Abaloparatide exhibits greater osteoanabolic response and higher cAMP stimulation and<i>β</i>‐arrestin recruitment than teriparatide
Bibliographic record
Abstract
Teriparatide and abaloparatide are parathyroid hormone receptor 1 (PTHR1) analogs with unexplained differential efficacy for the treatment of osteoporosis. Therefore, we compared the effects of abaloparatide and teriparatide on bone structure, turnover, and levels of receptor activator of nuclear factor-kappa B ligand (RANKL) and osteoprotegerin (OPG). Wild-type (WT) female mice were injected daily with vehicle or 20-80 µg/kg/day of teriparatide or abaloparatide for 30 days. Femurs and spines were examined by microcomputed tomography scanning and serum levels of bone turnover markers, RANKL, and OPG, were measured by ELISA. Both analogs similarly increased the distal femoral fractional trabecular bone volume, connectivity, and number, and reduced the structure model index (SMI) at 20-80 µg/kg/day doses. However, only abaloparatide exhibited a significant increase (13%) in trabecular thickness at 20 µg/kg/day dose. Femoral cortical evaluation showed that abaloparatide caused a greater dose-dependent increase in cortical thickness than teriparatide. Both teriparatide and abaloparatide increased lumbar 5 vertebral trabecular connectivity but had no or modest effect on other indices. Biochemical analysis demonstrated that abaloparatide promoted greater elevation of procollagen type 1 intact N-terminal propeptide, a bone formation marker, and tartrate-resistant acid phosphatase 5b levels, a bone resorption marker, and lowered the RANKL/OPG ratio. Furthermore, PTHR1 signaling was compared in cells treated with 0-100 nmol/L analog. Interestingly, abaloparatide had a markedly lower EC50 for cAMP formation (2.3-fold) and β-arrestin recruitment (1.6-fold) than teriparatide. Therefore, abaloparatide-improved efficacy can be attributed to enhanced bone formation and cortical structure, reduced RANKL/OPG ratio, and amplified Gs-cAMP and β-arrestin signaling.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".