Improved small molecule drug release from<i>in situ</i>forming poly(lactic-co-glycolic acid) scaffolds incorporating poly(β-amino ester) and hydroxyapatite microparticles
Bibliographic record
Abstract
In situ forming implants are an attractive choice for controlled drug release into a fixed location. Currently, rapidly solidifying solvent exchange systems suffer from a high initial burst, and sustained release behavior is tied to polymer precipitation and degradation rate. The present studies investigated addition of hydroxyapatite (HA) and drug-loaded poly(β-amino ester) (PBAE) microparticles to in situ forming poly(lactic-co-glycolic acid) (PLGA)-based systems to prolong release and reduce burst. PBAEs were synthesized, imbibed with simvastatin (osteogenic) or clodronate (anti-resorptive), and then ground into microparticles. Microparticles were mixed with or without HA into a PLGA solution, and the mixture was injected into buffer, leading to precipitation and creating solid scaffolds with embedded HA and PBAE microparticles. Simvastatin release was prolonged through 30 days, and burst release was reduced from 81 to 39% when loaded into PBAE microparticles. Clodronate burst was reduced from 49 to 32% after addition of HA filler, but release kinetics were unaffected after loading into PBAE microparticles. Scaffold dry mass remained unchanged through day 15, with a pronounced increase in degradation rate after day 30, while wet scaffolds experienced a mass increase through day 25 due to swelling. Porosity and pore size changed throughout degradation, likely due to a combination of swelling and degradation. The system offers improved release kinetics, multiple release profiles, and rapid solidification compared to traditional in situ forming implants.
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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) | 0.001 | 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".