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" Large amounts of sensor data can be efficiently
processed, " notes Smiatek, who adds that mathematical
models are also available that can support the
use of digital twins for bioprocess control, steering,
and prediction.
Another adoption driver is process control. " As one
starts getting into the process space-especially in
process monitoring-use of AI-based models stands
to offer solutions to monitor performance vis-à-vis
patterns and anomalies that start evolving across
batches, " Revankar explains. " Using AI-based tools to
process data across multiple sources and to facilitate
the contextualization of data across batches can
expedite the release of manufacturing batches. "
Adoption of AI would be simplified if regulations
were clarified. " The main challenges for ML- and
AI-based methods are missing guidelines in terms of
GMP application, " Smiatek comments. " It is unclear if
a proposed model or digital twin would satisfy GMP
requirements in addition to scientific validation criteria.
" AI and ML applications are mainly used where
GMP requirements do not apply, for example, in early
development. However, reasonable guidelines could
help extend the use of ML or AI approaches to GMP
environments. "
This view is shared by Gernaey, who proposes that
regulators, like industry participants, need time to
adapt. " I have the impression that regulators push for
more efficient production processes, " he says. " But of
course, they also require time to adapt to a new
reality where potentially more and more complex
algorithms are used to convert data to information.
What we need, I think, are published case studies
where the benefit of applying AI is clearly demonstrated,
including details about the methods that
have been used. "
In addition, the training of staff in the biopharmaceutical
industry could be revised to include
AI-enabled technologies. " The main difficulty in
exploiting AI isn't about technology, but more about
the requirement that people cultivate different skillsets, "
Gernaey maintains. " Alternatively, the solutions
that are implemented have to be made user-friendly
in such a way that the operator/process chemist can
readily use the information that is generated. "
To that end, industry could make greater use of
early drug and process development data to train AI
models. " Pharmaceutical companies usually produce
a lot of relevant research and development data, "
Smiatek points out. " With regard to an efficient use in
terms of advanced ML approaches, the corresponding
data sets need to be saved and structured
reasonably in modern data storage systems.
" Specifically, ML approaches require a lot of structured
data, such that the cleansing of data sheets
consumes a lot of working time. Thus, ML approaches
could benefit from the use of efficient data management
systems, such as electronic lab notebooks
(ELNs) or laboratory information management
systems (LIMSs), and the application of FAIR (findable,
accessible, interoperable, reusable) principles. "
Big data, big opportunity
Because biomanufacturers are familiar with PAT, they
are well placed to use AI and ML. " Most often, ML
techniques perform best if a lot of data is available, "
Smiatek says. " The biopharmaceutical industry can
indeed produce large data sets. "
Not all applications require more than limited
amounts of data. Such applications include active
learning. But PAT applications are more demanding.
They have encouraged biomanufacturers to deploy
sensor technologies and collect high-throughput
experimental measurements.
With PAT applications, ML as well as other
advanced statistics methods can be used to reveal
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