Rapid Discovery and Structure–Activity Relationships of Pyrazolopyrimidines That Potently Suppress Breast Cancer Cell Growth via SRC Kinase Inhibition with Exceptional Selectivity over ABL Kinase
Bibliographic record
Abstract
Novel pyrazolopyrimidines displaying high potency and selectivity toward SRC family kinases have been developed by combining ligand-based design and phenotypic screening in an iterative manner. Compounds were derived from the promiscuous kinase inhibitor PP1 to search for analogs that could potentially target a broad spectrum of kinases involved in cancer. Phenotypic screening against MCF7 mammary adenocarcinoma cells generated target-agnostic structure-activity relationships that biased subsequent designs toward breast cancer treatment rather than to a particular target. This strategy led to the discovery of two potent antiproliferative leads with phenotypically distinct anticancer mode of actions. Kinase profiling and further optimization resulted in eCF506, the first small molecule with subnanomolar IC50 for SRC that requires 3 orders of magnitude greater concentration to inhibit ABL. eCF506 exhibits excellent water solubility, an optimal DMPK profile and oral bioavailability, halts SRC-associated neuromast migration in zebrafish embryos without inducing life-threatening heart defects, and inhibits SRC phosphorylation in tumor xenografts in mice.
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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) | 0.001 | 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".