Enhancing Drug Development for Paediatric Pulmonary Hypertension—An Integrative Perspective
Bibliographic record
Abstract
As with adult pulmonary hypertension (PH), high morbidity and mortality persist with diverse types of paediatric PH. Despite major advances in pharmacologic therapies based on extensive studies in adult PH, few drugs have been comprehensively studied in neonates, infants, and children, leaving current paediatric PH care largely dependent on small observational studies and extrapolation of evidence from adult clinical trials. Challenges in developing successful clinical trials in children include the need to define distinct disease phenotypes with well-characterised natural history and outcomes, the lack of established age- and disease-specific study endpoints, small and heterogeneous paediatric populations, and the common off-label use of PH-targeted drug therapies without regulatory approval. From a regulatory perspective, sufficient studies of safety, pharmacokinetics, and pharmacodynamics in neonates and young children are often lacking, and the potential role for bridging biomarkers has been underexplored. Additional opportunities include developing innovative trial designs, employing real-world data from existing registries, and fostering collaborations among sponsors, regulatory authorities, physicians, patients, and their families. By reducing reliance on off-label drug use and leveraging paediatric PH registry data, this approach offers a path toward more effective and evidence-based treatment protocols for paediatric patients. This review provides an overview of integrated international perspectives from an interprofessional platform that includes academia, the pharmaceutical industry, and regulatory agencies surrounding the future design of clinical trials for paediatric PH. Ongoing evaluation and adaptation of these strategies will be essential for ensuring that paediatric PH patients receive the highest standard of care.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".