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Bioz 2017 Predictions: Why modern technology will catch up in the life science and biotech industry

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual VMblog.com series exclusive.

Contributed by Daniel Levitt, co-founder and CEO, and Dr. Karin Lachmi, co-founder, president and chief scientific officer, Bioz

Why modern technology will catch up in the life science and biotech industry

The life science sector has historically lagged when it comes to implementing modern technology that builds upon previous research. However, 2017 is the year biotech truly will catch up in terms of adopting new trending and emerging technologies in order to accelerate life science research and ultimately save lives. Machine learning, modern IT and cloud technology will take a greater place in advancing research methods in life science, and biotech companies will begin making serious strides.

To understand the direction the biotech industry is heading, it's crucial to know where we've been. Knowledge from previous scientific research is found in life science articles, but for researchers to access this information, they need to manually sift through hundreds of millions of article pages, and then spend countless hours reading through dense and complex article text in order to identify relevant insights relating to experimentation. Reading through articles to find relevant information is a cumbersome process that is compounded by the fact that a new life science article is published every 10 seconds. It can take even the most experienced researcher months to identify the correct antibodies to use in their experiment because they had no other choice but to perform this task manually.

To perform research, one has to come up with a hypothesis, a plan, and then conduct an experiment. This is time-consuming, and anything that can speed up this process, while reducing mistakes, is of great value to researchers. For a researcher to realize, months or years into an experiment, that an error has been made in the selection of an appropriate reagent or tool, can be devastating. Failures of this nature result in the human capital cost of wasted time, plus significant loss of money from the wasted reagents, tools and consumables. With $80 billion a year spent on life science experimentation and tools, the potential for savings is immense if researchers could, say, identify the correct antibodies ahead of time by knowing in advance what has worked for other researchers.

One may wonder why a solution wasn't available sooner, and the answer is that the technology just wasn't there yet. IT professionals weren't giving appropriate focus to the development of life science software, most likely due to the typical disconnect between life science researchers and software developers. On one side, researchers who work on drug discovery and basic research are not focused on developing software solutions. While on the other side, IT professionals are typically focused on developing software applications for consumers and enterprises, but are not focused on the life science domain. This has caused a disconnect, inadvertently resulting in wasted money and time on experiments that fail due to products not performing as expected.

However, 2017 is when this will change. Natural language processing (NLP), machine learning and artificial intelligence applied within a cloud-bio framework, will play an even bigger role in advancing methods and practices in both academic and biopharma research environments. This will be driven by more investments being made in companies that are not only in the biology space, but also in companies that are focused on what Andreessen Horowitz dubbed as "cloud biology," in which software is used to inspire innovation and improve life science research and drug discovery. Because of this, there will be less focus on traditional life science companies that are focused on pharmaceuticals and medical devices, with a strong shift towards utilizing the power of the computer, the cloud and other current software technologies to advance life science research.

There are approximately 10,000 companies selling life science tools, some of which sell great products and some that do not. The companies that begin implementing more efficient IT and software, including NLP, machine learning and the cloud, will rise to the top. Grant money from the National Institutes of Health will start to be used more wisely, and that $80 billion that is already being spent today, can be used more effectively. This will all translate into faster, more effective, drug discovery and better basic research into finding cures for cancer, Alzheimer's, Parkinson's and other diseases, and prove that software can indeed save lives

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About the Authors

Daniel Levitt, co-founder and CEO, Bioz

Daniel Levitt is a serial entrepreneur with 22 years of senior-level management experience in life sciences and high-technology companies. Co-founder and CEO of 8 companies (one was acquired by Microsoft), and holds a BA in Economics from UC Berkeley and an M.Sc. in Management from Boston University.

Daniel Levitt 

Dr. Karin Lachmi, co-founder, president and chief scientific officer, Bioz

Dr. Lachmi is an accomplished researcher, and previously served as a managing director of the western region of the Tel Aviv Sourasky Medical Center US. She also worked as a neuroscientist and researcher at Stanford. Dr. Lachmi originally earned a Ph.D. working on brain cancer at Tel Aviv University.

Karin Lachmi 

Published Friday, January 06, 2017 9:02 AM by David Marshall
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