Class II Eplet Mismatch Modulates Tacrolimus Trough Levels Required to Prevent Donor-Specific Antibody Development
Bibliographic record
Abstract
Despite more than two decades of use, the optimal maintenance dose of tacrolimus for kidney transplant recipients is unknown. We hypothesized that HLA class II de novo donor-specific antibody ( dn DSA) development correlates with tacrolimus trough levels and the recipient’s individualized alloimmune risk determined by HLA-DR/DQ epitope mismatch. A cohort of 596 renal transplant recipients with 50,011 serial tacrolimus trough levels had HLA-DR/DQ eplet mismatch determined using HLAMatchmaker software. We analyzed the frequency of tacrolimus trough levels below a series of thresholds <6 ng/ml and the mean tacrolimus levels before dn DSA development in the context of HLA-DR/DQ eplet mismatch. HLA-DR/DQ eplet mismatch was a significant multivariate predictor of dn DSA development. Recipients treated with a cyclosporin regimen had a 2.7-fold higher incidence of dn DSA development than recipients on a tacrolimus regimen. Recipients treated with tacrolimus who developed HLA-DR/DQ dn DSA had a higher proportion of tacrolimus trough levels <5 ng/ml, which continued to be significant after adjustment for HLA-DR/DQ eplet mismatch. Mean tacrolimus trough levels in the 6 months before dn DSA development were significantly lower than the levels >6 months before dn DSA development in the same patients. Recipients with a high-risk HLA eplet mismatch score were less likely to tolerate low tacrolimus levels without developing dn DSA. We conclude that HLA-DR/DQ eplet mismatch and tacrolimus trough levels are independent predictors of dn DSA development. Recipients with high HLA alloimmune risk should not target tacrolimus levels <5 ng/ml unless essential, and monitoring for dn DSA may be advisable in this setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".