Evidence map›Paper›PMID 38500691›Full record

ArticleDrug design, development and therapy2024

Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository.

Gehang Ju, Xin Liu, Wenyu Yang, Nuo Xu, Lulu Chen, Chenchen Zhang, Qingfeng He, Xiao Zhu, Dongsheng Ouyang

Open access · goldAbstract read
In one paragraph

Article in Drug design, development and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 17% 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

6 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Current approaches and advances in placental toxicology.Trends in endocrinology and metabolism: TEM · 2026
    Review
  3. Article
  4. Article
  5. Precision Medicine Strategies to Improve Isoniazid Therapy in Patients with Tuberculosis.European journal of drug metabolism and pharmacokinetics · 2024
    Review
  6. Article
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

9 authors at 4 institutions in 1 country.

Gehang JuDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Xin LiuDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Wenyu YangDepartment of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Nuo XuDepartment of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Lulu ChenHunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People's Republic of China.
Chenchen ZhangSchool of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People's Republic of China.
Qingfeng HeDepartment of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Xiao Zhu *Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Dongsheng Ouyang *Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Fudan University · CNSansure Biotech (China) · CNXiangya Hospital Central South University · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. Methods: A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Results: Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC Discussion: Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Indexed as

Arylamine N-AcetyltransferaseIsoniazidAdultAntitubercular AgentsChildComputer SimulationFemaleHumansInfantPhenotypePregnancyAntitubercular AgentsArylamine N-AcetyltransferaseIsoniazidNAT2 protein, humanIsoniazidmodel-informed precision dosingnonlinear mixed-effects modelpopulation pharmacokinetics

Identifiers

PMID38500691
PMCPMC10946406
OpenAlexW4392820405

What Socratic 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 Socratic graph.