Oral Ibuprofen Is More Effective than Intravenous Ibuprofen for Closure of a Patent Ductus Arteriosus: Can Pharmacokinetic Modeling Help Us to Understand Why?
Bibliographic record
Abstract
INTRODUCTION: Oral ibuprofen is more effective than intravenous (IV) ibuprofen for closure of a patent ductus arteriosus (PDA). This study explored whether higher concentrations of the biologically active S-enantiomer or increased R- to S-conversion following oral dosing could explain this finding. METHODS: Two datasets containing 370 S- and R-ibuprofen concentrations from 95 neonates with PDA treated with oral (n = 27, 28%) or IV ibuprofen were analyzed using nonlinear mixed effects modeling. Concentration-time profiles in typical neonates were explored and compared in different dosing or R- to S-conversion scenarios. RESULTS: Postnatal age (PNA), gestational age (GA), and being small for GA impacted S- and R-ibuprofen clearance. Upon oral dosing, S-ibuprofen concentrations were lower compared to IV ibuprofen for a large part of the dosing interval. We could show that R- to S-conversion will not exceed 45%. Exploration of a 30% presystemic R- to S-conversion resulted in a 25-32% increase in S-ibuprofen exposure following oral administration with AUC72h values varying between 700-2,213 mg*h/L (oral) and 531-1,762 (IV) for the standard or 1,704-2,893 (oral) and 1,295-2,271 mg*h/L (IV) for PNA-based dosing. DISCUSSION: The absence of higher S-ibuprofen concentrations does not support a beneficial concentration-time profile after oral dosing. While a fraction of up to 45% presystemic R- to S-conversion could not be ruled out, the impact of such a low conversion might be only relevant for the standard but not high dosing regimens, considering reported exposure-response targets. Perhaps, the lack of high peak concentrations observed following IV dosing may play a role in the observed effects upon oral dosing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".