Scottsdale, AZ – January 17, 2020 –Systems Oncology, an AI-based biopharmaceutical company, has established a research collaboration and licensing agreement with Texas A&M to commercially develop a series of pharmaceutical compounds based on Professor Stephen Safe’s work.
Safe’s lab discovered that their C-DIMs bind and inactivate a set of orphan nuclear receptors, NR4A1 and NR4A2, known to have increased activity in such conditions as breast cancer, glioblastoma, rhabdomyosarcoma, endometrial cancer, and endometriosis.
Because NR4A-nuclear receptors play a significant role in inflammation, T-cell exhaustion, and cell division, Safe and his team have primarily focused on cancer and endometriosis models, targeting the receptor as a means for treating these diseases.
Studies have now demonstrated that C-DIMs mimic the activities of immunotherapeutics in mouse models of breast cancer; this research—supported by the National Institutes of Health, Texas A&M AgriLife Research, and the Sid Kyle Chair Endowment—is also described in a recent publication by Cancer Research.
Before the treatment can be given to humans, C-DIMs must be tested and optimized for a targeted disease, which is where Systems Oncology comes in. Systems Oncology will steer the most promising C-DIM(s) through the pharmaceutical regulatory process through early-stage clinical trials and, subsequently, will form partnerships with other pharmaceutical companies to enable the clinical use of these C-DIMs in the future.
Additionally, Systems Oncology has made a commitment to sponsor further research and development activities in Safe’s lab over the next three years.
Full press release by Texas A&M: Texas A&M CVM Researcher Develops Potential Therapeutic Treatment For Cancers, Endometriosis
About Systems Oncology
Systems Oncology, LLC (SO) is an AI-based cancer therapy discovery and development company. SO has a multidisciplinary team of scientists and a revolutionary cognitive computing platform (Expansive.AI) able to intelligently integrate, model, and mine big data from hundreds of molecular, genomic, and biomedical datasets. This new kind of computational data mining has empowered the SO team to rapidly extract many therapeutically useful insights from complex multi-scalar systems models of cancer biology. This scalable data-driven approach has been used by SO to translate many unique biological insights into dozens of discovery projects and research collaborations with leading universities, producing one of the fastest growing pipelines of innovative cancer therapies in the industry. For more information, go to www.systemsoncology.com.