Characterization of the Apoptotic Response Induced by the Cyanine Dye D112: A Potentially Selective Anti-Cancer Compound
Bibliographic record
Abstract
Chemotherapeutic drugs that are used in anti-cancer treatments often cause the death of both cancerous and noncancerous cells. This non-selective toxicity is the root cause of untoward side effects that limits the effectiveness of therapy. In order to improve chemotherapeutic options for cancer patients, there is a need to identify novel compounds with higher discrimination for cancer cells. In the past, methine dyes that increase the sensitivity of photographic emulsions have been investigated for anti-cancer properties. In the 1970's, Kodak Laboratories initiated a screen of approximately 7000 dye structural variants for selective toxicity. Among these, D112 was identified as a promising compound with elevated toxicity against a colon cancer cell line in comparison to a non-transformed cell line. Despite these results changing industry priorities led to a halt in further studies on D112. We decided to revive investigations on D112 and have further characterized D112-induced cellular toxicity. We identified that in response to D112 treatment, the T-cell leukemia cell line Jurkat showed caspase activation, mitochondrial depolarization, and phosphatidylserine externalization, all of which are hallmarks of apoptosis. Chemical inhibition of caspase enzymatic activity and blockade of the mitochondrial pathway through Bcl-2 expression inhibited D112-induced apoptosis. At lower concentrations, D112 induced growth arrest. To gain insight into the molecular mechanism of D112 induced mitochondrial dysfunction, we analyzed the intracellular localization of D112, and found that D112 associated with mitochondria. Interestingly, in the cell lines that we tested, D112 showed increased toxicity toward transformed versus non-transformed cells. Results from this work identify D112 as a potentially interesting molecule warranting further investigation.
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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".