A high-throughput screening approach to discover potential colorectal cancer chemotherapeutics: repurposing drugs to identify novel disruptors of 14-3-3 proteins
Bibliographic record
Abstract
Selectively inducing apoptosis of cancer cells is an effective therapeutic strategy, but the success of existing chemotherapeutics is compromised by emergent tumor cell resistance and systemic off-target effects. Therefore, the discovery of new pro-apoptotic compounds with minimal systemic side effects remains an urgent need. 14-3-3 proteins are molecular scaffolds that serve as important regulators of cell survival. We previously demonstrated that 14-3-3ζ can sequester BAD, a pro-apoptotic member of the BCL-2 protein family, in the cytoplasm to inhibit the induction of apoptosis. Despite 14-3-3ζ being a critical regulator of cell survival, the identification of molecules that potently disrupt 14-3-3ζ actions has yet to materialize as a chemotherapeutic approach. Herein, we established a BRET-based, high-throughput drug screening approach (Z'-score = 0.52) to identify molecules that disrupt the binding of 14-3-3ζ to a BAD-derived fragment containing serine residues critical for their interactions. A drug library containing 1971 compounds was used for screening, and the capacity of identified hits to induce cell death was examined in NIH-3T3 fibroblasts and colorectal cancer cell lines, HT-29 and Caco-2. These results were mechanistically supported by both in silico structural analysis that suggest the possible mode of binding and direct biophysical measurements that demonstrate concentration-dependent target engagement. Terfenadine, penfluridol, and lomitapide have potential to either be repurposed as chemotherapeutics, or more likely, used as starting points for novel lead development. The described assay cascade demonstrates the feasibility of both expanding on these compounds and identifying novel disruptors of 14-3-3ζ to develop pro-apoptotic agents to treat pathogenic aberrant cell growth.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".