Identification and evaluation of novel drug combinations of Aurora kinase inhibitor CCT137690 for enhanced efficacy in oral cancer cells
Bibliographic record
Abstract
Oral cancer is the most prevalent subtype of head and neck cancers and arises mainly from squamous cells of the oral cavity. Patients with advanced metastatic disease have poor overall survival resulting primarily from limited treatment options. Recent advances in the understanding of molecular basis of oral tumorigenesis provide an opportunity for identification and validation of new drug targets. The deregulated expression of the Aurora family of mitotic kinases, for example, has been associated with pathogenesis and poor prognosis in oral cancer. Here, we have evaluated the efficacy of the pan-Aurora inhibitor (CCT137690) alone and in combination with different chemotherapeutic and targeted drugs to identify its synergistic partners in oral cancer cell lines (ORL-48 and ORL-115). CCT137690 effectively inhibits Aurora kinases in both the cell lines and displays potent antiproliferative activity towards them. Prolonged treatment of these cells with CCT137690 results in abrogated mitotic spindle formation, misaligned chromosome attachment and polyploidy that ultimately leads to apoptotic cell death. We further identified that inhibitors of EGFR (gefitinib) and PI3-kinase (pictilisib) synergize with CCT137690 to inhibit the proliferation of the oral cancer cell lines. Moreover, we demonstrate that polyethylene glycol-based nanocapsules harboring combinations of CCT137690 with gefitinib or pictilisib inhibit the growth of oral cancer cell lines in 3D spheroid cultures and induce apoptosis that is comparable to free drug combinations. In conclusion, we have demonstrated the in vitro efficacy of CCT137690 in oral cancer cell lines, identified novel drug combinations with CCT137690 and synthesized nanocapsules containing these drug combinations for co-administration.
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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) | 0.001 | 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".