Biosensors based on acrylic microgels

Biosensors and Bioelectronics 20 (2005) 2268–2275

Biosensors based on acrylic microgels夽 A comparative study of immobilized glucose oxidase and tyrosinase J. Rubio Retamab , M. S´anchez-Paniagua L´opezb , J.P. Herv´as P´ereza , G. Frutos Cabanillasc , E. L´opez-Cabarcosb , B. L´opez-Ruiza,∗ a Departamento de Qu´ımica Anal´ıtica, Facultad de Farmacia, Universidad Complutense de Madrid, 28040 Madrid, Spain Departamento de F´ısico-Qu´ımica Farmac´eutica, Facultad de Farmacia, Universidad Complutense de Madrid, 28040 Madrid, Spain Departamento de Estad´ıstica e Investigaci´on Operativa, Facultad de Farmacia, Universidad Complutense de Madrid, 28040 Madrid, Spain b

c

Received 14 July 2004; received in revised form 11 October 2004; accepted 12 October 2004 Available online 8 December 2004

Abstract Acrylic microgels are proposed as enzyme immobilizing support in amperometric biosensors. Two enzymes, glucose oxidase and tyrosinase, were entrapped in this matrix and their behaviour is compared. The optimum cross-linking of the polymeric matrix required to retain the enzyme, and to allow the diffusion of the substrate is different for each enzyme, 3.2% for glucose oxidase and 4.5% for tyrosinase. The effect of pH and temperature on the biosensor responses has been studied by experimental design methodology and predictions have been compared with independently performed experimental measurements. A quadratic effect of the variables studied (pH and T) on the biosensor response and the small or null interaction between them was confirmed. The pH results obtained with both methods are coincident revealing an reversible effect on the enzyme. However, the temperature optimum value obtained by experimental design was 10 ◦ C lower as a result of an activity decay due to irreversible thermal denaturation of both enzymes. © 2004 Elsevier B.V. All rights reserved. Keywords: Amperometric biosensor; Glucose oxidase; Tyrosinase; Experimental design

1. Introduction Immobilization of enzymes is one of the most important facets of biosensors research. Several methods have been employed for enzyme immobilization which includes adsorption onto insoluble materials, entrapment in polymeric gels, encapsulation in membranes, cross-linking with bifunctional or multifunctional reagents and linking to an insoluble carrier (Klibanov, 1983). In previous works (Rubio Retama et al., 2003), we have proposed to use polyacrylic microgels with immobilized 夽 The paper was presented at 8th World Congress on Biosensors 2004.

∗ Corresponding author. Present address: Departamento de Qu´ımica Anal´ıtica, Facultad de Farmacia, Universidad Compultense, Ciudad Universitaria s/n, 28023 Madrid, Spain. Tel.: +34 91 394 1756; fax: +34 91 394 1754. E-mail address: [email protected] (B. L´opez-Ruiz).

0956-5663/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.bios.2004.10.011

glucose oxidase (GOx) as biological component in glucose biosensors. Considering the good analytical results obtained with GOx we have used these microgels to immobilize tyrosinase (PPO) and to prepare a cathecol biosensor. The immobilization of enzymes within the microgels was performed using the concentrated emulsion polymerisation method (Rubio Retama et al., 2003). Herein, we report a study of the optimal microgel cross-linking required to immobilize enzymes with different molecular weights such as GOx (Mw = 160,000) and PPO (Mw = 128,000) for their use in biosensors. Moreover, as the net effect of pH and temperature on the extent of conversion of substrate to product may depend upon the enzyme-loading factor (Carr and Bowers, 1980), we have studied the influence of these variables in the behaviour of the biosensors when they operate in the low and high substrate concentration regions. The biosensor response at low concentration of substrate is of interest because of its analytical applications,

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whereas in the high concentration region, the biosensor response is highly dependent on the enzymatic activity allowing to know the effects of the immobilization system on the catalytic activity of the enzyme. There are few works reporting characterization and optimization of biosensors based on experiment design techniques (Dom´ınguez Renedo et al., 2004; Alonso Lomillo et al., 2003; Gonzalez-S´aiz and Pizarro, 2001; Sun et al., 1998). In this paper, we have used this technique with the aim to compare its predictions with independently measured experimental data. An individual experimental study of each variable (pH or temperature) was performed to determine the type of dependence and, in a second stage, the response surfaces were obtained by a procedure based on a two-factor and five-level central rotatable composite star design (Box et al., 1978). Response surface models may involve just main effects and interactions or they may also have quadratic and, possibly, cubic terms to account for curvature. A model involving only main effects and interactions may be appropriate to describe a response surface when the analysis of the results revealed no evidence of “pure quadratic” curvature in the response of interest and the design matrix originally used included the limits of the factor settings available to run the process. If the factor limits have been defined appropriately, then a process that requires a third-order model is highly unusual. We are interested in proving the convenience of this methodology in biosensors research because a reduced number of experimental tests is required to explore a wide range of experimental variables and, in addition, interactions between variables can be detected. The method was applied to a biosensor based on a robust enzyme, glucose oxidase, as model to compare with other enzyme, tyrosinase, more difficult to manipulate but very useful in analytical chemistry because it catalyzes the transformation of a large number of phenolic and nonphenolic aromatic compounds (Dur´an et al., 2002). Approximately 165 phenols are known with a toxic effect on plants and animals (Cosnier et al., 1999; Freire et al., 2002; Stanca et al., 2003), while others like catecholamines, play a central role in the organism as neurotransmitters (Lisdat et al., 1997). Moreover, because of its antioxidant action due to their high redox potentials, polyphenol benefits in health maintenance are increasingly reported in recent years (Capannesi et al., 2000; Mailley et al., 2004).

2. Experimental 2.1. Chemicals Acrylamide (AA), GOx (EC 1.1.3.4) from Aspergillus niger, 6000 units/g protein, PPO (EC 1.14.18.1) from Mushrooms, 3960 units/mg protein and catechol were purchased from Sigma (St. Louis, MO, USA). N,N methylenebisacrylamide (BIS) was obtained from Aldrich (St. Louis, MO, USA). Ammonium persulfate, N,N,N ,N tetramethylenediamine (TEMED) and the surfactant Span 80

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from Fluka (Buchs, Switzerland). d(+)-Glucose was from Merck (Darmstadt, Germany). Phosphate buffer solutions were prepared from stock solutions of sodium dehydrogenate phosphate and sodium acetate from Panreac and the pH was adjusted using sodium hydroxide (Panreac). The dialysis membrane (12,000–14,000 MWCO) was purchased from Spectrum Medical Industries. All reagents were used as received and the water was Milli Q quality (Millipore, Milford, MA, USA). 2.2. Apparatus and measurements Amperometric measurements at constant potential were performed with a Metrohm (Herisau, Switzerland) Polarecord potentiostat, Model E-506. All potentials are referred to a saturated calomel electrode (SCE). All electrochemical measurements were performed using 0.1 M phosphate buffer in a three-electrode cell with platinum and glassy carbon electrodes as working electrodes, a SCE as reference electrode and a platinum counter electrode. Scanning electron micrographs (SEM) of the microparticles were obtained with a JEOL JSM-6400 operating at an acceleration voltage of 20 kV and 5000 magnification. 2.3. Synthesis of the microgel Polyacrylamide (PAA) microgels have been prepared using the concentrated emulsion pathway (Rubio Retama et al., 2003). The amount of cross-linking, η (given as the ratio between the weights of the cross-linker, BIS, and the monomer, AA) was varied between 0.70% and 5.8%. The water/oil (W/O) concentrated emulsions were prepared by dropwise addition, using a syringe, of the dispersed phase consisting of AA (1.25 g), BIS (between 20 and 66.5 mg depending on η), ammonium persulfate (25 mg), TEMED (63 ␮l), and GOx (150 mg) or PPO (3 mg) dissolved in 5 ml of buffer, to the continuous oil phase (750 ␮l of dodecane and 250 ␮l of Span 80). The emulsion was homogenized by magnetic stirring and purged with nitrogen to remove residual oxygen. A concentrated W/O gel-like emulsion was obtained in which polymerization was started by adding TEMED (63 ␮l). After 1 h of reaction, the polymer was precipitated by washing with cold buffer solution. Microparticles were isolated by centrifugation (7500 rpm) for 20 min at 5 ◦ C and finally, were washed with water. The supernatant was always analyzed showing enzymatic activity when microparticles were synthesized with cross-linking 0.70% and 1.6%, and did not show activity when they were prepared with higher cross-linking contents. After solvent separation, microparticles were freeze-dried to remove residual solvent and water. The polymerization generates polyacrylamide microparticles with diameters between 0.9 and 15 ␮m. In a separated paper (Rubio Retama et al., 2003), we have reported the characterization of the microparticles by DSC, X-ray diffraction and scanning electron microscopy.

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2.4. Electrode preparation Depending on the electroactive species produced in the enzymatic reaction two types of electrodes were used. Platinum electrodes were selected for detection of the hydrogen peroxide produced by GOx, while a glassy carbon electrode was chosen to detect the catechol generated in the PPO reaction. The platinum electrodes were washed ultrasonically with hexane, acetone, isopropanol and distilled water. Then, they were heated at 60 ◦ C for 30 s with a solution comprising a 1:1:5 volume ratio of aqueous ammonia (0.1 M), hydrogen peroxide (20%) and distilled water. The electrode surfaces were then polished with 0.05-␮m alumina slurry paste and any residual abrasive particles were removed ultrasonically in water. After polishing, the glassy carbon electrode was rinsed with potassium hydroxide solution and finally sonicated in an ethanol and water mixture. An exactly weighed amount of microgel particles, 3 mg, was placed and held at the electrode surface by a dialysis membrane. The resulting electrode was washed with phosphate buffer and +0.6 V versus SCE (GOx-platinum electrode) and −0.1 V versus SCE (PPO-glassy carbon electrode) constant potential was applied until the background current decreased to a constant level. 2.5. Statistical methodology The value of statistically based experimental designs (the matrix of runs generated by specific combinations of inputs values) has been well established (Anderson and Whitcomb, 2000; Kraber et al., 2000; Box et al., 1978). In this work, a Box–Wilson response surface model will be evaluate from run data generate via Box–Wilson design. A Box–Wilson Composite Design (central composite design) contains an imbedded factorial or fractional design with center points that is augmented with a group of star points that allow estimation of curvature. The star points represent new extreme values (low and high) for each factor in the design, they are at a distance α from the center based on the properties desirables for the design and the number of factors. The value of α = ±1.414 allows simultaneously rotatability and orthogonality. Scaling the inputs to range [−1, 1] is used to increase model construction robustness. Scaling the inputs minimizes the correlation between the estimates of the coefficients of the model (Box and Draper, 1987; Myers and Montgomery, 2002). The covariance of the estimates, a metric of model stability, is dependent of the input design matrix and the lack of model fit. The response surface methods (RSM) allow us to estimate interactions and even quadratic effects are used to find improved or optimal process setting. 2.6. Statistical analysis All experiments were planned according to the central rotatable composite star design shown schematically in Table 1.

Table 1 Experimental matrix and factor levels for the two-factor and five-level central rotatable composite star design Coded factors

Glucose oxidase biosensor (factor levels)

Tyrosinase biosensor (factor levels)

pH

T

pH

T

pH

T

+1 0 −1 0 +1 0 −1 √ − 2 0 0 √ + 2 0 0 0

−1 0 +1 0 +1 0 −1 0 0 √ + 2 0 0 √ − 2 0

7.0 5.5 4.0 5.5 7.0 5.5 4.0 3.4 5.5 5.5 7.6 5.5 5.5 5.5

27 35 43 35 43 35 27 35 35 46 35 35 23.7 35

7.5 6.0 4.5 6.0 7.5 6.0 4.5 3.9 6.0 6.0 8.1 6.0 6.0 6.0

5 15 25 15 25 15 5 15 15 29.1 15 15 0.9 15

In this statistical design, each variable, temperature or pH, was essayed at five levels. The resulting biosensor responses (currents) were computer processed in order to obtain a response surface. The mathematical model was a second order polynomial: Y = β0 + β1 X1 + β2 X2 + β11 X12 + β22 X22 + β12 X1 X2 + ε (1) The β terms are the unknown coefficients, the terms X1 and X2 , represent the independent variables (T and pH), the variable Y represents the biosensor response and ε is the random error. The response surfaces were fitted using the Statgraphics data analysis package (STATGRAPHICS, 1992)

3. Results and discussion 3.1. Characterization of the microparticles Fig. 1 illustrates the scanning electron micrograph of polyacrylamide microparticles with GOx (a) and PPO (b). The size average of the microparticles with GOx was around 12 ␮m, and 6 ␮m when PPO was entrapped. These microparticles, once freeze-dried, preserve their morphology as well as their enzymatic activity during at least eight months (Rubio Retama et al., 2003). 3.2. Effect of the cross-linking on the immobilization of the enzyme To find the optimum pore size of the microgels allowing the enzyme to be retained inside, the polymeric matrix and diffusion of the species involved in the enzymatic reaction through the polymer, the response of biosensors prepared with microparticles with different cross-linking contents was measured. Fig. 2 shows the biosensor response versus in-

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Fig. 1. SEM micrograph of freeze-dried polyacryalmide microparticles with (a) GOx (cross-linking η = 3.2%) and (b) PPO (cross-linking η = 4.5%).

creasing concentration of substrate. The optimum response occurs for microgels with 3.2% cross-linking for entrapped GOx (Fig. 2a) and 4.5% for PPO (Fig. 2b). This is attributed to the different molecular weights of both enzymes, which for GOx is 160,000 Da whereas for PPO is 128,000 Da. Microgels with entrapped PPO required higher cross-linking to obtain the optimum biosensor response because of the smaller molecular weight of this enzyme. Furthermore, when choline oxidase (78,000 Da), was entrapped in the microgel, the optimal cross-linking value found was 7% (results still not published). 3.3. Effect of pH and temperature An individual study of each biosensor, by conventional methods, that is, essaying the influence of each factor independently, on the response, was initially carried out to know their experimental limits and to compare these results with predictions obtained by experimental design. The effect of the pH on the biosensor overall reaction was examined in 0.1 M acetate/phosphate buffer solutions from pH 3.5 to 8 at

25 ◦ C. According to Carr and Bowers (1980), when there is an excess of enzyme, a large change in pH, should have very little effect on the product concentration in the first-order kinetic regime. In contrast, at high substrate concentration, the electrode current will be very much dependent on the enzyme activity and therefore should be a strong function of pH. We have realized this study at two substrate concentrations, one corresponding to the first-order kinetic regime (2 mM of glucose and 20 ␮M of catechol), and the second one corresponding to the saturation domain (50 mM of glucose and 400 ␮M of catechol). Fig. 3 shows the bell-shaped pH profiles. Although, the glucose biosensor operates independent of the glucose concentration, the cathecol biosensor shows a more narrow response profile at high concentrations of catechol. The similarity of the pH profiles at both GOx concentrations seems to indicate that the immobilization support does not influence the enzymatic activity. Gonzalez-S´aiz and Pizarro (2001) found a similar behaviour when immobilizing alkaline phosphatase by entrapment in acrylamide gel. However, PPO is a less robust enzyme than GOx which could be the cause of the differences observed in their pH profiles.

Fig. 2. Influence of the cross-linking of the microgel with (a) GOx and (b) PPO entrapped on the biosensor response studied by calibration plot for (a) glucose and (b) catechol in stirred 0.1 M phosphate buffer, pH 6.0, at a potential of (a) +0.6 V vs. SCE and (b) −0.1 V vs. SCE and 25 ◦ C.

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Fig. 3. pH profiles of (a) GOx and (b) PPO biosensors at substrate concentration corresponding to the first-order kinetic regime (
) and corresponding to the saturation domain ().

The temperature range investigated was from 2 ◦ C to 50 ◦ C keeping the pH constant at 6.0 and performing all the experiments under oxygen saturation condition. As is illustrated in Fig. 4, the biosensor response profile shows no difference between high and low concentration of substrate. The deactivation of the immobilized enzymes occurs at 45 ◦ C for GOx and 25 ◦ C for PPO, very similar to the deactivation temperatures of these enzymes in solution, indicating that the protective effect of the polymer matrix in the enzyme activity is small. This study does not allow identifying if the deactivation takes place in a reversible or irreversible way. In order to find out, not only the individual effects of each variable on the biosensor response but also the interaction

between temperature and pH, an experimental design was performed. Moreover, this design is suitable to obtain the quadratic response surface. A two-factor and five-level central rotatable composite star design was used, and the quantitative analysis of the results was carried out by ANOVA. As Table 1 shows, 14 experiments were carried out corresponding to all the possible combinations of temperature and pH and were conducted in a fully randomised order. To estimate the residual value, eight replicates in the central point conditions were performed. Like in the conventional method, GOx and PPO were studied and compared at two substrate concentrations corresponding to the to first-order kinetic regime (2 mM of glucose and

Fig. 4. Temperature profiles of (a) GOx and (b) PPO biosensors at substrate concentration corresponding to the first-order kinetic regime ( ) and corresponding to the saturation (䊉).

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20 ␮M of catechol) and to saturation conditions (50 mM of glucose and 400 ␮M of catechol). According to the experimental results, the predicted statistical models were the following: GOx and low concentration of glucose: Y = 342 + 54 pH + 7 T − 98 pH2 − 78 T 2 + 0 pH T (R2 = 91.84%)

(2)

GOx and high concentration of glucose: Y = 3202 + 747 pH + 931 T −828 pH2 + 25 T 2 − 92 pH T (R2 = 39.94%)

(3)

PPO and low concentration of catechol: Y = 167 + 7.5 pH + 5.6 T − 65 pH2 − 49 T 2 − 7.5 pH T (R2 = 81.38%)

(4)

PPO and high concentration of catechol: Y = 872 − 4.6 pH + 109 T − 212 pH2 − 44 T 2 − 76 pH T (R2 = 36.29%)

(5)

As the adjusted R-squared statistics of the fitted models indicate, only the designs performed at low substrate concentrations could be considered from the analytical point of view, because those preformed at high substrate concentration account for less than 40% of the variability. The model confirms the quadratic effect of the variables on the biosensor response (Eqs. (2)–(5)) in agreement with the conventional method. Furthermore, interaction between variables in the low concentration regime does not exist (GOx, Eq. (2)) or is

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not significant (PPO, Eq. (4)). In order to visualize the effect of the factors and to make decisions on their influence on the dependent variable, the corresponding response surfaces were obtained. Fig. 5 shows the estimated response surfaces obtained from these fitted models for GOx and PPO biosensors at the two substrate concentration tested. As can be seen in Fig. 5, the glucose oxidase biosensor and tyrosinase biosensor, at low substrate concentration gave a “Peak” response surface but at high substrate concentration, the response surface corresponding to the name “Rising Ridge”. That proves the different behaviour of the biosensor for the two substrate concentrations considerated. Table 2 summarizes the optimal experimental conditions obtained by both methodologies. In the region of enzymatic saturation, the optimal values were similar. Nevertheless, in the region of low concentration of substrate, corresponding to the first-order kinetic regime, the pH optimal values were also concurrent, but in the temperature values, this coincidence was not observed. The optimum temperature found by experimental design was around 10 ◦ C lower than that obtained by the conventional method. Bearing in mind that analytical conclusions only can be considered from low concentrations, the concurrence in the optimal pH values confirms the reversible effect of the pH on the enzymatic activity; however, the startling difference found in the optimal temperature values could only be explained by the irreversible effect of this factor on the activity of both enzymes when the enzyme denaturation temperature is achieved. To confirm this explanation, an additional experiment was performed starting at the highest temperatures and decreasing until no response was registered. As expected, the response was considerably lower than that obtained by the conventional and direct assay. The random experimental runs carried

Fig. 5. Estimated response surfaces: (a) glucose oxidase biosensor and (b) tyrosinase biosensor. (1) Low substrate concentration and (2) high substrate concentration.

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Table 2 The optimal experimental conditions obtained by the conventional method (independently) and by experimental design Methodology

Conventional Experimental design Conventional Experimental design

Variables

pH pH T (◦ C) T (◦ C)

Glucose oxidase biosensor

Tyrosinase biosensor

2 mM (linear)

50 mM (saturation)

20 ␮M (linear)

400 ␮M (saturation)

6.0 5.9 45.0 35.4

6.0 6.1 45.0 46.0

6.0 6.1 25.0 15.6

6.0 5.6 25.0 29.0

out in the design allow detecting if the effect of the factors occurs in a reversible or non-reversible way. By contrast, the conventional methods study each variable in a serial way; as a result, the reversibility of the effects cannot be detected.

nology Ministry. We also thank A. Rodriguez (Electron Microscopy Centre, UCM) for valuable technical and professional assistance.

4. Conclusions

References

We have prepared glucose and cathecol biosensors by immobilizing GOx and PPO in acrylic microgels which were used as biological components of the biosensors. We have found that the optimum response for the glucose biosensor is obtained when the cross-linking is 3.2% whereas for cathecol, 4.5% is required and we attribute this difference to the molecular weight of each enzyme. We have applied experimental design methodology to study the behaviour of the biosensors as a function of pH and temperature, at low and high concentration of substrate and we have compared the predictions with independently performed experimental measurements. The statistical model confirms the quadratic effect of the variables studied (pH and T) on the biosensor response and the small or null interaction between them. Furthermore, the optimum pH for each enzyme is very close to that obtained with conventional methods due to the reversible effect of this factor on the enzyme. However, when comparing the results obtained by experimental design and separately, a great difference is observed in the temperature-dependent behaviour of both biosensors, which we attribute to irreversible changes in the activity of the enzyme due to its denaturation. The use of experimental design approach in the characterization of an amperometric biosensor has several advantages: First, with a very reduced number of experimental runs, more information can be obtained; second, the biosensor response surface can be built and from that, the optimal experimental conditions can be determined with the consequent improvement in the sensibility of the analytical device; third, this method allows to discern between the reversible or irreversible influence of some factors (i.e. the enzymatic activity) on the biosensor response.

Acknowledgments The authors acknowledge financial support from DGI (MAT2003-03051-C03-03) of the Spanish Science and Tech-

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