Novel peptide-based drugs for the treatment of sonic hedgehog-dependent medulloblastoma
Bibliographic record
Abstract
Copyright © 2015 Prous Science, S.A.U. or its licensors. All rights reserved. Medulloblastoma, the most common pediatric malignant brain tumor, consists of at least four distinct molecular subgroups. Hyperactivation of the sonic hedgehog (SHH) pathway is a hallmark of SHH subgroup medulloblastoma, affecting approximately 30% of pediatric patients. While small molecules that inhibit Smoothened (SMO), a major driver of the SHH pathway, are efficacious for the treatment of SHH subgroup medulloblastoma, the development of drug resistance is a consistent problem, and SMO inhibitors are ineffective for those tumors driven by mutations affecting SHH pathway components downstream of SMO. In addition, the spectrum of mutations driving SHH subgroup medulloblastoma displays strong demographic variability, suggesting that a suite of SHH pathway inhibitors may be required to combat the heterogeneity of the disease in infants, children and adults. These issues have provided a major impetus for the development of novel and more effective SHH therapeutics. Peptide-based drugs have received little attention in the context of the SHH pathway, despite having potentially significant advantages over small-molecule inhibitors. In particular, peptides can bind a larger and broader range of protein interfaces increasing the number of potential targets, in turn reducing the likelihood of drug resistance. Importantly also, peptides can be easily engineered for extended half-life, cell specificity and intracellular targeting. These advantages are discussed in the context of the variety of SHH pathway inhibitors that have been described so far, and the characteristics of critical components of the pathway that represent valid clinical targets.
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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".