Literature DB >> 21544017

Limited-sampling strategies for therapeutic drug monitoring of moxifloxacin in patients with tuberculosis.

Arianna D Pranger1, Jos G W Kosterink, Richard van Altena, Rob E Aarnoutse, Tjip S van der Werf, Donald R A Uges, Jan-Willem C Alffenaar.   

Abstract

BACKGROUND: Moxifloxacin (MFX) is a potent drug for multidrug resistant tuberculosis(TB) treatment and is also useful if first-line agents are not tolerated. Therapeutic drug monitoring may help to prevent treatment failure. Obtaining a full concentration-time curve of MFX for therapeutic drug monitoring is not feasible in most settings. Developing a limited-sampling strategy based on population pharmacokinetics (PK) may help to overcome this problem.
METHODS: Steady-state plasma concentrations after the administration of 400 mg of MFX once daily were determined in 21 patients with TB, using a validated liquid chromatography-tandem mass spectrometry method. A one-compartment population model was generated and crossvalidated. Monte Carlo data simulation (n=1000) was used to calculate limited-sampling strategies. The correlation between predicted MFX AUC0-24h (area under the concentration-time curve 0 to 24 hours) and observed AUC0-24h was investigated by Bland-Altman analysis. Finally, the predictive performance of the final model was tested prospectively using MFX profiles from patients with TB receiving 400, 600, or 800 mg once daily.
RESULTS: Median minimum inhibitory concentration of Mycobacterium tuberculosis isolates was 0.25 mg/L (interquartile range: 0.25-0.5 mg/L). The geometric mean AUC0-24h was 24.5 mg·h/L (range: 8.5-72.2 mg·h/L), which resulted in a geometric mean AUC0-24h/minimum inhibitory concentration ratio of 72 (range: 21-321). PK analysis, based on PK profiles of 400 mg of MFX once daily, resulted in a crossvalidated population PK model with the following parameters: apparent clearance (Cl) 18.5±8.6 L/h per 1.85 m, Vd 3.0±0.7 L/kg corrected lean body mass, Ka 1.15±1.16 h, and F was fixed at 1. After the Monte Carlo simulation, the best predicting strategy for MFX AUC0-24h for practical use was based on MFX concentrations 4 and 14 hours postdosing (r=0.90, prediction bias=-1.5%, and root mean square error=15%).
CONCLUSIONS: MFX AUC0-24h in patients with TB can be predicted with acceptable accuracy for clinical management, using limited sampling. AUC0-24h prediction based on 2 samples, 4 and 14 hours postdose, can be used to individualize treatment.

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Year:  2011        PMID: 21544017     DOI: 10.1097/FTD.0b013e31821b793c

Source DB:  PubMed          Journal:  Ther Drug Monit        ISSN: 0163-4356            Impact factor:   3.681


  14 in total

1.  Limited sampling strategy and target attainment analysis for levofloxacin in patients with tuberculosis.

Authors:  Abdullah Alsultan; Guohua An; Charles A Peloquin
Journal:  Antimicrob Agents Chemother       Date:  2015-04-13       Impact factor: 5.191

2.  Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

Authors:  Silke C Mueller; Bernd Drewelow
Journal:  Eur J Clin Pharmacol       Date:  2012-11-07       Impact factor: 2.953

Review 3.  Therapeutic drug monitoring in the treatment of tuberculosis: an update.

Authors:  Abdullah Alsultan; Charles A Peloquin
Journal:  Drugs       Date:  2014-06       Impact factor: 9.546

4.  Effect of genetic variation in UGT1A and ABCB1 on moxifloxacin pharmacokinetics in South African patients with tuberculosis.

Authors:  Anushka Naidoo; Veron Ramsuran; Maxwell Chirehwa; Paolo Denti; Helen McIlleron; Kogieleum Naidoo; Nonhlanhla Yende-Zuma; Ravesh Singh; Sinaye Ngcapu; Mamoonah Chaudhry; Michael S Pepper; Nesri Padayatchi
Journal:  Pharmacogenomics       Date:  2017-12-06       Impact factor: 2.533

5.  Pharmacokinetic Modeling and Limited Sampling Strategies Based on Healthy Volunteers for Monitoring of Ertapenem in Patients with Multidrug-Resistant Tuberculosis.

Authors:  S P van Rijn; M A Zuur; R van Altena; O W Akkerman; J H Proost; W C M de Lange; H A M Kerstjens; D J Touw; T S van der Werf; J G W Kosterink; J W C Alffenaar
Journal:  Antimicrob Agents Chemother       Date:  2017-03-24       Impact factor: 5.191

Review 6.  Targeting multidrug-resistant tuberculosis (MDR-TB) by therapeutic vaccines.

Authors:  Satria A Prabowo; Matthias I Gröschel; Ed D L Schmidt; Alena Skrahina; Traian Mihaescu; Serap Hastürk; Rotislav Mitrofanov; Edita Pimkina; Ildikó Visontai; Bouke de Jong; John L Stanford; Père-Joan Cardona; Stefan H E Kaufmann; Tjip S van der Werf
Journal:  Med Microbiol Immunol       Date:  2012-11-10       Impact factor: 3.402

7.  Limited Sampling Strategies Using Linear Regression and the Bayesian Approach for Therapeutic Drug Monitoring of Moxifloxacin in Tuberculosis Patients.

Authors:  Simone H J van den Elsen; Marieke G G Sturkenboom; Onno W Akkerman; Katerina Manika; Ioannis P Kioumis; Tjip S van der Werf; John L Johnson; Charles Peloquin; Daan J Touw; Jan-Willem C Alffenaar
Journal:  Antimicrob Agents Chemother       Date:  2019-06-24       Impact factor: 5.191

8.  Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.

Authors:  Robin J Svensson; Katarina Niward; Lina Davies Forsman; Judith Bruchfeld; Jakob Paues; Erik Eliasson; Thomas Schön; Ulrika S H Simonsson
Journal:  Br J Clin Pharmacol       Date:  2019-07-25       Impact factor: 4.335

9.  Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis.

Authors:  Marieke G G Sturkenboom; Leonie W Mulder; Arthur de Jager; Richard van Altena; Rob E Aarnoutse; Wiel C M de Lange; Johannes H Proost; Jos G W Kosterink; Tjip S van der Werf; Jan-Willem C Alffenaar
Journal:  Antimicrob Agents Chemother       Date:  2015-06-08       Impact factor: 5.191

10.  Effect of rifampicin and efavirenz on moxifloxacin concentrations when co-administered in patients with drug-susceptible TB.

Authors:  Anushka Naidoo; Maxwell Chirehwa; Helen McIlleron; Kogieleum Naidoo; Sabiha Essack; Nonhlanhla Yende-Zuma; Eddy Kimba-Phongi; John Adamson; Katya Govender; Nesri Padayatchi; Paolo Denti
Journal:  J Antimicrob Chemother       Date:  2017-05-01       Impact factor: 5.758

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