Bioluminescent kinase strips: A novel approach to targeted and flexible kinase inhibitor profiling
Bibliographic record
Abstract
In addition to target efficacy, drug safety is a major requirement during the drug discovery process and is influenced by target specificity. Therefore, it is imperative that every new drug candidate be profiled against various liability panels that include protein kinases. Here, an effective methodology to streamline kinase inhibitor profiling is described. An accessible standardized profiling system for 112 protein kinases covering all branches of the kinome was developed. This approach consists of creating different sets of kinases and their corresponding substrates in multi-tube strips. The kinase stocks are pre-standardized for optimal kinase activity and used for inhibitor profiling using a bioluminescent ADP detection assay. We show that these strips can routinely generate inhibitor selectivity profiles for small or broad kinase family panels. Lipid kinases were also assembled in strip format and profiled together with protein kinases. We identified two specific PI3K inhibitors that have off-target effects on CK2 that were not reported before and would have been missed if compounds were not profiled against lipid and protein kinases simultaneously. To validate the accuracy of the data generated by this method, we confirmed that the inhibition potencies observed are consistent with published values produced by more complex technologies such as radioactivity assays.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".