In order to determine the microbiological and pharmacokinetic parameters that best predicted the in vivo antistaphylococcal activity of the streptogramin RP 59500 (quinupristin-dalfopristin), we evaluated the activity in rabbit aortic endocarditis of three regimens of quinupristin-dalfopristin against five strains of Staphylococcus aureus with various streptogramin B-type antibiotic resistance phenotypes and susceptible to streptogramin A-type antibiotics. Quinupristin-dalfopristin was as active as vancomycin against three strains that were susceptible to its streptogramin B component quinupristin, including one strain that was inducibly resistant to erythromycin, but had a significantly decreased activity against two strains that were resistant to quinupristin, for all quinupristin-dalfopristin regimens tested (P < 0.05). The area under the concentration-time curve for quinupristin-dalfopristin in plasma divided by the MIC of quinupristin was the only parameter retained by multilinear regression that predicted the in vivo activity of quinupristin-dalfopristin (P ‫؍‬ 0.0001), emphazing the importance of determining the susceptibility to quinupristin in order to predict the in vivo activity of quinupristin-dalfopristin against S. aureus.

Aims To evaluate the distribution of population kinetic parameters for clozapine and their relationship to age and gender in patients on continuous treatment with the drug. Methods Retrospective therapeutic drug monitoring data (391 samples from 241 patients) were evaluated using the nonparametric maximum likelihood method. Patients treated concomitantly with drugs known to interact with clozapine were not included. The distribution of clozapine clearance was compared with the distribution of the activity of the drug metabolic enzyme CYP1A2 found in other populations, as recent studies indicate that CYP1A2 is a major determinant for clozapine elimination. The kinetic linearity for clozapine was studied in 41 patients who each provided data from more than one dose level. Conclusion The large kinetic variability for clozapine found in this study implies that the dose of clozapine needs to be individualised over a wide dose range. The similarity of the distribution of clozapine clearance in this study and the CYP1A2-activity in other populations support the assumption that CYP1A2 is a major determinant for clozapine elimination. ResultsKeywords: clozapine elimination of clozapine. When subjects who previously Introduction participated in a panel study of clozapine kinetics [8] on a later occasion were examined with regard to their The use of the neuroleptic drug clozapine has in recent years become widespread, despite the risk of causing CYP1A2-activity, a high correlation (r=−0.84, P<0.005) between the AUC of clozapine and the N3-demethylation agranulocytosis [1]. One reason is that some schizophrenic patients resistant to other drugs may respond to clozapine of caffeine was found [9]. This strongly supports the notion that clozapine is metabolized chiefly by CYP1A2. Another [2]. Another reason is that clozapine rarely induces extrapyramidal adverse effects, which is a major problem with other indication of a connection between clozapine elimination and CYP1A2 is that clozapine inhibits the degradation of neuroleptics.Clozapine is eliminated almost entirely by hepatic theophylline in vitro [10], where it is known that theophylline is metabolized by CYP1A2 [11]. metabolism [3]. Several metabolites are formed by demethylation and hydroxylation reactions [4, 5], but the pathways A study of 13 patients showed linear kinetics of clozapine under steady-state conditions at trough concentrations in and enzymes involved have not been mapped out in detail. One recent study of drug interactions with clozapine showed the range 60-250 mg ml −1 [12]. The peak concentration when adjusted for dose and the clearance at the dose levels that the antidepressant fluvoxamine effectively inhibits the elimination of clozapine, thereby increasing the dose-37.5, 75 and 150 mg day −1 were compared intraindividually and were found to be similar. Given that a relationship adjusted clozapine concentration by a factor of 5-10 [6]. Fluvoxamine is also a potent inhibitor of the enzyme between the kinetics of clozapine and the activity of CYP1A2 e...

In this paper 3 criteria to design experiments for Bayesian estimation of the parameters of nonlinear models with respect to their parameters, when a prior distribution is available, are presented: the determinant of the Bayesian information matrix, the determinant of the pre-posterior covariance matrix, and the expected information provided by an experiment. A procedure to simplify the computation of these criteria is proposed in the case of continuous prior distributions and is compared with the criterion obtained from a linearization of the model about the mean of the prior distribution for the parameters. This procedure is applied to two models commonly encountered in the area of pharmacokinetics and pharmacodynamics: the one-compartment open model with bolus intravenous single-dose injection and the Emax model. They both involve two parameters. Additive as well as multiplicative gaussian measurement errors are considered with normal prior distributions. Various combinations of the variances of the prior distribution and of the measurement error are studied. Our attention is restricted to designs with limited numbers of measurements (1 or 2 measurements). This situation often occurs in practice when Bayesian estimation is performed. The optimal Bayesian designs that result vary with the variances of the parameter distribution and with the measurement error. The two-point optimal designs sometimes differ from the D-optimal designs for the mean of the prior distribution and may consist of replicating measurements. For the studied cases, the determinant of the Bayesian information matrix and its linearized form lead to the same optimal designs. In some cases, the pre-posterior covariance matrix can be far from its lower bound, namely, the inverse of the Bayesian information matrix, especially for the Emax model and a multiplicative measurement error. The expected information provided by the experiment and the determinant of the pre-posterior covariance matrix generally lead to the same designs except for the Emax model and the multiplicative measurement error. Results show that these criteria can be easily computed and that they could be incorporated in modules for designing experiments.