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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Mark Gluzman1, Jacob G Scott2, Alexander Vladimirsky3

  • 1Center for Applied Mathematics, Cornell University, Ithaca, NY, USA.

Proceedings. Biological Sciences
|April 22, 2020
PubMed
Summary
This summary is machine-generated.

Adaptive cancer therapy, guided by tumor state rather than fixed schedules, shows promise. Optimizing these adaptive policies can significantly reduce drug use and improve recovery rates compared to standard maximum tolerated dose treatments.

Keywords:
Hamilton–Jacobi–Bellman equationadaptive therapyevolutionary game theoryoptimal treatment policytumour heterogeneity

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Area of Science:

  • Oncology
  • Mathematical Biology
  • Computational Science

Background:

  • Standard cancer treatments often rely on continuous maximum tolerated doses (MTD).
  • Adaptive drug therapy, adjusting doses based on tumor state, shows potential for improved efficiency.
  • Existing adaptive strategies have largely been developed without systematic optimization.

Purpose of the Study:

  • To develop a systematic method for optimizing adaptive cancer treatment policies.
  • To utilize evolutionary game theory and dynamic programming for optimizing adaptive therapy.
  • To compare optimized adaptive policies against standard MTD-based treatments.

Main Methods:

  • Employing an evolutionary game theory model to represent cancer dynamics.
  • Applying dynamic programming to find optimal adaptive treatment strategies.
  • Solving a Hamilton-Jacobi-Bellman equation to optimize drug usage and recovery time.

Main Results:

  • Optimized adaptive policies significantly decrease total drug requirements.
  • Adaptive strategies increase the range of initial tumor states from which recovery is possible.
  • The proposed method offers a systematic approach to adaptive cancer therapy optimization.

Conclusions:

  • Optimal control theory provides a promising framework for enhancing adaptive cancer treatment policies.
  • Integrating optimized adaptive therapies into clinical trial design is recommended.
  • Systematic optimization of adaptive therapy can lead to more efficient and effective cancer treatment.