Higher abatacept exposure after transplant decreases acute GVHD risk without increasing adverse events
Bibliographic record
Abstract
In the ABA2 study, the T-cell costimulation blockade agent, abatacept, was safe and effective in preventing acute graft-versus-host disease (aGVHD) after unrelated-donor hematopoietic cell transplant (HCT), leading to US Food and Drug Administration approval. Here, we performed a determination of abatacept pharmacokinetics (PK), which enabled an examination of how abatacept exposure-response relationships affected clinical outcomes. We performed a population PK analysis of IV abatacept using nonlinear mixed-effect modeling and assessed the association between abatacept exposure and key transplant outcomes. We tested the association between the trough after dose 1 (Ctrough_1) and grade (GR) 2 or 4 aGVHD (GR2-4 aGVHD) through day +100. An optimal Ctrough_1 threshold was identified via recursive partitioning and classification tree analysis. This demonstrated that abatacept PK was characterized by a 2-compartment model with first-order elimination. The ABA2 dosing regimen was based on previous work targeting a steady-state abatacept trough of 10 μg/mL. However, a higher Ctrough_1 (≥39 μg/mL, attained in ∼60% of patients on ABA2) was associated with a favorable GR2-4 aGVHD risk (hazard ratio, 0.35; 95% confidence interval, 0.19-0.65; P < .001), with a Ctrough_1 <39 μg/mL associated with GR2-4 aGVHD risk indistinguishable from placebo (P = .37). Importantly, no significant association was found between Ctrough_1 and key safety indicators, including relapse, and cytomegalovirus or Epstein-Barr virus viremia. These data demonstrate that a higher abatacept Ctrough_1 (≥39 μg/mL) was associated with a favorable GR2-4 aGVHD risk, without any observed exposure-toxicity relationships. This trial was registered at www.clinicaltrials.gov as #NCT01743131.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".