Abstract 4491: Discovery of potent small molecular tubulin inhibitors for the treatment of cancer.
Bibliographic record
Abstract
Abstract Microtubule is a well-established drug target for the treatment of cancer. Drugs acting at this target include paclitaxel and docetaxel that stabilize the microtubules and prevent cancer cells from going through mitosis. These drugs are very successful chemotherapy agents and widely used for the treatment of cancer. In this presentation, we report the discovery of several series of novel and potent small molecular tubulin inhibitors and the identification of IMP03138 as a preclinical candidate. IMP03138 has advantages over the most commonly used anti-cancer drug paclitaxel in several in vitro assays and in vivo efficacy studies. Importantly, IMP03138 is highly effective against multi-drug resistant cancer cells. IMP3138 is currently undergoing IND enabling studies. Citation Format: Sui Xiong Cai, Zenghui Yu, Lei Chen, Qingbing Xu, Lizhen Wu, Lijun Liu, Feng Yin, Guoxiang Wang, Yangzhen Jiang, Xiuyan Zhang, Lingsheng Kong, Qingli Bao, Ye Edward Tian. Discovery of potent small molecular tubulin inhibitors for the treatment of cancer. [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 4491. doi:10.1158/1538-7445.AM2013-4491
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".