Linking the Gastrointestinal Behavior of Ibuprofen with the Systemic Exposure between and within Humans—Part 1: Fasted State Conditions
Bibliographic record
Abstract
The goal of this project was to explore and to statistically evaluate the responsible gastrointestinal (GI) factors that are significant factors in explaining the systemic exposure of ibuprofen, between and within human subjects. In a previous study, we determined the solution and total concentrations of ibuprofen as a function of time in aspirated GI fluids, after oral administration of an 800 mg IR tablet (reference standard) of ibuprofen to 20 healthy volunteers in fasted state conditions. In addition, we determined luminal pH and motility pressure recordings that were simultaneously monitored along the GI tract. Blood samples were taken to determine ibuprofen plasma levels. In this work, an in-depth statistical and pharmacokinetic analysis was performed to explain which underlying GI variables are determining the systemic concentrations of ibuprofen between (inter-) and within (intra-) subjects. In addition, the obtained plasma profiles were deconvoluted to link the fraction absorbed with the fraction dissolved. Multiple linear regressions were performed to explain and quantitatively express the impact of underlying GI physiology on systemic exposure of the drug (in terms of plasma C max /AUC and plasma T max ). The exploratory analysis of the correlation between plasma C max /AUC and the time to the first phase III contractions postdose (TMMC-III) explains ∼40% of the variability in plasma C max for all fasted state subjects. We have experimentally shown that the in vivo intestinal dissolution of ibuprofen is dependent upon physiological variables like, in this case, pH and postdose phase III contractions. For the first time, this work presents a thorough statistical analysis explaining how the GI behavior of an ionized drug can explain the systemic exposure of the drug based on the individual profiles of participating subjects. This creates a scientifically based and rational framework that emphasizes the importance of including pH and motility in a predictive in vivo dissolution methodology to forecast the in vivo performance of a drug product. Moreover, as no extensive first-pass metabolism is considered for ibuprofen, this study demonstrates how intraluminal drug behavior is reflecting the systemic exposure of a drug.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".