Journal of Pharmacokinetics and Biopharmaceutics最新文献

Basic indirect pharmacodynamic models for agents which alter the generation of natural cells based on a life-span concept are introduced. It is assumed that cells (R) are produced at a constant rate (kin), survive for a specific duration TR, and then are lost. The rate of cell loss must equal the production rate but is delayed by TR. A therapeutic agent can stimulate or inhibit the production rate according to the Hill function: 1 +/- H(C(t)) where H(C(t)) contains capacity (Smax) and sensitivity (SC50) constants and C(t) is a pharmacokinetic function. Thus an operative model is [equation: see text] with the baseline condition R0 = kin.TR. One- and two-compartment catenary cell models were examined by simulation to describe the role of pharmacokinetics and cell properties. The area under the effect curve (AUCE) was derived. The models were applied to literature data to describe the stimulatory effects of single doses of hematopoietic growth factors such as granulocyte colony-stimulating factor (G-CSF) on neutrophils, thrombopoietin (TPO) on platelets, and erythropoietin (EPO) on reticulocytes in blood. The models described experimental data adequately and provided cell life-spans and SC50 values. The proposed cell production/loss models can be readily used to analyze the pharmacodynamics of agents which alter cell production yielding realistic physiological parameters.

The objective of the present study was to investigate the pharmacokinetics (PK) and pharmacodynamics (PD) of desmopressin in healthy male subjects at different levels of overhydration. Also, we examined if an indirect-response model could be related to renal physiology and the pharmacological action of desmopressin. Eight healthy male subjects participated in this open, randomized crossover study with three periods. Each subject was orally water loaded (0 to 20 ml.kg-1 body weight) on 3 study days in order to achieve three different levels of hydration. After the initial water load, urine was voided every 15 min and the volumes were measured. To ensure continuous overhydration the subjects replaced their fluid loss with drinking-water. When a steady-state diuresis was achieved after approximately 2 hr, 0.396 microgram of desmopressin was administered intravenously as a bolus injection. Blood was sampled and urine was collected at intervals throughout the study day (10 hr). An indirect-response model, where desmopressin was assumed to inhibit the elimination of response, was fit to the urine osmolarity data. There were no statistically significant effects of different levels of hydration, as expressed by urine flow rate at baseline, on the estimates of the PK and PD model parameters. The calculated terminal half-lives of elimination (t1/2 beta) ranged between 2.76 and 8.37 hr with an overall mean of 4.36 hr. The overall means of plasma clearance and the volumes of distribution of the central compartment (Vc) and at steady state (Vss) were estimated to be 1.34 (SD 0.35) ml.min-1.kg-1, 151 (SD28) ml.kg-1, and 386 (SD 63) ml.kg-1, respectively. High urine flow rate, indicating overhydration, produced a diluted urine and thus a low osmolarity at baseline (R0). The effect of the urine flow rate on the urine osmolarity at baseline was highly significant (p < 0.0001). The mean values for IC50 and the sigmodicity factor (gamma) were 3.7 (SD 1.2) pg ml-1 and 13.0 (SD 3.5), respectively. In most cases when there was a high urine flow rate at baseline, the model and the estimated PD parameters could be related to the pharmacological action of desmopressin and renal physiology. Thus, the indirect-response model used in this study offers a mechanistic approach of modeling the effect of desmopressin in overhydrated subjects.

Population approaches are appealing methods for detecting then assessing drug-drug interactions mainly because they can cope with sparse data and quantify the interindividual pharmacokinetic (PK) and pharmacodynamic (PD) variability. Unfortunately these methods sometime fail to detect interactions expected on biochemical and/or pharmacological basis and the reasons of these false negatives are somewhat unclear. The aim of this paper is firstly to propose a strategy to detect and assess PD drug-drug interactions when performing the analysis with a nonparametric population approach, then to evaluate the influence of some design variates (i.e., number of subjects, individual measurements) and of the PD interindividual variability level on the performances of the suggested strategy. Two interacting drugs A and B are considered, the drug B being supposed to exhibit by itself a pharmacological action of no interest in this work but increasing the A effect. Concentrations of A and B after concomitant administration are simulated as well as the effect under various combinations of design variates and PD variability levels in the context of a controlled trial. Replications of simulated data are then analyzed by the NPML method, the concentration of the drug B being included as a covariate. In a first step, no model relating the latter to each PD parameter is specified and the NPML results are then proceeded graphically, and also by examining the expected reductions of variance and entropy of the estimated PD parameter distribution provided by the covariate. In a further step, a simple second stage model suggested by the graphic approach is introduced, the fixed effect and its associated variance are estimated and a statistical test is then performed to compare this fixed effect to a given value. The performances of our strategy are also compared to those of a non-population-based approach method commonly used for detecting interactions. Our results illustrate the relevance of our strategy in a case where the concentration of one of the two drugs can be included as a covariate and show that an existing interaction can be detected more often than with a usual approach. The prominent role of the interindividual PD variability level and of the two controlled factors is also shown.

For anesthetic drugs undergoing nonorgan-based elimination, there is a definite trend towards using pharmacokinetic (PK) models in which elimination can occur from both central (k10) and peripheral compartments (k20). As the latter cannot be assessed directly, assumptions have to be made regarding its value. The primary purpose of this paper is to evaluate the impact of assuming various degrees of peripheral elimination on the estimation of PK parameters. For doing so, an explanatory model is presented where previously published data from our laboratory on three muscle relaxants, i.e., atracurium, doxacurium, and mivacurium, are used for simulations. The mathematical aspects for this explanatory model as well as for two specific applications are detailed. Our simulations show that muscle relaxants having a short elimination half-life are more affected by the presence of peripheral elimination as their distribution phase occupies the major proportion of their total area under the curve. Changes in the exit site dependent PK parameters (Vdss) are also mostly significant when k20 is smaller than k10. Although the physiological processes that determine drug distribution and those affecting peripheral elimination are independent, the two are mathematically tied together in the two-compartment model with both central and peripheral elimination. It follows that, as greater importance is given to k20, the rate of transfer from the central compartment (k12) increases. However, as a result of a proportional increase in the volume of the peripheral compartment, peripheral concentrations remain unchanged whether or not peripheral elimination is assumed. These findings point out the limitations of compartmental analysis when peripheral elimination cannot be measured directly.

A theory is developed for estimation of a population value of AUC along with its standard deviation, in the case, when only one concentration-time (C-t) sample is available for each individual. This theory is based on model-independent pharmacokinetics. Integration methods are classified due to their applicability to the presented approach. The main goal of this work is to establish a statistical hypothesis-testing procedure which would make single C-t samples usable for bioequivalence studies. An application of the theory to a number of integration methods currently in use is analyzed in detail. A real data illustration is included.