Abstract 494: Fluorescent carbon dots: A novel bioimaging tool to reveal the mechanism of action of anticancer drugs in cells
Bibliographic record
Abstract
Abstract The mechanism of action of many cancer drugs at the subcellular level remains unknown. Yet, this knowledge could be critical in driving structure-activity-relationship studies to improve their efficacy and selectivity. To gain a deeper understanding of the mechanisms governing the action of anticancer drugs within cells, we are developing an innovative methodology to visualize drug interactions. Carbon dots (CDs) are carbon-based nanoparticles, distinguished not only by their remarkable optical properties but also by their minimal cytotoxicity, rendering them highly suitable for bioimaging applications. We recently found that amine-passivated CDs with a negative surface charge and amphiphilic properties were enriched in the cytosol of cultured human cells. These CDs emit fluorescence in the blue spectra with high quantum yields and are not cytotoxic. Importantly, their surface is decorated with amine functional groups that can be used for coupling to polyethylene glycol to create a linker that can be covalently attached to drugs of interest. First, we are covalently linking amine-passivated CDs to Doxorubicin (Dox), a well-known anti-cancer drug that intercalates DNA and also has fluorescence properties that differ from the CDs. This will permit us to monitor and validate the conjugation of Dox to CDs, their cellular uptake and localization. The next step will be to use this approach to reveal the cellular mechanism of action of novel anticancer drugs. Specifically, our group has rationally designed thienoisoquinoline compounds with selectivity and high efficacy for triple negative breast cancer cells. In vitro they disrupt microtubule polymerization in the nanomolar range. However, in cells they cause mitotic arrest and spindle phenotypes that are distinct from colchicine, another microtubule-targeting drug. Our approach will reveal if these compounds target the centrosomes and/or microtubules and if this varies with cell types. This innovative approach could yield profound insights into the intracellular mechanisms of anticancer drugs to facilitate structure-activity-relationship studies, to improve their efficacy and selectivity for drug development. Citation Format: Adryanne Clermont-Paquette. Fluorescent carbon dots: A novel bioimaging tool to reveal the mechanism of action of anticancer drugs in cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 494.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".