<i>In vitro</i> and <i>In vivo</i> Activity of SKI-606, a Novel Src-Abl Inhibitor, against Imatinib-Resistant Bcr-Abl+ Neoplastic Cells
Bibliographic record
Abstract
Resistance to imatinib represents an important scientific and clinical issue in chronic myelogenous leukemia. In the present study, the effects of the novel inhibitor SKI-606 on various models of resistance to imatinib were studied. SKI-606 proved to be an active inhibitor of Bcr-Abl in several chronic myelogenous leukemia cell lines and transfectants, with IC(50) values in the low nanomolar range, 1 to 2 logs lower than those obtained with imatinib. Cells expressing activated forms of KIT or platelet-derived growth factor receptor (PDGFR), two additional targets of imatinib, were unaffected by SKI-606, whereas activity was found against PIM2. SKI-606 retained activity in cells where resistance to imatinib was caused by BCR-ABL gene amplification and in three of four Bcr-Abl point mutants tested. In vivo experiments confirmed SKI-606 activity in models where resistance was not caused by mutations as well as in cells carrying the Y253F, E255K, and D276G mutations. Modeling considerations attribute the superior activity of SKI-606 to its ability to bind a conformation of Bcr-Abl different from imatinib.
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) | 0.000 | 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 | 0.001 |
| 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".