Seco-Duocarmycin SA in Aggressive Glioblastoma Cell Lines
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is among the most lethal primary brain tumors and is characterized by significant cellular heterogeneity and resistance to conventional therapies. This study investigates the efficacy of seco-duocarmycin SA (seco-DSA), a novel DNA alkylating agent. Initial investigations using a colony formation assay revealed that seco-DSA exhibits remarkable potential with IC50 values lower than its natural DSA counterpart. Cell viability assay indicated that LN18 cells showed a markedly greater sensitivity to DSA than T98G cells. Furthermore, seco-DSA achieved its full cytotoxic effect within 8 h of drug incubation in GBM cell lines. Although seco-DSA induced a concentration-dependent increase in apoptotic cell death, the extent of apoptosis did not fully account for the observed decrease in cell viability. Instead, seco-DSA treatment resulted in significant cell cycle arrest in S and G2/M phases. These findings suggest that seco-DSA’s cytotoxicity in GBM cells is primarily due to its ability to disrupt cell cycle progression, though the precise mechanisms of action remain to be fully established, and further research is needed. Proteomic analysis of treated cells also indicates dysregulation of proteins involved in senescence, apoptosis, and DNA repair, alluding to seco-DSA-induced arrest as a major mechanism of GBM disruption. Data are available via ProteomeXchange with the dataset identifier “PXD061023”. Our reports promote the future exploration of seco-DSA’s therapeutic potential, representing a critical step toward developing a more targeted and effective treatment for GBM.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".