Quantitative, Wide-Spectrum Kinase Profiling in Live Cells for Assessing the Effect of Cellular ATP on Target Engagement
Bibliographic record
Abstract
For kinase inhibitors, intracellular target selectivity is fundamental to pharmacological mechanism. Although a number of acellular techniques have been developed to measure kinase binding or enzymatic inhibition, such approaches can fail to accurately predict engagement in cells. Here we report the application of an energy transfer technique that enabled the first broad-spectrum, equilibrium-based approach to quantitatively profile target occupancy and compound affinity in live cells. Using this method, we performed a selectivity profiling for clinically relevant kinase inhibitors against 178 full-length kinases, and a mechanistic interrogation of the potency offsets observed between cellular and biochemical analysis. For the multikinase inhibitor crizotinib, our approach accurately predicted cellular potency and revealed improved target selectivity compared with biochemical measurements. Due to cellular ATP, a number of putative crizotinib targets are unexpectedly disengaged in live cells at a clinically relevant drug dose.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".