Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

Saktiawati, Antonia Morita Iswari; Harkema, M.; Setyawan, Althaf; Subronto, Yanri Wijayanti; Sumardi, [No Value]; Stienstra, Ymkje; Aarnoutse, Rob E.; Magis-Escurra, Cécile; Kosterink, Jos G. W.; Werf, Tjip S van der; Alffenaar, Jan‐Willem C.; Sturkenboom, Marieke G G

doi:10.1007/s40262-019-00763-3

Cited by 24 publications

(22 citation statements)

References 31 publications

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“…According to Magis‐Escurra et al, the model combining C 4 and C 6 was the best to estimate the real AUC ( r = .98) . The combination of C 1 and C 8 gave the best prediction of isoniazid AUC in the study of Saktiawati et al…”

Section: Discussionmentioning

confidence: 95%

“…However, according to Cojutti et al the best correlation was obtained with C 5 model ( r = .950), while C 3 was poorly correlated with AUC . A recent study by Saktiawati et al performed in Asian population have shown that C 8 was associated with the best correlation as 1‐time‐point model ( r = .956) in fasted conditions . For some authors, isoniazid TDM could be based on a single time point.…”

Section: Discussionmentioning

confidence: 99%

“…For these reasons, an abbreviated regression analysis called limited sampling strategies (LSS), using few time points after dosing has been used to reliably predict the AUC and has been applied to a variety of drugs . However, the currently proposed models are very rare for isoniazid and have been established in different ethnic populations, but never in south Mediterranean populations. Our study aims to develop a reliable and practical LSS allowing the estimation of isoniazid AUC in Tunisian tuberculosis patients.…”

Section: What Is Known and Objectivementioning

confidence: 99%

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Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

et al. 2019

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What is known and objective: Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict the isoniazid area under the 24hour concentration-time curve (AUC). Experience with isoniazid is very limited, and no LSS has been developed in south-Mediterranean populations. Hence, we aimed to develop an accurate and convenient LSS for predicting isoniazid AUC in Tunisian patients with extrapulmonary tuberculosis. Methods: Pharmacokinetic profiles consisting of six blood samples each, collected during the 24-hour dosing interval, were obtained from 25 (6 men and 19 women) Tunisian patients with extrapulmonary tuberculosis. The AUC was calculated according to the linear trapezoidal rule. The isoniazid concentrations at each sampling time were correlated by a linear regression analysis with the measured AUC. We analysed all the developed models for their ability to estimate the isoniazid AUC. Error indices including the percentage of Mean Absolute Prediction Error (%MAE) and the percentage of Root Mean Squared Prediction Error (%RMSE) were used to evaluate the predictive performance. The agreement between predicted and measured AUCs was investigated using Bland and Altman and mountain plot analyses.Results and discussion: Among the 1-time-point estimations, the C 3 -predicted AUC showed the highest correlation with the measured one (r 2 = .906, %MAE = 10.45% and %RMSE = 2.69%). For the 2-time-point estimations, the model including the C 2 and C 6 provided the highest correlation between predicted and measured isoniazid AUC (r 2 = .960, %MAE = 8.02% and %RMSE = 1.75%). The C 0 /C 3 LSS model provided satisfactory correlation and agreement (r 2 = .930, %MAE = 10.19% and %RMSE = 2.32%).The best multilinear regression model for predicting the full isoniazid AUC was found to be the combination of 3 time points: C 0 , C 1 and C 6 (r 2 = .992, %MAE = 4.06% and %RMSE = 0.80%). The use of a 2-time-point LSS to predict AUC in our population could be sufficient. C 2 /C 6 combination has shown the best correlation but the

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: What Is Known and Objectivementioning

confidence: 99%

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Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

et al. 2019

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“…For instance, Prahl et al 31 measured the Cmax at 2hours post-dose, but Gengiah et al measured it at 2 1/2 hours post-dose. 22 For accurate prediction of AUC, although recent studies recommending a spare sample of plasma are emerging, 42 intensive blood sampling is essential. But estimation of AUC is also done using different approaches.…”

Section: Discussionmentioning

confidence: 99%

The Impact of First-Line Anti-Tubercular Drugs’ Pharmacokinetics on Treatment Outcome: A Systematic Review

Sileshi¹,

Tadesse²,

Makonnen³

et al. 2021

CPAA

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Background: Tuberculosis remains the major public health problem besides tremendous efforts to combat it. Most tuberculosis patients are treated with a standard dose of first-line anti-TB drugs. The cure rate, however, varies from patient to patient. Various factors have been related to anti-TB treatment failure. In recent years, studies associating lower plasma concentrations of anti-TB drugs with poor treatment outcomes are emerging although the results are inconclusive. Objective: Investigate the impact of first-line anti-tubercular drugs pharmacokinetics on treatment outcome. Methods: A systematic search of Pubmed, EMBASE, Web of Science, and the Cochrane Library for articles published in the English language between January 2010 to June 2020 was conducted to identify eligible studies describing associations of first-line anti-tubercular drug pharmacokinetics with treatment outcomes. The primary outcomes considered were pharmacokinetics parameter results and its association with treatment outcome. Results: The search identified 1754 articles of which twelve articles; ten prospective observational studies and two controlled clinical trials fulfilled the eligibility criteria. The majority of the studies showed target concentrations for the first-line anti-tubercular drugs below the current standard range. Among the twelve studies, eleven studies assessed rifampicin pharmacokinetics of which eight reported association of drug concentration and treatment outcomes. Similarly, four out of eight and three out of seven reported drug concentration and treatment outcome association for isoniazid and pyrazinamide, respectively. Despite the low plasma concentration, a favorable treatment outcome was achieved for the bulk of the patients. Irrespective of the inconsistency, an increase in exposure to rifampicin improved the outcome, and lower rifampicin, isoniazid, and pyrazinamide concentration are associated with poor outcome. No data are available for ethambutol associating its pharmacokinetics with treatment outcomes. Conclusion: The pharmacokinetics of first-line antitubercular drugs can influence treatment outcomes. Further controlled clinical studies are, however, required to establish these relationships.

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“…Recently, several LSSs were proposed for isoniazid alone or in combination with other first-line drugs. Limited sampling strategies were derived from a study with intense sampling of 20 patients conducted in Indonesia, who started on TB medication [ 34 ]. Both studies used the best subset selection, multiple linear regression to calculate LSS and the chosen LSS performed best at estimating the AUC 0–24 h .…”

Section: Anti-tb Drugsmentioning

confidence: 99%

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs

et al. 2021

Self Cite

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Tuberculosis (TB) is still the number one cause of death due to an infectious disease. Pharmacokinetics and pharmacodynamics of anti-TB drugs are key in the optimization of TB treatment and help to prevent slow response to treatment, acquired drug resistance, and adverse drug effects. The aim of this review was to provide an update on the pharmacokinetics and pharmacodynamics of anti-TB drugs and to show how population pharmacokinetics and Bayesian dose adjustment can be used to optimize treatment. We cover aspects on preclinical, clinical, and population pharmacokinetics of different drugs used for drug-susceptible TB and multidrug-resistant TB. Moreover, we include available data to support therapeutic drug monitoring of these drugs and known pharmacokinetic and pharmacodynamic targets that can be used for optimization of therapy. We have identified a wide range of population pharmacokinetic models for first- and second-line drugs used for TB, which included models built on NONMEM, Pmetrics, ADAPT, MWPharm, Monolix, Phoenix, and NPEM2 software. The first population models were built for isoniazid and rifampicin; however, in recent years, more data have emerged for both new anti-TB drugs, but also for defining targets of older anti-TB drugs. Since the introduction of therapeutic drug monitoring for TB over 3 decades ago, further development of therapeutic drug monitoring in TB next steps will again depend on academic and clinical initiatives. We recommend close collaboration between researchers and the World Health Organization to provide important guideline updates regarding therapeutic drug monitoring and pharmacokinetics/pharmacodynamics.

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Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

Cited by 24 publications

References 31 publications

Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

The Impact of First-Line Anti-Tubercular Drugs’ Pharmacokinetics on Treatment Outcome: A Systematic Review

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs

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