Impact of Drug Approval Pathways for Paediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Timely access to approved medications is a priority in paediatric inflammatory bowel disease [IBD]. To date, the timing of drug studies in paediatric IBD has been suboptimal, with most studies conducted long after approval has been granted for adult IBD. This delay in approval leads to extensive off-label prescribing of medications in children, often without clear guidance on optimal dosing. The European Medicines Agency [EMA] and U.S. Food and Drug Administration [FDA] have implemented drug development frameworks in an attempt to address these challenges. However, access to information on these regulatory pathways in paediatric IBD is limited. We summarised the time from adult to paediatric approval of IBD therapies, outlining the regulatory approval pathway between the EMA and FDA, with the goal of identifying areas for improvement. METHODS: We reviewed publicly accessible data from the EMA and the FDA to identify therapeutic agents approved over 2005-2021 for paediatric IBD. RESULTS AND CONCLUSIONS: Five drugs are currently approved for use in the paediatric IBD population, with long interval delays after adult approval. The impact of these drug development processes in paediatric IBD is awaited. Further consideration needs to be given to the age of enrolment along with novel, more efficient trial designs in an effort to improve access for paediatric IBD patients to newer therapies.
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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 | 0.036 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".