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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Annals of the New York Academy of Sciences 304 (1978), S. 0 
    ISSN: 1749-6632
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Natural Sciences in General
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 13 (1985), S. 185-201 
    ISSN: 1573-8744
    Keywords: least squares ; extended least squares ; maximum likelihood ; weighting ; precision ; parameter estimation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract The precision of pharmacokinetic parameter estimates from several least squares parameter estimation methods are compared. The methods can be thought of as differing with respect to the way they weight data. Three standard methods, Ordinary Least Squares (OLS-equal weighting), Weighted Least Squares with reciprocal squared observation weighting [WLS(y−2)], and log transform OLS (OLS(ln))-the log of the pharmacokinetic model is fit to the log of the observations-are compared along with two newer methods, Iteratively Reweighted Least Squares with reciprocal squared prediction weighting (IRLS,(f−2)), and Extended Least Squares with power function “weighting” (ELS(f−ξ)-here ξ is regarded as an unknown parameter). Tne values of the weights are more influenced by the data with the ELS(f−ξ) method than they are with the other methods. The methods are compared using simulated data from several pharmacokinetic models (monoexponential, Bateman, Michaelis-Menten) and several models for the observation error magnitude. For all methods, the true structural model form is assumed known. Each of the standard methods performs best when the actual observation error magnitude conforms to the assumption of the method, but OLS is generally least perturbed by wrong error models. In contrast, WLS(y−2) is the worst of all methods for all error models violating its assumption (and even for the one that does not, it is out performed by OLS(ln). Regarding the newer methods, IRLS(f−2) improves on OLS(ln), but is still often inferior to OLS. ELS(f−ξ), however, is nearly as good as OLS (OLS is only 1–2% better) when the OLS assumption obtains, and in all other cases ELS(f−ξ) does better than OLS. Thus, ELS(f−ξ.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 19 (1991), S. 79S 
    ISSN: 1573-8744
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 19 (1991), S. 691-712 
    ISSN: 1573-8744
    Keywords: pharmacokinetics ; pharmacodynamics ; linear system analysis ; nonlinear system analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract We present an approach to the analysis of pharmacodynamic (PD) data arising from non-steadystate experiments, meant to be used when only PD data, not pharmacokinetic (PK) data, are available. The approach allows estimation of the steady-state relationship between drug input and effect. The analysis is based on a model describing the time dependence of drug effect (E) on (unobserved) drug concentration (Ce) in an hypothetical effect compartment. The model consists of (i) a known model for the input rate of drug I(t), (ii) a parametric model; L(t, a) (a function of time t, and vector of parameters a), relating I to an observed variable X, (iii) a nonparametric model relating X to E. Ce is proportional to X. X(t) is given by I(X) * L(t, a)/AL, where L(t,α)=e −α 1 t * ∑ k=1 m , α2k e −α 2k+1 t, ∑ k=1 m α2k=1, AL=∫ 0 ∞ L(t) dt, and * indicates convolution.The nonparametric model relating X to Eis a cubic spline, a function of X and a vector of (linear) parameters β. The values of α and β are chosen to minimize the sum of squared residuals between predicted and observed E. We also describe a similar model, generalizing a previously described one, to analyze PK/PD data. Applications of the approach to different drug-effect relationships (verapamil-PR interval, hydroxazine-wheal and flare, flecainide and/or verapamil-PR, and left ventricular ejection fraction) are reported.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 23 (1995), S. 1-4 
    ISSN: 1573-8744
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 23 (1995), S. 651-672 
    ISSN: 1573-8744
    Keywords: population PK/PD ; residual error ; intraindividual variability ; autocorrelation ; replicates ; NONMEM
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Residual error models, traditionally used in population pharmacokinetic analyses, have been developed as if all sources of error have properties similar to those of assay error. Since assay error often is only a minor part of the difference between predicted and observed concentrations, other sources, with potentially other properties, should be considered. We have simulated three complex error structures. The first model acknowledges two separate sources of residual error, replication error plus pure residual (assay) error. Simulation results for this case suggest that ignoring these separate sources of error does not adversely affect parameter estimates. The second model allows serially correlated errors, as may occur with structural model misspecification. Ignoring this error structure leads to biased random-effect parameter estimates. A simple autocorrelation model, where the correlation between two errors is assumed to decrease exponentially with the time between them, provides more accurate estimates of the variability parameters in this case. The third model allows time-dependent error magnitude. This may be caused, for example, by inaccurate sample timing. A time-constant error model fit to time-varying error data can lead to bias in all population parameter estimates. A simple two-step time-dependent error model is sufficient to improve parameter estimates, even when the true time dependence is more complex. Using a real data set, we also illustrate the use of the different error models to facilitate the model building process, to provide information about error sources, and to provide more accurate parameter estimates.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 22 (1994), S. 431-445 
    ISSN: 1573-8744
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract In pharmacokinetic data analysis, it is frequently necessary to select the number of exponential terms in a polyexponential expression used to describe the concentration-time relationship. The performance characteristics of several selection criteria, the Akaike Information Criterion (AIC), and the Schwarz Criterion (SC), and theF test (α=0.05), were examined using Monte Carlo simulations. In particular, the ability of these criteria to select the correct model, to select a model allowing estimation of pharmacokinetic parameters with small bias and good precision, and to select a model allowing precise predictions of concentration was evaluated. To some extent interrelationships among these procedures is explainable. Results indicate that theF test tends to choose the simpler model more often than does either the AIC or SC, even when the more complex model is correct. Also, theF test is more sensitive to deficient sampling designs. Clearance estimates are generally very robust to the choice of the wrong model. Other pharmacokinetic parameters are more sensitive to model choice, particularly the apparent elimination rate constant. Prediction of concentrations is generally more precise when the correct model is chosen. The tendency for theF test (α=0.05) to choose the simpler model must be considered relative to the objectives of the study.
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 9 (1981), S. 59-127 
    ISSN: 1573-8744
    Keywords: Clinical Pharmacokinetic Summary ; population parameters ; data compilation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A standard format for a Clinical Pharmacokinetic Summary is proposed. It consists of a heading, table, notes, and references for each drug reviewed. The table presents a unified and logical set of clinically useful population pharmacokinetic parameters. They concern four major areas: absorption, distribution, elimination, and the relationship of concentration to effect. Within each major group, parameters dealing with extents and rates of processes are given. Each such parameter is really two: a population mean value (for example, average volume of distribution) and the standard deviation of individual values about this mean. The first value allows individual predictions of dosage or drug level to be made; the second allows computation of the likely proximity of subsequently observed quantities to those predictions. The table presents single consensus values for each population parameter, rather than a list of values. A procedure for computing these consensus values, and for revising them in the light of new data, or reinterpreted old data, is given. Examples of Summaries are given. The method appears applicable to a variety of drugs. We suggest our approach as a standard one for preparing Clinical Pharmacokinetic Summaries, and urge our colleagues to consider it for that purpose.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 10 (1982), S. 229-229 
    ISSN: 1573-8744
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 17 (1989), S. 463-491 
    ISSN: 1573-8744
    Keywords: linear systems theory ; semiparametric physiological modeling ; parametric physiological modeling ; arterial to tissue partition coefficient ; clearance rates ; convolution ; deconvolution ; simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract By regarding sampled tissues in a physiological model as linear subsystems, the usual advantages of flow models are preserved while mitigating two of their disadvantages, (i) the need for assumptions regarding intratissue kinetics, and (ii) the need to simultaneously fit data from several tissues. To apply the linear systems approach, both arterial blood and (interesting) tissue drug concentrations must be measured. The body is modeled as having an arterial compartment (A) distributing drug to different linear subsystems (tissues), connected in a specific way by blood flow. The response CA,with dimensions of concentration) of A is measured. Tissues receive input from A (and optionally from other tissues), and send output to the outside or to other parts of the body. The response CT,total amount of drug in the tissue (T) divided by the volume of T) from the T- th one, for example, of such tissues is also observed. From linear systems theory, CT can be expressed as the convolution of CA with a disposition function, F(t) (with dimensions 1/time). The function F(t)depends on the (unknown) structure of T, but has certain other constant properties: The integral F(t) dt is the steady state ratio of CT to CA,and the point F(0)is the clearance rate of drug from A to T divided by the volume of T. A formula for the clearance rate of drug from T to outside T can be derived. To estimate F(t)empirically, and thus mitigate disadvantage (i), we suggest that, first, a nonparametric (or parametric) function befitted to CA data yielding predicted values, ĈA,and, second, the convolution integral of CA with F(t)befitted to CT data using a deconvolution method. By so doing, each tissue's data are analyzed separately,thus mitigating disadvantage (ii). A method for system simulation is also proposed. The results of applying the approach to simulated data and to real thiopental data are reported.
    Type of Medium: Electronic Resource
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