1. INTRODUCTION:

The enhancement of solubility of biopharmaceutical classification system (BCS) class II drugs is highly challenging during the formulation development strategies. Numerous formulation approaches have been utilized to augment drug’s such as nanocrystals^1,2, complexation^3,4, solid dispersion (SD)^5,6, liquisolid compacts^7,8, self-microemulsifying drug delivery system ^9,10, and co-crystals^11,12.

SD formulations have several advantages like enhanced surface area, superior wetting characteristics, conversion of drug from crystalline to amorphous which leads to improvement in aqueous solubility of drugs. The techniques involved for development of SD includes melting method^13-15, solvent evaporation^16,17, spray drying^18,19. Fluvastatin sodium (FLS) is hypolipidemic drug which belongs to BCS class II having log P (4.17) and low water solubility (0.0039mg/mL) which causes lesser drug dissolution and therefore, poor oral bioavailability²⁰. In current research work, FLS-SD were manufactured by solvent-evaporation technique using two hydrophilic polymers i.e. poloxamer 407 and polyvinyl pyrollidone K-30 (PVP K-30)^21,22. Central composite design (CCD) was applied for formulation optimization with lesser experimental runs²³.

2. MATERIALS AND METHOD:

2.1. Materials:

FLS, PVP K-30 and Poloxamer 407 were purchased from All Well Pharmaceuticals Company, Chandigarh, India, Loba Chemicals (Mumbai, India) and Oniosome Healthcare Private Limited, Mohali, India, respectively.

2.2. Experimental Design:

CCD matrix for experimentation is given in Table 1and mathematical model between Y (response parameter) and X (factors) is given in equation1 ²³^,24.

Yi = b0 + b1X1 + b2X2 + b12X1X2 + b1² X1²+ b2² X2² Eq. (1)

Where, b0 denotes constant, b1, and b2 represents main effect, b12 indicates factor-interactions and b1² and b2² signified quadratic effects of factors.

Table 1: Investigated Variables in Design Model

Factors	Values assigned
	-1.41	-1	0	+1	+1.41
Drug: polymer (X₁)	1:1.59	1:2	1:3	1:4	1: 4.41
Surfactant concentration (% weight/volume) (X₂)	0.59	1	2	3	3.41
Dependent variables
T_50%(Minutes) (Y₁)
Q₉₀(%)(Y₂)
% Drug content (Y₃)
Layout
X₁	X₂
-1	-1
1	-1
-1	1
1	1
-1.41	0
1.41	0
0	-1.41
0	1.41
0	0
0	0
0	0
0	0
0	0

2.3. Manufacturing of FLS-SD:

FLS, PVP K30 and poloxamer 407 was dissolved in isopropyl alcohol at 1:20 (w/v) by vortex until formation of a transparent solution. Afterward, the solutions were subjected to removal of organic solvent via evaporation at 80°C in a water bath till appearance of dried solid (FLS-SD). Subsequently, the solid product was transferred to vacuum desiccators for 24 hours for removal of residual solvent from formulation. The resulting solid dispersions were pulverized through shifting via sieve #10, further sieved through # 22 and sealed in glass vials which were placed at room temperature in desiccators ^21,25-27.

2.4. Estimation of Response Variables:

In-vitro drug release from FLS-SDs was performed using USP dissolution paddle apparatus (Lab India, Mumbai) in 900ml phosphate buffer pH 6.8 (PB 6.8) under 100rpm at 37°C, n=3). Samples were withdrawn at particular time intervals and analyzed using Systronics ultraviolet spectrophotometer (AU-2701) at 300 nm. The t_50%(Y1) (time taken for 50% cumulative drug release; CDR) and Q90 (Y2) (% CDR within 90 min) were estimated from graph plotted between CDR (%) versus time (min) [25-30]. For determination of % drug content, FLS-SD equivalent to 10mg FLS was dissolved in PB 6.8, analyzed in UV spectrophotometer. The % drug content was calculated using equation 2^31-35.

Estimated quantity of FLS

Percentage drug content = ---------------------------------- ×100

Theoretical quantity of FLS

Eq. (2)

2.5. Model Validation:

The desirability function and optimized FLS-SD composition was evaluated with set criteria of minimum t_50% (Y1) while maximum Q90 (Y2) and % drug content (Y3) using Design-Expert software (Trial Version 11.1.2.0, Stat-Ease Inc., MN). The desirability of design model was validated by manufacturing of checkpoint FLS-SD batch and its evaluation for response parameters (experimental values) which were then compared with theoretical values (given by software) to observe the closeness between experimental and theoretical values^23,36-38.

2.6. In-Vitro Drug Release Profile and Release Kinetics Study:

The in-vitro drug release study from FLS, PM, and FLS-SD was inspected using USP paddle equipment as explained previously in section 2.4. The % CDR data were incorporated into in-vitro kinetic models. On the basis of values of regression coefficient (r²), the best fitted model was established to demonstrate the drug release trend. The release exponent value (n) was obtained from Korsmeyer-Peppas model and interpreted using the standard values like n < 0.45 (fickian diffusion), 0.46-0.88 (anomalous diffusion), 0.89 (case II transport) and > 0.89 (supercase II transport)^39,40.

3. RESULTS AND DISCUSSIONS:

3.1. t_50%(min) (Y1)

Since, the difference between the adjusted r²(0.9264) and predicted r² (0.7581) was less than 0.2 as obtained from fit summary statistics (Table 2). The p-value (lack of fit) for Y1 was found 0.12 (p>0.05) (Table 3). Insignificant lack-of-fit p-value showed good model fitting. This illustrated that quadratic design was best fitted for t_50%.The t_50%was significantly affected by drug: polymer (X1) and surfactant (X2) as main effect (p < 0.05) while interaction effect between X1 and X2 was non-significant (p > 0.05) The quadratic effect of X1² and X2² on Y1 was also found significant (p < 0.05) (Table 4). From polynomial equation 3, it has been revealed that drug: polymer (X1) and surfactant (X2) produced antagonistic outcome on t_50%(b1= -2.19; b2 = -1.33). This showed that increasing the amount of X1 and X2 in SD decreases the value of t_50%.

Y1 = 30.60 - 2.19X1 - 1.33X2 + 0.75X1X2 - 0.9857X1² - 2.49X2² Eq. (3)

This could be attributed to the solubilizing characteristics and surface tension reducing characteristics of PVP K-30 and Poloxamer 407 which augmented the in-vitro drug dissolution and reduced the value of t_50% (Figure 1)^41-47.

Table 2: Fit Summary Statistics for Response Parameters

Factor	Source	Sequential p-value	Lack of Fit p-value	Adjusted R²	Predicted R²
Y1	Linear	0.0319	0.0030	0.3977	0.1294
	2FI	0.5426	0.0024	0.3593	0.1100
	Quadratic	0.0002	0.1200	0.9264	0.7581
	Cubic	0.9528	0.0305	0.8990	-0.9820
Y2	Linear	< 0.0001	0.0114	0.8439	0.7266
	2FI	0.2530	0.0114	0.8512	0.7026
	Quadratic	0.0137	0.0561	0.9439	0.7997
	Cubic	0.0334	0.2562	0.9798	0.8271
Y3	Linear	0.0014	0.0072	0.6785	0.5182
	2FI	0.5703	0.0057	0.6560	0.2395
	Quadratic	0.0017	0.0897	0.9287	0.7569
	Cubic	0.7995	0.0256	0.9088	-0.8413

Table 3. ‘Lack of Fit’ Test Statistics for Response Parameters

Factor	Source	Sum of Squares	df	Mean Square	F-value	p-value
Y1	Linear	51.62	6	8.60	28.68	0.0030
	2FI	49.37	5	9.87	32.91	0.0024
	Quadratic	3.32	3	1.11	3.68	0.1200
	Cubic	3.23	1	3.23	10.76	0.0305
Y2	Linear	147.45	6	24.57	14.18	0.0114
	2FI	125.50	5	25.10	14.48	0.0114
	Quadratic	31.91	3	10.64	6.14	0.0561
	Cubic	3.04	1	3.04	1.75	0.2562
Y3	Linear	273.54	6	45.59	18.18	0.0072
	2FI	263.01	5	52.60	20.98	0.0057
	Quadratic	33.97	3	11.32	4.52	0.0897
	Cubic	30.20	1	30.20	12.04	0.0256

Table 4. Analysis of Variance for Response Parameters

Factor	Source	Sum of Squares	df	Mean Square	F-value	p-value
Y1	Model	100.71	5	20.14	31.22	0.0001*
	X1	38.22	1	38.22	59.24	0.0001*
	X2	14.19	1	14.19	22.00	0.0022*
	X1X2	2.25	1	2.25	3.49	0.1041
	X1²	6.70	1	6.70	10.39	0.0146*
	X2²	42.93	1	42.93	66.54	< 0.0001*
	Lack of Fit	3.32	3	1.11	3.68	0.1200
Y2	Model	1148.03	5	229.61	41.37	< 0.0001*
	X1	638.48	1	638.48	115.05	< 0.0001*
	X2	394.02	1	394.02	71.00	< 0.0001*
	X1X2	21.95	1	21.95	3.96	0.0871
	X1²	75.64	1	75.64	13.63	0.0077*
	X2²	9.56	1	9.56	1.72	0.2307
	Lack of Fit	31.91	3	10.64	6.14	0.0561
Y3	Model	1014.35	5	202.87	32.27	0.0001*
	X1	760.53	1	760.53	120.99	< 0.0001*
	X2	14.26	1	14.26	2.27	0.1758
	X1X2	10.53	1	10.53	1.68	0.2366
	X1²	222.16	1	222.16	35.34	0.0006*
	X2²	20.27	1	20.27	3.22	0.1156
	Lack of Fit	33.97	3	11.32	4.52	0.0897

^*p < 0.05

3.2. Q90 (Y2):

The Q90 was superlative fitted in quadratic model (adjusted r²= 0.9439 and predicted r² = 0.7997) (Table 2). The equation 4 explained the effect of independent parameters on Q90.

Y2 = 81.52 + 8.95X1 + 7.03X2 - 2.34X1X2 - 3.31X1² + 1.18X2² Eq. (4)

Q90 was considerably affected with change in values of X1, X2, and X1² (p < 0.05) (Table 4). The b1 and b2 were +8.95 and +7.03 which explained that increase in the drug: polymer and surfactant concentration resulted in considerable rise in Q90 which again is attributable to solubilizing action of PVP K-30 and Poloxamer 407^41-47 (Figure 2).

3.3.Percentage Drug Content (Y3)

The quadratic model was supreme fitted to the percentage drug content (adjusted r²= 0.9287) and predicted r² = 0.7569) (Table 2) and p-value for lack of fit was found 0.0897 (p > 0.05) (Table 3). The equation 5 elucidated the impact of independent parameters on percentage drug content:

Y3 = 83.33 + 9.76X1 + 1.34X2 + 1.62X1X2 - 5.68X1² - 1.71X2² Eq. (5)

With rise in drug: polymer, the percentage drug content in formulation was enhanced (b1= +9.76) (Table 4) (Figure 3)⁴³.

Figure 1. Response Surface Plots Depicting Effect of Various Factors on t_50%of FLS-SD.

Figure 2: Graphical Depiction of Influence of Factors on Q90 of FLS-SD.

Figure 3. 2-and 3-dimensional pictorial view of effect of factors on Percentage Drug Content of FLS-SD.

3.4. Model Validation:

The optimal values of optimized FLS-SD were 1:4.074 of drug: polymer and 3.102% of Poloxamer 407 having desirability function of 0.998 as explored by Design Expert Software. It also provides theoretical values of t_50%, Q90 and drug content corresponding to 23 min, 94.289% and 88.515%, respectively. An additional batch of FLS-MS was produced with optimal amount of X1 and X2. The experiential t_50%, Q90 and percentage drug content for formulated batch were estimated 24 min, 90.81% and 86.29%, respectively, which concurred intimately with the theoretical values^23,36-38.

3.5. In-Vitro Drug Release Study:

The percentage CDR from the FLS, PM and FLS-SD was found 25.43%, 27.54%, and 93.6% within 2 hour, respectively which revealed that dissolution enhancement of FLS was significant on manufacturing of solid dispersion formulation (^*p< 0.05) (Figure 4). This could be due surface activity of poloxamer 407 and amorphous characteristics of PVP K-30. The r² for the Zero-order, First-order, Korsmeyer-peppas, and Higuchi models for FLS-SD were 0.8336, 0.7594, 0.9539 and 0.9234, respectively. The regression equation for Korsmeyer-peppas was found “y=0.426x+1.1259” giving ‘n’ value was 0.426 (n <0.45) which revealed fickian drug release from FLS-SD^48-54.

Figure 4. In-Vitro Drug Release Pattern from Pure Drug, Physical Mixture, and Optimized FLS-SD. *Significance in comparison to FLS.

4. CONCLUSION:

This research work showed that solid dispersions of Fluvastatin sodium (FLS-SD) were successfully synthesized via solvent evaporation technique. CCD explored an optimized FLS-SD composition i.e. drug: polymer of 1:4.074 and 3.102% of surfactant/emulsifier. The % CDR from FLS, physical mixture and FLS-SD was 25.43%, 27.54%, and 93.6% within 2 hour which showed significant enhancement of dissolution rate of FLS on incorporation of drug in FLS-SD (^*p< 0.05). The drug release from FLS-SD was fabulously fitted in K-P model (r²= 0.9533). The n-value for K-P model was 0.426 which illustrated fickian drug release from FLS-SD. This investigation concluded that concluded that solid dispersion formulation by solvent evaporation technique could be effectively utilized for dissolution enhancement of BCS class II drug.

5. CONFLICT OF INTEREST:

The authors declare no conflict of interest.

6. ACKNOWLEDGEMENTS:

The authors express gratitude to Chitkara College of Pharmacy, Chitkara University, Punjab, India, for motivational support for the compilation of this research.

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Received on 23.07.2021 Modified on 10.03.2022

Research J. Pharm. and Tech 2022; 15(12):5659-5664.

DOI: 10.52711/0974-360X.2022.00954