Bibliographic record
Abstract
AMN107, A Designed Bcr-Abl Inhibitor Effective in Gleevec-Resistant CMLStudy finds new designer drug is potent treatment for chronic myelogenous leukemia More potent and highly selective therapy effective in treating Gleevec-resistant disease.A laboratory study led by researchers at Dana-Farber Cancer Institute has shown that a potent and highly selective therapy for chronic myelogenous leukemia (CML) may ultimately be more effective than Gleevec ® , the current standard of care.The researchers report in the February issue of Cancer Cell that the new compound, AMN107, is about 20 times more potent than Gleevec and is effective in treating Gleevec-resistant disease in model systems.Discovered by and in development with Novartis Pharma AG, AMN107 is a small molecule tyrosine kinase inhibitor."While Gleevec represents a major treatment advance for CMLapproximately 95% of patients treated with Gleevec achieve remission-there clearly is a need for therapies that produce longer remissions, are active against advanced disease, and can be used when Gleevec loses effectiveness," says Dana-Farber's James Griffin, M.D., senior author of the study.Gleevec shuts down CML by blocking the function of Bcr-Abl, the abnormal tyrosine kinase protein in the leukemic cells that causes them to grow too quickly.However, it does not bind very tightly to this protein, and patients can develop a resistant type of Bcr-Abl that no longer binds to Gleevec at all.Using rational drug design to circumvent these shortcomings, researchers at Novartis determined the crystal structure of Bcr-Abl, and then constructed compounds that would lock into the receptor more securely than Gleevec.Investigators at Dana-Farber tested the new compounds to measure their effectiveness against CML in laboratory cell cultures and mice with the disease.Data from the study published in Cancer Cell showed that in experiments with laboratory samples of CML cells, AMN107 killed the cells more effectively than Gleevec.In follow-up studies with mice with a human form of CML, AMN107 produced lengthier remissions than Gleevec and triggered remissions in animals in which the disease had become resistant to Gleevec.Side effects in the animals were minimal.Synthesized in August 2002, AMN107 entered early Phase I clinical studies in May 2004-21 months later.Data presented last December at the American Society of Hematology showed that AMN107 had demonstrated significant clinical activity in the most challenging setting: Gleevec resistant accelerated and blast crisis CML patients."We're very encouraged by the results so far," remarks Griffin, who is also a professor of medicine at Harvard Medical School."This is an elegant example of how rational drug design-developing drugs based on a molecular understanding of cell structures and processes-can be used to attack human diseases."The findings contribute to a larger Dana-Farber research effort, dubbed the "Kinase Project," which seeks to identify abnormal tyrosine kinases, enzymes that spark or halt growth, in cancer cells and test agents known to act against them.The Cancer Cell study's lead author is Ellen Weisberg, Ph.D., of Dana-Farber.Co-authors include researchers at Dana-Farber, Novartis, Brigham and Women's Hospital, and Children's Hospital Boston.The preclinical study was conducted as part of a research collaboration between Novartis Pharma AG and Dana-Farber.Dana-Farber Cancer Institute is a principal teaching affiliate of the Harvard Medical School and is among the leading cancer research and care centers in the
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".