Subcellular Distribution of a Fluorescence-Labeled Combi-Molecule Designed to Block Epidermal Growth Factor Receptor Tyrosine Kinase and Damage DNA with a Green Fluorescent Species
Bibliographic record
Abstract
To monitor the subcellular distribution of mixed epidermal growth factor (EGF) receptor (EGFR)-DNA targeting drugs termed combi-molecules, we designed AL237, a fluorescent prototype, to degrade into a green fluorescent DNA damaging species and FD105, a blue fluorescent EGFR inhibitor. Here we showed that AL237 damaged DNA in the 12.5 to 50 mumol/L range. Despite its size, it blocked EGFR phosphorylation in an enzyme assay (IC(50) = 0.27 mumol/L) and in MDA-MB468 breast cancer cells in the same concentration range as for DNA damage. This translated into inhibition of extracellular signal-regulated kinase 1/2 or BAD phosphorylation and downregulation of DNA repair proteins (XRCC1, ERCC1). Having shown that AL237 was a balanced EGFR-DNA targeting molecule, it was used as an imaging probe to show that (a) green and blue colors were primarily colocalized in the perinuclear and partially in the nucleus in EGFR- or ErbB2-expressing cells, (b) the blue fluorescence associated with FD105, but not the green, was colocalized with anti-EGFR red-labeled antibody, (c) the green fluorescence of nuclei was significantly more intense in NIH 3T3 cells expressing EGFR or ErbB2 than in their wild-type counterparts (P < 0.05). Similarly, the growth inhibitory potency of AL237 was selectively stronger in the transfectants. In summary, the results suggest that AL237 diffuses into the cells and localizes abundantly in the perinuclear region and partially in the nucleus where it degrades into EGFR and DNA targeting species. This bystander-like effect translates into high levels of DNA damage in the nucleus. Sufficient quinazoline levels are released in the cells to block EGF-induced activation of downstream signaling. Mol Cancer Ther; 9(4); 869-82. (c)2010 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".