Clinical effect and safety profile of pegzilarginase in patients with arginase 1 deficiency
Bibliographic record
Abstract
Hyperargininemia in patients with arginase 1 deficiency (ARG1-D) is considered a key driver of disease manifestations, including spasticity, developmental delay, and seizures. Pegzilarginase (AEB1102) is an investigational enzyme therapy which is being developed as a novel arginine lowering approach. We report the safety and efficacy of intravenously (IV) administered pegzilarginase in pediatric and adult ARG1-D patients (n = 16) from a Phase 1/2 study (101A) and the first 12 weeks of an open-label extension study (102A). Substantial disease burden at baseline included lower-limb spasticity, developmental delay, and previous hyperammonemic episodes in 75%, 56%, and 44% of patients, respectively. Baseline plasma arginine (pArg) was elevated (median 389 μM, range 238-566) on standard disease management. Once weekly repeat dosing resulted in a median decrease of pArg of 277 μM after 20 cumulative doses (n = 14) with pArg in the normal range (40 to 115 μM) in 50% of patients at 168 hours post dose (mean pegzilarginase dose 0.10 mg/kg). Lowering pArg was accompanied by improvements in one or more key mobility assessments (6MWT, GMFM-D & E) in 79% of patients. In 101A, seven hypersensitivity reactions occurred in four patients (out of 162 infusions administered). Other common treatment-related adverse events (AEs) included vomiting, hyperammonemia, pruritus, and abdominal pain. Treatment-related serious AEs that occurred in five patients were all observed in 101A. Pegzilarginase was effective in lowering pArg levels with an accompanying clinical response in patients with ARG1-D. The improvements with pegzilarginase occurred in patients receiving standard treatment approaches, which suggests that pegzilarginase could offer benefit over existing disease management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".