ArticleEuropean journal of clinical pharmacology2024
Estimation of linezolid exposure in patients with hepatic impairment using machine learning based on a population pharmacokinetic model.
Article in European journal of clinical pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
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Who cites it
3 citing papers in PubMed.
- Prediction of Zonisamide Concentration in Pediatric Patients With Epilepsy: A Machine Learning Approach.CNS neuroscience & therapeutics · 2026Article
- Model-Based Alternative Dosing Strategies for Subcutaneous Nivolumab to Improve Cost Effectiveness.Clinical pharmacokinetics · 2026Article
- High accumulation of linezolid and its major metabolite in the serum of patients with hepatic and renal dysfunction is significantly associated with thrombocytopenia and anemia.Microbiology spectrum · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.
methodsPatients with hepatic impairment who received linezolid were recruited. A population pharmacokinetic model (PPK) was then built using NONMEM software. And based on the final model, virtual patients with rich concentration values was constructed through Monte Carlo simulations (MCS), which were used to build machine learning (ML) models to predict linezolid exposure levels. Finally, we investigated the risk factors for thrombocytopenia in patients included.
resultsA PPK model with population typical values of 3.83 L/h and 34.1 L for clearance and volume of distribution was established, and the severe hepatic impairment was identified as a significant covariate of clearance. Then, we built a series of ML models to predict the area under 0 -24 h concentration-time curve (AUC
conclusionIn this study, we successfully constructed a PPK model for patients with hepatic impairment and used ML algorithm to estimate linezolid AUC
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Registered trials
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