EPPENDORF_Nov2021_UpstreamBioprocessingImprovingEfficiency - 26

Stars in Alignment for Artificial Intelligence in Bioprocessing
hidden correlations and to improve product or
process understanding. In principle, the algorithms
needed to support these methods are already available
from open or commercial source code platforms
compatible with Python, Matlab, or R. The remaining
difficulty, then, is efficient data storage and efficient
structuring. This difficulty can be addressed by
following the FAIR principles.
" New technologies are always welcome, " Smiatek
adds. " But at the moment, companies should have
enough to do applying their ML and advanced statistics
methods onto current or archived data sets. "
This opinion is shared by Revankar, who notes that
drug companies should use their data to train models.
" To benefit from all the stated promises of AI, " he
says, " manufacturers need to learn how to maximize
the utilization of the datasets already at their disposal
and identify, access, and integrate new data sources. "
Regulatory advances
There is also evidence that regulators expect more
biopharmaceutical companies to use AI in manufacturing.
A few years ago, the U.S. Food and Drug
Administration launched the emerging technologies
program.6
The aim was to promote innovation in
general, including the use of novel technologies such
as digital twins, advanced modeling, and other
ML-based techniques.
Such regulatory programs meet with Smiatek's
approval. " As long as strict guidelines are unavailable
for ML applications in terms of the bioprocess development
or manufacturing, " he states, " such programs
may help to establish novel approaches in companies
and to increase the confidence in their benefits. "
References
1. Fleming N. How artificial intelligence is changing drug discovery.
Nature 2018; 557: S55-S57.
2. Berg. FDA Orphan-Drug Designation of BPM31510 for the Treatment
of Pancreatic Cancer [press release]. Issued: January 22, 2018.
Accessed: December 31, 2020.
3. Yu P, Wilhelm K, Dubrac A, et al. FGF-dependent metabolic control
of vascular development. Nature 2017; 545: 224-228.
4. Smiatek J, Jung A, Bluhmki E. Towards a Digital Bioprocess Replica:
Computational Approaches in Biopharmaceutical Development
and Manufacturing. Trends Biotechnol. 2020; 38(10): 1141-1153.
5. Yang J, Knape MJ, Burkert O, et al. Artificial neural networks for the
prediction of solvation energies based on experimental and
computational data. Phys. Chem. Chem. Phys. 2020; 22(42):
24359-24364.
6. Emerging Technology Program. Food and Drug Administration
website. Updated: October 10, 2019. Accessed: December 31, 2020.
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