Clinical Laboratory Innovations: Nanoscale Detection of Chloramphenicol Using Nanomaterial Biosensors

ABSTRACT

Chloramphenicol (CAP) is an important broad‐spectrum antibiotic used to treat severe bacterial infections, but its clinical application is limited by serious toxic effects, including aplastic anaemia and bone marrow suppression. These care concerns underscore the need for fast, sensitive and reliable analytical methods for CAP detection in biological and food samples. In recent years, nanomaterial‐based biosensors have emerged as encouraging tools for this purpose, offering enhanced sensitivity, selectivity and analytical performance. This review examines recent advances in nanoscale detection of CAP using nanomaterial biosensors, with specific emphasis on nanomaterial‐based electrochemical platforms. The exceptional physicochemical properties of these materials, including enhanced electron transfer, large surface area, catalytic activity and signal amplification, have contributed to the development of highly effective sensing systems. These biosensors have shown strong potential for rapid analysis and trace detection in complex matrices. Moreover, several challenges remain before these platforms can be fully translated into routine clinical and regulatory use. Subjects such as matrix effects, reproducibility, stability and validation against reference methods require further attention. This review critically discusses current progress, highlights the limitations of existing approaches and outlines future directions for the development of practical laboratory‐based biosensing approaches for CAP detection.

Nanosensor schematic functionalized with specific bioreceptors that produce electrochemical signals upon Chloramphenicol binding. Resulting voltammetric readouts (differential pulse, square‐wave, and cyclic voltammetry) are analyzed to identify and quantify Chloramphenicol.

1. Introduction

Antibiotics are extensively used in both human and veterinary medicine to treat and prevent bacterial infections [1, 2]. On the other hand, their unregulated disposal contributes to serious environmental and ecological hazards. This increasing issue highlights the need to review current practices related to antibiotic prescription and consumption [3]. Additionally, the presence of antibiotic residues in food and water supplies has raised important concerns regarding food safety and environmental health, leading to the expansion of more advanced and reliable methods for detecting these contaminants across various samples. Chloramphenicol (CAP) is a powerful antibiotic that plays a crucial role in treating various infections, particularly those caused by susceptible bacteria [4, 5]. Its efficacy against a broad spectrum of Gram‐positive and Gram‐negative organisms makes it an essential option for managing serious infections such as typhoid fever, meningitis and certain respiratory tract infections [4, 5]. Recent studies have also explored CAP's potential role in cardiovascular disease management, suggesting it may possess anti‐inflammatory effects that could benefit conditions like atherosclerosis and other cardiovascular disorders [6, 7]. By mitigating inflammation, CAP may enhance vascular health and lower the risk of cardiovascular events. The significance of sensitive and rapid CAP quantification cannot be overstated, especially in clinical settings where timely therapeutic decisions are critical [6, 7]. Conventional methods for CAP quantification, such as high‐performance liquid chromatography (HPLC) and enzyme‐linked immunosorbent assays (ELISAs), while effective, often suffer from limitations, including lengthy processing times, the need for complex sample preparation and high operational costs [8, 9]. These methods may also lack the necessary sensitivity to detect low concentrations of CAP, which is particularly important given the narrow therapeutic window of this antibiotic and the potential for adverse effects, such as bone marrow suppression and aplastic anaemia [8, 9]. In contrast, biosensors offer significant advantages for the nanoscale CAP quantification. Utilizing nanomaterials, these biosensors can achieve enhanced sensitivity and specificity, allowing for the CAP quantification at nanomolar concentrations [10, 11]. The rapid response times of biosensors facilitate immediate monitoring of CAP levels, which is crucial for ensuring patient safety and optimizing therapeutic outcomes. Furthermore, biosensors can be designed for ease of use, portability and cost‐effectiveness, making them suitable for practical applications [10, 11]. This review critically discusses current progress, highlights the limitations of existing approaches and outlines future directions for the development of practical laboratory‐based biosensing strategies for CAP detection.

2. Chloramphenicol

CAP is a broad‐spectrum antibiotic that has been in clinical use since 1949 [12, 13]. Originally known as chloromycetin, it was first isolated from the soil bacterium Streptomyces venezuelae in 1947 [12, 13]. CAP functions as a protein synthesis inhibitor, exhibiting bacteriostatic properties, and is one of the few natural antibiotics with a nitro group in its structure. CAP has a unique chemical structure characterized by a nitro group and a dichloroacetamide side chain attached to a propanediol group [14, 15]. The core structure features a paranitrobenzene ring, which is one of the few natural compounds to possess both strong electron‐withdrawing groups—namely, the nitro and halogen groups. CAP's molecular configuration includes two stereogenic centres, with only the d‐threo isomer being biologically active in inhibiting bacterial protein synthesis [14, 15]. The overall simplicity of CAP's structure, combined with its functional groups, contributes to its ability to penetrate biological membranes effectively, allowing it to reach various tissues, including the central nervous system. This structural versatility is key to its broad‐spectrum antibiotic activity and its interactions with bacterial ribosomes.

The different structural illustrations of CAP presented in Figure 1 offer complementary visions that enhance the considerate of its stereochemistry and biological activity. The Fischer projection evidently shows the relative configuration of the two stereogenic centres and highlights the spatial plan of the d‐threo isomer, which is the only biologically active form. The Newman projection offers a conformational perspective along the carbon–carbon bond, revealing steric interactions between functional groups, while the skeletal formula offers a concise and clear depiction of atom connectivity and the positions of the nitro, dichloroacetamide and propanediol groups. Supportively, these illustrations allow readers to better appreciate why only the d‐threo isomer effectively binds to the peptidyltransferase centre of the bacterial ribosome, while the other stereoisomers are inactive. This structural clarity is mostly important for the increase of selective detection methods and biosensors, as accurate detection of the active isomer is essential for reliable quantification of CAP in pharmaceutical, food and biological samples.

FIGURE 1.

CAP structural forms: (A) Newman projection, (B) Fischer projection (showing the d‐threo isomer) and (C) skeletal formula, which illustrates the configuration of its two stereogenic centres. Notably, only the d‐threo isomer is effective in inhibiting bacterial protein synthesis, whereas the other stereoisomers, which do not occur naturally, are inactive [16].

Over 500 derivatives of CAP have been synthesized, including thiamphenicol and azidamphenicol for human use, and florfenicol for veterinary applications. CAP binds to the peptidyltransferase centre of the bacterial ribosome, inhibiting peptide bond formation and thus protein synthesis [17, 18]. It is effective against a wide range of bacteria, including Gram‐positive and Gram‐negative species, as well as atypical pathogens like mycoplasmas and rickettsia. Its clinical applications include treating bacterial conjunctivitis, otitis externa and severe infections such as meningitis and typhoid fever [17, 18]. However, due to the risk of serious side effects like aplastic anaemia, its use has become limited. Despite its initial popularity for its effectiveness and low cost, CAP can cause haematological disorders, including bone marrow suppression [19, 20]. This is particularly concerning in neonates and infants, who may develop grey syndrome due to the accumulation of toxic metabolites [19, 20]. Additionally, CAP's ability to penetrate tissues, including the brain, raises concerns about potential mitochondrial toxicity. Recent studies suggest that CAP may have potential in cancer therapy by inhibiting mitochondrial protein synthesis in cancer stem cells, which rely on robust mitochondrial activity for survival. This repurposing of antibiotics for anti‐cancer treatment is an emerging area of research, indicating that CAP may play a role beyond its traditional use as an antibiotic.

3. CAP Mechanisms of Action

Protein synthesis is a crucial cellular process where ribosomes translate genetic information from messenger RNAs (mRNAs) into proteins. In bacteria, the ribosome consists of three RNA chains and over 50 proteins, forming two subunits (30S and 50S) that combine to create the 70S ribosome [21, 22]. The translation process involves several protein factors and can be divided into four main steps: initiation, elongation, termination and ribosome recycling. Initiation begins on the 30S subunit with the recruitment of initiator formyl‐methionine transfer RNA (fMet‐tRNAi fMet) by initiation factors [21, 22]. The 50S subunit then joins to form the 70S initiation complex. During elongation, the ribosome decodes codons in the aminoacyl (A) site with the help of a ternary complex (aminoacyl‐tRNA, elongation factor Tu and GTP), leading to peptide bond formation and the extension of the peptide chain [23]. This process continues until a stop codon is reached, at which point release factors promote the release of the newly synthesized protein, and the ribosome is recycled into its subunits [23]. Antibiotics that target the 50S subunit, such as CAP, inhibit protein synthesis by binding to the peptidyl transferase centre (PTC) and competing with incoming aminoacyl‐tRNAs [24]. CAP's action is context‐specific, as it preferentially stalls ribosomes based on the specific amino acids in the nascent peptide chain. It is effective against a wide range of bacteria but not against certain species like Pseudomonas aeruginosa. The drug's binding is influenced by the growing polypeptide, making its inhibitory effects dependent on the sequence of the synthesized peptide.

As shown in Figure 2, CAP reversibly binds to a specific pocket in the PTC of the 50S subunit, blocking the transpeptidation reaction. This prevents the transfer of the growing peptide chain from the P‐site tRNA (t6) to the amino acid attached to the A‐site tRNA (t7). The inhibition is influenced by the nature of the amino acids in the nascent peptide, clarifying why ribosomal stalling occurs in a sequence‐dependent manner. These full interactions ultimately cease bacterial protein synthesis and limit bacterial proliferation.

FIGURE 2.

Mechanism of action of CAP on the bacterial ribosome. CAP binds to the peptidyl transferase centre (PTC) of the 50S ribosomal subunit (shown in green), precisely cooperating with residues in the 23S rRNA. This binding sterically hinders the proper putting of the aminoacyl moiety of the incoming charged tRNA (t7) at the A‐site, thereby inhibiting peptide bond creation during the elongation phase of protein synthesis. The drug specially stalls ribosomes in a context‐specific manner, depending on the amino acid sequence of the nascent polypeptide chain. Although CAP is generally bacteriostatic, it can exert bactericidal effects at higher concentrations. Particularly, its activity is ineffective against certain Gram‐negative bacteria such as Pseudomonas aeruginosa due to poor intracellular accumulation and efflux mechanisms [25].

4. CAP Conventional Quantification Methods

CAP is a broad‐spectrum antibiotic effective against a variety of bacterial infections. Due to its potential for serious side effects, including aplastic anaemia, detecting CAP in pharmaceuticals, food products and biological samples is crucial for ensuring safety and compliance with regulatory standards [24, 26]. Conventional methods for detecting CAP include several analytical techniques, each with its own advantages and limitations. Microbiological (bioassay) techniques determine biologically active CAP by measuring growth inhibition of a susceptible test organism on agar or in broth [27]. The sample is used to an inoculated medium, and the diameter or turbidity inhibition is correlated to antibiotic concentration via calibration curves [27]. These analyses reflect functional potency but lack molecular specificity and are susceptible to interference from other antimicrobials and matrix components that affect microbial growing. Typical limits of detection (LODs) reported are in the µg/mL range (∼1–10 µg/mL) with poor precision (coefficients of variation often >10%–20%) and long turnaround times (18–48 h), making them suitable for low‐cost potency screening but unsuitable for trace‐level quantification or fast results [24, 26]. HPLC, with UV, fluorescence or mass spectrometric (MS) detection, separates analytes on a stationary phase using gradient elution and typically uses sample cleanup such as solid‐phase extraction (SPE) [28]. HPLC–UV/FLD provides good specificity and a wide linear range; typical LODs are in the tens–hundreds of ng/mL to low µg/mL (≈0.01–1 µg/mL) depending on optics and matrix [29]. Coupling HPLC to tandem MS (LC–MS/MS) markedly improves sensitivity and selectivity, achieving LODs in the low ng/mL to sub‐ng/mL range and high accuracy. Principal sources of error include matrix effects and incomplete extraction; these are mitigated using isotope‐labelled internal standards, matrix‐matched calibration and rigorous sample cleanup. HPLC methods require specialized instrumentation and trained personnel but are widely accepted for confirmatory and regulatory analyses owing to their quantitative robustness [30, 31]. HPLC offers high sensitivity, specificity and the ability to analyse multiple samples simultaneously. However, it requires sophisticated equipment and trained personnel [30, 31]. Thin‐layer chromatography (TLC) is another technique that can be used for the CAP quantification [32, 33]. TLC separates analytes on a planar adsorbent layer and visualizes CAP with specific reagents or UV; high‐performance TLC (HPTLC) with densitometric scanning can give semi‐quantitative results [34]. TLC/HPTLC is low‐cost and rapid for screening, with typical LODs ≥1 µg/mL depending on derivatization and detection, but resolution, repeatability and quantitative accuracy are inferior to HPLC, and operator interpretation reduces reproducibility [35]. TLC is appropriate for preliminary screening or resource‐limited settings but requires confirmatory follow‐up for regulatory compliance [32, 33]. ELISAs use antibodies specific to CAP, frequently in competitive formats for small molecules, with colourimetric or chemiluminescent readouts [36]. ELISA offers high throughput and acceptable sensitivity. Specificity depends on antibody cross‐reactivity; structurally related compounds or matrix components can produce false positives or biased quantitation. Matrix effects can be mitigated by matrix‐matched standards or dilution, but positive ELISA results usually require confirmatory analysis by HPLC or LC–MS/MS for definitive identification and accurate quantitation in complex matrices [37]. Gas chromatography (GC) separates volatile analytes in the gas phase, but CAP needs chemical derivatization to increase volatility and thermal stability prior to GC analysis, which is commonly coupled to MS for detection. When derivatized and analysed by GC–MS, CAP can be quantified with high sensitivity and specificity; but derivatization increases method complexity and introduces risks of analyte degradation or incomplete reaction, so GC–MS is less commonly used than LC–MS for polar or thermally labile matrices [38]. The advantages, disadvantages and limits of quantification methods for CAP are summarized in Table 1.

TABLE 1.

The advantages, disadvantages and limits of quantification methods for chloramphenicol (CAP).

Method	Advantages	Disadvantages	LOD	Refs.
Microbiological assays	Quantity biologically active CAP, reflecting antimicrobial strength rather than just chemical presence; minimal sample preparation; suitable for screening bioactivity in complex matrices	Low analytical specificity due to interference from other antimicrobial compounds; poor precision and reproducibility; long incubation times (typically 18–24 h); not suitable for trace‐level quantification	Typically 1–10 µg/mL, dependent on organism sensitivity and analysis conditions	[39]
HPLC	High analytical specificity and accuracy when coupled with UV or MS detectors; capable of immediate multi‐residue analysis; robust for complex matrices with proper sample cleanup; widely validated	Necessitates extensive sample training (e.g., extraction, filtration and possible derivatization); relatively high operative cost; matrix effects can affect quantification without proper calibration or internal standards	Typically 0.01–1 µg/mL (UV detection) can reach ng/mL levels when coupled with MS	[40]
TLC	Fast and low‐cost screening tool; allows visualization of CAP presence; minimal instrumentation required; suitable for preliminary or field analysis	Limited measurable capability; low resolution and sensitivity; strong necessity on operator interpretation; not suitable for regulatory‐level residue analysis	Typically ≥1 µg/mL, depending on detection reagent and visualization method	[41]
ELISA	High amount and rapid screening; good sensitivity for trace detection; minimal sample training; suitable for large‐scale monitoring programmes	Cross‐reactivity with basically related compounds can lead to false positives; accuracy depends on antibody specificity; requires confirmatory methods (e.g., HPLC–MS) for regulatory compliance	Typically 0.1–10 ng/mL, depending on antibody affinity and kit design	[42]
GC	High resolution and sensitivity, particularly when coupled with MS; suitable for confirmatory analysis; high reproducibility	CAP needs for chemical derivatization due to low volatility and thermal instability; complex sample preparation; potential analyte degradation during analysis	Typically ∼1–10 ng/mL (GC–MS after derivatization)	[43]

Abbreviations: GC, gas chromatography; LOD, limit of detection; MS, mass spectrometric.

Complex matrices, such as serum, milk and environmental samples, introduce proteins, lipids, salts and co‐eluting organics that alter extraction efficiency, chromatographic behaviour and detector response, particularly in MS due to ion suppression/enhancement. Method performance must therefore be validated in each relevant matrix. Typical sample‐preparation strategies include protein precipitation, SPE (reversed‐phase, ion‐exchange and mixed‐mode), liquid–liquid extraction and derivatization. Use of internal standards, preferably isotopically labelled CAP, reduces bias from extraction losses and matrix‐induced signal variation. Validation should report LOD/LOQ, linear range, accuracy, precision (%RSD), selectivity, matrix effect assessment, carryover and stability; for regulatory purposes, confirmatory approaches require identification by retention time plus MS/MS transitions or ion ratios.

Conventional methods present trade‐offs between sensitivity, specificity and throughput. Microbiological assays measure biological activity but lack molecular specificity; TLC and HPTLC are low‐cost but limited in quantitation; ELISA is sensitive and fast but subject to cross‐reactivity; HPLC provides robust quantitation but may lack the ultimate sensitivity of MS; LC–MS/MS offers superior sensitivity and specificity but at higher cost and technical complexity. These limitations motivate development of advanced approaches—biosensors, nanomaterial‐enhanced electrochemical assays and portable MS—aimed at combining low LODs, high selectivity, rapid response and ease‐of‐use for on‐site monitoring. For definitive, trace‐level quantification in complex matrices, LC–MS/MS with isotope‐labelled internal standard and robust sample cleanup is preferred; ELISA is useful for high‐throughput screening (with LC–MS/MS confirmation); HPLC is effective for routine laboratory analyses; TLC and microbiological tests retain roles in preliminary or resource‐limited contexts. Robust sample preparation, use of appropriate internal standards and matrix‐specific validation are essential across all methods to ensure reliable CAP measurements.

Key design principles and performance drivers for CAP quantification are the detection target, required matrix complexity (serum, milk, environmental samples), LOD/LOQ (trace‐level confirmation vs. screening), selectivity needs (risk of cross‐reactivity or co‐elution), throughput and turnaround time, instrumentation availability and cost, and robustness/ease of sample preparation [44, 45]. The primary analytical drivers that determine method performance are analyte extraction efficiency, signal‐to‐noise ratio (instrument sensitivity and background), matrix effects (ion suppression/enhancement or biological interference), detector specificity (antibody affinity or MS transitions) and use of internal standards (ideally isotopically labelled CAP) to correct for losses and matrix bias [44, 45]. Basically, methods should be chosen to prioritize the dominant drivers: LC–MS/MS when low LOD, high specificity and robust matrix correction are required; ELISA for high‐throughput screening with subsequent confirmation; and TLC or microbiological assays for preliminary or resource‐limited testing.

4.1. Critical Appraisal and Recommendations

A critical appraisal of conventional CAP quantification methods highlights systematic weaknesses that limit real‐world reliability [46, 47]. Numerous studies report solvent‐based LODs without validation in representative matrices, inflating perceived performance and undermining applicability to serum, milk or environmental samples [46, 47]. Immunoassays and bioassays, while valuable for high‐throughput or functional screening, commonly show cross‐reactivity, matrix interference and kit‐to‐kit variability; positive results therefore require confirmation by LC–MS/MS for regulatory or forensic decisions [48, 49]. TLC/HPTLC and microbiological assays are suitable only for preliminary triage in resource‐limited contexts because of limited quantitative robustness and operator dependence [48, 49]. HPLC with UV/FLD is reliable for routine analysis but does not match the molecular specificity or ultimate sensitivity of mass spectrometry [50]. LC–MS/MS is the preferred method for trace‐level confirmation only when methods include isotopically labelled internal standards and matrix‐matched calibration or standard addition [50]. Comprehensive validation, including LOD/LOQ, recovery, precision, matrix effects, ion‐ratio/transition criteria and stability, is also required [51, 52]. Main drivers of bias across methods are partial extraction procedures and matrix effects; the scarcity of inter‐laboratory ring trials and certified reference materials further impedes method transferability [51, 52]. To improve consistency and comparability, future work should validate approaches in the actual target matrices, include isotope‐labelled internal standards for MS‐based assays, report full validation metrics, perform cross‐reactivity panels for immunoassays and provide detailed standard operating procedures (SOPs) to enable reproducibility. Adoption of these practices is essential for reliable, trace‐level CAP monitoring and for elevating emerging on‐site technologies to regulatory acceptability.

5. Biosensors

Biosensors are innovative analytical devices that combine biological components with a transducer to detect and quantify specific substances, such as biomolecules, pathogens or environmental pollutants. They operate on the principle of converting a biological response into an electrical signal, enabling real‐time monitoring and analysis [53, 54]. The versatility of biosensors allows them to be employed in various fields, including healthcare for disease diagnosis, food safety for contamination detection and environmental monitoring for pollutant assessment [53, 54]. With advancements in nanotechnology and materials science, biosensors have become more sensitive, selective and user‐friendly, making them essential tools for rapid and accurate analysis in both laboratory and field settings [55, 56]. Their ability to provide immediate results has revolutionized traditional diagnostic methods, paving the way for more efficient and accessible testing solutions. The structure of a biosensor typically consists of three main components: the biological recognition element, the transducer and the signal processing system. The biological recognition element is responsible for the specific interaction with the target analyte and can be composed of various biological materials, such as enzymes, antibodies, nucleic acids or whole cells [55, 56]. The choice of recognition element is crucial, as it determines the biosensor's specificity and sensitivity; for instance, antibodies can be used for immunosensors to detect specific proteins, whereas enzymes can catalyse reactions that produce measurable signals. The transducer plays a vital role by converting the biological response into a quantifiable signal [57, 58]. Depending on the type of biosensor, transducers can be electrochemical, optical, piezoelectric or thermal. Electrochemical transducers measure changes in current or voltage resulting from biochemical reactions, while optical transducers detect changes in light absorption or fluorescence [57, 58]. The design and material of the transducer significantly influence the overall performance of the biosensor, affecting factors such as sensitivity, response time and stability. The signal processing system interprets the signal generated by the transducer and converts it into a readable output. This component often includes amplification, filtering and data analysis algorithms to enhance the accuracy and reliability of the results. The output can be displayed on various platforms, such as digital screens or mobile devices, allowing for easy interpretation and real‐time monitoring. Overall, the integration of these components in a biosensor's structure enables effective detection and quantification of target analytes, making biosensors powerful tools in diagnostics, environmental monitoring and food safety. Ongoing advancements in materials and technology continue to enhance the design and functionality of biosensors, expanding their applications and improving their performance. Schematic illustration of biosensor technology is presented in Figure 3.

FIGURE 3.

A schematic illustration of a nanobiosensor platform for detecting analytes. The sensor comprises a transducer (electrochemical, optical or acoustic) modified with nanomaterials. A biorecognition element, which can be antibodies (Y‐shaped structures labelled A and B) or DNA strands, is immobilized on the nanomaterial layer to selectively bind to the target analyte. The diagram also includes examples of potential analytes such as bacteria or specific molecular targets. The inset shows representative electrochemical data (cyclic voltammetry curves) obtained during the ‘data analysing’ phase, alongside a calibration plot (inset within the inset) indicating a linear relationship between signal and concentration, which is crucial for quantitative analysis.

Biosensors can be categorized based on their transducer types, which include electrochemical, optical, piezoelectric and thermal biosensors. Table 2 presents these categories along with their working principles and advantages. Table 2 summarizes the characteristics of electrochemical, optical, piezoelectric and thermal biosensors, highlighting their unique detection mechanisms and applications in various fields.

TABLE 2.

Overview of different types of biosensors, including their working principles, advantages and relevant references.

Type of biosensor	Working principle	Advantages	Limitation	Refs.
Electrochemical	These biosensors measure the electrical signals (current, voltage or impedance) generated by biochemical reactions at an electrode surface. The interaction between the target analyte and the biological recognition element leads to a change in the electrical properties, which is then quantified	‐High sensitivity and specificity ‐Rapid response times ‐Cost‐effective and easy to miniaturize ‐Suitable for in‐field applications	‐Susceptible to interference from complex biological matrices ‐Electrode fouling can reduce performance ‐Often requires careful surface modification ‐Signal drift may affect reproducibility	[59, 60]
Optical	Optical biosensors detect changes in light properties, such as absorbance, fluorescence or refractive index, resulting from the interaction between the target analyte and the recognition element. These changes are measured using optical devices, providing a quantitative output	‐Label‐free detection ‐Real‐time monitoring ‐High sensitivity ‐Versatile applications in various fields	‐Often requires expensive instrumentation ‐Can be affected by background signal and sample turbidity ‐Data interpretation may be complex ‐Arrangement and calibration can be challenging	[61, 62]
Piezoelectric	These biosensors utilize piezoelectric materials that generate an electrical charge in response to mechanical stress. When the target analyte binds to the recognition element, it causes a mass change that alters the resonant frequency of the piezoelectric crystal, which can be measured	‐High sensitivity to mass changes ‐Real‐time monitoring ‐Can detect small quantities of analytes ‐Suitable for various applications, including biomolecular interactions	‐Sensitive to temperature and viscosity changes ‐Limited performance in highly viscous or complex samples ‐Needs stable environmental conditions ‐May have lower selectivity without proper surface functionalization	[63, 64]
Thermal	Thermal biosensors measure changes in temperature resulting from biochemical reactions. The binding of the target analyte to the recognition element can lead to exothermic or endothermic reactions, which are detected as changes in temperature	‐Simple and cost‐effective ‐Can be used for a wide range of analytes ‐Minimal sample preparation required ‐Suitable for both laboratory and field applications	‐Low sensitivity compared with other biosensor types ‐Strongly influenced by ambient temperature fluctuations ‐Limited real‐time ability in some designs ‐Difficult to use for very low analyte concentrations	[65, 66]

In biosensors, the process begins with a transducer, which serves as the base for various biological recognition elements. These elements, such as antibodies, nucleic acids (DNA/RNA/aptamers), enzymes or even microorganisms, are immobilized on the transducer's surface. When analytes (represented by small coloured shapes) are present, they interact specifically with the corresponding biological recognition element (e.g., a bound analyte interacting with an antibody). This binding event triggers a change that is converted into an electrical signal by the transducer. The diagram shows an electron (e⁻) transfer leading to a current (i), which represents the electrical signal.

The electrical signal generated during the bio‐detection step is then amplified. Following amplification, a signal processor analyses and interprets this amplified signal. Finally, the processed data are presented on a display unit for interpretation.

The diagram also includes examples of signal outputs that can be obtained, such as voltammetric signals (current vs. potential) and impedance signals (represented by Nyquist plots), which are characteristic of electrochemical detection methods. Schematic illustration of an electrochemical biosensor is presented in Figure 4.

FIGURE 4.

Schematic overview of an electrochemical biosensor: biological recognition elements (analytes, microorganisms, enzymes, nucleic acids and antibodies) interact at the transducer surface to produce an electrical signal, which is amplified and processed (voltammogram/impedance outputs shown) and then displayed by the readout unit; workflow is divided into bio‐detection and signal‐transmission steps [67].

5.1. Signal‐to‐Noise Considerations in Advanced Biosensor Design

A critical yet often underexplored issue in biosensor methodology is the difference between true signal amplification and accidental noise amplification. Several modern biosensors, mainly nanosensor‐based electrochemical and optical platforms, employ amplification approaches such as nanomaterial‐enhanced surface area, redox mediators, catalytic labels, enzymatic cascades or plasmonic improvement to improve sensitivity and lower LOD. Although these methods can substantially increase analytical response, the hypothesis that signal gain directly translates into improved analytical performance is not always valid. Amplification mechanisms may also increase background current, non‐specific adsorption, electrode fouling, optical scattering or matrix interference, ultimately compromising signal‐to‐noise ratio (SNR) rather than increasing it.

The literature commonly reports improved sensitivity in terms of lower detection limits, yet systematic evaluation of noise sources, including thermal noise, flicker (1/f) noise, shot noise, drift and biofouling‐induced variability, is relatively limited. In nanoscale systems, where surface phenomena dominate, stochastic fluctuations and heterogeneity of nanomaterial interfaces can further exacerbate baseline instability. For instance, increasing electrode roughness with nanostructures enhances faradaic current (signal), but it can simultaneously amplify capacitive charging currents and non‐specific interactions (noise). Without rigorous characterization of SNR, limit of blank (LOB) and LOQ, reported improvements may reflect amplified background rather than truly improved analyte‐specific response.

Addressing this gap requires standardized protocols for unique analyte‐specific amplification from baseline enhancement. Approaches such as surface passivation, controlled nanomaterial functionalization, differential measurement schemes, ratiometric sensing and advanced signal processing algorithms can help decouple signal from noise. Future biosensor progress should prioritize comprehensive reporting of noise analysis, reproducibility metrics and long‐term stability alongside sensitivity claims. A deeper understanding of the interplay between signal and noise amplification will be crucial for translating highly sensitive nanosensors into robust, clinically reliable diagnostic tools.

5.2. The Clinical Application of Biosensors: Enhancing Therapeutic Drug Monitoring (TDM) and Patient Care

Biosensors have developed as key tools in clinical applications, mainly in TDM services and patient care [68, 69]. Their capability to provide real‐time, accurate and specific measurements of biomolecules makes them valuable in several healthcare settings [68, 69]. In TDM, which involves measuring drug concentrations in blood to optimize efficacy and minimize toxicity, biosensors enable rapid, sensitive and precise quantification of CAP [70]. This is particularly important for drugs with a narrow therapeutic index. Among various biosensor platforms used in TDM, electrochemical biosensors are widely preferred due to their high sensitivity, excellent specificity and suitability for rapid drug quantification [70]. Biosensors can detect various therapeutic agents such as antibiotics, antiepileptics and immunosuppressants. Optical biosensors, based on light detection, enable real‐time monitoring of drug levels, interactions and pharmacokinetics. By providing rapid feedback, these sensors significantly support personalized medicine, enabling clinicians to adjust dosages based on individual patient responses [71]. Moreover, for patients with chronic conditions demanding long‐term medication, such as diabetes or hypertension, biosensors help maintain optimal drug levels, improving overall managing and outcomes. In terms of patient care, biosensors enable real‐time monitoring of several biomarkers, allowing for appropriate interventions [71]. Biosensors can detect various therapeutic agents such as antibiotics, antiepileptics and immunosuppressants. Optical biosensors, based on light detection, enable real‐time monitoring of drug levels, interactions and pharmacokinetics. By providing rapid feedback, these sensors significantly support personalized medicine, enabling clinicians to adjust dosages based on individual patient responses [72]. This is mainly beneficial in emergency settings or remote areas where access to laboratory conveniences is limited. Fast results can lead to earlier clinical decisions, improving patient outcomes. The advent of wearable biosensors has transformed patient care by allowing continuous health monitoring [72]. These strategies can track vital signs, physical activity and other health metrics, enabling patients to take an active role in their health management. For instance, wearable devices can monitor heart rate, oxygen saturation and even stress levels, providing valuable data for both patients and healthcare workers [73]. Despite their potential, the integration of biosensors into clinical practice faces encounters related to regulatory approval and standardization. Confirming that biosensors meet safety and efficacy standards is critical for widespread adoption. Additionally, for biosensors to be effective in TDM and patient care, they must be integrated into existing health information systems [74, 75]. This incorporation allows for continuous data sharing and analysis, enhancing clinical decision‐making. Looking ahead, progressions in nanotechnology, microfluidics and artificial intelligence are expected to enhance the capabilities of biosensors [74, 75]. Coming progresses may lead to more sensitive, specific and multifunctional biosensors that can monitor multiple biomarkers instantaneously, further improving patient care and TDM services. In summary, biosensors play a transformative role in TDM services and patient care by providing real‐time, precise monitoring of drug levels and health metrics. Their integration into clinical practice promises to enhance personalized medicine, improve chronic disease management and facilitate timely interventions. As equipment advances and challenges are addressed, the potential for biosensors in healthcare will continue to increase, ultimately leading to better patient results and more efficient healthcare delivery.

Robust validation and demonstration of analytical performance are essential before biosensors can be used reliably in TDM. Validation should report LOD/LOQ, linear dynamic range, accuracy, precision, selectivity against basically related compounds and common interferents, and stability under intended storage and use conditions. Spike‐and‐recovery experiments and matrix‐matched calibration curves are needed to quantify extraction efficiency and recovery in real samples; technique comparison against a gold‐standard technique (typically LC–MS/MS) using Bland–Altman and Passing–Bablok analyses should be included to demonstrate agreement and identify systematic bias.

Matrix effects and operational robustness must be explicitly evaluated for clinical matrices (whole blood, plasma and serum) and for real‐world utilization. Assess ion suppression/enhancement, non‐specific adsorption, protein binding and enzymatic degradation by testing across illustrative sample panels (different haematocrits, lipid levels, haemolysis and anticoagulants) and report %bias for each condition. Demonstrate robustness to environmental variables (temperature, humidity and mechanical stress) and user variation (finger‐prick, sample volume and intravenous sampling) with challenge studies, and include techniques for on‐device calibration, quality controls and approaches (antifouling coatings, surface blocking, sample pretreatment or dilution) to mitigate matrix interference.

5.3. Biosensors; Critical Appraisal and Validation Recommendations

Recent progresses in CAP biosensors (electrochemical nanosensors, aptamer‐ and antibody‐based platforms, and plasmonic/optical devices) reveal robust potential for fast, on‑site screening, but the literature shows recurring methodological shortcomings that limit applied efficacy and regulatory acceptance [76, 77]. Promising technical elements reported include nanostructured electrodes for improved surface area and lower apparent LODs, aptamer selection for enhanced selectivity and enzyme‐ or nanoparticle‐based amplification schemes that increase signal [76, 77]. On the other hand, various studies report sensitivity and LODs obtained in simple buffers rather than in representative clinical, food or environmental matrices; when these devices are challenged with serum, milk, honey or wastewater, performance often degrades substantially due to ion suppression, non‐specific adsorption, protein binding, fouling and optical scattering [78, 79]. A serious gap is the regular omission of orthogonal confirmation (typically LC–MS/MS with an isotopically labelled internal standard): without this, claims of trace‐level detection remain unproven and prone to false positives from cross‐reacting matrix artefacts or compounds.

To elevate biosensor research from proof‑of‑concept to reliable analytical tools for CAP monitoring, the following minimum validation and reporting standards are suggested. (A) Validate in the proposed matrices with spike‑and‑recovery at multiple concentrations and report LOB/LOD/LOQ determined by accepted statistical approaches [80]. (B) Quantify and report signal‑to‑noise and baseline stability (including LOB and LOQ) and separate true signal amplification from noise amplification (assess thermal, 1/f, shot noise, drift and biofouling contributions) [81]. (C) Validate selectivity with cross‑reactivity panels (CAP metabolites and structurally related antibiotics) and show agreement with a gold‑standard method (LC–MS/MS) using Passing–Bablok analyses or Bland–Altman [82, 83]. (D) Test operational robustness across realistic user and environmental conditions (haemolysis, haematocrit variation, lipid levels, temperature and mechanical stress) and include antifouling approaches and on‑device QC/calibration. (E) Provide full methodological transparency (SOPs, raw calibration data, stability and inter‑batch variability) and participate in inter‑laboratory ring trials where possible. Adopting these practices will clarify which state‑of‑the‑art biosensor studies represent genuine analytical progresses and which are premature for field or regulatory use, so guiding future development toward clinically and regulatory meaningful CAP detection.

6. CAP Biosensors

The detection of CAP, an antibiotic, using electrochemical sensors has gained significant attention due to the essential for sensitive and selective approaches for monitoring this compound in various matrices. A new label‐free electrochemiluminescence (ECL) biosensor utilizing a reagent‐based method has been developed, including AuNPs@CoFe‐PBA nanomaterials as highly effective catalysts for the luminol/hydrogen peroxide (H₂O₂) system. This biosensor indirectly activates the CRISPR/Cas 12a amplification system, allowing straightforward and efficient quantification of low‐abundance CAP [84]. A fluorescent molecule exhibiting aggregation‐induced emission properties has been designed, created and encapsulated within polystyrene nanospheres, working as an optical signalling probe in biosensors for the ultrasensitive, rapid and accurate detection of CAP in milk. This method employs magnetic nanoparticles linked to CAP antibodies as carriers for immunomagnetic separation [85]. An aptamer‐based biosensor has been advanced for the rapid fluorescence detection of CAP using a pyrene excimer switch, using a newly selected short DNA aptamer with high affinity. The aptamer is labelled with pyrene molecules at both ends. When CAP binds to the aptamer probe, it carries the two pyrene molecules into close nearness, resulting in the formation of a pyrene excimer, which leads to an increase in the fluorescence signal from the excimer [86]. A ‘Y‐shaped’ surface‐enhanced Raman scattering (SERS) biosensor has been planned for the ultrasensitive recognition of CAP in milk, using magnetic plasmonic superstructures composed of Fe₃O₄@SiO₂@Ag flowers along with the greatly efficient isothermal amplification strategy known as exponential amplification reaction (EXPAR) technology [87]. A new biosensing platform has been advanced using Fe/Zn‐MIL‐88B, which has been improved for dual‐mode detection of CAP through fluorescent and colourimetric methods. Zinc was integrated into the metal–organic framework (MOF) to adjust its internal electronic structure, leading to the creation of Fe/Zn‐MIL‐88B. This modification significantly improved its peroxidase‐like activity, so enhancing its colourimetric sensing capabilities [88]. A dual‐mode recognition platform has been formed that employs colourimetric and Raman techniques, based on the EXPAR strategy and a ‘core‐satellite’ structure formed by bimetallic nanozymes for the detection of CAP. Initially, DNA‐gated MOFs featuring cascaded amplification were utilized as nanocarriers for the colourimetric and Raman reporter molecules, specifically 3,3′,5,5′‐tetramethylbiphenyl (TMB) [89]. A simple, rapid and highly sensitive sensor has been settled for the detection of CAP. CdS shows exceptional properties as a semiconductor material, such as a large specific surface area, a rich pore construction and excellent electrical conductivity [90]. An ECL biosensor has been established for the sensitive detection of CAP. The improved ECL performance is attributed to the synergistic effects of N and P co‐doped Ti₃C₂‐Apt1 (N, P‐Ti₃C₂‐Apt1) nanoprobes and high‐intensity focused ultrasound (HIFU) pretreatment. The combination of N and P enhances the electrochemical performance of Ti₃C₂ [91]. A practical and reliable interfacial self‐assembly method was applied to form a uniform film of Au@Ag nanobipyramids (NBPs) on a structured array of SiO₂ nanospheres (SiO₂ NS), serving as a substrate that enhances Raman signals. Coupled with a deoxyribonucleic acid enzyme‐induced signal amplification method, this resulted in the creation of an original SERS biosensor designed for the selective and sensitive detection of CAP [92]. A novel plan for detecting antibiotics was developed, utilizing optical weak value amplification and employing an indirect competitive inhibition examine, which significantly enhances the system's sensitivity for recognizing small molecule antibiotics. The scientists selected CAP as a model compound and combined it with CAP‐bovine serum albumin conjugates to competitively bind to the CAP antibody [93]. A smartphone‐assisted electrochemical sensor utilizing graphene oxide (GO) and multi‐walled carbon nanotubes (MWCNTs) was developed for the sensitive detection of CAP. This system is compatible with both a conventional Autolab potentiostat and a compact PalmSens potentiostat. Studies using CV and DPV on the GO@MWCNT nanocomposite demonstrated outstanding electrocatalytic activity, along with an acceptable linear detection range, LOD and sensitivity [94]. An aptasensor was developed to detect CAP utilizing dual signal outputs from photoelectrochemistry (PEC) and colourimetry. The Fe3+‐doped porous tungsten trioxide was synthesized using the sol–gel method and applied to indium tin oxide (ITO) conductive glass, resulting in the formation of ITO/p‐W(Fe)O₃ [95]. A new self‐powered biosensor has been created for the detection of CAP using difunctional triple helix molecular switch (THMS)‐mediated DNA walkers. This biosensor employs the CAP aptamer as the recognition element, incorporates a DNA walker and capacitor for dual signal amplification and utilizes a digital multimeter (DMM) for data readout [96]. By creating magnetic multi‐‘hotspot’ nanoflower particles to serve as SERS substrates and integrating bimetallic peroxidase Au@Pt, dual‐mode detection of CAP was successfully accomplished. The synthesis of magnetic nanoparticles Fe₃O₄@Au enabled swift aggregation, thereby enhancing the SERS signal [97]. In one study, CAP aptamer and sulphhydryl (SH) complementary DNA strand (SH‐cDNA) were attached to gold and silver core–shell nanoparticles (Ag@Au CSNPs) and core–shell magnetic nanoparticles (Fe₃O₄@Au MNPs) through Au‐S covalent bonding. These were utilized as SERS signal probes and capture probes, respectively. The principle of competitive recognition by the aptamer was employed to enable the quantitative detection of CAP in fish [98]. Recombinant antibody‐based immunoassays have become essential methods for identifying antibiotic residues in food samples. Establishing a stable system for recombinant antibody production and improving detection sensitivity are vital for their application in biosensing. A single‐chain fragment variable (scFv) antibody was bioengineered to specifically target CAP using both Bacillus subtilis and HEK 293 expression systems, with the scFv derived from the HEK 293 system exhibiting significantly higher sensitivity [99]. An electrochemical sensor utilizing a dual recognition strategy involving molecularly imprinted polymers (MIPs) and aptamers (Apt) has been developed for the highly sensitive detection of CAP. In one study, MIPs and Apt provided dual recognition sites, significantly enhancing the sensor's specificity. Chitosan‐MWCNTs (CS‐MWNTs) and gold nanoparticles (AuNPs) were employed due to their outstanding electrical conductivity [100]. A target‐induced DNAzyme amplification strategy was developed in conjunction with peanut‐shaped FexOy@macro‐/mesoporous carbon (MMC)@Au and covalent organic frameworks (COF)@Au to create a SERS aptasensor for the ultrasensitive detection of CAP. Polydopamine was initially self‐polymerized on the peanut‐shaped Fe₂O₃ as a reductant, resulting in a uniform assembly of gold nanoparticles (AuNPs) with multiple hotspots, thereby ensuring high reproducibility [101]. Table 3 presents a comprehensive overview of the various methodologies and performance characteristics associated with CAP biosensors.

TABLE 3.

Summary of chloramphenicol (CAP) biosensors, outlining the type of biosensor, detection technique, NPs utilized, sample types, linear range and limit of detection (LOD).

Type	Technique	NPs	Samples	Linear range	LOD	Refs.
ECL	CRISPR/Cas 12a amplification system	AuNPs@CoFe‐PBA	Real	10 pg/mL–1 µg/mL	7.75 pg/mL	[84]
EL	Fluorescent immunosensor	PS nanospheres	Milk	100 pg/mL–500 ng/mL	100 pg/mL	[85]
EL	Aptasensor	—	Clinical	—	24.4 nmol/L	[86]
EL	Aptasensor‐SERS	Fe3O4@SiO2@Ag	Food	10−6 and 10−13 M	17.9 fM	[87]
EL	Fluorescent immunosensor	Fe/Zn‐MIL‐88B	Clinical	0.5–1000 ng/mL	0.064 ng/mL	[88]
EL	Fluorescent aptasensor	TMB@DNA/MOF	Complex	1.00 × 10−6–1.00 × 10−11	9.74 × 10−12 M	[89]
EL	Fluorescent immunosensor	Cd2In2S5/GCE	Food and water	10 nmol/L–20 µmol/L	3.8 nmol/L	[90]
ECL	Optical aptasensor	N, P‐Ti3C2	Food and environmental	—	0.01 ng/mL	[91]
EL	Aptasensor‐SERS	Au@Ag NBPs/SiO2	Food	1.0 × 10−12–1.0 × 10−6 mol/L	6.42 × 10−13 mol/L	[92]
EL	Fluorescent immunosensor	—	Clinical	up to 3.24 ng/mL	33.20 pg/mL	[93]
EL	Immunosensor	GO@MWCNT	Food and water	0–600 µM	46 nM	[94]
EL	Aptasensor‐PEC	ITO/p‐W(Fe)O3‐AUNPs	Real	5.0 pM–10.0 nM	1.72 pM	[95]
EL	Aptasensor	—	Milk	0.1–104 fM	0.012 fM	[96]
EL	SERS/EXPAR	Fe3O4@Au	—	—	4.96 × 10−13 M	[97]
EL	SERS	Fe3O4@Au MNPs	Fish	10−5–10−1 µg/mL	16 pg/mL	[98]
EL	HCR‐CRISPR/Cas12a	—	Food	—	55.23 pg/mL	[99]
EL	MIP	CS‐MWNTs/AuNPs	Clinical	10−8–10−2 g/L	3.3 × 10−9 g/L	[100]
EL	SERS	FexOy@MMC@Au	Clinical	—	7.35 fM	[101]

Abbreviations: CS‐MWNTs, chitosan‐multi‐walled carbon nanotubes; ECL, electrochemiluminescence; EXPAR, exponential amplification reaction; GO, graphene oxide; HCR, hybridization chain reaction; ITO, indium tin oxide; MIP, molecularly imprinted polymers; MOF, metal–organic framework; MWCNT, multi‐walled carbon nanotubes; PEC, photoelectrochemistry; SERS, surface‐enhanced Raman scattering; TMB, 3,3′,5,5′‐tetramethylbiphenyl.

As discussed above section, the development of advanced biosensors for the detection of CAP has seen significant innovation, particularly through the use of ECL and various nanomaterials. A notable advancement is the creation of a label‐free ECL biosensor that employs AuNPs@CoFe‐PBA nanomaterials as catalysts in a luminol/hydrogen peroxide system, which indirectly activates the CRISPR/Cas 12a amplification system. This method allows for the efficient quantification of low‐abundance CAP, demonstrating a linear detection range from 10 pg/mL to 1 µg/mL with an LOD of 7.75 pg/mL. Additionally, other innovative approaches include the use of fluorescent molecules encapsulated in polystyrene nanospheres for rapid detection in milk, and the development of aptamer‐based sensors utilizing pyrene excimer switches to enhance fluorescence signals upon CAP binding. Further advancements in biosensing technologies have introduced dual‐mode detection platforms and novel materials that enhance sensitivity and specificity. For instance, the integration of magnetic nanoparticles with CAP antibodies facilitates immunomagnetic separation, whereas the use of Fe/Zn‐MIL‐88B has improved colourimetric sensing capabilities through enhanced peroxidase‐like activity. Other techniques, such as the ‘Y‐shaped’ SERS biosensor utilizing magnetic plasmonic superstructures and the application of MIPs combined with aptamers, have shown promising results in achieving low detection limits and high specificity for CAP. These progresses highlight the ongoing efforts to create reliable, sensitive and fast biosensors for antibiotic detection in food and environmental samples, addressing serious public health concerns related to antibiotic residues. These developments address key analytical needs: rapid improvement, minimal sample training, high sensitivity/selectivity in complex milieus and potential for sensitive detection while modifying matrix interference and improving cost‐effectiveness. On the other hand, challenges remain in long‐term sensor stability, reproducibility across batches, full regulatory validation and seamless integration into routine laboratory or on‐site workflows. Continuous efforts in hybrid nanomaterials and signal amplification strategies are anticipated to further bridge the gap between laboratory research and practical analytical applications in food safety and clinical monitoring.

7. Clinical Relevance and Regulatory Context of CAP Detection

The clinical importance of CAP biosensing extends beyond analytical performance to its implications for patient safety, therapeutic monitoring and public health regulation. CAP is a broad‐spectrum antibiotic with severe haematological toxicity, particularly bone marrow suppression and aplastic anaemia, which can occur even at low systemic concentrations. Consequently, its clinical use is stringently limited and subject to international regulatory thresholds. For example, the European Union and the US FDA have established a zero‐tolerance policy for CAP in food products, with a minimum required performance limit (MRPL) of 0.3 µg/kg in animal‐derived samples. From a pharmacological standpoint, plasma concentrations exceeding approximately 10 µg/mL are associated with toxic side effects, while therapeutic levels typically remain between 5 and 20 µg/mL during short‐term controlled use. Advanced biosensing strategies aim to detect CAP at sub‐nanomolar levels, achieving detection limits significantly below these regulatory thresholds. This sensitivity ensures early identification of contamination or off‐label administration before reaching toxic exposure levels. Therefore, the integration of nanomaterial‐based, photonic and electrochemical biosensors is not only a technical advancement but also a critical enabler of clinical safety, supporting both regulatory compliance and therapeutic monitoring frameworks. Furthermore, the development of rapid, sensitive CAP detection systems is crucial for timely clinical intervention. In veterinary medicine, accidental or deliberate administration of CAP to food‐producing animals poses a significant public health risk due to its potential to cause serious adverse effects in consumers. Regulatory bodies worldwide have implemented strict surveillance programmes and established maximum residue limits (MRLs) to prevent economic fraud and protect consumer health. Biosensors that can operate in‐field or at the point of primary production offer a powerful tool for real‐time monitoring, enabling immediate corrective actions and ensuring compliance with international trade standards. The ability to quantify CAP accurately at trace levels, as facilitated by emerging biosensor technologies, directly addresses these critical concerns, bridging the gap between analytical capabilities and their real‐world impact on public health and regulatory enforcement.

Matrix effects remain a main challenge in biosensor‐based drug detection, as components present in biological samples such as salts, proteins, lipids, metabolites and other endogenous compounds can interfere with analyte recognition, alter transduction signals and reduce assay precision [102, 103]. To address these effects, biosensor design should incorporate selective recognition elements, effective surface passivation and anti‐fouling materials to minimize non‐specific adsorption [102, 103]. In addition, calibration approaches such as matrix‐matched standards, standard addition methods and the use of internal references can improve quantification consistency in complex samples [104, 105]. Sample pretreatment steps, including centrifugation (extraction), dilution and filtration, may further reduce interference when necessary. Consequently, comprehensive evaluation and mitigation of matrix effects are essential to ensure the robustness, reproducibility and clinical applicability of biosensor platforms for drug detection [104, 105].

8. Emerging Frontiers in Biosensing: From Lab‐to‐Clinic and Beyond

The field of biosensing is undergoing a rapid transformation, driven by synergistic advancements in nanomaterials, interdisciplinary collaborations and the burgeoning concept of ‘lab‐on‐chip’ and ‘hospital‐on‐chip’ technologies. These developments are not only enhancing analytical performance but also paving the way for ultra‐sensitive diagnostics and integrated healthcare monitoring systems.

Recent breakthroughs highlight the power of harnessing complex light‐matter interactions for nano‐optical biosensing, enabling highly sensitive and specific detection at the nanoscale. Innovations in this area are crucial for developing miniaturized optical systems capable of detecting biomarkers with unprecedented accuracy, aligning with the ‘nose‐on‐chip’ and ‘hospital‐on‐chip’ paradigms where complex analytical capabilities are integrated into compact devices.

Furthermore, the integration of fifth‐ and sixth‐generation intelligent nanomaterials is revolutionizing sensor design. This includes the development of chemiresistive nanosensors, particularly for breath analysis (breathomics) [106, 107]. As seen in emerging fifth‐generation chip technologies, these sensors show immense promise for complex disease diagnosis and continuous healthcare monitoring by detecting volatile organic compounds (VOCs) in exhaled breath [106, 107]. For instance, high‐performance sensors, such as those based on polyaniline–WO₃ nanocomposites, are being explored for portable applications like battery monitoring and non‐invasive diagnostics for conditions like irritable bowel syndrome, demonstrating the potential for breathomics‐based health assessment [108, 109]. However, the translation of these sophisticated ‘lab‐to‐clinic’ technologies faces significant hurdles [110]. Challenges in the development of fifth/sixth generation intelligent nanomaterial‐enabled biosensors often lie in achieving robust, reproducible and cost‐effective manufacturing processes. The dynamic landscape of these nanosensors necessitates a concerted effort to overcome issues related to sensor stability, calibration and signal processing in complex biological matrices. Crucially, the successful deployment of these advanced biosensors requires interdisciplinary efforts that bridge the gap between scientific innovation and social sciences. Promoting the widespread adoption and societal acceptance of biosensing technologies involves understanding user needs, ethical considerations, regulatory pathways and the socio‐economic impact. Collaborations involving material scientists, engineers, clinicians, ethicists and policymakers are vital to ensure that these groundbreaking technologies translate into accessible, equitable and impactful healthcare solutions. This holistic approach is key to realizing the full potential of biosensors beyond the confines of the research laboratory.

Despite notable analytical achievements reported across recent CAP biosensor trainings, a critical appraisal reveals recurring conceptual and practical flaws that limit translational potential. First, an overwhelming emphasis on reporting the lowest possible LOD has driven research toward progressively complex amplification schemes and exotic nanostructures, often at the expense of reproducibility, assay robustness and practicality. Several papers demonstrate femto‐ to picomolar LODs under idealized buffer conditions but fail to demonstrate comparable performance in realistic matrices or to report essential validation metrics. This focus on headline sensitivity obscures whether a method can consistently deliver reliable results in routine laboratory, clinical or field backgrounds.

Second, numerous important limitations recur across platforms and deserve obvious acknowledgement. Complex synthetic routes for hierarchical nanomaterials produce batch‐to‐batch variability and raise scale‐up and cost barriers; multistep sample pretreatments and amplification protocols increase operator dependence and error risk; and many signal‐amplification strategies (CRISPR, enzymatic cascades and hybridization chain reaction [HCR]) create fragility to environmental factors (temperature, nucleases and reagent stability). Moreover, insufficient attention has been paid to analytical specificity in realistic matrices: cross‐reactivity (in immunoassays and some aptamer formats), protein binding, non‐specific adsorption and ion‐suppression effects in MS‐based readouts can all produce false positives/negatives or biased quantitation if not systematically addressed.

Third, the field lacks standardized benchmarking and reporting practices that would enable objective comparison and identify genuinely disruptive advances. Heterogeneous reporting of LOD definitions, inconsistent use (or absence) of matrix‐matched calibrations and variable or missing validation statistics (recovery, LOQ and ruggedness studies) prevent meta‐analysis and risk overstating performance. Without decided minimum validation requirements covering representative clinical/food matrices, repeatability, stability and method‐comparison against LC–MS/MS claims of superiority remain problematic to substantiate and impede regulatory acceptance.

To advancement beyond proof‐of‐concept novelty, future work must obviously prioritize translatability: adopt standardized validation frameworks, quantify and minimize batch variability for nanomaterials, simplify sample workflows toward true sample‑to‑answer formats and evaluate cost‐per‐test and manufacturability alongside analytical metrics. Only by balancing sensitivity with robustness, specificity, reproducibility and economic feasibility can CAP biosensors move from remarkable laboratory demonstrations to reliable tools for clinical monitoring, food safety surveillance and regulatory enforcement.

Orthogonality of detection mechanisms refers to using methods based on distinct physical or biochemical principles so that concordant results provide independent confirmation and reduce false positives/negatives. For CAP, typical orthogonal pairs include immunoassays or aptamer/optical sensors (binding‑based) versus LC–MS/MS or GC–MS (mass/fragmentation‑based chemical identification), and microbiological bioassays (biological activity) versus chromatographic–mass methods (molecular identity); electrochemical or ECL sensors are orthogonal to mass‑based methods but not necessarily to other optical approaches. Studies should report which orthogonal methods were used, provide concordance statistics (per cent agreement, Bland–Altman or Passing–Bablok analysis) and debate mechanism‑specific interferences so that claimed positives are substantiated by independent evidence.

9. Conclusion and Future Direction

The studies summarized in Table 3 exhibit substantial advancement in the development of CAP biosensors, mainly in achieving very low LOD. Several platforms report detection limits in the femtomolar range, largely attributed to signal amplification strategies such as CRISPR/Cas12a systems, HCR, SERS‐based enhancement and nanocomposite materials (Fe₃O₄@Au, Au@Ag NBPs and MOF‐based structures). These results clearly indicate that nanomaterial‐assisted and amplification‐integrated systems considerably improve analytical sensitivity. On the other hand, a critical comparison of Table 3 also shows several limitations. First, reported linear ranges and LOD values vary extensively across platforms, partly due to differences in detection principles (ECL, EL, PEC and SERS) and experimental conditions, which complicates direct performance comparison. Some studies do not report complete analytical parameters, limiting reproducibility calculations. Furthermore, although many sensors achieve exceptional sensitivity in controlled environments, validation in real and complex matrices remains inconsistent. Even though applications in clinical, food, milk, fish and environmental samples are described, systematic inter‐matrix comparisons and long‐term stability assessments are often lacking. Another significant consideration is practical applicability. Several high‐performing systems rely on sophisticated nanostructures (e.g., AuNPs@CoFe‐PBA, Fe₃O₄@SiO₂@Ag, N,P‐Ti₃C₂ and MOF‐based composites), which may include complex synthesis procedures, high production costs or limited scalability. Moreover, although SERS‐ and CRISPR‐based methods provide notable sensitivity, they may require specialized equipment or multistep protocols, potentially limiting routine field implementation. Future research should therefore move beyond achieving ever‐lower LOD values and focus on improving reproducibility, standardization, cost‐effectiveness and operational simplicity. Creating uniform benchmarking criteria comprising consistent reporting of linear range, matrix effects, recovery and stability would enable more meaningful cross‐study comparisons. Additionally, the development of portable, integrated devices suitable for on‐site detection, along with multiplexed platforms capable of instantaneously monitoring multiple antibiotic residues, will improve real‐world applicability. Bridging the gap between laboratory‐scale innovation and regulatory validation remains a critical step for widespread adoption in food safety and environmental monitoring.

Beyond practical performance, the translation of CAP biosensors from laboratory investigation to widespread application is currently hindered by a ‘readiness gap’ across three critical dimensions: commercialization, reproducibility and clinical workflow integration, with emerging opportunities in AI‐driven sensing and sensitive diagnostics. However, the market for antibiotic residue testing is expanding under the pressure of severe food safety regulations, current research often overlooks the practical requests of industrial and clinical end‐users.

First, regarding commercialization, the field must transition from ordering extreme detection limits toward establishing a viable economic framework that includes low cost‐per‐test and scalable manufacturing of functionalized nanomaterials. Second, reproducibility remains an important hurdle; the high batch‐to‐batch variability inherent in complex sensor fabrication must be overcome through standardized, robust assembly protocols to ensure consistent performance across large‐scale production. Reaching this consistency is foundational for both commercial success and the integration of AI algorithms, which require reliable input data to train and operate effectively.

Finally, effective clinical and laboratory integration requires that future platforms move away from complex, multi‐step sample preparation. To be viable, these sensors must be redesigned as ‘sample‐to‐answer’ systems. This is where AI‐integrated sensing offers transformative potential. By embedding AI, biosensors can automate data interpretation, perform real‐time calibration and even predict sensor drift or failure, significantly enhancing their utility in POC devices. These AI‐enhanced systems must be compatible with existing automated workflows, providing rapid, reliable data that conform to established regulatory standards. Shifting toward this market‐oriented, systems‐based design approval AI for enhanced sensing and prioritizing POC functionality is crucial for bridging the divide between high‐performing prototypes and the working realities of industrial and clinical diagnostic environments.

Author Contributions

Nava Moghadasian Niaki: writing and editing original draft. Reza Kheradmand: writing and editing original draft. Seyedeh Giso Akbarian: writing and editing original draft. Elham Shaterian: supervision. Hamed Shaterian: supervision. Ahmad Mobed: supervision.

Funding

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.