Discovery of Pyridopyrimidinones that Selectively Inhibit the H1047R PI3Kα Mutant Protein
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The H1047R mutation of PIK3CA is highly prevalent in breast cancers and other solid tumors. Selectively targeting PI3Kα H1047R over PI3Kα WT is crucial due to the role that PI3Kα WT plays in normal cellular processes, including glucose homeostasis. Currently, only one PI3Kα H1047R -selective inhibitor has progressed into clinical trials, while three pan mutant (H1047R, H1047L, H1047Y, E542K, and E545K) selective PI3Kα inhibitors have also reached the clinical stage. Herein, we report the design and discovery of a series of pyridopyrimidinones that inhibit PI3Kα H1047R with high selectivity over PI3Kα WT, resulting in the discovery of compound 17 . When dosed in the HCC1954 tumor model in mice, 17 provided tumor regressions and a clear pharmacodynamic response. X-ray cocrystal structures from several PI3Kα inhibitors were obtained, revealing three distinct binding modes within PI3Kα H1047R including a previously reported cryptic pocket in the C-terminus of the kinase domain wherein we observe a ligand-induced interaction with Arg1047.
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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) | 0.003 | 0.001 |
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".