Quality by Design as a Tool in the Optimisation of Nanoparticle Preparation—A Case Study of PLGA Nanoparticles
Abstract
:1. Introduction
- Definition of quality attributes and selection of outcome parameters;
- Identification and evaluation of process variables that might have an impact on PLGA-NP properties;
- Identification of significant process parameters by screening of the most promising variables;
- First approach of optimisation using the results of the screening;
- Further optimisation and controlled adjustments of the products quality by varying the most influencing process parameters within a response surface design;
- Prediction and verification of the optimal process parameters.
2. Materials and Methods
2.1. Nanoparticle Preparation
2.2. Purification
2.3. Measurement of Size, Size Distribution and Zeta-Potential
2.4. Protein Quantification
2.5. Scanning Electron Microscopy (SEM)
2.6. Design of Experiements
3. Results and Discussion
3.1. Definition of Quality Attributes and Selection of Outcome Parameters
3.2. Identification and Evaluation of Process Parameters
3.3. Identification of Significant Process Parameters by Screening Design
3.3.1. Definitive Screening Design
3.3.2. Effect on Size and Size Distribution
3.3.3. Effect on Drug Loading and Loading Efficiency
3.4. First Approach of Optimisation with Screening Results
3.5. Further Optimisation by Response Surface Design
3.5.1. Central Composite Response Surface Design
3.5.2. Interactions
3.5.3. Predictability
3.5.4. Optimisation
3.6. Verification of the Optimal Process Parameters
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase | Composition | Quantity |
---|---|---|
Internal aqueous phase (W1) | Ovalbumin in phosphate buffer, pH 7.4 | 1600 µL |
Organic phase (O) | PLGA in ethyl acetate | 4 mL |
External aqueous phase (W2) | PVA in water | 12 g |
Stabiliser solution | PVA (1% (w/w)) in water | 40 g |
Parameter | Low | Medium | High |
---|---|---|---|
c (OVA) [%] | 2 | 3 | 4 |
c (PLGA) [%] | 3 | 6 | 9 |
Stirring speed W/O | 9500 rpm | - | 20,500 rpm |
Stirring time W/O [s] | 30 | 60 | 90 |
c (PVA) [%] | 2 | 3.5 | 5 |
Stirring speed W/O/W | 8000 rpm | - | 13,500 rpm |
Stirring time W/O/W [s] | 60 | 105 | 150 |
Outcome | z-Average | PDI | LE | LC | |
---|---|---|---|---|---|
Parameter | |||||
OVA conc. (%) | not sign. | not sign. | significant (p = 0.007) | significant (p = 0.000) | |
PLGA conc. (%) | significant (p = 0.0257) | not sign. | significant (p = 0.000) | significant (p = 0.000) | |
w/o stirring speed (rpm) | not sign. | not sign. | significant (p = 0.000) | significant (p = 0.002) | |
w/o stirring time (s) | not sign. | not sign. | not sign. | not sign. | |
PVA conc. (%) | significant (p = 0.00936) | not sign. | not sign. | not sign. | |
w/o/w stirring speed (rpm) | significant (p = 0.00893) | significant (p = 0.004) | not sign. | not sign. | |
w/o/w stirring time (s) | not sign. | not sign. | significant (p = 0.034) | not sign. | |
p-Value of model | 0.010 | 0.041 | 0.000 | 0.000 | |
R² (%) | 70.56 | 70.22 | 93.56 | 89.48 |
Response | Fit | Result (n = 3) ± SD |
---|---|---|
z-Average (d.nm) | 702 | 682.21 ± 11.94 |
PDI | 0.1989 | 0.30 ± 0.03 |
LE (%) | 19.88 | 24.25 ± 2.55 |
LC (%) | 3.3299 | 4.07 ± 0.41 |
Parameter | Low Axial | Low | Medium | High | High Axial |
---|---|---|---|---|---|
c (PLGA) [%] | 6.17 | 7 | 9 | 11 | 11.83 |
c (PVA) [%] | 2.17 | 3 | 5 | 7 | 7.83 |
Run | PLGA Conc. (%) | PVA Conc. (%) | z-Average (d.nm) | PDI | LE (%) | LC (%) |
---|---|---|---|---|---|---|
1 | 5 | 9 | 713.97 | 0.232 | 20.32 | 3.49 |
2 | 5 | 11.83 | 1204.67 | 0.353 | 27.17 | 3.54 |
3 | 3 | 11 | 1807.00 | 0.293 | 23.08 | 3.25 |
4 | 5 | 9 | 650.20 | 0.286 | 22.48 | 3.84 |
5 | 5 | 9 | 707.30 | 0.295 | 18.58 | 3.20 |
6 | 7 | 7 | 446.63 | 0.192 | 19.64 | 4.30 |
7 | 7.83 | 9 | 503.43 | 0.180 | 22.71 | 3.88 |
8 | 2.17 | 9 | 1810.33 | 0.311 | 19.53 | 3.36 |
9 | 5 | 9 | 606.73 | 0.255 | 23.21 | 3.96 |
10 | 7 | 11 | 565.50 | 0.257 | 25.20 | 3.54 |
11 | 5 | 6.17 | 506.23 | 0.242 | 18.20 | 4.51 |
12 | 3 | 7 | 873.43 | 0.351 | 18.90 | 4.14 |
13 | 5 | 9 | 695.43 | 0.301 | 22.61 | 3.86 |
Response | Fit | 95% CI | 95% PI |
---|---|---|---|
z-Average (d.nm) | 687.94 | 563.1; 812.7 | 496.0; 879.9 |
PDI | 0.291 | 0.236; 0.347 | 0.201; 0.382 |
LE (%) | 25.90 | 24.03; 27.77 | 22.24; 29.56 |
LC | 3.57 | 3.22; 3.93 | 2.96; 4.19 |
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Struzek, A.-M.; Scherließ, R. Quality by Design as a Tool in the Optimisation of Nanoparticle Preparation—A Case Study of PLGA Nanoparticles. Pharmaceutics 2023, 15, 617. https://doi.org/10.3390/pharmaceutics15020617
Struzek A-M, Scherließ R. Quality by Design as a Tool in the Optimisation of Nanoparticle Preparation—A Case Study of PLGA Nanoparticles. Pharmaceutics. 2023; 15(2):617. https://doi.org/10.3390/pharmaceutics15020617
Chicago/Turabian StyleStruzek, Anna-Maria, and Regina Scherließ. 2023. "Quality by Design as a Tool in the Optimisation of Nanoparticle Preparation—A Case Study of PLGA Nanoparticles" Pharmaceutics 15, no. 2: 617. https://doi.org/10.3390/pharmaceutics15020617