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An evaluation of optimal sampling strategy and adaptive study design
Author(s) -
Drusano George L,
Forrest Alan,
Snyder Merrill J,
Reed Michael D,
Blumer Jeffrey L
Publication year - 1988
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.1988.142
Subject(s) - estimator , mathematics , bayesian probability , population , maximum a posteriori estimation , statistics , sampling (signal processing) , prior probability , optimal design , least squares function approximation , bayes' theorem , a priori and a posteriori , mathematical optimization , computer science , medicine , maximum likelihood , philosophy , environmental health , filter (signal processing) , epistemology , computer vision
We have evaluated the utility of optimal sampling strategy coupled with adaptive study design in the determination of individual patient and population pharmacokinetic parameter values. In 9 patients with cystic fibrosis receiving a short (1 minute) infusion of ceftazidime pharmacokinetic parameter values were determined with a nonlinear least‐squares estimator analyzing a traditional, geometrically spaced set of 12 postinfusion serum samples drawn over 8 hours. These values were compared with values generated from four sample subsets of the 12 obtained at optimal times and analyzed by nonlinear least‐squares estimator, as well as a maximum a posteriori probability Bayesian estimator with prior distributions placed on β and clearance. The four sampling times were determined according to an adaptive design optimization technique that employs sequential updating of population prior distributions on parameter values. Compared with the 12‐point determination, the four optimal points analyzed with the maximum a posteriori probability Bayesian estimator faithfully reproduced both microscopic and hybrid pharmacokinetic parameter values for individual patients and, consequently, also produced accurate measures of population central tendency and dispersion. This has important implications in being able to more efficiently derive target patient population pharmacokinetic information for new drugs. This should also allow generation of better concentration‐effect relationships in populations of interest. Clinical Pharmacology and Therapeutics (1988) 44 , 232–238; doi: 10.1038/clpt.1988.142