The role of therapeutic drug monitoring in individualised drug dosage and exposure measurement in tuberculosis and HIV co-infection

Daskapan, Alper; Lange, Wiel C M de; Akkerman, Onno W.; Kosterink, Jos G. W.; Werf, Tjip S. van der; Stienstra, Ymkje; Alffenaar, Jan‐Willem C.

doi:10.1183/09031936.00142614

Cited by 19 publications

(13 citation statements)

References 10 publications

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“…This AUC was obtained in only 13 (24%) of the 55 patients studied. In the event that the observed MIC was lower, this might still result in a favorable AUC/MIC ratio (38). Furthermore, on the basis of the lower AUC 0 -24 value of 13 mg · h/liter for rifampin in the presence of a pyrazinamide AUC value over 363 mg · h/liter recently proposed by Pasipanodya et al (4), all our patients obtained sufficient exposure.…”

Section: Discussionmentioning

confidence: 96%

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

Sturkenboom

Mulder

Jager

et al. 2015

Antimicrob Agents Chemother

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f Rifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC 0 -24 ) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC 0 -24 values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n ‫؍‬ 1,000). The geometric mean AUC 0 -24 value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (T max ) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C 2 ) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with an r 2 value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of ؊0.4%. This study showed that the rifampin AUC 0 -24 in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

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Section: Discussionmentioning

confidence: 96%

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

Sturkenboom

Mulder

Jager

et al. 2015

Antimicrob Agents Chemother

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“…Taking the risk of bias assessment (Table 1) into account in relation to the studies included in the systematic review, we postulate that in patients prone to low FLD exposure, HIV infection might even further reduce drug exposure [66], leading to poor treatment outcome [9]. While not a substitute for clinical judgement, therapeutic drug monitoring (TDM) could be a powerful tool for identifying patients with subtherapeutic FLD levels at risk of poor treatment outcomes [62,67,68]. TDM performed early during TB treatment in patients at risk of subtherapeutic FLD levels may improve treatment response and may also prevent toxicity [68,69].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on the Effect of HIV Infection on the Pharmacokinetics of First-Line Tuberculosis Drugs

et al. 2018

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Introduction Contrasting findings have been published regarding the effect of human immunodeficiency virus (HIV) on tuberculosis (TB) drug pharmacokinetics (PK). Objectives The aim of this systematic review was to investigate the effect of HIV infection on the PK of the first-line TB drugs (FLDs) rifampicin, isoniazid, pyrazinamide and ethambutol by assessing all published literature. Methods Searches were performed in MEDLINE (through PubMed) and EMBASE to find original studies evaluating the effect of HIV infection on the PK of FLDs. The included studies were assessed for bias and clinical relevance. PK data were extracted to provide insight into the difference of FLD PK between HIV-positive and HIV-negative TB patients. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and its protocol was registered at PROSPERO (registration number CRD42017067250). Results Overall, 27 studies were eligible for inclusion. The available studies provide a heterogeneous dataset from which consistent results could not be obtained. In both HIV-positive and HIV-negative TB groups, rifampicin (13 of 15) and ethambutol (4 of 8) peak concentration (C max) often did not achieve the minimum reference values. More than half of the studies (11 of 20) that included both HIV-positive and HIV-negative TB groups showed statistically significantly altered FLD area under the concentration-time curve and/or C max for at least one FLD. Conclusions HIV infection may be one of several factors that reduce FLD exposure. We could not make general recommendations with respect to the role of dosing. There is a need for consistent and homogeneous studies to be conducted.

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“…Drug exposure over time (area under the concentration-time curve (AUC 0-24 )) and peak serum concentration (C max ) are the two parameters that, in combination with MIC, predict development of acquired drug resistance and are expressed as a ratio of AUC 0-24 /MIC or C max /MIC [4]. For instance, a patient with serum concentrations below the suggested therapeutic threshold may still achieve a successful treatment outcome because of a low MIC of the offending organism [5]. However, patients with altered pharmacokinetic parameters (e.g.…”

Section: Incorporating Therapeutic Drug Monitoring Into the World Heamentioning

confidence: 99%

Incorporating therapeutic drug monitoring into the World Health Organization hierarchy of tuberculosis diagnostics

et al. 2016

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Foster MW, et al. Eosinophil-associated lung diseases. A cry for surfactant proteins A and D help? Am J Respir Cell Mol Biol 2014; 51: 604-614. 13 Ofek I, Mesika A, Kalina M, et al. Surfactant protein D enhances phagocytosis and killing of unencapsulated phase variants of Klebsiella pneumoniae. Infect Immun 2001; 69: 24-33. 14 Scichilone N, Marchese R, Soresi S, et al. Deep inspiration-induced changes in lung volume decrease with severity of asthma. Respir Med 2007; 101: 951-956. 15 Pyrgos G, Scichilone N, Togias A, et al. Bronchodilation response to deep inspirations in asthma is dependent on airway distensibility and air trapping.

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The role of therapeutic drug monitoring in individualised drug dosage and exposure measurement in tuberculosis and HIV co-infection

Cited by 19 publications

References 10 publications

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

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

A Systematic Review on the Effect of HIV Infection on the Pharmacokinetics of First-Line Tuberculosis Drugs

Incorporating therapeutic drug monitoring into the World Health Organization hierarchy of tuberculosis diagnostics

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