The Interaction of Cationic Polymers and Their Bisphosphonate Derivatives with Hydroxyapatite
Bibliographic record
Abstract
Conjugating proteins with bisphosphonates (BPs), a class of molecules with exceptional affinity to hydroxyapatite (HA), is a feasible means to impart bone affinity to protein-based therapeutic agents. To increase the targeting effectiveness while minimizing protein modification, a polymeric linker containing multiple copies of BPs could be constructed for protein conjugation and targeting to bone. Towards this goal, poly(L-lysine) (PLL) and poly(ethylenimine) (PEI) were utilized as the polymeric backbones to incorporate a BP, namely 2-(3-mercaptopropylsulfanyl)-ethyl-1,1-bisphosphonic acid (thiolBP), by using N-hydroxysuccinimidyl polyethylene glycol maleimide and succinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carboxylate, respectively. In vitro and in vivo mineral affinity of the polymer-BP conjugates were determined in comparison with the unmodified polymers. The in vitro results indicated strong binding of the cationic polymers to HA in their unmodified form. BP conjugation did not enhance the inherent mineral affinity of the polymers; in contrast, certain modifications negatively affected the polymers' binding to the HA. In vivo results from a subcutaneous implant model in rats also showed no significant difference in mineral affinity of the BP modified and unmodified PEI. We conclude that thiolBP conjugation to the cationic polymers PLL and PEI was not beneficial for increasing the mineral affinity of the polymeric molecules. The strong interaction between the cationic polymers and HA may make the polymers suitable for imparting mineral affinity to bone-acting therapeutics.
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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".