Honokiol-Loaded Nanoemulsion for Glioblastoma Treatment: Statistical Optimization, Physicochemical Characterization, and an In Vitro Toxicity Assay
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Preparation of Honokiol-Loaded Nanoemulsion
2.3. Optimization of the Honokiol-Loaded Nanoemulsion Preparation Process
2.4. Characterization of the Honokiol-Loaded Nanoemulsion
2.4.1. Lipid Droplet Size Determination
2.4.2. Lipid Droplet Zeta Potential Evaluation
2.4.3. pH Measurement
2.4.4. OSM Measurement
2.4.5. Determination of Honokiol Concentration in Nanoemulsion Using Spectrophotometry UV-VIS
2.4.6. Determination of Honokiol Concentration in Nanoemulsion Using HPLC-FLD Method
2.4.7. Loading Efficiency of Honokiol in Nanoemulsion
2.4.8. Short- and Long-Term Stability Studies
High-Temperature Effect
Oxidative Degradation
Photostability
Long-Term Stability
2.5. In Vitro Cytotoxicity Studies
3. Results
3.1. Short-Term and Long-Term Stability Studies
3.2. In Vitro Cytotoxicity Studies
4. Discussion
4.1. MDD, PDI, and ZP of Optimal Formulation
4.2. pH and OSM of Optimal Formulations
4.3. Stability Studies
4.4. MTT Assay
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Formulation Code | X1 Shaking Speed (rpm) | X2 Shaking Time (min) | X3 HON Concentration (mg/mL) |
---|---|---|---|
F1 | 200 | 15 | 2 |
F2 | 400 | 15 | 2 |
F3 | 200 | 195 | 2 |
F4 | 400 | 195 | 2 |
F5 | 200 | 105 | 1 |
F6 | 400 | 105 | 1 |
F7 | 200 | 105 | 3 |
F8 | 400 | 105 | 3 |
F9 | 300 | 15 | 1 |
F10 | 300 | 195 | 1 |
F11 | 300 | 15 | 3 |
F12 | 300 | 195 | 3 |
F13 | 300 | 105 | 2 |
F14 | 300 | 105 | 2 |
F15 | 300 | 105 | 2 |
Formulation Code | LE% (SD) [%] | MDD (SD) [nm] | PDI (SD) | ZP (SD) [mV] |
---|---|---|---|---|
F1 | 88.52 (0.05) | 215.8 (1.5) | 0.07 ( 0.04) | −31.2 (0.6) |
F2 | 97.85 (2.40) | 206.3 (1.5) | 0.07 (0.02) | −28.0 (0.2) |
F3 | 93.78 (5.33) | 213.6 (0.8) | 0.07 (0.02) | −27.7 (0.8) |
F4 | 98.75 (0.52) | 205.4 (2.0) | 0.09 (0.02) | −27.1 (0.3) |
F5 | 99.77 (3.90) | 213.4 (2.1) | 0.09 (0.02) | −27.1 (0.1) |
F6 | 98.71 (0.88) | 206.7 (1.1) | 0.08 (0.00) | −30.8 (0.5) |
F7 | 85.41 (1.09) | 215.3 (1.0) | 0.08 (0.01) | −27.9 (0.3) |
F8 | 97.74 (2.91) | 204.9 (1.1) | 0.10 (0.02) | −28.1 (0.3) |
F9 | 94.02 (8.05) | 213.6 (2.1) | 0.04 (0.04) | −26.7 (0.3) |
F10 | 94.81 (7.17) | 214.5 (0.8) | 0.08 (0.02) | −26.9 (0.1) |
F11 | 87.78 (2.23) | 214.5 (1.6) | 0.09 (0.03) | −27.8 (0.2) |
F12 | 89.35 (2.58) | 215.1 (1.9) | 0.07 (0.02) | −27.4 (0.9) |
F13 | 92.62 (3.55) | 214.6 (0.5) | 0.09 (0.01) | −30.1 (0.5) |
F14 | 92.40 (4.74) | 211.8 (2.2) | 0.07 (0.02) | −25.7 (0.2) |
F15 | 93.38 (1.42) | 213.3 (0.8) | 0.09 (0.02) | −25.3 (0.4) |
Source | dF | SS | MS | F-Values | p-Values | p-Values |
---|---|---|---|---|---|---|
Model | 9 | 210.879 | 23.431 | 16.83 | 0.003 | < 0.05 |
Linear model | 3 | 152.351 | 50.784 | 36.47 | 0.001 | < 0.05 |
X1 | 1 | 151.670 | 151.670 | 108.93 | 0.000 | < 0.05 |
X2 | 1 | 0.333 | 0.333 | 0.24 | 0.645 | > 0.05 |
X3 | 1 | 0.347 | 0.347 | 0.25 | 0.639 | > 0.05 |
Quadratic model | 3 | 54.646 | 18.215 | 13.08 | 0.008 | < 0.05 |
X1 × X1 | 1 | 49.453 | 49.453 | 35.52 | 0.002 | < 0.05 |
X2 × X2 | 1 | 1.675 | 1.675 | 1.20 | 0.323 | > 0.05 |
X3 × X3 | 1 | 0.858 | 0.858 | 0.62 | 0.468 | > 0.05 |
Interactions | 3 | 3.882 | 1.294 | 0.93 | 0.491 | > 0.05 |
X1 × X2 | 1 | 0.380 | 0.380 | 0.27 | 0.624 | > 0.05 |
X1 × X3 | 1 | 3.484 | 3.484 | 2.50 | 0.175 | > 0.05 |
X2 × X3 | 1 | 0.018 | 0.018 | 0.01 | 0.914 | > 0.05 |
Error | 5 | 6.962 | 1.392 | |||
Lack of fit | 3 | 3.030 | 1.010 | 0.51 | 0.713 | > 0.05 |
Pure error | 2 | 3.932 | 1.966 | |||
Total | 14 | 217.841 | ||||
Regression equation | MDD [nm] = 191.36 + 0.1911 X1 − 0.0285 X2 + 0.116 X3 − 0.000366 X1 × X1 + 0.000083 X2 × X2 + 0.00482 X3 × X3 + 0.000034 X1×X2 − 0.000933 X1 × X3 − 0.000074 X2 × X3 |
Source | dF | SS | MS | F-values | p-Values | p-Values |
---|---|---|---|---|---|---|
Model | 9 | 269.446 | 29.9385 | 29.22 | 0.001 | <0.05 |
Linear model | 3 | 182.006 | 60.6685 | 59.22 | 0.000 | <0.05 |
X1 | 1 | 81.655 | 81.6552 | 79.70 | 0.000 | <0.05 |
X2 | 1 | 9.070 | 9.0703 | 8.85 | 0.031 | <0.05 |
X3 | 1 | 91.280 | 91.2800 | 89.10 | 0.000 | <0.05 |
Quadratic model | 3 | 37.784 | 12.5946 | 12.29 | 0.010 | <0.05 |
X1 × X1 | 1 | 31.464 | 31.4644 | 30.71 | 0.003 | <0.05 |
X2 × X2 | 1 | 3.683 | 3.6832 | 3.60 | 0.116 | >0.05 |
X3 × X3 | 1 | 0.365 | 0.3648 | 0.36 | 0.577 | >0.05 |
Interactions | 3 | 49.657 | 16.5523 | 16.16 | 0.005 | <0.05 |
X1 × X2 | 1 | 4.747 | 4.7473 | 4.63 | 0.084 | >0.05 |
X1 × X3 | 1 | 44.755 | 44.7554 | 43.69 | 0.001 | <0.05 |
X2 × X3 | 1 | 0.154 | 0.1543 | 0.15 | 0.714 | >0.05 |
Error | 5 | 5.123 | 1.0245 | |||
Lack of fit | 3 | 4.597 | 1.5322 | 5.83 | 0.150 | >0.05 |
Pure error | 2 | 0.526 | 0.2629 | |||
Total | 14 | 274.569 | ||||
Regression equation | LE [%] = 129.10 − 0.1974 X1 + 0.0697 X2 1.238 X3 + 0.000292 X1 × X1 − 0.000123 X2 × X2 0.00314 X3 × X3 0.000121 X1 × X2 + 0.003345 X1 × X3 + 0.000218 X2 × X3 |
Time | No Stress Condition (4 ± 2 °C, No Light Exposure) | Oxidative Stress Condition (25 ± 2 °C, No Light Exposure) | High-Temperature Condition (60 ± 1 °C, No Light Exposure) | Accelerated-Light Condition (35 ± 2 °C) |
---|---|---|---|---|
t = 0 h | 100.00 | 100.00 | 100.00 | 100.00 |
t = 24 h | 100.00 (0.47) | 99.92 (0.96) | 96.37 (3.62) | 91.72 (1.86) a |
t = 48 h | 100.12 (3.05) | 99.76 (1.84) | 97.25 (2.08) | 99.05 (1.21) b |
t = 72 h | 99.33 (1.29) | 99.15 (2.21) | 97.70 (1.34) | - |
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Gostyńska, A.; Czerniel, J.; Kuźmińska, J.; Brzozowski, J.; Majchrzak-Celińska, A.; Krajka-Kuźniak, V.; Stawny, M. Honokiol-Loaded Nanoemulsion for Glioblastoma Treatment: Statistical Optimization, Physicochemical Characterization, and an In Vitro Toxicity Assay. Pharmaceutics 2023, 15, 448. https://doi.org/10.3390/pharmaceutics15020448
Gostyńska A, Czerniel J, Kuźmińska J, Brzozowski J, Majchrzak-Celińska A, Krajka-Kuźniak V, Stawny M. Honokiol-Loaded Nanoemulsion for Glioblastoma Treatment: Statistical Optimization, Physicochemical Characterization, and an In Vitro Toxicity Assay. Pharmaceutics. 2023; 15(2):448. https://doi.org/10.3390/pharmaceutics15020448
Chicago/Turabian StyleGostyńska, Aleksandra, Joanna Czerniel, Joanna Kuźmińska, Jakub Brzozowski, Aleksandra Majchrzak-Celińska, Violetta Krajka-Kuźniak, and Maciej Stawny. 2023. "Honokiol-Loaded Nanoemulsion for Glioblastoma Treatment: Statistical Optimization, Physicochemical Characterization, and an In Vitro Toxicity Assay" Pharmaceutics 15, no. 2: 448. https://doi.org/10.3390/pharmaceutics15020448
APA StyleGostyńska, A., Czerniel, J., Kuźmińska, J., Brzozowski, J., Majchrzak-Celińska, A., Krajka-Kuźniak, V., & Stawny, M. (2023). Honokiol-Loaded Nanoemulsion for Glioblastoma Treatment: Statistical Optimization, Physicochemical Characterization, and an In Vitro Toxicity Assay. Pharmaceutics, 15(2), 448. https://doi.org/10.3390/pharmaceutics15020448