The combi-targeting concept: an optimized single-molecule dual-targeting model for the treatment of Chronic Myelogenous Leukemia
Bibliographic record
Abstract
LB-299 Blockade of bcr-abl by the inhibitor imatinib (GleevecTM) has proven efficacious in the therapy of CML. However resistance to the drug emerges at the advanced phases of the disease. Therefore, novel therapy models remained to be designed. We have developed a novel approach based on the development of a dual targeted agent termed combi-molecule designed to not only block bcr-abl but also to damage DNA. ZRF1, the first optimized prototype of the approach was “programmed” to degrade into another inhibitor ZRF0 + a methyldiazonium species. It was an approximately 2-fold stronger abl tyrosine kinase (TK) inhibitor than GleevecTM and a more potent DNA damaging agent than Temodar®. In the p53 wild-type Mo7p210 cells ZRF1 was approximately 1000-fold superior to that of equieffective combinations of GleevecTM+Temodar®. More importantly, its superior potency over GleevecTM was more pronounced in bcr-abl+ cells co-expressing wild-type p53. Studies to rationalize these results showed that, through its bcr-abl inhibitory function it down-regulates p53. However, sufficient levels of the latter protein are available for transactivating p21 and bax which are required for cell cycle arrest and apoptosis. The results suggest that in p53 wild-type cells, apoptosis is induced by the combi-molecule both through the intrinsic apoptotic machinery normally activated by bcr-abl inhibition and through the p53-controlled DNA damaging pathway. This additive effect translates into pronounced cell death in wild-type p53 expressing cells. The study conclusively demonstrated that p53 is a major determinant for the cytotoxic advantages of the novel combi-molecular approach in CML. The latter being a disease in which 70-85% of all the cases express wild-type p53.
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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) | 0.000 | 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) | 0.001 | 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 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".