Therapeutic Effects of FGF23 c-tail Fc in a Murine Preclinical Model of X-Linked Hypophosphatemia Via the Selective Modulation of Phosphate Reabsorption
Bibliographic record
Abstract
Fibroblast growth factor 23 (FGF23) is the causative factor of X-linked hypophosphatemia (XLH), a genetic disorder effecting 1:20,000 that is characterized by excessive phosphate excretion, elevated FGF23 levels and a rickets/osteomalacia phenotype. FGF23 inhibits phosphate reabsorption and suppresses 1α,25-dihydroxyvitamin D (1,25D) biosynthesis, analytes that differentially contribute to bone integrity and deleterious soft-tissue mineralization. As inhibition of ligand broadly modulates downstream targets, balancing efficacy and unwanted toxicity is difficult when targeting the FGF23 pathway. We demonstrate that a FGF23 c-tail-Fc fusion molecule selectively modulates the phosphate pathway in vivo by competitive antagonism of FGF23 binding to the FGFR/α klotho receptor complex. Repeated injection of FGF23 c-tail Fc in Hyp mice, a preclinical model of XLH, increases cell surface abundance of kidney NaPi transporters, normalizes phosphate excretion, and significantly improves bone architecture in the absence of soft-tissue mineralization. Repeated injection does not modulate either 1,25D or calcium in a physiologically relevant manner in either a wild-type or disease setting. These data suggest that bone integrity can be improved in models of XLH via the exclusive modulation of phosphate. We posit that the selective modulation of the phosphate pathway will increase the window between efficacy and safety risks, allowing increased efficacy to be achieved in the treatment of this chronic disease. © 2017 American Society for Bone and Mineral Research.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".