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The purpose of this study was to describe the nonlinear pharmacokinetics of piperacillin observed during intermittent infusion and continuous infusion by using a nonparametric population modeling approach. Data were 120 serum piperacillin concentration measurements from eight adult cystic fibrosis (CF) patients. Individual pharmacokinetic parameter estimates during intermittent infusion or continuous infusion were calculated by noncompartmental analysis and with a maximum iterative two-stage Bayesian estimator. To simultaneously describe concentration-time data during intermittent infusion and continuous infusion, nonlinear models were parameterized as two-compartment Michaelis-Menten models. Models were fit to the data with the nonparametric expectation maximization algorithm. The calculations were executed on a remote supercomputer. Nonlinear models were evaluated by log-likelihood estimates, residual plots, and R 2 values, and predictive performance was based on bias (mean weighted error [MWE]) and precision (mean weighted square error [MWSE]). A linear pharmacokinetic model could not describe combined intermittent infusion and continuous infusion data well. A good population model fit to the intermittent infusion and continuous infusion data was obtained with the constructed nonlinear models. Maximum a posteriori probability (MAP) Bayesian R 2 values for the nonlinear models were 0.96 to 0.97. Median parameter estimates for the best nonlinear model were as follows: K m , 58 ± 75 mg/liter (mean and standard deviation); V max , 1,904 ± 1,009 mg/h; volume of distribution of the central compartment, 14.1 ± 3.0 liters; k 12 , 0.63 ± 0.41 h −1 ; and k 21 , 0.37 ± 0.19 h −1 . The median bias (MWE) and precision (MWSE) values for MAP Bayesian estimation with the Michaelis-Menten model were 0.05 and 4.6 mg/liters, respectively. The developed nonlinear pharmacokinetic models can be used to optimize piperacillin therapy administered via continuous infusion in patients with CF and have distinct advantages over conventional linear models.

Combination therapy with antimicrobial agents can be used against bacteria that have reduced susceptibilities to single agents. We studied various tobramycin and ceftazidime dosing regimens against four resistant Pseudomonas aeruginosa strains in an in vitro pharmacokinetic model to determine the usability of combination therapy for the treatment of infections due to resistant bacterial strains. For the selection of an optimal dosing regimen it is necessary to determine which pharmacodynamic parameter best predicts efficacy during combination therapy and to find a simple method for susceptibility testing. An easy-to-use, previously described E-test method was evaluated as a test for susceptibility to combination therapy. That test resulted in a MIC combi , which is the MIC of, for example, tobramycin in the presence of ceftazidime. By dividing the tobramycin and ceftazidime concentration by the MIC combi at each time point during the dosing interval, fractional inhibitory concentration (FIC) curves were constructed, and from these curves new pharmacodynamic parameters for combination therapy were calculated (i.e., AUC combi , C max-combi , T >MIC-combi , and T >FICi , where AUC combi , C max-combi , T >MIC-combi , and T >FICi are the area under the FIC combi curve, the peak concentration of FIC combi , the time that the concentration of the combination is above the MIC combi , and the time above the FIC index, respectively). By stepwise multilinear regression analysis, the pharmacodynamic parameter T >FICi proved to be the best predictor of therapeutic efficacy during combination therapy with tobramycin and ceftazidime ( R 2 = 0.6821; P < 0.01). We conclude that for combination therapy with tobramycin and ceftazidime the T >FICi is the parameter best predictive of efficacy and that the E-test for susceptibility testing of combination therapy gives promising results. These new pharmacodynamic parameters for combination therapy promise to provide better insight into the rationale behind combination therapy.

The influence of half-life on the postantibiotic effect (PAE) of tobramycin against Pseudomonas aeruginosa and Staphylococcus aureus was investigated during one dosing interval. Tobramycin half-lives of 0.5 to 2.5 h were simulated in an in vitro model, and the PAE was determined by an enzymatic inactivation method at different time points, i.e., when the tobramycin concentrations were 20×, 5×, and 1× the MIC. At the time point during therapy when the tobramycin concentrations had declined to 1× the MIC, at a tobramycin half-life of 0.5 h, the times of the PAEs were approximately 0.7 and 1.7 h for P. aeruginosa and S. aureus , respectively, and the PAE disappeared completely at half-lives corresponding to those found in humans (i.e., 2 to 2.5 h). The PAE itself cannot be fully explained by the presence of free intrabacterial tobramycin or the emergence of resistant subpopulations. The explanation for the disappearance of the PAE during the dosing interval may therefore be explained by the repair of sublethal damage. Since the standard method of determining the PAE in animal models is somewhat different from the method used for measurement of the PAE in vitro, the conditions under which the PAE is measured in vivo were also simulated in the in vitro model. This resulted in PAEs longer than those found by the standard method of obtaining in vitro PAE measurements. We conclude that the PAE of tobramycin, as determined by conventional in vitro methods, has virtually no clinical importance. PAEs determined in vivo may have some clinical relevance, but they are probably primarily caused by sub-MIC effects.