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Estimation of linezolid exposure in patients with hepatic impairment using machine learning based on a population pharmacokinetic model.

Authors :
Liao, Ru
Chen, Lihong
Cheng, Xiaoliang
Li, Houli
Wang, Taotao
Dong, Yalin
Dong, Haiyan
Source :
European Journal of Clinical Pharmacology. May2024, p1-11.
Publication Year :
2024

Abstract

Purpose: To investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.Patients 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.A 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 (AUC0-24) of linezolid based on virtual patients from MCS. The results showed that the Xgboost models showed the best predictive performance and were superior to the methods for estimating linezolid AUC0-24 based on though concentration or daily dose. Finally, we found that baseline platelet count, linezolid AUC0-24, and combination with fluoroquinolones were independent risk factors for thrombocytopenia, and based on this, we proposed a method for calculating the toxicity threshold of linezolid.In this study, we successfully constructed a PPK model for patients with hepatic impairment and used ML algorithm to estimate linezolid AUC0-24 based on limited data. Finally, we provided a method to determine the toxicity threshold of linezolid.Methods: To investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.Patients 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.A 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 (AUC0-24) of linezolid based on virtual patients from MCS. The results showed that the Xgboost models showed the best predictive performance and were superior to the methods for estimating linezolid AUC0-24 based on though concentration or daily dose. Finally, we found that baseline platelet count, linezolid AUC0-24, and combination with fluoroquinolones were independent risk factors for thrombocytopenia, and based on this, we proposed a method for calculating the toxicity threshold of linezolid.In this study, we successfully constructed a PPK model for patients with hepatic impairment and used ML algorithm to estimate linezolid AUC0-24 based on limited data. Finally, we provided a method to determine the toxicity threshold of linezolid.Results: To investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.Patients 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.A 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 (AUC0-24) of linezolid based on virtual patients from MCS. The results showed that the Xgboost models showed the best predictive performance and were superior to the methods for estimating linezolid AUC0-24 based on though concentration or daily dose. Finally, we found that baseline platelet count, linezolid AUC0-24, and combination with fluoroquinolones were independent risk factors for thrombocytopenia, and based on this, we proposed a method for calculating the toxicity threshold of linezolid.In this study, we successfully constructed a PPK model for patients with hepatic impairment and used ML algorithm to estimate linezolid AUC0-24 based on limited data. Finally, we provided a method to determine the toxicity threshold of linezolid.Conclusion: To investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.Patients 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.A 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 (AUC0-24) of linezolid based on virtual patients from MCS. The results showed that the Xgboost models showed the best predictive performance and were superior to the methods for estimating linezolid AUC0-24 based on though concentration or daily dose. Finally, we found that baseline platelet count, linezolid AUC0-24, and combination with fluoroquinolones were independent risk factors for thrombocytopenia, and based on this, we proposed a method for calculating the toxicity threshold of linezolid.In this study, we successfully constructed a PPK model for patients with hepatic impairment and used ML algorithm to estimate linezolid AUC0-24 based on limited data. Finally, we provided a method to determine the toxicity threshold of linezolid. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00316970
Database :
Academic Search Index
Journal :
European Journal of Clinical Pharmacology
Publication Type :
Academic Journal
Accession number :
177103037
Full Text :
https://doi.org/10.1007/s00228-024-03698-2