The combi-targeting concept: a new cell signaling-based model for the selective targeting of the epidermal growth factor receptor (EGFR)- and Her2-expressing solid tumor cells by the complex combi-molecule RB24 (NSC 741279)
Bibliographic record
Abstract
4175 Within the context of the “Combi-Targeting” concept, we designed RB24 to inhibit EGFR or Her2 phosphorylation and to further degrade upon hydrolysis to RB10 (another EGFR/Her2 inhibitor) + a DNA methylating species. In vitro analyses using fluorescence microscopy, comet and Annexin-V binding assays showed that RB24 released RB10 in the perinuclear region, damaged DNA and induced apoptosis in the human MDA-MB-435 breast cancer cells. Transfection of the latter cells with ErbB1 or ErbB2 correlated with 2-3 fold higher levels of DNA damage, enhanced cell death by apoptosis and increased intensity of fluorescence associated with RB10 in the perinuclear region. The selective enhancement of DNA lesions in the transfected cells was abolished by co-incubation with exogenous RB10, suggesting that intact RB24 bound to its cognate perinuclear sites prior to releasing the alkylating species. Analysis of key signaling proteins in the cells demonstrated a DNA-damage dependent activation of JNK and down-regulation of Bad through inhibition of EGFR or Her2 phosphorylation by RB24. This translated in to a 10-20-fold stronger cytocidal activity by RB24 when compared with its clinical counterpart Temodal®.The results suggest a targeting model based on a by-stander effect that increases the levels of DNA damage in the transfectants and a cooperative enhancement of apoptosis through JNK activation and down regulation of Bad. This convergent mechanism may account for the remarkable potency of RB24 against the oncogenic expressing transfectants. The results in toto indicate that the Combi-Targeting concept may well represent a novel strategy to enhance the potency of alkylators in EGFR- and Her2-expressing cells.
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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".