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Compliance In Focus
Posted by Cristina Calvin on Wed, Jan 6, 2021

New FDA Guidance For Submitting Electronic Study Data

When you’ve spent so much time obtaining data about your drug or medical device, you want to ensure the FDA processes and reviews it efficiently.

electronic-study-data-submission

And these days, it’s much more likely you’ll be submitting that data electronically.

The FDA  recently issued updated guidance for submitting electronic standardized study data.

These updates clarify the information the FDA needs to be able to assess compliance with study data requirements and archive it properly.

Electronic submissions of standardized study data will be required for the following submission

  • Certain investigational new drug applications (INDs)
  • New drug applications (NDAs)
  • Abbreviated new drug applications (ANDAs)
  • Certain biologics license applications (BLAs)

What are the requirements  for electronic submission of standardized study data?

Currently, the FDA can process, review, and archive electronic submissions of clinical and nonclinical study data that use the standards specified in the Data Standards Catalog.  The Catalog provides a listing of currently supported and/or required standards, the timetable during which the FDA will support a particular standard, and other pertinent information.

When planning a study (including the design of case report forms, data management systems, and statistical analysis plans), the sponsor or applicant must determine which FDA-supported standards to use or request a waiver of those requirements.

FDA-supported standards listed in the Catalog are categorized as:

1. Exchange Format Standards

An exchange format standard specifies a particular way that information is encoded in a computer file.

Examples of format standards currently supported by FDA include: Adobe Portable Document Format (pdf), SAS, Institute Transport File format (xpt), and Extensible Markup Language (xml).

2. Study Data Standard

Study data standards describe a standard way of exchanging study data between computer systems. Study data standards may describe the data elements and relationships necessary to achieve the unambiguous exchange of information between disparate information systems. The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) and Standard Exchange for Nonclinical Data (SEND) are examples of study data standards for tabulations data. Analysis standards describe a standard data structure intended to support analysis. Analysis standards include extraction, transformation, and derivations of the original data. The CDISC Analysis Data Model (ADaM) is an example of a study data standard for analysis data.

3. Controlled Terminology Standard

The use of controlled terminology standards, also known as vocabularies, is an important component of study data standardization and is a critical component of achieving semantically interoperable data exchange.  Controlled terminology standards specify the key concepts that are represented as preferred terms, definitions, synonyms, codes, and code systems. Controlled terminology standards are maintained by external organizations (i.e., external to the sponsor or applicant). Sponsor- or applicant-defined custom terms are not considered controlled terminologies. Examples of controlled terminology standards include:

  • The National Drug File (NDF) — Reference Terminology for drug classifications
  • CDISC Controlled Terminology
  • Medical Dictionary for Regulatory Activities (MedDRA)

The FDA will not provide waivers to submit data that do not conform to any FDA-supported study data standard. However, sponsors or applicants may apply for a waiver from the requirement to use specific versions of FDA-supported standards for the submission of study data. FDA recognizes that standards development organizations may release version updates to standards in the interval between the start of the study and the submission of study data to the Agency. The Catalog may list more than one version of a supported standard (e.g. PDF versions 1.4-1.7, SDTM versions 1.2 and 1.3, MedDRA versions 8 or later).

Sponsors and applicants may use established FDA-sponsor meetings to discuss study data standardization plan  (e.g. at pre-IND stage) and work with the FDA technical staff regarding questions on  how to implement the FDA-supported study data standards. Sponsors and applicants may also arrange to submit sample data for a pre-submission technical review.

Technical specifications associated with this guidance are updated periodically at https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources.  These are:

  • Data Standards Catalog
  • Study Data Technical Conformance Guide
  • FDA Specific SEND Validation Rules
  • FDA Specific SDTM Validation Rules

Sponsors should use the established regulatory process to discuss with the review division the key data necessary to support a submission, the data elements that should be included in each dataset, and the organization of the data within the datasets. What data are collected and submitted is a decision that should be made based on scientific reasons, regulation requirements, and discussions with the review division.

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How IMARC Research Can Help

As a full-service contract research organization, IMARC offers data management services that can help you ensure FDA compliance.

We can help you select the right electronic data capture system, build it according to your specifications, and work with site monitors to ensure data is entered accurately and in a timely manner. We can sort, clean, and analyze your clinical trial data and assist with database locking as your trial comes to a close.

We can also help you recruit and manage data safety management boards or clinical events committees.

Outsourcing your data and safety management to us eliminates silos so you can focus on bringing your device to market faster.

For practical tips on how to improve collaboration between data and safety management, download our latest whitepaper.

Topics: Clinical Data Management

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