Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Ahmed Adnan Abu Sharar; Saleem Z. Ramadan; Samer Hasan Hussein-Al-Ali

doi:10.2478/msp-2021-0042

Figures & Tables

Methodology flow chart. NPs, nanoparticles

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

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

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

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

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

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

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

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

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

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	13.3%	64.2%	22.5%	4.0	129.0	126.6	22.0	18.7	92.0	91.6%
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

Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Figures & Tables

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

ANOVA for size-screening study

Screening DoE model with experimental values

ANOVA for LE-screening study

Pareto solutions

Validation experiments – data

ANOVA for ZP-screening study

Complete DoE study-data

Regression model evaluations

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