A stochastic control approach for dose regimen design is developed and applied to the problem of targeting the systemic exposure, defined as the area under the blood concentrationtime curve (AUC),of the anticancer drug teniposide in both the population and individual patients. The control objective involves maximizing the probability that AUCis within a selected target interval given either the population distribution for the kinetic model parameters (a priori control) or the posterior distribution for an individual patient (feedback control). Results of a detailed simulation study are presented, illustrating the feasibility of applying stochastic control principles to the design of dose regimens. The predictive ability of the calculated distributions of AUCfor the population and for individuals is evaluated in part by determining the percentage coverage of the computed 95% uncertainty intervals using the simulation results. For the a priori control phase, 94% of the simulated subjects had values of systemic exposure within the computed 95% uncertainty interval, while 93.4% of the simulated subjects had feedback control phase systemic exposure values within their computed 95%uncertainty intervals. Similar evaluation of the uncertainty intervals calculated for plasma concentrations further document the ability of the proposed stochastic control method to predict the uncertainty associated with future therapy.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
P. J. O'Dwyer and I. Slevin. The clinical pharmacology of etoposide and teniposide.Clin. Pharmacokin. 12:223–252 (1987).
J. H. Rodman, M. Abromowitch, J. A. Sinkule, F. A. Hayes, G. K. Rivera, and W. E. Evans. Clinical pharmacodynamics as a determinant of response in a Phase I trial.J. Clin. Oncol. 5:1007–1014 (1987).
G. K. Rivera, W. P. Bowman, S. B. Murphy,et al. VM-26 therapy with prednisone and vincristine for treatment of refractory acute lymphocytic leukemia.Med. Pediat. Oncol. 10:439–446 (1982).
W. P. Petros, J. H. Rodman, J. Mirro, and W. E. Evans. Pharmacokinetics of continuous infusion amsacrine and teniposide for treatment of relapsed childhood acute nonlymphocytic leukemia.Cancer Chemother. Pharmacol. 27:397–400 (1991).
J. H. Rodman, W. L. Furman, M. Sunderland, G. Rivera, and W. E. Evans. Escalating teniposide systemic exposure to increase dose intensity for pediatrie cancer patients.J. Clin. Oncol. 11:287–293 (1993).
S. Vozeh and J.-L. Steimer. Feedback control methods for drug dosage optimization,Clin. Pharmacokin. 10:457–476 (1985).
A. Schumitzky. Application of stochastic control theory to optimal design of dosage regimens. In D. Z. D'Argenio (ed.),Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Plenum Press, New York, 1991, pp. 137–152.
L. B. Sheiner, H. Halkin, C. Peck, B. Rosenberg, and K. L. Melmon. Improved computer-assisted digoxin therapy: a method using feedback of measured serum digoxin concentrations.Ann. Intern. Med. 82:619–627 (1975).
D. Z. D'Argenio and A. Schumitzky.ADAPT II User's Guide, Biomedical Simulations Resource, University of Southern California, Los Angeles, 1992.
D. Katz and D. Z. D'Argenio. Discrete approximation of multivariate densities with application to Bayesian estimation.Comp. Stat. Data Anal. 2:27–36 (1984).
D. Katz and D. Z. D'Argenio. Implementation and evaluation of control strategies for individualizing dosage regimens, with application to the aminoglycoside antibiotics.J. Pharmacokin. Biopharm. 14:523–537 (1986).
D. Z. D'Argenio and D. Katz. Application of stochastic control methods to the problem of individualizing intravenous theophylline therapy.Biomed. Meas. Inform. Contr. 2:115–122 (1988).
D. Z. D'Argenio. Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic experiments.Math. Biosci. 99:105–108 (1990).
J. Gaillot, J. L. Steimer, A. J. Mallet, J. Thebault, and A. Bieder. A priori lithium dosage regimen using population characteristics of pharmacokinetic parameters.J. Pharmacokin. Biopharm. 7:579–628 (1979).
O. Richter and D. Reinhardt. Methods for evaluating optimal dosage regimens and their application to theophylline.Int. J. Clin. Pharmacol. 20:564–575 (1982).
A. Mallet, F. Mentre, J. Gilles, A. W. Kelman, A. H. Thomson, S. M. Bryson, and B. Whiting. Handling covariates in population pharmacokinetics, with an application to gentamicin.Biomed. Meas. Infor. Contr. 2:138–146 (1988).
S. Amrani, E. Walter, Y. Lecourtier, and R. Gomeni. Robust control of uncertain pharmacokinetic systems.IFAC 9th Triennial World Congress, Budapest, 1984, pp. 3079–3083.
P. J. A. Lago. A first order approximation to the open-loop stochastic control of pharmacokinetic systems.IFAC Workshop on Decision Support for Patient Management, London, 1989, pp. 161–167.
A. Gelfand and A. F. M. Smith. Sampling-based approaches to calculating marginal densities.J. Am. Statist. Assoc. 85:398–409 (1990).
This work was supported in part by National Institutes of Health grant P41 RR01861, Leukemia Program Project Grant CA 20180, CORE Cancer Center Grant P30 CA21765; by a center of Excellence Grant form the State of Tennessee; and by the American Lebanese Syrian Associated Charities.
About this article
Cite this article
D'Argenio, D.Z., Rodman, J.H. Targeting the systemic exposure of teniposide in the population and the individual using a stochastic therapeutic objective. Journal of Pharmacokinetics and Biopharmaceutics 21, 223–251 (1993). https://doi.org/10.1007/BF01059772
- dose regimen design
- feedback control
- therapeutic drug monitoring
- stochastic control
- anticancer drug therapy
- systemic exposure
- therapeutic window