Robust Control of Repeated Drug Administration with Variable Doses Based on Uncertain Mathematical Model

Area of Science:

• Pharmacokinetics and Control Theory
• Biomedical Engineering
• Computational Pharmacology

Background:

• Traditional drug dosing strategies often rely on open-loop methods, which are insufficient due to pharmacokinetic parameter uncertainties.
• Trial-and-error approaches can lead to undesired drug concentration fluctuations.
• Accurate pharmacokinetic parameter knowledge, required for conservative designs, is often unrealistic.

Purpose of the Study:

• To design a robust, repeated drug administration strategy using system and control theory paradigms.
• To develop an algorithm for computing drug doses based on real-time blood sample analysis.
• To explore the application of this methodology for stabilizing unstable biological models, such as tumor growth.

Main Methods:

• Development of a discrete-time control algorithm for drug dosing.
• Utilizing blood sample data for adaptive dose computation.
• Application of closed-loop control principles, including integral controllers and state feedback.

Main Results:

• The proposed closed-loop control algorithm successfully reached and maintained target drug concentrations robustly.
• The control strategy demonstrated superiority over traditional open-loop methods, even with significant parametric uncertainties.
• Simulation experiments confirmed the robustness of the algorithm against large parametric uncertainties.

Conclusions:

• The developed closed-loop control strategy offers a robust and effective method for repeated drug administration.
• This approach overcomes limitations of traditional methods by accounting for pharmacokinetic uncertainties.
• The methodology shows promise for applications beyond drug delivery, including the stabilization of biological systems.