A comprehensive review in improving delivery of small-molecule chemotherapeutic agents overcoming the blood-brain/brain tumor barriers for glioblastoma treatment
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and lethal primary brain tumor which is highly resistant to conventional radiotherapy and chemotherapy, and cannot be effectively controlled by surgical resection. Due to inevitable recurrence of GBM, it remains essentially incurable with a median overall survival of less than 18 months after diagnosis. A great challenge in current therapies lies in the abrogated delivery of most of the chemotherapeutic agents to the tumor location in the presence of blood-brain barrier (BBB) and blood-brain tumor barrier (BBTB). These protective barriers serve as a selectively permeable hurdle reducing the efficacy of anti-tumor drugs in GBM therapy. This work systematically gives a comprehensive review on: (i) the characteristics of the BBB and the BBTB, (ii) the influence of BBB/BBTB on drug delivery and the screening strategy of small-molecule chemotherapeutic agents with promising BBB/BBTB-permeable potential, (iii) the strategies to overcome the BBB/BBTB as well as the techniques which can lead to transient BBB/BBTB opening or disruption allowing for improving BBB/BBTB-penetration of drugs. It is hoped that this review provide practical guidance for the future development of small BBB/BBTB-permeable agents against GBM as well as approaches enhancing drug delivery across the BBB/BBTB to GBM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".