Sup. Figure 3: Foretinib and nintedanib inhibited the TRK-A in the kinase inhibition profile assay. from Foretinib Overcomes Entrectinib Resistance Associated with the <i>NTRK1</i> G667C Mutation in <i>NTRK1</i> Fusion–Positive Tumor Cells in a Brain Metastasis Model
Bibliographic record
Abstract
The IC50 value of entrectinib, foretinib and nintedanib against TRK-A, TRK-B and TRK-C human kinases was determined by SignalChem's target profiling service (SignalChem, Richmond, BC, Canada). SignalChem uses a radioisotope assay format for profiling evaluation of all kinase and assays (in duplicate) were performed at 22oC for 30 min in a final volume of 25 mL. The assay was initiated by the addition of 33P-ATP and the reaction mixture incubated at room temperature 22oC for 30 minutes. After the incubation period, the assay was terminated by spotting 10 mL of the reaction mixture onto Multiscreen phosphocellulose P81 plate. The Multiscreen phosphocellulose P81 plate was washed 3 times for approximately 15 minutes each in a 1% phosphoric acid solution. The radioactivity on the P81 plate was counted in the presence of scintillation fluid in a Trilux scintillation counter. For detailed assay conditions for each protein kinase target, please see website (www.signalchem.com).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.098 |
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".