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Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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Related Experiment Video

Updated: Jan 9, 2026

Modeling Chemotherapy Resistant Leukemia In Vitro
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Delay-aware chemotherapy dosing via online critic learning.

Farshad Rahimi1, Mahdieh Samadi2

  • 1Faculty of Electrical and Computer Engineering, Sahand University of Technology, Tabriz, Iran. fa_rahimi@sut.ac.ir.

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|December 10, 2025
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This study introduces an adaptive chemotherapy control framework that adjusts drug doses in real-time. It effectively manages treatment delays and patient variations for better tumor suppression and reduced toxicity.

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Pharmacology

Background:

  • Chemotherapy dosing often faces challenges due to patient-specific variability and inherent delays in drug action.
  • Existing control strategies may not adequately address both pharmacokinetic and pharmacodynamic delays simultaneously.
  • Adaptive control offers a promising avenue for optimizing individualized cancer treatment.

Purpose of the Study:

  • To develop a delay-aware adaptive control framework for personalized chemotherapy.
  • To incorporate online critic learning for real-time dose adjustments.
  • To enhance tumor suppression and control toxicity in cancer patients.

Main Methods:

  • An adaptive control framework was designed, explicitly accounting for pharmacokinetic and pharmacodynamic delays.
  • An online critic network was employed to estimate the value function for guiding dose adjustments.
  • The framework was tested using simulations across diverse patient profiles with varying dynamics.

Main Results:

  • The proposed framework demonstrated effective tumor suppression across different patient simulations.
  • The system successfully controlled treatment-related toxicity.
  • Robustness was observed in response to variations in physiological delays and patient-specific parameters.

Conclusions:

  • The delay-aware adaptive control framework provides an effective strategy for individualized chemotherapy.
  • Online critic learning enables real-time adaptation to patient uncertainties and treatment delays.
  • This approach holds potential for improving clinical outcomes in cancer therapy.