Abstract 3808: Novel compound conferring selectivity for cancer cells
Bibliographic record
Abstract
Abstract There is a need to develop novel compounds that can effectively treat a broad range of cancers on their own, or in combination with approved therapies. As personalized medicine is developed, combinatorial approaches will become more common making it crucial to increase the repertoire of available drugs. In the past, drugs often were developed to target a biologically-relevant molecule, but structural limitations, stability and solubility issues, or lack of selectivity have hindered the clinical use of many of these drugs. Our approach was to first find a ‘high-quality’ compound that is selective for cancer cells, then characterize its mechanism of action and identify its target. High throughput screening (HTS) helped to rapidly identify a subset of compounds with selective toxicity toward MCF-7 (breast cancer) cells. Some of these compounds were further tested for their efficacy in HeLa (cervical cancer) cells, and we found one that selectively causes mitotic arrest at 250 nM in comparison to non-cancerous HFF-1 (foreskin fibroblast) cells. At 200 nM, this compound synergizes with drugs known to affect microtubule dynamics and cause mitotic arrest including Nocodazole and Paclitaxel (currently in use as an anti-cancer drug), causing them to be more effective at lower concentrations. Excitingly, this compound also provides a shielding effect for HFF-1 cells treated with Paclitaxel. To learn the mechanism of action for this compound, we performed immunofluorescence microscopy on HeLa and HFF-1 cells treated with a range of concentrations. We found that the mitotic spindle is improperly organized in HeLa cells at 250 nM, but not in HFF-1 cells. Interestingly, microtubules are completely gone in mitotic HeLa cells and are reduced in mitotic HFF-1 cells treated with >500 nM. Given that this compound synergizes with drugs that directly bind to tubulin subunits to modify their dynamics of assembly and disassembly, and differently affects cancerous vs. healthy cells, we hypothesize that it has a unique mechanism of action and may affect microtubule nucleation. We are continuing to characterize the compound, and will identify its molecular target. In addition, we are generating further iterations to explore the Structure-Activity Relationship, and optimize its efficacy. Our in vitro data shows that our approach has the potential to identify novel compounds with the potential for therapeutic use. Citation Format: Dilan B. Jaunky, Pat Forgione, Alisa Piekny. Novel compound conferring selectivity for cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3808.
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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) | 0.001 | 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) | 0.005 | 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".