Inhibition of Human Efflux Transporter ABCC2 (MRP2) by Self-emulsifying Drug Delivery System: Influences of Concentration and Combination of Excipients
Bibliographic record
Abstract
PURPOSE: This study investigated influences of concentration and combination of excipients, commonly used in self-emulsifying drug delivery systems (SEDDS), on inhibition of human efflux transporter ABCC2 (MRP2). METHODS: Ten commonly used excipients of SEDDS with inhibitory effect on MRP2 including Cremophor® EL, Cremophor® RH, Pluronic® F127, Maisine® 35-1, β-cyclodextrin, Labrasol®, Pluronic® F68, PEG 2000, PEG 400 and Transcutol® were studied with the Caco-2 cell model. Six excipients with inhibitory effect including Cremophor® EL, Cremophor® RH, Pluronic® F127, PEG 2000, PEG 400 and Transcutol® were further analyzed using the MRP2 vesicle assay and ATPase activity assay. Ultra-performance liquid-chromatography tandem mass spectrometry was used to measure scutellarin as the MRP2 substrate. RESULTS: In studying concentration-dependent effects, five excipients including Cremophor® EL, Cremophor® RH, Pluronic® F127, Maisine® 35-1 and β-cyclodextrin showed concentration-dependent decrease in efflux ratio of scutellarin. The other five excipients did not show such phenomenon, and their inhibitory effects were restricted to be above to certain critical or minimum concentrations. In studying combined effects, PEG 2000 and Pluronic® F127 both showed combined effect with Cremophor® EL on inhibiting MRP2. However, some combinations of excipients such as PEG 400 and Transcutol® with Cremophor® EL increased the scutellarin efflux ratio and decreased the transport of scutellarin and ATPase activity, compared to Cremophor® EL alone. CONCLUSION: The above results suggest that appropriate choice of excipients according to their concentration-dependent and combined effects on MRP2 inhibition can facilitate formulation of SEDDS for improving the bioavailability of drugs that are MRP2 substrates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".