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Journal of Pharmacokinetics and Biopharmaceutics

, Volume 14, Issue 5, pp 523–537 | Cite as

Implementation and evaluation of control strategies for individualizing dosage regimens, with application to the aminoglycoside antibiotics

  • Darryl Katz
  • David Z. D'Argenio
Pharmacometrics

Abstract

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.

Key words

stochastic control dosage regimen least squares Bayes' theorem Monte Carlo simulation 

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References

  1. 1.
    R. J. Sawchuk, D. E. Zaske, R. J. Cipolle, W. A. Wargin, and R. G. Strate. Kinetic model for gentamicin dosing with the use of individualized patient parameters.Clin. Pharmacol. Ther. 21:362–369 (1977).PubMedGoogle Scholar
  2. 2.
    L. B. Sheiner, B. Rosenberg, and K. L. Melmon. Modeling of individual pharmacokinetics for computer aided drug dosage.Comp. Biomed. Res. 5:441–459 (1972).CrossRefGoogle Scholar
  3. 3.
    L. B. Sheiner, S. Beal, B. Rosenberg, and V. V. Marathe. Forcasting individual pharmacokinetics.Clin. Pharmacol. Ther. 26:294–305 (1979).PubMedGoogle Scholar
  4. 4.
    O. Richter and D. Reinhardt. Methods for evaluating optimal dosage regimens and their application to theophylline.Int. J. Clin. Pharmacol. Ther. Toxicol. 20:564–575 (1982).PubMedGoogle Scholar
  5. 5.
    J. Gaillot, J. J. Steimer, A. J. Mallet, J. J. Thebault, and A. Bieder.A priori lithium dosage regimen using population characteristics of pharmacokinetic parameters.J. Pharmacokin. Biopharm. 7:579–628 (1979).CrossRefGoogle Scholar
  6. 6.
    D. Katz and D. Z. D'Argenio. Discrete approximation of multivariate densities with application to Bayesian estimation.Computational Stat. Data Anal. 2:27–36 (1984).CrossRefGoogle Scholar
  7. 7.
    D. Katz. Discrete approximations to continuous density functions that areL 1 optimal.Computational Stat. Data Anal. 1:175–181 (1983).CrossRefGoogle Scholar
  8. 8.
    International Mathematical and Statistical Libraries.IMSL Reference Manual, 9th ed., Houston, 1984.Google Scholar
  9. 9.
    S. J. Press.Applied Multivarite Analysis, Rinehart, Holt and Winston, 1972, pp. 138–140.Google Scholar

Copyright information

© Plenum Publishing Corporation 1986

Authors and Affiliations

  • Darryl Katz
    • 1
  • David Z. D'Argenio
    • 2
  1. 1.Department of MathematicsCalifornia State University at FullertonFullerton
  2. 2.Department of Biomedical EngineeringUniversity of Southern CaliforniaLos Angeles

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