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Molecular Fingerprint (mPrint)


Molecular Fingerprint (mPrint)
DESCRIPTION
PROBLEM
Just as sick people build up resistance to antibiotics, cancer patients build up resistance to targeted drugs. Resistance is a money sinkhole, costing payers over $55BN last year, with no guarantee of an effective treatment. More than 70% of the drugs in development will be considered "targeted therapies" however nearly 100% of patients who receive a targeted therapy develop resistance to it. bioSyntagma solves this problem by selling next generation sequencing diagnostic test services that predict drug effectiveness, likelihood of relapse and – most importantly – the probability of resistance. Our software output allows oncologists to tailor drugs with hyper precision and to avoid drugs that will not work on a given patient.
SOLUTION
bioSyntagma has developed a suite of AI-enabled technologies that can overcome acquired drug resistance. The company's proprietary hardware maps cancer patient biopsies to generate a unique "Molecular Fingerprint" for that patient's tumor. The Molecular Fingerprints are analyzed by advanced artificial intelligence algorithms to predict combination treatments unique for that specific patient that will overcome acquired drug resistance. This will truly enable personalized medicine and reduce costs.
There are currently no diagnostic tests on the market that can predict or prevent acquired drug resistance in cancer patients. Our solution is unique because of the novel technology that enables it and because of the unmet market need. Standard diagnostic tests use methods that have been demonstrated to produce bias and skew genetic results. Biosyntagma's novel technology eliminates bias, produces more accurate test results, and generates more breadth of data than any other method. This precise and broad data set is what uniquely enables our artificial intelligence to make treatment recommendations for patients.
CHALLENGES
Our biggest barrier will be obtaining access to medical records that correspond to patient samples. Accurately training AI requires curated, and correlated data sets. While we have the novel technology, constructing the equivalent of a "digital clinical trial" to train the AI will require strategic partners to gain access to records. However, there is great value for payers and hospital systems to partner for this work since they will directly benefit through reduced costs and enhanced care.
IMPACT
My Aunt was diagnosed with lung cancer - she was not a smoker. She was given chemotherapy and responded positively, however this was my first introduction to drug resistance in cancer. I knew you could become resistant to antibiotics, but had no idea it happened in cancer. She responded to treatment only for a few months before the tumor stopped shrinking. Over the course of 9 years she tried every commercially available chemotherapy before finally running out of drugs to try. She had become resistant to each one and eventually passed away. This is not just my Aunt's story, this happens every day. I received a call recently from someone who said, "I hear you can tell what treatments to give someone who has cancer. My wife is sick and the doctors don't know what to do. Can you help me?" I cried telling him, "Yes we can help, but not yet. We're almost there." His wife passed. I'm proud to work with the best team in the world, a team who wakes up in the morning and goes to war for the mission: to eliminate trial-and-error cancer treatments. To make sure no one else hears a doctor say they don't know what to do. To make sure no one's aunt ever runs out of options. To give more people more time to enjoy life.

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