Microfluidics-based PLGA nanoparticles of ratiometric multidrug: From encapsulation and release rates to cytotoxicity in human lens epithelial cells
Bibliographic record
Abstract
Multidrug nanomedicine is an effective therapeutic approach for the treatment of chronic diseases and cancers. However, co-encapsulation and release of drug combination at a fixed ratio by nanoparticles, particularly for long acting ocular formulations, remains challenging. Herein, poly (lactic-co-glycolic acid) nanoparticles ratiometrically co-encapsulating hydrophilic dual drugs, mitomycin C and doxorubicin, was obtained (D/M PLGANPs) by combining microfluidics and the Design of Experiments approaches. The formulation variable of lactide-to-glycolide ratios (L/G 50:50, 75:15 and 85:15) was used to achieve fast, medium and slow drug release rates of D/M PLGANPs. The dissolution of D/M PLGANPs in simulated intraocular fluid exhibited sustained release of dual drugs at the fixed ratio over 7 days, and analysis using the Korsmeyer-Peppas model showed mechanism of drug release to be governed by diffusion. More importantly, in human lens epithelial cells, the drug release rate was negatively correlated with drug potency. The slower drug release from D/M PLGANPs led to lower efficacy of drug combination against pathogenesis of cellular migration and proliferation, the key pathogenic processes of capsular opacification after cataract surgery. Compared to fast (L/G 50:50) and medium (L/G 75:15) drug release rate of D/M PLGANPs, the slow release formulation (L/G 85:15) exhibited the least cellular uptake of the dual drugs and the ratio of drug combination was not maintained intracellularly. The present study implicates the potential of using microfluidics for synthesizing polymeric nanoparticles of ratiometric drug combination and highlights the drug release rate as the critical determinant of efficacy for the long-acting nanomedicine design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".