Apoptosis Modulators: p53 Targeting
Bibliographic record
Abstract
Cancer is a disease initiated, driven, and sustained by genomic instability; many pleiotropic and overlapping signaling pathways contribute to oncogenesis and pathologic progression. Despite accumulating multiple genetic, epigenetic, and chromosomal abnormalities, cancer cells can become dependent on a single or a few oncogenic pathways for both maintenance of the malignant phenotype and cell survival; this phenomenon-termed “oncogene addiction”—has been reported in both cultured cell lines and animal models (1). Thus, reversal of only one or a few of these abnormalities can trigger massive apoptosis resulting in inhibition of cancer cell growth. Considerable progress in the treatment of cancer in recent years stems from the development and clinical application of drugs targeted to specific molecular pathways. Examples of these pathway-specific drugs are Gleevec (imatinib mesylate; Novartis Pharmaceutical Corp., East Hannover, New Jersey, U.S.A.) and Tarceva (erlotinib; Genentech, San Francisco, California, U.S.A.), both of which act as selective tyrosine kinase inhibitors. However, clinical success with these pathway-specific agents has been idiosyncratic. Agents that affect not only one, but various, albeit similar, pathways are being developed and have demonstrated improved clinical results. Such is the case of Sorafenib (Nevaxar; Wayne, New Jersey, U.S.A.), which was initially developed as a RAF-RAS kinase-targeted drug. Further studies showed that, in addition to targeting RAF kinase, Sorafenib also inhibits VEGF and PDGF receptor kinases, as well as KIT and FLT-3 kinases, culminating in tumor cell apoptosis and inhibition of angiogenesis. It appears that the combination of Sorafenib&s;s actions on multiple tyrosine kinase pathways induces an enhanced therapeutic response by this drug. Additional drugs which target multiple kinases that have demonstrated clinical promise are sunitinib (Sutent; Pfizer, Inc., New York, New York, U.S.A.), dasatinib (Sprycel; BristolMyers Squibb, New York, New York, U.S.A.), and lapatinib (GlaxoSmithKline, Brentford, London, U.K.). In contrast, agents which have demonstrated specificity against one kinase target do not always show clinical activity; this concept was exemplified by the lack of robust clinical activity of Iressa(gefitinib; Astrazeneca, Mississauga, Ontario, Canada), despite early enthusiasm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".