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Related Experiment Videos

Modeling, adaptive control, and optimal drug therapy.

R W Jelliffe1, A Schumitzky

  • 1Laboratory of Applied Pharmacokinetics, University of Southern California School of Medicine, Los Angeles 90033.

Medical Progress Through Technology
|May 1, 1990
PubMed
Summary
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This study reviews the evolution of pharmacokinetic models in drug therapy, highlighting adaptive control methods. A new Approximate Optimal Closed-Loop (AOCL) control strategy is introduced to simultaneously treat patients and learn about their unique models.

Area of Science:

  • Pharmacometrics
  • Pharmacokinetics
  • Therapeutic Drug Monitoring

Background:

  • Drug therapy development has been significantly advanced by pharmacokinetic models.
  • Adaptive control, a concept from engineering, has been integrated into therapeutics.
  • Historical contributions from Teorell, Augsberger, and Kruger-Thiemer laid the groundwork for current models.

Purpose of the Study:

  • To describe the historical development of pharmacokinetic models in drug therapy.
  • To review adaptive control strategies in pharmacokinetics, including regression and Bayesian methods.
  • To introduce and explain the Approximate Optimal Closed-Loop (AOCL) control method.

Main Methods:

  • Review of historical and current literature on pharmacokinetic modeling and adaptive control.

Related Experiment Videos

  • Description of adaptive control methods: linear/nonlinear least-squares regression and Maximum Aposteriori Probability (MAP) Bayesian methods.
  • Explanation of the Approximate Optimal Closed-Loop (AOCL) control strategy.

    • Main Results:

    • Pharmacokinetic modeling has evolved significantly, incorporating adaptive control techniques.
    • Optimal serum concentration monitoring strategies have been developed and evaluated.
    • The new AOCL method integrates therapeutic regimen adjustments with patient model learning.

    Conclusions:

    • Adaptive control has enhanced pharmacokinetic modeling in drug therapy.
    • The AOCL method offers a novel approach to optimize patient treatment and model learning concurrently.
    • This approach promises improved patient management by simultaneously treating and learning about individual patient models.