Methods to validate Hsp90 inhibitor specificity, to identify off-target effects, and to rethink approaches for further clinical development
Bibliographic record
Abstract
The molecular chaperone Hsp90 is one component of a highly complex and interactive cellular proteostasis network (PN) that participates in protein folding, directs misfolded and damaged proteins for destruction, and participates in regulating cellular transcriptional responses to environmental stress, thus promoting cell and organismal survival. Over the last 20 years, it has become clear that various disease states, including cancer, neurodegeneration, metabolic disorders, and infection by diverse microbes, impact the PN. Among PN components, Hsp90 was among the first to be pharmacologically targeted with small molecules. While the number of Hsp90 inhibitors described in the literature has dramatically increased since the first such small molecule was described in 1994, it has become increasingly apparent that not all of these agents have been sufficiently validated for specificity, mechanism of action, and lack of off-target effects. Given the less than expected activity of Hsp90 inhibitors in cancer-related human clinical trials, a re-evaluation of potentially confounding off-target effects, as well as confidence in target specificity and mechanism of action, is warranted. In this commentary, we provide feasible approaches to achieve these goals and we discuss additional considerations to improve the clinical efficacy of Hsp90 inhibitors in treating cancer and other diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".