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Use of Adaptive Control with Feedback to Individualize Suramin Dosing¹

Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Medicine [H. I. S., T. C.], Department of Pharmacy [J. M. J], Department of Molecular Pharmacology [W. T.], Memorial Sloan-Kettering Cancer Center, New York, New York 10021; Division of Developmental Therapeutics, University of Maryland Cancer Center [D. I. J., M. J. E., A. F.] Baltimore, Maryland 21201; and Department of Medicine, Cornell University Medical College [H. I. S.], New York, New York 10021

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 µg/ml) ranged from 7.7–21 days (mean, 13.2 days) with a decline to 150 µg/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.

¹ This work was supported by Contracts N01-CM-07303, N01-CM-57734, and N01-CM-57732, The Goldsmith/Perry Prostate Cancer Research Fund, and Grants CA-05826 and CA-29502-06 from the National Cancer Institute, NIH, U. S. Department of Health and Human Services.

² To whom requests for reprints should be addressed, at Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021.

³ Recipient of a Cancer Research Council (United Kingdom) Travelling Fellowship.

⁴ Present address: Department of Medicine and Pharmacy, State University of New York at Buffalo, Buffalo, NY 14260.

Received 6/ 7/91. Accepted 10/17/91.

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