APR Nov/Dec 2022 - 74

»
ROUNDTABLE
»
Biopharmaceuticals
and Biosimilars
Stacey Treichler
Director, Head of Marketing & Strategy, BioModalities
Catalent
Kaitlyn Vap
Life Sciences Lead Product Application Specialist
Sievers
Briana Nunez
Lead Researcher
Sievers Instruments' Microbiology Center of Excellence
Hayden Skalski
Life Sciences Product Application Specialist for the Sievers Instruments product
line at Veolia Water Technologies & Solutions
Over the last year, other than the pandemic, what
do you feel has been the major industry issue with
respect to biopharmaceutical product development
and manufacture?
Stacey Treichler, Director, Head of Marketing & Strategy,
BioModalities, Catalent: We are seeing continued growth in multispecific
antibodies, for example bi-specifics, tri-specifics, and T-cell
engagers. Results from multi-specifics in the clinic look promising, but
there can be unique challenges associated with their development and
manufacture. Those challenges range from upstream development
(e.g., optimizing the cell culture conditions for high titer) to downstream
development (e.g., ensuring purification of products with the correct
chain pairing), and analytical development (e.g., developing potency
assays that properly measure activity). As more of these molecules
advance through the clinic, the industry is working on solutions
around cell line development, purification, process, and analytical
development to improve manufacturing efficiencies.
Kaitlyn Vap, Life Sciences Lead Product Application Specialist,
Sievers: The pandemic has contributed to many industry issues
around supply chain, time-to-market,
regulatory scrutiny, etc.;
however, outside of the pandemic, the data-insight gap continues
to develop as a major industry issue for biopharmaceutical
product development and manufacturing. This issue has evolved
as the volume of data being collected in product development,
manufacturing, and clinical settings continues to grow
exponentially, but the capabilities of leveraging that data to make
decisions, predict, diagnose, and optimize do not match that level
of growth. Companies continue to operate with manual and siloed
processes, which ultimately leads to isolated data that does not
provide insight into process performance characteristics. With tools
like machine learning, predictive analytics, and artificial intelligence
continuing to emerge, it will be pertinent for the biopharmaceutical
industry to adopt software platforms that allow for the integration
of these tools for the purpose of optimizing drug development,
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| November/December 2022
APR Nov/Dec 2022

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