Abstract 3237: Development of direct inhibitors of KRAS mutants for the treatment of cancer
Bibliographic record
Abstract
Abstract Oncogenic KRAS mutations occur in ~ 30% of all human cancer. All attempts to develop inhibitors of oncogenic KRAS have failed to reach the clinic for decades and promote the perception of ‘undruggable’ KRAS proteins. Given the recent advances in understanding of mechanism of oncogenic KRAS, there is renewed enthusiasm toward development of direct inhibitors of KRAS. In a recent breakthrough, Shokat et al. discovered a “pocket," where they designed a series of mutant-specific small molecular inhibitors that are covalently binding to the mutant Cys 12 in KRAS protein (KRAS G12C). In human cancer, G12C, G12D and G12V are the predominant KRAS mutants. The G12C mutation accounts for 29,700 new diagnoses annually (lung, colon and pancreatic). The G12D and G12V, annually account for 53,700 and 39,100 new cancer diagnoses, respectively, occurring most frequently in Pancreatic ductal adenocarcinoma (PDAC) (51% G12D and 30% G12V) and colorectal cancer (45% G12D and 27% G12V). Thus, there is an urgent need to develop direct inhibitor of KRAS 12D and 12V mutants. By computer-aided drug design techniques, we have discovered a series of novel chemical inhibitors that potently inhibit KRAS G12D mutant in reporter assays using HEK293 cells and in Western blot analysis using PANC10.05 and SNU-C2B cells, which express endogenous KRAS G12D mutant. The direct binding of our compounds with KRAS G12D mutant was confirmed by Surface Plasmon Resonance (SPR) analysis. Our compounds also inhibit KRAS G12V mutant. Chemical optimization of these initial hits as direct inhibitors of multiple KRAS mutants is in progress. Citation Format: Xiaohong Tian, Guoyan Geng, Jian Hui Wu. Development of direct inhibitors of KRAS mutants for the treatment of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3237. doi:10.1158/1538-7445.AM2017-3237
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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) | 0.001 | 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 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".