Recent estimates indicate that the value of the global biotechnology market will exceed USD
775.2 billion by 2024, growing at an
impressive CAGR of 9.9%. Biotechnology
covers a variety of verticals, including
bioagriculture, bioservices, bioindustrial and
biopharmaceutical. The last of these makes
up the lion’s share of the biotechnology
market and is an area of growing investment.
Biopharmaceutical companies continue
to be on the lookout for technological
improvements to increase their efficiency
and bring products to market quicker.
Automation, and the integration of other
technological systems via the Internet
of Things (Io T) and analytics platforms
like machine learning, will form a major part
of the future of scientific laboratory work.
A particular area of focus is bioprocess
development, where the conditions for the
optimum production of a new drug product
are developed. This is a time-consuming and
complicated process that can be dramatically
improved using modern software and
informatics solutions. High throughput
technologies such as microbioreactor
systems are already recognized as valuable
additions to development labs, but there are
still challenges to be overcome before these
‘islands of automation’ can be used to their
Why is automation needed?
Biotechnology labs in general, and
biopharmaceutical development labs in
particular, are often a study in contrasts
between highly sophisticated scientific
methods and instruments on the one
hand and paper lab notebooks, binders
and repetitive manual tasks on the other.
In addition to often quoted efficiency
improvement gains, one of the other key
objectives of automation is to improve the
reproducibility of experimental results.
A Nature survey from 2016 revealed that,
of the 1,576 researchers who responded,
more than 70 percent had tried and failed to
reproduce someone else’s experiment. Even
more surprisingly, more than 50 percent had
failed to reproduce their own experiments.
While it isn’t possible to attribute the problems
with reproducibility to a single factor, it’s easy
to see how eliminating sources of human error
through automation can help.
The rise of the lab robot
The first thing many people think of when
it comes to lab automation is robots. The
use of robots in the lab isn’t a new concept;
Taking Biotechnology to the
Next Level With Automation
Automation, and the integration of other technological systems via IoT and analytics platforms
like machine learning, will form a major part of the future of scientific laboratory work
Robots could someday connect to corporate inventories and sample management and
dispensing systems, meaning that scientists could design experiments and then effectively walk
away and let the automation do the work. Credit: Wladimir Bulgar/ Shutterstock.com