Physiologically based pharmacokinetic modeling revealed minimal codeine intestinal metabolism in first‐pass removal in rats
Bibliographic record
Abstract
The physiologically based model with segregated flow to the intestine (SFM-PBPK; partial, lower flow to enterocyte region vs. greater flow to serosal region) was found to describe the first-pass glucuronidation of morphine (M) to morphine-3β-glucuronide (MG) in rats after intraduodenal (i.d.) and intravenous (i.v.) administration better than the traditional model (TM), for which a single intestinal flow perfused the whole of the intestinal tissue. The segregated flow model (SFM) described a disproportionately greater extent of intestinal morphine glucuronidation for i.d. vs. i.v. administration. The present study applied the same PBPK modeling approaches to examine the contributions of the intestine and liver on the first-pass metabolism of the precursor, codeine (C, 3-methylmorphine) in the rat. Unexpectedly, the profiles of codeine, morphine and morphine-3β-glucuronide in whole blood, bile and urine, assayed by LCMS, were equally well described by both the TM-PBPK and SFM-PBPK. The fitted parameters for the models were similar, and the net formation intrinsic clearance of morphine (from codeine) for the liver was much higher, being 9- to 13-fold that of the intestine. Simulations, based on the absence of intestinal formation of morphine, correlated well with observations. The lack of discrimination of SFM and TM with the codeine data did not invalidate the SFM-PBPK model but rather suggests that the liver is the only major organ for codeine metabolism. Because of little or no contribution by the intestine to the metabolism of codeine, both the TM- and SFM-PBPK models are equally consistent with the data. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".