Brain Uptake of a Fluorescent Vector Targeting the Transferrin Receptor: A Novel Application of<i>in Situ</i>Brain Perfusion
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) targeting blood-brain barrier (BBB) transporters are being developed for brain drug targeting. However, brain uptake quantification remains a challenge, particularly for large compounds, and often requires the use of radioactivity. In this work, we adapted an in situ brain perfusion technique for a fluorescent mAb raised against the mouse transferrin receptor (TfR) (clone Ri7). We first confirmed in vitro that the internalization of fluorolabeled Ri7 mAbs is saturable and dependent on the TfR in N2A and bEnd5 cells. We next showed that the brain uptake coefficient (Clup) of 100 μg (∼220 nM) of Ri7 mAbs fluorolabeled with Alexa Fluor 750 (AF750) was 0.27 ± 0.05 μL g(-1) s(-1) after subtraction of values obtained with a control IgG. A linear relationship was observed between the distribution volume VD (μL g(-1)) and the perfusion time (s) over 30-120 s (r(2) = 0.997), confirming the metabolic stability of the AF750-Ri7 mAbs during perfusion. Co-perfusion of increasing quantities of unlabeled Ri7 decreased the AF750-Ri7 Clup down to control IgG levels over 500 nM, consistent with a saturable mechanism. Fluorescence microscopy analysis showed a vascular distribution of perfused AF750-Ri7 in the brain and colocalization with a marker of basal lamina. To our knowledge, this is the first reported use of the in situ brain perfusion technique combined with quantification of compounds labeled with near-infrared fluorophores. Furthermore, this study confirms the accumulation of the antitransferrin receptor Ri7 mAb in the brain of mice through a saturable uptake mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".