Chitosan-Coated PLGA Nanoparticles Loaded with Peganum harmala Alkaloids with Promising Antibacterial and Wound Healing Activities
Abstract
:1. Introduction
2. Results and Discussion
2.1. Formulation Optimization by Three-Factor, Three-Level Box–Behnken Response Surface Design (33 BBD)
2.1.1. Influence of the Independent Factors on Particle Size (PS)
2.1.2. Influence of the Independent Factors on PDI
2.1.3. Influence of the Independent Factors on ZP
2.1.4. Influence of the Independent Factors on Entrapment Efficiency (EE %)
2.2. Characterization of the Optimized H/CS/PLGA NPs
2.3. ATR-FTIR Analysis of the Optimal H/CS/PLGA NPs Formulation
2.4. In Vitro Release Study for the Optimal H/CS/PLGA NPs Formulation
2.5. Antimicrobial Assay for the Optimal H/CS/PLGA NPs Formulation
2.6. Cytotoxicity Assay
2.7. In Vitro Scratch Wound Healing Assay
3. Conclusions
4. Materials and Methods
4.1. Materials
4.2. Plant Material
4.3. Methods
4.3.1. Extraction and Isolation of Major P. harmala Alkaloids
4.3.2. Experimental Design
4.3.3. Preparation of H/CS/PLGA NPs
4.3.4. Average Particle Size, Polydispersity Index (PDI), and Zeta Potential Measurements
4.3.5. Determination of Entrapment Efficiency (EE%)
4.3.6. Formulation Optimization
4.3.7. Characterization of the Optimal H/CS/PLGA NPs Formulation
4.3.8. In Vitro Release Study of HARF from the Optimal H/CS/PLGA NPs Formulation
4.3.9. Antimicrobial Assay for the Optimal H/CS/PLGA NPs Formulation
Inoculum Preparation (Colony Suspension Method)
Broth Macrodilution Method
4.3.10. Cytotoxicity Assay
Cell Culture
Sulforhodamine B Colorimetric Assay
4.3.11. In Vitro Scratch Wound Healing Assay
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Response | Model | R2 | Adjusted R2 | Predicted R2 | Constraints | Predicted | Observed | 95% Prediction Interval | |
---|---|---|---|---|---|---|---|---|---|
Y1 | Particle Size (nm) | Quadratic model | 0.96 | 0.90 | 0.61 | In range | 204.84 | 202.27 ± 2.44 | 198.56–211.13 |
Y2 | PDI | Quadratic model | 0.97 | 0.92 | 0.53 | Minimize | 0.10 | 0.23 ± 0.01 | 0.07–0.13 |
Y3 | Zeta Potential (mV) | Linear model | 0.91 | 0.89 | 0.87 | Maximize | 8.79 | 9.22 ± 0.94 | 7.12–10.45 |
Y4 | Entrapment Efficiency (%) | 2FI model | 0.95 | 0.92 | 0.84 | Maximize (lower limit: 80%) | 89.50 | 86.77 ± 4.18 | 79.99–99.01 |
Factors | Levels of Factors | Levels for the Optimized Formulation | |||
---|---|---|---|---|---|
Low (−1) | Medium (0) | High (+1) | |||
A | HARF:PLGA weight ratio | 0.1:1 | 0.3:1 | 0.5:1 | 0.10 |
B | CS:PLGA weight ratio | 0 | 0.4:1 | 0.8:1 | 0.60 |
C | Sonication time (min) | 4 | 8 | 12 | 12 |
Bacterial Strain | Minimum Inhibitory Concentration (MIC in mg/mL) | ||
---|---|---|---|
Harmala Alkaloid Rich Fraction (HARF) | CS/PLGA NPs | H/CS/PLGA NPs | |
Staphylococcus aureus | 0.5 | 0.18 | 0.13 |
Escherichia coli | 0.5 | 0.18 | 0.06 |
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Azzazy, H.M.E.-S.; Fahmy, S.A.; Mahdy, N.K.; Meselhy, M.R.; Bakowsky, U. Chitosan-Coated PLGA Nanoparticles Loaded with Peganum harmala Alkaloids with Promising Antibacterial and Wound Healing Activities. Nanomaterials 2021, 11, 2438. https://doi.org/10.3390/nano11092438
Azzazy HME-S, Fahmy SA, Mahdy NK, Meselhy MR, Bakowsky U. Chitosan-Coated PLGA Nanoparticles Loaded with Peganum harmala Alkaloids with Promising Antibacterial and Wound Healing Activities. Nanomaterials. 2021; 11(9):2438. https://doi.org/10.3390/nano11092438
Chicago/Turabian StyleAzzazy, Hassan Mohamed El-Said, Sherif Ashraf Fahmy, Noha Khalil Mahdy, Meselhy Ragab Meselhy, and Udo Bakowsky. 2021. "Chitosan-Coated PLGA Nanoparticles Loaded with Peganum harmala Alkaloids with Promising Antibacterial and Wound Healing Activities" Nanomaterials 11, no. 9: 2438. https://doi.org/10.3390/nano11092438
APA StyleAzzazy, H. M. E. -S., Fahmy, S. A., Mahdy, N. K., Meselhy, M. R., & Bakowsky, U. (2021). Chitosan-Coated PLGA Nanoparticles Loaded with Peganum harmala Alkaloids with Promising Antibacterial and Wound Healing Activities. Nanomaterials, 11(9), 2438. https://doi.org/10.3390/nano11092438