Design, Characterization, and Optimization of Controlled Drug Delivery System Containing Antibiotic Drug/s

The objective of this work was design, characterization, and optimization of controlled drug delivery system containing antibiotic drug/s. Osmotic drug delivery system was chosen as controlled drug delivery system. The porous osmotic pump tablets were designed using Plackett-Burman and Box-Behnken factorial design to find out the best formulation. For screening of three categories of polymers, six independent variables were chosen for Plackett-Burman design. Osmotic agent sodium chloride and microcrystalline cellulose, pore forming agent sodium lauryl sulphate and sucrose, and coating agent ethyl cellulose and cellulose acetate were chosen as independent variables. Optimization of osmotic tablets was done by Box-Behnken design by selecting three independent variables. Osmotic agent sodium chloride, pore forming agent sodium lauryl sulphate, and coating agent cellulose acetate were chosen as independent variables. The result of Plackett-Burman and Box-Behnken design and ANOVA studies revealed that osmotic agent and pore former had significant effect on the drug release up to 12 hr. The observed independent variables were found to be very close to predicted values of most satisfactory formulation which demonstrates the feasibility of the optimization procedure in successful development of porous osmotic pump tablets containing antibiotic drug/s by using sodium chloride, sodium lauryl sulphate, and cellulose acetate as key excipients.


Introduction
Oral controlled drug delivery system can provide continuous delivery of drugs at controlled rate and predictable kinetics throughout the GI transit. Oral controlled drug delivery system targets drug delivery to a specific region for either local or systemic effect throughout the GI transit. This system also gives zero-order release profile [1]. Oral controlled release system can provide better effectiveness in treatment of chronic disease, reduce side effects, and improve patient compliance due to less frequent dosing interval.
Drug release from oral controlled release dosage forms are affected by pH of GI fluid, GI motility, and presence of food in GI tract. Drug release from osmotic drug delivery system is independent of pH and other physiochemical parameters and it is possible to modulate the release characteristic by optimizing the properties of drug and system [2,3].
Osmotic pressure is used as driving force for osmotic drug delivery systems to release the drug in controlled manner. Osmotic pressure created due to imbibition of fluid from external environment into the dosage form regulates the delivery of drug from osmotic device. Osmotic drug delivery technique is the most interesting and widely acceptable among all other technologies used for the same purpose. Intensive research has been carried out on osmotic systems and several patents are also published. These systems can be used for both routes of administration, that is, oral and parenteral. Oral osmotic systems are known as gastrointestinal therapeutic systems (GITS). Parenteral osmotic drug delivery includes implantable pumps [3].
Dicloxacillin sodium and amoxicillin trihydrate are -Lactam antibiotics. Dicloxacillin sodium and amoxicillin trihydrate have short half-life and high protein binding. The drug that shows linear pharmacokinetics is suitable for oral controlled release tablets and it would be advantageous to 2 Journal of Drug Delivery slow down its release in GI tract not only to prolong its therapeutic action but also to minimize side effects of drugs.

Materials.
Dicloxacillin sodium was obtained as gift sample from Suvik Hitek Pvt. Ltd. (Gandhinagar, India). Amoxicillin trihydrate was obtained as gift sample from Astral Life Care (Mumbai, India). Sodium chloride was purchased from Merck Pharmaceutical (Mumbai, India). Sodium lauryl sulphate was purchased from Bombay Tablet (Gandhinagar, India). Cellulose acetate and PVP K30 were purchased from Chemdyes Corporation (Gujarat). Magnesium stearate and talc were purchased from Suvik Hitek Pvt. Ltd. (Gandhinagar, India). (DSC). DSC studies were carried out for the pure drug, physical mixtures of drug and excipients, and placebo of the porous osmotic pump tablets to study the compatibility. The analysis was performed under nitrogen (nitrogen flow rate 50 mL/min) in order to eliminate oxidative and pyrolytic effects at a standard heating rate of 10 ∘ C/min over a temperature range of 50 ∘ C-400 ∘ C using Universal V4 5A TA instruments.

Preparation of Core Tablets.
Core tablets of dicloxacillin sodium were prepared by wet granulation method. All the ingredients were sieved through # 40 sieve. Individual ingredients, sufficient for a batch of 25 tablets, were weighed on a digital weighing balance as per Table 1. All the ingredients (except PVP K30, magnesium stearate, and talc) were mixed in mortar and pestle using geometric dilution method. The dry blend was granulated with sufficient quantity of PVP K30 which was dissolved in isopropyl alcohol. The powder mass was dried at 60 ∘ C in hot air oven for 6 h and passed through # 20 sieve. Then dried granules were mixed with magnesium stearate and talc for 3 min. Tablets were prepared by 9 mm concave die punch set using rotary tablet punching machine [4,5].

Method of Preparation of Tablet Coat Solution.
Cellulose acetate and PEG 400 were added to 3/4th of the total volume of acetone and stirred at 35 rpm using propeller stirrer for half an hour till the solution was clear. Magnesium stearate and coloring agent were triturated thoroughly in a mortar and added to the above solution and stirring continued further. Finally, the volume was made up with acetone [4,5] (see Table 2).

2.4.
Coating of the Core Tablets. Tablet coating was done using coating pan apparatus. Speed of coating pan was set at 30 rpm, and inlet air temperature and flow rate were 50 ∘ C and 3.2 kg/min, respectively. Spraying rate for coating solution was kept at 4-5 mL/min. Number of tablets per batch was fixed at 50 tablets. Ten tablets of test batch were mixed with 40 dummy tablets. Empty coating pan was run at above set parameters for 5 min. Tablets were loaded to the pan and  2) for 2 hr and then in pH 6.8 phosphate buffer for 10 hr, at 37 ± 0.5 ∘ C and 100 rpm. A sample (1 mL) of the solution was withdrawn from the dissolution apparatus hourly for 12 h, and the samples were replaced with fresh dissolution medium. The samples were passed through Whatman filter paper after dilution and the absorption of these solutions was measured at 273 nm. The cumulative percentage drug release was calculated.

Curve Fitting Analysis.
For the determination of the drug release kinetics from the porous osmotic pump tablet, the in vitro release data were analyzed by zero-order, first-order, Higuchi, and Korsmeyer and Peppas equations [6].

Zero-Order Release Kinetics.
To study the zero-order release kinetics the release data was fitted into the following equation: where " " is the amount of drug release, " 0 " is the zeroorder release rate constant, and " " is the release time. The graph is plotted percentage cumulative drug release (% CDR) versus time.

First-Order Release Kinetics.
To study the first-order release kinetics the release rate data are fitted into the following equation: where " " is the fraction of drug release, " 1 " is the firstorder release rate constant, and " " is the release time.

Higuchi Release Model.
To study the Higuchi release model the release rate data are fitted into the following equation: where " " is the fraction of drug release, " " is the release rate constant, and " " is the release time. The graph is plotted as % CDR versus square root of time.
where / ∞ is the fraction of drug release, " KP " is the release rate constant, " " is the release time, and " " is the diffusion exponent related to mechanism of drug release. The graph is plotted as log % CDR versus log time [6].

Optimization of Osmotic Tablet by Box-Behnken Factorial Design.
In this optimization technique, the desirability approach was used to generate the optimum settings for the formulation. From the trial batches, three independent variables were found to affect drug release significantly. Concentration of coating agent (NaCl) and pore forming agent (SLS) and concentration of coating agent (cellulose acetate) were taken as independent variables [8,9]. For the optimized formulation, the drug release at 2 hr, 6 hr, and 12 hr and release exponent ( ) were kept in target. Formulation of osmotic tablets of factorial batches is shown in Tables 5 and  6.

Effect of pH on Drug
Release. The optimized formulation of porous osmotic pump tablets was tested for the effect of pH on drug release. The best formulations were undergone in dissolution studies in 0.1 N HCl, 6.8 pH phosphate buffer, 7.5 pH phosphate buffer, and distilled water in rotation speed of 100 rpm and 37 ± 0.5 ∘ C using USP dissolution test apparatus type 1. and at 37 ± 0.5 ∘ C in 7.5 pH phosphate buffer for 8 h using USP dissolution test apparatus type 1.

Stability Studies.
The stability studies were carried out as per the ICH and WHO guidelines of stability testing. Optimized formulations were kept inside the stability chamber maintained at 45 ∘ C and 75% RH for the period of 30 days. At the end of the stability study period, samples were analyzed for parameters like physical characteristics, drug content, and in vitro drug release.

Screening of Polymers by Plackett-Burman Factorial Design
3.2.1. Physicochemical Properties. Twelve batches were prepared for screening of polymers. The mean values of hardness, friability, thickness, weight, and drug content of prepared porous osmotic pump tablets are shown in Table 7.

In Vitro Dissolution Study.
To study all the possible combinations of all factors at all levels, a six-factor, twolevel Plackett-Burman factorial design was constructed and conducted in a fully randomized order. Six factors, NaCl( 1), MCC( 2), SLS( 3), sucrose( 4), EC( 5), and CA ( 6), were selected as independent variables. Twelve batches were prepared to study Plackett-Burman factorial design for osmotic tablets. Two checkpoint batches were also evaluated to validate the design. The dependent variables (responses) studied were % drug release after 1 hr, 6 hr, and 12 hr of dissolution. Results of the drug release profile obtained for osmotic tablets are shown in Figures 3(a), 3(b), 3(c), and 3(d).
Effect of formulation variable on drug release at 1 hr, 6 hr, and 12 hr was carried out using Design-Expert Software (Version 7.1.6, Stat-Ease Inc., Minneapolis, MN).

Effect of Formulation Variable on Drug Release at 1 hr (Y1).
From the equation, factor value of 1 was +2.08 and 2 was −1.41 indicating that 1 had more effect on drug release than 2. Factor value of 3 was +5.75 and 4 was +0.41 indicating that 3 had more effect on drug release than 4. Factor value of 5 was +0.08 and 6 was +0.53 indicating that 6 had more effect on drug release than 5. Positive sign of 1, 3 , and 6 indicates positive effect on drug release.
The relationship between formulation variables ( 1 and 2) and 1 was further elucidated using 3D surface plot. From Figure 4(b) it can be concluded that factor NaCl( 1) had more osmotic effect on drug release while MCC( 2) had no significant effect on drug release. The relationship between formulation variables ( 3 and 4) and 1 was further elucidated using 3D surface plot. From Figure 4(d) it can be concluded that factor SLS( 3) had more pore forming effect on drug release while sucrose ( 4) had no significant effect on drug release.

Effect of Formulation Variable on Drug Release at 6 hr ( 2).
From the equation, factor value of 1 was +6.66 and 2 was −3.66 indicating that 1 had more effect on drug release than 2. Factor value of 3 was +12.66 and 4 was +0.66 indicating that 3 had more effect on drug release than 4. Factor value of 5 was −0.16 and 6 was +3.83 indicating that 6 had more effect on drug release than 5. Positive sign of 1, 3, and 6 indicates positive effect on drug release.
The relationship between formulation variables ( 1 and 2) and 2 was further elucidated using 3D surface plot. From  The relationship between formulation variables ( 3 and 4) and 2 was further elucidated using 3D surface plot. From Figure 5(d) it can be concluded that factor SLS( 3) had more pore forming effect on drug release while sucrose ( 4) had no significant effect on drug release.

Effect of Formulation Variable on Drug Release at 12 hr ( 3).
From the equation, factor value of 1 was +5.08 and 2 was −5.19 indicating that 1 had more effect on drug release than 2. Factor value of 3 was +19.75 and 4 was −1.08 indicating that 3 had more effect on drug release than 4. Factor value of 5 was +2.41 and 6 was +7.25 indicating that 6 had more effect on drug release than 5. Positive sign of 1, 3, and 6 indicates positive effect on drug release.
The relationship between formulation variables ( 1 and 2) and 3 was further elucidated using 3D surface plot. From Figure 6(b) it can be concluded that factor NaCl( 1) had more osmotic effect on drug release while MCC( 2) had no significant effect on drug release. The relationship between formulation variables ( 3 and 4) and 3 was further elucidated using 3D surface plot. From Figure 6(d) it can be concluded that factor SLS( 3) had more pore forming effect on drug release while sucrose ( 4) had no significant effect on drug release.

Optimization of Osmotic Tablet by Box-Behnken Factorial Design
3.3.1. Physiochemical Parameter. 15 batches were prepared for optimization of osmotic tablets. Tablets were evaluated for uniformity of weight, uniformity of contents, tablet thickness and diameter, and hardness and friability. Results of the physiochemical tests obtained are shown in Table 8.

In Vitro Dissolution Study.
To study all the possible combinations of all factors at all levels, a three-factor, three-level Box-Behnken factorial design was constructed and conducted in a fully randomized order. Three factors, NaCl( 1), SLS( 2), and CA ( 3), were selected as independent variables. 15 batches were prepared to study Box-Behnken factorial design for osmotic tablets. Two checkpoint batches were also evaluated to validate the design. The dependent variables (responses) studied were % drug release after 1 hr, 6 hr, and 12 hr of dissolution. Results of the drug release profile obtained for osmotic tablets are shown in Figures 7(a), 7(b), 7(c), and 7(d). Figure 7(a) contains dissolution profile for batches F13-F16. Figure 7(b) contains dissolution profile for batches F17-F20. Figure 7(c) contains dissolution profile for batches F21-F124. Figure 7(d) contains dissolution profile for batches F25-F127.

Effect of Formulation Variable on Drug Release at 1 hr (Y1).
Equation shows that coefficients 1 and 2 bear a positive sign and 3 bears a negative sign and coefficient value for 1 is 7.50, 2 is 0.25, and 3 is −0.50. So it indicates that 1 has more effect on drug release than 2 and 3. Figures 8(a) and The relationship between formulation variables ( 1, 2, and 3) and 1 was further elucidated using cube and 3D surface plot. From Figure 8(a) it can be concluded that factor Nacl( 1) has more effect on drug release than SLS( 2). From Figure 8(b) it can be concluded that factor 3 (Coating agent) has negative effect on drug release. As we increase the level of 3, it decreases drug release.

Effect of Formulation Variable on Drug Release at 6 hr (Y2).
Equation shows that coefficients 1 and 2 bear a positive sign and 3 bears a negative sign and coefficient value for 1 is 11.62, 2 is 0.38, and 3 is −0.75. So it indicates that 1 has The relationship between formulation variables ( 1, 2, and 3) and 2 was further elucidated using cube and 3D surface plot. From Figure 9(a) it can be concluded that factor NaCl( 1) has more effect on drug release than SLS( 2). From Figure 9(b) it can be concluded that factor 3 (Coating agent) has negative effect on drug release. As we increase the level of 3, it decreases drug release.

Effect of Formulation Variable on Drug Release at 12 hr (Y3).
Equation shows that coefficients 1 and 2 bear a positive sign and 3 bears a negative sign and coefficient value for 1 is 17.50, 2 is 0.88, and 3 is −0.80. So it indicates that 1 has more effect on drug release than 2 and 3. Figures 10(a) and 10(b) show cube and 3D surface plot for 2 suggesting effect of variables as described above. Consider The relationship between formulation variables ( 1, 2, and 3) and 3 was further elucidated using cube and 3D surface plot. From Figure 10(a) it can be concluded that factor NaCl( 1) has more effect on drug release than SLS( 2). From Figure 10(b) it can be concluded that factor 3 (coating agent) has negative effect on drug release. As we increase the level of 3, it decreases drug release.

Selection of Optimized
Batch. Selection of best batch was carried out using Design-Expert Software (Version 7.1.6, Stat-Ease Inc., Minneapolis, MN). After statistical analysis the desirability function was applied to select the best batch.
The desirable values selected for dependent variables 1, 2, and 3 are given in Table 9. Desirable value range selected that was 5% varies from optimum value.
Batch F22 came closest to satisfying all the selection criteria. The results were further reinstated using the overlay plot in Figure 11. The yellow region of the plot indicates the area where all the selection criteria are satisfied. Batch F22 falls in this yellow area, indicating the formulation having amount of osmotic agent (150 mg), pore forming agent (15 mg), and coating agent (2%) that possessed the desirable characteristics.

So F22 Batch Was Selected as Optimized Batch
3.5.1. Effect of pH on Drug Release. When formulation F22 was subjected to in vitro release studies in buffers with different pH and distilled water, no significant differences in the release profiles were seen compared to that in phosphate buffer pH 6.8. Thus the fluid in different parts of the GI tract will scarcely affect drug release from the osmotic system.

Effect of Agitation
Intensity on Drug Release. The release profile of dicloxacillin sodium from the optimized formulation F22 was independent of the agitational intensity of the release media.

Osmotic Tablet of Amoxicillin Trihydrate
Optimized batch of amoxicillin trihydrate was prepared using F22 batch composition of Box-Behnken design batches. From the drug release data and also release pattern shown in Figure 12 it can be concluded that there is no significant difference between two drug release profiles.

Release Kinetics and Release Mechanism
Six kinetic models were used for controlled release curve fitting to select the most appropriate model. The dissolution data for optimized batch was fitted to the zero-order, firstorder, Higuchi, Hixson-Crowell, Korsmeyer-Peppas, and Weibull models. Best fitting model was selected on the basis of highest correlation coefficient and lowest value. Comparative statistical parameters for all the models were obtained as shown in Table 11. Drug release mechanism was explored on the basis of release exponent ( ) value.
Model fitting results revealed that the Korsmeyer-Peppas model was best fitted to the release kinetics ( 2 = 0.9960, highest; = 6.8629, lowest). Higuchi model was also close to the Korsmeyer-Peppas model. Hence test was performed for both models. It revealed significant difference between the two models. Hence Korsmeyer-Peppas model was finally selected as best fitted model. Release exponent was found Model fitting results revealed that the Korsmeyer-Peppas model was best fitted to the release kinetics ( 2 = 0.9960, highest, = 6.8629, lowest). Higuchi model was also close to the Korsmeyer-Peppas model. Hence test was performed for both models. It revealed significant difference between two models. Hence Korsmeyer-Peppas model was finally selected as best fitted model. Release exponent was found to be 0.580, indicating that the drug was released from the formulation by anomalous (non-Fickian) mechanism.

Stability Study
After the 1-month storage of formulation F22, values of all parameters like hardness, diameter, thickness, % drug content, and friability were checked periodically and found to be almost similar to the initial values. The drug profile was similar to the initial profile shown in Figure 13. There was not any significant change in any value and also no changes in the physical appearance. So it can be said that formulation is stable (see Table 12).

Conclusion
The observed independent variables were found to be very close to predicted values of optimized formulation which demonstrates the feasibility of the optimization procedure in successful development of porous osmotic pump tablets containing dicloxacillin sodium and amoxicillin trihydrate as model drug by using sodium chloride (150 mg) as osmotic agent, sodium lauryl sulphate (15 mg) as pore former, cellulose acetate (2%) as coating agent, and control membrane permeability. Batch F22 was selected as optimized batch. Stability studies also revealed that optimized formulation is stable. From the comparison of dissolution profile of optimized batch for both drugs (dicloxacillin sodium and amoxicillin trihydrate) it can be concluded that there was no significance difference in drug release observed, so it concludes that porous osmotic pump tablets of antibiotic drugs were successfully developed (see Table 10).