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Biosensor-Based Drug Delivery Systems: Innovations, Applications, and… 239
Fig. 3 The components of biosensor involved in efficient identification of analyte. (Adapted from [19])
3.1 Approach Toward Designing Biosensors
3.1.1 Selection of the Analyte and Bioreceptors
3.1.2 Immobilization of Biosensors
The selection of the analyte and bioreceptor requires digging deep
into reverse pharmacology. That means, the root cause of the
infection and the knowledge of pathogenesis must be known to
the developer. Say, for example, foot ulcers are a common problem
in chronic diabetic patients. Wound healing is poor in diabetic
patients due to increasing circulatory concentrations of glucose.
Biosensors are designed to analyze the minute-to-minute condition
of wound healing in a bandaged wound without the need of
opening it and providing therapy accordingly.
It is crucial in their synthesis, as improper or inadequate fixation of
the surface can lead to enzyme leaching or inactivation. Enzymes,
serving as the biorecognition element, can be immobilized using
physical methods, primarily through adsorption or encapsulation.
Physical adsorption involves attaching the bioreceptor onto an inert
solid material through van der Waals forces, electrostatic interactions, ionic bonds, or hydrogen bonding.
This m
ethod i
s simple, cost-effective, preserves bioreceptor
activity, and requires no modification of biological elements or
matrix generation. However, it is sensitive to changes in pH, temperature, and ionic strength due to its reliance on weak interactions,
which can affect operational and storage stability.
Biorecognition elements
are typically embedded within the
three-dimensional network of organic or inorganic materials.
Organic materials include polydimethylsiloxane, photopolymers,
240 Disha Pant et al.
gelatin, alginate, cellulose, acetate phthalate, modified polypropylene, and polyacrylamide. Inorganic materials such as activated carbon and porous ceramic materials are also used. Common
techniques for immobilization include electro polymerization, the
sol-gel process, and microencapsulation.
Electro polymerization involves applying current or potential
to an aqueous solution or electrolyte containing biomolecules and
monomers. This leads to either reduction or oxidation of the
monomer on the electrode surface, forming reactive radicals that
polymerize and trap enzymes near the electrode in the solution.
Commonly used electropolymerized films include aniline, pyrrole,
and thiophene.
The sol-gel process is widely employed for enzyme entrapment,
involving the hydrolysis and condensation of metal alkoxides at low
temperatures. This results in the formation of a nanoporous material network that encapsulates biomolecules under mild conditions,
offering thermal and chemical stability and ease of synthesis.
Microencapsulation is another economical method where
enzymes are enclosed within a spherical semi-permeable membrane. This membrane can be made from polymeric, lipoidal,
lipoprotein-based, or non-ionic materials. Two preferred microencapsulation techniques are phase separation (coacervation) of
enzyme micro-droplets in water-immiscible liquid phases and interfacial polymerization at the interface of immiscible substances.
These techniques effectively encapsulate enzymes within a polymeric membrane, ensuring their protection and functionality [
30–
33].
Chemical or irreversible immobilization involves creating
robust chemical bonds, such as covalent binding or cross-linking,
between functional groups of the biorecognition element and the
transducer surface. Chemical immobilization methods are classified
into direct covalent binding and covalent cross-linking.
Direct C
ovalent B
inding: Direct covalent binding is the predominant technique for enzyme immobilization, where the biorecognition element forms strong bonds with either the electrode/
transducer surface or an inert matrix in a membrane. The process
involves two main steps: synthesis of a functional polymer and
subsequent covalent immobilization. This binding relies on interactions between functional groups of the biorecognition element
(typically amino acid side chains) and reactive groups on the transducer or membrane matrix surface.
Advantages of
direct covalent binding include high resistance
to environmental fluctuations, minimal leakage of enzymes, and
robust bonding between the biorecognition element and the
matrix. Drawbacks include the use of harsh chemicals and irreversibility once the matrix is used.
Biosensor-Based Drug Delivery Systems: Innovations, Applications, and… 241
Covalent Cross-Linking: Covalent cross-linking involves creating intermolecular covalent bonds between biorecognition elements (enzymes) or between biorecognition elements and inert
proteins (e.g., bovine serum albumin). This process utilizes multifunctional reagents as linkers to connect enzyme molecules into 3D
cross-linked structures anchored to the transducer surface. Optimal
conditions for cross-linking include pH, temperature, and ionic
strength adjustments.
Benefits of covalent cross-linking include reduced enzyme leakage, strong chemical bonds, and the ability to optimize the environment for biorecognition elements using stabilizing agents.
However, drawbacks include potential cross-linking between protein molecules rather than with the matrix, which can lead to partial
protein denaturation and limit application versatility [
30–33].
3.1.3 Selection of Transducer
Electrochemical biosensors rely on the electrochemical properties
of the analyte and transducer.
Electrochemical biosensors are extensively researched and utilized, leveraging the electrochemical properties of both the analyte
and the transducer for operation. Renowned for their high sensitivity, selectivity, and detection capabilities, they operate through
electrochemical reactions occurring at the transducer surface
between the bioreceptor and analyte. These reactions produce
discernible electrochemical signals, including voltage, current,
impedance, and capacitance. Electrochemical biosensors are classified based on their transduction principles into potentiometric,
amperometric, impedimetric, conductometric, and voltammetric
34–36].
types [
Potentiometric biosensors detect charge accumulation from
the interaction between an analyte and a bioreceptor at the working
electrode, measured against a reference electrode with no current
flow. They use ion-selective electrodes and ion-sensitive field-effect
transistors to convert biochemical reactions into potential signals.
Amperometric biosensors,
operating
in two or three-electrode
configurations, measure current from electrochemical reactions at
the working electrode under a constant potential, providing a
sensitive, fast, precise, and linear response proportional to analyte
concentration. Despite these advantages, they suffer from poor
selectivity and interference from other electroactive substances.
Conductometric b
iosensors m
easure changes in conductance
between electrode pairs due to electrochemical reactions in the
analyte, often used alongside impedimetric biosensors to monitor
metabolic processes in living systems.
Impedimetric biosensors
measure electrical impedance at the
electrode/electrolyte interface using a small sinusoidal excitation
signal and analyzing the in/out-of-phase current response as a
function of frequency. This technique involves applying low amplitude AC voltage at the sensor electrode and measuring the resulting
current with an impedance analyzer.
242 Disha Pant et al.
Voltammetric biosensors detect analytes by measuring the current during controlled variations of the applied potential, offering
highly sensitive measurements and the capability for simultaneous
detection of multiple analytes.
Optical biosensors are analytical instruments that combine a
biorecognition element with an optical transducer system. They
operate by producing signals directly proportional to the analyte
concentration, offering real-time and label-free detection in parallel. These biosensors employ various biorecognition elements, such
as enzymes, antibodies, aptamers, whole cells, and tissues. Within
optical biosensors, the transduction process leads to modifications
in absorption, transmission, reflection, refraction, phase, amplitude, frequency, and/or light polarization. These alterations arise
in response to physical or chemical changes initiated by the biorecognition elements [
34–36]
The most commonly used optical-based biosensors are
fluorescence-based optical biosensors, chemiluminescence-based
optical biosensors, SPR-based optical biosensors, and optical
fiber-based optical biosensors [
30].
Fluorescence-based optical biosensors utilize fluorescence
labeling to detect analytes or molecules. These biosensors are
highly valued for medical diagnosis, environmental monitoring,
and food quality assessment due to their high selectivity, sensitivity,
and quick response time. Various fluorescent dyes, such as quantum
dots, traditional dyes, and fluorescent proteins, are employed.
Fluorescence-based biosensors operate through fluorescent
quenching (turn-off), fluorescent enhancement (turn-on), and
fluorescence resonance energy transfer (FRET).
FRET-based optical biosensors, noted for their higher sensitivity, are particularly prominent in studying intercellular processes,
detecting changes on the scale of angstroms to nanometers. They
are extensively used in clinical applications like cancer therapy and
aptamer analysis. A FRET sensor is based on a carbon dots (CDs)/
Au NR assembly to detect lead ions, achieving a linear detection
range from 0 to 155 μM with a detection limit of 0.05 μM [
Chemiluminescence-based o
ptical b
iosensors, which emit light
30].
as a result of chemical reactions, are highly valued for their simplicity, low detection limits, wide calibration ranges, and cost-effective
instrumentation. Recent advancements have incorporated nanomaterials to enhance intrinsic sensitivity and broaden application areas.
A chemiluminescence-based biosensor using graphene oxide can
detect DNA, demonstrating high sensitivity and selectivity with a
linear range of 0.1–3 nM and a detection limit of 34 pM [
SPR-based biosensors
detect changes in the refractive index
30].
due to molecular interactions at a metal surface via surface plasmon
waves, functioning as a label-free biosensing technology. When
polarized light illuminates a metal surface at the interface of two
media with different refractive indices, it generates electron charge
Biosensor-Based Drug Delivery Systems: Innovations, Applications, and… 243
density waves (plasmons) at a specific angle, reducing the intensity
of the reflected light, which correlates with the mass on the surface.
This principle is used for various applications, including disease
diagnosis and environmental and food quality monitoring. Extending SPR to metal-based nanomaterials like gold and silver nanoparticles results in localized sur face plasmon resonance (LSPR), where
plasma oscillations occur locally at the nanostructure surfa
Rashidi et al. developed
an SPR-based DNA biosensor using gold
ce.
nanostars to detect a donkey meat marker, achieving a detection
limit of 1.0 nM with a relative standard deviation of 0.85%.
Optical fiber-based biosensors use an optical field to measure
biological species such as whole cells, proteins, and aptamers,
providing a promising alternative to traditional biomolecule assessment methods. These sensors often employ evanescent field sensing, which occurs in tapered optical fibers. When light passes
through the fiber, an evanescent wave is generated at the sample
interface due to total internal reflection, decaying exponentially
with distance and capable of exciting fluorescence near the sensing
surface. Tapered optical fibers are utilized with various optical
transduction methods, including changes in refractive index,
absorption, fluorescence, and SPR.
Gravimetric biosensors
operate
based on changes in mass, particularly in the binding material like proteins or antibodies on their
surface, resulting in detectable signals. These biosensors utilize thin
piezoelectric quartz crystals that vibrate at a specific frequency
influenced by both the applied current and the mass of the material
being detected. Piezoelectric-based biosensors, magnetoelasticbased biosensors (MES), and quartz crystal microbalance (QCM)
sensors are most commonly used for gravimetric transduction.
herm
A t
al biosensor utilizes the fundamental properties of
biological reactions, such as whether they are exothermic or endothermic, by measuring the heat energy absorbed or released during
the reaction. The total heat energy absorbed or evolved, or the
resulting temperature change (ΔT) detected by the thermal biosensor, is directly proportional to the enthalpy (ΔH) and the total
number of product molecules (np) generated in the biochemical
reaction. Conversely, it is inversely proportional to the heat capacity
(Cp) of the reaction, expressed as ΔT =-(np ΔH) / Cp.
Acoustic biosensors
function by detecting alterations in the
physical characteristics of an acoustic wave, which can be linked to
the quantity of absorbed analyte. Piezoelectric materials are frequently employed as sensor transducers due to their capability to
generate and propagate acoustic waves in a frequency-dependent
fashion. In the propagation of acoustic waves, the ideal resonant
frequency is greatly influenced by the physical dimensions and
attributes of the piezoelectric crystal. Changes in the mass of materials on the crystal’s surface can lead to observable fluctuations in
the crystal’s natural resonant frequency.
244 Disha Pant et al.
3.2 Green Biosensors
Herbal extracts have garnered significant interest in biosensorbased drug delivery systems due to their unique biochemical properties and potential therapeutic benefits. These natural extracts,
derived from plants with medicinal properties, are rich sources of
bioactive compounds such as polyphenols, alkaloids, and flavonoids, which exhibit antioxidant, antimicrobial, and antiinflammatory properties. Integrating herbal extracts into biosensors enables targeted and controlled drug delivery, where the biosensor can detect specific biomarkers or conditions and trigger the
release of therapeutic compounds encapsulated within nanoparticles or hydrogels. This approach not only enhances the precision
and efficacy of drug delivery but also leverages the biocompatibility
and sustainability of herbal ingredients. Moreover, the use of herbal
extracts aligns with the growing preference for natural and
eco-friendly healthcare solutions, offering promising avenues for
developing next-generation biosensor technologies with enhanced
therapeutic potential. Herbal molecules incorporated in metalbased nanoparticles are listed in Table
4.
Table 4
Green biosensors
S.
Green biosensor Property Reference
No.
1. Silver nanoparticles (AgNPs)
synthesized using quercetin
2. AgNPs synthesized using onion peel Detection of toxic mercury in the liquid
3. AgNPs using pine nut extract Reducing and stabilizing agent
4. Fluorescent probes from AuNPs using
papaya juice
5. Papain-stabilized gold nanoclusters Biosensor for sensitive and selective detection
6. CuONPs synthesized from the stem
latex of peepal (Ficus religiosa)
Copper oxide nanoparticles (CuONPs)
synthesized using Caesalpinia
bonducella seed extract
Quercetin as the reducing agent and used to
modify graphite electrodes to fabricate a
third-generation lactose biosensor
phase
electrochemical sensor for the
determination of paracetamol
Capping and reducing agents were used to
develop a
and stable biosensor for the detection of
L-lysine
of D-penicillamine
Fabrication of an electrochemical biosensor
for the detection of pesticides
Electrochemical b
of riboflavin
highly selective,
iosensor f
biocompatible,
or the detection
[37]
[38]
[39]
[40]
[41]
[42]
[43]
Biosensor-Based Drug Delivery Systems: Innovations, Applications, and… 245
3.3 Challenges in Development of Biosensors-Based Drug Delivery Systems
Preparing biosensors for drug delivery presents several challenges,
including:
1. Biocompatibility: Ensuring that biosensors are compatible with
the human body without causing an immune response or
adverse reactions is crucial. Materials used must not provoke
inflammation, infection, or toxicity over long-term
implantation.
2. Biofouling: The accumulation of proteins, cells, and other
biological materials on sensor surfaces can hinder sensor performance and accuracy. Effective strategies to prevent or minimize biofouling are essential to maintain sensor functionality.
3. Stability and Longevity: Biosensors need to maintain their
stability and sensitivity over extended periods. This includes
resistance to degradation from the physiological environment,
such as varying pH levels, enzymatic activity, and mechanical
stress.
4. Selective and Sensitive Detection: Biosensors must be highly
selective and sensitive to specific biomarkers related to drug
delivery and disease states. Cross-reactivity with non-target
molecules and low signal-to-noise ratios can reduce the effectiveness of the sensor.
5. Integration with Drug Delivery Systems: Designing biosensors
that can seamlessly integrate with drug delivery mechanisms is
challenging. This includes synchronization of the sensor with
the drug release system to ensure timely and appropriate therapeutic interventions.
6. Miniaturization and Power Requirements: Reducing the size of
biosensors to fit within implantable devices while ensuring they
have adequate power sources for long-term operation is a
significant challenge. Energy-efficient designs and alternative
power sources like bio-batteries or wireless power transfer need
to be considered.
7. Data Transmission and Security: Ensuring reliable transmission
of data from the biosensor to external monitoring systems,
often wirelessly, is critical. Data security and privacy concerns
must also be addressed to protect patient information.
8. Calibration and
Maintenance:
Developing biosensors that
require minimal calibration and maintenance over their operational life is important for practical and widespread use. Automated or self-calibrating systems can help address this issue.
9. Regulatory
and Ethical Considerations: Biosensors for drug
delivery must comply with stringent regulatory standards and
undergo extensive testing to ensure safety and efficacy. Ethical
considerations, particularly regarding patient consent and data
handling, must also be addressed.
246 Disha Pant et al.
10. Cost and Scalability: Producing biosensors that are costeffective and scalable for mass production without compromising quality is a key challenge. Economic viability is crucial for
widespread adoption in clinical settings.
Addressing these challenges requires interdisciplinary research
and collaboration among materials scientists, engineers, biologists,
and medical professionals to develop robust and reliable biosensors
for drug deliver y applications.
4 Future Perspectives: Can Artificial Intelligence Be a Boon in Biosensor-Based
Drug Delivery Systems
AI-based computer modeling offers a cost-effective alternative to
traditional laboratory methods, which can be labor-intensive and
slow. Utilizing machine-based intelligence, AI performs complex
analytical tasks using computers or computer-controlled robots,
surpassing the capabilities of human-based natural intelligence. AI
and its subfields can simulate biological processes related to gene
delivery with high precision, evaluate the effectiveness of gene/
drug delivery vehicles, control gene/drug delivery parameters, and
model cells and their intracellular organelles to develop highly
efficient and non-toxic gene delivery agents. AI encompasses
machine learning (ML), neural networks (NNs), expert systems,
deep learning (DL), computer vision, robotics, and reinforcement
learning.
Recent advancements
major challenge for scientists: the binding of cellular and humoral
components such as apolipoproteins, immune proteins, or complement components to nanomedicine after injection into the bloodstream. This protein binding, known as the protein corona (PC),
affects the physicochemical properties, reactivity, immune response,
stability, macrophage uptake, cellular recognition, and fate of nanocarriers. Predicting protein corona formation is challenging due to
the complexities of nanomaterial properties, reaction conditions,
and extracellular protein composition. However, machine learning
can accurately and quantitatively predict the functional composition of the protein corona and cellular responses (such as cytokine
release and macrophage uptake). Machine learning algorithms,
such as random forest, can predict protein corona formation by
analyzi
fluorescence properties. For instance, the formation of a protein
corona on silver nanoparticles can be predicted using a machine
learning algorithm based on a random forest, which combines
solution conditions with the physicochemical characteristics of
proteins and nanoparticles to provide accurate predictions. Additionally, ML-based approaches, including support vector machines,
ng
nanomaterial descriptors like size, surface charge, and
in nanobiomedicine have presented a
Biosensor-Based Drug Delivery Systems: Innovations, Applications, and… 247
multivariate adaptive regression splines (EARTH), partial least
squares (PLS), multiple linear regression (MLR), and projection
pursuit regression (PPR), have been used to predict the bioactivity
of surface-modified gold nanoparticles and analyze the composition of the protein corona.
5 Benefits of Artificial Intelligence in Developm ent of Biosensor-Based Drug
Delivery
Artificial intelligence significantly enhances the development of
biosensor-based drug delivery systems by offering several key benefits. First, AI-driven models can optimize biosensor sensitivity and
specificity by analyzing vast datasets to identify patterns and correlations that might be missed by traditional methods. This leads to
the creation of more accurate and reliable sensors. Second, AI
algorithms can predict the optimal conditions for drug release,
ensuring that medications are delivered at the right time and in
the right amounts, improving therapeutic efficacy and minimizing
side effects. Third, AI can facilitate real-time monitoring and feedback, allowing for adaptive drug delivery that responds dynamically
to changes in a patient’s condition. This is particularly valuable in
managing chronic diseases, where continuous monitoring and
timely interventions are crucial. Furthermore, AI can assist in the
design of biosensors that are more biocompatible and resistant to
biofouling, extending their lifespan and functionality in vivo. Overall, the integration of AI into biosensor-based drug delivery systems
holds the promise of more personalized, efficient, and effective
healthcare solutions.
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