ArticleThe pharmacogenomics journal2021
Development of a population pharmacokinetic model and Bayesian estimators for isoniazid in Tunisian tuberculosis patients.
Article in The pharmacogenomics journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Efficacy, safety, and pharmacokinetics of isoniazid affected by NAT2 polymorphisms in patients with tuberculosis: A systematic review.Clinical and translational science · 2024Pooled it
- Influence of N-acetyltransferase 2 (NAT2) genotype/single nucleotide polymorphisms on clearance of isoniazid in tuberculosis patients: a systematic review of population pharmacokinetic models.European journal of clinical pharmacology · 2022Pooled it
- Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management.Pharmaceutics · 2022Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a population pharmacokinetic model using full pharmacokinetic (PK) profiles of isoniazid (INH) taking into account demographic and genetic covariates and to develop Bayesian estimators for predicting INH area under the curve (AUC) in Tunisian tuberculosis patients. The INH concentrations in the building data set were fitted using a one- to three-compartment model. The impact of the different covariates was assessed on the PK parameters of the best model. The best limited sampling strategy (LSS) for estimating the INH AUC was selected by comparing the predicted values to an independent data set. INH PK was best described using a three-compartment model with lag-time absorption. The different studied covariates did not have any impact on the PK parameters of the building model. The Bayesian estimation using one-point concentrations gave the lowest values of prediction errors for the C
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Registered trials
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