Mitigating Deficiencies in Evidence during Regulatory Assessments of Advanced Therapies: A Comparative Study with Other Biologicals
Bibliographic record
Abstract
Advanced therapy medicinal products (ATMPs) comprising cell therapy, gene therapy, and tissue-engineered products, offer a multitude of novel therapeutic approaches to a wide range of severe and debilitating diseases. To date, several advanced therapies have received marketing authorization for a variety of indications. However, some products showed disappointing market performance, leading to their withdrawal. The available evidence for quality, safety, and efficacy at product launch can play a crucial rule in their market success. To evaluate the sufficiency of evidence in submissions of advanced therapies for marketing authorization and to benchmark them against more established biological products, we conducted a matched comparison of the regulatory submissions between ATMPs and other biologicals. We applied a quantitative assessment of the regulatory objections and divergence from the expected data requirements as indicators of sufficiency of evidence and regulatory flexibilty, respectively. Our results demonstrated that product manufacturing was challenging regardless of the product type. Advanced therapies displayed critical deficiencies in the submitted clinical data. The submitted non-clinical data packages benefited the most from regulatory flexibility. Additionally, ATMP developers need to comply with more commitments in the post-approval phase, which might add pressure on market performance. Mitigating such observed deficiencies in future product development, may leverage their potential for market success.
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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 | 0.444 | 0.652 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".