Harnessing Bone-Liver Crosstalk: A Dual-Action LYTAC Approach for Bone-Specific Accumulation and Liver-Specific Protein Degradation in Bone Disorders
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Despite significant progress in extracellular targeted protein degradation (eTPD), existing approaches rarely achieved tissue-specific drug accumulation while maintaining efficient systemic clearance, a critical challenge in treating bone disorders. In this study, we introduced GalNAc-Apc001, a novel aptamer-based lysosome-targeting chimera (LYTAC) that uniquely combined bone-specific retention with hepatocyte-mediated clearance through a spatiotemporally controlled mechanism. By conjugating a tri-N-acetylgalactosamine (GalNAc) moiety to a bone-homing sclerostin aptamer (Apc001), we engineered a bifunctional molecule capable of accumulating in bone via hydroxyapatite binding, capturing circulating sclerostin with high affinity and directing it to hepatocytes for ASGPR-mediated lysosomal degradation. In the absence of ASGPR-positive cells, GalNAc-Apc001 functioned via the conventional aptamer mechanism of binding inhibition, demonstrating efficacy comparable to that of Apc001 but notably lower than that of a sclerostin antibody. However, in ASGPR-positive cell coculture systems, GalNAc-Apc001 achieved a 40% greater activation of the Wnt signaling pathway compared to the sclerostin antibody, effectively reversing sclerostin-mediated inhibition (96 vs 60% recovery). Pharmacologically, GalNAc-Apc001 exhibited superior therapeutic efficacy by mitigating the suppressive effects of sclerostin on Wnt signaling, upregulating bone formation markers, and enhancing bone mass in a Col1a2 +/G610C osteogenesis imperfecta mouse model. These findings provided compelling mechanistic evidence that the spatiotemporal control of protein degradation could resolve the inherent trade-off between tissue targeting and systemic clearance, supporting the clinical potential of GalNAc-Apc001 in bone disorders.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".