ABSTRACT: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.Metabolism-based pharmacokinetic (PK) interactions are well recognized as a source of clinically significant adverse drug reactions Huang and Lesko, 2004). The early forecast of clinically significant metabolism-based pharmacokinetic drug-drug interactions is an increasingly important aspect of drug development. The assessment of drug-drug interaction potential before the conduct of a clinical trial often involves simple algebraic calculations. One such calculation is the ratio of the expected clinical exposure to the in vitro inhibition constant (K i ) of a new drug entity for a specific cytochrome P450 (Sahajwalla et al., 1999;Bjornsson et al., 2003). The primary sources of quantitative errors associated with this prediction approach are attributed to the experimental procedures used to determine the in vitro parameters, the variability in the intrinsic factors associated with PK properties of the inhibitor (e.g., absorption kinetics, plasma protein binding, and tissue partition coefficients), or the substrate (i.e., the fraction of total clearance attributed to the elimination pathway of interest), the extrinsic factors associated with the clinical study designs (e.g., dosing scheme, sampling design, and population demographics), and, most notably, the uncertainty in the effective concentration of the inhibitor at the enzyme site. In addition, complex drug disposition proper...

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.

ABSTRACT:The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3A, as 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.

In drug-drug interaction (DDI) research, a two drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor/inducer or substrate's PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. In this paper, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The first level model is a study-specific sample mean model; the second level model is a random effect model connecting different PK studies; and all priors of PK parameters are specified in the third level model. A Monte Carlo Markov chain (MCMC) PK parameter estimation procedure is developed, and DDI prediction for a future study is conducted based on the PK models of two drugs and posterior distributions of the PK parameters. The performance of Bayesian meta-analysis in DDI prediction is demonstrated through a ketoconazole-midazolam example. The biases of DDI prediction are evaluated through statistical simulation studies. The DDI marker, ratio of area under the concentration curves, is predicted with little bias (less than 5 per cent), and its 90 per cent credible interval coverage rate is close to the nominal level. Sensitivity analysis is conducted to justify prior distribution selections.

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ObjectiveTo characterize the pharmacokinetics (PK) of vancomycin in patients in the initial phase of septic shock.MethodsTwelve patients with septic shock received an intravenous infusion of vancomycin 30 mg/kg over 2 h. The vancomycin PK study was conducted during the first 12 h of the regimen. Serum vancomycin concentration–time data were analyzed using the standard model-independent analysis and the compartment model.ResultsFor the noncompartment analysis the mean values ± standard deviation (SD) of the estimated clearance and volume of distribution of vancomycin at steady state were 6.05±1.06 L/h and 78.73±21.78 L, respectively. For the compartmental analysis, the majority of vancomycin concentration–time profiles were best described by a two-compartment PK model. Thus, the two-compartmental first-order elimination model was used for the analysis. The mean ± SD of the total clearance (3.70±1.25 L/h) of vancomycin was higher than that obtained from patients without septic shock. In contrast, the volume of the central compartment (8.34±4.36 L) and volume of peripheral compartment (30.99±7.84 L) did not increase when compared with patients without septic shock.ConclusionThe total clearance of vancomycin was increased in septic shock patients. However, the volume of the central compartment and peripheral compartment did not increase. Consequently, a loading dose of vancomycin should be considered in all patients with septic shock.

Vancomycin administered during the last hour of CAHP-210 dialysis results in 24% less vancomycin exposure than when administered post-haemodialysis. This intra-dialytic drug loss should be accounted for when dosing vancomycin in this manner.

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