A human-like glutaminase-free asparaginase is highly efficacious in ASNSlow leukemia and solid cancer mouse xenograft models
Bibliographic record
Abstract
L-asparaginase (L-ASNase) is crucial in treating pediatric acute lymphoblastic leukemia (ALL), but its use is hampered by side effects from the immunogenicity and L-glutaminase (L-GLNase) co-activity of FDA-approved bacterial L-ASNases, often leading to treatment discontinuation and poor outcomes. The toxicity of these L-ASNases makes them especially challenging to use in adult cancer patients. To overcome these issues, we developed EBD-200, a humanized guinea pig L-ASNase with low Km and no L-GLNase activity, eliminating glutamine-related toxicity. EBD-200 showed comparable anti-cancer effects to PEGylated L-ASNase in ASNS low ALL, melanoma and liver cancer models, with improved tolerability. Its potent anti-cancer efficacy and enhanced safety profile suggest that EBD-200 could benefit ALL patients and broaden treatment options for ASNS low solid cancers. • Similar durable in vivo anti-leukemic effect is seen with EBD-200 and PEG-ASNase. • Lower toxicity of EBD-200 versus PEG-ASNase correlates with lack of GLNase activity. • EBD-200 effectively treats ASNS low solid cancers with hypermethylated ASNS promoter.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".