Population pharmacokinetics of tobramycin.

Aarons, Leon; Vozeh, S; Wenk, Markus R.; Weiß, Peter; Folláth, Ferenc

doi:10.1111/j.1365-2125.1989.tb05431.x

Cited by 77 publications

(110 citation statements)

References 23 publications

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“…Applications are numerous, some of which can be found in the British where Y ij indicates the response from subject i at time j, and the h i are the individual parameters belonging to Journal of Clinical Pharmacology [7,8]. An exhaustive bibliography with methodological references and refersubject i.…”

Section: Introductionmentioning

confidence: 99%

Estimating impossible curves using NONMEM

Schoemaker

Cohen

1996

Brit J Clinical Pharma

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1 On fitting model equations to experimental data, the situation may arise that individual subjects provide insufficient information to obtain adequate parameter estimates due to the fact that not all aspects are exhibited by all subjects or that the models are simply too complex. This may be solved by applying nonlinear mixed effect modelling to the data, which integrates the information provided by different subjects. 2 We aim to provide insight into the methodology and its use in these situations, illustrated by three examples: determination of pharmacokinetics in a rising dose design, where the lower doses provide insufficient information (due to assay limitations) to estimate terminal half-life; determination of the kinetics of the low molecular weight heparin enoxaparine (ClexaneB) using anti-Xa activity, effectively dealing with lingering low/basal activity; simultaneous estimation of the pharmacokinetics and pharmacodynamics of the low molecular weight heparin dalteparine (FragminB) after subcutaneous and intravenous administration.Keywords parameter estimation NONMEM rising dose design low molecular weight heparin Introduction Nonlinear mixed effect modelling

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Section: Introductionmentioning

confidence: 99%

Estimating impossible curves using NONMEM

Schoemaker

Cohen

1996

Brit J Clinical Pharma

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“…The idea is that pharmacokinetics accounts for some of the variability in the dose-response relationship. However, in order to transform dose into concentration, a pharmacokinetic model is required, based on an analysis of concentration-time data and preferably incorporating relevant patient characteristics, allowing it to be used for individualized therapy [2]. The complexity of a pharmacokinetic model depends on the level and quality of information available and on its purpose.…”

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confidence: 99%

Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed

Aarons

2005

Brit J Clinical Pharma

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Pharmacokinetics is the science of drug absorption, distribution, and elimination, or more specifically the quantification of those processes, leading to the understanding, interpretation, and prediction of blood concentration-time profiles. Occasionally, concentration-time data from other physiological fluids or tissues are available, but it is the lack of data in relevant tissues and organs that limits one's ability to get at the underlying mechanisms determining the blood profile. For example, blood concentration data are used to evaluate drug absorption for orally administered drugs, even though absorption is a multistep erratic process under the control of many factors, and measurements within the gut are not usually available.Nevertheless, blood concentration can be a useful biomarker, and sometimes a surrogate [1], to guide therapy. The idea is that pharmacokinetics accounts for some of the variability in the dose-response relationship. However, in order to transform dose into concentration, a pharmacokinetic model is required, based on an analysis of concentration-time data and preferably incorporating relevant patient characteristics, allowing it to be used for individualized therapy [2]. The complexity of a pharmacokinetic model depends on the level and quality of information available and on its purpose. A hierarchy of pharmacokinetic modelsPharmacokinetic models can be classified in order of increasing complexity. At the lowest level is the empirical model most often described by a sum of exponential terms, as in the following equation:This model adequately describes typical concentrationtime profiles and can be used to derive primary pharma- cokinetic parameters, such as clearance and half-life. It can also be used to devise a dosage regimen for a subject who has the same set of parameters [C i , λ i ]. Although useful for data description and interpolation, empirical models are very poor at extrapolation. This is because the parameters do not have a physiological interpretation, and it is difficult to predict how they change when the underlying physiology changes. For example, with a biexponential model after an intravenous bolus dose it is not obvious how the coefficients C 1 and C 2 change with age. This problem can be partly addressed by adopting a compartmental approach with a so-called physiological parameterization. A classical two-compartment model is shown in Figure 1. The concentration-time profiles that result from this model and a biexponential model are the same. However, with this model it is easier to relate the parameters to physiological processes. For example, clearance can be related to renal function, perhaps through creatinine clearance measurements, and the effect of renal impairment on the shape of the concentration-time profile can be predicted. Nevertheless, the compartments in these classical compartment models do not represent real physical spaces, and their meaning has been misrepresented by many authors. Consequently, these models should be viewed as semi-mechanistic mod...

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“…A number of applications can be found in the pharmacokinetic literature (Aarons, 1992;Sheiner and Ludden, 1992;Vozeh et al, 1996;Yuh et al, 1994) . Until recently, little attention had been paid to these approaches in toxicokinetics.…”

Section: Population Modelsmentioning

confidence: 99%

Applications of population approaches in toxicology

Bois

2001

Toxicology Letters

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Many experimental or observational studies in toxicology are best analysed in a population framework.Recent examples include investigations of the extent and origin of intra-individual variability in toxicity studies, incorporation of genotypic information to address intra-individual variability, optimal design of experiments, and extension of toxicokinetic modelling to the analysis of biomarker studies in industrial hygiene or epidemiological contexts. Bayesian statistics provide powerful numerical methods for fitting population models, particularly when complex mechanistic toxicokinetic or toxicodynamic models are involved. Challenges and limitations to the use of population models, in terms of basic structure, computational burden, ease of implementation and data accessibility, are identified and discussed.

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Population pharmacokinetics of tobramycin.

Cited by 77 publications

References 23 publications

Estimating impossible curves using NONMEM

Estimating impossible curves using NONMEM

Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed

Applications of population approaches in toxicology

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