POPULATION PHARMACOKINETICS MODELING OF LEVOFLOXACIN AFTER ORAL ADMINISTRATION IN HEALTHY INDONESIAN VOLUNTEERS
Abstract
Research focusing on the utility of population-based modelling in Indonesia is still minimal. This study aimed to implement the population-based approach for the pharmacokinetics profiles of levofloxacin, one of the broad-spectrum antibiotics commonly used in the treatment of infection.
A single 500 mg levofloxacin film-coated tablet was administered orally to 18 healthy volunteers (10 males and 8 females, age between 18-55 years), evaluated based on the liver and kidney function, blood and urine parameters, disease history, physical examination, blood pressure, heart rate, ECG, body weight, and the absence of viral infections (i.e., HCV, Hepatitis B, and HIV). Blood samples were periodically collected within 24 hours after dosing. Levofloxacin-plasma-concentration (Cp) was measured using a validated bioanalytical HPLC method based on selectivity, accuracy, precision, stability, LLOQ, and linearity evaluations. The population modelling of Cp time profiles used Monolix (v. 2020 R1) and NONMEM (v. 7.43). The best model was evaluated from the one-compartment and two-compartment model with and without the absorption lag-time. Moreover, the modelling was further assessed by involving and searching the appropriate covariate, including sex, age, weight, BMI, height, systolic, diastolic, pulse, respiratory rate, albumin and bilirubin concentration, SGOT, SGPT, urea concentration, total clearance, haemoglobin, lymphocytes, monocyte, eosinophile, basophil, HCT, red blood cells, and platelet.
A two-compartment model with a lag-time best describes pharmacokinetic profiles of levofloxacin. Both Monolix and NONMEM estimated similar population and individual parameter estimates of CL (Central-clearance), Vd (central-distribution-volume), Q (peripheral-clearance), V2 (peripheral-distribution-volume), Ka (absorption-rate-constant) and lag-time. Both Monolix and NONMEM confirm age and height as covariates, improving the modelling by influencing the Ka and Vd, respectively.
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