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Topics Covered
- The need for optimizing cell culture processes in biotherapeutic manufacturing
- Key features of a new predictive model to support cell culture processes
- Development and validation of the model
- Key challenges and how they were met
- Wider implications of the model in biopharmaceutical manufacturing
Biography
Shyam Panjwani is currently working as a Principal Data Scientist at Bayer Pharmaceuticals, Berkeley. He holds a PhD degree from University of Houston and a bachelor’s degree from Indian Institute of Technology, Kanpur in chemical engineering. He has 8+ of experience in applying AI/ML/statistics for biologics manufacturing processes, biological assays, and process development. Before joining Bayer, he worked with Halliburton and Air Products
Talk Citation
Panjwani, S. (2024, December 25). Predictive modelling for cell culture processes in biotherapeutic manufacturing [Audio file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved February 9, 2025, from https://doi.org/10.69645/OTCC5164.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Shyam Panjwani has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Audio Interview
Predictive modelling for cell culture processes in biotherapeutic manufacturing
Published on December 25, 2024
18 min
A selection of talks on Pharmaceutical Sciences
Transcript
Please wait while the transcript is being prepared...
0:00
Interviewer: Dr.
Shyam Panjwani,
you and your
colleagues published
a paper earlier this
year presenting
a new predictive
model for optimizing
cell culture processes in
biotherapeutic manufacturing and
we would like to discuss
this paper with you in
this short interview.
To kick things off, can you
describe the objective of
the project described
in your paper and
what was the unmet
need behind it?
Dr. Panjwani: Sure!
First of all, thank you
Ail for inviting me and
I'll be happy to share
my research with
your readers and audience.
The objective of
our research was to
enhance the efficiency and
the predictability of the
cell culture process,
specifically in the production
of biotherapeutics.
Our research
specifically aimed at
addressing the unmet need of
a data-driven predictive
model in application
that could predict
bioreactor potency using
at-line process parameters,
days in advance.
There is a reason why we
picked potency because
potency is an attribute which
is not typically measured
on the production floor.
The samples are sent
to a laboratory and so
there is a delay in getting
the potency measurement value.
If we have predictive models
like what we developed,
then we can use those models to
predict potency in the absence
of the measured value.
These kinds of models
actually do not only support
the business objective
but also fulfills
the regulatory requirement that
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