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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
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External Links
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 October 31, 2025, from https://doi.org/10.69645/OTCC5164.Export Citation (RIS)
Publication History
- Published on December 25, 2024
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
                
              Other Talks in the Playlist: Research and Clinical Interviews
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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