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Not for implementation. Contains non-binding recommendations.
This guidance is being distributed for comment purposes only.
Although you can comment on any guidance at any time (see 21 CFR 10.115(g)(5)), to ensure that the FDA considers your comment on a draft guidance before it begins work on the final version of the guidance, submit either online or written comments on the draft guidance before the close date.
If unable to submit comments online, please mail written comments to:
Dockets Management Food and Drug Administration 5630 Fishers Lane, Rm 1061 Rockville, MD 20852
All written comments should be identified with this document's docket number: FDA-2021-D-1155
The purpose of this document is to provide guidance to animal drug sponsors on specific areas of the approval process where the available scientific literature may be useful to support the approval of a new animal drug application (NADA), an abbreviated new animal drug application (ANADA), or a conditionally approved new animal drug application (CNADA), as well as methodologies to ensure the validity of conclusions drawn by animal drug sponsors from the scientific literature to support an approval.
Use of published scientific literature is of interest because it makes use of existing knowledge and may reduce the number of animals needed for studies to support approval, and in some cases may provide greater inferential value compared to individual studies conducted for the purpose of supporting an approval. Scientific literature may also be used to respond to specific regulatory questions, identify data gaps, and inform protocol design.
However, use of scientific literature to support drug approval has its limitations. Although there may be a wealth of published data on a drug or drug class, underlying study protocols and original study data and records generally are not available, making it difficult to determine the quality of studies and confirm aspects of study conduct or design necessary to make a safety or effectiveness determination. Implementation of systematic methods to screen and analyze information across multiple publications may reduce the uncertainty resulting from analyzing limited information in individual studies.
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