Article

Pharmacokinetic-pharmacodynamic modeling of the effect of fluvoxamine on p-chloroamphetamine-induced behavior.

Department of Drug Metabolism and Pharmacokinetics, Johnson and Johnson Pharmaceutical Research and Development, a Division of Janssen Pharmaceutica N.V., Beerse, Belgium.
European Journal of Pharmaceutical Sciences (Impact Factor: 2.99). 11/2007; 32(3):200-8. DOI: 10.1016/j.ejps.2007.07.004
Source: PubMed

ABSTRACT The pharmacokinetic-pharmacodynamic (PK-PD) correlation of the effect of fluvoxamine on para-chloroamphetamine (PCA)-induced behavior was determined in the rat. Rats (n=66) with permanent arterial and venous cannulas received a 30-min intravenous infusion of 1.0, 3.7 or 7.3 mg kg(-1) fluvoxamine. At various time points after the start of fluvoxamine administration, a single dose of PCA (2.5 mg kg(-1)) was injected in the tail vein and resulting behavioral effects, excitation (EXC), flat body posture (FBP) and forepaw trampling (FT), were immediately scored (scores: 0, 1, 2 or 3) over a period of 5 min. In each individual animal the time course of the fluvoxamine plasma concentration was determined up to the time of PCA administration. Observed behavioral effects were related to fluvoxamine plasma concentrations. Fluvoxamine pharmacokinetics was described by a population three-compartment pharmacokinetic model. The effects of fluvoxamine on PCA-induced behavior (probability of EXC, FBP and FT) were directly related to fluvoxamine plasma concentration on the basis of the proportional odds model. For EXC, EC(50) values for the cumulative probabilities P(Y<1), P(Y<2), P(Y<3) were 237+/-39, 174+/-28 and 100+/-20 ng ml(-1), respectively. Slightly higher EC(50) values were obtained for the corresponding effects on FBP and FT. This investigation demonstrates the feasibility of PK-PD modeling of categorical drug effects in animal behavioral pharmacology. This constitutes a basis for the future development of a mechanism-based PK-PD model for fluvoxamine in this paradigm.

0 Bookmarks
 · 
137 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Characterizing the relationship between the pharmacokinetics (PK, concentration vs. time) and pharmacodynamics (PD, effect vs. time) is an important tool in the discovery and development of new drugs in the pharmaceutical industry. The purpose of this publication is to serve as a guide for drug discovery scientists toward optimal design and conduct of PK/PD studies in the research phase. This review is a result of the collaborative efforts of DMPK scientists from various Metabolism and Pharmacokinetic (MAP) departments of the global organization Novartis Institute of Biomedical Research (NIBR). We recommend that PK/PD strategies be implemented in early research phases of drug discovery projects to enable successful transition to drug development. Effective PK/PD study design, analysis, and interpretation can help scientists elucidate the relationship between PK and PD, understand the mechanism of drug action, and identify PK properties for further improvement and optimal compound design. Additionally, PK/PD modeling can help increase the translation of in vitro compound potency to the in vivo setting, reduce the number of in vivo animal studies, and improve translation of findings from preclinical species into the clinical setting. This review focuses on three important elements of successful PK/PD studies, namely partnership among key scientists involved in the study execution; parameters that influence study designs; and data analysis and interpretation. Specific examples and case studies are highlighted to help demonstrate key points for consideration. The intent is to provide a broad PK/PD foundation for colleagues in the pharmaceutical industry and serve as a tool to promote appropriate discussions on early research project teams with key scientists involved in PK/PD studies.
    Frontiers in Pharmacology 01/2014; 5:174.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Clinical outcomes are often described as events: death, stroke, epileptic seizure, multiple sclerosis lesions, recurrence of cancer, disease progression, pain, infection and bacterial/viral eradication, severe toxic adverse effect, resistance to treatment, etc. They may be quantified as time-to-event, counts of events per time interval (rates), their severity grade, or a combination of these. Such data are discrete and require specific modelling structures and methods. This article references the most common modelling approaches for categorical, count and time-to-event data, and reviews examples of such models applied in the analysis of pharmacodynamic data. Modelling is useful for identification of influential factors related to the clinical outcome, characterization and quantification of their impact, for making better informed predictions and clinical decisions, assessments of efficacy of therapeutic interventions, optimizing the individual treatments and drug development studies.
    Clinical Pharmacokinetics 12/2012; 51(12):767-86. · 5.49 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A pharmacokinetic (PK) model is proposed for estimation of total and free brain concentrations of fluvoxamine. Rats with arterial and venous cannulas and a microdialysis probe in the frontal cortex received intravenous infusions of 1, 3.7 or 7.3 mg.kg(-1) of fluvoxamine. With increasing dose a disproportional increase in brain concentrations was observed. The kinetics of brain distribution was estimated by simultaneous analysis of plasma, free brain ECF and total brain tissue concentrations. The PK model consists of three compartments for fluvoxamine concentrations in plasma in combination with a catenary two compartment model for distribution into the brain. In this catenary model, the mass exchange between a shallow perfusion-limited and a deep brain compartment is described by a passive diffusion term and a saturable active efflux term. The model resulted in precise estimates of the parameters describing passive influx into (k in) of 0.16 min(-1) and efflux from the shallow brain compartment (k out) of 0.019 min(-1) and the fluvoxamine concentration at which 50% of the maximum active efflux (C 50) is reached of 710 ng.ml(-1). The proposed brain distribution model constitutes a basis for precise characterization of the PK-PD correlation of fluvoxamine by taking into account the non-linearity in brain distribution.
    Pharmaceutical Research 05/2008; 25(4):792-804. · 4.74 Impact Factor

Full-text (2 Sources)

Download
108 Downloads
Available from
Jun 1, 2014