Prediction of Oral Absorption of Low-Solubility Drugs by Using Rat Simulated Gastrointestinal Fluids: The Importance of Regional Differences in Membrane Permeability and Solubility
Bibliographic record
Abstract
PURPOSE: This study aimed to develop a novel approach for predicting the oral absorption of low-solubility drugs by considering regional differences in solubility and permeability within the gastrointestinal (GI) tract. METHODS: Simulated GI fluids were prepared to reflect rat in vivo bile acid and phospholipid concentrations in the upper and lower small intestine. The saturated solubility and permeability of griseofulvin (GF) and albendazole (AZ), a drug with low aqueous solubility, were measured using these simulated fluids, and fraction absorbed (Fa) at time t after oral administration was calculated. RESULTS: The saturated solubility of GF and AZ, a drug with low aqueous solubility, differed considerably between the simulated GI fluids. Large regional differences in drugs concentration were also observed following oral administration in vivo. The predicted Fa values using solubility and permeability data of the simulated GI fluid were found to correspond closely to the in vivo data. CONCLUSION: These results indicated the importance of evaluating regional differences in drug solubility and permeability in order to predict oral absorption of low-solubility drugs accurately. The new methodology developed in the present study could be useful for new oral drug development.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".