Improving Brain Delivery of Biomolecules via BBB Modulation in Mouse and Rat: Detection using MRI, NIRF, and Mass Spectrometry
Bibliographic record
Abstract
There is an urgent need to develop new and alternative methods to deliver functional biomolecules to the brain for diagnosis and treatment of brain diseases.The goal of this study was to evaluate the activity of blood-brain barrier (BBB) modulators (i.e., HAV and ADT peptides) to deliver functional biomolecules (i.e., galbumin, IRdye800cw-cLABL, and cIBR7) to the brains of mice and rats.HAV6, cHAVc3, and ADTC5 peptides but not HAV4 peptide significantly enhanced the brain delivery of 65 kDa galbumin compared to control in Balb/c mice as quantified by magnetic resonance imaging (MRI).Ten-minute pretreatment with ADTC5 peptide still significantly increased brain delivery of galbumin; however, no enhancement was observed after 10-min pretreatment with HAV6.There was no enhancement of galbumin deposition following 40-min pretreatment with ADTC5 or HAV6, suggesting a short duration of the BBB opening for large molecules.ADTC5 peptide also improved the brain delivery of IRdye800cw-cLABL peptide about 3.5-fold compared to control in Balb/c mice as detected by near infrared fluorescence (NIRF).The BBB modulator activity of ADTC5 to deliver cIBR7 peptide was also evaluated in vivo using Sprague-Dawley rats.The amount of cIBR7 in the brain was detected by LC-MS/MS.ADTC5 peptide enhanced the delivery of cIBR7 peptide into rat brain about 4-fold compared to control and the intact cIBR7 can be efficiently extracted and detected in rat brain.In conclusion, HAV and ADT peptides enhance the brain delivery of functional peptides (e.g., cLABL and cIBR7) and protein (e.g., 65 kDa galbumin) in two animal models, and the duration of the BBB opening for a large molecule (e.g., galbumin) was short.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".