APR Sept/Oct 2023 - 96

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FORMULATION AND DEVELOPMENT
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The Role of Data in the Pharmaceutical Lifecycle
Andrew A. Anderson
Vice President Innovation & Informatics Strategy
ACD/Labs
Data plays a critical role in the discovery, development, and sustained
delivery of new and existing medicines to patients. There are a variety
of scientific, regulatory, project, clinical, and supply chain-related
decisions stakeholders must make to matriculate through this full
lifecycle. Data is what informs these decisions. Stakeholders must
carefully consider the implementation of systems to assure that all
this data, in its various forms, dispositions, and locations is available
for effective decision making. Implementation of an enterprise data
strategy must account for a variety of factors.
A futuristic data-driven experience in the pharmaceutical industry
might look a little like this:
No paper, no writing implements, and few keyboards.
Communication is made through radio frequency via RFID
scanners and receivers.
Scientists and operations staff wear augmented reality interfaces
with visual and natural language processing-based audio
support, on top of the usual PPE.
Instruments and laboratory equipment is equipped with IoT
adapters that enable data streaming. When scientists approach
an instrument, their RFID detector opens the acquisition console
on their heads-up display (AR HUD) allowing for automatic
initiation of experiments.
Data from acquisition is streamed via 5G (maybe 6G!) to the cloud.
ML-based analysis and the scientist's interpretation is streamed
to decision support interfaces.
How do we get closer to this seamlessly data-driven future?
Pharmaceutical Product Lifecycle
Data Strategy
When stakeholders first embark on strategy development, it's best
to start with identifying the sources of data as a function of the
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unit operations across the lifecycle. In the pharmaceutical industry
this lifecycle starts in Research and Development. The goal of
pharmaceutical research (also known as discovery) is to identify
molecular entities that are suitable for clinical investigation. Today,
automation and digital measurement outputs must be carefully
accounted for in data strategy development.
Moreover, consider the degree to which software systems support the
completion of specific unit operations. Rather than rely on scientists to
document or transcribe information into human-readable documents
or presentations, today's stakeholders can leverage the ability to
automate the streaming of formatted output from such instrumental
or robotic data sources and rely on software-based decision support
systems. The resultant systems afford data-driven decision making
(as opposed to the document-driven paradigm of the past). When
confronting the challenge of implementing a practical data strategy,
the following considerations are essential:
* Business process analysis-the steps to discover, develop, and
supply new therapies to patients
* Data-generating activities-the unit operations across the
lifecycle that generate data
* Data governance model-systems and procedures needed to
glean the most benefit from data-generating activities
* Digital knowledge transfer between unit operations-when
matriculating through these unit operations, how does
knowledge flow as the composition of teams' change? For
example, how does knowledge transfer from discovery to
development upon candidate nomination? Assuring successful/
efficient knowledge transfer is essential to keep the trains
running on time.
Here I will discuss the typical set of dependent stages for discovery
and development project teams. Each step produces a variety of data
that informs project team decision-making and should be carefully
evaluated for successful implementation.
APR Sept/Oct 2023

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