Abstract 2243A: Discovery of potent hedgehog pathway inhibitors as potential anticancer agents.
Bibliographic record
Abstract
Abstract The hedgehog (Hh) pathway is critically important for embryonic development, and interference with the Hh pathway could result in severe birth defect. The Hh pathway is known to be highly activated in certain cancers such as basal cell carcinoma (BCC) and medulloblastoma. The approval of hedgehog pathway inhibitor Erivedge (vismodegib, GDC-0445) for the treatment of adults BCC by FDA in early 2012 validated Hh pathway as an anticancer target.In this presentation, we report the discovery of novel hedgehog pathway inhibitors and the identification of lead compounds that are 10-100 fold more potent in multiple in vitro assays than GDC-0449. Several of these potent compounds have been found to have good oral bioavailability and brain penetration. The in vivo efficacy studies of selected compounds are in progress. Citation Format: Sui Xiong Cai, Kai Xie, Sicun Li, Lizhen Wu, Lijun Liu, Feng Yin, Chengyun Gu, Xiaozhu Wang, Sishun Kang, Shengzhi Chen, Zheng Meng, Yan Zhang, Guoxiang Wang, Hongqiang Fei, Dongmei Wang, Ye Edward Tian. Discovery of potent hedgehog pathway inhibitors as potential anticancer agents. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2243A. doi:10.1158/1538-7445.AM2013-2243A
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 0.003 |
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".