BTP-7, a novel peptide for therapeutic targeting of malignant brain tumors
Bibliographic record
Abstract
Abstract Background Targeted therapies for malignant brain cancer that are currently available have little clinical activity, highlighting an urgent need for the development of novel precision medicines. Brevican (Bcan), a central nervous system (CNS)-specific extracellular matrix protein is upregulated in glioma cells. A brevican isoform lacking glycosylation, dg-Bcan, is a unique glioma marker and thus represents a valuable target for anti-cancer therapy. In this study, we aimed to find a versatile dg-Bcan specific ligand to facilitate glioma targeting. Methods We screened a D-peptide library to identify dg- B can- T argeting P eptide (BTP) candidates, which were characterized extensively through binding kinetic analyses, cell uptake tests and animal studies. Results The top candidate, BTP-7 binds dg-Bcan with high affinity and specificity, is preferentially internalized by Bcan-expressing glioma cells and can cross the blood-brain barrier in vitro and in mice. Functionalization of camptothecin with BTP-7 led to increased drug delivery to intracranial glioblastoma and cytotoxicity in tumor tissues, as well as prolonged survival in tumor-bearing mice. Conclusion dg-Bcan is an attractive therapeutic target for high-grade gliomas, and BTP-7 represents a promising lead candidate for further development into novel targeted therapeutics. Key points BTP-7 is a high affinity peptide ligand for the dg-Bcan protein and Bcan-expressing cells. BTP-7 targets human intracranial GBM xenografts in mice. Functionalization of a toxic anti-cancer drug with BTP-7 enables targeted delivery of the therapeutic to intracranial GBM in mice Importance of the Study Targeted therapies for malignant brain cancer that are currently available have little clinical activity, highlighting an urgent need for the development of novel precision medicines that can selectively recognize and kill high-grade glioma tissues. A protein called dg-Bcan is an ideal target because it is present only in the extracellular matrix of high-grade glioma cells and is absent from normal brain tissues. Here, we describe the discovery of a novel dg- B can- T argeting P eptide, called BTP-7 that can bind specifically to high-grade glioma cells/tissues, and thus serve as a promising drug delivery vehicle.
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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 | 0.000 |
| 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".