The Design and Development of an Injectable Thermoresponsive Hydrogel for Controlled Simvastatin Release in Bone Repair Applications
Bibliographic record
Abstract
Osteoporotic vertebrae are a uniquely challenging tissue for local delivery due to the complex geometry of cancellous bone, the proximity of the spinal cord, and the need for reliable site retention. These challenges can be met with the use of stimuli responsive, state transiting formulations by leveraging their unique capacity for minimally invasive implantation as a liquid, sol–gel transition in response to stimuli, and finally, release of a loaded therapeutic. Here, we present the formulation development of a thermosensitive methylcellulose–collagen hydrogel, functionalised with controlled release simvastatin, recently shown to enhance osteogenesis while also impeding osteoclast activity. We first optimised a formulation with collagen content of 0.4% w/v to achieve a thermosensitive system with sol–gel transition at 29 °C, shear-thinning/injectable properties, low cytotoxicity, and high biocompatibility. Incorporation of nano-hydroxyapatite for enhanced bone tissue mimicry revealed optimal performance at 100% w/collagen content, showing long-term hydrolytic stability, maintaining more than 100% of its mass after 28 days. A loading concentration of 1 mg of simvastatin to 1 g of hydrogel displayed sustained release of simvastatin over 35 days. Finally, the release of simvastatin from the hydrogel into in vitro conditions prevented the formation of osteoclasts but failed to boost osteogenesis. Together these findings reveal a series of desirable stimuli-responsive hydrogel properties, achieving minimally invasive application coupled with sustained release of a hydrophobic compound, which is potentially useful for spatially complex bone regeneration. Further this work demonstrates the challenge of dosing sustained release systems to achieve simultaneous osteogenesis and anti-osteoclastogenic effects.
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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".