Use of adaptive control with feedback to individualize suramin dosing.

Suramin is the first putative growth factor inhibitor in clinical trial that has demonstrated antitumor activity. Administration of suramin is complicated by a narrow therapeutic index and significant interpatient variability of measured pharmacokinetic parameters. Because both antitumor response and dose-limiting toxicities are related to plasma suramin concentration profiles, individualized dose schedules are required for optimal administration of the compound. In this report, the use of optimal sampling theory to derive sparse data monitoring and control strategies for use with suramin is described. A fixed rate continuous infusion schedule was used in seven patients, and the time to peak concentration (280-300 micrograms/ml) ranged from 7.7-21 days (mean, 13.2 days) with a decline to 150 micrograms/ml in 3-22 days (mean, 11 days). An initial population pharmacokinetic model was fit using a maximum likelihood algorithm. The mean volume of the central compartment was 4.5 +/- 6.7 liters/m2, volume of the peripheral compartment 10.6 +/- 1.4 liters/m2, distributional half-life 25 +/- 5.4 h, and elimination half-life 29.7 +/- 6.9 h. The terminal half-life was shorter than previously reported. These parameters were used as the initial population model for an iterative 2-stage analysis. The resulting distributional half-life of 22.3 +/- 2.7 h and elimination half-life of 28.2 +/- 5.0 h were similar, reflecting the intensive sampling. The iterative 2-stage analysis model was then used to determine the optimal sampling times and to simulate 20 data sets for a protocol designed to maintain plasma concentrations in a defined concentration range. This strategy is currently under investigation in phase I clinical trials.