We are innovators and pioneers in biotech, complex systems and big data.
TMed was founded by pioneers who launched the revolutions in biotechnology, genomics and big data for health and were faced with potentially fatal diseases in their own families. We realized that finding the best treatment for our loved ones was confusing and time consuming. The heart of the problem was the fact that it is hard to predict in advance what medicines will work and for whom. What we, our friends and family members needed was help in finding the most effective medicines as fast as possible.
In April 2013, Stuart Kauffman—a TMed founder whose research on the critical role of genetic networking's effect on our predisposition to stay healthy or get disease, the platform for much biomedical research around the world—lost his beloved wife Elizabeth to pancreatic cancer despite her courage and heroic efforts. Like many others, Stu was confronted with a dilemma: When standard of care is not a good option, when clinical trials are risky or not available, when alternative therapy is unproven and time is running out, what do you do?
At his request we met in November 2013 and asked ourselves three questions:
- Can we use information about the treatment and experiences of a multitude of others—those with both unique and common patient characteristics— to determine what the optimal treatment is for you or your loved one?
- Can we analyze this information with systems biology, big data analytics and cloud computing to help people with life-threatening illnesses learn what medicines are best for them?
- Can we do this and reduce the time and confusion people struggle with as well?
We think we can and think the time is now
We don't have to treat individuals as if they are 'sort of like' other patients. The time and cost of genomics and healthcare big data has dramatically decreased while computing power has soared. We see a breakthrough opportunity to go beyond determining if a medicine treats a disease in an 'average' patient half the time. We can find out which treatments will work successfully for individual patients, especially those with life-threatening diseases.
And we can do so starting now.