Harnessing Bone-Liver Crosstalk: A Dual-Action LYTAC Approach for Bone-Specific Accumulation and Liver-Specific Protein Degradation in Bone Disorders
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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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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 | 0.000 | 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) | 0.000 | 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 it