Chemotherapy optimization based on mathematical models is a promising direction of
personalized medicine. Personalizing, thus optimizing treatments, may have multiple
advantages, from fewer side effects to lower costs. However, personalization is a
complicated process in practice. We discuss a mathematical model of tumor growth and
therapy optimization algorithms that can be used to personalize therapies. The therapy
generation is based on the concept of keeping the drug level over a specified value.
A mixed-effect model is used for parametric identification, and the doses are calculated
using a two-compartment model for drug pharmacokinetics, and a nonlinear pharmacodynamics
and tumor dynamics model. We propose personalized therapy generation algorithms for
having a maximal effect and minimal effective doses. We handle inter- and intra-patient
variability for the minimal effective dose therapy. Results from mouse experiments
for the personalized therapy are discussed and the algorithms are compared to a generic
protocol based on overall survival. The experimental results show that the introduced
algorithms significantly increased the overall survival of the mice, demonstrating
that by control engineering methods an efficient modality of cancer therapy may be
possible.
