We propose a model of drug pharmacodynamic response that when integrated with a pharmacokinetic model allows characterization of the temporal aspects of pharmacodynamics as well as the time-independent sensitivity component. The total model can accommodate extremes of effect. It allows fitting of simultaneous plasma concentration (Cp) and effect data from the initial distribution phase of drug administration, or from any non-equilibrium phase. The model postulates a hypothetical effect compartment, the dynamics of which are adjusted to reflect the temporal dynamics of drug effect. The effect compartment is modeled as an additional compartment linked to the plasma compartment by a first-order process, but whose exponential does not enter into the pharmacokinetic solution for the mass of drug in the body. The hypothetical amount of drug in the effect compartment is then related to the observed effect by the Hill equation, a nonlinear sigmoid form. Nonlinear least-squares data fitting is used for parameter estimation. The model is demonstrated on two different sets of Cp and effect data for the drug d-tubocurarine (dTC). In 7 normal subjects, the (mean +/- SD) rate constant for equilibration of dTC effect (paralysis) and Cp is 0.13 +/- 0.04 min-1 and the (mean +/- SD) steady-state Cp required to produce 50% paralysis is 0.37 +/- 0.05 microgram/ml.

1. Population pharmacokinetic parameters of tobramycin were determined in a heterogenous group of 97 patients using serum samples drawn for the routine monitoring of tobramycin concentrations, following multiple dosing regimens. 2. To describe the accumulation kinetics of tobramycin a two‐compartment pharmacokinetic model was required. The best fit to the data was obtained when drug clearance (1 h‐1) was related linearly to creatinine clearance (proportionality constant: 0.059 +/‐ 0.002 x CLcr (ml min‐1)) and initial volume of distribution (1) was related linearly to body weight (proportionality constant: 0.327 +/‐ 0.014 x body weight (kg)). The intersubject variability in these two parameters was 32% and 3%, respectively, whilst the residual or intrasubject variability amounted to 21% of the tobramycin concentration. The terminal half‐life of tobramycin, 26.6 +/‐ 9.4 h, was appreciably shorter than previously reported. 3. The population pharmacokinetic model was validated against data obtained from 34 independent patients and the predicted and observed concentrations were found to be in good agreement. The population pharmacokinetic model was used to design a priori dosing recommendations for tobramycin.

Most previously suggested methods for predicting phenytoin dosage from steady-state drug levels (Cpss) measured in the clinical setting fail to fully exploit all relevant (population) information. A bayesian prediction method, applicable to any drug, is available. It appropriately combines all types of information. In this paper, we compare the Bayesian method as applied to phenytoin to two other prediction methods (and a baseline, nonfeedback one). Actual doses are compared to predictions in 49 patients. Each method is optimized, as far as possible, for the test data. The comparison favors the Bayesian method. Since each of the other prediction methods for phenytoin can be shown to be a theoretically suboptimal special case of the Bayesian one, the superiority of the latter may be a general phenomenon. Because the pharmacokinetic model linking steady-state phenytoin levels and dosage is so simple, a good approximation of the general Bayesian method can be implemented as a graphical device, or as a program for a programmable calculator. We present and describe both of these approximations.

The most important points revealed by this analysis were: (1) when interpreting the results of interaction studies, it is important to consider not only the mean of the interaction effect but also the observed and the theoretically conceivable extreme effects in individual subjects and (2) a drug with a high interaction potential may represent a high risk even if an adequate warning is included in the product information. The need for specific pharmacokinetic and pharmacodynamic interaction studies with new drugs and the limitations of the pivotal clinical efficacy and safety studies during phase III in order to reveal such interactions are discussed.

Netilmicin pharmacokinetics were studied in neonates of 27 to 42 weeks' gestational age and 0.8 to 5.0 kg body weight in their first 2 weeks of life by the population pharmacokinetic approach. The data were best described by a two-compartment model. Clearance depends on body weight, gestational age, and postnatal age. Volume of distribution of the central and peripheral compartments was also related to body weight. Including these patient characteristics in the population pharmacokinetic regression model resulted in a marked reduction of the unexplained interindividual variability. This enabled us to derive dosage recommendations that result in peak and average concentrations within the desired range for 95% of the neonates with gestational age above 31 weeks, thus avoiding the need for individual drug-level monitoring in a well-defined large group of patients. Only for infants with gestational age less than 31 weeks who are less than 6 days old is individual dose adjustment based on serum concentration measurements required.