Analytics-Driven Supplement Evaluation (ASE)
Description
Exponential increase in post-approval chemistry, manufacturing, and controls (CMC) change submissions, with 80% being Changes Being Effected (CBE-30/0) notifications that may be suitable for systematic analytics-driven evaluation.
Detailed example
Using a Convolutional Neural Network (NN) model, in combination with a rules-based approach, produces an output that helps staff triage CBE submission review
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
This AI use case supports the triage and staff assignment process for the review of post-market Change Being Effected (CBE) supplement submissions, improving review efficiency and consistency while ensuring appropriate regulatory oversight.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Data submitted in applicants' supplemental submissions