Do we need full compliance data for population pharmacokinetic analysis? View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1996-06-01

AUTHORS

Pascal Girard, Lewis B. Sheiner, Helen Kastrissios, Terrence F. Blaschke

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. More... »

PAGES

265-282

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02353671

DOI

http://dx.doi.org/10.1007/bf02353671

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005925235

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/8970015


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