Formulation process is a very complex activity which sometimes implicates taking decisions about parameters or variables to obtain the best results in a high variability or uncertainty context. Therefore, robust optimization tools can be very useful for obtaining high quality formulations. This paper proposes the optimization of different responses through the robust Taguchi method. Each response was evaluated like a noise variable, allowing the application of Taguchi techniques to obtain a response under the point of view of the signal to noise ratio. A
The use of natural polymers for the design of drug delivery systems has long been the subject of great interest during the past decades. Sodium alginate is a sodium salt of alginic acid, a naturally occurring nontoxic polysaccharide found in marine brown algae. Alginate has been widely used as food and pharmaceutical additives, such as tablet disintegrant, thickening, and suspending agent. It contains two uronic acids,
The addition of a polycation (poly-L-lysine or chitosan) to the gelation medium induces the formation of polyanionic-polycationic complexes, which stabilizes the ionic gel network and reduces the alginate permeability [
The main advantage of using alginate to encapsulate drugs is that the alginate gelation process occurs under very mild conditions without using high temperatures or chemical crosslinking agents. Another advantage of using alginate is that the alginate gel can also be converted to sol by adding chelating agents, such as Na+ and EDTA. However, the drug releasing properties of Ca-sodium alginate matrices suffer from some serious problems. Firstly, the drugs could be leaked during the gel formation due to the long immersion time, which decreased the encapsulation efficiency. Secondly, the burst release of the drugs from pure Ca-sodium alginate beads is severe due to the quick breakdown of beads in the
Therefore, many factors are involved in the formulation of alginate microspheres. Some of them are summarized in Table
Some of the factors affecting the encapsulation process of drugs into alginate beads.
Factor | Drug | Effect | Reference | |
---|---|---|---|---|
Related with the formulation | ||||
Gliclazide | ||||
Alginate concentration | + EE | |||
+ diameter | ||||
−porosity |
[ | |||
−release rate | ||||
+ swelling | ||||
BSA | + EE | [ | ||
Glucose | ||||
Volume of the alginate solution | Gliclazide | −diameter | [ | |
Divalent cation concentration | BSA | −EE | [ | |
VEGF | ||||
Glucose | ||||
Crosslinker/alginate ratio | Neutral (xanthan gums, ethylcellulose, PVA) | Diclofenac sodium | + EE | [ |
+ swelling | ||||
Cationic (chitosan, poli-L-lysine) | Proteins Oligonucleotides | + EE | [ | |
Waxy compounds (Compritol 888 ATO, Precirol ATO 5, Gelucires, glyceryl palmitostearate, magnesium aluminum silicate) | Tamsulosin, diclofenac sodium | + EE | [ | |
Anionic (CMC, Eudragit L100, dextran sulphate) and cationic | Insulin | +EE | ||
+ diameter | ||||
−release rate | ||||
Surfactant presence | Gliclazide | −diameter |
[ | |
+ EE | ||||
Related with the process | ||||
Needle size | BSA | −diameter | [ | |
Coaxial air volume | BSA | −diameter | [ | |
Drying process | BSA | [ | ||
Stirring rate | Gliclazide | −EE |
[ | |
−diameter |
Drug release from calcium-alginate beads depends on the swelling of the beads and the diffusion of the drug in the gel matrix [
Polymeric materials have been widely used in order to conveniently modify and modulate the drug release from controlled-release microparticles. However, a large number of factors, including the chemical-physical properties of the raw materials (both drug and excipients), the composition and the relative amounts of the components in the formulations, as well as the manufacturing process parameters, can influence the drug release behavior from the final products [
In the present study, we attempted to reinforce calcium-alginate beads containing methylene blue as a model drug by incorporating Carbopol 940 as hydrophilic polymer. This is a poly acrylic acid in anionic form that contains many free hydroxyl groups. Since it can form a gel, it has been used in combination with other polymers to control the drug release.
There are previous reports about the effect of carbomers on characteristics of calcium-alginate beads [
The preparation of calcium-alginate beads for drug entrapment is a stepwise procedure. The industrial experimentation includes a great number of parameters and a full factorial design that yield large number of experiments. Conventional optimization procedures involve altering one independent factor at a time keeping all others remain constant, which enables to assess the impact of those particular parameters on the process performance. These procedures are time consuming and cumbersome because require more experiments and cannot provide information about the mutual interactions of the parameters [
Statistical experimental design methods, such as the fractional factorial design [
The Taguchi method focuses on product robustness against uncontrollable influences, or noise. The method is designed to reduce variability, and optimize function or performance with the lowest cost components. Taguchi method of orthogonal array design of experiment (DOE) involves the study of any given system by a set of independent variables (factors) over a specific region of interest (levels). This approach also facilitates to identify the influence of individual factors, and establish the relationship between variables and operational conditions. In this methodology, the desired design is sought by selecting the best performance under conditions that produce consistent performance [
Robustness is essential in the production of nearly all products. The variation in the quality of the product may follow from environmental factors and/or manufacturing variables that cannot be easily controlled (noise factors). In addition to emphasizing that the variability in the quality of the product and factors interactions should be reduced [
This technique is somehow not very well known for optimization of pharmaceutical processes. This paper offers a rapid and efficient methodology to study and optimize pharmaceutical formulations, based on the Taguchi orthogonal array design. To do this, as an example, we used alginate-Carbopol beads formulated by ionotropic gelation with methylene blue (MB) as hydrophilic drug model.
After the selection of noise and control factors, we selected the profiles (responses) to optimize with the aim to demonstrate the applicability of this methodology in the optimization of different profiles such as drug release, swelling rate, or the morphology of the beads. Afterward, the experimental design and data analysis procedure, we selected the best profiles for Taguchi optimization and then we compared the results with predicted ones by lineal regression. Finally we compared the accuracy of the optimization.
Sodium alginate (viscosity 2% w/v: 250 cP) and triethanolamine (TEA) were purchased from Sigma Aldrich. Calcium chloride (Panreac, Spain) was selected as cation donor salt. MB was used as hydrophilic drug model. Carbopol 940 (Acofarma, Spain) was added to control the drug release from the alginate beads. Sodium tripolyphosphate and Tris(hydroxymethyl) aminomethane (TRIS) were obtained from Sigma (Spain).
The beads were prepared by following the extrusion/precipitation method [
Factors and their corresponding levels implemented for the construction of
Factor | Levels used, actual (coded) | ||
---|---|---|---|
Low (−1) | Medium (0) | High (+1) | |
Independent variables | |||
A: addition of TEA | No | Yes | |
B: concentration of Carbopol (% w/v) | 1 | 2 | 4 |
C: flux rate of polymeric solution (mL/min) | 1 | 2 | 3 |
D: stirring rate of stirrer (rpm) | 100 | 300 | 500 |
E: reaction time of beads into CaCl2 solution (min) | 5 | 30 | 60 |
F: drying method | Room | Rotary evaporator | Oven |
G: NaCl (% w/v) | 0 | 0.01 | 0.1 |
H: pH | 7.2 | 7.6 | 8.0 |
The MB content in the dry samples was determined by dissolving the beads, previously weighed, in a tripolyphosphate solution (0.03 M; 100 mL). The resulting solution was filtered and the drug concentration was analyzed by UV spectrophotometry, at 664 nm, using a UV-visible spectrophotometer (Hitachi U-2000). Entrapment efficiency was calculated as the percentage (w/w) of the theoretical drug content. Results were based on triplicate determinations.
The drug release profiles of beads were determined using the USP 28 paddle method. The dissolution medium was 900 mL (sink conditions) of Tris-buffer solution adjusted at different pH and NaCl concentration values as a function of the experiment. The stirring speed was 50 rpm and the temperature was maintained at
Tris-buffer solution was selected as the dissolution medium because in the formation of alginate gel, alginate reaction with calcium ions turns reversible with sodium ions, and a disaggregation of the beads occurs [
The amount of absorbed water was determined following described methods by many authors [
Swelling percentage (
Morphological characteristics of beads were observed by Scanning Electron Microscopy, SEM (Philips XL30). The samples were sputter-coated with Au/Pd using a vacuum evaporator (Edwards) and examined at 20 kV accelerating voltage.
The size and shape of the beads were determined using image analyzer software (Image Pro Plus). The length and breadth were measured from the digital images of each bead and its size calculated from the average of these two dimensions [
For each formulation, 30 beads were randomly selected for measurement; the results were then averaged.
Optimization methodology adopted in this study was divided into four phases (with various steps): planning, performance, analysis, and validation. The schematic representation of the designed methodology was depicted in Figure
Schematic diagram of the steps involved in the Taguchi methodology.
First step in Phase I is to determine the factors to be optimized in the production and characterization of alginate-Carbopol beads that have critical effect on the drug release, encapsulation efficiency, and in the morphological properties of the beads. The goal is to evaluate the effect of different factors on the elaboration of the beads and to obtain the perfect combination between the levels of the factors to optimize theirs properties.
Six process parameters (flux rate, stirring rate, incubation time, drying mechanism, NaCl concentration, and pH of the dissolution medium) and two formulation parameters (presence of TEA and Carbopol concentration) were evaluated. The normal practice is to experiment with the feasible range, so that the variation inherent in the process does not mask the factors effects. The controls factor and theirs levels are exposed in Table
The responses to be optimized from the characterization of alginate-Carbopol beads are those that have critical effect on the drug release, encapsulation efficiency, and morphological properties of the beads. Selected responses are exposed in Table
Test conditions to optimize.
Profile analyzed | Selected variable |
---|---|
Drug release | Time to release 50 percent of entrapment drug ( |
Area under curve (AUC) | |
Entrapment efficiency | Entrapment efficiency (EE) |
Swelling performance | Swelling rate (SR) |
Shape of the particles | Shape factor (SF) |
Size of the particles | Mean diameter (Dm) |
The fundamental principle of the Taguchi method is to improve the quality of a product by minimizing the effect of the causes of variation without eliminating the causes [
The Taguchi approach provides an opportunity to select a suitable orthogonal array depending on the number of control factors and theirs levels [
Orthogonal array is a matrix of numbers arranged in rows and columns. Each row represents the level of factors in each run and each column represents a specific level for a factor that can be changed for each run.
In the Taguchi method, quality is measured by the deviation of a characteristic from its target value, and a loss function
In order to measure the quadratic loss function, the Taguchi introduces the signal-to-noise (
where
For each type of characteristics, with the above
The design was performed by an
After phase I, in which the factors to be optimized, theirs levels and the experimental matrix were selected, the experiments were carried out by triplicate (Table
Experimental layout using a
Run | AUC | EE | SR | SF | Dm | ||||||||
Mean | Mean | Mean | Mean | Mean | Mean | ||||||||
1 | 73.00 | 28.82 | 268.68 | 49.11 | 48.43 | 37.04 | 2863.78 | 69.14 | 0.52 | −5.74 | 1.34 | −2.71 | |
2 | 50.00 | 27.96 | 421.78 | 49.29 | 54.88 | 38.26 | 1937.37 | 65.74 | 0.20 | −13.88 | 1.47 | −3.43 | |
3 | 69.33 | 35.57 | 117.23 | 48.60 | 53.63 | 38.08 | 1320.67 | 62.42 | 0.25 | −12.06 | 1.53 | −3.70 | |
4 | 91.33 | 31.94 | 461.89 | 48.23 | 54.88 | 38.17 | 1681.62 | 64.51 | 0.58 | −4.77 | 1.72 | −2.96 | |
5 | 102.33 | 32.18 | 405.74 | 48.66 | 59.00 | 38.92 | 2177.52 | 66.76 | 0.27 | −11.32 | 1.64 | −4.50 | |
6 | 198.33 | 25.60 | 348.62 | 47.04 | 53.51 | 38.08 | 1119.52 | 60.98 | 0.65 | −3.83 | 1.69 | −4.61 | |
7 | 118.67 | 19.17 | 354.48 | 48.22 | 54.34 | 38.21 | 1610.31 | 64.14 | 0.30 | −10.47 | 1.51 | −3.65 | |
8 | 293.33 | 28.10 | 257.57 | 46.46 | 54.46 | 38.24 | 1082.83 | 60.69 | 0.25 | −12.01 | 1.54 | −3.73 | |
9 | 106.00 | 18.80 | 427.85 | 47.90 | 57.14 | 38.65 | 1459.06 | 63.28 | 0.55 | −5.26 | 1.66 | −4.42 | |
10 | 60.00 | 24.77 | 448.50 | 48.89 | 59.73 | 39.04 | 2786.18 | 68.90 | 0.19 | −14.68 | 2.07 | −6.33 | |
11 | 82.67 | 22.18 | 634.32 | 48.71 | 59.33 | 38.97 | 1151.14 | 61.22 | 0.26 | −11.95 | 1.83 | −5.27 | |
12 | 119.33 | 40.29 | 401.70 | 47.93 | 56.07 | 38.49 | 3685.23 | 71.32 | 0.55 | −5.20 | 1.57 | −3.97 | |
13 | 111.67 | 23.30 | 1326.03 | 47.92 | 58.83 | 38.91 | 1868.79 | 65.39 | 0.40 | −9.30 | 1.73 | −5.12 | |
14 | 148.67 | 36.17 | 225.15 | 47.45 | 59.88 | 39.06 | 3003.21 | 69.55 | 0.20 | −13.88 | 1.72 | −4.70 | |
15 | 209.33 | 36.72 | 469.47 | 47.14 | 59.49 | 39.00 | 2017.31 | 66.10 | 0.38 | −8.50 | 1.56 | −4.01 | |
16 | 238.67 | 40.29 | 1052.68 | 46.04 | 53.97 | 38.15 | 3094.41 | 69.81 | 0.35 | −9.26 | 1.67 | −4.54 | |
17 | 142.00 | 32.25 | 536.70 | 48.08 | 56.39 | 38.52 | 1554.57 | 63.83 | 0.34 | −9.40 | 1.41 | −3.04 | |
18 | 138.33 | 33.61 | 841.84 | 47.79 | 60.02 | 39.08 | 1420.16 | 63.05 | 0.26 | −11.71 | 2.81 | −8.99 | |
At this stage, a statistical analysis of variance was performed to determine which test parameters were statistically significant for every variable. Moreover, the influence of each factor on the different response variables selected was determined.
After the analysis of the data, we proceeded to determine the optimal set parameter configuration. Optimization process was made applying the marginal means methodology (also called average rate or main effects analysis).
In order to do that, we obtained the average value of
With
In order to validate this methodology, validation experiments were further performed using the optimized conditions obtained for each response and then the results will be compared to the predicted by regression. If final results are similar to the predicted, we could validate the proposed methodology.
The application of the experimental design methodology has the objective of identifying the most significant factors influencing the formulation of alginate-Carbopol beads containing a water-soluble drug, and establishing the best levels for optimizing different response variables.
Results obtained for these parameters, expressed as mean values, are presented in Table
ANOVA results: effect of the variables on
Factors | AUC | EE | |||||||
---|---|---|---|---|---|---|---|---|---|
A | 89.52 | <0.001 | 16.48 | 144.1 | <0.001 | −1892 | 11.2 | 0.004 | 5.57 |
B | 2072 | <0.001 | 97.11 | 1070 | <0.001 | −6316 | 0.27 | 0.607 | 1.07 |
C | 132.5 | <0.001 | 24.56 | 136 | <0.001 | −2252 | 1.41 | 0.25 | 2.42 |
D | 564 | <0.001 | 50.67 | 251.7 | <0.001 | −3063 | 0.003 | 0.955 | −0.12 |
E | 172.9 | <0.001 | −28.06 | 52.87 | <0.001 | 1404 | 1.27 | 0.274 | 2.30 |
F | 83.07 | <0.001 | −19.44 | 21.36 | <0.001 | 892.2 | 2.18 | 0.157 | 3.01 |
G | 0.01 | 0.918 | −0.22 | 0.01 | 0.921 | −19.4 | 0.67 | 0.425 | 1.66 |
H | 213.4 | <0.001 | −31.17 | 3.20 | 0.082 | 345.2 | 0.53 | 0.474 | −1.49 |
Factors | SR | SF | DM | ||||||
F-ratio | |||||||||
A | 1842 | <0.001 | 592 | 34.79 | <0.001 | −0.072 | 12.78 | <0.001 | 0.255 |
B | 1208 | <0.001 | −587.2 | 0.78 | 0.383 | 0.013 | 2.19 | 0.148 | 0.128 |
C | 808.8 | <0.001 | −480.5 | 11.9 | <0.001 | 0.051 | 2.19 | 0.148 | 0.130 |
D | 274.4 | <0.001 | 279.9 | 69.35 | <0.001 | −0.124 | 1.69 | 0.201 | 0.115 |
E | 0.00 | 0.99 | −0.22 | 4.49 | 0.041 | −0.032 | 2.73 | 0.108 | −0.142 |
F | 62.8 | <0.001 | −133.9 | 100.4 | <0.001 | −0.149 | 0.80 | 0.377 | 0.077 |
G | 5856 | <0.001 | −1293 | 23.4 | <0.001 | −0.072 | 8.78 | 0.005 | 0.261 |
H | 25.09 | <0.001 | 84.64 | 15.63 | <0.001 | 0.059 | 8.15 | 0.007 | −0.251 |
Contribution degrees of each factor to every response have been graphically showed in Figure
Scree plot obtained for the variables using the results of the ANOVA analysis. (a) Time to release 50% of entrapment drug, (b) area under the curve, (c) entrapment efficiency, (d) swelling rate, (e) shape factor, and (f) Diameter medium. *Values with statistical significance
As shown in Table
The influence of the Carbopol percentage on drug release profile is due to the effect of this polymer on the structure of the beads, as was discussed by other authors [
All these contribute to produce a slowing in MB release rate (increasing the
According to ANOVA analysis, the factor with higher contribution and statistical significance on the entrapment capacity of the drug was the incorporation of TEA into formulations.
TEA is a weak base that ionizes carboxylic groups in the chains of carbomers. As a result, the structure of the gel changes its conformation, going from a relaxed state in spiral form to a rigid form, which provides viscosity and consistency to the beads. This structural conformation increases the entrapment efficiency. The same results have been obtained by other authors when they added chitosan to the alginate matrix to prepare beads [
Once the base was added, the increase of entrapment efficiency is as consequence of the creation of a higher network density and the electrostatic interaction between the drug and the polymers [
After the ANOVA analysis, we can determine that practically all factors have statistical influence on this dependent variable. The factor presenting the higher influence was NaCl concentration in the dissolution medium followed by TEA addition and Carbopol concentration.
According to ANOM test for the factor NaCl concentration (data not showed), the presence of sodium ions acts hindering the swelling process. Swelling percentage will increase as NaCl concentration decreases (
This result can be explained on the basis of the swelling mechanism of these polymeric structures alginate-Carbopol. It is well accepted that the alginate beads in an aqueous medium without sodium ions do not swell well, but when these ions are present in the medium, the preformed complex between the alginate and calcium turns unstable [
Digital images corresponding to samples during swelling tests (after 180 minutes). (a) batch 11 (0.1 M NaCl), (b) batch 15 (0.01 M NaCl) and (c) batch 16 (without NaCl).
In addition, TEA incorporation produces a rigid structure inside the beads and increase carboxylic groups with negative charge. These groups have a high affinity for water molecules so more quantity of water can get inside the bead, increasing the swelling of the beads in time and intensity. These results were visualized by photographing the samples during the swelling process tests. We can notice a higher swelling in batches with TEA addition (Figure
Digital images corresponding to samples during the swelling test. (a) Batch 17 (with TEA) and (b) batch 8 (without TEA).
According to ANOVA analysis, the stirring rate and the drying method of beads are the factors that have the higher influence on this morphological parameter.
Stirring rate has an important statistical significance concerning the shape and size parameters [
SEM images of alginate-Carbopol beads prepared with different stirring rates (a) 100 rpm (batch 11) and (b) 500 rpm (batch 14).
From the evaluation of the drying process, we must keep in mind the characteristics of every treatment, because they can affect many properties of the beads [
When the beads were dried at environmental conditions, the water evaporation is slow and progressive. This slow loss of water allow the colloidal structure to settle slowly without getting to fracture point, obtaining at the end more spherical and smooth beads (Figure
SEM images of beads dried during: (a) room temperature process (batch 8); (b) oven process (batch 11); (c) rotary evaporator process (batch 4).
During the oven process, the drying temperature was about 100°C and hence, heat energy was supplied to the system, producing a strong and quick loss of water from the beads. As a consequence, the shape was altered producing roughs on the surface of the beads (Figure
When the gelled structures were yielded in a rotary evaporator, the process was submitted to 80°C into a thermostatic bath and applying vacuum. In this case, the evaporation process is quicker than oven process even when we are applying less temperature. With the quicker loss of water, we obtain more irregular beads than oven process but with lesser imperfections on the surface of the beads (see Figure
Analyzing the effect produced by the different characteristics of each drying method, we can conclude that the irregular surface of the beads may be resulted mainly from the heat energy and the alteration on the shape of the beads is determined by the water loss rate.
All these characteristics not only are going to influence on shape and size parameters, but also they are going to affect the release process [
According to ANOVA test, TEA addition was the most significant contributor factor on particle size of the beads. As was explained above, the solutions containing TEA were more viscous. Then, when this polymer solution was dripped into CaCl2 solution, the gelled beads presented higher sizes and higher entrapment efficiencies [
The appropriate selection of the parameters can improve the quality of the product, especially when at least two quality characteristics are to be simultaneously considered. The selection of the response parameters should account for the objectives of the study and the feasibility and ease of handling the optimization process [
The previous experiments revealed that the selected factors affected the release profile, the entrapment efficiency, the swelling process, and the morphological and dimensional properties of the beads depending to the analyzed variable. In the beginning of the optimization process, the marginal means graph for every response variable at each level of factors has been determined (Figure
Optimization process carried out in each variable for robust method.
Variable | Aim in Taguchi | Robust |
---|---|---|
Smaller is best (to120 minutes) | ||
AUC | Bigger is best | |
EE | Bigger is best | |
SR | Bigger is best | |
SF | Bigger is best | |
Dm | Smaller is best |
Marginal means graphs of the
In order to validate this methodology, the confirmation tests were performed using their optimal combination of parameters levels. By using the predicted optimal combinations (Table
Results obtained by confirmation test. SD: standard deviation; CI: confidence interval.
Variable | Mean | Predicted | SD | CI |
---|---|---|---|---|
180 | 186 | 6.34 | ||
AUC | 19359.31 | 26086.38 | 613.57 | |
EE | 85.55 | 87.28 | 2.32 | |
SR | 3073.33 | 3509.44 | 122.20 | |
SF | 0.44 | 0.47 | 0.10 | |
Dm | 1.37 | 1.55 | 0.15 |
For every evaluated response and following the combinations obtained by the Taguchi optimization and after confirmation tests by triplicate, we could see that predicted results are inside the confidence intervals obtained by practically all the optimal batches.
The Taguchi optimization selects the best combination under conditions that produce consistent performance to obtain as possible an optimal value. In the Taguchi method, the robustness of a process is taken into more consideration than the final value of the optimized response.
The success of the results by applying the Taguchi method is partly due to the ability to get good results in formulations or contexts with high variability. As argued in earlier sections, this variability is not always controllable by the researcher, so efficient methods to work in uncertain environments are always interesting to apply.
Applying the Taguchi method to our results, we demonstrated that Taguchi method makes the final product less sensitive to variations than classical optimizations and that this methodology can be used as a tool for the optimization of other pharmaceutical forms.
In the present work, we have focused on the applicability of the Taguchi robust methodology in the pharmaceutical field to optimize pharmaceutical processes. A technological process based on the production of alginate-Carbopol beads has been selected as an example, taking into account our experience in that field.
Various formulation variables such stirring rate, flux rate, reaction time, drying method, and polymer concentration, and other variables such as NaCl concentration and pH were selected in the study. The influence of these parameters on drug entrapment efficiency, particle size, surface morphology, swelling behavior, and
From the ANOVA tests, it may be concluded that the drug release rate from the beads was mainly affected by the Carbopol concentration and the stirring rate of the stirred during the bead formation process. The alginate-Carbopol MB-loaded beads had higher swelling percentages in absence of NaCl in the dissolution medium, and using higher polymer concentrations and with TEA into the polymeric solution. Beads dried at ambient conditions showed more satisfactory sustained release profile.
The Taguchi design method was used to optimize the parameter values for obtaining the desired characteristics. Finally, the confirmation tests to verify the model prediction were carried out and the obtained values were very similar to the predicted ones. This result reveals the feasibility of robust optimization. Therefore, this methodology was then validated, being a perfect complement to the classical DOE methodology in environments with high variability and noise conditions.
The authors declare no financial relationships related to any products involved in this study that might lead to a conflict of interest.