Reducing residual solvent levels in poly (D, L-lactic-co-glycolic acid) microspheres: a roadmap for scalable industrial production
Bibliographic record
Abstract
OBJECTIVE: Poly (D, L-lactic-co-glycolic acid) (PLGA) microspheres have garnered significant attention as biocompatible and biodegradable carriers for sustained drug delivery. However, the production of PLGA microspheres typically involves organic solvents, such as ethyl acetate and benzyl alcohol. Residual solvents are undesirable given their potential toxicity and adverse effects on product stability. Effective solvent removal is critical for ensuring the safety and functionality of microspheres. METHOD: In this study, 12 formulations were designed by altering the conditions of solvent extraction, washing, and solvent evaporation steps to reduce residual solvents and determine critical parameters in process. Microspheres were evaluated based on residual solvent content, drug loading, size, morphology, moisture content, injectability, and release kinetics. RESULT: In five formulations (F06-F10), at least the residual amount of one organic solvent was significantly reduced. Prolonging the microspheres' residence time in ethanolic solution during the second extraction phase (F11) resulted in notable organic solvent reductions (ethyl acetate 93% and benzyl alcohol 60% compared to formulation F01). Further, these microparticles were spherical with a geometric diameter of 75.8 μm, a drug loading percentage of 33.7%, and a reasonable release profile, representing significant achievements. CONCLUSION: This study highlighted the importance of some modifications in preparing PLGA microspheres that have not been reported previously. These modifications greatly affected the residual solvent amount as well as other physicochemical properties of microspheres including size, morphology, and release profile. Overall, some practical methods could be used for feasible industrial production of PLGA microspheres.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".