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Three strategies are implemented for controlling serum concentrations by determining individualized dosage regimens. The methods incorporate, respectively, nonlinear least squares parameter estimation, Bayesian maximum a posterioriprobability estimation, and a stochastic control procedure that minimizes the expected value of an appropriate therapeutic cost. The performance of the three dose regimen calculation strategies was evaluated using Monte Carlo simulations of a typical therapeutic protocol for tobramycin.

A two-observation protocol for estimating theophylline clearance during a constant-rate intravenous infusion is used to examine the importance of blood sampling schedules with regard to the information content of resulting concentration data. Guided by a theory for calculating maximally informative sample times, population simulations are used to assess the effect of specific sampling times on the precision of resulting clearance estimates and subsequent predictions of theophylline plasma concentrations. The simulations incorporated noise terms for intersubject variability, dosing errors, sample collection errors, and assay error. Clearance was estimated using Chiou 's method, least squares, and a Bayesian estimation procedure. The results of these simulations suggest that clinically significant estimation and prediction errors may result when using the above two-point protocol for estimating theophylline clearance if the time separating the two blood samples is less than one population mean elimination half-life.

An optimization procedure is presented for selecting sample times for estimating mean residence time using noncompartmental data analysis. Performance of this experimental design strategy is evaluated using Monte Carlo simulations of a model for digoxin kinetics. The contribution of measurement error to the overall expected error in MRT estimates is examined and its importance emphasized. Also, the sensitivity of the proposed experimental design strategy to required assumptions is investigated using the digoxin model, and the importance of accurate estimates of extrapolated areas is noted.

A sequential estimation procedure is presented which uses optimal sampling times to estimate the parameters of a model from data obtained from a group of subjects. This optimal sampling sequential estimation procedure utilizes parameter estimates from previous subjects in the group to determine the optimal sampling times for the next subject. Parameter estimates obtained from the optimal sampling procedure are compared to those obtained from a conventional sampling scheme by using Monte Carlo simulations which include noise terms for both assay error and intersubject variability. The results of these numerical experiments, for the two examples considered here, show that the parameter estimates obtained from data collected at optimal sampling times have significantly less variability than those generated using the conventional sampling procedure. We conclude that optimal sampling and preexperiment simulation may be useful tools for designing informative pharmacokinetic experiments.