Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Methods
2.2.1. Experimental Design and Optimization Process
2.2.2. Preparation of Carvedilol-Loaded BSA-Based Nanoparticles
2.2.3. In Vitro Evaluation of Carvedilol-Loaded BSA-Based Nanoparticles
2.2.4. In Vitro Evaluation of Optimized Carvedilol-Loaded BSA-Based Nanoparticle Formulation
2.2.5. In Vivo Evaluation of Optimized Carvedilol-Loaded BSA-Based Nanoparticle Formulation
3. Results and Discussion
3.1. Preparation of Carvedilol-Loaded BSA-Based Nanoparticles
3.2. Experimental Design and Statistical Analysis
3.2.1. Effect of Independent Factors (A and B) on the Particle Size of Carvedilol-Loaded Nanoparticles (Y1 Response)
3.2.2. Effect of Independent Factors (A and B) on Entrapment Efficiency of Carvedilol-Loaded Nanoparticles (Y2 Response)
3.2.3. Effect of Independent Factors (A and B) on T50 of Carvedilol-Loaded Nanoparticles (Y3 Response)
3.2.4. Optimization Process
3.3. In Vitro Evaluation of Optimized Carvedilol-Loaded Nanoparticle Formulation
3.3.1. TEM Study
3.3.2. DSC Study
3.4. In Vivo Evaluation of Optimized Carvedilol-Loaded Nanoparticle Formulation
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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Independent Factors | Unit | Symbol | Actual Levels (Coded) | ||
---|---|---|---|---|---|
Low (−1) | Medium (0) | High (+1) | |||
BSA concentration | % | A | 0.5 | 1 | 1.5 |
Carvedilol percentage in BSA nanoparticles | % | B | 6 | 8.5 | 11 |
Dependent responses | Unit | Symbol | Goal | ||
Particle size | nm | Y1 | Minimize | ||
Entrapment efficiency | % | Y2 | Maximize | ||
T50 | d | Y3 | Target to 3.5 d |
Formulation | Actual Levels | Responses | |||
---|---|---|---|---|---|
A (%) | B (%) | Y1 (nm) | Y2 (%) | Y3 (d) | |
F1 | 0.5 | 6 | 123.33 ± 7.83 | 93.14 ± 0.97 | 4.46 ± 0.62 |
F2 | 0.5 | 8.5 | 147.84 ± 22.84 | 88.32 ± 1.03 | 3.05 ± 0.38 |
F3 | 1 | 8.5 | 177.56 ± 13.21 | 94.41 ± 0.77 | 5.47 ± 0.71 |
F4 | 1.5 | 8.5 | 196.66 ± 16.11 | 97.57 ± 0.91 | 7.29 ± 0.79 |
F5 | 1 | 6 | 148.60 ± 22.16 | 96.19 ± 1.31 | 6.23 ± 0.78 |
F6 | 1.5 | 6 | 167.83 ± 7.08 | 98.89 ± 1.02 | 8.51 ± 0.79 |
F7 | 1.5 | 11 | 243.96 ± 13.26 | 94.13 ± 0.98 | 2.25 ± 0.55 |
F8 | 1 | 11 | 210.36 ± 13.39 | 90.34 ± 0.42 | 1.41 ± 0.33 |
F9 | 0.5 | 11 | 186.30 ± 18.41 | 78.28 ± 1.25 | 0.14 ± 0.05 |
Source | Y1 Response | Y2 Response | Y3 Response | ||||||
---|---|---|---|---|---|---|---|---|---|
F-Value | p-Value | Significance | F-Value | p-Value | Significance | F-Value | p-Value | Significance | |
Model | 164.01 | 0.0007 | Significant | 29.31 | 0.0095 | Significant | 66.20 | 0.0029 | Significant |
A | 292.75 | 0.0004 | Significant | 76.26 | 0.0032 | Significant | 95.27 | 0.0023 | Significant |
B | 518.14 | 0.0002 | Significant | 51.98 | 0.0055 | Significant | 208.89 | 0.0007 | Significant |
AB | 3.34 | 0.1652 | Non-significant | 12.26 | 0.0394 | Significant | 4.97 | 0.1120 | Non-significant |
A2 | 0.217 | 0.6731 | Non-significant | 3.56 | 0.1555 | Non-significant | 0.0794 | 0.7964 | Non-significant |
B2 | 5.63 | 0.0983 | Non-significant | 2.48 | 0.2136 | Non-significant | 21.82 | 0.0185 | Significant |
Formulation | Zero Order Model | First Order Model | Higuchi Model | Hixson–Crowell Model | Korsmeyer–Peppas Model | ||
---|---|---|---|---|---|---|---|
R2 | R2 | R2 | R2 | R2 | n | T50 | |
F1 | 0.4897 | 0.6793 | 0.9095 | 0.6239 | 0.9854 | 0.327 | 4.46 |
F2 | 0.4309 | 0.6665 | 0.8847 | 0.5990 | 0.9789 | 0.313 | 3.05 |
F3 | 0.6357 | 0.7785 | 0.9559 | 0.7370 | 0.9880 | 0.374 | 5.47 |
F4 | 0.7322 | 0.8309 | 0.9781 | 0.8023 | 0.9910 | 0.412 | 7.29 |
F5 | 0.7837 | 0.8824 | 0.9909 | 0.8547 | 0.9951 | 0.447 | 6.23 |
F6 | 0.8429 | 0.9075 | 0.9882 | 0.8887 | 0.9882 | 0.503 | 8.51 |
F7 | −0.0716 | 0.2968 | 0.6720 | 0.1896 | 0.9929 | 0.226 | 2.25 |
F8 | −0.0883 | 0.3443 | 0.6621 | 0.2227 | 0.9915 | 0.224 | 1.41 |
F9 | −1.1107 | 0.3320 | 0.0792 | −0.2575 | 0.9962 | 0.140 | 0.14 |
Optimized | 0.5762 | 0.7423 | 0.9436 | 0.6948 | 0.9959 | 0.348 | 4.77 |
Parameters | Pure Carvedilol Suspension | Optimized Carvedilol-BSA Nanoparticles |
---|---|---|
t1/2 (h) | 4.34 ± 0.77 | 23.56 ± 1.92 |
AUC0–72 (ng/mL.h) | 3115.09 ± 784.53 | 10,154.75 ± 2652.49 |
AUC0–∞ (ng/mL.h) | 3671.54 ± 1041.31 | 11,804.37 ± 3425.80 |
MRT (h) | 4.98 ± 0.77 | 32.21 ± 3.03 |
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Attia, M.S.; Radwan, M.F.; Ibrahim, T.S.; Ibrahim, T.M. Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance. Pharmaceutics 2023, 15, 1425. https://doi.org/10.3390/pharmaceutics15051425
Attia MS, Radwan MF, Ibrahim TS, Ibrahim TM. Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance. Pharmaceutics. 2023; 15(5):1425. https://doi.org/10.3390/pharmaceutics15051425
Chicago/Turabian StyleAttia, Mohamed S., Mohamed F. Radwan, Tarek S. Ibrahim, and Tarek M. Ibrahim. 2023. "Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance" Pharmaceutics 15, no. 5: 1425. https://doi.org/10.3390/pharmaceutics15051425
APA StyleAttia, M. S., Radwan, M. F., Ibrahim, T. S., & Ibrahim, T. M. (2023). Development of Carvedilol-Loaded Albumin-Based Nanoparticles with Factorial Design to Optimize In Vitro and In Vivo Performance. Pharmaceutics, 15(5), 1425. https://doi.org/10.3390/pharmaceutics15051425