Development of a peptide-based delivery platform for targeting malignant brain tumors
Bibliographic record
Abstract
Despite extensive molecular characterization, human glioblastoma remains a fatal disease with survival rates measured in months. Little improvement is seen with standard surgery, radiotherapy and chemotherapy. Clinical progress is hampered by the inability to detect and target glioblastoma disease reservoirs based on a diffuse invasive pattern and the presence of molecular and phenotypic heterogeneity. The goal of this study was to target the invasive and stem-like glioblastoma cells that evade first-line treatments using agents capable of delivering imaging enhancers or biotherapeutic cargo. To accomplish this, a combinatorial phage display library was biopanned against glioblastoma cell model systems that accurately recapitulate the intra- and inter-tumor heterogeneity and infiltrative nature of the disease. Candidate peptides were screened for specificity and ability to target glioblastoma cells in vivo. Cargo-conjugated peptides delivered contrast-enhancing agents to highly infiltrative tumor populations in intracranial xenograft models without the obvious need for blood brain barrier disruption. Simultaneous use of five independent targeting peptides provided greater coverage of this complex tumor and selected peptides have the capacity to deliver a therapeutic cargo (oncolytic virus VSVΔM51) to the tumor cells in vivo. Herein, we have identified a series of peptides with utility as an innovative platform to assist in targeting glioblastoma for the purpose of diagnostic or prognostic imaging, image-guided surgery, and/or improved delivery of therapeutic agents to glioblastoma cells implicated in disease relapse.
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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) | 0.000 | 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 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".