Why We Need Proper PBPK Models to Examine Intestine and Liver Oral Drug Absorption
Bibliographic record
Abstract
Intestinal transporters and enzymes are factors that can influence the absorption of orally administrated drugs. Compartmental models are no longer adequate to describe the sequential handling of drugs and metabolites by the intestine and liver during oral drug absorption, especially when intestinal removal is substantial relative to the liver, and when induction/inhibition elicits different extents of change for identical intestinal and hepatic enzymes or transporters. In this review, we described PBPK models for the intestine (with differential flow patterns: traditional model, TM, and segregated flow model, SFM, and QGut model) as well as semi- or whole bodyphysiological- based pharmacokinetic (PBPK) models to describe the impact of the flow pattern, and the intestinal transporters and enzymes and their attendant heterogeneities on intestinal (FI or FG) and oral (Fsys) bioavailability. The modeling efforts have led to a refinement in providing mechanistic insight on the accurate prediction of drug and metabolite profiles for DDI, pharmacogenomics, age factors and disease conditions. Keywords: Intestine models; segregated flow model, QGut model, physiological-based pharmacokinetic (PBPK) models, enzymes; transporters; intestinal flow, enterocyte flow
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".