Comparative Supply Chain Risk Management for Biologics and Pharmaceuticals (messenger RNA (mRNA) vaccines and gene therapies): FDA, Health Canada, and EMA Approaches
Bibliographic record
Abstract
This study examines the comparative Supply Chain Risk Management (SCRM) frameworks applied to biologics and pharmaceuticals, specifically messenger RNA (mRNA) vaccines and gene therapies, across the U.S. Food and Drug Administration (FDA), Health Canada, and the European Medicines Agency (EMA). Biologics and advanced therapies present unique manufacturing and distribution challenges due to their complexity, sensitivity to environmental conditions, and reliance on high-integrity cold-chain systems. Using a comparative case study methodology supported by Failure Mode and Effects Analysis (FMEA), this research evaluates how regulatory agencies manage risks related to production, quality assurance, and distribution. Findings indicate that harmonized risk management practices grounded in International Council for Harmonisation (ICH) Q9 (Quality Risk Management) and Q10 (Pharmaceutical Quality System) principles can reduce supply disruptions by approximately 30% and improve compliance outcomes. The study highlights the growing emphasis on data integrity, Good Manufacturing Practice (GMP) compliance, and cross-border cooperation, especially under emergency pathways such as the FDA’s Emergency Use Authorization (EUA) and EMA’s accelerated approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".