Using mathematical modeling, researchers have determined the best combination and sequencing of targeted therapies for the treatment of chronic myeloid leukemia (CML). Their results have been published in PLOS Computational Biology.
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CML is commonly treated with several targeted therapies, including the drugs imatinib, nilotinib, and dasatinib. However, some patients with CML relapse or do not respond to treatment when these drugs are administered alone, because of pre-existing resistance mutations within cancer cells. On the other hand, administering the targeted drugs simultaneously can result in severe side effects for patients with CML.
Researchers led by Qie He and Kevin Leder, Department of Industrial and Systems Engineering, University of Minnesota, (Minneapolis, MN), studied the effects of combining the targeted CML drugs. In order to avoid side effects, researchers first combined previously established mathematical models to predict how CML cells with different pre-existing mutations would respond to each of the three targeted drugs. They then analyzed different treatment schedules and targeted drug sequences to find strategies that would maximize time until disease progression for hypothetical patients with different mutations.
Results of the study indicated that personalized sequential combination treatment schedules result in optimal outcomes for patients with CML. Under a wide range of patient scenarios, sequential combination treatment was expected to increase life expectancy and lower chance of relapse at a higher rate than administering any of the three drugs alone.
Researchers cautioned that these findings are still theoretical and will need to be validated experimentally with patients before they can be used in clinical decisions. However, CML treatment could be on its way to a customizable, personalized approach based on a patient’s individual cancer cell characteristics.
"We have discovered that designing optimal anti-cancer therapies based on cellular parameters can lead to significant improvement in patient survival. This has the potential to help in the search for personalized medicine," said Dr Leder.
