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Pharmacometrics in tuberculosis: progress and opportunities. Int J Antimicrob Agents 2022; 60:106620. [PMID: 35724859 DOI: 10.1016/j.ijantimicag.2022.106620] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2022] [Accepted: 06/12/2022] [Indexed: 11/22/2022]
Abstract
Tuberculosis remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employs quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures, and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of antituberculosis drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically-based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multidrug regimens in terms of both efficacy and safety, informing regimen design for future study. In this short clinically-focused review, we explore what has been done, and what opportunities exist for pharmacometrics to impact tuberculosis pharmacotherapy.
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Cantrell JM, Chung CH, Chandrasekaran S. Machine learning to design antimicrobial combination therapies: promises and pitfalls. Drug Discov Today 2022; 27:1639-1651. [DOI: 10.1016/j.drudis.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/20/2022] [Accepted: 04/04/2022] [Indexed: 01/13/2023]
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Zheng X, Davies Forsman L, Bao Z, Xie Y, Ning Z, Schön T, Bruchfeld J, Xu B, Alffenaar JW, Hu Y. Drug exposure and susceptibility of second-line drugs correlate with treatment response in patients with multidrug-resistant tuberculosis: a multi-centre prospective cohort study in China. Eur Respir J 2021; 59:13993003.01925-2021. [PMID: 34737224 PMCID: PMC8943270 DOI: 10.1183/13993003.01925-2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/21/2021] [Indexed: 11/15/2022]
Abstract
Background Understanding the impact of drug exposure and susceptibility on treatment response of multidrug-resistant tuberculosis (MDR-TB) will help to optimise treatment. This study aimed to investigate the association between drug exposure, susceptibility and response to MDR-TB treatment. Methods Drug exposure and susceptibility for second-line drugs were measured for patients with MDR-TB. Multivariate analysis was applied to investigate the impact of drug exposure and susceptibility on sputum culture conversion and treatment outcome. Probability of target attainment was evaluated. Random Forest and CART (Classification and Regression Tree) analysis was used to identify key predictors and their clinical targets among patients on World Health Organization-recommended regimens. Results Drug exposure and corresponding susceptibility were available for 197 patients with MDR-TB. The probability of target attainment was highly variable, ranging from 0% for ethambutol to 97% for linezolid, while patients with fluoroquinolones above targets had a higher probability of 2-month culture conversion (56.3% versus 28.6%; adjusted OR 2.91, 95% CI 1.42–5.94) and favourable outcome (88.8% versus 68.8%; adjusted OR 2.89, 95% CI 1.16–7.17). Higher exposure values of fluoroquinolones, linezolid and pyrazinamide were associated with earlier sputum culture conversion. CART analysis selected moxifloxacin area under the drug concentration–time curve/minimum inhibitory concentration (AUC0–24h/MIC) of 231 and linezolid AUC0–24h/MIC of 287 as best predictors for 6-month culture conversion in patients receiving identical Group A-based regimens. These associations were confirmed in multivariate analysis. Conclusions Our findings indicate that target attainment of TB drugs is associated with response to treatment. The CART-derived thresholds may serve as targets for early dose adjustment in a future randomised controlled study to improve MDR-TB treatment outcome. Drug exposure and susceptibility were proved to be associated with treatment responses during multidrug-resistant tuberculosis treatment, and identified thresholds may serve as targets for dose adjustment in future clinical studies to improve treatment efficacyhttps://bit.ly/3pZQbFU
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Affiliation(s)
- Xubin Zheng
- Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
| | - Lina Davies Forsman
- Division of Infectious Diseases, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Infectious Disease, Karolinska University Hospital, Stockholm, Sweden
| | - Ziwei Bao
- The Fifth People's Hospital of Suzhou, Jiangsu, China
| | - Yan Xie
- Zigong City Centre for Disease Control and Prevention, Sichuan, China
| | - Zhu Ning
- Zigong City Centre for Disease Control and Prevention, Sichuan, China
| | - Thomas Schön
- Department of Infectious Diseases, Linköping University Hospital and Kalmar County Hospital, Sweden.,Division of Inflammation and Infectious Diseases, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Judith Bruchfeld
- Division of Infectious Diseases, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Infectious Disease, Karolinska University Hospital, Stockholm, Sweden
| | - Biao Xu
- Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
| | - Jan-Willem Alffenaar
- Faculty of Medicine and Health, School of Pharmacy, University of Sydney, Sydney, Australia.,Westmead hospital, Sydney, Australia.,Marie Bashir Institute of Infectious Diseases and Biosecurity, University of Sydney, Sydney, Australia
| | - Yi Hu
- Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
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