APR Sept/Oct 2023 - 93

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has meant adopting new technology, including sequencing platforms,
specialized bioinformatics, proteomics, and molecular biology tools to
scrutinize data and characterize modifications, and cloud-computing
to analyze huge volumes of data.
The next gamechanger? Getting those software tools to work
seamlessly, so the data can be layered and flow smoothly through the
entire discovery process.
The Next Great Change: Uniting Tools
and Data
Biologics R&D can be a tangle to navigate: the complexity and scale of
the biological systems, the analysis workflows, the data and storage
requirements, all the way down to the manufacturing and regulatory
requirements. These produce an incredible amount of complex data
that researchers need to sift through to make the right decisions that
lead to discoveries.
Rich data visualization is a critical part of the R&D process. And, certainly,
visualization of a wide array of data types can help researchers better
understand and validate their work. But biologics teams don't want to
see their data as disconnected. They need to access and interrogate
how the data relate to each other at any given moment throughout
the discovery process. Context is king and they want to use it-quickly
and collaboratively-to propel their research forward. What makes a
difference for biologics R&D teams is to close the gaps on software
tools and data to accelerate innovation and make better research
decisions, all while stretching research dollars.
Unified Data Flow Example Scenarios
Imagine a new researcher joins an antibody discovery team and
needs insight into previous efforts. In the past, this might have been
impossible due to poor record keeping and brain drain. But if the
team's work has been detailed and preserved in a centralized FAIR
data repository, the new member can access all historical R&D data.
They can see what work has been done, when, how, and by whom. For
example, they can quickly search for and explore registered antibodies,
see all associated data like associated clones or expression vectors,
and view production-related data (such expression and purification
information) and assay results.
Next, they may want to narrow down which antibody candidates
to pursue by pulling in assay data to " lay over " their sequence data
and annotations (like amino acid liabilities) and then performing
alignments. With these data and tools at their fingertips, the new
team member can quickly onboard, avoid redundant efforts, build off
existing knowledge, and focus on the best antibody candidates. This
kind of access to the data also lets the new researcher easily go back
and add new data to a prior analysis, which is common when new
insights or trends have emerged since the original analysis. Adding
new data may be necessary to re-evaluate the original conclusions or
to discover new patterns.
Another situation to consider is a biologics program director who
needs to decide which disease target to pursue next. This typically
involves an incredible amount of legwork to manually collect, process,
and collate all the data needed to qualify protein targets, which is
often done in an external program that traps the results forever. If
that director could instead view relevant data in simple-to-interpret
reports and dashboards, they could more quickly uncover the most
promising paths. Imagine, for example, the time that could be saved
and knowledge gained if it were easy to collate data across a wide
array of assay experiments, perform bulk analyses like multiple curve
calculations, view the results, and then quickly filter through their data
to produce figures that communicate discoveries clearly.
Discovery Through Tightly
Integrated Software
Biologics R&D teams benefit immensely when all their research tools
are seamlessly connected. While it's useful to select test results and
visualize the associated data at once, it's a gamechanger to move one
step further and allow researchers to quickly branch their research
questions, register and track the process changes, easily order or
perform new tests or analyses-all from a central hub. Think of it like
" Google Maps " for science, helping researchers orient themselves, see
where they have been, where they want to go, and what's the best way
to get there.
On top of integrating R&D instruments and software, the data these
instruments produce must be linked to get the most value out of the
data. Creating a centralized data repository can be a huge undertaking.
There are numerous considerations, including standardizing file
formats, managing copious amounts of data, establishing permission
controls for internal users and external partners, and facilitating
backend data exchange. All data within a central depository must be
findable, accessible, interoperable, and reusable, or " FAIR. "
A centralized research platform can deliver quick access to both
the software tools and data that teams need to accelerate biologics
discovery. With everyone from lab techs to antibody scientists to
program directors united on the same platform, teams can flow
data between their specialty research tools, build off each other's
knowledge, streamline workflows and lab requests and make datadriven
decisions at every step.
Author Biography
Christian Olsen is the Associate VP, Industry Principal of Biologics at
Dotmatics, a provider of R&D scientific software connecting science, data
and decision-making.
www.americanpharmaceuticalreview.com |
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