jrnls.org logo
Submit

BROWSE ALL JOURNALS

SEE ALL 19 JOURNALS
International Journal of Pharmaceutical InvestigationNOW IN
Review

Biosensor-Based Innovations in Therapeutic Drug Monitoring: Bridging Traditional Methods and Precision Medicine

K Pavani Reddy1, Katika Nizamuddin1, Haranath Chinthaginjala2, E Bhargav2, Kavadi Kartikeya1, Kothakota Kousar1, Chennavaram Kurmila1, Maddileti Repollu2★★ Corresponding author

  1. 1Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (Autonomous), KR. Palli Cross, (Affiliated to JNTU-Anantapur), Ananthapuramu, Andhra Pradesh, INDIA.
  2. 2Department of Pharmaceutics, Raghavendra Institute of Pharmaceutical Education and Research (Autonomous), KR. Palli Cross, (Affiliated to JNTU-Anantapur), Ananthapuramu, Andhra Pradesh, INDIA.

CORRESPONDENCE

Maddileti Repollu

Mr. Repollu Maddileti Department of Pharmaceutics, Raghavendra Institute of Pharmaceutical Education and Research (Autonomous), KR. Palli Cross-515721, (affiliated to JNTU-Anantapur), Ananthapuramu, Andhra Pradesh, INDIA.

madhurepalle9160@gmail.com

Received: 21-04-2026; Revised: 04-05-2026; Accepted: 19-06-2026.

Volume 16, Issue 4 · pp. 1216–1230 · PUBLISHED Oct-Dec 2026 · DOI: 10.5530/ijpi.20260148

View on IJPI original site ↗

ABSTRACT

Therapeutic Drug Monitoring (TDM) serves as a crucial component of personalized medicine, allowing healthcare providers to tailor drug dosages according to individual pharmacokinetic and pharmacodynamic profiles, thereby maximizing effectiveness and minimizing adverse effects. This detailed review investigates the essential principles and justifications for TDM, stressing its importance for medications with narrow therapeutic windows, considerable variations among patients, non-linear pharmacokinetics, and intricate drug interactions. It outlines the conventional TDM process from evaluating the patient and collecting samples to analyzing measurements, interpreting results, and continuous enhancement while also showcasing alternative methods that include point-of-care testing, non-invasive sampling methods, and AI-based modeling. A critical analysis of traditional analytical techniques such as LC-MS/MS, HPLC-UV/FLD, GC-MS, immunoassays, and atomic spectroscopy is provided, examining their principles, uses, benefits, and drawbacks. Following this, the article highlights the emerging trend towards the use of biosensors, motivated by the limitations of standard approaches like long turnaround times, invasiveness, and expensive procedures. Recent innovations in biosensors featuring continuous monitoring, non-invasive sampling, detailed PK/PD profiling, multiplexing, AI incorporation, CRISPR related systems, and nanomaterials are explored, demonstrating their capacity to transform TDM through real-time, decentralized, and patient-focused methods. The challenges faced in adopting biosensors, such as issues with stability, scalability, and regulatory obstacles, are discussed, emphasizing the importance of hybrid models that integrate both traditional and cutting-edge technologies. Ultimately, this review argues that the incorporation of biosensors in clinical settings can significantly improve patient outcomes in the age of precision medicine.

KEYWORDS

0% READ

FULL TEXT

INTRODUCTION

Therapeutic Drug Monitoring (TDM) is a basis of personalized medicine within clinical pharmacology, fundamentally aimed at optimizing individual medication therapy to enhance effectiveness and minimize adverse effects (Manubolu et al., 2024; Vermeire et al., 2020). At its core, TDM involves systematic measurement of drug concentrations in biological matrices, typically blood or plasma, but also increasingly in alternative fluids like saliva, cerebrospinal fluid, or urine (Manubolu et al., 2024; Ates et al., 2020; Kang & Lee, 2009). This process is driven by the premise that drug concentration in the body correlates more reliably with therapeutic and toxic effects than the administered dose alone (Manubolu et al., 2024; Fishberger et al., 2022).

The goal of TDM is to precisely adjust drug dosages to maintain concentrations within a predefined therapeutic window or target range, a concentration interval associated with optimal therapeutic response and minimal toxicity (Manubolu et al., 2024; Ates et al., 2020). This isn’t a one-time assessment but a dynamic, feedback-driven process. It begins with the initial prescription, considering patient-specific factors like age, weight, organ function, and concomitant medications to establish a preliminary dosing schedule (Manubolu et al., 2024; Kwaw et al., 2022). Subsequently, measured drug concentrations, interpreted within the context of sampling time, prior dosing history, and patient clinical status, guide necessary dosage modifications (Manubolu et al., 2024; Kang & Lee, 2009).

Foundational to TDM are Pharmacokinetic (PK) principles-what the body does to the drug. Key PK characteristics, such as clearance, volume of distribution, and half-life, are critical in guiding dosage adjustments (Manubolu et al., 2024). TDM relies on a definable relationship between the dose and the drug’s concentration in blood, and, in turn, between that concentration and the drug’s therapeutic or toxic effects (Manubolu et al., 2024; Amponsah & Pathak, 2022). This comprehensive approach allows clinicians to make informed decisions for optimal patient benefit across diverse healthcare scenarios (Manubolu et al., 2024; Kang & Lee, 2009).

The Rationale for TDM

While TDM is not necessary for the majority of prescriptions, it becomes indispensable in specific situations where drug therapy demands careful management (Manubolu et al., 2024; Edenya & Atoe, 2024). The need for TDM stems from several crucial factors

Narrow Therapeutic Index

Drugs with a Narrow Therapeutic Index (NTI) are prime candidates for TDM because they possess a small margin between their minimum effective concentration and their minimum toxic concentration (Alakeel, 2024; Kwaw et al., 2022; Fishberger et al., 2022). Even slight deviations in concentration can lead to either subtherapeutic effects (treatment failure) or severe toxicity.

Inter-Patient Pharmacokinetic Variablity

Individual patients can exhibit significant variability in drug pharmacokinetics, meaning their bodies handle drugs differently (Alakeel, 2024; Kwaw et al., 2022; De Rose et al., 2020). This variability can arise from numerous factors.

Age and developmental changes

Neonates and children, for instance, have rapidly changing organ function and metabolic pathways, making their drug responses highly unpredictable (Alakeel, 2024; De Rose et al., 2020). Their clearance rates may necessitate higher mg/kg doses than adults (Patsalos et al., 2008). Elderly patients also exhibit altered pharmacokinetics and are more prone to drug interactions due to polypharmacy and age-related physiological changes (Patsalos et al., 2008).

Organ function

Renal and hepatic impairments significantly alter drug clearance and metabolism, affecting drug concentrations. Critically ill patients, who may experience augmented renal clearance or acute kidney injury, particularly benefit from TDM (Fishberger et al., 2022).

Genetics

Genetic polymorphisms, particularly in enzymes like cytochrome P-450 (CYP) and transporters, can lead to wide inter-individual variability in drug metabolism (Fishberger et al., 2022; Widmer et al., 2014; Mueller-Schoell et al., 2021). This genetic background can profoundly impact how a patient absorbs, distributes, metabolizes, and excretes a drug (De Rose et al., 2020).

Concomitant medications and comorbidities

The presence of other drugs or disease states can alter a drug’s pharmacokinetics (Manubolu et al., 2024; Kwaw et al., 2022).

Non-Linear Pharmacokinetics

Some drugs exhibit non-linear pharmacokinetics, where a proportional increase in dose does not lead to a proportional increase in drug concentration (Manubolu et al., 2024; Kwaw et al., 2022). This is often due to saturation of metabolic enzymes or transport proteins at clinically relevant concentrations. Phenytoin is a classic example: its binding sites can become saturated at relatively low doses, leading to a rapid and unpredictable increase in plasma concentration with subsequent dose increments (Fishberger et al., 2022). For such drugs, TDM is crucial to avoid unintended toxicity.

Inadequate Clinical Endpoints

For many medications, directly monitoring their therapeutic effect is challenging or impossible (Kwaw et al., 2022; Kang & Lee, 2009). This is particularly true for:

Prophylactic drugs

Medications like lithium (for manic-depressive attacks) or immunosuppressants (to prevent transplant rejection) are administered to prevent an event, making direct clinical response difficult to assess (Kwaw et al., 2022). TDM ensures sufficient drug exposure without relying on the absence of a negative outcome (Fishberger et al., 2022).

Drugs with delayed or subtle effects

Some drugs, including many anticancer agents, may have effects that are not immediately apparent or are difficult to quantify with simple clinical observations (Paci et al., 2014). For these, drug concentration can serve as a valuable surrogate marker for efficacy and safety (Kwaw et al., 2022).

Drug and Disease Interactions

TDM is invaluable in managing complex drug-drug and drug-disease interactions that can significantly alter drug concentrations or patient response (Manubolu et al., 2024; Kwaw et al., 2022; Kang & Lee, 2009).

In patients taking multiple medications simultaneously, especially those with enzyme inducing or inhibiting properties, TDM helps identify potential interactions and allows for timely dosage adjustments (Manubolu et al., 2024; Johannessen & Landmark, 2008). For instance, rifampin, an enzyme inducer, can increase the clearance of cyclosporine, necessitating higher cyclosporine doses (Fishberger et al., 2022).

HIV patients often receive complex antiretroviral regimens alongside other medications, making them highly susceptible to significant drug-drug interactions. TDM helps verify adequate concentrations for both HIV and concomitant drugs (Peloquin, 2002).

Parkinson’s disease patients on levodopa frequently take other drugs for age-related comorbidities, which can interfere with levodopa plasma levels and increase side effects. TDM can help manage this variability (Müller et al., 2024).

Economic and Regulatory Imperatives

Beyond direct patient care, TDM also offers significant economic benefits and addresses regulatory considerations (Kwaw et al., 2022). By optimizing dosing, TDM can:

Reduce healthcare costs

By preventing subtherapeutic concentrations that lead to treatment failure, or supratherapeutic levels causing toxicity requiring hospitalization, TDM can shorten hospital stays and reduce the overall burden on healthcare systems (Ates et al., 2020; Kang & Lee, 2009).

Improve resource allocation

For expensive medications, especially novel oral targeted anticancer therapies, TDM ensures that costly drugs are used optimally, maximizing their therapeutic potential and justifying their cost from a public health perspective (Widmer et al., 2014).

Monitor compliance

TDM is an effective tool for assessing patient adherence to medication regimens (Manubolu et al., 2024; Kang & Lee, 2009). Non-compliance is a major cause of treatment failure and can be identified by unexpectedly low drug concentrations (Edenya & Atoe, 2024).

TDM Workflow

Patient Assessment and Drug Selection

It begins with the patient assessment, where we establish a preliminary dosage regimen tailored to the individual’s clinical status, considering factors like age, weight, organ function, and concomitant therapies (Alakeel, 2024; Kwaw et al., 2022). Not all drugs require TDM; it is reserved for those where the dose-response relationship is poorly predictable and where there are significant consequences of under- or overdosing (Manubolu et al., 2024; Ates et al., 2020; Kang & Lee, 2009).

Dosing Initiation and Target Setting

Once a drug is selected for monitoring, the goal is to define and achieve a therapeutic concentration range - a "window" within which the drug is most effective with minimal toxicity (Manubolu et al., 2024; Kwaw et al., 2022). This involves setting an initial dosage regimen and then, critically, individualizing it. For many Antiepileptic Drugs (AEDs), for example, the concept of an "individual therapeutic concentration" has emerged, acknowledging that optimal levels can vary greatly among patients based on their unique response (Patsalos et al., 2008; Johannessen & Landmark, 2008).

Sample Collection

This is a pivotal step, as accurate measurement relies heavily on proper sample collection. The choice of biological matrix is vital; while blood (serum or plasma) is the most common and often recommended specimen (Manubolu et al., 2024; Ates et al., 2020), other matrices like urine, saliva, and Cerebrospinal Fluid (CSF) may also be used (Resztak et al., 2025; Amponsah & Pathak, 2022). For certain drugs like cyclosporine A, whole blood must be collected due to its distribution between red blood cells and plasma (Manubolu et al., 2024).

Timing of sample collection is perhaps the most critical factor, as it can dramatically influence interpretation (Manubolu et al., 2024; Kwaw et al., 2022; Fishberger et al., 2022).

Steady-state samples are generally preferred, collected after 4-5 half-lives of consistent dosing, when the drug’s intake and elimination are in equilibrium (Kwaw et al., 2022; Patsalos et al., 2008).

Trough concentrations (Cmin), measured just before the next dose, reflect the lowest drug level and are often used to assess steady-state and ensure sufficient ongoing exposure, particularly for drugs like vancomycin and linezolid (Manubolu et al., 2024; Dogan et al., 2025; Roberts et al., 2012).

Peak concentrations (Cmax), measured after drug administration (e.g., 30-60 min post-IV infusion), capture maximum drug exposure and are important for drugs with rapid onset or short half-lives, such as aminoglycosides (Manubolu et al., 2024; Roberts et al., 2012).

Analytical Measurement

Once collected, samples undergo laboratory evaluation to quantify drug concentrations. Common analytical techniques include:

Immunoassays (FPIA, EMIT, ELISA)

These are often easy to use, automated, and rely on validated kits, but can suffer from low specificity and cross-reactivity with metabolites or similar compounds, potentially leading to inaccurate results (Campbell & Williams, 1998; Fishberger et al., 2022).

Chromatography (HPLC, LC-MS/MS, GC-MS)

These methods offer higher sensitivity and specificity but are typically more expensive, require sophisticated equipment, and demand highly trained personnel and complex sample preparation (Manubolu et al., 2024; Ates et al., 2020; Kwaw et al., 2022).

Rigorous quality control measures are essential throughout this process, including instrument calibration, assay validation, and participation in external quality assurance programs, to ensure the reliability and accuracy of TDM results (Manubolu et al., 2024; Kang & Lee, 2009).

INTERPRETATION OF RESULTS

Interpreting TDM results involves a holistic view of the patient’s data (Manubolu et al., 2024; Kwaw et al., 2022).

Comparison with therapeutic ranges

Drug concentrations are compared against established reference ranges. However, it’s crucial to remember that these ranges are probabilistic and many patients may achieve optimal outcomes outside them (Kang & Lee, 2009; Ghiculescu, 2008).

Assessment of pharmacokinetic parameters

Evaluating parameters like Cmax, Cmin, AUC, and half-life helps understand how the drug is absorbed, distributed, metabolized, and eliminated in that specific patient (Dogan et al., 2025; Roberts et al., 2012).

Consideration of patient factors

Individual variables such as age, weight, renal and hepatic function, genetics, comorbidities, and concomitant medications (which can lead to significant drug-drug interactions) profoundly impact drug behavior and must be carefully integrated into the interpretation (Kwaw et al., 2022; Patsalos et al., 2008).

Dose Adjustment and Treatment Optimization

Based on comprehensive interpretation, healthcare providers make informed decisions to adjust the drug therapy (Manubolu et al., 2024). This might involve increasing or decreasing the dose, or altering the dosing frequency (Manubolu et al., 2024; Dogan et al., 2025). The aim is to optimize therapeutic efficacy while minimizing adverse effects (Manubolu et al., 2024). TDM is particularly valuable in managing drug-drug interactions by allowing clinicians to adjust regimens or select alternative therapies to maintain efficacy and safety (Fishberger et al., 2022; Johannessen & Landmark, 2008). Evidence-based algorithms often guide these adjustments (Manubolu et al., 2024; Vermeire et al., 2020).

Ongoing Monitoring and Evaluation

The TDM process is not a one-time event; it’s a continuous cycle. Treatment decisions and their rationale are meticulously documented (Manubolu et al., 2024). Follow-up monitoring is scheduled to assess the patient’s response, track for adverse effects, and ensure ongoing therapeutic optimization (Manubolu et al., 2024). TDM can significantly reduce healthcare costs by decreasing hospitalizations and the incidence of adverse drug reactions (Kwaw et al., 2022; Kang & Lee, 2009; Widmer et al., 2014). It’s a fundamental tool for quality assurance in drug therapy (Johannessen & Landmark, 2008).

TRADITIONAL ANALYTICAL METHODS IN TDM

Liquid Chromatography-Tandem Mass Spectroscopy (LC-MS)

Principle

At the forefront of modern TDM is Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS), widely regarded as the gold standard for quantitative detection of small molecules (Zhang & Garg, 2024; Wang et al., 2025). This powerful analytical technique operates by first separating compounds within a complex biological sample using Liquid Chromatography (LC) (Bekoe et al., 2022; Zhang & Garg, 2024). Following separation, the isolated analytes are ionized, typically via Electrospray Ionization (ESI), and then introduced into a tandem Mass Spectrometer (MS/MS) (Pigliasco et al., 2024; Zhang & Garg, 2024). The MS/MS component then selects a "precursor ion" and fragments it into specific "product ions," which are then detected (Zhang & Garg, 2024). This process, often performed in "Multiple Reaction Monitoring" (MRM) mode, provides exceptional specificity by identifying drugs based on unique fragmentation patterns (Zhang & Garg, 2024; Gqamana & Zhang, 2024). To ensure high accuracy and compensate for variations in ionization and extraction efficiency, Stable Isotope-Labeled Internal Standards (SIL-IS) are almost universally employed, behaving identically to the target analyte but with a different mass (Kocur et al., 2024; Adaway & Keevil, 2012).

Key Advantages

LC-MS/MS offers unparalleled specificity and sensitivity, enabling the definitive identification and quantification of drugs, even at very low concentrations, overcoming limitations of older immunoassay methods prone to cross-reactivity (Adaway & Keevil, 2012; Zhang & Garg, 2024). This inherent specificity has made it indispensable for compounds lacking natural chromophores or fluorophores, or those with significant metabolite interference (Adaway & Keevil, 2012).

Perhaps one of its most transformative benefits is its revolutionary multiplexing capability, allowing for the simultaneous measurement of dozens to hundreds of analytes from a single sample aliquot (Zhang & Garg, 2024; Adaway & Keevil, 2012). This has revolutionized monitoring in complex clinical scenarios, such as in Intensive Care Units (ICUs), where patients often receive multiple antibiotics (Qi et al., 2025; Radovanovic et al., 2022). For instance, recent methods can simultaneously quantify 10-15 antibiotics in human plasma within minutes, a significant leap from traditional methods that would require multiple, time-consuming assays (Radovanovic et al., 2022). "The ability to quantify multiple drugs simultaneously in critically ill patients has significantly improved the optimization of antimicrobial therapy," allowing for timely dose adjustments that improve patient outcomes and potentially reduce drug resistance (Qi et al., 2025; Radovanovic et al., 2022).

LC-MS/MS methods often feature streamlined sample preparation, employing simple techniques like protein precipitation or "dilute and shoot," which significantly reduce pre-analytical steps and analysis time (Radovanovic et al., 2022; Adaway & Keevil, 2012). This simplicity, coupled with rapid chromatographic separation, translates to fast turnaround times, with total run times per sample often ranging from 2 to 15 minutes (Radovanovic et al., 2022; Wang et al., 2025). Even with calibration and controls, results can often be available within hours, crucially before the next dosing interval, directly impacting clinical decisions (Radovanovic et al., 2022). The versatility of LC-MS/MS is remarkable, covering a broad spectrum of drugs across various therapeutic classes. These include, but are not limited to:

Other critical drugs including oral antineoplastics, antidepressants, and cardiovascular agents (Gaspar et al., 2021). The typical Lower Limit of Quantification (LLOQ) for these assays can be as low as 0.005 ng/mL for some analgesics or 0.82 ng/mL for norfenfluramine, extending to wide calibration ranges suitable for clinical monitoring (Wang et al., 2025; Pigliasco et al., 2024).

Challenges and Considerations

Despite its advantages, LC-MS/MS comes with significant hurdles. The high initial capital investment for instrumentation is substantial, making it inaccessible for many laboratories (Zhang & Garg, 2024; Gaspar et al., 2021). Furthermore, its operation and method development demand highly specialized and trained personnel, a critical bottleneck for widespread adoption (Adaway & Keevil, 2012; Seger & Salzmann, 2020).

HPLC (High Performance Liquid Chromatography)

Principle

High-Performance Liquid Chromatography (HPLC) is a chromatographic separation technique that leverages the differing interactions of compounds with a stationary phase (typically a column) and a mobile phase (solvent system) (Tuzimski & Petruczynik, 2020). This allows for the effective separation of complex mixtures, making it a versatile tool in pharmaceutical analysis (Bekoe et al., 2022).

UV Detection (HPLC-UV)

This detection method relies on the principle that many drug molecules absorb UV light. When electrons within a molecule’s atoms are promoted from a ground state to an excited state by absorbing energy from UV light at specific wavelengths, this absorption can be quantified (MohammadiArani et al., 2023). This makes HPLC-UV particularly suitable for drugs with chromophores (light-absorbing groups) in their structure (Tuzimski & Petruczynik, 2020).

Fluorescence Detection (HPLC-FLD)

For compounds that naturally fluoresce or can be chemically derivatized to become fluorescent, FLD offers enhanced sensitivity. It measures the light emitted by molecules after they absorb light at a specific excitation wavelength (Tuzimski & Petruczynik, 2020).

Key Advantages

HPLC-UV/FLD methods are often favored for routine TDM due to their simplicity, cost-effectiveness, and lower maintenance requirements compared to LC-MS/MS systems, making them highly suitable for general hospital laboratories (Tuzimski & Petruczynik, 2020; Boccardi et al., 2024). As one study on ceftobiprole noted, "LC-MS/MS availability is still not so obliquitous in hospitals for both its high cost and the need of specialized staff, while HPLC-UV is sensitive and accurate enough to guide clinician decision". This highlights the pragmatic choice of HPLC-UV in budget-limited clinical settings to ensure TDM implementation (Boccardi et al., 2024).

Sample Preparation

A crucial step for accurate analysis in biological matrices is sample preparation, which typically involves removing interfering substances while retaining the analytes of interest. Common techniques include:

Protein Precipitation (PP)

A widely used, straightforward method where proteins are precipitated (e.g., using acetonitrile or sulphosalicylic acid), and the supernatant containing the drug is then analyzed (Tuzimski & Petruczynik, 2020; Boccardi et al., 2024).

Liquid-Liquid Extraction (LLE) and Solid-Phase Extraction (SPE)

These are employed for more extensive cleanup and concentration, especially for drugs present at lower concentrations or in complex matrices (Tuzimski & Petruczynik, 2020).

Derivatization

For drugs lacking a natural chromophore, pre-column derivatization is essential to enable UV or fluorescence detection. A notable example is Valproic Acid (VPA), an anticonvulsant with a therapeutic range of 50-100 μg/mL (Kaewpradit et al., 2024). VPA lacks a chromophore, making direct detection challenging. However, a recent innovative method utilized microwave-assisted derivatization (MAD) with phenylhydrazine hydrochloride (PH HCl), allowing for efficient HPLC-UV detection (Kaewpradit et al., 2024). This streamlined process, optimized at 450W for 50 seconds, significantly improved feasibility and cost-effectiveness, offering "a viable alternative to conventional HPLC methodologies, offering a balance of efficiency and economic practicality for VPA quantification" (Kaewpradit et al., 2024). The reported sensitivity for VPA in plasma using this MAD method was adequate for TDM, with a concentration of 30 µg/mL being detectable (Kaewpradit et al., 2024).

Drugs Commonly Monitored with HPLC-UV/FLD: Many drugs across various therapeutic classes are quantified using HPLC-UV/FLD due to their suitable concentrations in biological samples and UV absorption properties.

Carbamazepine (CBZ) and its Metabolites

A widely used antiepileptic drug, TDM for CBZ is crucial due to its narrow therapeutic window and significant inter-individual variability (Milosheska & Roškar, 2023). HPLC-UV methods have been extensively developed for CBZ and its active metabolites, such as carbamazepine-10,11-epoxide (CBZ-E) and 10-Monohydroxycarbazepine (MHD) (MohammadiArani et al., 2023; Milosheska & Roškar, 2023). A recent HPLC-UV method simultaneously quantified 12 antiepileptic drugs and 3 metabolites, including CBZ, CBZ-E, and MHD, in a single run using a Phenyl-Hexyl column, which enhances interactions with aromatic compounds (Milosheska & Roškar, 2023). This method reported LOQ values of 0.5 mg/L for CBZ and 0.2 mg/L for CBZ-E.

Vancomycin

This important antibiotic requires TDM to maintain effective plasma concentrations, typically within 10.0-20.0 μg/mL (Jiang et al., 2024). HPLC-UV methods, detecting at 240 nm, have been validated for its quantification, with a reported LLOQ of 1 µg/mL (Tuzimski & Petruczynik, 2020). The necessity for TDM is underscored by observations of a high proportion of subtherapeutic concentrations in patients.

Ceftobiprole (CB)

A newer cephalosporin, Ceftobiprole (CB) also benefits from TDM. HPLC-UV methods have been developed to support routine monitoring, offering a linear range of 0.5-50.0 mg/L and an LOQ of 0.5 mg/L. Its simple protein precipitation sample preparation makes it practical for busy hospital labs (Boccardi et al., 2024).

Other Antibiotics

HPLC-UV is often suitable for various antibiotics (e.g., piperacillin, tazobactam, daptomycin, linezolid) because they are frequently administered in high doses, leading to concentrations in biological samples that are readily detectable by UV (Tuzimski & Petruczynik, 2020).

Anticancer Drugs

For many anticancer drugs like enzalutamide and methotrexate, HPLC-UV is utilized, particularly when high doses result in high plasma concentrations. For enzalutamide, the LOD was 0.20 µg/mL and LLOQ 0.50 µg/mL. Methotrexate can be quantified by HPLC-UV with an LLOQ of 0.05 µmol/L (approximately 0.0227 µg/mL) (Tuzimski & Petruczynik, 2020).

Safinamide (SAF)

An anti-Parkinsonian drug, SAF can be quantified using HPLC-DAD, which also detects degradation products and impurities. Its methods emphasize eco-friendly and time-efficient aspects (Ibrahim et al., 2024), with linearity up to 150 µg/mL (Ibrahim et al., 2024).

Limitations

While highly valuable, HPLC-UV/FLD methods do have limitations that sometimes necessitate the use of more advanced techniques:

Sensitivity for Low Concentration Drugs

While adequate for many drugs, HPLC-UV/FLD may lack the sensitivity required for drugs present at very low concentrations in biological matrices (Bekoe et al., 2022; Tuzimski & Petruczynik, 2020). UPLC and LC-MS/MS, for instance, can quantify drugs down to picogram levels (Vanitha Madhuri et al., 2024).

Specificity and Interference Challenges

Although HPLC offers good specificity through chromatographic separation (Tuzimski & Petruczynik, 2020; Nassar et al., 2025), interference from co-administered medications or endogenous matrix components can still be a concern, especially in complex clinical samples (Milosheska & Roškar, 2023). Careful evaluation of selectivity is crucial.

Requirement for Derivatization

For drugs that do not possess intrinsic chromophores or fluorophores (like Valproic acid), a derivatization step is necessary to enable detection by UV or FLD (Tuzimski & Petruczynik, 2020). This additional step can add complexity and time to the analytical process (Kaewpradit et al., 2024).

Run Time for Complex Mixtures

While some HPLC methods are rapid, achieving separation for multiple analytes with diverse physicochemical properties can lead to longer run times (Boccardi et al., 2024; Zayed et al., 2024; Nassar et al., 2025). For very complex or high-throughput needs, LC-MS/MS might offer significantly shorter analysis times (Jiang et al., 2024; Lee et al., 2024).

Initial Capital Investment

Compared to simpler analytical techniques like immunoassays, HPLC systems represent a higher initial capital investment for laboratories, although their running costs are generally considered low (Bekoe et al., 2022).

Gas Chromatography (GC)

Principle

Gas Chromatography, particularly when coupled with Mass Spectrometry (GC-MS), stands out as a powerful analytical tool for drug quantification (Nikolaou et al., 2015; Ando et al., 2020; Hahn et al., 2017; Chiadmi & Schlatter, 2015). At its core, GC separates compounds based on their volatility and interaction with a stationary phase inside a heated column (Ikeda et al., 2014). Once separated, these compounds are detected, often by a mass spectrometer, which identifies them based on their unique fragmentation patterns. GC-FID (Flame Ionization Detection) and GC-ECD (Electron Capture Detection) are other common GC detectors, offering high sensitivity for specific types of compounds.

Derivatization

A significant aspect of GC analysis, especially for polar or less volatile compounds, is derivatization (Nikolaou et al., 2015). This chemical modification makes the analyte more volatile and stable, improves chromatographic behavior (reducing peak tailing), and often enhances detection sensitivity.

Lacosamide, an antiepileptic drug, requires silylation using N-methyl-N-tert-butyldimethylsilyltrifluoroacetamide (MTBSTFA) with 1% tert-butyldimethylsilylchloride (TBDMSCl) before GC-MS analysis due to its polar amino groups (Nikolaou et al., 2015). This method, validated for human plasma, was reported as the first of its kind using GC-MS for lacosamide determination.

Valproic acid (VPA), widely used for epilepsy, has been quantified in tears and plasma. For tears, a GC/ECNCI/MS method converted VPA directly into its pentafluorobenzyl ester (PFB-ester) derivative without prior extraction (Nakajima et al., 2000). In another GC-EI-MS method for dried plasma spots, VPA was trimethylsilylated using N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) (Ikeda et al., 2014). GC-FID methods for VPA in plasma have also been reported using dispersive liquid-liquid microextraction (DLLME) (Fazeli-Bakhtiyari et al., 2015).

Theophylline, a bronchodilator, was determined in tears and plasma using GC-EI-MS after being converted to its pentafluorobenzoyl amide (PFB-amide) derivative (Nakajima et al., 2003).

Mitotane, a key drug for adrenal cortical carcinoma, was analyzed in plasma using a simplified GC-EI-MS method after deproteination and liquid-liquid extraction (Ando et al., 2020).

Topiramate, another antiepileptic, can be measured in Dried Blood Spots (DBS) using GC-MS after a flash methylation with Trimethylanilinium Hydroxide (TMAH). This is particularly advantageous as whole blood is considered a more suitable matrix for TOP TDM due to its concentration-dependent binding to erythrocytes (Hahn et al., 2017).

Methadone and its principal metabolite, EDDP, have been quantified in human plasma using GC-MS with derivatization by bis(trimethylsilyl)trifluoroacetamide (BSTFA) + 1% Trimethylchlorosilane (TMCS) (Chiadmi & Schlatter, 2015).

Immunoassays

Principle

Immunoassays are analytical techniques that leverage the highly specific binding affinity between antibodies and antigens for the detection and quantification of target analytes (Bekoe et al., 2022). For small drug molecules like many therapeutics, a competitive immunoassay format is typically employed, where the drug in the sample competes with a labeled drug derivative for binding to a specific antibody. The signal intensity is inversely proportional to the drug concentration (Cavalera et al., 2023). These assays can be categorized as heterogeneous (requiring a separation step) or homogeneous (no separation needed) (Kozo et al., 2017). Many modern immunoassays are designed for automated clinical chemistry analyzers, making them a common sight in routine laboratories (Kozo et al., 2017).

Gemcitabine

A rapid, homogeneous nanoparticle immunoassay was developed for this anticancer agent, utilizing a monoclonal antibody coated on nanoparticles (Kozo et al., 2017). This assay was specifically designed to be robust and not affected by the major metabolite dFdU or the blood stabilizer THU (Kozo et al., 2017).

Busulfan

A nanoparticle immunoassay for busulfan, an alkylating agent used in stem cell transplantation, demonstrated good precision, accuracy, and linearity (Hilaire et al., 2021). It showed good correlation with mass spectrometry methods.

Immunosuppressants

Immunoassays are widely used for routine monitoring of critical drugs like tacrolimus, cyclosporine, sirolimus, everolimus, and mycophenolic acid due to their speed and ease of operation (Dasgupta, 2016).

Tacrolimus

While immunoassays like CMIA are routinely used and FDA-approved, they often exhibit a positive bias compared to the LC-MS/MS gold standard, largely due to cross-reactivity with active metabolites (e.g., M-II). Issues like low hematocrit and endogenous antibodies (e.g., rheumatoid factor) can also cause false-positive results with certain immunoassay platforms (Dasgupta, 2016).

Cyclosporine

Immunoassays for cyclosporine often lack specificity because they cannot distinguish the parent drug from its numerous metabolites, which increase over the course of treatment, leading to positive bias (Dasgupta, 2016; Wang et al., 2021). Whole blood is the preferred sample matrix for these drugs due to their distribution within blood components.

Tenofovir (TFV)

ELISA and Lateral Flow Immunoassays (LFIAs) have been developed for TFV in saliva, requiring high sensitivity due to low drug levels in this matrix (Cavalera et al., 2023). LFIAs offer rapid, on-site screening suitable for low-resource settings.

Methotrexate (MTX)

Various immunoassays are used for MTX monitoring, but concerns exist regarding the influence of metabolites like 7-hydroxymethotrexate on assay results and clinical decision-making (Descoeur et al., 2022).

TNF-alpha inhibitors (Infliximab, Adalimumab)

ELISAs are extensively used, but new chemiluminescent and fluorescence-based immunoassays offer faster turnaround times and higher throughput, potentially enabling more proactive TDM (Kim et al., 2024; Van Aalen et al., 2023; Vroemen et al., 2024).

Pros:

Cons:

Atomic Spectroscopy & Inductively Coupled Plasma-Mass Spectrometry

Principle

Atomic Absorption Spectrometry (AAS)

AAS measures the absorption of light by free atoms in a sample, which is proportional to their concentration (Clases & Gonzalez De Vega, 2022). For lithium, this involves monitoring the partial resolution of its isotopic shift in the electronic transition (Winckelmann et al., 2022).

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

This powerful technique ionizes elements in an argon plasma, extracts the ions, and separates them based on their mass-to-charge ratio (m/z) before detection (Clases & Gonzalez De Vega, 2022). ICP-MS is renowned for its high sensitivity, isotopic selectivity, and vast linear dynamic range, making it ideal for trace element analysis (Davison et al., 2023). Hyphenated techniques, such as Liquid Chromatography-ICP-MS (LC-ICP-MS) and Laser Ablation-ICP-MS (LA-ICP-MS), couple separation or direct solid sampling with ICP-MS, enabling elemental speciation and bioimaging (Clases & Gonzalez De Vega, 2022).

Drugs Monitored and Practical Considerations

Lithium

Due to its narrow therapeutic range, close monitoring of lithium concentration is mandatory (Winckelmann et al., 2022). ICP-MS and AAS methods are utilized, with isotope dilution techniques offering traceable and precise determination with reduced uncertainties and less matrix sensitivity (Winckelmann et al., 2022).

Metal-Based Anticancer Drugs (e.g., Platinum/Cisplatin, Ruthenium)

ICP-MS is invaluable for understanding the pharmacokinetics and pharmacodynamics of these drugs.

It can directly measure metal content in drug-exposed cells to determine uptake rates and intracellular localization (Timerbaev, 2021; Clases & Gonzalez De Vega, 2022).

LA-ICP-MS provides elemental bioimaging to visualize drug distribution and accumulation within tissues at high spatial resolution, crucial for understanding efficacy and side effects (e.g., platinum deposition in tumors, liver, kidney) (Clases & Gonzalez De Vega, 2022).

Gadolinium-Based Contrast Agents (GBCAs)

ICP-MS is used to study the distribution and deposition of gadolinium in tissues, particularly in the brain, relevant for patient safety due to potential retention (Timerbaev, 2021).

Bismuth-Based Antibiotics: ICP-MS aids in understanding their mechanism of action by identifying bismuth-containing proteins (Timerbaev, 2021).

Pros:

  • High sensitivity and selectivity, capable of detecting major, trace, and toxicologically relevant elements (Clases & Gonzalez De Vega, 2022).
  • Multi-elemental capability, allowing comprehensive profiling of elements in biological samples (Clases & Gonzalez De Vega, 2022).
  • Elemental speciation analysis when coupled with chromatography, providing insight into the functional forms of elements (Clases & Gonzalez De Vega, 2022).
  • Elemental bioimaging with LA-ICP-MS, offering high spatial resolution to visualize drug distribution in tissues (Clases & Gonzalez De Vega, 2022).
  • Isotope Dilution Analysis (IDA) provides absolute quantification with high accuracy, less susceptibility to matrix effects, and reduced uncertainties (Winckelmann et al., 2022).

Cons:

  • High initial capital investment and running costs (Winckelmann et al., 2022).
  • Requires extensive expertise and highly trained personnel for operation and method development (Clases & Gonzalez De Vega, 2022).
  • Often involves laborious sample preparation steps (e.g., acid digestion for total metal content) (Timerbaev, 2021).
  • Lack of Certified Reference Materials (CRMs) for all matrices and elements, which can complicate validation and standardization (Clases & Gonzalez De Vega, 2022).
  • While generally robust, can still face matrix effects or spectral interferences, though advanced hardware (e.g., collision/reaction cells, tandem MS) mitigates these (Timerbaev, 2021).

LIMITATIONS OF TRADITIONAL LAB-BASED METHODS DRIVING THE SHIFT TO BIOSENSORS

Prolonged Turnaround Times

ELISA, a commonly used immunoassay, typically requires several days for results, primarily because laboratories conduct batch testing, accumulating samples before analysis (Kim et al., 2024). Similarly, chromatographic methods such as HPLC and LC-MS/MS involve laborious sample preparation and long processing times, which can extend to hours or even days (Garzón et al., 2019; Casian et al., 2025; Chen et al., 2025; Astúa et al., 2025). This delay is particularly critical for drugs with narrow therapeutic windows, where rapid adjustments are essential to prevent adverse effects like vancomycin’s potential for kidney and ear toxicity (Hemdan et al., 2024; Kudłacik-Kramarczyk et al., 2025).

Invasiveness and Sample Volume

Traditional methods usually necessitate blood sample collection by trained personnel, which is invasive, can cause patient discomfort, and is impractical for vulnerable populations such as neonates or critically ill patients (Awad et al., 2025; Sedighi et al., 2026). Large sample volumes (e.g., ≥1 mL) are often required, limiting frequent monitoring (Garzón et al., 2020).

Centralisation and Accessibility

The need for specialised laboratories and trained staff means TDM is often confined to centralised facilities, making it unfeasible for widespread, routine application, especially in rural or resource-limited areas (Kim et al., 2024; Garzón et al., 2019; Garzón et al., 2020; Awad et al., 2025; Astúa et al., 2025). This centralisation can lead to significant delays (e.g., 24-hour waits) in receiving crucial results, particularly impacting timely clinical decision-making (Awad et al., 2025).

High Costs

HPLC and LC-MS/MS instruments are expensive, and their operation requires costly reagents and highly skilled personnel, leading to substantial per-test costs (e.g., reported costs of €15-€80 per test, though not explicitly stated in the provided sources, it is implied by the emphasis on cost-effectiveness of biosensors) (Garzón et al., 2019; Garzón et al., 2020; Awad et al., 2025). ELISA also contributes to costs through the need for calibration curves and trained staff (Kim et al., 2024).

Interference Effects

Immunoassays can be susceptible to interference from matrix components, co-administered drugs, and metabolites, potentially leading to inaccurate results (Chen et al., 2025).

These limitations collectively underscore the urgent demand for alternative, more efficient solutions for TDM, driving a significant shift towards biosensors for faster, less invasive, and decentralised monitoring (Jarnda et al., 2025; Awad et al., 2025).

Biosensors: Their Transformative Role in TDM

A biosensor is an advanced analytical device that precisely detects subtle changes in complex biological processes, converting these variations into clear, measurable signals (Hemdan et al., 2024; Kudłacik-Kramarczyk et al., 2025). It typically integrates a biological recognition element (e.g., enzymes, antibodies, nucleic acids) with a physicochemical transducer that converts the biological interaction into a quantifiable signal (e.g., electrical, optical, or mass-sensitive) (Jarnda et al., 2025).

In TDM, biosensors play a pivotal role by providing real-time or near-real-time monitoring of drug concentrations in biological samples (Hemdan et al., 2025). This capability moves beyond the ’snapshot’ measurements of traditional methods, enabling continuous monitoring of vital physiological and biochemical parameters (Vo & Trinh, 2024; Mayol et al., 2025; Frumento & Țălu, 2025; Duan et al., 2025).

ADVANCEMENTS OF BIOSENSORS IN TDM

Continuous Monitoring and Point-of-Care (POCT) Integration

Optical Biosensors and Microfluidic Paper-Based Analytical Devices (µPADs) have emerged as cost-effective, portable, and highly sensitive tools (Garzón et al., 2020; Jarnda et al., 2025). µPADs, for instance, can provide rapid results, often within minutes, for biomarkers like glucose and uric acid (Jarnda et al., 2025).

Electrochemical Biosensors are particularly attractive for POCT due to their high sensitivity, portability, and direct electronic output, eliminating the need for additional detectors (Frumento & Țălu, 2025; Lehr et al., 2026; Belcastro & Arduini, 2025).

Wearable and Implantable Biosensors represent a significant frontier, offering continuous, non-invasive or minimally invasive monitoring of physiological parameters and drug levels directly on or within the body (Vo & Trinh, 2024; Hemdan et al., 2024; Duan et al., 2025). LC-MS/MS, while a gold standard for drug quantification, is now seen as a reference for validating these emerging biosensor technologies (Casian et al., 2025; Astúa et al., 2025; Minichmayr et al., 2024).

Smartphone-Assisted Systems enable user-friendly, real-time data exchange and analysis, making TDM more accessible at home or in remote settings (Awad et al., 2025; Hemdan et al., 2025). For example, the MediMeter app, combined with colorimetric and electrochemical biosensors, successfully quantified paracetamol in artificial saliva within minutes, demonstrating potential for remote drug monitoring (Awad et al., 2025).

Plasmonic Biosensors offer high specificity and sensitivity for rapid antibiotic monitoring, with results in under 20 minutes for amikacin and colistin in blood serum (Astúa et al., 2025).

Ultra-Low Volume and Non-Invasive Sample Analysis

Biosensors can analyse diverse biological fluids, including saliva, sweat, tears, and interstitial fluid (ISF), offering less invasive alternatives to blood sampling (Awad et al., 2025; Duan et al., 2025).

Saliva-based sensors are explored for drugs like paracetamol and potentially tacrolimus, offering advantages like non-invasiveness and cost-effectiveness (Jarnda et al., 2025).

Sweat-based biosensors enable dynamic profiling of stress hormones (Tu et al., 2025) and continuous monitoring of electrolytes and metabolites (Frumento & Țălu, 2025).

Tear-based biosensors, such as contact lenses, can quantify glucose (Garzón et al., 2020) and ocular proteins like TNF-α (Duan et al., 2025).

Microneedle (MN) patches provide minimally invasive access to ISF, allowing continuous monitoring of drugs like phenoxymethylpenicillin, tobramycin, and vancomycin, as well as metabolites and cytokines (Mayol et al., 2025; Vo & Trinh, 2024; Hemdan et al., 2024). This allows for the detection of unbound, active drug levels.

Paper-based SERS Nanosensors facilitate rapid detection of drugs like flucytosine in undiluted serum and whole blood by physically separating the analyte from blood components (Agogo-Mawuli & Siderovski, 2022).

High-Resolution Pharmacokinetic/Pharmacodynamic (PK/PD) Profiling

Aptamer-based electrochemical (E-AB) sensors can provide high-resolution temporal datasets for real-time pharmacokinetic analysis in living animals, linking drug elimination rates to organ function (e.g., tobramycin in rats and kidney function) (Minichmayr et al., 2024; Mayol et al., 2025; Lehr et al., 2026). This significantly enhances the understanding of drug dynamics compared to traditional snapshot measurements.

Digital Twins, virtual replicas of an individual’s physiological systems, can simulate the impact of interventions, providing a proactive approach to healthcare by integrating real-time biosensor data (Soumma et al., 2025).

Model-Informed Precision Dosing (MIPD) integrates mathematical predictions with patient-specific factors and biosensor data, moving beyond traditional TDM for more accurate dosing (Minichmayr et al., 2024).

Multiplexed Detection Capabilities

Biosensors can detect multiple analytes simultaneously, offering comprehensive diagnostic profiles from a single test (Jarnda et al., 2025; Ranjan et al., 2025). µPADs, for instance, can perform multiple analyses on one strip.

Surface Plasmon Resonance imaging (SPRi) allows for the simultaneous study of multiple biomolecular interactions, making it suitable for multiplexed analysis of monoclonal antibodies or cardiovascular biomarkers (Garzón et al., 2019; Hemdan et al., 2024; Puiu & Bala, 2016; Mondal et al., 2023).

Researchers have developed an integrated microfluidic platform for swiftly measuring cardiovascular disease biomarkers such as NT-proBNP, fibrinogen, cTnI, and CRP (Hemdan et al., 2024).

Integration of Artificial Intelligence (AI)

AI-powered biosensors revolutionize health monitoring by enabling real-time data analysis, pattern recognition, and predictive modelling (Soumma et al., 2025).

Machine learning algorithms enhance the precision of wearable devices in processing physiological signals, such as glucose levels and heart rate variability (Soumma et al., 2025).

AI assists in optimizing sensor design, managing large datasets, and improving the overall performance and reliability of electrochemical diagnostics (Ranjan et al., 2025). For example, AI has been used to significantly lower the limit of detection for tyrosine and uric acid using electrochemical eMoSx-LIG sensors in sweat and saliva.

Large Language Models (LLMs) can process and contextualize vast amounts of health data from biosensors, generating personalized health insights and recommendations (Soumma et al., 2025).

Counterfactual explanations generated by AI can enhance trust and transparency by illustrating how changes in lifestyle or medication would affect health outcomes, offering actionable insights (Soumma et al., 2025).

AI can overcome challenges related to signal complexity, noise, and data interpretation, identifying specific patterns often undetectable by traditional methods (Frumento & Țălu, 2025; Ranjan et al., 2025).

Advanced Biorecognition and Transduction Mechanisms

Aptamer and Oligonucleotide-Based Biosensors

Aptamers, known as "nucleic acid antibodies," offer high specificity, stability, and versatility for a wide range of targets beyond what natural antibodies or enzymes can detect (Mayol et al., 2025; Frumento & Țălu, 2025; Chen et al., 2025). They enable rapid, label-free, and real-time detection of various biomarkers for diagnostics, including metabolites and therapeutic drugs like vancomycin and imatinib (Mayol et al., 2025).

CRISPR-Cas Systems

These systems have been adapted for ultrasensitive detection of nucleic acids and, more recently, protein biomarkers (Sheng et al., 2021). The MARPLE platform, for instance, leverages antibody induced proximity effects to activate CRISPR-Cas13a-mediated signal amplification, enabling femtomolar detection of clinically relevant antibodies such as trastuzumab and anti-MUC1 in human serum under one-pot conditions (Spezzani et al., 2025). An immunocapture magnetic bead-enhanced biosensor also uses CRISPR-Cas13a for rapid, ultrasensitive SARS-CoV-2 nucleic acid detection (Han et al., 2023).

Nanomaterials

The integration of nanomaterials (e.g., gold nanoparticles, carbon nanotubes, graphene, quantum dots) significantly enhances biosensor sensitivity, selectivity, and durability, allowing for the detection of analytes at ultra-low concentrations (Jarnda et al., 2025; Vo & Trinh, 2024; Hemdan et al., 2024; Kudłacik-Kramarczyk et al., 2025; Hemdan et al., 2025). They are crucial in improving the electrochemical performance of biosensors due to their high surface-to-volume ratios.

Bridging Elements and Future Directions

While biosensors promise to revolutionize TDM, traditional analytical methods like LC-MS/MS remain crucial as "gold standards" for validation (Casian et al., 2025; Astúa et al., 2025; Minichmayr et al., 2024; Lehr et al., 2026). The future of healthcare will likely see hybrid diagnostic models, where biosensor-based POCT devices act as first-line triage, providing rapid, on-site insights, with confirmatory testing or more complex analyses still performed by centralized laboratories using conventional methods when necessary (Frumento & Țălu, 2025; Lehr et al., 2026). This collaboration ensures both speed and definitive reliability. Despite considerable advancements, several challenges persist in the widespread adoption of biosensors for TDM:

Sensor Stability and Biocompatibility

Ensuring long-term stability of reagents and sensor materials in complex biological environments, along with overcoming issues like biofouling and immunological degradation, remains critical (Kudłacik-Kramarczyk et al., 2025; Sedighi et al., 2026; Jarnda et al., 2025; Mayol et al., 2025).

Miniaturization and Manufacturing Scalability

While significant progress has been made, further miniaturization is needed for seamless integration into the body without disrupting physiological processes (Jarnda et al., 2025; Kudłacik-Kramarczyk et al., 2025). Scaling up production from lab prototypes to mass manufacturing while maintaining consistent performance is also a hurdle (Hemdan et al., 2025).

Calibration and Interference Effects

The need for precise calibration and the mitigation of interference from other compounds or sample matrix components are ongoing challenges (Chen et al., 2025; Jarnda et al., 2025; Kudłacik-Kramarczyk et al., 2025).

Data Integration and Regulatory Approval

Seamless data interoperability with Electronic Health Records (EHRs) and telemedicine platforms is crucial (Awad et al., 2025; Jarnda et al., 2025). Navigating stringent regulatory frameworks (e.g., FDA, EMA) for medical device approval is a complex and time-consuming process (Frumento & Țălu, 2025; Hemdan et al., 2025).

Ethical Considerations

As biosensors become more pervasive and AI-driven, addressing data privacy concerns and ensuring informed consent for continuous monitoring are paramount (Sedighi et al., 2026; Hemdan et al., 2025; Soumma et al., 2025).

CONCLUSION

Therapeutic Drug Monitoring (TDM) has advanced significantly, from its foundation in how drugs behave in the body to the integration of modern biosensors for personalized medicine. Traditional methods, while highly accurate, often involve delays, invasive procedures, and high resource demands that can hinder prompt patient care especially in cases with narrow safe dose ranges and vulnerable patients. Biosensors offer an effective solution, providing real-time, minimally invasive, and adaptable monitoring that better suits the demands of clinical practice. However, as with emerging technologies, challenges remain maintaining sensor stability in complex biological settings, achieving cost-effective mass production, reducing measurement errors, ensuring smooth integration with electronic health systems, and addressing ethical issues related to data privacy and patient autonomy.

REFERENCES

As published

Showing references and in-text citations exactly as published.

  1. 1.Adaway, J. E.; Keevil, B. G.. Therapeutic drug monitoring and LC-MS/MS. Journal of Chromatography B. 2012;883-884:33–49. https://doi.org/10.1016/j.jchromb.2011.09.041DOIGOOGLE SCHOLAR
  2. 2.Agogo-Mawuli, P. S.; Siderovski, D. P.. Recent advances in nanosensors for therapeutic drug monitoring (TDM). Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;233–253. https://doi.org/10.1007/978-3-031-12098-6_11DOIGOOGLE SCHOLAR
  3. 3.Alakeel, Y. S.. Therapeutic drug monitoring. Manual of pediatric cardiac care. Springer. 2024;579–592. https://doi.org/10.1007/978-981-99-5683-8_81DOIGOOGLE SCHOLAR
  4. 4.Amponsah, S. K.; Pathak, Y. V.. Therapeutic and toxic concentrations of drugs in biological matrices. Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;1–7. https://doi.org/10.1007/978-3-031-12398-6_1DOIGOOGLE SCHOLAR
  5. 5.Andes, D.; Pascual, A.; Marchetti, O.. Antifungal therapeutic drug monitoring: Established and emerging indications. Antimicrobial Agents and Chemotherapy. 2009;53(1):24–34. https://doi.org/10.1128/AAC.00705-08DOIGOOGLE SCHOLAR
  6. 6.Ando, M.; Hirabatake, M.; Yasui, H.; Fukushima, S.; Sugioka, N.; Hashida, T.. A simplified method for therapeutic drug monitoring of mitotane by gas chromatography‐electron ionization‐mass spectrometry. Biomedical Chromatography. 2020;34(3):Article e4776. https://doi.org/10.1002/bmc.4776DOIGOOGLE SCHOLAR
  7. 7.Astúa, A.; Estevez, M.-C.; Luque, S.; Grau, S.; Sorlí, L.; Montero, M. et al. Therapeutic drug monitoring of amikacin and colistin in patients with multidrug-resistant Gram-negative infections using a portable plasmonic biosensor. Analytical Chemistry. 2025;97(23):12051–12059. https://doi.org/10.1021/acs.analchem.5c01234DOIGOOGLE SCHOLAR
  8. 8.Ates, H. C.; Roberts, J. A.; Lipman, J.; Cass, A. E. G.; Urban, G. A.; Dincer, C.. On-site therapeutic drug monitoring. Trends in Biotechnology. 2020;38(11):1262–1277. https://doi.org/10.1016/j.tibtech.2020.04.008DOIGOOGLE SCHOLAR
  9. 9.Avataneo, V.; D’Avolio, A.; Cusato, J.; Cantù, M.; De Nicolò, A.. LC-MS application for therapeutic drug monitoring in alternative matrices. Journal of Pharmaceutical and Biomedical Analysis. 2019;166:40–51. https://doi.org/10.1016/j.jpba.2018.12.040DOIGOOGLE SCHOLAR
  10. 10.Awad, A.; Rodríguez-Pombo, L.; Simón, P. E.; Álvarez, A. C.; Alvarez-Lorenzo, C.; Basit, A. W. et al. Smartphone biosensors for non-invasive drug monitoring in saliva. Biosensors. 2025;15(3):Article 163. https://doi.org/10.3390/bios15030163DOIGOOGLE SCHOLAR
  11. 11.Bekoe, S. O.; Asare-Nkansah, S.; Opuni, K. F. M.. Analytical techniques for therapeutic drug monitoring and clinical toxicology. Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;9–20. https://doi.org/10.1007/978-3-031-12398-6_2DOIGOOGLE SCHOLAR
  12. 12.Belcastro, L.; Arduini, F.. Recent advances in electrochemical paper-based analytical devices for drug analyses. Electrochimica Acta. 2025. https://doi.org/10.1016/j.electacta.2025.147084DOIGOOGLE SCHOLAR
  13. 13.Boccardi, D.; Marini, V.; Baiardi, G.; Cameran Caviglia, M.; Sacco, F.; Piras, F. et al. Ceftobiprole quantification in human serum by HPLC-UV to implement routine TDM in clinical practice. Clinica Chimica Acta. 2024;561:Article 119806. https://doi.org/10.1016/j.cca.2024.119806DOIGOOGLE SCHOLAR
  14. 14.Bodor, G. S.; Rands, A. J.. Quantitative LC-MS/MS method for the simultaneous measurement of six antiepileptics and pentobarbital in human serum. Clinical applications of mass spectrometry in drug analysis (Methods in Molecular Biology: Vol. 2737 pp. 43-54). Humana. 2024. https://doi.org/10.1007/978-1-0716-3541-4_4DOIGOOGLE SCHOLAR
  15. 15.Cafaro, A.; Barco, S.; Pigliasco, F.; Russo, C.; Mariani, M.; Mesini, A. et al. Therapeutic drug monitoring of glycopeptide antimicrobials: An overview of liquid chromatography-tandem mass spectrometry methods. Journal of Mass Spectrometry and Advances in the Clinical Lab. 2024;31:33–39. https://doi.org/10.1016/j.jmsacl.2023.12.004DOIGOOGLE SCHOLAR
  16. 16.Campbell, T. J.; Williams, K. M.. Therapeutic drug monitoring: Antiarrhythmic drugs. British Journal of Clinical Pharmacology. 1998;46(4):307–319. https://doi.org/10.1046/j.1365-2125.1998.t01-1-00768.xDOIGOOGLE SCHOLAR
  17. 17.Casian, M.; Hosu-Stancioiu, O.; Manea, I.; Suárez, D.; Díaz, N.; Lobo-Castañón, M. J. et al. Disposable electrochemical aptasensor for rapid and selective vancomycin detection in clinical samples: Bridging affinity selection, computational modeling and clinical validation. Analytica Chimica Acta. 2025;1374:Article 344519. https://doi.org/10.1016/j.aca.2025.344519DOIGOOGLE SCHOLAR
  18. 18.Cavalera, S.; Serra, T.; Abad-Fuentes, A.; Mercader, J. V.; Abad-Somovilla, A.; De Nardo, F. et al. Development and in-house validation of an enzyme-linked immunosorbent assay and a lateral flow immunoassay for the dosage of tenofovir in human saliva. Biosensors. 2023;13(6):Article 667. https://doi.org/10.3390/bios13060667DOIGOOGLE SCHOLAR
  19. 19.Chen, J.; Alberi, L.; Pétermann, Y.; Buclin, T.; Guidi, M.; Carrara, S.. Imatinib detection by memristive biosensors for therapeutic drug monitoring [Preprint]. SSRN. 2025. https://doi.org/10.2139/ssrn.4891234DOIGOOGLE SCHOLAR
  20. 20.Chiadmi, F.; Schlatter, J.. Determination and validation of a solid-phase extraction gas chromatography-mass spectrometry for the quantification of methadone and its principal metabolite in human plasma. Analytical Chemistry Insights. 2015;10:ACI.S25554. https://doi.org/10.4137/ACI.S25554DOIGOOGLE SCHOLAR
  21. 21.Clases, D.; Gonzalez De Vega, R.. Facets of ICP-MS and their potential in the medical sciences—Part 1: Fundamentals, stand-alone and hyphenated techniques. Analytical and Bioanalytical Chemistry. 2022;414(25):7337–7361. https://doi.org/10.1007/s00216-022-04266-0DOIGOOGLE SCHOLAR
  22. 22.Clases, D.; Gonzalez De Vega, R.. Facets of ICP-MS and their potential in the medical sciences—Part 2: Nanomedicine, immunochemistry, mass cytometry, and bioassays. Analytical and Bioanalytical Chemistry. 2022;414(25):7363–7383. https://doi.org/10.1007/s00216-022-04267-zDOIGOOGLE SCHOLAR
  23. 23.Dasgupta, A.. Limitations of immunoassays used for therapeutic drug monitoring of immunosuppressants. Personalized immunosuppression in transplantation: Role of drug monitoring and genetics. Elsevier. 2016;29–56. https://doi.org/10.1016/B978-0-12-800817-8.00003-2DOIGOOGLE SCHOLAR
  24. 24.Dasgupta, A.. Limitations of immunoassays used for therapeutic drug monitoring of immunosuppressants. Personalized immunosuppression in transplantation: Role of drug monitoring and genetics. Elsevier. 2016;29–56. https://doi.org/10.1016/B978-0-12-800817-8.00003-2DOIGOOGLE SCHOLAR
  25. 25.Davies Forsman, L.; Kim, H. Y.; Nguyen, T. A.; Alffenaar, J. W. C.. Salivary therapeutic drug monitoring of antimicrobial therapy: Feasible or futile?. Clinical Pharmacokinetics. 2024;63(3):269–278. https://doi.org/10.1007/s40262-024-01320-6DOIGOOGLE SCHOLAR
  26. 26.Davison, C.; Beste, D.; Bailey, M.; Felipe-Sotelo, M.. Expanding the boundaries of atomic spectroscopy at the single-cell level: Critical review of SP-ICP-MS, LIBS and LA-ICP-MS advances for the elemental analysis of tissues and single cells. Analytical and Bioanalytical Chemistry. 2023;415(28):6931–6950. https://doi.org/10.1007/s00216-023-04788-0DOIGOOGLE SCHOLAR
  27. 27.De Rose, D. U.; Cairoli, S.; Dionisi, M.; Santisi, A.; Massenzi, L.; Goffredo, B. M. et al. Therapeutic drug monitoring is a feasible tool to personalize drug administration in neonates using new techniques: An overview on the pharmacokinetics and pharmacodynamics in neonatal age. International Journal of Molecular Sciences. 2020;21(16):Article 5898. https://doi.org/10.3390/ijms21165898DOIGOOGLE SCHOLAR
  28. 28.Dejaco, A.; Paal, M.; Gruber, M.; Drettwan, D.; Marquardt, C.; Helou, M. et al. Therapeutic drug monitoring of piperacillin: Agreement between quantitative nuclear magnetic resonance spectroscopy and liquid chromatography with tandem mass spectrometry. Clinica Chimica Acta. 2025;577:Article 120448. https://doi.org/10.1016/j.cca.2025.120448DOIGOOGLE SCHOLAR
  29. 29.Descoeur, J.; Dupuy, A.-M.; Bargnoux, A.-S.; Cristol, J. P.; Mathieu, O.. Comparison of four immunoassays to an HPLC method for the therapeutic drug monitoring of methotrexate: Influence of the hydroxylated metabolite levels and impact on clinical threshold. Journal of Oncology Pharmacy Practice. 2022;28(1):55–63. https://doi.org/10.1177/10781552211010257DOIGOOGLE SCHOLAR
  30. 30.Dogan, C. Z.; Kara, E.; Pinar, A.; Demirkan, K.; Metan, G.. Therapeutic drug monitoring in patients treated with vancomycin: A single center, prospective, observational, real-world study. European Journal of Clinical Microbiology & Infectious Diseases. 2025. https://doi.org/10.1007/s10096-025-04912-3DOIGOOGLE SCHOLAR
  31. 31.Duan, H.; Peng, S.; He, S.; Tang, S.; Goda, K.; Wang, C. H. et al. Wearable electrochemical biosensors for advanced healthcare monitoring. Advanced Science. 2025;12(2):Article 2411433. https://doi.org/10.1002/advs.202411433DOIGOOGLE SCHOLAR
  32. 32.Edenya, O. O.; Atoe, K.. Therapeutic drugs monitoring and the consequences for health and well-being. Journal of Applied Sciences and Environmental Management. 2024;28(10):3143–3150.GOOGLE SCHOLAR
  33. 33.Fazeli-Bakhtiyari, R.; Panahi-Azar, V.; Sorouraddin, M. H.; Jouyban, A.. Determination of valproic acid in human plasma using dispersive liquid-liquid microextraction followed by gas chromatography-flame ionization detection. Iranian Journal of Basic Medical Sciences. 2015;18(10):979–985.GOOGLE SCHOLAR
  34. 34.Fishberger, G.; Natarelli, N.; Le, D.; Liaw, D.; Naz, A.; Ward, C.. Plasma therapeutic drug monitoring and clinical toxicology. Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;21–42. https://doi.org/10.1007/978-3-031-12398-6_3DOIGOOGLE SCHOLAR
  35. 35.Frumento, D.; Țălu. Biosensors for human cell analysis: Immune cell dynamics and diagnostic capabilities. Biomedical Materials & Devices. 2025. https://doi.org/10.1007/s44174-025-00490-0DOIGOOGLE SCHOLAR
  36. 36.Gando, Y.; Yasu, T.. A simple HPLC-UV method for ivosidenib determination in human plasma. Journal of Chromatographic Science. 2024;62(6):580–584. https://doi.org/10.1093/chromsci/bmad098DOIGOOGLE SCHOLAR
  37. 37.Garzón, V.; Bustos, R. H.; Pinacho, D. G.. Personalized medicine for antibiotics: The role of nanobiosensors in therapeutic drug monitoring. Journal of Personalized Medicine. 2020;10(4):Article 147. https://doi.org/10.3390/jpm10040147DOIGOOGLE SCHOLAR
  38. 38.Garzón, V.; Pinacho, D.; Bustos, R. H.; Garzón, G.; Bustamante, S.. Optical biosensors for therapeutic drug monitoring. Biosensors. 2019;9(4):Article 132. https://doi.org/10.3390/bios9040132DOIGOOGLE SCHOLAR
  39. 39.Gaspar, V. P.; Ibrahim, S.; Zahedi, R. P.; Borchers, C. H.. Utility, promise, and limitations of liquid chromatography‐mass spectrometry‐based therapeutic drug monitoring in precision medicine. Journal of Mass Spectrometry. 2021;56(11):Article e4788. https://doi.org/10.1002/jms.4788DOIGOOGLE SCHOLAR
  40. 40.Ghiculescu, R. A.. Therapeutic drug monitoring: Which drugs, why, when and how to do it. Australian Prescriber. 2008;31(2):42–44. https://doi.org/10.18773/austprescr.2008.030DOIGOOGLE SCHOLAR
  41. 41.Gqamana, P. P.; Zhang, Y. V.. Quantitative LC-MS/MS analysis of amphetamines and cocaine in human urine. Clinical applications of mass spectrometry in drug analysis (Methods in Molecular Biology: Vol. 2737 pp. 15-23). Humana. 2024. https://doi.org/10.1007/978-1-0716-3541-4_2DOIGOOGLE SCHOLAR
  42. 42.Hahn, R. Z.; Antunes, M. V.; Costa Arnhold, P.; Andriguetti, N. B.; Verza, S. G.; Linden, R.. Determination of topiramate in dried blood spots using single-quadrupole gas chromatography-mass spectrometry after flash methylation with trimethylanilinium hydroxide. Journal of Chromatography B. 2017;1046:131–137. https://doi.org/10.1016/j.jchromb.2017.01.025DOIGOOGLE SCHOLAR
  43. 43.Han, Y.; Li, F.; Yang, L.; Guo, X.; Dong, X.; Niu, M. et al. Immunocapture magnetic beads enhanced and ultrasensitive CRISPR-Cas13a-assisted electrochemical biosensor for rapid detection of SARS-CoV-2. Biosensors. 2023;13(6):Article 597. https://doi.org/10.3390/bios13060597DOIGOOGLE SCHOLAR
  44. 44.Hemdan, M.; Abuelhaded, K.; Shaker, A. A. S.; Ashour, M. M.; Abdelaziz, M. M.; Dahab, M. I.. Recent advances in nano-enhanced biosensors: Innovations in design, applications in healthcare, environmental monitoring, and food safety, and emerging research challenges. Sensing and Bio-Sensing Research. 2025;48:Article 100783. https://doi.org/10.1016/j.sbsr.2025.100783DOIGOOGLE SCHOLAR
  45. 45.Hemdan, M.; Ali, M. A.; Doghish, A. S.; Mageed, S. S. A.; Elazab, I. M.; Khalil, M. M.. Innovations in biosensor technologies for healthcare diagnostics and therapeutic drug monitoring: Applications, recent progress, and future research challenges. Sensors. 2024;24(16):Article 5143. https://doi.org/10.3390/s24165143DOIGOOGLE SCHOLAR
  46. 46.Hesham, N.; Hegazy, M. A.; Wagdy, H. A.. Therapeutic drug monitoring of six contraindicated/co-administered drugs by simple and green RP-HPLC-PDA; application to spiked human plasma. BMC Chemistry. 2024;18(1):Article 66. https://doi.org/10.1186/s13065-024-00995-2DOIGOOGLE SCHOLAR
  47. 47.Hilaire, M. R.; Gill, R. V.; Courtney, J. B.; Baburina, I.; Gardiner, J.; Milone, M. C. et al. Evaluation of a nanoparticle-based busulfan immunoassay for rapid analysis on routine clinical analyzers. Therapeutic Drug Monitoring. 2021;43(6):766–771. https://doi.org/10.1097/FTD.0000000000000923DOIGOOGLE SCHOLAR
  48. 48.Ibrahim, E. A.; Saad, S. S.; Hegazy, M. A.; Abdel Fattah, L. E.; Marzouk, H. M.. A novel stability-indicating chromatographic quantification of the antiparkinsonian drug safinamide in its pharmaceutical formulation employing HPTLC densitometry and ion-pair HPLC-DAD. BMC Chemistry. 2024;18(1):Article 212. https://doi.org/10.1186/s13065-024-01232-0DOIGOOGLE SCHOLAR
  49. 49.Ikeda, K.; Ikawa, K.; Yokoshige, S.; Yoshikawa, S.; Morikawa, N.. Gas chromatography-electron ionization-mass spectrometry quantitation of valproic acid and gabapentin, using dried plasma spots, for therapeutic drug monitoring in in‐home medical care. Biomedical Chromatography. 2014;28(12):1756–1762. https://doi.org/10.1002/bmc.3210DOIGOOGLE SCHOLAR
  50. 50.Jarnda, K. V.; Dai, H.; Ali, A.; Bestman, P. L.; Trafialek, J.; Roberts-Jarnda, G. P.. A review on optical biosensors for monitoring of uric acid and blood glucose using portable POCT devices: Status, challenges, and future horizons. Biosensors. 2025;15(4):Article 222. https://doi.org/10.3390/bios15040222DOIGOOGLE SCHOLAR
  51. 51.Jiang, X.; Qin, Y.; Lei, R.; Han, Y.; Yang, J.; Zhang, G. et al. A rapid and simple HPLC-MS/MS method for the therapeutic drug monitoring of six special-grade antimicrobials in pediatric patients. Heliyon. 2024;10(1):Article e24198. https://doi.org/10.1016/j.heliyon.2024.e24198DOIGOOGLE SCHOLAR
  52. 52.Johannessen, S. I.; Landmark, C. J.. Value of therapeutic drug monitoring in epilepsy. Expert Review of Neurotherapeutics. 2008;8(6):929–939. https://doi.org/10.1586/14737175.8.6.929DOIGOOGLE SCHOLAR
  53. 53.Johnston, A.; Holt, D. W.. Therapeutic drug monitoring of immunosuppressant drugs. British Journal of Clinical Pharmacology. 1999;47(4):339–350. https://doi.org/10.1046/j.1365-2125.1999.00911.xDOIGOOGLE SCHOLAR
  54. 54.Kaewpradit, S.; Yusakul, G.; Rojsitthisak, P.; Jantarat, C.. A simple and rapid HPLC-UV method for the determination of valproic acid in human plasma using microwave-assisted derivatization with phenylhydrazine hydrochloride. Heliyon. 2024;10(6):Article e27875. https://doi.org/10.1016/j.heliyon.2024.e27875DOIGOOGLE SCHOLAR
  55. 55.Kang, J. S.; Lee, M. H.. Overview of therapeutic drug monitoring. Korean Journal of Internal Medicine. 2009;24(1):1–10. https://doi.org/10.3904/kjim.2009.24.1.1DOIGOOGLE SCHOLAR
  56. 56.Kim, E. S.; Chon, H.; Kwon, Y.; Lee, M.; Kim, M. J.; Choe, Y. H.. Fluorescence-based lateral flow immunoassay for quantification of infliximab: Analytical and clinical performance evaluation. Therapeutic Drug Monitoring. 2024;46(4):460–467. https://doi.org/10.1097/FTD.0000000000001180DOIGOOGLE SCHOLAR
  57. 57.Kim, E. S.; Chon, H.; Kwon, Y.; Lee, M.; Kim, M. J.; Choe, Y. H.. Fluorescence-based lateral flow immunoassay for quantification of infliximab: Analytical and clinical performance evaluation. Therapeutic Drug Monitoring. 2024;46(4):460–467. https://doi.org/10.1097/FTD.0000000000001180DOIGOOGLE SCHOLAR
  58. 58.Kocur, A.; Czajkowska, A.; Moczulski, M.; Kot, B.; Rubik, J.; Pawiński, T.. Assessment of dried serum spots (DSS) and volumetric-absorptive microsampling (VAMS) techniques in therapeutic drug monitoring of (val)ganciclovir—comparative study in analytical and clinical practice. International Journal of Molecular Sciences. 2024;25(16):Article 8760. https://doi.org/10.3390/ijms25168760DOIGOOGLE SCHOLAR
  59. 59.Koren, G.. Therapeutic drug monitoring principles in the neonate. Clinical Chemistry. 1997;43(1):222–227. https://doi.org/10.1093/clinchem/43.1.222DOIGOOGLE SCHOLAR
  60. 60.Kozo, D.; Ross, M. W.; Jarrah, J.; Barrett, M.; Harney, R. L.; Courtney, J. B. et al. Rapid homogeneous immunoassay to quantify gemcitabine in plasma for therapeutic drug monitoring. Therapeutic Drug Monitoring. 2017;39(3):235–242. https://doi.org/10.1097/FTD.0000000000000388DOIGOOGLE SCHOLAR
  61. 61.Kudłacik-Kramarczyk, S.; Kieres, W.; Przybyłowicz, A.; Ziejewska, C.; Marczyk, J.; Krzan, M.. Recent advances in micro- and nano-enhanced intravascular biosensors for real-time monitoring, early disease diagnosis, and drug therapy monitoring. Sensors. 2025;25(15):Article 4855. https://doi.org/10.3390/s25154855DOIGOOGLE SCHOLAR
  62. 62.Kwaw, A.; Forkuo Donkor, A.; Buabeng, K. O.. Therapeutic drug monitoring and optimal pharmacotherapy with medicines of narrow therapeutic index. Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;87–94. https://doi.org/10.1007/978-3-031-12398-6_6DOIGOOGLE SCHOLAR
  63. 63.Lee, S.; Yang, S.; Shim, W. S.; Song, E.; Han, S.; Park, S. S. et al. Development and validation of an improved HPLC-MS/MS method for quantifying total and unbound lenalidomide in human plasma. Pharmaceutics. 2024;16(10):Article 1340. https://doi.org/10.3390/pharmaceutics16101340DOIGOOGLE SCHOLAR
  64. 64.Lehr, J.; Sattar, I.; McAuley, M.. Electrochemical sensors for diagnosis and monitoring of acute lymphoblastic leukaemia. Clinica Chimica Acta. 2026;578:Article 120522. https://doi.org/10.1016/j.cca.2025.120522DOIGOOGLE SCHOLAR
  65. 65.Liu, J.; Wu, L.; Zhu, Z.; Yan, C.; Zhang, Y.; Yang, T. et al. Dual‐responsive fluorescent probes: Advances in biosensing, diagnosis and therapy. Advanced Functional Materials. 2025. https://doi.org/10.1002/adfm.202415602DOIGOOGLE SCHOLAR
  66. 66.Macrelli, A.; Villa, N. S.; Lucotti, A.; Dellasega, D.; Ossi, P. M.; Tommasini, M.. Sensing the anti-epileptic drug perampanel with paper-based spinning SERS substrates. Molecules. 2021;27(1):Article 30. https://doi.org/10.3390/molecules27010030DOIGOOGLE SCHOLAR
  67. 67.Manubolu, K.; Gubbala, Y. D.; Methukumelli, T.. A short guide to clinical pharmacokinetics. Springer. 2024. https://doi.org/10.1007/978-981-97-4283-7DOIGOOGLE SCHOLAR
  68. 68.Mayol, B.; Qubbaj, I. Z.; Nava-Granados, J.; Vasquez, K.; Keene, S. T.; Sempionatto, J. R.. Aptamer and oligonucleotide-based biosensors for health applications. Biosensors. 2025;15(5):Article 277. https://doi.org/10.3390/bios15050277DOIGOOGLE SCHOLAR
  69. 69.Milosheska, D.; Roškar, R.. Simple HPLC-UV method for therapeutic drug monitoring of 12 antiepileptic drugs and their main metabolites in human plasma. Molecules. 2023;28(23):Article 7830. https://doi.org/10.3390/molecules28237830DOIGOOGLE SCHOLAR
  70. 70.Minichmayr, I. K.; Dreesen, E.; Centanni, M.; Wang, Z.; Hoffert, Y.; Friberg, L. E.. Model-informed precision dosing: State of the art and future perspectives. Advanced Drug Delivery Reviews. 2024;215:Article 115421. https://doi.org/10.1016/j.addr.2024.115421DOIGOOGLE SCHOLAR
  71. 71.MohammadiArani, M. E.; Zibaseresht, R.; Chamanara, M.; Mosaed, R.; MahboubiRabbani, M.; Bayanati, H.. A review of HPLC methods developed for quantitative analysis of carbamazepine in plasma samples. Journal of Chemical Reviews. 2023;6(4):588–607. https://doi.org/10.22034/jcr.2023.404941.1399DOIGOOGLE SCHOLAR
  72. 72.Mondal, H. S.; Ahmed, K. A.; Birbilis, N.; Hossain, M. Z.. Machine learning for detecting DNA attachment on SPR biosensor. Scientific Reports. 2023;13(1):Article 3742. https://doi.org/10.1038/s41598-023-30809-2DOIGOOGLE SCHOLAR
  73. 73.Mueller-Schoell, A.; Groenland, S. L.; Scherf-Clavel, O.; van Dyk, M.; Huisinga, W.; Michelet, R. et al. Therapeutic drug monitoring of oral targeted antineoplastic drugs. European Journal of Clinical Pharmacology. 2021;77(4):441–464. https://doi.org/10.1007/s00228-020-03036-8DOIGOOGLE SCHOLAR
  74. 74.Müller, T.; Gerlach, M.; Hefner, G.; Hiemke, C.; Jost, W. H.; Riederer, P.. Therapeutic drug monitoring in Parkinson’s disease. Journal of Neural Transmission. 2024;131(10):1247–1262. https://doi.org/10.1007/s00702-024-02828-5DOIGOOGLE SCHOLAR
  75. 75.Nakajima, M.; Sato, S.; Yamato, S.; Shimada, K.; Kitagawa, S.; Honda, A. et al. Assessment of tear concentrations on therapeutic drug monitoring. III. Determination of theophylline in tears by gas chromatography/mass spectrometry with electron ionization mode. Drug Metabolism and Pharmacokinetics. 2003;18(2):139–145. https://doi.org/10.2133/dmpk.18.139DOIGOOGLE SCHOLAR
  76. 76.Nakajima, M.; Yamato, S.; Shimada, K.; Sato, S.; Kitagawa, S.; Honda, A. et al. Assessment of drug concentrations in tears in therapeutic drug monitoring: I. Determination of valproic acid in tears by gas chromatography/mass spectrometry with EC/NCI mode. Therapeutic Drug Monitoring. 2000;22(6):716–722. https://doi.org/10.1097/00007691-200012000-00015DOIGOOGLE SCHOLAR
  77. 77.Nassar, M. W.; Serag, A.; Hasan, M. A.; Kamel, M.. Development and validation of a RP-HPLC method for simultaneous determination of five COVID-19 antiviral drugs in pharmaceutical formulations. Scientific Reports. 2025;15(1):Article 25470. https://doi.org/10.1038/s41598-025-00012-3DOIGOOGLE SCHOLAR
  78. 78.Nikolaou, P.; Papoutsis, I.; Spiliopoulou, C.; Voudris, C.; Athanaselis, S.. A fully validated method for the determination of lacosamide in human plasma using gas chromatography with mass spectrometry: Application for therapeutic drug monitoring. Journal of Separation Science. 2015;38(2):260–266. https://doi.org/10.1002/jssc.201400882DOIGOOGLE SCHOLAR
  79. 79.Paci, A.; Veal, G.; Bardin, C.; Levéque, D.; Widmer, N.; Beijnen, J. et al. Review of therapeutic drug monitoring of anticancer drugs part 1—cytotoxics. European Journal of Cancer. 2014;50(12):2010–2019. https://doi.org/10.1016/j.ejca.2014.04.014DOIGOOGLE SCHOLAR
  80. 80.Patsalos, P. N.; Berry, D. J.; Bourgeois, B. F. D.; Cloyd, J. C.; Glauser, T. A.; Johannessen, S. I. et al. Antiepileptic drugs—best practice guidelines for therapeutic drug monitoring: A position paper by the subcommission on therapeutic drug monitoring, ILAE Commission on Therapeutic Strategies. Epilepsia. 2008;49(7):1239–1276. https://doi.org/10.1111/j.1528-1167.2008.01561.xDOIGOOGLE SCHOLAR
  81. 81.Peloquin, C. A.. Therapeutic drug monitoring in the treatment of tuberculosis. Drugs. 2002;62(15):2169–2183. https://doi.org/10.2165/00003495-200262150-00001DOIGOOGLE SCHOLAR
  82. 82.Pigliasco, F.; Cafaro, A.; Barco, S.; Stella, M.; Mattioli, F.; Riva, A. et al. Innovative LC-MS/MS method for therapeutic drug monitoring of fenfluramine and cannabidiol in the plasma of pediatric patients with epilepsy. Journal of Pharmaceutical and Biomedical Analysis. 2024;245:Article 116174. https://doi.org/10.1016/j.jpba.2024.116174DOIGOOGLE SCHOLAR
  83. 83.Puiu, M.; Bala, C.. SPR and SPR imaging: Recent trends in developing nanodevices for detection and real-time monitoring of biomolecular events. Sensors. 2016;16(6):Article 870. https://doi.org/10.3390/s16060870DOIGOOGLE SCHOLAR
  84. 84.Qi, Z.; Zhang, S.; Li, J.; Qian, S.; Song, D.; Ye, X.. LC-MS/MS analysis of five antibiotics in dried blood spots for therapeutic drug monitoring of ICU patients. Journal of Chromatography B. 2025;1263:Article 124699. https://doi.org/10.1016/j.jchromb.2025.124699DOIGOOGLE SCHOLAR
  85. 85.Qi, Z.; Zhang, S.; Li, J.; Qian, S.; Song, D.; Ye, X.. LC-MS/MS analysis of five antibiotics in dried blood spots for therapeutic drug monitoring of ICU patients. Journal of Chromatography B. 2025;1263:Article 124699. https://doi.org/10.1016/j.jchromb.2025.124699DOIGOOGLE SCHOLAR
  86. 86.Radovanovic, M.; Day, R. O.; Jones, G. D. R.; Galettis, P.; Norris, R. L. G.. LC-MS/MS method for simultaneous quantification of ten antibiotics in human plasma for routine therapeutic drug monitoring. Journal of Mass Spectrometry and Advances in the Clinical Lab. 2022;26:48–59. https://doi.org/10.1016/j.jmsacl.2022.11.001DOIGOOGLE SCHOLAR
  87. 87.Ranjan, K.; Barar, B.; Prasad, M.; Prasad, G.. Application of artificial intelligence in electrochemical diagnostics for human health. Discovery Electrochemistry. 2025;2(1):Article 27. https://doi.org/10.1007/s44302-025-00027-5DOIGOOGLE SCHOLAR
  88. 88.Resztak, M.; Czyrski, A.; Sobiak, J.. Saliva as a matrix for therapeutic drug monitoring and disease biomarkers in children and adolescents. Pharmacological Reports. 2025;77(4):921–961. https://doi.org/10.1007/s43440-025-00732-7DOIGOOGLE SCHOLAR
  89. 89.Roberts, J. A.; Norris, R.; Paterson, D. L.; Martin, J. H.. Therapeutic drug monitoring of antimicrobials. British Journal of Clinical Pharmacology. 2012;73(1):27–36. https://doi.org/10.1111/j.1365-2125.2011.04080.xDOIGOOGLE SCHOLAR
  90. 90.Saikia, S.; Prajapati, J. B.; Prajapati, B. G.; Padma, V. V.; Pathak, Y. V.. The role of artificial intelligence in therapeutic drug monitoring and clinical toxicity. Recent advances in therapeutic drug monitoring and clinical toxicology. Springer. 2022;67–85. https://doi.org/10.1007/978-3-031-12398-6_5DOIGOOGLE SCHOLAR
  91. 91.Sedighi, A.; Kou, T.; Huang, H.; Li, Y.. Noninvasive on-skin biosensors for monitoring diabetes mellitus. Nano-Micro Letters. 2026;18(1):Article 16. https://doi.org/10.1007/s40820-025-01843-9DOIGOOGLE SCHOLAR
  92. 92.Seger, C.; Salzmann, L.. After another decade: LC-MS/MS became routine in clinical diagnostics. Clinical Biochemistry. 2020;82:2–11. https://doi.org/10.1016/j.clinbiochem.2020.03.004DOIGOOGLE SCHOLAR
  93. 93.Sheng, Y.; Zhang, T.; Zhang, S.; Johnston, M.; Zheng, X.; Shan, Y. et al. A CRISPR/Cas13a-powered catalytic electrochemical biosensor for successive and highly sensitive RNA diagnostics. Biosensors and Bioelectronics. 2021;178:Article 113027. https://doi.org/10.1016/j.bios.2021.113027DOIGOOGLE SCHOLAR
  94. 94.Soumma, S. B.; Mamun, A.; Ghasemzadeh, H.. AI-powered wearable sensors for health monitoring and clinical decision making. Computer Science and Mathematics. 2025.GOOGLE SCHOLAR
  95. 95.Spezzani, E.; Capelli, L.; Di Lena, D.; Chamorro-Garcia, A.; Ippodrino, R.; Porchetta, A. et al. MARPLE: A proximity-triggered CRISPR-Cas13 platform for ultrasensitive antibody detection. Chemistry. 2025. https://doi.org/10.1002/chem.202501234DOIGOOGLE SCHOLAR
  96. 96.Thu, N. Q.; Tien, N. T. N.; Yen, N. T. H.; Duong, T. H.; Long, N. P.; Nguyen, H. T.. Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management. Journal of Pharmaceutical Analysis. 2024;14(1):16–38. https://doi.org/10.1016/j.jpha.2023.100939DOIGOOGLE SCHOLAR
  97. 97.Timerbaev, A. R.. Application of ICP-MS to the development of metal-based drugs and diagnostic agents: Where do we stand?. Journal of Analytical Atomic Spectrometry. 2021;36(2):254–266. https://doi.org/10.1039/D0JA00396ADOIGOOGLE SCHOLAR
  98. 98.Tu, J.; Yeom, J.; Ulloa, J. C.; Solomon, S. A.; Min, J.; Heng, W. et al. Stressomic: A wearable microfluidic biosensor for dynamic profiling of multiple stress hormones in sweat. Science Advances. 2025. https://doi.org/10.1126/sciadv.adn1234DOIGOOGLE SCHOLAR
  99. 99.Tuzimski, T.; Petruczynik, A.. Review of chromatographic methods coupled with modern detection techniques applied in the therapeutic drugs monitoring (TDM). Molecules. 2020;25(17):Article 4026. https://doi.org/10.3390/molecules25174026DOIGOOGLE SCHOLAR
  100. 100.Van Aalen, E. A.; De Vries, I. R.; Hanckmann, E. T. L.; Stevens, J. R. F.; Romagnoli, T. R.; Derijks, L. J. J.. Point-of-care therapeutic drug monitoring of tumour necrosis factor- inhibitors using a single step immunoassay. Sensors and Diagnostics. 2023;2(6):1492–1500. https://doi.org/10.1039/D3SD00152BDOIGOOGLE SCHOLAR
  101. 101.Vanitha Madhuri, T.; Pandreka, Manasa Krishna; Geetha Gayatri, B.; Yamini, M.; Abhishek, G.; Gope., Edward Raju. Advances in high-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC): Review article. Journal of Pharmaceutical and Industrial Research. 2024;2(6):39–46. https://doi.org/10.5281/zenodo.13977009DOIGOOGLE SCHOLAR
  102. 102.Vermeire, S.; Dreesen, E.; Papamichael, K.; Dubinsky, M. C.. How, when, and for whom should we perform therapeutic drug monitoring?. Clinical Gastroenterology and Hepatology. 2020;18(6):1291–1299. https://doi.org/10.1016/j.cgh.2019.09.041DOIGOOGLE SCHOLAR
  103. 103.Vo, D. K.; Trinh, K. T. L.. Advances in wearable biosensors for healthcare: Current trends, applications, and future perspectives. Biosensors. 2024;14(11):Article 560. https://doi.org/10.3390/bios14110560DOIGOOGLE SCHOLAR
  104. 104.Vroemen, W. H. M.; Agata, S. S.; Van Beers, J. J. B. C.; Damoiseaux, J. G. M. C.. Therapeutic drug monitoring of infliximab and adalimumab through concentration and anti-drug antibodies assessment; Comparison of Sanquin Diagnostics and Theradiag assays. Antibodies. 2024;13(3):Article 73. https://doi.org/10.3390/antib13030073DOIGOOGLE SCHOLAR
  105. 105.Wang, C.; Zeng, H. S.; Liu, K. X.; Lin, Y. N.; Yang, H.; Xie, X. Y. et al. Biosensor-based therapy powered by synthetic biology. Smart Materials in Medicine. 2023;4:212–224. https://doi.org/10.1016/j.smaim.2022.10.003DOIGOOGLE SCHOLAR
  106. 106.Wang, X.; Qin, W.; Chen, W.; Liu, H.; Zhang, D.; Zhang, X.. Validation of a novel UPLC HRMS method for human whole blood cyclosporine and comparison with a CMIA immunoassay. Experimental and Therapeutic Medicine. 2021;21(3):Article 191. https://doi.org/10.3892/etm.2021.9705DOIGOOGLE SCHOLAR
  107. 107.Wang, Y.; Diao, Z.; Qu, Y.; Fan, K.; Feng, C.; Lv, B. et al. LC-MS/MS quantification of omadacycline in human plasma for therapeutic drug monitoring: Method development and clinical application. Scientific Reports. 2025;15(1):Article 27728. https://doi.org/10.1038/s41598-025-13396-3DOIGOOGLE SCHOLAR
  108. 108.Widmer, N.; Bardin, C.; Chatelut, E.; Paci, A.; Beijnen, J.; Levéque, D. et al. Review of therapeutic drug monitoring of anticancer drugs part two—targeted therapies. European Journal of Cancer. 2014;50(12):2020–2036. https://doi.org/10.1016/j.ejca.2014.04.015DOIGOOGLE SCHOLAR
  109. 109.Winckelmann, A.; Morcillo, D.; Richter, S.; Recknagel, S.; Riedel, J.; Vogl, J. et al. Determination of lithium in human serum by isotope dilution atomic absorption spectrometry. Analytical and Bioanalytical Chemistry. 2022;414(1):251–256. https://doi.org/10.1007/s00216-021-03740-1DOIGOOGLE SCHOLAR
  110. 110.Wolken, J. K.; Cao, W.; Cui, M.; Jin, Z.. Therapeutic drug monitoring of mycophenolic acid and azole antifungals on two distinct LC-MS/MS instruments. Journal of Mass Spectrometry and Advances in the Clinical Lab. 2024;33:7–13. https://doi.org/10.1016/j.jmsacl.2024.06.002DOIGOOGLE SCHOLAR
  111. 111.Yao, M.; Fang, B.; Yang, J.; Wang, S.; Chen, F.. Simultaneous determination of 14 analgesics in postoperative analgesic solution by HPLC-DAD and LC-MS/MS. BMC Chemistry. 2024;18(1):Article 10. https://doi.org/10.1186/s13065-023-01026-2DOIGOOGLE SCHOLAR
  112. 112.Zayed, A. L.; Hadieh, M.; Al Hroot, J.; Hameedat, F.; Jaber, S. A.. Development of a rapid HPLC-fluorescence method for monitoring warfarin metabolites formation: In vitro studies for evaluating the effect of piperine on warfarin metabolism and plasma coagulation. Heliyon. 2024;10(10):Article e31266. https://doi.org/10.1016/j.heliyon.2024.e31266DOIGOOGLE SCHOLAR
  113. 113.Zhang, Y. V.; Garg, U.. Mass spectrometry in clinical laboratory: Applications in therapeutic drug monitoring and toxicology. Clinical applications of mass spectrometry in drug analysis (Methods in Molecular Biology: Vol. 2737 pp. 1-13). Humana. 2024. https://doi.org/10.1007/978-1-0716-3541-4_1DOIGOOGLE SCHOLAR

Cite this article

SELECT FORMAT

Reddy, K. P., Nizamuddin, K., Chinthaginjala, H., Bhargav, E., Kartikeya, K., Kousar, K., Kurmila, C., & Repollu, M. (2026). Biosensor-Based Innovations in Therapeutic Drug Monitoring: Bridging Traditional Methods and Precision Medicine. International Journal of Pharmaceutical Investigation, 16(4), 1216–1230. https://doi.org/10.5530/ijpi.20260148