Bisphosphonate Conjugation to Proteins as a Means To Impart Bone Affinity
Bibliographic record
Abstract
Growth factors are endogenous proteins capable of stimulating new bone formation, but their clinical benefit for systemic stimulation of bone mass has not been demonstrated. The critical challenge is to deliver a significant dose of the proteins to bone after intravenous injection. This challenge may be overcome by derivatizing proteins with ligands that exhibit a high bone affinity (e.g., bisphosphonates). To demonstrate the feasibility of this approach, 1-amino-1,1-diphosphonate methane (aminoBP) was conjugated to a model protein, albumin. The conjugation was performed by (1) converting the amino group of aminoBP to a thiol group using 2-iminothiolane, (2) derivatizing the albumin amino groups with a thiol-reactive sulfosuccinimidyl-4-(N-maleimidomethyl)-1-cyclohexane carboxylate, and (3) reacting the derivatized albumin with thiolated aminoBP. Typically, 1-4 aminoBP molecules per albumin were obtained. The conjugated albumin exhibited a high affinity to hydroxyapatite that was proportional to the extent of conjugation. The conjugates were shown to exhibit a high affinity to bone matrix in vitro in a serum-containing medium. Once bound to bone matrix, the conjugates were found to desorb more slowly than the unmodified albumin, especially from bone whose organic matrix was removed by ashing. In conclusion, conjugation of bisphosphonates to albumin was shown to impart a high bone affinity to the protein, and such conjugates can be potentially targeted to bone.
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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".