Imparting Mineral Affinity to Fetuin by Bisphosphonate Conjugation: A Comparison of Three Bisphosphonate Conjugation Schemes
Bibliographic record
Abstract
Protein conjugation to bisphosphonic acids (BPs), such as 1-amino-1,1-diphosphonate methane (aminoBP) and 3,5-di(ethylamino-2,2-bisphosphono)benzoic acid (diBP), was proposed as a foundation for bone-specific delivery of protein therapeutics. This study was performed to directly compare the mineral affinity of protein-BP conjugates prepared by three different approaches. Fetuin, serving as a model protein, was derivatized with BPs by the following approaches: (i) by attaching the aminoBPs onto protein lysines using succinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carboxylate (SMCC); (ii) by attaching the aminoBPs onto protein carbohydrates using 4-(maleimidomethyl)-cyclohexane-1-carboxyl-hydrazide (MMCCH), and (iii) by conjugating diBP to protein lysines using the carbodiimide chemistry. The results indicated that conjugation of aminoBP and diBP to fetuin by all three means unequivocally enhanced the protein's affinity for hydroxyapatite in vitro. Similarly, conjugation of aminoBP and diBP onto fetuin increased the protein's retention in a mineral-containing matrix (Pro-Osteon) when the proteins were implanted in a rat subcutaneous model. Upon parenteral administration, however, no discernible differences were found between the SMCC- or MMCCH-linked conjugates and unmodified fetuin to target to bony tissues. DiBP-fetuin conjugates, however, led to successful bone targeting after intravenous injection in rats. We conclude that all three conjugation schemes were equally effective in imparting an affinity to the proteins toward mineral-containing matrices. Bone targeting, however, was achieved only with diBP conjugation to fetuin, supportive of the superior ability of this BP with a higher density of bisphosphonic acid groups.
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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".