Pharmacokinetic Modeling, Simulation, and Development of a Limited Sampling Strategy of Cycloserine in Patients with Multidrug-/Extensively Drug-Resistant Tuberculosis

BACKGROUND AND OBJECTIVE: Multidrug-resistant tuberculosis has much poorer treatment outcomes compared with drug-susceptible tuberculosis because second-line drugs for treating multidrug resistant tuberculosis are less effective and are frequently associated with side effects. Optimization of drug treatment is urgently needed. Cycloserine is a second-line tuberculosis drug with variable pharmacokinetics and thus variable exposure when programmatic doses are used. The objective of this study was to develop a population pharmacokinetic model of cycloserine to assess drug exposure and to develop a limited sampling strategy for cycloserine exposure monitoring. MATERIAL AND METHODS: Patients with multidrug-/extensively drug-resistant tuberculosis who were treated for > 7 days with cycloserine were eligible for inclusion. Patients received cycloserine 500 mg (body weight ≤ 50 kg) or 750 mg (body weight > 50 kg) once daily. MW/Pharm 3.83 (Mediware, Groningen, The Netherlands) was used to parameterize the population pharmacokinetic model. The model was compared with pharmacokinetic values from the literature and evaluated with a bootstrap analysis, Monte Carlo simulation, and an external dataset. Monte Carlo simulations were used to develop a limited sampling strategy. RESULTS: Cycloserine plasma concentration vs time curves were obtained from 15 hospitalized patients (nine male, six female, median age 35 years). Mean dose/kg body weight was 11.5 mg/kg (standard deviation 2.04 mg/kg). Median area under the concentration-time curve over 24 h (AUC0-24 h) of cycloserine was 888 h mg/L (interquartile range 728-1252 h mg/L) and median maximum concentration of cycloserine was 23.31 mg/L (interquartile range 20.14-33.30 mg/L). The final population pharmacokinetic model consisted of the following pharmacokinetic parameters [mean (standard deviation)]: absorption constant Ka_po of 0.39 (0.31) h-1, distribution over the central compartment (Vd) of 0.54 (0.26) L/kg LBM, renal clearance as fraction of the estimated glomerular filtration rate of 0.092 (0.038), and metabolic clearance of 1.05 (0.75) L/h. The population pharmacokinetic model was successfully evaluated with a bootstrap analysis, Monte Carlo simulation, and an external dataset of Chinese patients (difference of 14.6% and 19.5% in measured and calculated concentrations and AUC0-24 h, respectively). Root-mean-squared-errors found in predicting the AUC0-24 h using a one- (4 h) and a two- (2 h and 7 h) limited sampling strategy were 1.60% and 0.14%, respectively. CONCLUSIONS: This developed population pharmacokinetic model can be used to calculate cycloserine concentrations and exposure in patients with multidrug-/extensively drug-resistant tuberculosis. This model was successfully validated by internal and external validation methods. This study showed that the AUC0-24 h of cycloserine can be estimated in patients with multidrug-/extensively drug-resistant tuberculosis using a 1- or 2-point limited sampling strategy in combination with the developed population pharmacokinetic model. This strategy can be used in studies to correlate drug exposure with clinical outcome. This study also showed that good target attainment rates, expressed by time above the minimal inhibitory concentration, were obtained for cycloserine with a minimal inhibitory concentration of 5 and 10 mg/L, but low rates with a minimal inhibitory concentration of 20 and 32.5 mg/L

Risk factors contributing to a low darunavir plasma concentration

Darunavir is an efficacious drug; however, pharmacokinetic variability has been reported. The objective of this study was to find predisposing factors for low darunavir plasma concentrations in patients starting the once- or twice-daily dosage. Darunavir plasma concentrations from January 2010 till December 2014 of human immunodeficiency virus-infected individuals treated in the outpatient clinic of the University Medical Center Groningen were retrospectively reviewed. The first darunavir plasma concentration of patients within 8weeks after initiation of darunavir therapy was selected. A dichotomous logistic regression analysis was conducted to select the set of variables best predicting a darunavir concentration below median population pharmacokinetic curve. In total 113 patients were included. The variables best predicting a darunavir concentration besides food intake included age together with estimated glomerular filtration rate (Hosmer-Lemeshow test P=0.945, Nagelkerke R-2=0.284). Systematic evaluation of therapeutic drug monitoring results may help to identify patients at risk for low drug exposure

Prognostic impact of elevated lactate levels on mortality in critically ill patients with and without preadmission metformin treatment:a Danish registry-based cohort study

BACKGROUND: Lactate is a robust prognostic marker for the outcome of critically ill patients. Several small studies reported that metformin users have higher lactate levels at ICU admission without a concomitant increase in mortality. However, this has not been investigated in a larger cohort. We aimed to determine whether the association between lactate levels around ICU admission and mortality is different in metformin users compared to metformin nonusers. METHODS: This cohort study included patients admitted to ICUs in northern Denmark between January 2010 and August 2017 with any circulating lactate measured around ICU admission, which was defined as 12 h before until 6 h after admission. The association between the mean of the lactate levels measured during this period and 30-day mortality was determined for metformin users and nonusers by modelling restricted cubic splines obtained from a Cox regression model. RESULTS: Of 37,293 included patients, 3183 (9%) used metformin. The median (interquartile range) lactate level was 1.8 (1.2-3.2) in metformin users and 1.6 (1.0-2.7) mmol/L in metformin nonusers. Lactate levels were strongly associated with mortality for both metformin users and nonusers. However, the association of lactate with mortality was different for metformin users, with a lower mortality rate in metformin users than in nonusers when admitted with similar lactate levels. This was observed over the whole range of lactate levels, and consequently, the relation of lactate with mortality was shifted rightwards for metformin users. CONCLUSION: In this large observational cohort of critically ill patients, early lactate levels were strongly associated with mortality. Irrespective of the degree of hyperlactataemia, similar lactate levels were associated with a lower mortality rate in metformin users compared with metformin nonusers. Therefore, lactate levels around ICU admission should be interpreted according to metformin use

Ganciclovir Therapeutic Drug Monitoring:A Case Series

This paper presents three cases of immunocompromised patients for whom therapeutic drug monitoring (TDM) of ganciclovir in combination with cytomegalovirus (CMV) viral load measurement was used to guide treatment. The first patient is diagnosed with Thymoma A, the second is a heart transplant recipient and the third is an HIV positive patient. These patients were all diagnosed with CMV and treated with ganciclovir. Our case studies illustrate how TDM guided dosing can be helpful in the management of these complex cases.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal

Dried blood spots can help decrease the burden on patients dually infected with multidrug-resistant tuberculosis and HIV

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Use of Salivary Iodine Concentrations to Estimate the Iodine Status of Adults in Clinical Practice

BACKGROUND: Measurement of the 24-h urinary iodine concentration or urinary iodine excretion (UIE) is the gold standard to determine iodine status; however, this method is inconvenient. The use of salivary iodine could be a possible alternative since salivary glands express the sodium-iodine symporter. OBJECTIVES: We aimed to establish the correlation between the salivary iodine secretion and UIE, to evaluate the clinical applicability of the iodine saliva measurement. METHODS: We collected 24-h urine and saliva samples from 40 participants ≥18 y: 20 healthy volunteers with no specific diet (group 1), 10 patients with differentiated thyroid cancer with a low dietary intake (<50 μg/d, group 2), and 10 patients with a high iodine status as the result of the use of amiodarone (group 3). Urinary and salivary iodine were measured using a validated inductively coupled plasma MS method. To correct for differences in water content, the salivary iodine concentration (SIC) was corrected for salivary protein and urea concentrations (SI/SP and SI/SU, respectively). The intra- and inter-individual CVs were calculated, and the Kruskal-Wallis test and Spearman's correlation were used. RESULTS: The intra-individual CVs for SIC, SI/SP, and SI/SU were 63.8%, 37.7%, and 26.9%, respectively. The inter-individual CVs for SIC, SI/SP, and SI/SU were 77.5%, 41.6% and 47.0%, respectively. We found significant differences (P < 0.01) in urinary and salivary iodine concentrations between all groups [the 24-h UIE values were 176 μg/d (IQR, 96.1–213 μg/d), 26.0 μg/d (IQR, 22.0–37.0 μg/d), and 10.0*10(3) μg/d (IQR, 7.57*10(3)–11.4*10(3) μg/d) in groups 1–3, respectively; the SIC values were 136 μg/L (IQR, 86.3–308 μg/L), 71.5 μg/L (IQR, 29.5–94.5 μg/L), and 14.3*10(3) μg/L (IQR, 10.6*10(3)–25.6*10(3) μg/L) in groups 1–3, respectively]. Correlations between the 24-h UIE and SIC, SI/SP, and SI/SU values were strong (ρ = 0.80, ρ = 0.90, and ρ = 0.86, respectively; P < 0.01). CONCLUSIONS: Strong correlations were found between salivary and urinary iodine in adults with different daily iodine intakes. A salivary iodine measurement can be performed to assess the total iodine body pool, with the recommendation to correct for salivary protein or urea

Optimization of Fluconazole Dosing for the Prevention and Treatment of Invasive Candidiasis Based on the Pharmacokinetics of Fluconazole in Critically Ill Patients

The efficacy of fluconazole is related to the area under the plasma concentration-time curve (AUC) over the MIC of the microorganism. Physiological changes in critically ill patients may affect the exposure of fluconazole, and therefore dosing adjustments might be needed. The aim of this study was to evaluate variability in fluconazole drug concentration in intensive care unit (ICU) patients and to develop a pharmacokinetic model to support personalized fluconazole dosing. A prospective observational pharmacokinetic study was performed in critically ill patients receiving fluconazole either as prophylaxis or as treatment. The association between fluconazole exposure and patient variables was studied. Pharmacokinetic modeling was performed with a nonparametric adaptive grid (NPAG) algorithm using R package Pmetrics. Data from 33 patients were available for pharmacokinetic analysis. Patients on dialysis and solid organ transplant patients had a significantly lower exposure to fluconazole. The population was best described with a one-compartment model, where the mean volume of distribution was 51.52 liters (standard deviation [SD], 19.81) and the mean clearance was 0.767 liters/h (SD, 0.46). Creatinine clearance was tested as a potential covariate in the model, but was not included in the final population model. A significant positive correlation was found between the fluconazole exposure (AUC) and the trough concentration (C-min). Substantial variability in fluconazole plasma concentrations in critically ill adults was observed, where the majority of patients were underexposed. Fluconazole C-min therapeutic drug monitoring (TDM)-guided dosing can be used to optimize therapy in critically ill patients