Final Guidance and Implementation Date The U.S. Food and Drug Administration (FDA) has released long-anticipated final guidance with the implementation date for all promotional materials submissions via electronic common technical document (eCTD) format. The guidance titled, “Providing Regulatory Submissions in Electronic and Non-Electronic Format — Promotional Labeling and Advertising Materials for Human Prescription...
DIA is an opportunity to share challenges, strategies, and other insights to advance our common goal of achieving regulatory success. As one of our valued eCTD Summit community members, we hope you’ll schedule a meeting with us or visit our booth #1117, which is sure to be a premium destination at DIA 2019.
Health Canada announced an update to the mandatory use of the electronic Common Technical Document (eCTD) format. As of June 1, 2019, the remainder of Division 8 regulatory activity types (human drugs only) must be submitted in eCTD format.
Synchrogenix was well represented at DIA’s Annual Regulatory Submissions, Information, and Document Management (RSIDM) Forum held earlier this month. One of the reasons RSIDM is such a valuable meeting for Regulatory Operations professionals is because it is so well attended by U.S. FDA staff members who participate both as speakers and fellow attendees. We particularly look forward to the “Ask the Regulators” sessions, where FDA representatives answer questions directly from the attendees. Following are highlights from the ‘Ask the Regulators’ sessions.
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The CDISC Standard for the Exchange of Nonclinical Data (SEND) provides the structure and implementation rules for the submission of computer readable datasets. SEND is one of the required standards for data submission to the U.S. Food and Drug Administration (FDA) and specifies a way to collect and present nonclinical data in a consistent format. The overall package consists of several components, but the focus is on individual study endpoint data which are typically mapped to datasets in domains with several variables in each study dataset.