Telo Genomics Corp. announced the launch of a validation study to accelerate the commercialization of its lead product in development for smoldering multiple myeloma (SMM) patients. The company also announced that it has recently implemented in its workflow several proprietary automation solutions that employed cutting edge machine learning and artificial intelligence algorithms. The company has recently received the SMM patient samples pertaining to a validation study that the company is launching.

The clinical study will be conducted under the clinical leadership of Dr. Hans Knecht, Head of Hematology, Jewish General Hospital & McGill University, Montreal, Canada and TELO's Clinical Advisor. The samples were received in collaboration with the Francois Baclesse Cancer Center, Caen, France. The launched study goal is to accelerate the validation of the company's ongoing collaboration with the Mayo clinic to develop the company's lead product, a prognostic test for smoldering multiple myeloma (SMM) patients.

The company's lead test in development for SMM patients has the potential to identify high risk SMM patients that will benefit from immediate treatment intervention, and equally important, the test also has the potential to confirm disease stability of low risk SMM patients who can be safely monitored and tested as often as every three months. The total addressable market for this TeloView test will is conservatively estimated to be over 200,000 SMM patients in the USA alone with the potential for over 500,000 tests per year. Multiple Myeloma (MM) is a cancer that forms in plasma cells, a type of white blood cells.

To date MM is a deadly incurable cancer. MM is preceded by 2 asymptomatic forms of the disease (precursors), recognized as monoclonal gammopathy of undetermined significance (MGUS) and SMM. MGUS progression rate to full stage MM is approximately 1% per year, hence considered low risk of progression.

SMM progression rate to full stage MM is 15% per year. The progression of SMM patients to full stage MM constitutes a major concern to health care professionals in the management of the MM disease. The company's lead product in development for SMM is expected to provide healthcare professionals a long-waited-for solution for the management of this group of patients.

Currently SMM patients are monitored but not treated. The MM clinical community is diligently seeking non-invasive prognostic modalities to identify high risk SMM patients and confirm the disease stability of low risk SMM patients. The urgency in establishing a prognostic tool for SMM patients was repeatedly emphasized by MM key opinion leaders during the American Society of Hematology (ASH) annual meeting that took place in December 2021, and the International Myeloma Workshop that was held in September 2021.

Currently a number of clinical trials to treat SMM patients are ongoing, however the progress of these trials is restrained by the lack of an effective tool to identify high risk patients, a true unmet market need. The treatment cost of diagnosed full stage MM patient exceeds $100,000 per year. Treating any of the 80% of stable SMM patient group unnecessarily at this cost would create a substantial burden on health care systems. The company also announced that over the last 12-months it has conducted several internal R&D projects to enhance its throughput, maximize accuracy and elevate its efficiency.

These projects included: i) increased automation and batch processing to the microscopy component of the workflow, ii) introduction of machine learning algorithms to facilitate automated target cell selection, a key step in the single cell analysis that enriches the value of TeloView analytics, and iii) enhanced the processing capacity of the TeloView platform. These projects are now completed, validated and implemented. The implementation of these enhancements to the company's workflow increased the efficiency and productivity of TeloView by over 40%.

The key advantages of these improvements include: i) expedite the completion of clinical studies, ii) simplify the process of technology adoption by potential partners or licensees in the future, iii) lower sample processing cost by allowing the company's high qualified human assets to maximize multitasking, iv) minimize the probabilities of human introduced errors.