Impact of Small Molecules Immunosuppressants on P-Glycoprotein Activity and T-cell Function
Bibliographic record
Abstract
PURPOSE: P-glycoprotein (Pgp) is a member of the ABC-transporter family that transports substances across cellular membranes acting as an efflux pump extruding drugs out of the cells. Pgp plays a key role on the pharmacokinetics of several drugs. Herein, we have studied the effects of immunosuppressants on Pgp function, assessing rhodamine-123 (Rho123) uptake and efflux in different T-cell subsets. METHODS: Different immunosuppressants such as Cyclosporine (CsA), Rapamycin (Rapa) and Tacrolimus (Tac) were used to assess the in vitro effect on Pgp function of main T-cell subsets among healthy volunteers. We measured Rho123 uptake, efflux and kinetic of extrusion in CD4+ and CD8+ subsets by flow cytometry. Antigen-specific memory T-cell responses were assessed by measuring T-cell proliferation and cytokine secretion using an allogeneic mixed lymphocyte reaction. RESULTS: Rho123 uptake in groups treated with CsA and CsA+Rapa was significantly decreased compared to non-treated group and the other immunosupressants in both T cells subsets. Pgp activity was also reduced in CsA and CsA+Rapa compared to the other immunosupressants but it was only significant in the CsA group for CD8+ subset. Kinetic extrusion of Rho123 by Pgp in all groups was faster in CD8+ T cells. All immunosuppressants and the specific Pgp inhibitor PSC833 diminished antigen-primed T-cell proliferation, especially CD8+ T-cell subset. CONCLUSIONS: Our data indicate that small molecules immunosuppressants, especially CsA, inhibit Pgp activity and T-cell function being the CD8+ T cells more susceptible to this effect. These findings support the importance of Pgp when designing combined immunosuppressive regimens.
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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".