Young-Kyung Choi (1), Jong-Lyul Ghim (1,2), Dae Yeon Kim (3), Jae-Gook Shin (1,2)
(1) Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea, (2) Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea, (3) Masan National Tuberculosis Hospital, Mokpo, Republic of Korea
Objectives: Cycloserine is a bacteriostatic anti-tuberculosis drug used in combination for the treatment of multidrug-resistant tuberculosis (MDR-TB). Cycloserine remains a ‘second-line’ TB drug because of its frequent CNS effects. Symptoms range from headache and somnolence to severe psychosis, seizures, and suicidal ideas. Serious CNS toxicity may be associated with elevated serum or plasma concentrations, because there is no appreciable barrier to CNS entry for cycloserine. And low plasma concentrations may lead to therapeutic failure and development of drug resistance due to incomplete eradication of Mycobacterium tuberculosis. Therefore, it is important to clarify dosing conditions that may impair or promote achievement of adequate cycloserine plasma concentrations. However, the pharmacokinetics (PK) of cycloserine has not been studied in Korean patients. The objectives of this study was to describe the population PK of cycloserine in Korean patients with MDR-TB and to identify significant covariates affecting its disposition.
Methods: Serial blood samples were collected from the patients and population PK analysis was performed using nonlinear mixed effects modeling (NONMEM, version 7.3: Icon, Inc., Ellicott City, MD, USA). Population was undertaken using the first-order conditional estimation (FOCE) method with interaction. Sex, age, weight, comorbidities, and creatinine clearance (calculated by Modification of Diet in Renal Disease formula) were investigated as potential covariates on the PK parameters.
Results: Twenty-five Korean patients with MDR-TB aged 20 to 78 years included in this study. The mean body weight and creatinine clearance were 56.6 kg (range, 46-73) and 87.6 mL/min/1.73m2 (range, 41.7-118.3), respectively. A one-compartment model with first-order absorption and elimination described PK of cycloserine. The estimated total body clearance and volume of distribution were 0.84 L/hr and 22.8 L, respectively. Clearance was decreased according to creatinine clearance which was explained using power model. We were not able to characterize fully the creatinine clearance in our model, and this was mostly due to the lack of sufficient patients who have low or/and high CLCR. Cycloserine does not bind to plasma proteins and approximately 70% of dose is excreted by the kidney, unaltered, within 72 hours. Besides, base model incorporating CLCR on CL/F was not only reduced the AIC level but also improved visual inspections of goodness-of-fit plots. Furthermore, when the model included the effect of CLCR on CL/F, objective function value is slightly decreased from the simple exponential error model (△OFV=3.412). For these reasons, it is reasonable to base model including CLCR on CL/F. Other covariates from demographic and clinical information did not significantly further explain the PKs of cycloserine.
Conclusions: The PK behaviors of cycloserine were well described by the developed population PK model. This model can be helpful to set therapeutic drug monitoring system for anti-TB drugs including cycloserine. Additional studies will be needed to further validate the suggested results.
References:
[1] Zhu M, Nix DE, Adam RD, Childs JM, Peloquin CA. Pharmacokinetics of cycloserine under fasting conditions and with high-fat meal, orange juice, and antacids. Pharmacotherapy. 2001 Aug;21(8):891-7.
[2] Park SI, Oh J, Jang K, Yoon J, Moon SJ, Park JS, Lee JH, Song J, Jang IJ, Yu KS, Chung JY. Pharmacokinetics of Second-Line Antituberculosis Drugs after Multiple Administrations in Healthy Volunteers. Antimicrob Agents Chemother. 2015 Aug;59(8):4429-35.
Reference: PAGE 27 (2018) Abstr 8611 [www.page-meeting.org/?abstract=8611]
Poster: Drug/Disease Modelling - Infection