Principles of alemtuzumab immunoablation in hematopoietic cell transplantation for non‐malignant diseases in children: A review
Bibliographic record
Abstract
Alemtuzumab is a humanized mAb targeted to CD52. Alemtuzumab is highly immunosuppressive with the ability to deplete T and B cells (in addition to other immune cell lines). A growing understanding of the PKs, dosing, and timing of administration of alemtuzumab has allowed for the study of its use as a conditioning agent for allogeneic HCT. The highly immunosuppressive properties of the drug are particularly appealing in the setting of non-malignant HCT, where GVHD provides no clinical benefit and relapse of malignancy is not applicable. In addition, the degree of immune suppression achieved with alemtuzumab has allowed for a reduction in the intensity of myeloablative cytotoxic agents included in some HCT conditioning regimens, allowing for fewer acute and late toxicities. This review paper will provide a comprehensive summary of the mechanism of action, PKs, dosing, and timing of alemtuzumab, a brief description of its use in various allogeneic HCT protocols for non-malignant conditions and a summary of the data regarding its use for GVHD therapy. The goal of this review was to provide an understanding as to how alemtuzumab might be safely incorporated into HCT conditioning regimens for children with non-malignant disease, allowing for expanded access to curative HCT therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".