Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2026 Mar 17.
doi: 10.1007/s40262-026-01626-4. Online ahead of print.

Single Saliva Sample Linezolid Dosing for Multidrug-Resistant Tuberculosis: A Population Pharmacokinetic Modelling of Plasma and Saliva

Thi A Nguyen  1   2   3 , Tri P Nguyen  4 , Anh T Nguyen  5   6   7 , Luong V Dinh  8 , Hoa B Nguyen  8 , Hoa D Vu  9 , Tram N B Nguyen  7 , Dang Vu  7 , Greg J Fox  5   6 , Jan-Willem C Alffenaar  1   2   3 , Sophie L Stocker  10   11   12   13   14
Affiliations

Single Saliva Sample Linezolid Dosing for Multidrug-Resistant Tuberculosis: A Population Pharmacokinetic Modelling of Plasma and Saliva

Thi A Nguyen et al. Clin Pharmacokinet. .

Abstract

Background and objective: Therapeutic drug monitoring (TDM) of linezolid for treating multidrug-resistant tuberculosis (MDR-TB) is recommended but is hindered by the invasive and logistical challenges of plasma sampling. Saliva is a promising alternative, but a saliva pharmacokinetic model to inform dosing is lacking. This study aimed to develop a saliva-based population pharmacokinetic (popPK) model and evaluate limited sampling strategies for linezolid in MDR-TB.

Methods: Plasma and saliva samples were collected at pre-dose, 2-, and 5-h post-dose from adults treated with linezolid for ≥ 7 days. A saliva-plasma popPK model was developed in NONMEM with covariate analysis. Bayesian estimation and Monte Carlo simulations were used to evaluate the predictive performance of saliva limited sampling strategies for predicting plasma AUC0-24. A ±20% bias versus reference was considered acceptable.

Results: One-compartment plasma model incorporating a saliva hypothetical effect compartment with first-order absorption and elimination best described the data (102 paired saliva-plasma samples, 17 patients). Body weight influenced volume of distribution with an exponent of 1.1 (95% CI 1.01-1.18). The three-sample (pre-dose, 2-, and 5-h) saliva strategy adequately predicted plasma AUC0-24 with < 5% median bias. A single saliva sample at pre-dose or 2 h post-dose provided accurate plasma AUC0-24 predictions (< 5% median bias).

Conclusions: The validated model enables reliable estimation of plasma AUC0-24 using limited saliva samples. A single saliva sample (pre-dose or 2 h post-dose) can accurately predict plasma AUC0-24, supporting saliva-based TDM as a practical alternative to plasma-based monitoring of linezolid in MDR-TB.

PubMed Disclaimer

Conflict of interest statement

Declarations. Consent to participate: Written informed consent was obtained from all participants prior to study enrolment, in accordance with the approved protocol. Consent for publication: Not applicable. Code available: The model codes that support the findings of this study are accessible in the Supplementary Materials (Table S7). Data availability: The data supporting this study's findings may be available on request from the principal investigator of the study (J.W. Alffenaar). Ethics approval: Ethical approval was granted by the University of Sydney, Australia (Protocol No. 2021/082, dated 21.10.2021) and the National Lung Hospital, Vietnam (Approval No. 25/21/CN-HDDD, dated 27/04/2021). Author’s contributions: T.A. Nguyen conceptualised the study, performed drug concentration analysis, performed the pharmacometrics data analyses and wrote the manuscript. T.P. Nguyen provided support for the pharmacometrics data analyses and revised the manuscript. A.T. Nguyen, L.V. Dinh , B.H. Nguyen, D.H. Vu, T.N.B. Nguyen, And D. Vu revised the manuscript. G.J. Fox conceptualised the study and revised the manuscript. J.W. Alffenaar conceptualised the study, supervised the analysis and revised the manuscript. S.L. Stocker conceptualised the study, supervised the analysis and revised the manuscript. All authors revised the manuscript and accepted submission to the journal. Conflict of interest: Jan-Willem C. Alffenaar and Sophie L. Stocker are Editorial Board members of Clinical Pharmacokinetics. They were not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

    1. WHO. Global Tuberculosis Report 2024 (Section 1.2) [Internet]. https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-heal... .
    1. WHO. Tuberculosis resurges as top infectious disease killer [Internet]. https://www.who.int/news/item/29-10-2024-tuberculosis-resurges-as-top-in... .
    1. WHO. WHO consolidated guidelines on tuberculosis: module 4: treatment and care, 2025 [Internet]. https://www.who.int/publications/i/item/9789240063129 .
    1. Ahmad N, Ahuja SD, Akkerman OW, Alffenaar J-WC, Anderson LF, Baghaei P, et al. Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: an individual patient data meta-analysis. Lancet. 2018;392:821–34. https://doi.org/10.1016/S0140-6736(18)31644-1 . - DOI - PubMed - PMC
    1. Conradie F, Diacon AH, Ngubane N, Howell P, Everitt D, Crook AM, et al. Treatment of highly drug-resistant pulmonary tuberculosis. N Engl J Med. 2020;382:893–902. https://doi.org/10.1056/NEJMoa1901814 . - DOI - PubMed - PMC

LinkOut - more resources