Novartis Comments to the Docket
Regulatory New Drug Review: Solutions for Study Data Exchange
Standards; Notice of Meeting: Request for Comments
(Docket No. FDA-2012-N-0780)
Author(s):
Melissa Binz, Peter Mesenbrink
Document type:
Response to Request for Comments
Document status:
Final
Release date:
03-Oct-2012
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Docket No. FDA-2012-N-0780
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Table of contents
Table of contents ................................................................................................................. 2
Question 1:........................................................................................................................... 3
Question 2 ............................................................................................................................ 4
Question 3:........................................................................................................................... 5
Question 4:........................................................................................................................... 5
Question 5:........................................................................................................................... 6
Question 6:........................................................................................................................... 7
Question 7:........................................................................................................................... 7
Question 8:........................................................................................................................... 8
Question 9:........................................................................................................................... 9
Question 10:....................................................................................................................... 10
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Novartis is pleased to have the opportunity to provide feedback to Docket No. FDA-2012-N0780. Please find our responses below. We look forward to sharing our viewpoints with
FDA and our PHARMA industry peers at the public meeting on November 5, 2012.
Question 1:
What are the most pressing challenges that industry faces with regard to study
data management? Please address each of the following areas: (a) Study
design/set-up, (b) capture, (c) integration, (d) analysis, (e) reporting, and (f)
regulatory submission. What opportunities/solutions exist to meet each
challenge?
Response:
Study Design/setup
As the pharmaceutical industry strives to make research and development more efficient, even
as it evolves towards the development of personalized medicines, adaptive study design and
sub-studies within the context of a clinical trial will become critical to success. While these
concepts help to maximize the value of research dollars spent, as well as the value from
subject-level study data, it cannot be ignored that shifting to this model is increasing the
complexity of study protocols and with the data management both at the research site level as
well as at the Sponsor. It will be important for researchers to focus more on the value of each
data point to be collected so that stress within the process caused by over-collection of nonessential data can be minimized.
Capture
The EMA Reflection paper on expectations for electronic source data and data transcribed to
electronic data collection tools in clinical trials is currently causing much discussion across
industry. Novartis sees a difference between eSource which is facilitated (paid for) by the
Sponsor and is then directly inputted into the Sponsor’s data capture tool or otherwise used
directly; eSource which is utilized/owned by the research site itself and for which the Sponsor
obtains permission to access the system to withdraw data for the sponsor data capture system;
and finally, paper source which is actually transcribed into the Sponsor’s data capture system.
It would be helpful to have an open discussion and evaluation of the requirements for each of
these scenarios as they relate to this paper and an official guide for auditors and industry that
explicitly defines, by scenario, the expectations.
In addition to the current debate around the regulation of electronic source data and recent
findings by auditors, the pharmaceutical industry continues to struggle with site-to-sponsor
data submission metrics. Novartis sees value in time-from-visit-to-submission-to-sponsor
regulation for sites. PHARMA currently manages this with varying levels of success and
having regulation which holds sites to quality metrics in this area would help to eliminate
individual sponsor-site negotiations as well as improve subject safety oversight.
Such
timelines/expectations exist for serious adverse events (SAEs); however, no
timelines/expectations exist for other clinical data despite audit findings suggesting that the
lag from visit to data entry is too long.
Integration & Analysis & Reporting
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As industry moves more rapidly towards data standardization with the formalization
introduced in PDUFA V, the changing regulations on required data capture, or preferences for
controlled terminology used, creates additional workload when attempting to pool data from
clinical studies that span many years and potentially many versions of data exchange
standards. Once defined, data standards should be kept backwards compatible and/or
mapping expectations defined to expedite and/or clarify any necessary mapping to the new
structure/terminology.
With respect to analysis and reporting, if there are rules or expectations on how to impute
missing data and/or special statistical methodologies which the agency recommends, having
these explicitly defined will help so that as required if one needs to go back retrospectively
and update analysis data sets so that pooled analyses and meta-analyses can be conducted this
will become more time consuming. In addition, if vast changes in platforms occur which
make compatibility not plausible, agreements need to be place with respect to how metaanalyses can be presented if it is not possible to have available complete integrated analysis
data sets across all clinical studies. This is why, early on in the clinical development process
as part of the data standards plan there should be clear agreement between Agency and
Sponsor on the overall pooling strategy and which parameters and types of studies will be
included as part of the data pool and the analyses that will be conducted on complete data and
which analyses will be conducted on summary data.
Regulatory Submission
Current submission file formats are not up-to-date (SAS Version 5 Transport files) with
existing technology and/or trends in data management systems which creates additional
complexities when designing the collection instrument and/or after data export to
accommodate the restrictions based on years-old technology. Sponsor companies who
maintain up-to-date technologies, consistent with what is currently specified as required,
should be able to utilize the new features instead of having to work backwards to reformat
data.
Question 2
How could FDA’s regulatory requirements make the study data management
process more efficient?
Response:
As discussed in Question 1, many factors are introducing additional complexity into clinical
research and thus the collection of data to support statistical analysis of clinical trial data. So
that these additional factors can be focused on and addressed, Novartis believes that it would
be beneficial to have, clear and explicit requirements from the Agency for essential data to be
specified to the individual element level. Further, it will be critical to have cross-ReviewDivision alignment and/or defined, specific Review Division exceptions and additions would
allow Industry to perform focused research and data collection instead of attempting to collect
maximum amounts of data in order address potential ad hoc questions from reviewers.
Throughout the course of a product development lifecycle, various data and biomarkers are
explored and later abandoned. Novartis sees this as part of the normal scientific process.
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Current position papers seem to suggest that all data collected in the course of researching a
compound be sent along with the submission regardless of final relevance and/or the
sparseness of data availability. It would be good to evaluate the need for data inclusion in a
submission when specific data was not collected across the full cycle of the product’s
development. Novartis believes this could be facilitated by at the End-of-Phase II meeting
with the review divisions where they can agree upon the particulars of data submission
requirements and the overall Data Standards Plan that will be utilized in Phase III studies.
Question 3:
What does industry need to make clinical trials data management more
effective and efficient? Please describe the tools, techniques, and processes
that would help as well as the regulatory guidance documents that would be
useful in this area
Response:
Clear expectations around the required data points to be collected and reported in order to
achieve approval will help in making decisions around case report form (CRF) design more
focused and ultimately more consistent for the research sites. This should not restrict
Sponsors from collecting additional data they feel necessary for quality trial and subject
oversight, but would eliminate the continued collection of non-essential clinical data. For
sponsors submitting data beyond these expectations, it would be helpful if the Review
Division would provide utilization metrics for the data that was submitted versus what the
reviewers used to determine approval status. If reviewers are not using certain data elements
or entire sets of data, highlighting this to the Sponsors would potentially help them adjust their
data collection strategy for future similar compounds or submissions in that disease area. This
would result in more focused submissions of data to the reviewers as well as less stress on the
sites to collect non-essential data. Accordingly, the required clinical data exchange standards
for that disease area should be updated to reflect the requirements.
In addition to data collection requirements themselves, defining and auditing against data
quality expectations for sites would help to ensure both timely submissions of subject data to
Sponsors as well higher quality upon initial data entry. If sponsors were required to report at
the end of studies to both the site as well as to regulators the time-to-entry, percentage of data
corrected after initial entry, as well as the query rate for individual sites it might be possible to
further eliminate fraud and encourage self-improvement at the site itself all while reducing
some of the stress inherent in the data management area.
Question 4:
What data standards are you currently using for the conduct of regulated
research studies?
Response:
At Novartis, the clinical data standards that are in use in clinical research studies are aligned
with the principals of CDASH Version 1.1 for all safety domains that are defined within the
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implementation guide. Wherever possible the naming convention for data collection defined
as part of the submission data standard (SDS) are utilized with the exception that to allow for
multiple CRF views to collect the same data element, instead of the domain name being the
prefix for the data element, e.g. AE + data element name, the data domain wherever possible
is the suffix for data collection to distinguish between those data elements used for collection
and those data elements used for submission. This distinction is performed in the events that
there is any flexible in the attributes for a particular data element across CRF views.
Wherever possible, 3rd party clinical data follows the vertical structure that is expected by
SDTM and ADaM to minimize the amount of conformance and processing of clinical data
received by vendors. Novartis has developed a substantial number of custom domains for
which SDTM domains have not yet been defined. All of these data domains follow either the
model for the Findings, Interventions, or Event classes that are part of the SDTM model.
For analysis and reporting all analysis data sets that will be submitted from 2013 will follow
the ADaM data set structures including the Adverse Event and Time-to-Event ADaM
structures that were defined earlier this year.
Question 5:
Would Health Level Seven v31 (e.g. messages, structured documents and
Clinical Data Architecture) be a viable study data exchange standard? Please
explain advantages and disadvantages. What would be the impact (e.g.,
financial, technical, or in terms of implementation or change in business
processes)?
Response:
It is not immediately clear what advantages would exist by Health Level Seven v3 (HL7) as a
study data exchange method. Like the Operational Data Model (ODM), any information
stored in HL7 would not be in a format that is easily usable by downstream users for data
review and statistical analysis and would require conversion or manipulation before such data
exchanged in this format could be utilized. Because HL7 is trying to integrated concepts
across not only clinical trials but data from other settings as well as the structure of clinical
reports to implement at a large scale and to educate research sites and Sponsors on the
intricacies needed to understand all the components of HL7 would far and exceed the
struggles already currently faced in transitioning research sites and Sponsors on understanding
the different CDISC concepts defined within CDASH/SDTM/ADaM.
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Question 6:
Would CDISC Operational Data Model2 be a viable study data exchange
standard? Please explain advantages and disadvantages. What would be the
impact (e.g., financial, technical, or in terms of implementation or change in
business processes)?
Response:
Given that CDISC ODM is kept closer in synch with the CDISC standards themselves and the
industry has been watching and moving towards being able to provide CDISC SDTMcompliant submissions, it would seem to make sense that using CDISC ODM as an exchange
standard makes more sense than any other. This being said, ODM does not easily correspond
with the relational technology that is in widespread use across the industry for both data
storage and data analysis and thusly the exchange standard concept is still very immature in
this industry. Further, it is felt that there is still work to be done in ensuring that ODM has
the appropriate types of extensions to fully support the data that will be submitted via this
mechanism. Considering the evolving nature of science and research, a responsive oversight
and approval organization would need to be in place to approve new extensions such that
submission timelines were not adversely impacted. Moving to ODM as an exchange standard
will require that industry learn the particulars of new technology not currently in widespread
use and define new processes to ensure accuracy and reliability of data in ODM.
In order to move towards an XML-based data exchange standard efficiently, the tools
typically used in clinical research and in the analysis of clinical data need to be fashioned to
work seamlessly either with the XML data directly or to efficiently package and then unwrap
the data from its XML tags. Without this unobtrusive functionality in place, sponsors will
incur new burdens for what is currently seen as minimal return on the investment.
Question 7:
Are there other open data exchange standards that should be evaluated?
Please explain advantages and disadvantages. What would be the impact (e.g.,
financial, technical, or in terms of implementation or change in business
processes)?
Response:
Novartis is not aware of any other open exchange standard that we would recommend for
evaluation at this time.
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Question 8:
What would be a reasonable phased implementation period for each
recommended exchange standard? And should supporting multiple,
concurrent study data exchange standards be evaluated (please explain
advantages and disadvantages of this approach)? What can FDA do to help
industry to be more prepared for, or reduce burden of, a migration to a new
study data exchange standard?
Response:
Once an exchange standard has been approved and is posted on the FDA data standards
website, at minimum a 12 month grace period should allowed for a Sponsor who has not used
a previous version of the exchange and up to 24 months should be allowed for transitioning
from a previous version of the exchange standard in use to the new version of the exchange
standard.
If multiple concurrent study data exchange standards are permitted, this would only be
permitted if the different study data exchange standards were compatible and there was a clear
algorithm how you could convert to one version or another as was the case with different
versions of a data exchange data standards. This would allow for flexibility as study data
exchange standards are being developed for a particular disease area if one method allowed
for easier end-to-end data flow from data collection to analysis and reporting.
The advantage of having such flexibility in choosing the best study data exchange standard for
a particular disease area needs to be balanced by several complicating factors

Compounds are often evaluated across multiple disease areas and therapeutic areas
and thus if the exchange standards were not compatible and one chose different
standards for different disease areas this would hinder data pooling considerably

Educating research sites and Sponsors to be knowledgeable on multiple study data
exchange standards would be timing consuming and subject to a considerable learning
curve that would lead to possible errors if sites and Sponsors were conducting research
studies for a compound in different disease areas that used different exchange
standards.

The different IT systems within Sponsors needing to maintain the end-to-end data flow
from the different exchange standards would be difficult to manage and maintain.

It would not be easy switch from one exchange standard to another once decision was
made to select one exchange standard for a disease area within or across compounds as
well as across disease areas within a compound

Alignment across Review Divisions when Compounds are being submitted across
Review Divisions would be challenging but necessary to facilitate the ability to
integrate/pool data across studies/disease areas for conducting meta-analyses during
the lifecycle of the compound.
The FDA can facilitate the decisions around study data exchange standards and the level of
burden to Sponsors by allowing clear and transparent public review of any proposal for the
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establishment of a study data exchange standard within a disease area or across different
disease areas that are often known to be evaluated together (e.g. if a biologic compound is
being evaluated in plaque psoriasis it is often also being evaluated in rheumatoid arthritis).
Once a decision is made the communication of this decision and timeframe of implementation
should be made clear through the FDA Data Standards website.
Question 9:
FDA encourages sponsors to design study data collection systems so that
relationships between data elements, as well as relationships across data
domains, can be captured at the point of data entry. Describe the challenges, to
and opportunities for, accomplishing this goal.
Response:
At Novartis, the end-to-end process by which clinical study data is collected, conformed, and
reported is metadata driven which allows all data elements to have all of their attributes
clearly defined across all the systems where the data element is used. The key attributes that
are defined for the data element include, as applicable:

the data domain where it is collected,

the CRF view where it was collected,

which SDTM domain the data element is submitted to

how values of the data element are created (defaulted, entered, derived),

where in the end-to-end process the data element is created,

Does the data element collected get submitted to SDTM?

if derived, the name of the derivation required to create the data element

what are the inputs to that derivation, in which IT system is the derivation to create the
data element performed,

are there any dependencies with other data elements in creating this derived data
element,

is there any controlled terminology,

what is the controlled terminology associated with that data element,

the SAS and Oracle data formats, is the data element required, expected,

or permissible for a particular data domain,

the version of SDTM that is being followed (currently 3.1.2 for everything except for
TS (Trial Summary) which is using 3.1.3),

the version of the data element within the internal system
These data elements are defined on a per domain basis and will appear in each data domain
that they contribute to unless the data element appears in all data domains or a class of data
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domains then these elements are stored in a “COMMON” data domain that adds certain data
elements to the appropriate data domains.
By collecting data in a metadata driven system, this allows one to have full traceability of all
data elements that will be included as part of the SDTM and ADaM data exchanges with the
Agency.
The other significant challenge faced is that when following the recommendations for data
collection defined by CDASH and SDS, these methods are not always conducive to the
optimal way that we would submit data to the Agency in SDTM- and ADaM-compliant
format where the former may be horizontal and the latter is vertical. Thus, a transformation or
mapping of the data is often required to take the data from its collected format into a format
that is usable submission and for statistical analysis.
Question 10:
What other comments would you care to share with FDA concerning the
general topic of data exchange standards?
Response:
There are three areas of general concern with respect to exchange standards
Transition management
One of the key areas of concern within Novartis and across the other Sponsors is the proper
handling of transition management during the successful implementation of the PDUFA V IT
Goals to allow us the move towards the mandatory use of data standards to support clinical
and non-clinical research and regulatory review. Novartis believes that this will be achievable
through ongoing collaboration between FDA, industry and other stakeholders to develop a
clear, predictable process and plan for the development and implementation of new and
revised data standards.
Evolution of data standards guidances
With the understanding that the regulatory guidance and resource of information on data
standards will continue to evolve over time, Novartis urges the FDA to continue to seek
public consultation as these guidances and webpages are developed. Novartis recommends
that the Agency provide clarity between recommendations and requirements for use of data
standards. Novartis believes that the time when specific standards should be required for use
should be determined based on the issuing of a final guidance which describes the
requirements for usage. Novartis is aligned with the PhrMA position that FDA’s PDUFA IT
5-year plan should serve as a roadmap for forthcoming changes, providing sponsors with
predictability and certainty about when and how changes will be managed. In addition,
Novartis is aligned with the PhRMA position that the FDA data standards webpage should
distinguish between recommendations and requirements (with associated timelines), with
updates to the webpages as appropriate with the proper communications through the process
of issuing guidance or regulation.
Another area where controlled management of change will be important is when controlled
terminologies are updated and new therapeutic area data standards become available. It will
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also need to be made clear in research areas like immunology where the number of potential
orphan diseases is quite large what the expectations will be of the Agency with respect to the
development and creation of the appropriate data standards and controlled terminologies in
disease areas that will potentially not be part of the FDA’s 5-year IT plan. Guidance for
orphan diseases will be important because standards development will likely involve custom
terminology that will not be defined by the FDA or available on the NCI website.
We believe that the process by which all draft guidance and resource pages are updated
should be clear and transparent so that the interpretation and implementation of these
guidances is done consistently across the Review Divisions within the Agency where often
compounds are being developed across multiple Revision Divisions at the Agency and within
the Industry. Thus a process should be defined that clearly describes the timing of the
implementation clock with version changes in standards so that Sponsors can evaluate the
impact of the change on ongoing and future clinical trials as well as how long older versions
of the standards will be supported by the Agency.
Clarification is also needed from the Agency with respect to the use of medical reference
dictionaries and which ones are recognized by the FDA as being standard for the use in the
collection and reporting of clinical and non-clinical data and whether deviations from the
official endorsed dictionary list is permitted. Further clarity is also required on the process by
which controlled terminology is approved and how to address if the approval timelines do not
coincide with the timelines for dossier submission for regulatory approval.
The scope of studies to be covered by the data standards should be clarified as well as the
level of flexibility will exist during the implementation of the FDA 5-year IT plan where
Sponsors will have a considerable volume of legacy clinical studies that were or will be
reported in a non-CDISC compliant format. In addition, the expectations relative to the data
submitted as part of the IND needs to be delineated as much of the non-clinical data that will
be included as part of such submissions will originate from non-US sources and/or may have
been collected 10 or more years ago during period when data standards were lacking in this
area.
Process clarification
One area of great concern regarding processes focuses on the expectation of the Agency with
respect to submission of clinical or non-clinical study data which has been converted to
CDISC-compliant format after it has been reported in a legacy format. Will this be required
for all clinical studies or will it be possible for this to be a point of discussion during the
lifecycle of the compound to negotiate that only those studies that provide data that either
would appear in product labeling or support product labeling should require conversion while
all other data can remain in its original format and other approaches to meta-analyses be
considered if there is need to evaluate safety risks across all the subjects exposed to a
particular compound.