BioImagene Develops Companion Algorithms™ to Further Enable Personalized Medicine
News Jul 02, 2009
BioImagene announced has that the company is advancing its goal of bridging personalized medicine and the clinical practice of pathology by providing Companion Algorithms™.
These specialized algorithms, which BioImagene develops for use with its Virtuoso™ suite of web-based software, aid pathologists in the quantitative assessment of specialized diagnostic tests used to determine patient suitability for specific cancer therapies.
As pharmaceutical companies work to develop companion diagnostics to individualize therapy for cancer patients, Companion Algorithms further enable pathologists to correctly identify and measure specific biomarkers used to determine appropriate treatment options for patients.
“Companion diagnostics will play an increasing role in cancer care as physicians strive to provide the therapies that are most likely to be advantageous to individual patients,” said Keith J. Kaplan, M.D., Mayo Clinic. “Companion Algorithms bring personalized medicine one step closer to reality and can help the pathologist provide the most actionable information to the oncologist.”
Ajit Singh, Ph.D., chief executive officer of BioImagene, commented: “The Human Genome Project opened the doors to research in the field of biomarkers and cancer diagnostics; however, challenges still exist in using biomarkers to identify subpopulations of patients that are likely to respond favorably to targeted treatments. Providing pathologists with Companion Algorithms will ultimately move our industry closer to the goal of personalized medicine.”
BioImagene’s Companion Algorithms can be used by pathologists to aid in the interpretation of digitized images of cancer diagnostic tests including immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH). Digitized images can be generated by one of BioImagene’s iScan slide scanning systems, such as Coreo, Concerto, or Solo.
Chinese researchers have developed interfacially polymerized porous polymer particles for low- abundance glycopeptide separation. These polymer particles - with hydrophilic-hydrophobic heterostructured nanopores - can separate low-abundance glycopeptides from complex biological samples with high-abundance background molecules efficiently.