Drive the Lab of the
Better storage structures, more cloud availability and cheaper data are
propelling the use of AI and machine learning in the laboratory.
Automate the laboratory or die? Well not quite, but the ability to do more in the laboratory with automated solutions is fast becoming a reality for
many in R&D. Automation, and the integration of other
technological systems via Io T and analytics platforms
like machine learning, is obviously going to be a major
part of the future of scientific laboratory work. These
technologies are already making waves in the industry
by removing mundane tasks from researchers while
increasing accuracy and efficiency. More importantly,
it’s part of the “lab of the future” that’s no longer in the
future—it’s already here.
Automation, and other new technologies, influence
the way laboratories operate—how they collect the necessary data to create drugs, form hypotheses or fund
research. With pressure to drive efficiency and cut costs,
organizations are embracing automation, with impacts
for both laboratory scientists and the drug discovery
Removing mundane tasks is one area that appeals to
scientists from all walks of life. Estimates from many
studies over the years put time wasted doing tasks like
copying data, reviewing data, aggregating data and writing reports in the 10s of hours per week. Take this away
from any scientist and they will likely jump for joy—but
that is only the tip of the iceberg.
Automation of routine “manual tasks” also gets scientists to sit up and take note. Robotics has been applied
to areas like high throughput screening in the past, but
it is becoming more pervasive now—making inroads
into DMPK, bioanalysis and process development. All
can be automated to a point, and this can have dramatic
efficiency impacts, resulting in an increase of throughput.
It is a complex task and the “full automated lab” is
a while away but progress is being made with newer
instruments that are modular and can be daisy chained
with sample preparation and automated data analysis.
by Paul Denny-Gouldson, Vice President Strategic Strategies, IDBS
Robotics has been applied to areas like high throughput screening in the
past, but it is becoming more pervasive now.
This is not simple and still requires detailed knowledge of IT and robotics to get a solution working. Some
instrument vendors are taking a more holistic approach
to the problem by merging many tests, IT and process
into a single instrument solution—thus removing the
need for bespoke software and robotics. This works
extremely well for known processes but is not so useful
for adhoc or emerging testing types.
High performance computing
Finding data that is of importance to a project is one
burden that many lab managers and leaders in R&D dislike. It often means lots of wasted time and dead ends,
leaving most frustrated and annoyed. Scientists like to
do science and so data munging and investigations are
tedious at best. Automation of “data discovery” is starting to emerge as a trend with the advent of better storage infrastructures, cloud availability and cheaper cost