples from the 1000 Genomes Project. In the clinical cohort, coverage was >100x for 1498/1500 (99.8%) amplicons across the 100 samples. One third (33%) of participants were identified as slow, 47% were intermediate and 20% were rapid isoniazid acetylators. Isoniazid clearance was significantly impacted by acetylator status (p<0.0001) with median (IQR) clearances of 11.2 L/h (9.3-13.4), 27.2 L/h (22.0-31.7), and 45.1 L/h (34.1-51.1) in slow, intermediate, and rapid acetylators. Rifampin clearance was 17.3% (2.50-29.9) lower in individuals with homozygous Conclusion: Targeted sequencing can enable detection of polymorphisms influencing TB drug metabolism on a low-cost, portable instrument to personalize dosing for TB treatment or prevention.

Indexed as

isoniazidNanoporeNAT2pharmacogenomicstargeted sequencingtuberculosis

Identifiers

PMID37732197
PMCPMC10508808
OpenAlexW4386587330

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.