Intelligent Multi-sensor Arrays for Next Generation Diagnostic

Biodevices

Hadar Ben-Yoav

Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel

Keywords: Electrochemical Sensors, Lab-on-a-Chip, Intelligent Sensors, Chemometrics, Redox, Biomedical

Diagnostics.

Abstract: Redox molecules play an essential role in many biological pathways, including energy metabolism,

biosynthesis, and cell respiration. Moreover, a specific spectrum of redox molecules (comprising their type,

concentration, and redox state) is involved in the development of diseases, including cancer. Thus, profiling

the redox spectrum in the body is highly beneficial for a wide range of biomedical applications, from in vivo

diagnostics to in situ monitoring of cell metabolism. Yet, the fundamental challenge lies within the ability to

efficiently extract and analyze physiologically and medically relevant information from redox molecules in

biofluids. This paper will discuss the advantageous approach to rapidly and continuously monitor redox

molecules in biofluids using electrochemical lab-on-a-chip biodevices. The current approaches will be

discussed to selectively measure redox molecules from biofluids without diminishing the rapid and

continuous monitoring capabilities of these translational biodevices, i.e., by simultaneously analyzing

multiple diagnostic redox biomarkers in a stand-alone operation, without any sample pretreatment or

elimination of other interfering molecules. Then, the promising approach of using intelligent multi-sensor

arrays will be highlighted for the rapid detection of a spectrum of redox molecules and the current

challenges impeding their important utilization for next generation biomedical diagnostic devices.

1 INTRODUCTION

‘Redox’ molecules—i.e., molecules that transfer

electrons through a Reduction-Oxidation reaction—

play an essential role in many biological pathways,

including energy metabolism, biosynthesis, and cell

respiration (Jones and Sies, 2015). Moreover, the

specific ‘redox landscape’ in the body—i.e., the

type, concentration, and redox state of various redox

molecules—plays an important role in regulation

and communication between different physiological

pathways (Griffiths, Gao et al., 2017; Sies, 2017),

and it is even involved in the development of

diseases (Swomley and Butterfield, 2015;

Chandrasekaran, Idelchik et al., 2017), including

cancer (Yuan, Liu et al., 2015). Importantly, in

2017, the European Cooperation in Science and

Technology [COST] Action ‘EU-ROS’ expressed

the significance and need to “providing new insights

and tools for better understanding redox biology and

medicine and, in the long run, to finding new

therapeutic strategies to target dysregulated redox

processes in various diseases” (Egea, Fabregat et al.,

2017). Thus, profiling the ‘redox landscape’ in the

body is highly beneficial for a wide range of

biomedical applications, from in vivo diagnostics to

in situ monitoring of cell metabolism. Yet, the

fundamental challenge lies within the ability to

efficiently extract and analyze physiologically and

medically relevant information from redox

molecules in biofluids.

Electrochemical lab-on-a-chip are well suited for

the analytical task of measuring redox molecules

(Bandodkar and Wang, 2014; Katz, Fernandez et al.,

2015; Katz, 2016; Sekretaryova, Eriksson et al.,

2016). These translational, cost-effective, and

portable miniaturized biodevices enable a rapid and

continuous monitoring with superior sensitivity of

multiple redox molecules without externally-added

labels [‘unlabeled’], by directly recording

electrochemical reactions happening between the

molecules and an electrode, generating a unique

electronic signal according to the redox potential of

a molecule of interest. Thus, these ‘portable

laboratories’ provide the means in which the sensor

continuously measures the in situ levels of unlabeled

236

Ben-Yoav, H.

Intelligent Multi-sensor Arrays for Next Generation Diagnostic Biodevices.

DOI: 10.5220/0006653602360240

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 1: BIODEVICES, pages 236-240

ISBN: 978-989-758-277-6

redox-active diagnostic markers in the sample

(Bandodkar, Jia et al., 2015; Topkaya, Azimzadeh et

al., 2016). However, the selectivity of

electrochemical lab-on-a-chip towards several

diagnostic redox biomarkers dramatically decreases

in the presence of multicomponent samples [e.g.,

biofluids] due to the presence of other redox

molecules generating overlapping electrochemical

signals, impeding the ability to distinguish between

the various unlabeled, redox biomarkers in the

sample (Corrie, Coffey et al., 2015; Rocchitta,

Spanu et al., 2016).

This paper will focus on current approaches to

overcome the key challenge of selectivity in

biofluids without diminishing the rapid and

continuous monitoring capabilities of

electrochemical lab-on-a-chip biodevices, i.e., by

simultaneously analyzing multiple diagnostic redox

biomarkers from biofluids in a stand-alone

operation, without any sample pretreatment or

elimination of other interfering molecules. First, the

commonly used analytical approaches will be

reviewed that enable the selective detection of

specific redox biomarkers. Then, the promising

approach of using intelligent multi-sensor arrays will

be highlighted for the detection of a spectrum of

redox molecules and the current challenges

impeding unleashing the potential utilization for

biomedical diagnostics.

2 ELECTROCHEMICAL

LAB-ON-A-CHIP BIODEVICES

FOR REDOX BIOMARKERS

DETECTION

2.1 Detection of Specific Diagnostic

Redox Biomarkers

The signal in electrochemical sensors – namely, the

electronic current generated at the surface of the

electrode – is determined by three main factors: (a)

the properties of the molecules, e.g., their standard

redox potentials, molecular weights, particle

charges, diffusion rates, and concentrations; (b) the

properties of the electrode used, e.g., electrode

material type and electrode geometry; and (c) the

properties of the redox reaction at the solid–

electrolyte interface, e.g., the rate of electron transfer

between the molecules and the electrode and the

thickness of the electric double layer (Bard and

Faulkner, 2001). For samples containing more than

one redox molecule [such as biofluids], the resulting

electrochemical signal is complex and must undergo

digital deconvolution through direct and simple

signal processing methods (Jakubowska, 2011; Xia

and Behnamian, 2015). Naturally, however, the level

of complexity increases as a function of the number

of molecules in the solution, making such a

separation impractical for solutions with multiple

redox molecules – and practically impossible when

these molecules have overlapping electrochemical

signals.

Another approach to discriminate between

molecules in multicomponent mixtures is to use a

pre-separation step [e.g., liquid chromatography],

which increases the selectivity of the sensor by

physically separating the molecule of interest

(Huang, Lin et al., 2011; Kutter, 2012; Sikanen,

Aura et al., 2012; Chan, Pasikanti et al., 2015;

Fekete, Guillarme et al., 2016; Hong, Yang et al.,

2016; Songa and Okonkwo, 2016). This solution

indeed increases separation efficiency but it is

laborious, time-consuming, and increases the

number of analytic steps and the overall cost of the

discrimination step, thus preventing its effective

utilization for in situ recording of the numerous

molecules continuously generated in the body.

An alternative approach involves modifying the

surface of the electrode with a film that recognizes a

specific type of molecule in the sample, thus

increasing the selectivity of the sensor to the specific

electrochemical signal generated by this molecule

(Xie, Liu et al., 2015; Bala and Gorski, 2016;

Weltin, Kieninger et al., 2016). Both artificial and

biological receptors [e.g., enzymes] can be used to

produce such highly selective films (Palchetti, 2016;

Weltin, Kieninger et al., 2016), and, indeed, these

modified electrodes can be integrated into an array

of specific sensors [Figure 1] that can analyze the

types of the redox molecules at suitable limit-of-

detection values [usually between nano- and femto-

molars (Bougrini, Florea et al., 2016)]. However, the

development of such films is costly and is multiplied

by the number of the detected molecules and

required electrodes, thereby limiting the multi-

molecule detection capabilities of such modified

electrodes (Bunyakul and Baeumner, 2015; Zaffino,

Galan et al., 2015; Heikenfeld, 2016). Moreover,

with the growing evidence that a disease is no more

governed by a specific biomarker but profiles of

molecules, a single or combination of highly

selective measurements may be inadequate as

appropriate biomarkers are seldom known for

complex diseases.

Intelligent Multi-sensor Arrays for Next Generation Diagnostic Biodevices

237

Figure 1: Array of electrodes modified with films that are

specific to multiple diagnostic redox biomarkers.

2.2 Detection of a Spectrum of

Diagnostic Redox Biomarkers

Recently, a promising trend has emerged based on

arrays of partially-selective electrodes that

simultaneously cross-react with multiple redox

molecules in the mixture [Figure 2]. Such an array

of electrodes thus generates a set of complex

electrochemical signals that are analyzed using

intelligent chemometric algorithms [e.g., pattern

recognition algorithms] to facilitate the in situ

analysis of these molecules (Cipri, Schulz et al.,

2016; Kilmartin, 2016; Wadehra and Patil, 2016)).

Inspired by the sensory system of taste in

mammals, wherein several taste receptors on the

tongue can respond to a large variety of flavor-

inducing substances, these intelligent

electrochemical sensors enable fast response, low-

cost, portability, ease-of-use, and simultaneous

detection of a large spectrum of redox molecules in

one step without preforming any pretreatment, as

data processing stage may offset any matrix or

interference effect from the sample itself, drifts or

nonlinearity obtained with the sensors (Cetó,

Voelcker et al., 2016).

Figure 2: Array of electrodes modified with partially-

selective films that cross-react with multiple diagnostic

redox biomarkers.

The cross-reactivity is achieved by using

electrodes consisting of materials that possess

different electron transfer rates, thereby generating

slightly diverse electrochemical signals from the

analyzed redox mixture. To extract information

about the redox molecule of interest [concentration,

type, etc.], machine learning algorithms—including

multivariate analysis models, such as principal

component analysis, artificial neural networks, and

others—are used together with training sets of

calibration mixtures, thus shifting the complexity of

the analysis from the physical domain to the digital

processing domain (Górski, Kubiak et al., 2016).

However, despite the vast contribution of such

‘intelligent sensors’ to differentiate between groups

of food types or environmental conditions (Wei,

Yang et al., 2018), utilizing them for biomedical

applications is more challenging, mainly because

biofluids contain an abundance of redox molecules,

BIODEVICES 2018 - 11th International Conference on Biomedical Electronics and Devices

238

which generate overlapping electrochemical signals

that are hard to distinguish.

To solve this issue and provide an accurate and

meaningful profile of the redox molecules in

biofluids, previous studies suggested modifying

electrodes with films that provide various

reactivities between the electrode and the redox

molecules based on the films’ properties, i.e., their

electrocatalytic activity [e.g., noble metals as

electrode material or conductive polymers to coat

the electrodes (Wei, Yang et al., 2018)] or their

biofunctionality [e.g., enzymes immobilized on the

electrode (Cetó, Voelcker et al., 2016)]. Indeed, such

approaches have enabled, for the first time, profiling

a predetermined set of redox molecules—e.g., for

identifying the redox state of specific molecules

involved in a specific type of cancer [namely,

prostate cancer (Pascual, Campos et al., 2016)].

However, despite this marked advancement in the

ability to differentiate between groups of specific,

disease-oriented ‘redox landscapes’, this approach is

currently lacking the ability to extract redox

information from all redox molecules present in a

biofluid at a given time, hence limiting the diagnosis

to a specific biomedical condition.

3 CONCLUSIONS

This paper highlights the potential contribution of

intelligent multi-sensor arrays for the important need

to profile the ‘redox landscape’ in the body. By

overcoming the fundamental challenge of selectivity

in biofluids, these intelligent sensors will enable the

rapid and continuous quantitative analyses of redox

information in biofluids. Such an achievement is

highly beneficial for a wide range of biomedical

applications, from in vivo diagnostics to in situ

monitoring of cell metabolism, and will offer the

next generation of diagnostic biodevices that can be

used to study and monitor disease initiation and

development. Ultimately, integrating these

intelligent sensors in electrochemical lab-on-a-chip

biodevices will facilitate the development of highly

sensitive monitoring tools for the continuous in vivo

monitoring of various biomedical conditions.

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