Spatial control of bone formation using a porous polymer scaffold co-delivering anabolic rhBMP-2 and anti-resorptive agents
Bibliographic record
Abstract
Current clinical delivery of recombinant human bone morphogenetic proteins (rhBMPs) utilises freeze-dried collagen. Despite effective new bone generation, rhBMP via collagen can be limited by significant complications due to inflammation and uncontrolled bone formation. This study aimed to produce an alternative rhBMP local delivery system to permit more controllable and superior rhBMP-induced bone formation. Cylindrical porous poly(lactic-co-glycolic acid) (PLGA) scaffolds were manufactured by thermally-induced phase separation. Scaffolds were encapsulated with anabolic rhBMP-2 (20 µg) ± anti-resorptive agents: zoledronic acid (5 µg ZA), ZA pre-adsorbed onto hydroxyapatite microparticles, (5 µg ZA/2% HA) or IkappaB kinase (IKK) inhibitor (10 µg PS-1145). Scaffolds were inserted in a 6-mm critical-sized femoral defect in Wistar rats, and compared against rhBMP-2 via collagen. The regenerate region was examined at 6 weeks by 3D microCT and descriptive histology. MicroCT and histology revealed rhBMP-induced bone was more restricted in the PLGA scaffolds than collagen scaffolds (-92.3% TV, p < 0.01). The regenerate formed by PLGA + rhBMP-2/ZA/HA showed comparable bone volume to rhBMP-2 via collagen, and bone mineral density was +9.1% higher (p < 0.01). Local adjunct ZA/HA or PS-1145 significantly enhanced PLGA + rhBMP-induced bone formation by +78.2% and +52.0%, respectively (p ≤ 0.01). Mechanistically, MG-63 human osteoblast-like cells showed cellular invasion and proliferation within PLGA scaffolds. In conclusion, PLGA scaffolds enabled superior spatial control of rhBMP-induced bone formation over clinically-used collagen. The PLGA scaffold has the potential to avoid uncontrollable bone formation-related safety issues and to customise bone shape by scaffold design. Moreover, local treatment with anti-resorptive agents incorporated within the scaffold further augmented rhBMP-induced bone formation.
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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) | 0.000 | 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 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".