Interplay of transporters and enzymes in the Caco‐2 cell monolayer: I. effect of altered apical secretion
Bibliographic record
Abstract
Interactions are expected between transporters and enzymes that compete for the substrate within the cell, and controversy exists for data interpretation on the interplay between transporters and enzymes. In the Caco-2 cell monolayer, the increase in mean residence time (MRT) of drug accompanying increased secretion has been construed as the reason for increased metabolism, whereas others hold an opposite view that increased secretion would evoke decreased metabolism in this closed system. A catenary Caco-2 cell model was used to simulate the effects of altered secretion on metabolism, estimated as the fraction of dose metabolized (f(met)) or the extraction ratio (ER). The simulations showed that both f(met) and ER varied inversely with the transporter-mediated intrinsic clearance for apical secretion (CL(int,sec)) under linear conditions. Under non-linear conditions of saturable metabolism, apical absorption, basolateral influx or efflux, the simulated f(met) consistently bore a reciprocal relationship with CL(int,sec). For saturable apical absorption or basolateral efflux, a reciprocal relationship between the ER and CL(int,sec) was also found. However, under conditions of saturable metabolism or basolateral influx, the pattern of change in the ER became dependent on the administration sites, showing increasing patterns for apical dosing but decreasing patterns for basolateral dosing with increasing values of CL(int,sec). In general, f(met) consistently demonstrated an inverse relationship with CL(int,sec), whereas ER, failing to include the drug in the donor side, would not show the same pattern of change in metabolism especially for apical dosing, since a substantial amount of drug was back-secreted into the apical compartment.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".