Biotech's oldest problem, solved?


Investors are going crazy for everything and anything associated with the Internet of Things, or machine-to-machine communication. After all, the field has the potential to add significant efficiency gains throughout the global economy in applications ranging from household appliances to agriculture to industrial manufacturing. While it is rarely discussed, the Internet of Things also has the potential to revolutionize biotechnology laboratories and research and development efforts -- perhaps creating substantially more value for biotech than in any other headline-grabbing applications. The leaders in automated biotech lab services might not be household names today, but they're racing to build next-generation biofabrication facilities that will be difficult, if not impossible (or, at the very least, very expensive) to compete with. In other words, the early movers could be the only players in the market for quite some time, which could force future biotech companies to adopt business models strikingly different from those observed today.

The biotech facilities of the future will rely increasingly on automation. Why? Consider what a typical day in a traditional biotech research lab looks like. Researchers sit at the bench, furiously hand write detailed parameters of their experiment (temperature, pH, oxygen content, and the like), tediously move small volumes in and out of test tubes hundreds of times, and track labels for dozens or hundreds (or more) of samples. One big problem: humans make mistakes. They write down the wrong parameter (or forget altogether), add the wrong liquid to the wrong test tube, and mislabel samples. Even when an experiment is performed perfectly, two labs can produce very different results. Reproducibility is biotech's oldest problem. In 2011, healthcare leader Bayer took a random sample of drug development studies published in peer reviewed journals, while Amgen did the same in 2012. Each company performed the same experiments outlined in the studies in their state-of-the-art facilities to see if they could produce the same published result. Bayer found that no more than 25% of the results could be reproduced, while Amgen could only replicate results 11% of the time. That's pathetic -- and costly. It is cited as a leading reason for high drug failure rates in healthcare applications of biotech and has decimated early industrial biotech companies such as Amyris that attempted to scale chemical production from 2-liter bioreactors to their 200,000-liter commercial scale counterparts. It highlights the finicky nature of biology (in our current limited understanding) and that humans simply aren't the best way to conduct biotech research.

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