Evaluating FDA compliance actions like warning letters and Form 483s is a good way to determine agency expectations and to focus internal evaluations of manufacturing facilities and quality systems.
FDA warning letters to finished dose manufacturers include numbered paragraphs with a reference to the specific CFR that is being cited, followed by examples that support the citations. For example:
Your firm failed to thoroughly investigate any unexplained discrepancy or failure of a batch or any of its components to meet any of its specifications, whether or not the batch has already been distributed (21 CFR 211.192).
For example, your investigations into out-of-specification (OOS) results and process deviations were inadequate…(68 more words in the example text)
However, 483s do not generally include CFR citations; warning letters for products that do not fall under the CFR, such as APIs and excipients, also do not contain CFR codes. How can those deficiencies be mapped to the CFRs?
At Redica Systems, we have used machine learning and Natural Language Processing tools to isolate language used in these documents such that observations and citations can be mapped back to the FDA Quality Systems and subsequently to a CFR. This is especially useful in documents that do not cite the CFR, but also in drug GMP warning letters in which language is used in the example supporting text that infers additional deficiencies that we call “hiding in plain sight.”
Here is an example from an FDA warning letter in December 2022 to DuPont Nutrition excipient manufacturing operations that shows how the language in the letter (left column) maps to specific quality system areas and the corresponding CFRs (right column) for both the primary citation (in bold) and the supporting example text:
Inferred CFR Codes and Quality System Labeling are two of many examples of how Redica Systems structures and organizes previously unstructured data.
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