Aroonrut Lucksiri

STOCHASTIC PREDICTION OF CYP3A-MEDIATED INHIBITION OF MIDAZOLAM CLEARANCE BY KETOCONAZOLE

Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time- and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.

Physiologically Based Pharmacokinetic Model of Mechanism-Based Inhibition of CYP3A by Clarithromycin

The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. KI and kinact values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 μM for Ki and 0.4 and 4 h−1 for kinact in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in KI, kinact, and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.

Influence of Hemodialysis on Gentamicin Pharmacokinetics, Removal During Hemodialysis, and Recommended Dosing

BACKGROUND AND OBJECTIVES Aminoglycoside antibiotics are commonly used in chronic kidney disease stage 5 patients. The purpose of this study was to characterize gentamicin pharmacokinetics, dialytic clearance, and removal by hemodialysis and to develop appropriate dosing strategies. Design Setting, Participants, and Measurements: Eight subjects receiving chronic, thrice-weekly hemodialysis with no measurable residual renal function received gentamicin after a hemodialysis session. Blood samples were collected serially, and serum concentrations of gentamicin were determined. RESULTS Median (range) systemic clearance, volume of distribution at steady state, and terminal elimination half-life were 3.89 ml/min (2.69-4.81 ml/min), 13.5 L (8.7-17.9 L), and 39.4 h (32.0-53.6 h), respectively. Median (range) dialytic clearance, estimated amount removed, and percent maximum rebound were 103.5 ml/min (87.2-132.7 ml/min), 39.6 mg (19.7-43.9 mg), and 38.7% (0%-71.8%), respectively. Gentamicin dialytic clearance was statistically significantly related to creatinine dialytic clearance (r(2) = 0.52, P = 0.04), although this relationship is not likely to be strong enough to serve as a surrogate for gentamicin monitoring. The pharmacokinetic model was used to simulate gentamicin serum concentrations over a one-wk period. CONCLUSIONS In clinical situations where gentamicin is used as the primary therapy in a patient receiving hemodialysis with a CAHP hemodialyzer, conventional doses after each dialysis session are not as efficient at achieving treatment targets as predialysis dosing with larger doses.

Levofloxacin pharmacokinetics in ESRD and removal by the cellulose acetate high performance-210 hemodialyzer

BACKGROUND No published data are available describing the pharmacokinetics of intravenous levofloxacin in patients with end-stage renal disease (ESRD). Objectives of this study are to determine the pharmacokinetics and dialytic clearance of levofloxacin and develop dosing strategies in these patients. METHODS Eight noninfected subjects receiving long-term thrice-weekly hemodialysis, with no measurable residual renal function, were administered intravenous levofloxacin, 250 mg, over 1 hour after a scheduled hemodialysis session. Blood samples were collected serially during the interdialytic period, during the next intradialytic period, and immediately after the next hemodialysis session. Serum concentrations of levofloxacin were determined by high-performance liquid chromatography. Differential equations describing a 2-compartment open-infusion pharmacokinetic model were fit to each individual subjects serum concentration-time data by iterative nonlinear weighted least-squares regression analysis using Adapt II (Biomedical Simulations Resource, University of Southern California, Los Angeles, CA). Ratios of maximum serum concentration (C(max)) to minimum inhibitory concentration (MIC) were calculated for common respiratory pathogens by using MIC for 90% of isolates (MIC90) data from published studies. RESULTS All subjects completed the study, and no adverse events were reported. Median systemic clearance, volume of distribution at steady state, elimination half-life, and C(max) were 37.0 mL/min (range, 12.8 to 42.7 mL/min), 103.3 L (range, 39.8 to 139.3 L), 34.4 hours (range, 28.4 to 39.3 hours), and 5.2 microg/mL (range, 4.1 to 11.3 microg/mL), respectively. Median dialytic clearance and levofloxacin reduction ratios were 84.4 mL/min (range, 61.8 to 107.6 mL/min) and 0.244 (range, 0.181 to 0.412), respectively. Median C(max)-MIC90 ratios were 10 or greater for Haemophilus influenzae, Moraxella catarrhalis, Enterobacter cloacae, and Klebsiella pneumoniae, approximately 5 for Streptococcus pneumoniae, and less than 1 for Pseudomonas aeruginosa. CONCLUSION The administration of levofloxacin to patients with ESRD as 500 mg initially, followed by 250 mg every 48 hours, will provide adequate C(max)-MIC ratios after the first and subsequent doses for most patients with respiratory tract infections caused by organisms with levofloxacin MICs of 1 microg/mL or less.

Pharmacokinetics and pharmacodynamics of meropenem in children with severe infection

This study aimed to describe the pharmacokinetic (PK) characteristics of meropenem in children with severe infections and to assess the pharmacokinetic/pharmacodynamic (PK/PD) profiles of various meropenem dosage regimens in these patients. Fourteen children with severe infections received intravenous (i.v.) bolus doses of meropenem (20 mg/kg/dose) every 8 h (q8h). Serum samples were obtained before and serially after the second dose of meropenem, and a population PK analysis was performed. The final model was used to simulate serum concentration-time profiles with various dosage regimens. The PK/PD target was to achieve a serum meropenem concentration higher than the minimum inhibitory concentration (MIC) of the causative organism (i.e. Pseudomonas aeruginosa and Enterobacteriaceae) for ≥40% of the dosing interval (40%T>MIC). The median age and weight of the children were 6.0 years and 20.0 kg, respectively. Meropenem serum concentration-time profiles were best described by a two-compartmental model with first-order elimination. The simulations showed that the probabilities of target attainment (PTAs) for organisms with an MIC of 1 mg/L were 0.678 and 1.000 following i.v. bolus and 3-h infusion of meropenem (20 mg/kg/dose), respectively. Using a 3-h infusion of a 20 mg/kg/dose, the PTA was 0.999 and 0.765 for organisms with MICs of 4 mg/L and 8 mg/L, respectively. Meropenem given as i.v. bolus doses of 20 mg/kg/dose q8h appeared to be inadequate for PK/PD target attainment for organisms with an MIC of 1 mg/L. The simulations showed that meropenem administration via a 3-h infusion using the same dose improved the PTA.

PIII-55Prediction of clarithromycin nonlinear pharmacokinetics and its interaction with midazolam using a physiologically-based pharmacokinetic model

The scale‐up of in vitro data to estimate drug‐drug interactions is problematic for mechanism‐based inhibitors, such as clarithromycin (CLAR), because of inherently nonlinear disposition.

OIV-B-4

The prediction of the extent of drug‐drug interactions (DDIs) between the mechanism‐based inhibitors, erythromycin (ERY) and diltiazem (DTZ), and the CYP3A substrate midazolam (MDZ) is confounded by the time and concentration‐dependant clearance of the inhibitors.

A Bayesian analysis of published pharmacokinetic data - a ketoconazole example

In drug‐drug interaction research, an inhibitor/inducers pharmacokinetic model is usually neither directly available from clinical studies nor published literature, and sometimes published results were inconsistent. A well known CYP3A inhibitor, ketoconazole, was reported to follow either a one‐ or two‐compartment model. In order to establish its optimal PK model from published data, an innovative Bayesian meta‐analysis was proposed.