APR Nov/Dec 2022 - 71

« FORMULATION AND DEVELOPMENT
What's Keeping Your Enterprise from Fully Leveraging
Its Data?
Jim Olson
CEO
Flywheel
Research & development (R&D) processes are increasingly being
redefined within the framework of digital transformation. With these
initiatives, pharma organizations aim to improve speed to market and
reduce the considerable R&D costs of drug discovery and development.
Artificial intelligence (AI) and machine learning (ML) are key parts
of this transformation. However, organizations are discovering that
harnessing these approaches isn't the work of just a few weeks or
months. Some companies have gone through several iterations of
digital transformation in R&D, with costly failures along the way. A
recurring issue among these iterations has been the problem of how
to efficiently de-silo data and enable enterprise-wide access.
Machine learning is data-hungry (especially for diverse data), and
unless research enterprises have outstanding data governance models
in place, they face challenges in realizing a true digital transformation.
Often, the challenge is in breaking data out of institutional silos, uniting
them in a central repository (or a centrally managed and normalized
set of repositories), and standardizing them for machine learning.
Decades of ingrained culture, pieced-together tech stacks, and
homegrown systems make strong headwinds for life science leaders
as they seek to move their research teams forward. But the risks
of the status quo are impossible to ignore: without modern data
management, organizations are wasting money, missing innovative
opportunities, under-leveraging their assets, and potentially even
facing compliance risks.
Why Is Standardizing Data
Such a Challenge?
It doesn't take long to understand at a high level why data management
is so difficult for pharma companies. The data they hold are old and
new, simple and complex, and sourced from multiple places, ranging
from internal or external research results to clinical trials that are still
underway. In addition to what is located in a company's own archives,
data can be held externally by development partners such as contract
research organizations (CROs) and clinical sites.
To explore just one example of the difficulty in standardizing even
newly captured data, consider a global clinical trial with hundreds
of patients that has prescribed an imaging protocol with five types
of examinations for every patient at multiple time points. With
imaging being performed via multiple sites, devices, providers, and
languages, this trial could generate thousands of unique metadata
tags and descriptions.
As this example illustrates, the sum of an organization's data, having
been captured over time by different researchers, different devices,
and using different organizational conventions, results in a collection
of heterogeneous data with untold variation, even if data is provided in
the same format (e.g., DICOM). The older the data, the more challenges
may arise when attempting to curate those data. Over a dataset's
lifecycle, as the data have moved through transfer and analysis
pipelines and changed format, the likelihood that metadata have
been manipulated or even removed increases. Significant portions
of the data will likely predate AI/ML and will not have been acquired,
archived or organized with such applications in mind. However, as
already mentioned, ML is data-hungry, and even legacy data or data
that are part of long-term longitudinal studies with issues like these
are worth curating to create bigger datasets for training.
Traditionally, in order to harness disparate data for ML, research teams
must spend countless hours locating datasets and manually curating
them to a common standard. This curation entails tasks including
selection, classification, transformation, validation, and preservation
of research data and supporting material. The time required by this
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APR Nov/Dec 2022

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