The role of vorasidenib in the treatment of isocitrate dehydrogenase-mutant glioma
Bibliographic record
Abstract
Isocitrate dehydrogenase (IDH)-mutant gliomas are the most common malignant primary brain tumors in young adults. This condition imposes a substantial burden on patients and their caregivers, marked by neurocognitive deficits and high mortality rates due to tumor progression, coupled with significant morbidity from current treatment modalities. Although surgery, radiation therapy, and chemotherapy improve survival, these treatments can adversely affect cognitive function, quality of life, finances, employment status, and overall independence. Consequently, there is an urgent need for innovative strategies that delay progression and the use of radiation therapy and chemotherapy. The recent Federal Drug Administration (FDA) approval of vorasidenib, a brain-penetrant small molecule targeting mutant IDH1/2 proteins, heralds a shift in the therapeutic landscape for IDH-mutant gliomas. In this review, we address the role of vorasidenib in the treatment of IDH-mutant gliomas, providing a roadmap for its incorporation into daily practice. We discuss ongoing clinical trials with vorasidenib and other IDH inhibitors, as single-agent or in combination with other therapies, as well as current challenges and future directions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".