New Tool Optimizes Drug Selection for Chronic Myelogenous Leukemia
A computational model personalizes treatment for CML, optimizing drug selection and enhancing outcomes.
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Chronic myelogenous leukemia (CML) is a rare blood cancer caused by a chromosomal abnormality that produces an atypical gene. This gene drives the rapid proliferation of immature white blood cells, which can obstruct blood vessels and lead to cancer development. In Norway, about 70 new cases of CML are diagnosed each year. Despite its serious implications, CML often progresses silently, with symptoms only appearing years after its onset.
“CML is a form of cancer that many people live with for a long time without knowing it. Symptoms can be absent for several years before the patient becomes visibly ill,"
Dr. Astrid S. de Wijn.
Current treatment options
Stem cell transplantation is the most effective treatment for CML. However, many patients can avoid this invasive procedure with a class of drugs known as tyrosine kinase inhibitors (TKIs). These medications target enzymes essential for the uncontrolled cell growth associated with CML, offering a less invasive alternative for disease management. There are five FDA-approved TKIs available, and selecting the most effective drug for a patient remains a challenge due to variability in individual responses and drug resistance caused by cellular mutations.
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Subscribe for FREEA new computational approach to treatment selection
Researchers at the Norwegian University of Science and Technology (NTNU), Linnaeus University in Sweden, and the Universidade de São Paulo have developed a computational model to optimize the selection of TKIs for individual patients. The model evaluates the effectiveness of various drugs based on patient-specific characteristics, aiding clinicians in prescribing the most suitable treatment.
The method employs algorithms to analyze biological and clinical data, predicting how specific mutations in a patient’s cancer cells might affect their response to each drug. This tailored approach reduces the likelihood of resistance developing and enhances the effectiveness of the prescribed medication.
“The new method can help those affected by chronic myelogenous leukemia,”
Jennifer Sheehan.
Implications for personalized medicine
The advancement underscores the role of computational tools in personalizing treatment plans. By integrating patient-specific data with predictive models, clinicians can move away from a one-size-fits-all approach, enhancing treatment outcomes and minimizing side effects. While the current application focuses on CML, similar models may eventually be adapted for other forms of cancer and diseases that require personalized drug regimens.
Reference: Roadnight Sheehan J, De Wijn AS, Freire TS, Friedman R. Beyond IC50—A computational dynamic model of drug resistance in enzyme inhibition treatment. Gallo J, ed. PLoS Comput Biol. 2024;20(11):e1012570. doi: 10.1371/journal.pcbi.1012570
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