Reversible Dimers of the Atypical Antipsychotic Quetiapine Inhibit P-Glycoprotein-Mediated Efflux in Vitro with Increased Binding Affinity and in Situ at the Blood-Brain Barrier
Bibliographic record
Abstract
The multidrug resistance transporter P-glycoprotein (P-gp) is highly expressed in the capillary endothelial cells of the blood-brain barrier (BBB) where it functions to limit the brain penetration of many drugs, including antipsychotic agents used to treat schizophrenia. Therefore, in an effort to inhibit the transporter, we designed dimers of the antipsychotic drug and P-gp substrate quetiapine (QT), linked by variable length tethers. In P-gp overexpressing cells and in human brain capillary endothelial hCMEC/D3 cells, the dimer with the shortest tether length (QT2C2) (1) was the most potent inhibitor showing >80-fold better inhibition of P-gp-mediated transport than monomeric QT. The dimers, which are linked via ester moieties, are designed to revert to the therapeutic monomer once inside the target cells. We demonstrated that the addition of two sterically blocking methyl groups to the linker (QT2C2Me2, 8) increased the half-life of the molecule in plasma 10-fold as compared to the dimer lacking methyl groups (QT2C2, 1), while retaining inhibitory potency for P-gp transport and sensitivity to cellular esterases. Experiments with purified P-gp demonstrated that QT2C2 (1) and QT2C2Me2 (8) interacted with both the H- and R-binding sites of the transporter with binding affinities 20- to 30-fold higher than that of monomeric QT. Using isolated rat brain capillaries, QT2C2Me2 (8) was a more potent inhibitor of P-gp transport than QT. Lastly, we showed that QT2C2Me2 (8) increased the accumulation of the P-gp substrate verapamil in rat brain in situ three times more than QT. Together, these results indicate that the QT dimer QT2C2Me2 (8) strongly inhibited P-gp transport activity in human brain capillary endothelial cells, in rat brain capillaries, and at the BBB in an animal model.
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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 | 0.001 |
| 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".