Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure.

BACKGROUND: Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS: This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS: In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS: Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.

Recommendation of pharmacokinetics/pharmacodynamics target of ethambutol to suppress tuberculosis resistance: A population pharmacokinetics study on a large prospective cohort.

BACKGROUND: The ability of ethambutol (EMB) to suppress bacterial resistance has been demonstrated in a time-dependent manner. Through the development of a population pharmacokinetics (PK) model, this study aimed to suggest the PK/pharmacodynamics (PD) target and identify the significant covariates that influence interindividual variability (IIV) in the PK of EMB. METHODS: In total, 837 patients from 20 medical centres across Korea were enrolled in this study. The non-linear mixed-effect method was used to establish and validate the population PK model. RESULTS: A two-compartment model with transit compartment absorption was sufficient to describe the PK of EMB. Body weight and renal function were identified as significant covariates that affect IIV of the apparent clearance (CL/F) of EMB. Patients with moderate renal function showed 35% and 55% lower CL/F (CL/F 89.9 L/h) compared with those with mild and normal renal function, respectively. All the renal function groups with simulated doses ranging from 800 to 1200 mg achieved area under the curve over minimum inhibitory concentration (MIC) >119, and maintained T>MIC for >23 h for MIC of 0.5 µg/mL. Based on our simulation result, it is suggested that doses of 800, 1000, and 1200 mg should obtain the T>MIC target of 4, 6, and 8 h, respectively. This model was validated internally and externally. CONCLUSION: This study provides insight into the PK/PD indexes of EMB for three different renal function groups and T>MIC targets for different doses. The results could be used to provide optimal-dose suggestions for EMB.

Development of population pharmacokinetics model of isoniazid in Indonesian patients with tuberculosis.

OBJECTIVES: No population pharmacokinetics (PK) model of isoniazid (INH) has been reported for the Indonesian population with tuberculosis (TB). Therefore, we aimed to develop a population PK model to optimize pharmacotherapy of INH on the basis of therapeutic drug monitoring (TDM) implementation in Indonesian patients with TB. MATERIALS AND METHODS: INH concentrations, N-acetyltransferase 2 (NAT2) genotypes, and clinical data were collected from Dr. Soetomo General Academic Hospital, Indonesia. A nonlinear mixed-effect model was used to develop and validate the population PK model. RESULTS: A total of 107 patients with TB (with 153 samples) were involved in this study. A one-compartment model with allometric scaling for bodyweight effect described well the PK of INH. The NAT2 acetylator phenotype significantly affected INH clearance. The mean clearance rates for the rapid, intermediate, and slow NAT2 acetylator phenotypes were 55.9, 37.8, and 17.7 L/h, respectively. Our model was well-validated through visual predictive checks and bootstrapping. CONCLUSIONS: We established the population PK model for INH in Indonesian patients with TB using the NAT2 acetylator phenotype as a significant covariate. Our Bayesian forecasting model should enable optimization of TB treatment for INH in Indonesian patients with TB.