In vitro evidence of synergistic efficacy with asciminib combined with reduced dose of ATP-binding pocket tyrosine kinase inhibitors according to the ABL1 kinase domain mutation profile
Bibliographic record
Abstract
Abstract Background Asciminib (ASC), inhibiting ABL1 myristoyl pocket, has a different action mechanism from ATP-binding pocket inhibitors (ABPIs). We hypothesized that tyrosine kinase inhibitor (TKI) resistance mediated by ABL1 kinase domain mutation (KDM) can be reversed by combination of ASC with ABPI. Methods The efficacy and synergy of combination of ASC with ABPIs was evaluated in 11 different BaF3 cell lines including wild type (WT), G250E, E255K, T315A, M351T, F317L, F317V, H396P, Y253F, M244V, T315I mutant ones and WT K562 cell line. Results Combining fixed dose ASC with the reduced doses of ABPI was feasible to inhibit CML/WT cell lines completely. According to sensitivity to the combination of ABPIs with fixed dose ASC, ABL1 KDM cell lines are stratified into high (G250E, E255K, T315A), intermediate (M351T, F317L) or low sensitivity (F317V, H396P, Y253F, M244V and T315I). Reduced dose ABPI combined with fixed dose ASC showed similar efficacy to full dose ABPIs alone in high and intermediate sensitive cells. Ponatinib dose can be reduced to 25% when combined with ASC, but exerting similar efficacy to full dose ponatinib. Conclusion The present study provides in vitro evidence of the synergistic efficacy of the combination of ASC with reduced dose of ABPI including dasatinib/ponatinib.
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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) | 0.001 | 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 | 0.001 |
| 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".