Evaluation of drug efflux transporter liabilities of darifenacin in cell culture models of the blood–brain and blood–ocular barriers
Bibliographic record
Abstract
AIMS: The objective of the present study was to evaluate drug efflux transporter interactions of darifenacin and examine the impact of such transporter interactions on darifenacin permeability in an in vitro model of the blood-brain barrier (BBB) and blood-ocular barrier (BOB). METHODS: Cell membranes expressing human P-glycoprotein (P-gp), multidrug resistance-associated protein (MRP), and breast cancer resistance protein (BCRP) were examined for ATPase activity following darifenacin exposure (0-10 µM). Primary cultured bovine brain microvessel endothelial cells (BBMEC) and P-gp transfected Manin-Darby canine kidney epithelial cells (MDCKMDR1) were used to examine darifenacin permeability and drug efflux transporter responses. RESULTS: Concentration-dependent increases in ATPase activity was observed in P-gp membranes following darifenacin exposure. Both MRP and BCRP membrane preparations were unresponsive to darifenacin. Studies in both BBMEC and MDCKMDR1 monolayers confirmed a P-gp interaction for darifenacin and significantly greater efflux (basolateral to apical) permeability for darifenacin that was reduced by the P-gp inhibitor, elacridar. CONCLUSIONS: Darifenacin is a substrate for the P-gp drug efflux transporter present in both BBB and BOB. The P-gp drug efflux transporter liabilities of darifenacin may limit its penetration into brain and ocular tissue thereby reducing side effect potential.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".