Integrated Platform for Brain Imaging and Drug Delivery Across the Blood–Brain Barrier
Bibliographic record
Abstract
The development of imaging and therapeutic agents against neuronal targets is hampered by the limited access of probes into the central nervous system across the blood-brain barrier (BBB). The evaluation of drug penetration into the brain in experimental models often requires complex procedures, including drug radiolabeling, as well as determinations in multiple animals for each condition or time point. Prospective in vivo imaging of drug biodistribution may provide an alternative to "classical" pharmacokinetics and biodistribution studies in that a contrast-enhanced imaging signal could serve as a surrogate for the amount of drug or biologic delivered to the organ of interest. For the brain-targeting applications, it is necessary to develop formulation strategies that enable a simultaneous drug and contrast agent delivery across the BBB. In this chapter, we describe methods for encapsulating drugs into liposome nanocarriers with surface display of both the imaging contrast agent for one or multiple imaging modalities and the single-domain antibody that undergoes receptor-mediated transcytosis across the BBB. Contrast-enhanced imaging signal detected in the brain after intravenous injection of such formulation(s) is proportional to the amount of drug delivered into the brain parenchyma. This method allows for a prospective, noninvasive estimation of drug delivery, accumulation, and elimination from the brain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".