Library

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Electronic Resource  (4)
  • population pharmacokinetics  (4)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 24 (1996), S. 265-282 
    ISSN: 1573-8744
    Keywords: compliance ; MEMS ; population pharmacokinetics ; Markov chain model
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract For population pharmacokinetic analysis of multiple oral doses one of the key issues is knowing as precisely as possible the dose inputs in order to fit a model to the input-output (dose-concentration) relationship. Recently developed electronic monitoring devices, placed on pill containers, permit precise records to be obtained over months, of the time/date opening of the container. Such records are reported to be the most reliable measurement of drug taking behavior for ambulatory patients. To investigate strategies for using and summarizing this new abundant information, a Markov chain process model was developed, that simulates compliance data from real data from electronically monitored patients, and data simulations and analyses were conducted. Results indicate that traditional population pharmacokinetic analysis methods that ignore actual dosing information tend to estimate biased clearance and volume and markedly overestimate random interindividual variability. The best dosing information summarization strategies consist of initially estimating population pharmacokinetic parameters, using no covariates and only a limited number of dose records, the latter chosen based on an a priori estimate of the half-life of the drug in the compartment of interest; then resummarizing the dose records using either population or individual posterior Bayes parameter estimates from the first population fit; and finally reestimating the population parameters using the newly summarized dose records. Such summarization strategies yield the same parameter estimates as using full dosing information records while reducing by at least 75% the CPU time needed for a population pharmacokinetic analysis.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    ISSN: 1573-8744
    Keywords: Taxotere ; docetaxel ; population pharmacokinetics ; NONMEM ; model building ; model validation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A sparse sampling strategy (3 samples per patient, 521 patients) was implemented in 22 Phase 2 studies of docetaxel (Taxotere®) at the first treatment cycle for a prospective population pharmacokinetic evaluation. In addition to the 521 Phase 2 patients, 26 (data rich) patients from Phase 1 studies were included in the analysis. NONMEM analysis of an index set of 280 patients demonstrated that docetaxel clearance (CL) is related to α1-acid glycoprotein (AAG) level, hepatic function (HEP), age (AGE), and body surface area (BSA). The index set population model prediction ofCL was compared to that of a naive predictor (NP) using a validation set of 267 patients. Qualitatively, the dependence ofCL onAAG, AGE, BSA, andHEP seen in the index set population model was supported in the validation set. Quantitatively, for the validation set patients overall, the performance (bias, precision) of the model was good (7 and 21%, respectively), although not better than that of theNP. However, in all the subpopulations with decreasedCL, the model performed better than theNP; the more theCL differed from the population average, the better the performance. For example, in the subpopulation of patients withAAG levels〉2.27 g/L (n=26) bias and precision of model predictions were 24 and 32% vs. 53 and 53%, respectively, for theNP. The prediction ofCL using the model was better (than that of theNP) in 73% of the patients. The population model was redetermined using the whole population of 547 patients and a new covariate, albumin plasma level, was found to be a significant predictor in addition to those found previously. In the final model,HEP, AAG, andBSA are the main predictors of docetaxelCL.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 8 (1980), S. 553-571 
    ISSN: 1573-8744
    Keywords: nonlinear regression ; population pharmacokinetics ; Michaelis-Menten model ; phenytoin ; statistics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual intraindividual variability plus measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters are estimated either by fitting all individual's data together as though there were no individual kinetic differences (the naive pooled data approach), or by fitting each individual's data separately, and then combining the individual parameter estimates (the two-stage approach). A third approach, NONMEM, takes a middle course between these, and avoids shortcomings of each of them. A data set consisting of 124 steady-state phenytoin concentration-dosage pairs from 49 patients, obtained in the routine course of their therapy, was analyzed by each method. The resulting population parameter estimates differ considerably (population mean Km, for example, is estimated as 1.57, 5.36, and 4.44 μg/ml by the naive pooled data, two-stage, and NONMEM approaches, respectively). Simulations of the data were analyzed to investigate these differences. The simulations indicate that the pooled data approach fails to estimate variabilities and produces imprecise estimates of mean kinetics. The two-stage appproach produces good estimates of mean kinetics, but biased and imprecise estimates of interindividual variability. NONMEM produces accurate and precise estimates of all parameters, and also reasonable confidence intervals for them. This performance is exactly what is expected from theoretical considerations and provides empirical support for the use of NONMEM when estimating population pharmacokinetics from routine type patient data.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 25 (1997), S. 615-642 
    ISSN: 1573-8744
    Keywords: semiparametric ; population pharmacokinetics ; mixed-effects model ; random-effects ; longitudinal spline ; nonparametric
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract We propose a semiparametric method to estimate model-independent pharmacokinetic (PK) measures such as area under concentration–time, peak concentration and time to peak concentration (Tpeak ), for noisy population PK data from a sparsely sampled prospectively designed trial. The method is developed within the mixed-effect model framework, for the single-dose and steady-state case. We describe individual concentration vs. time using a longitudinal spline, consisting of a template spline, common to all individuals, and an individual-specific distortion spline accounting for individual differences. We impose a number of constraints on the longitudinal spline, including (i) it has a decreasing tail, (ii) its typical Tpeak is near the modal Tpeak observed in the population data, and (iii) its value is zero at time zero (single dose), or the same nonzero value at the beginning and end of a dosing interval (steady state). We test our method using simulated data and compare its performance to that of a parametric and a nonparametric method. An actual data example is also shown. The performance of the method is as good or better than that of a standard nonparametric method, and when the analysis model is misspecified, the method is superior to a standard parametric one. Since it is often not apparent that an analysis model is correct, we propose this approach as a general method for analysis.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...