Advances in malignant glioma drug discovery
Bibliographic record
Abstract
INTRODUCTION: Outcome for patients with glioblastoma (GBM), the most common malignant primary brain tumor among adults, remains poor. However, two key treatment options have recently generated meaningful improvements in outcome for GBM patients. The addition of temozolomide (a methylating chemotherapeutic agent) to surgical resection and radiation therapy increases survival and is the first evidence that systemic chemotherapy can benefit GBM patients. Also, bevacizumab (a humanized mAb against VEGF) has significant antitumor activity among recurrent GBM patients. Additional areas of ongoing research are generating more therapeutic options that offer exciting potential to build on these results and further improve the outcome for malignant glioma patients. AREAS COVERED: This review describes three foci of advanced clinical research aimed at improving the outcome of GBM patients: protracted temozolomide dosing, VEGF-inhibiting agents and integrin inhibitors. This review also discusses potential clinical trial strategies to evaluate irreversible EGFR inhibitors as well as therapeutics targeting PI3K and the hedgehog signaling pathway. EXPERT OPINION: Several factors limit the efficacy of therapeutics targeting GBM. However, significant advances from basic science laboratories have recently generated important insights into the pathophysiology and molecular genetic abnormalities of these tumors. Efforts to translate these findings into innovative treatment strategies offer substantial promise to overcome therapeutic hurdles and treat individual patients more effectively. Improved understanding of malignant glioma biology and factors associated with treatment response will probably lead to improved therapeutic options and a better patient outcome.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".