Whole-body physiologically based pharmacokinetic population modelling of oral drug administration: inter-individual variability of cimetidine absorption
Bibliographic record
Abstract
OBJECTIVES: Inter-individual variability of gastrointestinal physiology and transit properties can greatly influence the pharmacokinetics of an orally administered drug in vivo. To predict the expected range of pharmacokinetic plasma concentrations after oral drug administration, a physiologically based pharmacokinetic population model for gastrointestinal transit and absorption was developed and evaluated. METHODS: Mean values and variability measures of model parameters affecting the rate and extent of cimetidine absorption, such as gastric emptying, intestinal transit times and effective surface area of the small intestine, were obtained from the literature. Various scenarios incorporating different extents of inter-individual physiological variability were simulated and the simulation results were compared with experimental human study data obtained after oral cimetidine administration of four different tablets with varying release kinetics. KEY FINDINGS: The inter-individual variability in effective surface area was the largest contributor to absorption variability. Based on in-vitro dissolution profiles, the mean plasma cimetidine concentration-time profiles as well as the inter-individual variability could be well described for three cimetidine formulations. In the case of the formulation with the slowest dissolution kinetic, model predictions on the basis of the in-vitro dissolution profile underestimated the plasma exposure. CONCLUSIONS: The model facilitates predictions of the inter-individual pharmacokinetic variability after oral drug administration for immediate and extended-release formulations of cimetidine, given reasonable in-vitro dissolution kinetics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".