Proteasome Inhibitors against Glioblastoma—Overview of Molecular Mechanisms of Cytotoxicity, Progress in Clinical Trials, and Perspective for Use in Personalized Medicine
Bibliographic record
Abstract
Proteasome inhibitors are moieties targeting the proteolytic activity of a proteasome, with demonstrated efficacy in certain hematological malignancies and candidate drugs in other types of cancer, including glioblastoma (GBM). They disturb the levels of proteasome-regulated proteins and lead to the cell cycle inhibition and apoptosis of GBM cells. The accumulation of cell cycle inhibitors p21 and p27, and decreased levels of prosurvival molecules NFKB, survivin, and MGMT, underlie proteasome inhibitors’ cytotoxicity when used alone or in combination with the anti-GBM cytostatic drug temozolomide (TMZ). The evidence gathered in preclinical studies substantiated the design of clinical trials that employed the two most promising proteasome inhibitors, bortezomib and marizomib. The drug safety profile, maximum tolerated dose, and interaction with other drugs were initially evaluated, mainly in recurrent GBM patients. A phase III study on newly diagnosed GBM patients who received marizomib as an adjuvant to the Stupp protocol was designed and completed in 2021, with the Stupp protocol receiving patients as a parallel control arm. The data from this phase III study indicate that marizomib does not improve the PFS and OS of GBM patients; however, further analysis of the genetic and epigenetic background of each patient tumor may shed some light on the sensitivity of individual patients to proteasome inhibition. The mutational and epigenetic makeup of GBM cells, like genetic alterations to TP53 and PTEN, or MGMT promoter methylation levels may actually determine the response to proteasome inhibition.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".