Theoretical consideration of the properties of intestinal flow models on route‐dependent drug removal: Segregated Flow (SFM) vs. Traditional (TM)
Bibliographic record
Abstract
Abstract The intestine is endowed with a plethora of enzymes and transporters and regulates the flow of substrate to the liver. Physiologically‐based pharmacokinetic models have surfaced to describe intestinal removal. The traditional model (TM) describes the intestinal flow as a whole perfusing the entire tissue that contains the intestinal transporters and enzymes. The segregated flow model (SFM) describes that only a fraction (fQ < 0.2) of the intestinal blood flow perfuses the enterocyte region where the intestinal enzymes and transporters are housed, rendering a lower drug distribution/intestinal clearance when drug enters via the circulation than from the gut lumen. As shown by simulations, a higher intestinal clearance and extraction ratio (EI,iv) exists for the TM than for SFM after iv dosing. By contrast, the EI,po after po dosing is higher for the SFM, due to the smaller volume of distribution for the enterocyte region and a lower flow rate that result in increased mean residence time and higher drug extraction. Under MBI (mechanism‐based inhibition), the AUCR,po after oral bolus is the highest for drug when inhibitor is given orally, with SFM > TM. Competitive inhibition of intestinal enzymes leads to higher liver metabolism; again, when both drug and inhibitor are given orally, changes in the SFM > TM. However, less definitive patterns result with inhibition of both intestinal and liver enzymes. In conclusion, differences exist for EI and drug‐drug interaction (DDI) between the TM and SFM. The fractional intestinal blood flow (fQ) is a key factor affecting different extents of intestinal/liver metabolism of the drug after oral as well as intravenous administration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".