Heat Shock Protein 70 Inhibitors. 1. 2,5′-Thiodipyrimidine and 5-(Phenylthio)pyrimidine Acrylamides as Irreversible Binders to an Allosteric Site on Heat Shock Protein 70
Bibliographic record
Abstract
Heat shock protein 70 (Hsp70) is an important emerging cancer target whose inhibition may affect multiple cancer-associated signaling pathways and, moreover, result in significant cancer cell apoptosis. Despite considerable interest from both academia and pharmaceutical companies in the discovery and development of druglike Hsp70 inhibitors, little success has been reported so far. Here we describe structure-activity relationship studies in the first rationally designed Hsp70 inhibitor class that binds to a novel allosteric pocket located in the N-terminal domain of the protein. These 2,5'-thiodipyrimidine and 5-(phenylthio)pyrimidine acrylamides take advantage of an active cysteine embedded in the allosteric pocket to act as covalent protein modifiers upon binding. The study identifies derivatives 17a and 20a, which selectively bind to Hsp70 in cancer cells. Addition of high nanomolar to low micromolar concentrations of these inhibitors to cancer cells leads to a reduction in the steady-state levels of Hsp70-sheltered oncoproteins, an effect associated with inhibition of cancer cell growth and apoptosis. In summary, the described scaffolds represent a viable starting point for the development of druglike Hsp70 inhibitors as novel anticancer therapeutics.
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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) | 0.000 | 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) | 0.001 | 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".