The Concept of Divergent Targeting through the Activation and Inhibition of Receptors as a Novel Chemotherapeutic Strategy: Signaling Responses to Strong DNA-Reactive Combinatorial Mimicries
Bibliographic record
Abstract
Recently, we reported the combination of multitargeted ErbB1 inhibitor-DNA damage combi-molecules with OCT in order to downregulate ErbB1 and activate SSTRs. Absence of translation to cell kill was believed to be partially due to insufficient ErbB1 blockage and DNA damage. In this study, we evaluated cell response to molecules that damage DNA more aggressively and induce stronger attenuation of ErbB1 phosphorylation. We used three cell lines expressing low levels (U87MG) or transfected to overexpress wildtype (U87/EGFR) or a variant (U87/EGFRvIII) of ErbB1. The results showed that Iressa ± HN2 and the combi-molecules, ZRBA4 and ZR2003, significantly blocked ErbB1 phosphorylation in U87MG cells. Addition of OCT significantly altered cell cycle distribution. Analysis of the DNA damage response pathway revealed strong upregulation of p53 by HN2 and the combi-molecules. Apoptosis was only induced by a 48 h exposure to HN2. All other treatments resulted in cell necrosis. This is in agreement with Akt-Bad pathway activation and survivin upregulation. Despite strong DNA damaging properties and downregulation of ErbB1 phosphorylation by these molecules, the strongest effect of SSTR activation was on cell cycle distribution. Therefore, any enhanced antiproliferative effects of combining ErbB1 inhibition with SSTR activation must be addressed in the context of cell cycle arrest.
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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 | 0.001 |
| 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".