Understanding Microencapsulation and Performance of Composition-Equivalent PLGA Microspheres for 1-Month Controlled Release of Leuprolide
Bibliographic record
Abstract
Poly(lactic-co-glycolic acid) (PLGA) microspheres are the most commonly used and commercially successful long-acting release depots (LARs) for delivery of peptide drugs. However, the approval of generic versions of these products are slow. Due to the complexity of the manufacturing process, concerns about bioequivalence of generic complex drug products have been raised by the U.S. Food and Drug Administration (FDA). In order to address these challenges, this work aims to help fill in the knowledge gap between: (a) raw materials and manufacturing parameters, (b) critical quality attributes, and (c) release performance and mechanisms for PLGA LARs encapsulating the model drug, leuprolide. The 1-month Lupron Depot® (LD) encapsulating water-soluble leuprolide in PLGA microspheres is the first injectable microsphere product launched in the US market. It is also a benchmark product upon which modern LAR products are often compared. Here, we describe the reverse engineering of the LD composition and important product attributes. Analyzed contents of the formulation and the determined PLGA characteristics matched well with the official values stated in the package insert and those found in the literature, respectively. The gelatin was identified as type B consistent with ~ 300 bloom. The 11-μm volume-median microspheres in the LD displayed very low content of residual moisture (< 0.5%) and methylene chloride (< 1 ppm). Composition-equivalent PLGA microsphere formulations to the LD were prepared as a function of raw material and manufacturing variables. The following variables were adjusted at constant theoretical loading of 16.4% leuprolide: polymer supplier/ polymerization type, gelatin supplier/ bloom number, polymer concentration, 1st homogenization speed and time, volume of primary water phase, 2nd homogenization time, volume of secondary water phase and stirring rate. The encapsulation efficiency (EE) of gelatin (101 ± 1%) was observed to be higher than the EE of leuprolide (42%- 63%). Desirable conditions of polymer concentration, homogenization time and volume of secondary water phase were critical to achieving high EE of leuprolide. The prepared formulations displayed a larger median particle size, a more porous surface, and higher residual solvents compared to the LD. The microspheres prepared with the identified LD raw materials possessed the same glass transition temperature as the LD. The leuprolide release kinetics of the formulations were also highly similar to the LD exhibiting zero-order kinetics after a ~20% initial burst release and displayed the same release versus mass loss kinetics. The correlations between the process variables and emulsion size were established. The dimensionless Sauter mean diameter of primary emulsion droplet was proportional to the product of key dimensionless groups raised to appropriate power indices. A new dimensionless group (total surface energy/total energy input to fluid) was used to rationalize insertion of a proportionate time dependence in the scaling of the Sauter mean diameter. The increased viscosity of primary emulsion inhibited drug loss during microencapsulation while increased droplet size enhanced drug leakage to outer water phase. The Sauter mean diameter of secondary emulsion was also found proportional to the product of three dimensionless groups raised to appropriate power indices. In summary, the rigorous approach of reverse engineering, characterization of composition-equivalent formulations and understanding of emulsion formation in the microencapsulation process described in this thesis could be useful for further development of generic or new peptide loaded PLGA microspheres, and for guiding decisions on the influence of process variables on product physicochemical attributes and release performance.
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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 | 0.000 | 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) | 0.000 | 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".