The Caspase Selective Inhibitor EP1013 Augments Human Islet Graft Function and Longevity in Marginal Mass Islet Transplantation in Mice
Bibliographic record
Abstract
OBJECTIVE: Clinical islet transplantation can provide insulin independence in patients with type 1 diabetes, but chronic graft failure has been observed. This has been attributed in part to loss of >or=60% of the transplanted islets in the peritransplant period, resulting in a marginal implant mass. Strategies designed to maximize survival of the initial islet mass are likely to have major impact in enhancing long-term clinical outcomes. EP1013 (N-benzyloxycabonyl-Val Asp-fluoromethyl ketone [zVD-FMK]), is a broad-spectrum caspase selective inhibitor with no observed toxicity in rodents. RESEARCH DESIGN AND METHODS: The therapeutic benefit of EP1013 was examined in a syngeneic rodent islet transplant model using deceased donor human islets to determine whether the amount of tissue required to restore euglycemia in diabetic animals could be reduced. RESULTS: EP1013 (combined pretransplant islet culture for 2 h and in vivo treatment for days 0-5 posttransplant) significantly improved marginal islet mass function following syngeneic islet transplantation in mice, even at lower doses, compared with previous studies using the pan-caspase inhibitor N-benzyloxycabonyl-Val Ala-Asp-fluoromethyl ketone (zVAD-FMK). EP1013 supplementation in vitro improved human islet yields following prolonged culture and reversed diabetes following implantation of a marginal human islet mass (80-90% reduction) into mice. CONCLUSIONS: Our data suggest that EP1013 therapy will markedly reduce the islet mass required in clinical islet transplantation, improving insulin independence rates following single-donor infusion.
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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) | 0.001 | 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 | 0.001 |
| 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".