Learning from our failures in blood–brain permeability: what can be done for new drug discovery?
Bibliographic record
Abstract
Many existing pharmaceuticals are rendered ineffective in the treatment of cerebral diseases due to a permeability barrier well known as the blood-brain barrier (BBB). Such barrier between the blood within brain capillaries and the extracellular fluid in brain tissue has motivated several approaches aimed at delivering therapeutics to the brain. These approaches rely on strategies that can be classified as molecular modifications, the use of BBB bypassing pathways, and BBB disruptions. Although several of these approaches that have been investigated so far show promising results, none has addressed the optimization of the ratio of the dose of the drug molecules that contributes to the therapeutic effects. As such, the extensive research efforts, such as prioritizing the enhancement of the BBB permeability alone is likely to fail to provide the best therapeutic effects for a given dose if prior systemic circulation is not avoided while enhancing the spatial targeting only to regions of the brain that need treatment. Hence, new therapeutics for the brain could be synthesized to take advantage of recent technologies for non-systemic delivery and spatially targeted brain uptake.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.029 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".