Targeting insulin‐like growth factor‐1 receptor (IGF1R) for brain delivery of biologics
Bibliographic record
Abstract
The blood-brain barrier (BBB) prevents the majority of drugs from crossing into the brain and reaching neurons. To overcome this challenge, safe and non-invasive technologies targeting receptor-mediated pathways have been developed. In this study, three single-domain antibodies (sdAbs; IGF1R3, IGF1R4, and IGF1R5) targeting the extracellular domain of the human insulin-like growth factor-1 receptor (IGF1R), generated by llama immunization, showed enhanced transmigration across the rat BBB model (SV-ARBEC) in vitro. The rate of brain uptake of these sdAbs fused to mouse Fc (sdAb-mFc) in vivo was estimated using the fluorescent in situ brain perfusion (ISBP) technique followed by optical brain imaging and distribution volume evaluation. Compared to the brains perfused with the negative control A20.1-mFc, the brains perfused with anti-IGF1R sdAbs showed a significant increase of the total fluorescence intensity (~2-fold, p < .01) and the distribution volume (~4-fold, p < .01). The concentration curve for IGF1R4-mFc demonstrated a linear accumulation plateauing at approximately 400 µg (~1 µM), suggesting a saturable mechanism of transport. Capillary depletion and mass spectrometry analyses of brain parenchyma post-ISBP confirmed the IGF1R4-mFc brain uptake with ~25% of the total amount being accumulated in the parenchymal fraction in contrast to undetectable levels of A20.1-mFc after a 5-min perfusion protocol. Systemic administration of IGF1R4-mFc fused with the non-BBB crossing analgesic peptide galanin (2 and 5 mg/kg) induced a dose-dependent suppression of thermal hyperalgesia in the Hargreaves pain model. In conclusion, novel anti-IGF1R sdAbs showed receptor-mediated brain uptake with pharmacologically effective parenchymal delivery of non-permeable neuroactive peptides.
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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) | 0.000 | 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 | 0.001 |
| 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".