Optimal response surface design of Gum tragacanth-based poly[(acrylic acid)-co-acrylamide] IPN hydrogel for the controlled release of the antihypertensive drug losartan potassium

Abstract

The present study proposes the development and optimization of a new interpenetrating polymer network (IPN), consisting of Gum tragacanth, poly(acrylic acid) (PAA), and poly(acrylamide) (PAAm), for the in situ controlled release of losartan potassium under different pH conditions at 37 °C. Solvent amount and monomer concentration were chosen as the process variables and percentage swelling was taken as the process response. ANOVA model fits were made for the data and gave the cubic model as the best fit, with a predicted R² = 0.976. The maximum desirability was observed to be 19.63 mL solvent and 3.28 × 10⁻⁴ mol L⁻¹ monomer concentration, at which the percentage swelling was found to be 266. Whereas, at 23.7 mL solvent and 6.7 × 10⁻⁴ mol L⁻¹ monomer, the percentage swelling was found to be at the minimum (139%). The model was validated at the optimal points for developing devices with the maximum swelling capacity. Drug release through the synthesized matrix was found to show non-fickian behavior at pH 2.0, 7.0, and 9.2, with an increasing trend in gel characteristic constant (k). At each pH medium, the initial diffusion coefficient (D_I) was found to be higher than the lateral diffusion coefficient (D_L).

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1. Introduction

Many researchers around the world are focusing their efforts on the design and development of multipolymer devices, because of their better performance compared to that of individual components. Since homopolymer and copolymer hydrogels alone cannot always meet the divergent applications' demands, both in terms of properties and performance, interpenetrating polymer networks (IPN) systems are seen as a better approach. IPNs, comprised of two or more polymer networks formed in the presence of one another, have been found to be versatile suitable materials for biomedical applications. Many IPNs are prepared from water soluble polymers through cross-linking, using chemical or enzymatic initiators.^1–5 Different properties of IPN-systems, like porosity, elasticity and the response to external stimuli, can be tuned with respect to the types of cross-linking agents and their proportions.

Since IPNs are capable of delivering drugs at a constant rate for specific time intervals, scientists are using them as devices for controlled drug delivery. Such devices are also used in tissue engineering as scaffolds.⁶ Ekici and Saraydin prepared IPN hydrogels based on chitosan, poly(N-vinyl pyrrolidene), and poly(acrylic acid) for the gastrointestinal release of amoxicillin.⁷ PAAc-based hydrogels have been investigated as a device for bioadhesive release and a Ag/P(HEMA/IA)/PVP interpenetrating network has been used as an antimicrobial agent.^8,9

Gum tragacanth is a gummy exudation obtained from shrubby locoweeds in the native regions of the Eastern Mediterranean and South Western Asia, with Iran the biggest producer. Gum tragacanth is a carbohydrate biopolymer with a molecular weight of 840 000 g mol⁻¹ and is comprised of two main fractions. One is tragacanthin and the other is bassorin. On mixing with water, only the soluble fraction called tragacanthin dissolves and gives a colloidal hydrogel, while the insoluble fraction bassorin swells to form a gel.^10–13

The present work deals with the synthesis of a new interpenetrating polymer network (IPN) consisting of Gum tragacanth, poly(acrylic acid) (PAA), and poly(acrylamide) (PAAm), using glutaraldehyde as the cross-linker. Two process variables (solvent and monomer) were varied as per the IV-optimal design strategy of the response surface methodology (RSM). The maximum percentage swelling was taken as response indicator for the process. An ANOVA model was constructed which best fits the response data. Finally, the desirability function and ramp functions were used for process optimization. The synthesized IPN contains hydrophilic chains, which help in the easy loading and sustained release of the drug from the synthesized IPN. The synthesized IPN is therefore a promising approach to achieve in vitro buoyancy and to also improve the absorption of losartan potassium in the gastrointestinal tract for a longer time, thus making it a suitable device for controlled drug release. Thus herein, the synthesized IPN matrix was used for the controlled release of the antihypertensive drug losartan potassium^14,15 under varying pH and time.

Interpenetrating polymer networks are better than some other hydrogels or drug delivery carriers, because they offer better performances over conventional individual polymers and all class of materials. The superior properties of IPNs, like swelling capacity, stability, biocompatibility, nontoxicity and biodegradability, have attracted considerable attention for delivering bioactive molecules. In the last few years, various research reports on IPN-based delivery systems have shown the potential for these carriers as novel carriers in controlled drug delivery.²⁴

2. Materials and methods

Gum tragacanth, potassium persulphate and ascorbic acid were purchased from SD Fine Chemical Pvt. Ltd. Acrylic acid and acrylamide were obtained from Merck. The model drug losartan potassium was obtained from Cipla. All the above chemicals were of analytical grade and were used as-received. Deionised water was used in carrying out all the reactions in the experiments.

FTIR spectra of Gum tragacanth, Gt-cl-poly(AA) [Gum tragacanth-cross-linked-poly(acrylic acid)], and Gt-cl-poly(AA-ip-AAm) [Gum tragacanth-poly(acrylic acid-co-acrylamide) interpenetrating polymer network] were recorded using a Perkin Elmer spectrophotometer to determine their structures. Samples were thoroughly mixed with dried KBr and the pellets were prepared by compression under a vacuum. Scanning electron microscopy (SEM) of the samples were taken on a LEO-435VF, LEO Electron Microscopy Ltd. Drug release was studied using an UV-Vis spectrophotometer (Double beam Systronic-2201) with a sensitivity of 0.1 ppm and a drug loading of 50 ppm.

2.1 Preparation of the IPNs

Gt-cl-poly(AA) were prepared by the method describe earlier,¹⁶ suspended in deionised water. A known concentration of acrylamide was added with continuous stirring and kept overnight. To the reaction mixture, ascorbic acid-potassium persulphate, as an initiator and glutaraldehyde, as a cross-linker, were added with continuous stirring. The reaction was carried-out at a specific temperature and time. The homopolymer was removed with aqueous extraction. Synthesized IPN was dried at 50 °C, until a constant weight was obtained. The concentration of acrylamide was optimized with respect to the percentage swelling and was calculated using eqn (1).

Percentage swelling (P_s) = (W_s − W_d)/W_d × 100 (1)

where, W_s and W_d are the weights of swollen and dry polymer, respectively.

2.2 Swelling studies

A pre-weighed dry IPN sample was immersed in distilled water and after every 2 h, the weight gained by the sample was noted until equilibrium was attained. The swollen IPN sample was separated from the water using a mesh screen and drained on the sieve for 5 min until no redundant water was left behind. The percentage swelling of the synthesized IPN was calculated using eqn (1).

2.3 Experimental design

RSM is an efficient technique for process optimization in a minimal experimental run. In this paper, an IV optimal design was used. The design was used to minimize the integral of the prediction variance across the design space. These designs were built algorithmically using a CONVERT algorithm to provide the lower prediction variance across the entire design space. IV-optimal designs were best used with response surface designs. With the response surface designs, the goal was to model the true response surface with greater precision. Lack-of-fit points were added to the design to fill the largest gaps by selecting a group of points that could maximize the minimum distance to another point. Similarly, replicates were chosen that could best support the optimality criterion, along with the additional center points. There were two process variables and one response variable, along with the range given in Table 1. There were a total of 22 runs, as shown in Table 2, which included six model points, five runs used to estimate the lack of fit, four replicates at the center point and an additional seven runs at the edges and vertices.

Table 1 Design summary for IV-optimal response surface

Factor	Name	Units	Minimum	Maximum
			Actual (coded)
A	Solvent	mL	17.5 (−1)	25 (+1)
B	Monomer × 10^−4	mol L^−1	2.11 (−1)	7 (+1)
Response variable			Actual
Ps	Percentage swelling	%	140.6	270.0

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Table 2 Optimal response surface design with coded and actual values with percentage swelling

Std	Factor 1	Factor 2	Factor 1	Factor 2	Response
	Solvent	Monomer	Solvent (mL)	Monomer × 10^−4 (mol L^−1)	Percentage swelling
	Coded values		Actual values		Actual	Predicted
1	−1.00	−1.00	17.50	2.11	194.9	197.3
2	−1.00	−1.00	17.50	2.11	199.3	197.3
3	−0.20	−1.00	20.50	2.11	221.4	226.9
4	1.00	−1.00	25.00	2.11	184.2	185.9
5	1.00	−1.00	25.00	2.11	189.8	185.9
6	0.40	−0.63	22.75	3.01	235.7	235.3
7	−0.56	−0.42	19.15	3.53	270.0	263.5
8	1.00	−0.28	25.00	3.87	213.5	217.7
9	0.00	0.00	21.25	4.56	227.6	227.8
10	0.00	0.00	21.25	4.56	220.4	227.8
11	0.00	0.00	21.25	4.56	232.6	227.8
12	0.00	0.00	21.25	4.56	228.0	227.8
13	−1.00	0.11	17.50	4.81	201.6	204.0
14	−1.00	0.11	17.50	4.81	204.4	204.0
15	0.66	0.36	23.73	5.44	171.7	167.7
16	−1.00	1.00	17.50	7.00	158.9	158.1
17	−0.10	1.00	20.88	7.00	166.2	169.7
18	−0.10	1.00	20.88	7.00	171.7	169.7
19	1.00	1.00	25.00	7.00	140.6	141.3
20	−0.44	−0.52	19.60	3.29	265.2	265.9
21	−0.20	−1.00	20.50	2.11	229.8	226.9
22	1.00	1.00	25.00	7.00	142.1	141.3

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2.4 Drug loading and release studies

There are two methods of drug loading onto the IPN matrix. In one method, the drug is mixed with the monomer, followed by the initiator, with or without the cross-linking agent, and then allowed to polymerize. This method entraps the drug within the matrix. Whereas in the second method, gel is swelled in the drug solutions until equilibrium is attained, followed by drying to obtain the release device. The second method of drug imbibing has certain advantages over the first, as the polymerization conditions may have an adverse effect on the drug properties. It may also cause difficulty in the device purification. In the present work, the second method of drug loading was followed. A solution containing 2 g L⁻¹ of drug was prepared in distilled water. λ_max was noted and the calibration curve was prepared for the absorbance vs. the drug concentration. The drug loaded IPN was placed in different pH media to assess the drug release behavior. All the drug release experiments were carried out at 37 °C. The concentration of the losartan potassium released was taken after every two hours. This was followed by the drug release kinetics with respect to pH and time. An UV-Vis spectrophotometer (Double beam Systronic-2201) was used for the evaluation of the drug release through the synthesized IPN.

2.5 Mathematical analysis of the drug release behavior

A mathematical model was used to study the drug release behavior through the synthesized IPN. Liquid uptake, i.e., weight gain (M_s), was evaluated using eqn (2).

M_s = ktⁿ (2)

where k is the gel characteristic constant, and n is the diffusion exponent. n = 0.5 revealed the normal fickian diffusion, but if the value of n lies between 0.5 to 1, it signifies an anomalous or non-fickian diffusion. However, if n > 1, then it signifies a Case II diffusion. Fick's power law eqn (3) was used to evaluate the drug release through the IPN.^15–18

M_t/M_∞ = ktⁿ (3)

where, M_t is the fractional release of the drug at different time intervals and M_∞ is the fractional release of the drug at equilibrium. This equation could be applied for the 60% release of the drug. The value of n and k are obtained from the slope and intercept of the plot between M_t/M_∞ versus ln(t).

2.6 Diffusion coefficient of the drug release

Ficks law was used to describe the diffusion process.^19–23 The initial diffusion coefficient (D_I) was calculated using eqn (4).

M_t/M_∞ = 4 × D_I/πl² (4)

where, M_t/M_∞ is the fractional release of the drug. M_t and M_∞ are the drug release at time t and at equilibrium, respectively. L is the thickness of the IPN sample used.

The average diffusion coefficient (D_A) was calculated from eqn (5).

D_A = 0.049l²/t^1/2 (5)

where, t_1/2 is the time required for 50% release of the drug.

The lateral diffusion coefficient (D_L) was calculated using eqn (6).

M_t/M_∞ = 1 − (8/π²)exp(−π²Dt/l²) (6)

D_L was also evaluated using the slope of the plot between ln(1 − M_t/M_∞) vs. time as per eqn (7).

D_L = (slope l²/8) (7)

3. Results and discussion

3.1 RSM modeling

The experimental design, along with the actual response data, is given in Table 2. The ANOVA sequential model sum of squares proposed a cubic model as the best fit model. Although, the quadratic model would have explained the good model variance (Adjusted R² > 0.8), its lack of fit was found to be significant. This indicated that the model noise was more than the signal and the model could not be navigated in the design space; whereas, the cubic model was highly significant (adjusted R² = 0.984, F-value = 144) with a nonsignificant lack of fit (p = 0.253 at F = 1.61). Looking at the individual model terms, few model terms were nonsignificant at p-values greater than 0.05. Thus, the model was again generated after eliminating the nonsignificant model terms. The reduced cubic model was again significant at F = 173, with the lack of fit not significant (p = 0.355). The model statistics, like the coefficient of variation (CV) of 2.14% and the predicted R² = 0.976, which was closes to the adjusted R² = 0.985, indicated an excellent model fitting. Also, the signal to noise (S/N) ratio of 44.6 indicated that the model noise was not significant, compared to the signal. Thus, the model could be navigated in the design space. The unit-less regression equation, in terms of coded factors, is given in eqn (8) and in terms of actual factors in eqn (9). The results of the ANOVA statistics are given in Table 3.

P_s = 227.81 − 42.81A − 87.49B − 1.34A × B − 22.07A² − 35.09B² + 6.30A²B + 35.76A³ + 60.23B³ (8)

where P_s = percentage swelling; A: solvent and B: monomer are in coded units.

P_s = −7484 + 1010A + 360B − 7.93A × B − 45.63A² − 62.17B² + 0.183A²B + 0.678A³ + 4.12B³ (9)

where P_s = percentage swelling; A: solvent is in mL and B: monomer × 10⁻⁴ is in mol L⁻¹.

Table 3 ANOVA table for reduced cubic model

Source	Sum of squares	df	Mean square	F-value	p-value
					Prob > F
a Significant at p < 0.05.b Not significant at p < 0.05.
Model	26 234	8	3279	173.9	<0.0001^a
A-Solvent	811	1	811	43.0	<0.0001^a
B-Monomer	3254	1	3254	172.6	<0.0001^a
A × B	12	1	12	0.7	0.4334^b
A^2	1950	1	1950	103.4	<0.0001^a
B^2	4662	1	4662	247.3	<0.0001^a
A^2B	95	1	95	5.0	0.0431^a
A^3	499	1	499	26.4	0.0002^a
B^3	1446	1	1446	76.7	<0.0001^a
Lack of fit	88	4	22	1.3	0.3550^b
Model statistics
Std. dev.	4.34		R^2	0.991
Mean	203.2		Adj R^2	0.985
C.V.%	2.14		Pred R^2	0.976
PRESS	630		Adeq precision	44.9

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Before moving to the response surface plots, the diagnostic statistics were studied using normal plots of the residuals to satisfy the normal distribution of error, as given in Fig. 1a. The actual vs. predicted response plot for the percentage swelling is given in Fig. 1b.

Fig. 1 (a) Normal plot of residuals and (b) actual vs. predicted plot for percentage swelling.

The response plots were generated using regression equations. The percentage swelling is taken as the response and the solvent and monomer concentrations were varied in the designed space. The maximum percentage swelling was obtained in the solvent range of 19 mL to 20 mL. Similarly, the monomer was in the range of 2.8 × 10⁻⁴ mol L⁻¹ to 3.5 × 10⁻⁴ mol L⁻¹, to achieve the maximum percentage swelling (Fig. 2). Whereas, the minimum percentage swelling was obtained when the maximum amount of solvent was added (25 mL) with the maximum amount of monomer addition (7 × 10⁻⁴ mol L⁻¹). This indirectly indicated that the solvent and monomer had a weak interaction and may be interacting through higher terms.

Fig. 2 (a) 2-D and (b) 3-D contour plots showing the percentage swelling vs. solvent and monomer concentration.

3.2 Process optimization

Numerical optimization was achieved through the perturbation plot and desirability plot. The independent variables were set within the range, and the percentage swelling was set to the maximum. Through the random sampling point, the best process conditions were achieved at 19.63 mL solvent and 3.28 × 10⁻⁴ mol L⁻¹ monomer. The maximum percentage swelling achieved at optimized conditions was found to be 266. The results were verified by validation testing at the new proposed conditions. Using experimental design optimization, there was approximately a 92% increase in percentage swelling (140 to 270), depicted in the desirability plot (Fig. 3a). The perturbation plot helped to compare the response sensitivity at the optimized conditions. In this, one variable was found to change while holding the other constant under optimized conditions. A higher slope or curvature by a factor shows the response sensitivity. The monomer was more sensitive than the solvent under optimized conditions (Fig. 3a). Graphical optimization, commonly known as an overlay plot, was studied. The central elliptical (yellow) area represents those process conditions at which the minimum percentage swelling was 250. To achieve the minimum percentage swelling of 250, the solvent should range between 18 mL to 21.69 mL and the monomer concentration between 2.4 × 10⁻⁴ mol L⁻¹ to 4.2 × 10⁻⁴ mol L⁻¹ (Fig. 3b).

Fig. 3 (a) Perturbation plot showing the response sensitivity using the desirability function. (b) Overlay plot for achieving the minimum percentage swelling of 250.

3.3 Characterization studies

FT-IR spectroscopy. In the case of Gum tragacanth, broad peaks obtained were at 3427.08 cm⁻¹ (due to O–H stretching of the carbohydrate e), 2934.78 cm⁻¹ (CH₂ asymmetric stretching), 1039.07 cm⁻¹ (C–O stretching region as complex bands, resulting from C–O and C–O–C stretching vibrations), and 638 cm⁻¹ was (due to the pyranose ring). In the case of Gt-cl-poly(AA), additional peaks were obtained at 1750.23 cm⁻¹ and 1613.40 cm⁻¹, due to C=O stretching in carboxylic acid. Peaks at 2854.31 cm⁻¹, 2659.42 cm⁻¹, and 2521.73 cm⁻¹ were due to O–H stretching of the carboxylic acid, while for Gt-cl-poly(AA-ip-AAm), peaks near 1450 cm⁻¹ were due to –NH₂ plane bending of the amide II band. Peaks at 1200 and 1600 cm⁻¹ were due to –C–N stretching of the amide III band and –C=O stretching of the amide I band, respectively (Fig. 4).

Fig. 4 FT-IR spectrum of (a) Gum tragacanth, (b) Gt-cl-poly(AA), and (c) Gt-cl-poly(AA-ip-AAm).

SEM. Gum tragacanth, Gt-cl-poly(AA) and Gt-cl-poly(AA-ip-AAm) were morphologically examined with the help of scanning electron micrographs (SEM). Samples were gold plated in order to achieve a good conductive effect. SEM of Gum tragacanth showed no deposition of poly(monomeric) chains and is without any type of cross-linking (Fig. 5a). Whereas, the SEM of Gt-cl-poly(AA) revealed the cross-linking between the –OH groups of polysaccharide chains of the backbone and poly(AA) chains through glutaraldehyde linkage (Fig. 5b). However, the introduction of poly(AAm) chains in between the poly(AA) grafted polysaccharide chains caused physical and chemical interactions between the poly(AA) grafted polysaccharide chains and poly(AAm) chains, resulting in the formation of the IPN system. Comparison of the scanning electron micrograph of Gum tragacanth, Gt-cl-poly(AA) and Gt-cl-poly(AA-ip-AAm) revealed a clear cut distinction between them (Fig. 5a–c).

Fig. 5 SEM of (a) Gum tragacanth, (b) Gt-cl-poly(AA), and (c) Gt-cl-poly(AA-ip-AAm).

3.4 Drug release at varying pH and time

Losartan potassium was selected as a model drug. The drug loading efficiency of the synthesized IPN was found to be 79%. The synthesized IPN contained hydrophilic chains. These chains help in easy slipping of the drug molecules in the synthesized IPN matrix and thus lead to the easy loading of the drug into the matrix. These groups also help in the easy dissolution, as well as the easy diffusion of the drug through the matrix. Synthesized IPN hydrogel was found to swell constantly as a function of time, so it was found to be a suitable device for controlled release of drug. Various pH solutions 2.0, 7.0, and 9.2 were used as releasing media. The drug release behavior was studied at different pH. It was found from the results that the initial release of losartan potassium was 40.56 ppm at 2 hour time intervals (D_I = 4.523, pH = 7.0), followed by 41.67 ppm at 2.0 pH (D_I = 4.441), and 52.4 ppm at pH 9.2 (D_I = 6.547). However, the final release of the drug occurring after 34 h was found to be maximum in alkaline medium (D_L = 1.98, pH = 9.2) and minimum in neutral medium, see Table 4. The drug release was found to increase with increases in pH, due to the fact that –COOH groups on the polymeric chain are in a unionized form at lower pH, resulting in the collapsed state of the IPN hydrogel matrix. The drug release rate was a little higher at higher pH, because these groups are partially ionized to –COO⁻. These ionic charges repel each other leading to increased drug diffusion from the device. The drug release was also found to be dependent on time. Initially, the drug release was slow but it kept on increasing with the passage of time, and after reaching the maximum, equilibrium was attained with a constant release of losartan potassium (Fig. 6). This could be due to the fact that in the initial stage, the drug loaded polymer matrix swelled slowly under different pH media, which allowed a slow diffusion of the drug, which further increase with time until equilibrium was attained.

Table 4 Diffusion exponent, gel characteristic constant and diffusion coefficients of losartan potassium release behavior through loaded Gt-cl-poly(AA-ip-AAm) at different pH

Sample	pH	Diffusion exponent ‘n’	Gel characteristic constant ‘k’ × 10^−2	Diffusion coefficient (m^2 h^−1) × 10^−7
				DI	DA	DL
Gt-cl-poly(AA-ip-AAm)	2.0	0.879	0.053	4.441	0.2552	2.38
	7.0	0.892	0.054	4.523	0.2722	2.36
	9.2	0.788	0.073	6.547	0.3062	1.98

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Fig. 6 Effect of pH on losartan potassium release behavior through Gt-cl-poly(AA-ip-AAm), (a) conc. vs. time, (b) ln(M_t/M_∞) vs. ln(t) and (c) M_t/M_∞ vs. t^1/2.

4. Conclusions

Response surface methodology is an effective experimental design tool for maximizing drug uptake capacity. A novel interpenetrating polymeric network (IPN) was successfully synthesized and was found to possess the desired swelling capacity. The IPN was found to be pH sensitive towards the controlled release of the antihypertensive drug losartan potassium. The final release of the drug from the matrix occurred after 34 h, which indicated that the synthesized device could be used as a drug carrier for the prolonged release of losartan potassium.

Acknowledgements

The financial support by Ministry of Human Resource Development, New Delhi for providing fellowship (no. F. 9-2/2007 TS.I) to Saruchi is acknowledged.

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