More than a decade ago Dr. Svetomer Markovic, now an oncologist at The Mayo Clinic and professor at the Mayo Graduate School of Medicine, felt that biomarkers found in the blood could expose the activity focus of the immune system. He eventually reached out to a retired NASA engineer (Harold Frisch) who had for many years built math models of complex systems for NASA and a Math professor at the Texas A&M University (Dr. James Turner). After much discussion with Dr. Markovic, they agreed that the action of the immune system was reflected in the population of cells and cytokines at any moment. They decided to create a dynamic Math model that would expose the details of the Immune System activities. They settled on using daily blood analysis of 24 pretreatment, stage IV melanoma cancer patients over a period of two weeks. They chose to measure populations of 50 biomarkers (15 cells and 35 cytokines) known to have some relationship to cancer.
The math model was based on the known interactions of cells and cytokines in healthy people. The model consisted of 50 equations with about 28,000 variables. Using advanced math techniques familiar from the NASA experience and the most powerful desktop computers available, the model was solved for the variables. Techniques such as Singular Variable Decomposition and other related Math yielded a set of data that represented the current action of the immune system in each patient. The process and programs used to produce the results they call CICD (Cancer Immune Control Dynamics).
The data resulting from CICD were very encouraging. The differences and similarities between patients confirmed many known characteristics of the immune system actions. However, all of these conclusions were in engineering terms and relating the results back to the medical team proved difficult.
Then the team expanded to include three more members. Allan Sprau, the founder/CEO and President of Metafile Information Systems, Jeremy Waldner a gifted student at Rochester's Century High School, Virginia McElroy an honors graduate in Molecular Biology from Lehigh University, all joined to form a Joint Development Study with Mayo.
The new members focused on the abstraction provided by the Math techniques and led to the development of presentation diagrams they called DaVinci Maps. One of the biggest challenges was to present these abstract DaVinci map results in biological terms that could be understood by the medical community and could be related back to actionable information for the medical staff. The project expanded by adding the same data from 28 healthy volunteers. The DaVinci presentations were expanded to include presentations that compared the immune systems of healthy volunteers with those of seriously ill cancer patients. The presentations were so revealing regarding the differences and similarities of the two groups that Math for Medicine, Inc. was formed to take the technology to the medical market.
Math for Medicine, Inc. is owned by the team members mentioned above and Mayo Ventures. An exclusive license to the patent and procedures embodied in the software are owned by Math for Medicine, Inc. Several manuscripts are in preparation that will be submitted for publication in respected peer-reviewed journals.
CICD can measure the activity of the immune system components in people with suspected or diagnosed illnesses. Almost every known illness has an expression through unique immune system activity. In cancer particularly, there is no effective way to detect that the immune system is in the very beginning stages of battling cancer. Traditional early diagnosis depends on discovering cancer modified cells or cancer tumors. By that time cancer is already advancing rapidly. CICD has the potential to see defensive immune activities very early in the progression of disease. Earlier detection of illness revealed through CICD measurements, available through inexpensive, minimally intrusive blood testing, would save lives while reducing costs dramatically.
CICD could eventually provide insight to the initial treatment plan decision-making.
CICD could eventually enable treatment plan adjustment mid-course due to observations provided by DaVinci presentations.
The first market for CICD is to assist administrators of drug studies and trials. CICD can provide valuable progress information on each patient and the study group as a whole compared to healthy people. This information will provide researchers with a profile of the patients that are doing well versus those not doing well. These profiles would enable the selection of patients that have a higher success profile before administering the drug when the study is complete.