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Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Ahmed Adnan Abu Sharar
Ahmed Adnan Abu Sharar
, 
Saleem Z. Ramadan
Saleem Z. Ramadan
 et 
Samer Hasan Hussein-Al-Ali
Samer Hasan Hussein-Al-Ali
   | 27 mars 2022
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Publié en ligne: 27 mars 2022
Pages: 517 - 544
Reçu: 02 déc. 2021
Accepté: 26 janv. 2022
DOI: https://doi.org/10.2478/msp-2021-0042
Mots clés
© 2021 Ahmed Adnan Abu Sharar et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Methodology flow chart. NPs, nanoparticles
Methodology flow chart. NPs, nanoparticles

Fig. 2

NPs fabrication process flow chart. CS, chitosan; DSC, differential scanning calorimetry; FTIR, Fourier-transform infrared spectroscopy; FZN, fluphenazine; NPs, nanoparticles; TPP, tripolyphosphate; XRD, X-ray diffraction
NPs fabrication process flow chart. CS, chitosan; DSC, differential scanning calorimetry; FTIR, Fourier-transform infrared spectroscopy; FZN, fluphenazine; NPs, nanoparticles; TPP, tripolyphosphate; XRD, X-ray diffraction

Fig. 3

Simples design plot. CS, chitosan; FZN, fluphenazine; TPP, tripolyphosphate
Simples design plot. CS, chitosan; FZN, fluphenazine; TPP, tripolyphosphate

Fig. 4

NSGA-II process – adapted from Acampora et al. [46]. NSGA-II, non-dominated sorting genetic algorithm
NSGA-II process – adapted from Acampora et al. [46]. NSGA-II, non-dominated sorting genetic algorithm

Fig. 5

Main effect plots. LE, loading efficiency; NPs, nanoparticles
Main effect plots. LE, loading efficiency; NPs, nanoparticles

Fig. 6

Contour plots. CS, chitosan; LE, loading efficiency; NPs, nanoparticles; TPP, tripolyphosphate; ZP, zeta potential
Contour plots. CS, chitosan; LE, loading efficiency; NPs, nanoparticles; TPP, tripolyphosphate; ZP, zeta potential

Fig. 7

Analysis of residuals. LE, loading efficiency; NPs, nanoparticles; ZP, zeta potential
Analysis of residuals. LE, loading efficiency; NPs, nanoparticles; ZP, zeta potential

Fig. 8

DSC Thermograms. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). DSC, differential scanning calorimetry; FZN, fluphenazine
DSC Thermograms. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). DSC, differential scanning calorimetry; FZN, fluphenazine

Fig. 9

In-vitro drug release study
In-vitro drug release study

Fig. 10

XRD-diffractograms. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). (D) Physical Mixture of FZN with CSNPs. FZN, fluphenazine; XRD, X-ray diffraction
XRD-diffractograms. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). (D) Physical Mixture of FZN with CSNPs. FZN, fluphenazine; XRD, X-ray diffraction

Fig. 11

Chemical Interactions between the CSNPs and FZN drug. (A) FZN chemical structure. (B) CS chemical structure. (C) TPP chemical structure. CS, chitosan; FZN, fluphenazine; TPP, tripolyphosphate
Chemical Interactions between the CSNPs and FZN drug. (A) FZN chemical structure. (B) CS chemical structure. (C) TPP chemical structure. CS, chitosan; FZN, fluphenazine; TPP, tripolyphosphate

Fig. 12

FTIR spectrums. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). FTIR, Fourier-transform infrared spectroscopy; FZN, fluphenazine
FTIR spectrums. (A) Drug free (FZN). (B) Blank (CSNPs). (C) Nanocomposite (FZNCSNPs). FTIR, Fourier-transform infrared spectroscopy; FZN, fluphenazine

ANOVA for size-screening study

Source DF Seq SS Adj SS Adj MS F-value P-value
Regression 11 65411864 65411864 5946533 901.35 0.000
Component only
Linear 2 16852012 25941727 12970863 1966.06 0.000
Quadratic 1 10563934 10563934 10563934 1601.23 0.000
A × B 1 10563934 10563934 10563934 1601.23 0.000
Component × X1
Linear 3 26622324 34654360 11551453 1750.92 0.000
A × X1 1 16413096 19779395 19779395 2998.07 0.000
B × X1 1 9152068 955902 955902 144.89 0.000
C × X1 1 1057159 3609045 3609045 547.04 0.000
Quadratic 1 11360884 11360884 11360884 1722.03 0.000
A × B × X1 1 11360884 11360884 11360884 1722.03 0.000
Component × X2
Linear 3 6511 12619 4206 0.64 0.629
A × X2 1 520 10879 10879 1.65 0.268
B × X2 1 1446 1701 1701 0.26 0.638
C × X2 1 4545 10744 10744 1.63 0.271
Quadratic 1 6200 6200 6200 0.94 0.387
A × B × X2 1 6200 6200 6200 0.94 0.387
Residual Error 4 26390 26390 6597
Total 15 65438254

Screening DoE model with experimental values

Runs Independent variables Responses
(A) (B) (C) (X1) (X2) Size (nm) ZP (mV) LE (%)
Run 1 15 62.5 22.5 6.0 18 252 7.9 97.6
Run 2 15 22.5 62.5 6.0 3 219 4.0 96.8
Run 3 5 22.5 72.5 6.0 18 324 4.4 94.0
Run 4 5 72.5 22.5 4.0 3 152 24.0 85.8
Run 5 15 62.5 22.5 6.0 3 258 8.0 97.4
Run 6 5 22.5 72.5 6.0 3 389 4.3 94.2
Run 7 15 22.5 62.5 6.0 18 197 4.5 97.0
Run 8 5 72.5 22.5 6.0 3 279 7.2 92.6
Run 9 15 62.5 22.5 4.0 3 148 24.1 91.2
Run 10 15 62.5 22.5 4.0 18 159 21.2 91.1
Run 11 5 22.5 72.5 4.0 18 549 6.9 83.4
Run 12 15 22.5 62.5 4.0 3 642 7.6 84.6
Run 13 5 72.5 22.5 4.0 18 150 28.0 84.0
Run 14 5 72.5 22.5 6.0 18 279 7.9 92.7
Run 15 5 22.5 72.5 4.0 3 302 6.8 83.3
Run 16 15 22.5 62.5 4.0 18 631 7.6 84.8

ANOVA for LE-screening study

Source DF Seq SS Adj SS Adj MS F-value P-value
Regression 11 448.552 448.552 40.7774 172.15 0.000
Component Only
Linear 2 68.186 10.791 5.3954 22.78 0.007
Quadratic 1 14.811 14.811 14.8113 62.53 0.001
A × B 1 14.811 14.811 14.8113 62.53 0.001
Component × X1
Linear 3 361.159 190.177 63.3923 267.62 0.000
A × X1 1 274.911 4.726 4.7263 19.95 0.011
B × X1 1 7.779 10.489 10.4893 44.28 0.003
C × X1 1 78.468 18.425 18.4246 77.78 0.001
Quadratic 1 3.629 3.629 3.6288 15.32 0.017
A × B × X1 1 3.629 3.629 3.6288 15.32 0.017
Component × X2
Linear 3 0.694 0.733 0.2442 1.03 0.469
A × X2 1 0.001 0.000 0.0000 0.00 0.995
B × X2 1 0.681 0.493 0.4929 2.08 0.223
C × X2 1 0.013 0.018 0.0177 0.07 0.798
Quadratic 1 0.073 0.073 0.0732 0.31 0.608
A × B × X2 1 0.073 0.073 0.0732 0.31 0.608
Residual Error 4 0.948 0.948 0.2369
Total 15 449.499

Pareto solutions

Solution no. Size ZP LE
S1 215.6 21.5 88.8
S2 204.1 21.4 89.4
S3 198.0 21.3 89.6
S4 191.9 21.2 89.8
S5 183.2 20.8 89.5
S6 171.2 20.7 90.4
S7 167.2 20.6 90.7
S8 165.3 20.5 90.6
S9 153.0 20.2 91.1
S10 147.9 20.0 91.2
S11 145.5 19.9 91.3
S12 143.2 19.8 91.3
S13 141.0 19.7 91.4
S14 138.4 19.5 91.3
S15 136.5 19.2 91.1
S16 132.7 19.0 91.2
S17 129.4 18.9 91.4
S18 127.2 18.8 91.6
S19 126.6 18.7 91.6
S20 164.6 4.9 91.7
S21 164.6 4.9 91.7
S22 165.1 5.0 91.7
S23 168.9 5.1 91.7
S24 177.1 5.2 91.7
S25 173.6 4.9 91.8
S26 177.9 5.1 91.8
S27 177.2 5.0 91.9
S28 177.2 5.0 91.9
S29 184.3 5.2 91.8
S30 186.1 5.3 91.8
S31 190.7 5.4 91.7
S32 181.4 5.1 91.9
S33 189.1 5.3 91.9
S34 192.7 5.4 91.8
S35 195.5 5.5 91.8
S36 199.9 5.6 91.7
S37 189.8 5.1 92.0
S38 194.2 5.4 91.9
S39 200.4 5.6 91.8
S40 194.4 5.2 92.0
S41 194.5 5.3 92.0
S42 200.7 5.6 91.9
S43 203.7 5.7 91.8
S44 201.8 5.2 92.1
S45 205.4 5.7 91.9
S46 214.8 6.0 91.7
S47 207.3 5.3 92.1
S48 205.6 5.6 92.0
S49 208.8 5.8 91.9
S50 211.3 5.5 92.1
S51 216.9 6.0 91.9
S52 217.2 6.1 91.9
S53 220.8 6.2 91.8
S54 221.9 5.5 92.2
S55 217.0 5.9 92.0
S56 217.8 6.0 92.0
S57 221.5 6.2 91.9
S58 223.7 6.3 91.8
S59 225.9 6.4 91.8
S60 230.7 6.5 91.7
S61 223.2 6.0 92.1
S62 223.6 6.1 92.0
S63 227.2 6.4 91.9
S64 231.8 6.6 91.8
S65 234.8 6.7 91.8
S66 238.9 6.8 91.7

Validation experiments – data

Run codes NPs input fabrication parameters Size (nm) ZP (mV) LE (%)
A B C X−1 Experimental Values Predicted using Eq. (5) Experimental Predicted using Eq. (6) Experimental Values Predicted using Eq. (7)
V1 131.0 20.4 93.0
V2 130.0 21.3 91.3
V3 128.2 20.0 92.6
V4 13.3% 64.2% 22.5% 4.0 125.4 126.6 19.0 18.7 91.4 91.6%
V5 129.0 22.0 92.0
V6 132.0 20.0 93.1
V7 125.4 17.7 92.5
V8 126.0 18.0 90.4
V9 127.0 18.4 92.5
Mean 128.2 126.6 19.6 18.7 92.1 91.6
Standard Deviation 2.45 – 1.48 – 0.90 –
Test of hypothesis H0: μ = 126.6 H0: μ =18.7 H0: μ = 91.6
H1: μ ≠ 126.6 H1: μ ≠ 18.7 H1: μ ≠ 91.6
P-value P-value = 0.082 P-value = 0.092 P-value = 0.140
95% CI for μ (126.3–130.1 nm) (18.5 – 20.8 mV) (91.4–92.8%)

ANOVA for ZP-screening study

Source DF Seq SS Adj SS Adj MS F-value P-value
Regression 11 1008.68 1008.68 91.698 77.30 0.000
Component only
Linear 2 426.63 256.25 128.124 108.01 0.000
Quadratic 1 0.23 0.23 0.231 0.19 0.682
A × B 1 0.23 0.23 0.231 0.19 0.682
Component × X1
Linear 3 573.36 152.82 50.940 42.94 0.002
A × X1 1 282.00 0.29 0.288 0.24 0.648
B × X1 1 284.68 109.49 109.488 92.30 0.001
C × X1 1 6.68 5.15 5.147 4.34 0.106
Quadratic 1 0.61 0.61 0.606 0.51 0.514
A × B × X1 1 0.61 0.61 0.606 0.51 0.514
Component × X2
Linear 3 4.00 7.66 2.553 2.15 0.236
A × X2 1 0.08 0.91 0.914 0.77 0.430
B × X2 1 2.84 6.67 6.671 5.62 0.077
C × X2 1 1.08 0.67 0.671 0.57 0.494
Quadratic 1 3.84 3.84 3.842 3.24 0.146
A × B × X2 1 3.84 3.84 3.842 3.24 0.146
Residual Error 4 4.74 4.74 1.186
Total 15 1013.42

Complete DoE study-data

Run codes Non-dependent variables Responses
(FZN) (CS) (TPP) (pH) Size ZP LE
F01-1 5.0 72.50 22.50 6.0 225 7 82
F01-2 5.0 72.50 22.50 6.0 221 7 81
F01-3 5.0 72.50 22.50 6.0 221 8 81
F02-1 12.5 53.75 33.75 4.0 253 14 80
F02-2 12.5 53.75 33.75 4.0 262 16 80
F02-3 12.5 53.75 33.75 4.0 257 16 79
F03-1 10.0 45.00 45.00 6.0 255 5 87
F03-2 10.0 45.00 45.00 6.0 262 5 90
F03-3 10.0 45.00 45.00 6.0 259 6 90
F04-1 15.0 42.50 42.50 4.0 626 12 83
F04-2 15.0 42.50 42.50 4.0 625 10 82
F04-3 15.0 42.50 42.50 4.0 627 12 82
F05-1 7.5 33.75 58.75 4.0 687 7 80
F05-2 7.5 33.75 58.75 4.0 686 9 81
F05-3 7.5 33.75 58.75 4.0 670 9 79
F06-1 5.0 47.50 47.50 6.0 209 5 84
F06-2 5.0 47.50 47.50 6.0 203 5 88
F06-3 5.0 47.50 47.50 6.0 217 5 83
F07-1 15.0 22.50 62.50 4.0 499 9 70
F07-2 15.0 22.50 62.50 4.0 514 7 70
F07-3 15.0 22.50 62.50 4.0 502 9 69
F08-1 7.5 33.75 58.75 6.0 297 4 88
F08-2 7.5 33.75 58.75 6.0 307 4 87
F08-3 7.5 33.75 58.75 6.0 317 4 87
F09-1 10.0 45.00 45.00 4.0 501 16 82
F09-2 10.0 45.00 45.00 4.0 506 16 83
F09-3 10.0 45.00 45.00 4.0 504 16 81
F10-1 7.5 58.75 33.75 6.0 164 6 87
F10-2 7.5 58.75 33.75 6.0 179 6 86
F10-3 7.5 58.75 33.75 6.0 164 6 86
F11-1 12.5 53.75 33.75 6.0 299 6 91
F11-2 12.5 53.75 33.75 6.0 296 6 91
F11-3 12.5 53.75 33.75 6.0 297 6 91
F12-1 15.0 62.50 22.50 6.0 228 8 91
F12-2 15.0 62.50 22.50 6.0 221 8 91
F12-3 15.0 62.50 22.50 6.0 230 8 92
F13-1 5.0 22.50 72.50 6.0 276 4 84
F13-2 5.0 22.50 72.50 6.0 264 4 84
F13-3 5.0 22.50 72.50 6.0 272 4 84
F14-1 12.5 33.75 53.75 6.0 226 5 91
F14-2 12.5 33.75 53.75 6.0 225 5 91
F14-3 12.5 33.75 53.75 6.0 225 6 91
F15-1 7.5 58.75 33.75 4.0 360 16 74
F15-2 7.5 58.75 33.75 4.0 360 16 74
F15-3 7.5 58.75 33.75 4.0 360 19 74
F16-1 10.0 22.50 67.50 6.0 277 4 89
F16-2 10.0 22.50 67.50 6.0 265 5 90
F16-3 10.0 22.50 67.50 6.0 267 4 90
F17-1 10.0 67.50 22.50 4.0 195 20 88
F17-2 10.0 67.50 22.50 4.0 190 22 88
F17-3 10.0 67.50 22.50 4.0 191 23 88
F18-1 5.0 72.50 22.50 4.0 167 16 82
F18-2 5.0 72.50 22.50 4.0 168 22 82
F18-3 5.0 72.50 22.50 4.0 164 22 82
F19-1 10.0 22.50 67.50 4.0 353 7 83
F19-2 10.0 22.50 67.50 4.0 351 7 83
F19-3 10.0 22.50 67.50 4.0 351 7 83
F20-1 5.0 22.50 72.50 4.0 428 7 79
F20-2 5.0 22.50 72.50 4.0 433 8 79
F20-3 5.0 22.50 72.50 4.0 427 7 79
F21-1 15.0 22.50 62.50 6.0 458 5 96
F21-2 15.0 22.50 62.50 6.0 438 5 96
F21-3 15.0 22.50 62.50 6.0 447 5 96
F22-1 5.0 47.50 47.50 4.0 414 11 80
F22-2 5.0 47.50 47.50 4.0 404 12 80
F22-3 5.0 47.50 47.50 4.0 424 16 80
F23-1 10.0 67.50 22.50 6.0 239 7 89
F23-2 10.0 67.50 22.50 6.0 237 7 89
F23-3 10.0 67.50 22.50 6.0 240 8 89
F24-1 15.0 62.50 22.50 4.0 185 18 91
F24-2 15.0 62.50 22.50 4.0 181 20 91
F24-3 15.0 62.50 22.50 4.0 183 20 91
F25-1 12.5 33.75 53.75 4.0 528 10 82
F25-2 12.5 33.75 53.75 4.0 511 12 82
F25-3 12.5 33.75 53.75 4.0 507 14 82
F26-1 15.0 42.50 42.50 6.0 232 6 92
F26-2 15.0 42.50 42.50 6.0 230 6 92
F26-3 15.0 42.50 42.50 6.0 230 6 92

Regression model evaluations

Model R-seq (%) R-seq (adj) (%) R-seq (pred) (%)
Model (1): size 96.23 95.24 94.12
Model (2): ZP 96.05 95.09 93.79
Model (3): IE 94.44 92.98 91.61
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