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

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Abstract

Purpose

To investigate the pharmacokinetic changes of linezolid in patients with hepatic impairment and to explore a method to predict linezolid exposure.

Methods

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.

Results

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.

Conclusion

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.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We would like to thank authors and all the participants for their time and effort.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82173898) and the Institutional foundation of the first affiliated hospital of Xi’an Jiaotong University (No. 2019ZYTS-01).

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Author notes

  1. Yalin Dong and Haiyan Dong contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacy, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, China

    Ru Liao, Xiaoliang Cheng, Houli Li, Taotao Wang, Yalin Dong & Haiyan Dong

  2. Department of International Medical Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, China

    Lihong Chen

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  1. Ru Liao
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Contributions

RL: conception, design, data collection, analysis of data, interpretation of results, drafting, and revision of manuscript. HY: conception, design, and major revision of manuscript, and funding acquisition. YL: conception, design, major revision of manuscript. LH: data collection, and revision of manuscript. XL, HL and TT: interpretation of results, and revision of manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Yalin Dong or Haiyan Dong.

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Ethical approval

This study was approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (Xi’an, China). The institutional review board (IRB) approval number was “[XJTU1AF2019LSK-163].”

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Liao, R., Chen, L., Cheng, X. et al. Estimation of linezolid exposure in patients with hepatic impairment using machine learning based on a population pharmacokinetic model. Eur J Clin Pharmacol (2024). https://doi.org/10.1007/s00228-024-03698-2

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