Is it possible to predict which cancer therapies will be effective before administering them to the patient? Even when an effective therapy is identified, many patients cannot tolerate it or develop resistance to it. By linking genetic testing and patient clinical data with drug response information, our teams will predict which drugs and drug combinations will be effective cancer therapies and at what dose. This type of precision medicine approach may improve treatment outcomes, reduce the side effects and costs of therapies, and even help discover new drugs or treatment schemes. Our model uses data from viability testing of live patient cells and high-throughput flow cytometry (HTFC) tests after applying therapeutic drugs to cancer cells in the lab. The researchers use this information to validate drugs and combinations of drugs outside of the patients, avoiding side-effects and speeding up drug efficacy tests.
In this event, Serena Giliberto PhD student and Pilar Ayuda-Duran will present their precision medicine approach and show recent data enabling them to predict which drugs and drug combinations are effective cancer therapies and at what dose
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