Evidence map›Paper›PMID 33674941›Full record

ReviewClinical pharmacokinetics2021

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs.

Marieke G G Sturkenboom, Anne-Grete Märtson, Elin M Svensson, Derek J Sloan, Kelly E Dooley, Simone H J van den Elsen, Paolo Denti, Charles A Peloquin, Rob E Aarnoutse, Jan-Willem C Alffenaar

Open access · hybridAbstract readReview
In one paragraph

Review in Clinical pharmacokinetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 1% of its field
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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.

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  20. CPhaMAS: The first pharmacokinetic analysis cloud platform developed by China.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 8 institutions in 6 countries.

Marieke G G SturkenboomDepartment of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Anne-Grete MärtsonDepartment of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Elin M SvenssonDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.
Derek J SloanInstitute of Infection and Global Health, University of Liverpool, Liverpool, UK.
Kelly E DooleyDepartment of Medicine, Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Simone H J van den ElsenDepartment of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Paolo DentiDivision of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Charles A PeloquinDepartment of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Rob E AarnoutseDepartment of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands.
Jan-Willem C AlffenaarDepartment of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. johannes.alffenaar@sydney.edu.au.ORCID 0000-0001-6703-0288
University Medical Center Groningen · NLJohns Hopkins University · USRadboud University Nijmegen · NLThe University of Sydney · AUUniversity of Cape Town · ZAUniversity of Florida · USUniversity of Liverpool · GBUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) is still the number one cause of death due to an infectious disease. Pharmacokinetics and pharmacodynamics of anti-TB drugs are key in the optimization of TB treatment and help to prevent slow response to treatment, acquired drug resistance, and adverse drug effects. The aim of this review was to provide an update on the pharmacokinetics and pharmacodynamics of anti-TB drugs and to show how population pharmacokinetics and Bayesian dose adjustment can be used to optimize treatment. We cover aspects on preclinical, clinical, and population pharmacokinetics of different drugs used for drug-susceptible TB and multidrug-resistant TB. Moreover, we include available data to support therapeutic drug monitoring of these drugs and known pharmacokinetic and pharmacodynamic targets that can be used for optimization of therapy. We have identified a wide range of population pharmacokinetic models for first- and second-line drugs used for TB, which included models built on NONMEM, Pmetrics, ADAPT, MWPharm, Monolix, Phoenix, and NPEM2 software. The first population models were built for isoniazid and rifampicin; however, in recent years, more data have emerged for both new anti-TB drugs, but also for defining targets of older anti-TB drugs. Since the introduction of therapeutic drug monitoring for TB over 3 decades ago, further development of therapeutic drug monitoring in TB next steps will again depend on academic and clinical initiatives. We recommend close collaboration between researchers and the World Health Organization to provide important guideline updates regarding therapeutic drug monitoring and pharmacokinetics/pharmacodynamics.

Indexed as

Pharmaceutical PreparationsTuberculosisAntitubercular AgentsBayes TheoremHumansIsoniazidAntitubercular AgentsIsoniazidPharmaceutical Preparations

Identifiers

PMID33674941
PMCPMC7935699
OpenAlexW3135702228

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.