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As a molecular Virologist coming from a traditional science background I often get asked why I believe in digital health. The challenges for digital health are apparent: limited clinical validations, limited investor returns (where are those Digital Health unicorns?), and limited scalability due to local regulatory specifications of healthcare systems. The upside potential, however, is enormous: treating disease with digital approaches as done for depression and schizophrenia, for example (keyword “Digital Therapeutics”), increasing treatment success by tailoring intake regimen to individual patient´s behavior and improving clinical trial processes are a just a few highlights of how digitization can impact how we do health & care.
During my PhD thesis work, I genetically modified virus strains to study why some influenza viruses are more dangerous than others using traditional molecular assays and experiments. One laboratory experiment to test whether one single mutation affects pathogenicity (aka “danger potential” and yes we worked in safe BSL3+ environment) of the virus took several days and sometimes even weeks to get results. Negative result? Start the experiment again! This meant going through a strict safety procedure every 2 hours in a course of a week (day and night): changing into a heavy astronaut-like suit, entering the low air pressure laboratory room with heavy equipment, perform experiment, change out of suit and shower.
Ten years fast forward and I met the startup Turbine from Hungary. They got selected out of 600 companies to enter the Bayer Grants4Apps (G4A) Accelerator program with their cancer cell simulation technology using Artificial Intelligence (AI). The Turbine team had developed an AI algorithm that mimics how cancer cells respond to various drug treatments and the ones with highest efficacy could be identified in minutes. We are talking about an up to 10,000 fold increase in knowing the results! Why does the simulation work so well? The Turbine algorithm utilizes data from all publicly available scientific publications and input data from industry to teach their algorithm to behave like a cell. For me, this is where science is going now: leveraging more the vast intelligence and knowledge that is already out there and speed up how we develop new treatments to deliver it to patients in need faster!
Am I saying digital approaches will completely replace molecular biology approaches? No. To me they are complementary and powerful when used in combination. Data science approaches like AI require data from biological readouts to learn how to effectively simulate real-life scenarios. New data insights can then guide follow-up wet lab research and even clinical study design. It truly functions like a ying and yang – in a closed loop!
This is why I am passionate being part of shaping the Digital Health movement with G4A at Bayer, and working together with dynamic driven minds out there in the startup world at our Accelerator. We are again open for applications in amazing focus areas in digital therapeutics, blockchain, FemTech and many more. Check out details at G4A.Health and join our mission! We are looking for the NEXT BIG DIGICORN!