Halofuginone Synergistically Enhances Anti-Proliferation of Rapamycin in T Cells and Reduces Cytotoxicity of Cyclosporine in Cultured Renal Tubular Epithelial Cells
Bibliographic record
Abstract
Both rapamycin (RAPA) and cyclosporin A (CsA) are commonly used for immunosuppression, however their adverse side effects limit their application. Thus, it is of interest to develop novel means to enhance or preserve the immunosuppressive activity of RAPA or CsA while reducing their toxicity. Halofuginone (HF) has been recently tested as a potential immunosuppressant. This study investigated the interaction of HF with RAPA or with CsA in cell cultures. Cell proliferation in cultures was determined using methylthiazol tetrazolium assay, and cell apoptosis assessed by flow cytometric analysis and Western blot. The drug-drug interaction was determined according to Loewe's equation or Bliss independence. Here, we showed that addition of HF to anti-CD 3 antibody-stimulated splenocyte cultures induced synergistic suppression of T cell proliferation in the presence of RAPA, indicated by an interaction index (γ) value of < 1.0 between HF and RAPA, but not in those with CsA. The synergistic interaction of RAPA with HF in the suppression of T cell proliferation was also seen in a mixed lymphocyte reaction and Jurkat T cell growth, and was positively correlated with an increase in cell apoptosis, but not with proline depletion. In cultured kidney tubular epithelial cells, HF attenuated the cytotoxicity of CsA. In conclusion, these data indicate that HF synergistically enhances anti-T cell proliferation of RAPA and reduces the nephrotoxicity of CsA in vitro, suggesting the potential use of HF for enhancing anti-T cell proliferation of RAPA and reducing CsA-mediated nephrotoxicity.
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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".