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Bayer is investing nearly $100M in diabetes management startup One Drop


One Drop's diabetes supplies
One Drop's diabetes supplies (courtesy image)
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One Drop, a diabetes management platform that has its roots in Austin, has raised almost $100 million in new funding and expanded its partnership with pharmaceutical and life science giant Bayer.

The funding includes $34.7 million in Series C investment led by Bayer, as well as $64 million in development fees and future commercial milestone payments.

The partnership and financing will help accelerate One Drop’s expansion from its current focus on diabetes, high blood pressure and high cholesterol into Bayer’s cardiology, oncology and women’s health portfolio.

One Drop was founded in Austin in 2015 by RazorFish co-founder Jeff Dachis. It raised a $1 million seed round and followed that months later with an $8 million Series A round led by RRE Ventures that year. BoxGroup, Launch Fund, Capital Factory and Neu Ventures were also early investors. In 2016, One Drop became one of the first apps to be integrated into Apple’s CareKit.

The startup began shifting some operations to New York after its initial funding rounds. It now has offices and job openings in both cities.

One Drop’s app has now been downloaded more than 3 million times. Along with monitoring and education, the startup uses its data to forecast health outcomes, as well as develop long-term blood pressure and weight outcomes forecasts. For example, One Drop users can get glucose forecasts and real-time advice on how to improve outcomes.

"To deliver better health outcomes and cost savings at a population level, we must focus on individuals," Dachis said in a statement. "One Drop delivers a personalized health transformation program that dynamically tunes to your needs and behaviors each day, empowering each individual to achieve the best possible health outcomes. In contrast, other platforms lacking our data wealth and predictive engine continue to focus on telemedicine and remote monitoring, reacting to problems that have already occurred and delivering a one-size-fits-all model that works for some, but fails for most."


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