Vacuolar H+-ATPases (V-ATPases) as therapeutic targets: a brief review and recent developments
Bibliographic record
Abstract
-ATPases (V-ATPases) are multi-subunit enzymes that play housekeeping roles in eukaryotic cells by acidifying lysosomes, late endosomes, Golgi, and other membrane-bounded compartments. Beyond that, V-ATPases have specialized functions in certain cell types linked to diseases including osteoporosis and cancer. Efforts to identify strategies to develop inhibitors selective for V-ATPases that are involved in disease progression have been ongoing for more than two decades, but so far have not yielded a therapeutic agent that has been translated to the clinic. Recent basic science studies have identified unexpected roles for V-ATPases in nutrient and energy sensing, and renin/angiotensin signaling, which offer additional incentives for considering V-ATPases as therapeutic targets. This article briefly reviews efforts to utilize inhibitors of V-ATPases as drugs. Primary focus is on recent "rational" efforts to identify small molecule inhibitors of the V-ATPases that are selectively expressed in osteoclasts and cancer cells. Enoxacin and bis-enoxacin are two molecules that emerged from these efforts. These molecules block a binding interaction between V-ATPases and microfilaments that occurs in osteoclasts, but not most other cell types, which relates to the specialized function of V-ATPases in bone resorption. Enoxacin and bis-enoxacin have proven useful in the treatment of bone diseases and cancer in animal models and display therapeutic effects that are different, and perhaps better, than current drugs. These results provide evidence that agents targeting subsets of V-ATPases may prove useful in the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".