Do we need full compliance data for population pharmacokinetic analysis?
- 77 Downloads
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.
Key Wordscompliance MEMS population pharmacokinetics Markov chain model
Unable to display preview. Download preview PDF.
- 5.New Strategies in Drug Development and Clinical Evaluation The Population Approach, M. Rowland and L. Aarons (eds.). Brussels, Commission of the European Communities, 1992.Google Scholar
- 6.L. Aarons. Practical issues in possible implementation of population pharmacokinetics in drug development discussion. In M. Rowland and L. Aarons. (eds.),New Strategies in Drug Development and Clinical Evaluation. The Population Approach, Commission of the European Communities, Brussels, 1992.Google Scholar
- 12.S. L. Beal and L. B. Sheiner.NONMEM User's Guide, Part I.NONMEM Project Group (ed.), San Francisco, University of California at San Francisco, 1992.Google Scholar
- 14.S. L. Beal and L. B. Sheiner.NONMEM User's Guide, Part VII.NONMEM Project Group (ed.), San Francisco, University of California at San Francisco, 1992.Google Scholar
- 15.S. M. Ross.Introduction to Probability Models. In Z. W. Birnbaum and C. Lukacs (eds.), Academic Press, New York, 1980.Google Scholar
- 16.H. Kastrissios, J. Suarez, B. S. Flowers, and T. F. Blaschke. Could decreased compliance in an AIDS clinical trial affect analysis of outcomes?Clin. Pharmacol. Ther. 57:190 (1995).Google Scholar
- 17.Statistical Sciences.S-PLUS Programmer's Manual, Version 3.2, StatSci, a division of MathSoft, Seattle, 1993.Google Scholar
- 21.D. Gillings. The application of the principle of intention to treat to the analysis of clinical trials.Drug Inform. J. 25:411–424 (1991).Google Scholar