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Area of Science:

  • Regulatory Science
  • Artificial Intelligence in Healthcare
  • Natural Language Processing (NLP)

Background:

  • US Food and Drug Administration (FDA) regulatory submissions require structured documents for efficient reviewer allocation.
  • Inconsistent document structuring hinders holistic data review, impacting drug safety and efficacy assessments.
  • Current practices often lead to information being spread across sections, complicating comprehensive analysis.

Purpose of the Study:

  • To evaluate an AI-based NLP methodology, Bidirectional Encoder Representations from Transformers (BERT), for automatic classification of free-text information into standardized regulatory sections.
  • To support a more holistic review of drug safety and efficacy by improving document organization.
  • To assess the model's performance on both well-structured and less-structured FDA labeling documents.

Main Methods:

  • Developed and trained a BERT-based NLP model to classify text into standardized sections.
  • Utilized FDA labeling documents, specifically the Physician Label Rule (PLR) structure, for model development.
  • Evaluated the model on diverse datasets including PLR-based, non-PLR, and Summary of Product Characteristic (SmPC) labeling documents.

Main Results:

  • The model achieved high accuracy during training: 96% for binary and 88% for multiclass classification.
  • Testing accuracies demonstrated robust performance: Binary model achieved 95% (PLR), 88% (non-PLR), 88% (SmPC); Multiclass model achieved 82% (PLR), 73% (non-PLR), 68% (SmPC).
  • The AI language model effectively processed unformatted documents, classifying free text into standardized sections.

Conclusions:

  • AI language models, specifically BERT, offer an advanced regulatory science approach for processing unformatted documents.
  • Automatic classification of free texts into standardized sections can significantly enhance the efficiency and effectiveness of the drug review process.
  • This methodology holds promise for improving regulatory agency operations and ensuring comprehensive drug safety and efficacy evaluations.