Blood-Brain Barrier Disruption in Neuro-Oncology: Strategies, Failures, and Challenges to Overcome
Bibliographic record
Abstract
The blood-brain barrier (BBB) presents a formidable challenge in the development of effective therapeutics in neuro-oncology. This has fueled several decades of efforts to develop strategies for disrupting the BBB, but progress has not been satisfactory. As such, numerous drug- and device-based methods are currently being investigated in humans. Through a focused assessment of completed, active, and pending clinical trials, our first aim in this review is to outline the scientific foundation, successes, and limitations of the BBBD strategies developed to date. Among 35 registered trials relevant to BBBD in neuro-oncology in the ClinicalTrials.gov database, mannitol was the most common drug-based method, followed by RMP-7 and regadenoson. MR-guided focused ultrasound was the most common device-based method, followed by MR-guided laser ablation, ultrasound, and transcranial magnetic stimulation. While most early-phase studies focusing on safety and tolerability have met stated objectives, advanced-phase studies focusing on survival differences and objective tumor response have been limited by heterogeneous populations and tumors, along with a lack of control arms. Based on shared challenges among all methods, our second objective is to discuss strategies for confirmation of BBBD, choice of systemic agent and drug design, alignment of BBBD method with real-world clinical workflow, and consideration of inadvertent toxicity associated with disrupting an evolutionarily-refined barrier. Finally, we conclude with a strategic proposal to approach future studies assessing BBBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".