Ruben van der Galiën1, Natasha Van't Boveneind-Vrubleuskaya1,2, Charles Peloquin3, Alena Skrahina4, Daan J Touw1, Jan-Willem C Alffenaar5,6,7. 1. Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands. 2. Department of Public Health TB Control, Metropolitan Public Health Service Haaglanden, The Hague, The Netherlands. 3. Infectious Disease Pharmacokinetics Laboratory, College of Pharmacy, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. 4. Republican Research and Practical Centre for Pulmonology and Tuberculosis, Minsk, Republic of Belarus. 5. Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands. johannes.alffenaar@sydney.edu.au. 6. School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. johannes.alffenaar@sydney.edu.au. 7. Westmead Hospital, Sydney, NSW, Australia. johannes.alffenaar@sydney.edu.au.
Abstract
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.
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.
Authors: Marieke G G Sturkenboom; Anne-Grete Märtson; Elin M Svensson; Derek J Sloan; Kelly E Dooley; Simone H J van den Elsen; Paolo Denti; Charles A Peloquin; Rob E Aarnoutse; Jan-Willem C Alffenaar Journal: Clin Pharmacokinet Date: 2021-03-06 Impact factor: 6.447