Collaboration will develop phenotypic personalized medicine platform for cancer patients

IntelliCyt and Notable Labs will further the development of a predictive personalized medicine platform for cancer patients.

IntelliCyt (Albuquerque, NM), which develops integrated platforms that accelerate drug discovery, antibody discovery, and immuno-oncology research, is collaborating with Notable Labs (San Francisco, CA) to further the development of a predictive personalized medicine platform. The two companies recognize that next-generation platforms must integrate immune profiling in physiologically relevant ex vivo microenvironments (cancer patient cells, in this case) to ensure clinical translation of laboratory results.

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Notable Labs helps oncologists identify effective therapies that they can prescribe by testing them on a patient's own cells. This approach promises tailored treatment options fast enough for doctors to put to use. By testing a broad set of drugs and drug combinations, the company is able to functionally profile cancer cells, which leads to a better understanding of the biological mechanisms of the patient's cancer. Providing this information to physicians can improve treatment outcomes while reducing side effects.

Notable Labs will use IntelliCyt's iQue Screener PLUS platform in a fully automated, high-throughput laboratory to immune-profile phenotypic and functional endpoints of cells and secreted proteins through cell- and bead-based assays. Prioritized treatment options will be highlighted in reports to clinicians using Notable Lab's screening process with IntelliCyt's immune-profiling technology.

The platform, which integrates an instrument, software and reagent system, offers a proprietary high-throughput sampling capability that enables the rapid assessment of cells and beads in suspension in 96-, 384-, and 1536-well plates. Miniaturization of the assay is possible to save reagent cost and conserve samples, and high-content, multiplex readouts are generated on each individual cell or bead via the flow cytometry-based detection system.

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