DDIS-36. BTP-7, A NOVEL PEPTIDE FOR THERAPEUTIC TARGETING OF MALIGNANT BRAIN TUMORS
Bibliographic record
Abstract
Abstract High-grade gliomas are deadly cancers, and current standard-of-care has demonstrated limited success. The ability to specifically target glioma cells allows for the development of safer and more efficacious brain cancer therapy strategies. Brevican, a CNS-specific extracellular matrix protein is upregulated in glioma cells and its expression correlates with tumor progression. Particularly, a brevican isoform lacking glycosylation, B/bΔg is a unique glioma marker and not expressed in non-cancerous tissues. Therefore, B/bΔg represents a valuable target for anti-cancer strategies. Here, we describe the utilization of state-of-the-art platforms to screen a one-bead-one-compound combinatorial peptide library to discover a novel “B/bΔg-Targeting Peptides”, called BTP-7 that can bind B/bΔg with high affinity and specificity. BTP-7 displayed 260 nanomolar affinity for recombinant B/bΔg protein. Binding to a specific site on B/bΔg was confirmed in a competitive binding assay using BTP-7 functionalized with a UV-crosslinker and BTP-7 had little association with the fully glycosylated isoform of brevican (control). Scrambling of the BTP-7 sequence led to complete abrogation of B/bΔg binding. Furthermore, BTP-7 is preferentially taken up by B/bΔg-expressing glioma cells compared with non-expressing cells. We also discovered that BTP-7 can cross the blood-brain barrier in both the in vitro BBB organoid model and in mice. BTP-7 displayed 10x greater binding to intracranial GBM-6 tumors than control peptides, and 4x higher tumor uptake than in normal brain tissues. Conjugation of BTP-7 to camptothecin (an anti-tumor drug) via a cleavable linker led to increased DNA damage in intracranial GBM-6 tumors and prolonged survival in tumor-bearing mice. Our results show the potential of BTP-7 for the development of next-generation targeted therapeutics that could greatly benefit the outcome of patients with advanced brain cancer.
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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".