Computational Designed and Optimized Liposomal Curcumin-Embedded Bifunctional Cross-Linked Hydrogels for Wound Healing
Abstract
:1. Introduction
2. Results and Discussion
2.1. Preparation and Optimization of Curcumin-Loaded Liposomes by DoE
2.2. Differential Scanning Calorimetry
2.3. Antioxidant Activity
2.4. Cytotoxicity Assessment
2.5. Preparation and Optimization of Hydrogel by DoE
2.6. Addition of the Synthesized PNVP-ITA to the Hydrogel
2.7. Drug Loading and In Vitro Drug Release Study
2.8. In Vivo Skin Recovery Study
3. Conclusions
4. Materials and Methods
4.1. Materials
4.2. Preparation and Optimization of Curcumin-Loaded Liposomes by DoE
4.3. Characterizations of Curcumin-Loaded Liposomes
4.3.1. Particle Size, PDI, and ZP
4.3.2. Drug Determination
4.4. Differential Scanning Calorimetry
4.5. Antioxidant Activity
4.6. Cytotoxicity Assessment
4.7. Synthesis of Poly(N-Vinylpyrrolidone-Co-Itaconic Acid)
4.8. Preparation and Optimization of Hydrogel by DoE
4.9. Characterizations of HA/PVA Hydrogels
4.9.1. Water Absorption
4.9.2. Erosion
4.9.3. Mechanical Properties
4.10. Preparation of Curcumin Liposome-Embedded Hydrogels
4.11. Morphology
4.12. In Vitro Drug Release Study
4.13. In Vivo Skin Recovery Study
4.14. Data Analysis
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Formulation | Input Factors (X) | Output Responses (Y) | |||||
---|---|---|---|---|---|---|---|
X1: Tween® 20 (%) | X2: Oleic Acid (%) | X3: CUR (%) | Y1: Size (nm) | Y2: PDI | Y3: ZP (mV) | Y4: CUR Content (µg/mg) | |
L1 | 1.5 | 0.75 | 5.5 | 86.61 | 0.2 | −35.7 | 6.6 |
L2 | 0 | 0.75 | 10 | 114.1 | 0.39 | −17.9 | 1.9 |
L3 | 1.5 | 0.75 | 5.5 | 87.2 | 0.18 | −37.3 | 11.2 |
L4 | 3 | 1.5 | 5.5 | 80.2 | 0.14 | −34.7 | 10.8 |
L5 | 0 | 0 | 5.5 | 78.1 | 0.15 | 7.3 | 10.4 |
L6 | 0 | 0.75 | 1 | 96.9 | 0.2 | −25.3 | 2.1 |
L7 | 1.5 | 1.5 | 1 | 99.5 | 0.16 | −45 | 3.5 |
L8 | 1.5 | 1.5 | 10 | 110 | 0.17 | −39.7 | 7.2 |
L9 | 3 | 0 | 5.5 | 58.2 | 0.01 | −12.4 | 17.6 |
L10 | 3 | 0.75 | 1 | 63 | 0.25 | −30.5 | 3.4 |
L11 | 1.5 | 0.75 | 5.5 | 75.1 | 0.2 | −35.7 | 14 |
L12 | 1.5 | 0.75 | 5.5 | 88.6 | 0.2 | −38.6 | 11.7 |
L13 | 1.5 | 0.75 | 5.5 | 76.7 | 0.22 | −34 | 10.1 |
L14 | 3 | 0.75 | 10 | 61.2 | 0.22 | −32.4 | 17 |
L15 | 1.5 | 0 | 1 | 64.4 | 0.17 | −9.2 | 5.9 |
L16 | 1.5 | 0 | 10 | 73.2 | 0.21 | −7.57 | 21.8 |
L17 | 0 | 1.5 | 5.5 | 112.6 | 0.21 | −40.3 | 2 |
Responses | Model | p-Value | Lack of Fit | R2 |
---|---|---|---|---|
Particle size | Linear | <0.0001 | 0.5558 | 0.8750 |
PDI | Quadratic | 0.0093 | 0.0106 | 0.7161 |
Zeta potential | Quadratic | <0.0001 | 0.6991 | 0.9885 |
CUR content | Linear | 0.0012 | 0.2322 | 0.6240 |
Multiple regression equation model (coded equation) | ||||
Y1 = 83.86 − 17.39X1 + 16.05X2 + 4.34X3 Y2 = 0.2032 − 0.0413X1 + 0.0175X2 + 0.0262X3 − 0.0550X1X3 − 0.0796X22 + 0.0579X32 Y3 = −36.26 − 4.23X1 − 17.23X2 + 1.55X3 + 6.32X1X2 − 2.32X1X3 + 0.9175X2X3 + 7.54X12 − 8.70X22 + 2.20X32 Y4 = 10.72 + 4.05X1 − 4.03X2 + 4.12X3 |
Variables | Goal | Solution | Desirability | Confirmation | p-Value |
---|---|---|---|---|---|
Tween® 20 | in range | 2.718 | 1.000 | ||
Oleic acid | in range | 0.041 | |||
CUR | in range | 8.116 | |||
Size | minimize | 55.80 ± 6.35 | 64.02 ± 3.37 | 0.1187 | |
PDI | none | 0.082 ± 0.003 | 0.107 ± 0.026 | 0.2262 | |
Zeta potential | in range | −15.03 ± 1.56 | −17.26 ± 3.56 | 0.1979 | |
CUR content | maximize | 22.09 ± 2.22 | 19.92 ± 0.54 | 0.1758 |
Formulation | Input Factors (A) | Output Responses (R) | |||
---|---|---|---|---|---|
A1: HA (%) | A2: PVA (%) | R1: Swelling (%) | R2: Young’s Modulus (Pa/%) | R3: Erosion (%) | |
H1 | 2.5 | 12.5 | 168.45 | 97.43 | 38.02 |
H2 | 2.5 | 12.5 | 191.72 | 100.28 | 37.16 |
H3 | 5 | 10 | 208.37 | 146.03 | 38.55 |
H4 | 0 | 15 | 123.93 | 100.06 | 20.03 |
H5 | 0 | 15 | 120.39 | 98.02 | 16.31 |
H6 | 5 | 10 | 172.02 | 143.87 | 37.16 |
H7 | 1.25 | 13.75 | 213.11 | 103.63 | 35.64 |
H8 | 3.75 | 11.25 | 161.23 | 106.19 | 37.71 |
H9 | 5 | 10 | 187.00 | 142.91 | 37.90 |
H10 | 0 | 15 | 121.55 | 99.35 | 18.51 |
Responses | Model | p-Value | Lack of Fit | R2 |
---|---|---|---|---|
%Swelling | Cubic | 0.0241 | 0.8901 | 0.8424 |
Young’s modulus | Cubic | <0.0001 | 0.8076 | 0.9957 |
%Erosion | Cubic | 0.0083 | 0.2588 | 0.9788 |
Multiple regression equation model (coded equation) | ||||
%Swelling = 189.03A1 + 121.86A2 + 95.18A1A2 − 295.83A1A2 (A1 − A2) Young’s modulus = 144.29A1 + 99.16A2 − 90.84A1A2 − 106.25A1A2(A1 − A2) %Erosion = 37.95A1 + 18.36A2 + 40.52A1A2 − 41.18A1A2(A1 − A2) |
Variables | Goal | Solution | Desirability | Confirmation | p-Value |
---|---|---|---|---|---|
HA | in range | 5 | 0.41 | ||
PVA | in range | 10 | |||
Swelling | maximize | 177.9 ± 12.6 | 176.2 ± 12.0 | 0.8731 | |
Young’s modulus | maximize | 133.6 ± 14 | 131.0 ± 18.0 | 0.8297 | |
Erosion | minimize | 38.3 ± 1.4 | 38.3 ± 1.8 | 0.9850 |
Kinetic Model | Parameter | CUR Suspensions | CUR-Ls | CUR-L @HA/PVA/PNVP-ITA Hydrogels |
---|---|---|---|---|
Zero-order | R2 | 0.8595 | 0.7688 | 0.8682 |
MSC | 1.6768 | 1.1787 | 1.2668 | |
Equation | F = 0.02t | F = 0.053t | F = 0.099t | |
First-order | R2 | 0.8610 | 0.7908 | 0.9139 |
MSC | 1.6876 | 1.2789 | 0.9367 | |
Equation | ) | ) | ||
Higuchi | R2 | 0.8762 | 0.9280 | 0.9186 |
MSC | 1.8035 | 2.1456 | 0.1710 | |
Equation | ||||
Krosmeyer-Peppas | R2 | 0.8219 | 0.9649 | 0.9850 |
MSC | 1.8019 | 1.9199 | 2.3622 | |
Equation | F = 0.162tn−1 | F = 0.957tn−1 | F = 12.191tn−1 | |
n | 0.668 | 0.538 | 0.232 |
Day 0 | Day 3 | Day 5 | Day 7 | Day 10 | |
---|---|---|---|---|---|
Negative control | 0.0 ± 0.0 | 30.8 ± 4.2 | 46.2 ± 4.1 | 53.8 ± 5.6 | 65.1 ± 5.4 |
Commercial Patch | 0.0 ± 0.0 | 27.3 ± 8.0 | 45.5 ± 3.9 | 81.8 ± 6.5 * | 84.3 ± 6.3 * |
CUR-L@HA/PVA/PNVP-ITA | 0.0 ± 0.0 | 25.0 ± 5.0 | 66.0 ± 5.6 *,# | 88.3 ± 2.1 * | 95.2 ± 2.1 *,# |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Pornpitchanarong, C.; Aye, K.C.; Arunprasert, K.; Opanasopit, P.; Patrojanasophon, P. Computational Designed and Optimized Liposomal Curcumin-Embedded Bifunctional Cross-Linked Hydrogels for Wound Healing. Gels 2024, 10, 598. https://doi.org/10.3390/gels10090598
Pornpitchanarong C, Aye KC, Arunprasert K, Opanasopit P, Patrojanasophon P. Computational Designed and Optimized Liposomal Curcumin-Embedded Bifunctional Cross-Linked Hydrogels for Wound Healing. Gels. 2024; 10(9):598. https://doi.org/10.3390/gels10090598
Chicago/Turabian StylePornpitchanarong, Chaiyakarn, Khin Cho Aye, Kwanputtha Arunprasert, Praneet Opanasopit, and Prasopchai Patrojanasophon. 2024. "Computational Designed and Optimized Liposomal Curcumin-Embedded Bifunctional Cross-Linked Hydrogels for Wound Healing" Gels 10, no. 9: 598. https://doi.org/10.3390/gels10090598
APA StylePornpitchanarong, C., Aye, K. C., Arunprasert, K., Opanasopit, P., & Patrojanasophon, P. (2024). Computational Designed and Optimized Liposomal Curcumin-Embedded Bifunctional Cross-Linked Hydrogels for Wound Healing. Gels, 10(9), 598. https://doi.org/10.3390/gels10090598