EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 24

Stars in Alignment for Artificial Intelligence in Bioprocessing
Process development
Another major advantage of AI is its ability to shorten
process development. According to Smiatek, process
development modeling provides significantly higher
precision and agreement with experimental data
than do older modeling techniques.
" ML methods in terms of supervised and
unsupervised learning techniques are often used for
the classification and analysis of experimental data, "
he notes. " Hence, there exist a plethora of potential
applications for ML methods in terms of modeling,
analysis, and execution of bioprocesses. "
In support of this view, Smiatek cites a recent
report that ANN has been used to predict the solvation
energies and entropies for distinct ion pairs in
various protic and aprotic solvents.5
" The high accuracy
of such approaches, " he argues, " provides even
deeper insights into the underlying molecular
mechanisms or process correlations, which helps to
improve drug product formulations, and to enable
the more efficient design of novel drug molecules or
bioprocesses. "
Equipment maintenance
" I see introduction of AI in the biopharmaceutical
industry as a process that will take place in several
phases, " Gernaey says. " I believe that the first phase
will have focus on use of AI for predictive maintenance,
that is, the monitoring of specific critical
pieces of equipment for changes in behavior. "
Such monitoring can prompt the servicing of
equipment whenever deterioration in performance is
detected. Otherwise, servicing may be reactive, instigated
by the unscheduled breakdown of equipment,
which leads to loss of production capacity and,
potentially, loss of valuable product.
According to Gernaey, this type of predictive
maintenance can be considered a " local " AI
24 | GENengnews.com
application. " Some of these predictive maintenance
projects have already been implemented in the
bio-based industry, " he remarks.
" That first phase will build confidence with
industry, " he continues. " In a second phase, more
complex challenges will be tackled, where data from
significant parts of a production process can be
processed at once, in an attempt to extract knowledge
on process performance that can be used
toward improving process operations in real time. "
The trend toward real-time maintenance and
process improvement is already impacting technology
design. " Like our customers, we are looking at
applying AI-based condition monitoring technologies, "
Revankar remarks. " Many areas such as service
and maintenance could benefit.
" As biomanufacturing starts embracing strategies
toward continuous processing, the condition of the
underlying assets forms an important and fundamental
factor. AI-based technologies stand to offer
some immediate and tangible benefits there. "
Adoption drivers
The biopharmaceutical industry often lags other
industries when it comes to new technology, and
to a degree, this is true for AI in biomanufacturing.
However, for some ancillary tasks, the approach is
already well established.
" AI and ML approaches in the biopharmaceutical
industry are still in their infancy, " Smiatek observes.
" However, useful applications already include
laboratory automation, detection of impurities in raw
materials, process control, and improved modeling
techniques. "
The biggest adoption driver for AI and ML is
process analytical technology (PAT), which is already
established. Many drug firms already have the
information technology infrastructure in place to
implement AI.
https://www.genengnews.com/ http://www.GENengnews.com


Table of Contents for the Digital Edition of EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency

EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 1
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 2
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - Contents
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 4
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 5
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 6
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 7
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 8
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 9
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 10
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 11
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 12
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 13
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 14
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 15
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 16
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 17
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 18
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 19
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 20
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 21
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 22
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 23
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 24
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 25
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 26
EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 27