Antibody Therapies for Acute Myeloid Leukemia: Unconjugated, Toxin-Conjugated, Radio-Conjugated and Multivalent Formats
Bibliographic record
Abstract
In recent decades, therapy for acute myeloid leukemia (AML) has remained relatively unchanged, with chemotherapy regimens primarily consisting of an induction regimen based on a daunorubicin and cytarabine backbone, followed by consolidation chemotherapy. Patients who are relapsed or refractory can be treated with allogeneic hematopoietic stem-cell transplantation with modest benefits to event-free and overall survival. Other modalities of immunotherapy include antibody therapies, which hold considerable promise and can be categorized into unconjugated classical antibodies, multivalent recombinant antibodies (bi-, tri- and quad-specific), toxin-conjugated antibodies and radio-conjugated antibodies. While unconjugated antibodies can facilitate Natural Killer (NK) cell antibody-dependent cell-mediated cytotoxicity (ADCC), bi- and tri-specific antibodies can engage either NK cells or T-cells to redirect cytotoxicity against AML targets in a highly efficient manner, similarly to classic ADCC. Finally, toxin-conjugated and radio-conjugated antibodies can increase the potency of antibody therapies. Several AML tumour-associated antigens are at the forefront of targeted therapy development, which include CD33, CD123, CD13, CLL-1 and CD38 and which may be present on both AML blasts and leukemic stem cells. This review focused on antibody therapies for AML, including pre-clinical studies of these agents and those that are either entering or have been tested in early phase clinical trials. Antibodies for checkpoint inhibition and microenvironment targeting in AML were excluded from this review.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".