Simvastatin Embedded into Poly(Lactic-Co-Glycolic Acid)-Based Scaffolds in Promoting Preclinical Bone Regeneration: A Systematic Review
Bibliographic record
Abstract
Simvastatin embedded into poly(lactic-co-glycolic acid) (PLGA)-based scaffolds can stimulate bone regeneration in preclinical models. However, the ideal pharmacological dose has not been evaluated. This systematic review reports on the simvastatin doses used in preclinical studies and evaluates the regeneration of critical-sized bone defects. References were selected in a two-phase process. Electronic databases (Embase, LILACS, LIVIVO, PubMed, SCOPUS, and Web of Science) and grey literature databases (Google Scholar, Open Grey, and ProQuest) were searched until September 2022. The risk of bias was considered to be low based on the SYRCLE tool. We identified four studies in rat, two in parietal and two in calvaria bone, one in mouse parietal bone, and one in rabbit femur bone. Simvastatin, ranging from 8 to 100 µg, significantly increased bone formation in five studies, as compared to the scaffold alone based on µ-computed tomography, histomorphometric, and radiography analysis. The median increase in bone formation caused by simvastatin was 2.1-fold compared to the PLGA-based scaffold alone. There was, however, no significant correlation between the relative bone gain and the doses of simvastatin (p = 0.37). The data suggest that relatively lower doses of simvastatin can consistently promote preclinical bone regeneration. However, the interpretation of these data must consider the heterogenicity of the PLGA-scaffolds, the defect anatomy, the observation period, and the evaluation method.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".