New Strategies for the Conduct of Clinical Trials in Pediatric Pulmonary Arterial Hypertension: Outcome of a Multistakeholder Meeting With Patients, Academia, Industry, and Regulators, Held at the European Medicines Agency on Monday, June 12, 2017
Bibliographic record
Abstract
Aims: Drug development for paediatric pulmonary arterial hypertension (PAH) ispressingly needed. Experts from the US Food and Drug Administration, EuropeanMedicines Agency, Health Canada, key opinion leaders, academia, patients, and industry representatives held a workshop on 12th June 2017 dedicated to addressing challenges and unmet needs. This report summarises the approaches proposed during the meeting to address key issues in extrapolation, trial design, and study endpoints in pediatric drug development.Methods and Results: A pre-workshop stakeholder survey was conducted and showed that most respondents believe the pathophysiology of heritable PAH and some forms of idiopathic PAH is thought to be sufficiently similar in adult and paediatric patients, although the clinical manifestations may differ. In this situation, placebo-controlled trials might not be required to confirm clinical benefit in paediatrics. The study endpoints used to support drug approvals in adults were reviewed to determine if these existing study endpoints can be applied in paediatric PAH efficacy trials. It showed that non-invasive study endpoints, such as the time to clinical worsening, WHO functionalclass, and 6-Minute-Walk-Test could be applicable in paediatric PAH trials, although each presents some limitations in paediatrics.Conclusion: Extrapolation of efficacy from informative adult studies may be appropriate in some forms of PAH. Initial dose-finding studies and exposure-response modelling are warranted in paediatric PAH, followed by an efficacy and safety study to explore the response to treatment and exposure-response relationship. A novel, non-invasive, developmentally-appropriate, and reliable study endpoint needs to be developed.
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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 | 0.759 | 0.556 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".