Pharmaceutical Commerce - September/October 2017 - 28

Clinical Operations
The power of strategic data standardization
Global efforts to collect clinical data in a more standardized form will lead to more research innovation
By Sy Pretorius, MD, Martin Roessner, MS, Michelle Hoiseth and Michael Goedde, Parexel International

The pharmaceutical
industry is deluged
with data. However,
because data is
seldom standardized,
companies cannot
fully tap its potential
to substantially reduce
t h e f a i l u re r a te s i n
d r u g d e ve l o p m e n t .
[1] By consciously
streamlining and
unifying data,
drugmakers can
improve efficiency
throughout the four
stages of clinical
development-from
design and star t-up
through execution and
submission.
As a case in point,
during the clinical
development design
phase, companies can
integ r ate data from
multiple sources to
build a clear patient
profile. Social media,
patient focus groups
and disease advocacy
groups are all excellent
sources of potentially valuable data. For
endpoint selection, pertinent data sources
may include similar trials, basic research,
regulatory filings and payer data. Dose
selection can also be better met with
data from preclinical studies, population
data and other sources that would allow
successful simulations and predictions of
dose response curves.
In our experience, it is important for
companies to leverage both their internal,
as well external, data. This is something
that we routinely do when we optimize
protocols-both from a design, as well as an
execution perspective. To enable the above,
we have created a metadata repository
(MDR), which applies standardization and
traceability from protocol generation (e.g.,
endpoints, visit structure), through data
collection, mapping, statistical analysis and
reporting, on through preparation of the
clinical study report (CSR) (see graphic).
Conventionally, the various individuals and
teams responsible for these activities start
from scratch to write their own sections of
protocol, develop the electronic database
and program the statistical analysis. This
process can be cumbersome and has the
potential to cause issues down the line. Our
MDR approach allows us to standardize and
expedite these tasks through automation,
ensuring consistency in language and data
collection.
An example of how metadata
standardization works (and an illustration

of data complexity overall) can be seen
in this example: the collection of gender
information. Every clinical study collects this
information in the demography domain.
The clinical data behind this concept
would be "male," "female," "unknown," and
"undifferentiated." This gender information
for a given subject will be collected in
different places by different people and
systems for a given study (e.g. clinical data
base, central laboratory, randomization
system, specialty lab, clinical management
system, etc.). The metadata for this item
should follow the established industry
Clinical Data Interchange Standards
Consortium (CDISC) Standards, with "sex"
as an item name and the values "M" and
"F" for male and female. However, in many
cases even in this very simple example we
see different data sources using metadata
such as "sex," "gender" and actual clinical
data such as "male, female," "M, F," "man,
woman," "0, 1," "1, 2" or other variations.
When these different data sources are then
being put together, an enormous amount
of mapping through programming needs
to be put in place to allow to combine data.
The complexity level increases for other
examples, such as laboratory data with
different test codes.
Common protocols
Research organizations around the
globe are increasingly advocating and
implementing data standardization.
In 2012, the collaborative non-profit
organization, TransCelerate Biopharma,
introduced a Common Protocol Template
(CPT ) to prov ide a foundat ion for
electronic protocols. [2] In addition, the US
Food and Drug Administration (FDA) and
the National Institutes of Health (NIH) last
month announced availability of their own
joint template, including sample language
and instructions, to help researchers prepare
Phase II and III clinical trial protocol. [3]
Many fresh efforts to harness disparate data
sets are likely to influence the conduct and
design of clinical research going forward.
The 21st Century Cures Act, passed by
Congress several months ago, is a case in
point. It may enable drugmakers requesting
re g u l a to r y re v i e w o f e x p e r i m e n t a l
treatments to bypass full clinical trials and
instead submit "data summaries" and "realworld evidence," such as observational
studies and insurance claims data. [4] For
its part, the Patient-Centered Outcomes
Research Institute (PCORI) seeks to
enhance engagement of clinical research
stakeholders, including patients, caregivers
and prescribing physicians. As PCORI and
other groups fund investigations of highly
complex issues, such as the best means of
managing symptoms of end-stage illness
across disease categories, standardization
of data and methods for analyzing it will be

28 Visit our website at www.PharmaceuticalCommerce.com September | October 2017

The metadata repository (MDR) enables data to be standardized. Credit: Parexel

key. [5]
Around the globe, it's clear that dataoriented research efforts will soon afford
drugmakers access to data truly meaningful
for clinical development. Some of the
most visible include the United Kingdom's
Clinical Practice Research Datalink
(CPRD), the European Commission's
eHealth Interoperability Framework,
and the Structured Data Capture (SDC)
Initiative. Guidance issued by FDA [6] and
the European Medicines Agency (EMA)
[7] also make plain that electronic health
records (EHRs) will have a growing impact
on healthcare and research.
Companies can harness standardized
data during the start-up phase of
development. Examples include site
enrollment and performance on previous
trials, epidemiological data, etc. Critical to
unlocking insights from these standardized
data sources is expertise with data storage
architecture, ability to search across
different data structures, data visualization
capabilities, predictive analytics and more.
In the clinical development execution
phase, standardized operational data can
help companies save time and money by
making it easier to determine whether the
data being gathered from patients are in
line with trial objectives. As a best practice,
organizations should leverage a statistical
monitoring approach to help data withstand
regulatory scrutiny. Visual presentations
of scientific data should be geared towards
people with specific responsibilities and
skills, such as data management, clinical,
medical and statistical team members.
Wearable technology
Beyond data processing, technological
advances in data capture are also changing
what's possible in the execution of clinical
trials. Wearable devices equipped with
sensors are swiftly becoming viable means

for collecting clinical data from human
patients in a real-world setting. Eventually,
d a t a c a p t u re d v i a w e a r a b l e s e n s o r
technology might be taken directly from a
person's electronic health record and into
databases established for hybrid studies
and/or site-less clinical trials.
Data standardization is already making a
big impact on the final steps of clinical drug
development-analysis and submission-
and its importance will only increase.
Specific data formats are now widely
used, including Clinical Data Acquisition
Standards Harmonization (CDASH), for
data collection standards, the Study Data
Tabulation Model (SDTM), for submission
of clinical and non-clinical data to FDA.
The Analysis Data Model (ADaM), an
increasingly popular description of dataset
and metadata standards, is guiding the
creation of powerful, flexible structures
for generating, analyzing and replicating
clinical data. CDASH, SDTM and ADaM are
championed by the CDISC, an international
consortium well-regarded for its work to "let
data speak the same language." In addition,
genomics and proteomics technologies
dependent on massive data sets are not only
helping drugmakers discover fresh targets,
but also increasing their ability to predict
and identify specific subgroups of patients
likely to respond to treatment.
In May, FDA approve d Ke y t r uda
( p em bro l i z u m a b ) for p a t i en t s t h a t
screen positive for a particular tumor
continued on page 34

ABOUT THE AUTHORS
Sy Pretorius, MD, serves as senior vice
president and chief scientific officer at
Parexel, where Martin Roessner, MS, is vice
president, global biostatistics; Michelle Hoiseth
serves as vice president, Parexel Access; and
Michael Goedde is vice president, clinical data
management and database programming.


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