Drug Exposure and Minimum Inhibitory Concentration Predict Pulmonary Tuberculosis Treatment Response.


Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
02 11 2021
Historique:
received: 03 06 2020
pubmed: 19 10 2020
medline: 11 11 2021
entrez: 18 10 2020
Statut: ppublish

Résumé

Prospective studies correlating pharmacokinetic/pharmacodynamic (PK/PD) indices to clinical responses are urgently needed. This study aimed to find clinically relevant PK/PD thresholds that can be used for treatment optimization. Pharmacokinetic sampling and minimum inhibitory concentration (MIC) measurements were performed for patients with culture-confirmed tuberculosis (TB). Classification and regression tree (CART) analysis was applied to obtain PK and/or PD thresholds for first-line drugs predictive of 2-week/month culture conversion, treatment outcome determined at 6-8 months, acute kidney injury (AKI), and drug-induced liver injury (DILI). Least absolute shrinkage and selection operator (LASSO) logistic regression was used for model development and validation. Finally, 168 and 52 patients with TB were included in development and validation cohorts for analysis, respectively. Area under the concentration-time curve (AUC)/MIC below CART-derived thresholds for pyrazinamide of 8.42, pyrazinamide of 2.79, or rifampicin of 435.45 were the predominant predictors of 2-week culture conversion, 2-month culture conversion, or treatment success, respectively. Isoniazid AUC >21.78 mg · h/L or rifampicin AUC >82.01 mg · h/L were predictive of DILI or AKI during TB treatment. The predictive performance of trained LASSO models in the validation cohort was evaluated by receiver operating characteristic curves and ranged from 0.625 to 0.978. PK/PD indices and drug exposure of TB drugs were associated with clinical outcome and adverse events. The effect of CART-derived thresholds for individualized dosing on treatment outcome should be studied in a randomized controlled trial.

Sections du résumé

BACKGROUND
Prospective studies correlating pharmacokinetic/pharmacodynamic (PK/PD) indices to clinical responses are urgently needed. This study aimed to find clinically relevant PK/PD thresholds that can be used for treatment optimization.
METHODS
Pharmacokinetic sampling and minimum inhibitory concentration (MIC) measurements were performed for patients with culture-confirmed tuberculosis (TB). Classification and regression tree (CART) analysis was applied to obtain PK and/or PD thresholds for first-line drugs predictive of 2-week/month culture conversion, treatment outcome determined at 6-8 months, acute kidney injury (AKI), and drug-induced liver injury (DILI). Least absolute shrinkage and selection operator (LASSO) logistic regression was used for model development and validation.
RESULTS
Finally, 168 and 52 patients with TB were included in development and validation cohorts for analysis, respectively. Area under the concentration-time curve (AUC)/MIC below CART-derived thresholds for pyrazinamide of 8.42, pyrazinamide of 2.79, or rifampicin of 435.45 were the predominant predictors of 2-week culture conversion, 2-month culture conversion, or treatment success, respectively. Isoniazid AUC >21.78 mg · h/L or rifampicin AUC >82.01 mg · h/L were predictive of DILI or AKI during TB treatment. The predictive performance of trained LASSO models in the validation cohort was evaluated by receiver operating characteristic curves and ranged from 0.625 to 0.978.
CONCLUSIONS
PK/PD indices and drug exposure of TB drugs were associated with clinical outcome and adverse events. The effect of CART-derived thresholds for individualized dosing on treatment outcome should be studied in a randomized controlled trial.

Identifiants

pubmed: 33070176
pii: 5929670
doi: 10.1093/cid/ciaa1569
doi:

Substances chimiques

Antitubercular Agents 0
Pharmaceutical Preparations 0
Pyrazinamide 2KNI5N06TI

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e3520-e3528

Subventions

Organisme : National Natural Science Foundation of China
ID : 81874273

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Auteurs

Xubin Zheng (X)

Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China.

Ziwei Bao (Z)

The Fifth People's Hospital of Suzhou, Suzhou, China.

Lina Davies Forsman (LD)

Division of Infectious Diseases, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden.
Department of Infectious Disease, Karolinska University Hospital, Stockholm, Sweden.

Yi Hu (Y)

Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China.

Weihua Ren (W)

Central Laboratory, First Affiliated Hospital, Henan University of Science and Technology, Luoyang, China.

Yazhou Gao (Y)

Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China.

Xuliang Li (X)

Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China.

Sven Hoffner (S)

Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.

Judith Bruchfeld (J)

Division of Infectious Diseases, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden.
Department of Infectious Disease, Karolinska University Hospital, Stockholm, Sweden.

Jan-Willem Alffenaar (JW)

University of Sydney, Faculty of Medicine and Health, School of Pharmacy, Sydney, Australia.
Westmead Hospital, Sydney, Australia.
Marie Bashir Institute of Infectious Diseases and Biosecurity, University of Sydney, Sydney, Australia.

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Classifications MeSH