Bibliographic record
Abstract
Background: The clinical success of the tyrosine kinase inhibitor imatinib (Gleevec; STI-571) in the treatment of several leukemias has emphasized the proof-of-concept that molecularly targeted drug design is a viable approach to cancer therapy. However, the emergence of imatinib-resistant phenotypes has spurred a vast amount of research towards finding newer and more potent kinase inhibitors that can overcome drug resistance. Unexpectedly, the newest inhibitors are often less specific than imatinib, inhibiting not only BCR-Abl (the target of imatinib), but also the Src family of tyrosine kinases, which have recently been shown to be downstream effectors of BCR-Abl. Objective: This review summarizes some of the new BCR-Abl inhibitors that have followed from the teaming of combinatorial library searches and structure-based drug design, giving attention to the structural aspects of drug recognition. Conclusion: The use of lower-specificity inhibitors seemingly undermines the rationale behind targeted therapy, yet it appears to be a critical aspect of overcoming drug resistance. Combination therapy with a cocktail of drugs, including an inhibitor of the T315I resistance mutation, will be the next maneuver in the battle against BCR-Abl in the treatment of chronic myelogenous leukemia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".