Recent Treatment Advances and the Role of Nanotechnology, Combination Products, and Immunotherapy in Changing the Therapeutic Landscape of Acute Myeloid Leukemia
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is the most common acute leukemia that is becoming more prevalent particularly in the older (65 years of age or older) population. For decades, "7 + 3" remission induction therapy with cytarabine and an anthracycline, followed by consolidation therapy, has been the standard of care treatment for AML. This stagnancy in AML treatment has resulted in less than ideal treatment outcomes for AML patients, especially for elderly patients and those with unfavourable profiles. Over the past two years, six new therapeutic agents have received regulatory approval, suggesting that a number of obstacles to treating AML have been addressed and the treatment landscape for AML is finally changing. This review outlines the challenges and obstacles in treating AML and highlights the advances in AML treatment made in recent years, including Vyxeos®, midostaurin, gemtuzumab ozogamicin, and venetoclax, with particular emphasis on combination treatment strategies. We also discuss the potential utility of new combination products such as one that we call "EnFlaM", which comprises an encapsulated nanoformulation of flavopiridol and mitoxantrone. Finally, we provide a review on the immunotherapeutic landscape of AML, discussing yet another angle through which novel treatments can be designed to further improve treatment outcomes for AML patients.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".