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Aug 6, 2003 - Abstract This paper is a review of the authors' publica- tions concerning the development of biosensors based on enzyme field-effect transistors ...
Anal Bioanal Chem (2003) 377 : 496–506 DOI 10.1007/s00216-003-2134-4

REVIEW

Sergei V. Dzyadevych · Alexey P. Soldatkin · Yaroslav I. Korpan · Valentyna N. Arkhypova · Anna V. El’skaya · Jean-Marc Chovelon · Claude Martelet · Nicole Jaffrezic-Renault

Biosensors based on enzyme field-effect transistors for determination of some substrates and inhibitors Received: 7 March 2003 / Revised: 14 June 2003 / Accepted: 24 June 2003 / Published online: 6 August 2003 © Springer-Verlag 2003

Abstract This paper is a review of the authors’ publications concerning the development of biosensors based on enzyme field-effect transistors (ENFETs) for direct substrates or inhibitors analysis. Such biosensors were designed by using immobilised enzymes and ion-selective field-effect transistors (ISFETs). Highly specific, sensitive, simple, fast and cheap determination of different substances renders them as promising tools in medicine, biotechnology, environmental control, agriculture and the food industry. The biosensors based on ENFETs and direct enzyme analysis for determination of concentrations of different substrates (glucose, urea, penicillin, formaldehyde, creatinine, etc.) have been developed and their laboratory prototypes were fabricated. Improvement of the analytical characteristics of such biosensors may be achieved by using a differential mode of measurement, working solutions with different buffer concentrations and specific agents, negatively or positively charged additional membranes, or genetically modified enzymes. These approaches allow one to decrease the effect of the buffer capacity influence on the sensor response in an aim to increase the sensitivity of the biosensors and to extend their dynamic ranges. Biosensors for the determination of concentrations of different toxic substances (organophosphorous pesticides,

S. V. Dzyadevych (✉) · A. P. Soldatkin · Y. I. Korpan · V. N. Arkhypova · A. V. El’skaya Laboratory of Biomolecular Electronics, Institute of Molecular Biology & Genetics, National Academy of Sciences of Ukraine, 150 Zabolotnogo Str., 03143 Kiev, Ukraine e-mail: [email protected] J. Chovelon Laboratoire d’Application de la Chimie à l’Environnement, UMR 5634 CNRS-Université Claude Bernard Lyon 1, 43, boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex, France C. Martelet · N. Jaffrezic-Renault Laboratoire d’Ingénierie et Fonctionnalisation des Surfaces, FRE 2608, Ecole Centrale de Lyon, 36 avenue Guy de Collongue, 69134 Ecully Cedex, France

heavy metal ions, hypochlorite, glycoalkaloids, etc.) were designed on the basis of reversible and/or irreversible enzyme inhibition effect(s). The conception of an enzymatic multibiosensor for the determination of different toxic substances based on the enzyme inhibition effect is also described. We will discuss the respective advantages and disadvantages of biosensors based on the ENFETs developed and also demonstrate their practical application. Keywords Biosensors · ENFETs · Enzyme · Substrates · Inhibitors · Multibiosensor

Introduction During recent decades there has been unprecedented interest in the development of analytical devices for the detection, quantification and monitoring of different biological and chemical compounds. Improvements in instrumentation, sampling and sample preparation techniques have become essential to keep up with the requirements of detection at low levels in the ppb or ppt range, as well as to achieve a faster analysis. Furthermore, analytical data have to comply with increasingly drastic new analytical regulations especially in the field of medicine, drinking water and food control. This has led to the development of various new methodologies, which can significantly differ from the conventional macroanalytical and semi-microanalytical approaches. The design of biosensors is probably one of the most promising ways to solve some problems concerning sensitive, fast, repetitive and cheap measurements. The dynamic field of biosensors is covered by an extensive number of reviews [1, 2, 3]. As a rule a biosensor is a self-contained device containing two functional parts: a bioselective membrane in direct contact with a physical transducer, which transforms the biorecognition event into an electrical or optical signal. The amplitude of this signal depends on the concentration of the analysed compound (analyte) in the sample. Biologically active materials used for the construc-

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tion of biosensor systems can be divided into two main groups: catalytic (enzymes, cells, tissues) and noncatalytic or affinity (antibodies, receptors, nucleic acids). Electrochemical (amperometric, potentiometric, conductometric or impedimetric, and ion charge or field-effect), optical, calorimetric and acoustic transducers are currently used in measuring systems [4]. Biosensors based on semiconductor structures have received considerable attention, since it was expected that the innovative potential of microelectronics might result in the appearance of new biosensor technologies. These technologies are well adapted to the large-scale production of miniaturised devices. Ion-sensitive field-effect transistors (ISFETs) are devices that have been developed for such purposes; these use integrated circuit technologies to obtain advantageous sensors compared to standard non-integrated pH electrodes. The first ISFETs reported by Bergveld [5, 6] consisted of a metal/oxide/silicon transistor in which the gate conductor is replaced by the tested liquid itself. These devices had a channel conductance which varied with pH of the solution. Later, Matsuo and Wise [7] improved the device by using nitride as the gate-sensitive membrane. ISFETs are not restricted to H+ sensing; other ions can be detected if additional sensitive membranes are deposited over the inorganic membrane and placed in contact with the liquid. When such ISFETs are coupled with a biocatalytic layer (enzyme), they become biosensors, and are called enzyme field-effect transistors (ENFETs). Caras and Janata used them for the first time for penicillin detection in 1980 [8]. However, in spite of initial optimism, progress in this field was slower than expected [9]. The major obstacles preventing rapid progress in the practical application of ENFETs (including glucose-sensitive ENFETs) were recognised and can be summarised as following: (1) physical instability and light sensitivity of semiconductor structures; (2) dramatic decrease of the biosensor response with the increase of buffer capacity and ionic strength of the tested solution, preventing applications in highly buffered media; (3) limitation of the dynamic range of the glucose biosensor response because of insufficient concentration of dissolved oxygen in the measured samples. Taking into consideration rather attractive features of ENFETs, our efforts were directed to the elaboration of var-

Fig. 1a, b General schematic representation of MOSFET (a) and ISFET (b)

ious biosensors based on ENFETs with improved working characteristics. In this paper our achievements in the development of ENFET-based biosensors are reviewed. Such biosensors are used for both direct and inhibitory analysis, and were designed for applications in medical, food and environmental fields.

Description and characterisation of ISFETs The ISFET is a classical MOSFET with the gate connection separated from the chip in the form of a reference electrode inserted in an aqueous solution, which is in contact with the gate oxide (Fig. 1). Theory, technology and instrumentation for ISFETs were quite well reviewed by Bergveld [10]. A MOSFET is essentially a p-type silicon substrate in which two n-type regions have been diffused (the source and drain, respectively), separated by a p-type channel and overlaid with a metallized gate electrode. The source is electrically biased with respect to drain by the applied potential Vds. In the conventional operational mode, a voltage Vg is applied between the silicon substrate and the gate electrode to create an electric field beneath the gate and attract or repel electrons on the surface of the substrate under the gate. There is also a threshold potential VT, at which silicon changes from p-type to n-type, and inversion occurs. With a small positive Vds and VgVT, there is surface inversion, and p-Si becomes n-Si, and current can pass from drain to source. Vg modulates the number of electrons from inversion layer and so controls the conductance. Id flows from source to drain and is proportional to both the electrical resistance of the surface inversion layer and Vds. This current may be measured directly at constant Vg. Alternatively, we can keep Id constant by changing Vg and measuring the latter by using “a source and drain follower circuit” [10]. In the case of pH-sensitive ISFET (pH-FET), the electrical signal depends on the surface potential of the dielectric of the transistor gate. This surface potential can be modified by an accumulation of charges at the dielectric surface of the FET gate, and in the case of a pH-FET, it can be caused by pH variations occurring near the transistor’s gate.

498 Fig. 2 Schematic representation of ENFET from R&D Institute of Microdevices (Kiev, Ukraine)

Fig. 3 General view of ISFETs from RIMD (1), LAAS (2), ESIEE (3), and ITIMS (4)

The ENFET can be fabricated from ISFETs by applying a thin overlayer of enzyme-loaded gel on the pH-selective layer (Fig. 2). It is based on the principle a pH changes, locally occurring inside the biological-sensitive membrane, caused by an enzymatic reaction involving the substrate to be assayed. The various ISFET transducers used were fabricated at the R&D Institute of Microdevices (RIMD, Kiev, Ukraine), CNRS Laboratory for Analysis and Architecture of Systems (LAAS, Toulouse, France), ESIEE Paris (ESIEE, Noisy-le-Grand, France) and the International Training Institute for Materials Science (ITIMS, Hanoi, Vietnam) (see Fig. 3). The sensor chip from RIMD contained two identical ISFETs with an ion-selective layer of Si3N4 whose design and operational mode were reported elsewhere [11, 12]. The ISFETs were operated under a constant drain current and drain-source voltage mode (Id=200 µA, Vds=1 V). The pH sensitivity of the ISFETs was linear in the pH range 2–12 with a slope of 30–40 mV pH–1. The sensor chip from LAAS, including a SiO2/Si3N4 pH-sensitive ISFET, was fabricated using standard P-well silicon technology. It was operated at Id=100 µA, Vds=1 V. The pH sensitivity of the ISFETs was linear in the pH range 3–9 with a slope of 40–50 mV pH–1 [13].

The sensor chip from ESIEE included one ISFET with an implanted channel, whose threshold voltage was around –3 V. It was operated at Id=200 µA, Vds=1 V. The pH sensitivity due to a Si3N4-sensitive layer is 15 mV pH–1 [14]. The sensor chip from ITIMS used both ISFETs able to work at Id=200–500 µA, Vds=0.2–1 V. These had silicon dioxide as the ion-selective layer. The output signal of the sensors was linear in the pH range 2.5–8 with a slope of 11–13 mV pH–1 [15]. All ISFET transducers were physical stable and had similar operational characteristics. A proper mode of ISFET operation is achieved when it is electrically isolated from the solution by a reverse biased diode formed by an n-type source-channel-drain region and a p-type substrate [12]. The leakage currents through the reverse biased diode and, consequently, through the substrate and the reference electrode depend on the p–n junction quality and normally do not exceed the nanoampere level. Such leakage currents do not affect the ISFET performance. The ohmic contact used to bias the substrate is at the same time used to short-circuit the photovoltage in order to suppress the ISFET light sensitivity. A differential pair of ISFETs (one covered with an enzyme-containing membrane and the other with a blank one) is usually employed to compensate for common interferences, bulk pH and temperature changes. For the both ISFETs, the “source and drain follower circuit” can be used, from which the output voltages are connected to an additional differential amplifier. In the case of biosensors from RIMD [11, 12], the proposed differential mode of operation allows the use of a non-encapsulated substrate of the chip itself as a quasireference electrode (Fig. 2). This makes it unnecessary to use any conventional reference electrode or separate fabricated noble metal quasi-reference electrode to render the biosensor operational. In the case of the individual mode of operation, each ISFET has to be used together with a conventional reference electrode.

ENFETs based on direct analysis Introduction Our group and some other authors have developed biosensors based on ENFETs with direct enzyme analysis for the determination of glucose, urea, acetylcholine chloride,

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butyrylcholine chloride, penicillin, formaldehyde, 4-chlorophenol and creatinine. The resulting pH changes are produced by enzymatically catalysed conversion of different substrates according to Equations

(3)

where AOX is alcohol oxidase, PPox is polyphenoloxidase and CD is creatinine deiminase. The main analytical characteristics of the developed biosensors are presented in Table 1. The biosensors demonstrated reproducible and stable response after addition of substrates with a measurement time within 1–4 min. The influence of the pH, buffer capacity and ionic strength of the samples on the biosensor response has been studied [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]. Unfortunately, the analytical application of ENFETs could be restricted by the dramatic decrease of the sensor response when increasing the buffer capacity of the tested sample, a non-linear dependence of the enzyme kinetics and buffer capacity of a sample on pH, dependence of the response on ionic strength and cosubstrate limitation of the enzymatic reaction rate (in the case of the glucose sensor).

(4)

Improvement of ENFETs analytical characteristics using additional membranes

(1)

(2)

(5)

(6)

(7)

and 8. &' FUHDWLQLQH +  2 → 1 0HWK\OK\GDQWRLQ1+ 

(8)

The response of most of enzyme sensors presented in Table 1 is dependent on the buffer capacity and ionic strength of the analyte. As a potential solution to overcome the problem of buffer dependency, the possibility of using a coulometric pH control system was investigated. This system consists of an ISFET with an integrated noble-metal electrode around its pH-sensitive gate. This electrode can either be used as anode or cathode to produce hydroxyl ions or protons through electrolysis of water. Through the generation of these ions, pH changes located in the vicinity of the ISFET can be used to determine the buffer capacity. The authors have shown the feasibility of such a system for rapid acid–base titration [36] and in a new type of carbon dioxide sensor with an excellent long-term stability [37]. The application of this technique for the improvement of enzyme electrodes has also been proposed [38, 39].

Table 1 Analytical characteristics of biosensors based on ENFETs (without additional charged membranes) Substrate (enzyme)

Dynamic range (M)

Glucose (glucose oxidase)

10 –4–3¥10 –3

Urea (urease)

10 –4–5¥10 –3

Acetylcholin chloride (acetylcholinesterase) Butyrilcholin chloride (Butyrilcholinesterase) Penicillin (penicillinase) Formaldehyde (alcohol oxidase) Creatinine (creatinine deiminase) 4-Chlorophenol (tyrosinase)

10 –5–5¥10 –3 10 –4–10–2 10 –4–1.5¥10 –2 5¥10 –3–2¥10 –1 1¥10 –5–5¥10 –3 2¥10 –4–8¥10 –3

Operational mode

kinetic steady-state kinetic steady-state kinetic steady-state kinetic steady-state steady-state steady-state steady-state steady-state

Time of analysis (min)

Stability

0.1–0.2 1–2 0.1–0.2 1–3 0.1–0.2 1–3 0.1–0.2 1–3 1–2 1–3 1–2 2–4

>20

90

[16, 17, 18, 19]

>20

90

>30

90

[19, 20, 21, 22, 23, 24] [25, 26]

>30

90

[25, 26]

>20 >20 >20 >15

90 30 180 15

Operational (h)

References Storage (days)

[8, 27, 28] [29, 30] [31, 32, 33] [34, 35]

500

The possibility of overcoming these difficulties through a more simple way has been investigated in the case of the glucose and urea biosensor [40, 41, 42, 43, 44]. It was shown that characteristics such as dependence of the sensor response on buffer and salt concentration, sensitivity, detection limit, reproducibility and operational stability of biosensors based on ENFETs can be improved by using permselective additional membranes having different properties in terms of electrical charge. For example, the formation of an additional Nafion membrane on the top of the enzyme-containing membrane results in a substantial reduction of the influence of the buffer concentration on the sensor response and in extension of its dynamic range. Such a reduction in the influence of the buffer capacity on the glucose ENFET response may be explained by taking into account the properties of Nafion as a cation-exchange membrane [40]. The negatively charged sulfonate groups inside the Nafion membrane create a barrier for the diffusion of negatively charged ions. With the phosphate buffer used in this investigation, the buffer-mediated mechanism of the diffusion of protons out of the enzyme layer operates owing to the movement of neutral and negatively charged buffer species across the membranes. The presence of an additional Nafion membrane effectively blocks the transfer of charged buffer ions and thus drastically reduced the contribution of the “carrier-mediated” mechanism to the total diffusional flux of protons across the Nafion membrane. The additional Nafion membrane also Fig. 4a–c Typical response curves of the urea-sensitive ENFETs without (a) and with 5% Nafion (b) and 5% PVP (c) additional membranes versus urea in a model solution and in a solution containing diluted serum. Measurement conditions: baseline is obtained in a phosphate buffer, pH 7,4; arrows indicate the points of different aliquots adding and washing

limits the diffusion of glucose through the membrane more efficiently than the oxygen one, which results in an extension of the sensor dynamic range. Typical response curves of ENFETs (without and with additional membranes) versus urea concentration in model buffer solution and in solution containing diluted serum were obtained and compared (Fig. 4). As can be seen from Fig. 4a, the response shapes differ for urea detection in a model solution and in a solution containing serum aliquots. In the case of model buffer solution the biosensor responses for urea addition presented a classical form. ENFETs without additional membranes showed an unusual signal shape when the serum portions containing urea were added in buffer solution. This shape of signal, with a transient part, can introduce errors in the measurements. Moreover, it does not allow urea analyses in serum using a kinetic mode, thereby restricting the possibility of data processing and practical utilisation of such biosensors. The addition of serum amounts with no urea content (after treatment with soluble urease) demonstrated a non-specific sensor response. A similar signal was obtained by adding BSA in the analysed solution. As can be seen from Fig. 4b and c the presence of additional Nafion or poly(4-vinylpyridine-co-styrene) (PVP) membranes efficiently improves the sensor response. Biosensors with additional Nafion and PVP membranes give a classical response shape for urea measurements both in model solution and in diluted serum samples. The addi-

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Extension of the dynamic range for urea biosensor based on ENFETs

Fig. 5 The comparative results of glucose determination in rat blood serum

tion of serum amounts with no urea contents (after treatment with soluble urease) demonstrated a reduction of the non-specific sensor response. Additional membranes effectively exclude blood cells, serum proteins and other blood constituents. The phenomena of reducing protein influence on the sensor response by additional membrane can be explained by a) an effective limitation of the charged protein diffusion into enzyme and reference membranes; b) a decrease of the non-specific protein sorption on the sensitive surface. The possibility of application of ENFETs with an additional Nafion layer on the top of enzymatic membrane for glucose determination in a blood serum of rats has been demonstrated [44]. The results of glucose determination on 29 blood serum samples from different Wistar rats from the Chernobyl exclusion zone are presented in Fig. 5. As can be seen, the correlation between the three methods (ENFET, “Eksan-G” from the Panevezhis precision mechanics plant [45] and “Diagluc” from the L’viv bacterial preparation plant [46]) is quite good. It was shown that discrepancy of data obtained with the glucose-sensitive ENFETs is y=1.09x–0.69 (R=0.88) relative to the “Eksan-G” analyser and y=0.85x+2.38 (R=0.91) relative to “Diagluc”. The high reproducibility and operational stability of the glucose and urea biosensor based on ENFETs with additional permselective membranes were therefore demonstrated. Intra-sample estimation shows high reproducibility with a metering error of approximately 2% for each sample. The test for operational stability of the glucosesensitive ENFETs showed that the sensor response was stable over 48 h for a continuous use, which corresponds to 200–300 measurements for each sensor. Moreover, the storage stability was more than 3 months.

The main drawback for urea biosensor based on ENFETs is a narrow dynamic range. Such sensors cannot be used for direct urea measurements in human blood, where normal concentration varies from 6 to 8 mM and can even reach higher levels for pathologic situations. A pre-dilution of serum samples has to be used for urea detection [43]. The dynamic range for urea determination using ENFETbased biosensors can substantially be shifted to higher concentrations by adding specific reagents such as sodium tetraborate, which acts as a competitive inhibitor of urease [47]. The biosensor response was investigated in a phosphate buffer with different concentrations of tetraborate anion. The results showed that the apparent Michaelis– Menten constant increases from 4.3 to 79.3 mM for experiments realised without and with 0.5 mM sodium tetraborate, respectively (the lower detection limit increases from 0.05 to 1 mM). The inhibition effect is completely reversible and the initial state of the biosensor can be recovered through a simple washing with water or phosphate buffer. By using various concentrations of the inhibitor, it is possible to determine the concentration range of urea in a three-decade range, thus suggesting that the biosensor could be used in routine analysis for urea assays as in urine and serum. Another way to overcome the problem of the narrow dynamic range of the urea biosensor is to directly modulate the affinity of urease as biorecognition element. It is necessary to underline that during the last few years the use of genetically modified enzymes in the development of biosensors with improved characteristics is of great interest [48, 49]. We used a new enzyme preparation – recombinant urease from E. coli with a genetically modified active site of urease [50]. The modification of the active site of urease was done in such a way that the enzyme has

Fig. 6 Calibration curves for ENFETs based on the recombinant urease. Measurements were done in 5 mM phosphate buffer, pH 7.4 (1) and in 150 mM NaCl+5% BSA, pH 7.4 (2)

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decreased affinity between its active site and the specific substrate. Such a urease preparation is characterised by a high value of Km (around 200 mM; this value was directly obtained from the enzyme supplier). This biosensor demonstrates a quite broad dynamic range (from 1 mM to 80 mM of urea, covering the variations of urea concentrations in plasma for either normal patients or those presenting a renal dysfunction) working in the sample solutions modelling plasmatic and dialysis liquid (Fig. 6). Moreover this biosensor demonstrated a quite good reproducibility and high storage stability. Working characteristics of the biosensor developed fit with the medical requirements needed for a direct urea measurement in plasma and dialysis liquids.

protocol included measurement of the biosensor response to a fixed concentration of the urea before and after the incubation of the biosensor for a definite time in a solution containing heavy metal ions. Detection limits estimated for heavy metal ions giving a decrease of the substrate signal equal to three times the blank value concentration were 1.0×10–6 M for Hg2+, 2.0×10–6 M for Cu2+, 5.0×10–6 M for Cd2+, 1.0×10–5 M for Co2+, 2.0×10–5 M for Pb2+ and 1.0×10–4 M for Sr2+. Thus, the sequence of heavy metal ions relative to their toxicity toward urease is Hg2+>Cu2+> Cd2+>Co2+>Pb2+>Sr2+. This reaction is reversible in the presence of a strong chelating agent such as EDTA [61], which is why EDTA was used as reactivating reagent. Irreversible inhibition of cholinesterases

ENFETs based on inhibitory analysis Biosensors based on ENFETs and inhibition phenomena have been designed for determination of different toxic components [51, 52, 53, 54, 55, 56, 57, 58, 59, 60]. The main analytical characteristics of such biosensors are presented in Table 2. Inhibition of urease The immobilised urease can be inactivated by heavy metal ions through their direct interaction with the sulfhydryl groups of the enzyme active site [52, 53, 54]. The assay

The determination of organophosphorus and carbamate compounds is based on their ability to inhibit cholinesterases by interaction with the serine hydroxyl group in the enzyme active site. The decrease in cholinesterase activity after its interaction with pesticides can be effectively monitored by the ENFET-based biosensors, facilitating the toxicity assessment of organophosphorous and carbamate pesticides. The calibration curves of biosensors based on acetyl cholinesterase and ENFETs for different pesticides are shown in Fig. 7. They are linear in a semilogarithmic plot for all pesticides tested. Detection limits estimated for inhibitor concentrations were 3.0×10–11 M for diisopropyl

Table 2 Analytical characteristics of biosensors based on ENFETs and inhibitory analysis Toxic components

Hg2+ Cu2+ Cd2+ Co2+ Pb2+ Sr2+ Diisopropyl fluorophosphate Diisopropyl fluorophosphate Trichlorfon Trichlorfon Paraoxon-ethyl Paraoxon-ethyl Paraoxon-methyl Paraoxon-methyl Parathion-methyl Carbofuran Hypochlorite a-Solanine a-Chaconine

Enzyme

Dynamic range (M)

Time of analysis (min)

Stability

References

Operational (h)

Storage (days)

urease urease urease urease urease urease acetyl cholinesterase

10 –6–5¥10 –5 2¥10 –6–10–4 5¥10 –6–2¥10 –4 10 –5–5¥10 –4 2¥10 –5–5¥10 –3 10 –4–5¥10 –3 3¥10 –11–5¥10 –7

20 20 20 20 20 20 15

>10 >10 >10 >10 >10 >10 >10

30 30 30 30 30 30 30

[51, 52, 53] [51, 52] [51, 52] [51, 52] [51, 52] [51, 52] [25, 54, 55]

butyryl cholinesterase

5¥10 –11–10–7

15

>10

30

[25, 54, 55]

acetyl cholinesterase butyryl cholinesterase acetyl cholinesterase butyryl cholinesterase acetyl cholinesterase butyryl cholinesterase acetyl cholinesterase acetyl cholinesterase acetyl cholinesterase butyryl cholinesterase butyryl cholinesterase

2¥10 –7–10–5 1¥10 –6–10–5 10 –6–5¥10 –5 5¥10 –7–5¥10 –5 10 –6–5¥10 –5 5¥10 –6–5¥10 –5 5¥10 –6–10–4 2¥10 –6–10–4 10 –5–3¥10 –4 5¥10 –7–10–4 2¥10 –7–10–4

15 15 15 15 15 15 15 15 15 5 5

>10 >10 >10 >10 >10 >10 >10 >10 disposable >30 >30

30 30 30 30 30 30 30 30 30 30 30

[25, 26, 54] [25, 26, 56] [25, 54, 55] [25, 54, 55] [25, 54, 55] [25, 54, 55] [55, 57] [57] [58] [59, 60] [59, 60]

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Fig. 7 Calibration curves of biosensor based on acetyl cholinesterase and ENFETs for diisopropyl fluorophosphate (1), trichlorphon (2), paraoxon-ethyl (3), paraoxon-methyl (4), carbofuran (5) and parathion-methyl (6). Measurements were conducted in triplicate in 5.0 mM phosphate buffer, pH 7.5, using 2.0 mM AcChCl and 20 min inhibition time

fluorophosphate, 5.0×10–7 M for paraoxon-ethyl, 5.0×10–6 M for paraoxon-methyl, 2.0×10–7 M for trichlorfon, 5.0×10–6 M for parathion-methyl and 2.0×10–6 M for carbofuran. Thus, a maximal toxicity was observed for diisopropyl fluorophosphate, while the minimal toxicity was for parathionmethyl for both enzymes (Table 2). The conditions for practical application of the biosensors based on ENFETs and inhibition processes analysis have been investigated and critically evaluated for optimisation. Enzyme reactivation after inhibition of organophosphorous pesticides using pyridine-2-aldoxime methyliodide (PAM-2) has been demonstrated [25]. A repeated use of the biosensor for the determination of the same pesticide concentration, after reactivation, gives a quite similar value of inhibition. The biosensor shows reproducible values of the inhibition level for at least 30 consecutive cycles of inhibition–reactivation. The initial response of the biosensor decreases during this series by around 20–40%. In the case of inhibition by hypochlorite species, the use of PAM-2 treatment did not give a positive restoration effect for the inhibited enzyme [58]. This fact can be used as a basis for the conclusion that the inhibition of acetyl cholinesterase by hypochlorite species probably presents a mechanism different to those involved in the case of inhibition by organophosphorous pesticides. Figure 8 shows a correlation between parathion-methyl photodegradation, which is accompanied by formation of the main photodegradation products (HPLC results), and the toxicity of sample solution assessed by biosensors based on acetyl cholinesterase and ENFETs. Toxicity was assessed using mixtures corresponding to parathion-methyl and its photodegradation products, which were formed or consumed during photolysis [62]. Curve 4 shows that the inhibition effect registered by the enzyme biosensor in-

Fig. 8 Kinetics of parathion-methyl photodegradation (1) and formation of 4-nitrophenol (2) and paraoxon-methyl (3) with their further photodegradation measured by HPLC, along with solution toxicity determined by ENFET-based biosensor (4)

creases dramatically as soon as photodegradation begins. In addition, the toxicity curve does not follow exactly the curve of appearance of paraoxon-methyl, which is more toxic toward acetyl cholinesterase than the precursor pesticide. The maximal sample toxicity is obtained about 40 min after irradiation [57, 62]. It is important to note that even after an almost complete degradation of parathion-methyl (t >160 min), the mixture still exhibited a relatively high toxicity, mainly due to paraoxon-methyl. Reversible inhibition of cholinesterases The feasibility of butyryl cholinesterase biosensors based on pH-sensitive field-effect transistors for sensitive detection of glycoalkaloids α-solanine and α-chaconine have been reported [59, 60]. Figure 9 demonstrates the results

Fig. 9 Calibration curves for detection of different glycoalkaloids (α-chaconine (1), tomatine (2), and α-solanine (3)). Measurements were conducted with 1 mM butyryl choline chloride in 5 mM phosphate buffer, pH 7.2

504 Table 3 Levels of total glycoalkaloids content in different varieties of potatoes (metering error is about 5%) Potato variety

Monalisa Pompadour Elkana Agata Caesar

Biosensor method (mg kg–1 fresh weight) Standard addition

Calibration curve

145 250 550 120 130

157 212 460 105 143

Reference HPLC method (mg kg–1 fresh weight)

138 253 611

of immobilised butyryl cholinesterase inhibition by different glycoalkaloids in potatoes. As can be seen, the total potato glycoalkaloids can be detected within the range 0.2–100 µM depending on the type of alkaloid, with a detection limits of 0.2 µM for α-chaconine, 0.5 µM for α-solanine and 0.5 µM for tomatine. The dynamic ranges for the compounds examined show that such biosensors are suitable for a quantitative detection of glycoalkaloids in potato samples. The total glycoalkaloid content in potato juice was determined by two methods: standard additions and using a calibration curve [63]. The results of determination of the glycoalkaloids concentration in potato samples obtained by the biosensor are presented in Table 3 compared with similar data obtained by a method based on standard complex pre-treatment procedure followed by HPLC analysis. There is good correlation between these results. A biosensor for such reproducible and sensitive detection will certainly provide a simple and quick method for screening a great number of samples directly in fresh potato juice without any sample pre-treatment. The biosensor is operational on a crude extract, or even directly on a suspension of potato or plant material. In comparison with biosensors for glycoalkaloids determination (reversible inhibition), the practical application of cholinesterase biosensors for pesticide determination (irreversible inhibition) has significant limitations. The irreversible inhibition measurements result in the decrease in biosensor responses, so that the lifetime of a biosensor is limited to 10–20 measurements irrespective of the real stability of the immobilised enzyme. The necessity of a permanent reloading or reactivation of an enzymatic layer complicates the operation of a biosensor and reduces the reproducibility. In the case of glycoalkaloids determination these problems were overcome, as a reversible type of enzyme inhibition was used. In general, it was shown that enzyme biosensor based on ENFETs might serve as a reliable tool for the estimation of the overall toxicity level in liquid samples. The sensitivity of detection is comparable to the sensitivity provided by other enzyme biosensor-based assay techniques.

Table 4 Level of inhibition of enzymes by different toxic substances (level of inhibition, %)

10 µM trichlorfon 50 µM trichlorfon 1 mM trichlorfon 100 µM carbofuran 10 µM Ag+ 50 µM Ag+ 10 µM Hg2+ 50 µM Hg2+ Mixture No 1a Mixture No 3b Mixture No 4c Mixture No 2d

Urease

BuChE

AChE

0 0 0 0 0 10 15 40 20 95 100 30

50 70 100 100 3 7 3 7 100 100 100 100

5 25 85 50 25 70 10 70 30 90 100 35

aMixture No 1 10 µM Ag++10 µM Hg2++10 µM trichlorfon+10 µM carbofuran bMixture No 2 unknown sample; it was prepared on basis of solution which contains 10 µM Hg2+ and 10 µM carbofuran with addition of Ag+ and trichlorfon cMixture No 3 50 µM Ag++20 µM Hg2++50 µM trichlorfon+20 µM carbofuran dMixture No 4 50 µM Ag++50 µM Hg2++50 µM trichlorfon+50 µM carbofuran

Conception of a multibiosensor based on enzyme inhibition analysis The conception of a multibiosensor based on enzyme inhibition analysis for determination of different toxins has been described, and the possibility of practical application has also been shown [26]. The data on the enzyme inhibition by definite toxins and their mixtures are presented in Table 4. For semi-quantitative determination of mixed toxic components in a sample, the multivariate correspondence analysis is used to extract the important characteristics of these data [64]. The important feature of this method is the simultaneous representation of objects (concentrations of different toxins presented in rows in Table 4) and variables (nature of enzyme presented in columns in Table 4). Correspondence analysis defines the axis, which provides the best fit for both the row points and the column points. A second axis is determined, which best fits the data subject to being orthogonal to the first. Best fit is in the least square sense, relative to the χ-squared distance. This can be viewed as a weighted Euclidean distance between profiles. Multivariate correspondence analysis is well tailored for inhibition data processing because they are coded data between 0 and 100. Principal component analysis, which is a correlation analysis, would be less adapted to this type of coded data. The free statistical software R from Cran.rproject (equivalent to S-Plus) was used. Two factors were extracted and represented along orthogonal axes in Fig. 10, and data from Table 4 were represented in the plane defined by these two axes. As can be seen from this figure, the composition of the unknown sample is close to that of mixture 1. It proves that despite a rather small amount of experimental points for each toxic substance and their mixtures, the method

505

Fig. 10 Representation of data from Table 4 using multivariate correspondence analysis

described allows one to perform half-quantitative analysis of the sample composition quite reliably. More accurate and reliable results could be obtained by using a greater number of different sensors (enzymes) and more experimental data for each of them.

Conclusions Different biosensors based on ENFETs were fabricated and thoroughly investigated. This technology is well adapted to the large-scale production of miniaturised devices compared to standard non-integrated pH electrodes. Concerning application, the obtained results demonstrate the possibility to improve and to modulate their main characteristics in order to fit with the specific analytical requirements needed in the specific application field. It is noteworthy that all biosensors designed are complementary to traditional analytical techniques. Biosensors constitute a complementary additional system for fast and early warning about the presence of various substances. More accurate but time-consuming and expensive classical methods could be used for further validation and additional investigations of the samples previously tested by biosensor arrays. They can provide a way to save time and costs, with a possibility of making rapid decisions on local environmental problems or in the medical field where cheap and throwaway devices can be complementary tools for home monitoring or early diagnostic testing.

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