Cancer poses significant challenges due to the development of resistance and the likelihood of relapse. Resistance may arise from permanent genetic changes in cancer cells or non-genetic alterations in cancer cell behavior induced by treatment. Standard of care in cancer treatments typically involves administering the maximum tolerated dose of a drug to eradicate drug-sensitive cells effectively.
However, this approach often fails in the long term because drug-resistant cancer cells can grow more rapidly when all drug-sensitive cancer cells are killed off. An evolution-based treatment approach, called adaptive therapy, personalizes treatment dose or breaks based on individual patient responses. The goal of adaptive therapy is to maintain a sufficient number of sensitive cells to control the growth of resistant cells.
Recent studies and clinical trials have demonstrated that adaptive therapy could delay resistance more effectively compared to the standard of care. Determining the dose and treatment breaks for each patient is challenging because cancer is a complex evolving system, and every patient is different. Mathematical models can be helpful in designing such patient-specific treatment strategies.
Indeed, several mathematical models have been developed to explore the effects of various treatment strategies on patient outcomes. However, existing mathematical models often overlook the impact of acquired resistance and cancer cell plasticity. 'Acquired resistance' encompasse.
