Feasibility and Safety of Ex Vivo Delivery of Rituximab to Lung Allografts in Transplant Recipients at High Risk for Epstein-Barr Virus–associated Posttransplant Lymphoproliferative Disorder
Bibliographic record
Abstract
Background. Ex vivo lung perfusion (EVLP) offers a novel platform for delivering targeted therapies directly to donor lungs before transplantation, potentially reducing systemic side effects. Our study evaluated the feasibility and safety of rituximab delivery to donor lungs from Epstein-Barr virus (EBV)-seropositive donors for transplantation into EBV-seronegative recipients (D + /R – ) to reduce the risk of EBV-associated posttransplant lymphoproliferative disorder (PTLD), which remains a major obstacle in the transplant setting. Methods. A pilot study was conducted involving 5 EBV-seronegative lung transplant recipients. Donor lungs were perfused with 500 mg rituximab during EVLP for 3–4 h. Primary outcomes included safety and feasibility, assessed by monitoring lung function during perfusion, posttransplant complications, and graft dysfunction. Secondary outcomes included EBV DNAemia, PTLD incidence, peripheral B-cell frequencies, EBV blood transcripts, and rituximab serum levels. Results. Rituximab delivery via EVLP was feasible and safe, with no significant deviations in lung function or adverse events linked to treatment. One patient experienced primary graft dysfunction. Peripheral B-cell counts were reduced immediately posttransplant and remained low in some patients, whereas others rebounded over the weeks posttransplant, and serum rituximab levels were undetectable after 2 wk. Three patients developed EBV DNAemia and 2 developed PTLD within 2 y, although PTLD lesions were not observed in transplanted lungs. Conclusions. EVLP-based rituximab delivery is a feasible and promising strategy for targeting donor-transmitted EBV with minimal systemic exposure. Although the findings suggest potential clinical benefit, the development of PTLD in extrathoracic sites underscores the need for further optimization and larger studies to evaluate efficacy and refine the intervention.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".