High-Throughput Screening Identifies Cardiac Glycosides as Potent Inhibitors of Human Tissue Kallikrein Expression: Implications for Cancer Therapies
Bibliographic record
Abstract
PURPOSE: Human tissue kallikreins (KLK) comprise a subgroup of 15 homologous secreted serine proteases. Primarily known for their clinical use as cancer biomarkers (e.g., PSA), KLKs have recently been directly implicated in cancer-related processes, including invasion, angiogenesis, and tumor growth regulation. Therefore, the identification of compounds that would modulate expression of KLKs might be of considerable therapeutic value. EXPERIMENTAL DESIGN: A cell-based high-throughput screening (HTS) of three small molecule libraries ( approximately 4,500 compounds) was undertaken; KLK expression in the breast cancer cell line MDA-MB-468 was assessed with sensitive ELISAs. RESULTS: The initial screening resulted in 66 "putative hits" that decreased KLK5 expression by at least 50% over control. Secondary screening and mini-dose-response assays resulted in 21 "validated hits." These 21 compounds were clustered in only three distinct functional families and were further analyzed in vitro to determine their effectiveness (IC(50)s). Hits that failed to show dose-responsiveness or interfered with the viability of the cells were excluded. Multiple members of the cardiac glycoside family were found to be novel inhibitors of KLK expression, acting at low concentrations (10-50 nmol/L). Furthermore, members of the same family induced marked decreases in c-MYC and c-FOS expression, in a dose-dependent manner that correlated the KLK inhibition, suggesting a transcriptional mechanism of regulation of KLK expression. CONCLUSIONS: We conclude that cardiac glycosides can dramatically suppress the transcription of KLKs and that these effects may be linked to proto-oncogene (c-myc/fos) expression. These findings may partially explain the recently realized antineoplastic actions of cardiac glycosides.
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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) | 0.001 | 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 | 0.001 |
| 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".