PEGylated Recombinant Adenosine Deaminase Maintains Detoxification and Lymphocyte Counts in Patients with ADA-SCID
Bibliographic record
Abstract
PURPOSE: Metabolic detoxification with enzyme replacement therapy (ERT) promotes immune recovery in patients with adenosine deaminase (ADA)-deficient severe combined immunodeficiency (ADA-SCID). Elapegademase is a PEGylated recombinant bovine ADA ERT developed to replace the now-discontinued bovine-derived pegademase. This study was a 1-way crossover from pegademase to elapegademase in 7 patients with ADA-SCID to assess efficacy and safety outcomes for elapegademase. METHODS: After once-weekly pegademase dosage was adjusted to achieve therapeutic metabolic detoxification and trough ADA activity, patients transitioned to a bioequivalent dose of elapegademase. Maintenance of metabolic detoxification and adequate ADA activity were evaluated periodically. RESULTS: One patient withdrew after 2 doses of an early elapegademase formulation due to injection-site pain caused by EDTA. The 6 remaining patients completed 71-216 weeks of elapegademase therapy with a formulation that did not contain EDTA. In these patients, elapegademase improved ADA activity compared with pegademase and maintained metabolic detoxification. Total lymphocyte counts increased for all completer patients from between 1.2- and 2.1-fold at the end of study compared with baseline. Elapegademase had a comparable safety profile to pegademase; no patient developed a severe infectious complication. Three patients had transient, non-neutralizing antibodies to pegademase, elapegademase, and/or polyethylene glycol ≤ 47 weeks of treatment without effect on trough plasma ADA activity or trough erythrocyte deoxyadenosine nucleotide levels. CONCLUSION: Elapegademase was safe, well tolerated, achieved stable trough plasma ADA activity with weekly dosing, was effective in maintaining metabolic detoxification, and was associated with maintenance or improvements in lymphocyte counts compared with pegademase therapy in patients with ADA-SCID.
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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 | 0.001 |
| 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".