Histone Deacetylase Inhibitors and Diabetic Kidney Disease
Bibliographic record
Abstract
Despite recent clinical trial advances and improvements in clinical care, kidney disease due to diabetes remains the most common cause of chronic kidney failure worldwide. In the search for new treatments, recent attentions have turned to drug repurposing opportunities, including study of the histone deacetylase (HDAC) inhibitor class of agents. HDACs are a group of enzymes that remove functional acetyl groups from histone and non-histone proteins and they can affect cellular function through both epigenetic and non-epigenetic means. Over the past decade, several HDAC inhibitors have been adopted into clinical practice, primarily for the treatment of hematological malignancy, whereas other existing therapies (for instance valproate) have been found to have HDAC inhibitory effects. Here we review the current HDAC inhibitors in the clinic and under development; the literature evidence supporting the renoprotective effects of HDAC inhibitors in experimental diabetic kidney disease; and the adverse effect profiles that may prevent existing therapies from entering the clinic for this indication. Whereas recent research efforts have shed light on the fundamental actions of HDACs in the diabetic kidney, whether these efforts will translate into novel therapies for patients will require more specific and better-tolerated therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".