Cathepsin K inhibitors promote osteoclast-osteoblast communication and engagement of osteogenesis
Bibliographic record
Abstract
Cathepsin K inhibitors are well known for their inhibitory activity against bone resorption but were also reported to preserve bone formation in clinical trials, in contrast with other bone resorption antagonists. Here, we show cathepsin K inhibitors favor the crosstalk between osteoclasts and osteoblasts and help engaging the osteogenic process required for proper bone remodeling. Therefore, we used a novel approach, co-culturing human osteoclasts and osteoblast lineage cells on bone slices and monitoring through time-lapse their response to an active site (odanacatib) or an ectosteric (T06) cathepsin K inhibitor. Both inhibitors prevent the shift from pit to trench resorption mode and thus lead to a marked increase in pit-eroded surface lined with undigested collagen. Importantly, pit-eroded surfaces prove to receive significantly more and longer visits of osteoblast lineage cells. Furthermore, resorption achieved under CatK inhibition promotes osteoblast differentiation as shown by upregulation of alkaline phosphatase and type 1 collagen, and down regulation of RANKL. We propose a model where high cathepsin K activity levels lead to both aggressive bone resorption and compromised bone formation, and where low cathepsin K levels result in both slower resorption and faster initiation of formation. This model fits the current knowledge on the effect of collagen/collagenolysis on osteoclast activity and osteoblast chemotaxis. The combined effects of cathepsin K on resorption and formation render cathepsin K inhibitors unique tools to prevent bone loss. They stress the clinical interest of developing ectosteric inhibitors that may not have the side effects of active site inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".