Biosensors and Nanotheranostics

1. Introduction

For most of the twentieth century, therapeutic and diagnostic practice rested on a simple premise: identify a disease, select an agent known to act against it, and administer that agent at a dose established in a reference population. The premise has served medicine well, but its limitations have become difficult to ignore. Many drugs are poorly soluble in water, are cleared from the circulation within minutes to hours, and distribute indiscriminately into healthy tissues, where they cause dose-limiting toxicity (Parvin et al., 2025; Tivay et al., 2025). Equally important, conventional interventions are largely reactive and uniform. A fixed dose is given regardless of the physiological fluctuations that occur within a single patient over a day, and regardless of the molecular heterogeneity that distinguishes one patient's tumor or infection from another's (Shakitha and Devi, 2025; Umer & Arshed, 2025). Biological barriers such as the vascular endothelium, dense extracellular matrix and the blood–brain barrier compound the problem by limiting how much of an administered dose actually reaches its target (Tivay et al., 2025).

Nanotechnology entered this landscape with a different set of tools. When matter is engineered within the 1–100 nm range, properties emerge that are absent in the bulk: surface-area-to-volume ratios rise steeply, quantum confinement alters optical and electronic behavior, and surface chemistry can be tuned with near-molecular precision (Fahim et al., 2025; Mahmoud et al., 2026). These attributes, together with distinctive optical, magnetic and catalytic properties, allow nanomaterials to interact with cells and biomolecules on their own length scale (Mahmoud et al., 2026; Umer & Arshed, 2025). It is this capacity for precise interaction that has made nanotechnology central to the broader movement toward precision healthcare, in which diagnosis and treatment are tailored to the biology of the individual (Fahim et al., 2025; Parvin et al., 2025).

The first generation of nanomedicines, including conventional liposomes, polymeric nanoparticles and simple inorganic particles, demonstrated that nanoscale carriers could improve drug solubility and circulation time. Most relied on passive accumulation in tumors through the enhanced permeability and retention (EPR) effect (Parvin et al., 2025). Clinical experience has since shown that passive targeting is variable and often modest. Static carriers tend to leak their cargo prematurely or release it in an uncontrolled burst; they acquire a corona of adsorbed plasma proteins that masks targeting ligands; and they rarely discriminate between diseased and healthy cells (Shakitha and Devi, 2025; Mahmoud et al., 2026). These shortcomings prompted a shift in design philosophy, from carriers that simply hold a drug to "smart" nanomaterials that perceive specific features of their microenvironment, or respond to an externally applied signal, and alter their behavior accordingly (Mahmoud et al., 2026; Parvin et al., 2025).

Two families of triggers underpin this responsiveness. Endogenous triggers exploit the biochemical signatures of disease itself: the mildly acidic extracellular pH of solid tumors (about 6.5–6.8) and the more acidic endosomes and lysosomes (pH 4.5–6.0), elevated concentrations of reactive oxygen species (ROS), overexpressed enzymes such as matrix metalloproteinases, and the steep glutathione (GSH) gradient between the cytosol and the extracellular space (Cinti et al., 2024; Parvin et al., 2025; Tivay et al., 2025). In response, a well-designed carrier may cleave a chemical bond, reverse its surface charge or undergo a phase transition, releasing its payload at the diseased site while largely sparing healthy tissue (Parvin et al., 2025). Exogenous triggers, by contrast, are applied from outside the body. Near-infrared (NIR) light, alternating magnetic fields, ultrasound and localized heating give the clinician spatial and temporal control over drug release, photothermal and photodynamic therapy, or hyperthermia (Shakitha and Devi, 2025; Parvin et al., 2025). The same responsive principles have been extended beyond cancer, for example to antibacterial "nano-shields" that act selectively at sites of infection (Ioannou et al., 2024).

Responsiveness is only part of the story. In diagnostics, nanomaterials such as two-dimensional MXenes, graphene derivatives, metal–organic frameworks (MOFs), quantum dots and plasmonic noble-metal particles now serve as transducer layers and recognition interfaces in highly sensitive biosensors (Shakitha and Devi, 2025; Sharma, 2026). Where diagnostic and therapeutic functions are built into one platform, the result is theranostics: a single nanostructure that can visualize a lesion, track its own biodistribution, release a drug on demand and report on how the tissue responds (Parvin et al., 2025; Umer & Arshed, 2025). More recently, the field has begun to converge with artificial intelligence (AI), digital health and soft bioelectronics. Machine-learning models are being used to predict pharmacokinetics and cellular uptake and to optimize particle size, charge and geometry before synthesis, while nanosensors embedded in skin patches, microneedle arrays and point-of-care devices stream physiological data to cloud-based analytics (Parvin et al., 2025; Sharma, 2026; Tivay et al., 2025; Umer & Arshed, 2025).

Despite this momentum, the path from bench to bedside remains narrow. Manufacturing complex nanoplatforms at scale under Good Manufacturing Practice (GMP), achieving batch-to-batch reproducibility, preventing biofouling-induced signal drift, establishing long-term safety and satisfying regulators whose frameworks were written for conventional drugs are all unresolved challenges (Shakitha and Devi, 2025; Parvin et al., 2025; Tivay et al., 2025). Addressing them requires a critical, cross-disciplinary appraisal that draws together materials chemistry, pharmacology, oncology, immunology and bioengineering (Shakitha and Devi, 2025; Parvin et al., 2025).

The present review was undertaken with that appraisal in mind. Its aims are fivefold: (i) to classify the principal classes of smart nanomaterials (0D, 1D, 2D and 3D architectures, including MXenes, graphene derivatives, MOFs, polymeric nanogels, lipid carriers and plasmonic hybrids) according to composition, dimensionality and surface functionalization (Shakitha and Devi, 2025; Mahmoud et al., 2026; Parvin et al., 2025; Umer & Arshed, 2025); (ii) to explain how endogenous and exogenous trigger mechanisms produce site-specific drug release and targeted immunomodulation (Cinti et al., 2024; Ioannou et al., 2024; Mahmoud et al., 2026; Parvin et al., 2025); (iii) to survey advances in ultrasensitive biosensing, real-time bioimaging, closed-loop theranostic implants and regenerative scaffolds (Shakitha and Devi, 2025; Parvin et al., 2025; Sharma, 2026); (iv) to assess how AI-driven design and wearable technologies are reshaping the field (Parvin et al., 2025; Sharma, 2026; Tivay et al., 2025); and (v) to analyze the translational bottlenecks of biocompatibility, immunogenicity, biofouling, manufacturing and regulation, and to outline a realistic roadmap for clinical adoption (Shakitha and Devi, 2025; Parvin et al., 2025; Tivay et al., 2025). 

2. Smart Nanomaterials for Stimuli-Responsive Delivery, Theranostics, and AI-Enabled Precision Diagnostics

2.1 Architectural Taxonomy and Functional Classification

2.1.1 Physicochemical foundations of nanoscale design

Nanomedicine depends, at its core, on the deliberate design and functionalization of nanoscale architectures (Shakitha and Devi, 2025; Parvin et al., 2025; Umer & Arshed, 2025). Materials engineered within the 1–100 nm range differ from their bulk counterparts in ways that matter clinically: they present very large surface areas per unit volume, show quantum confinement effects, transfer charge rapidly and can be tuned to resonate with specific wavelengths of light (Shakitha and Devi, 2025; Fahim et al., 2025; Parvin et al., 2025). A useful way to organize the field is by dimensionality, which runs from zero-dimensional (0D) quantum dots and metal clusters, through one-dimensional (1D) nanotubes and nanorods and two-dimensional (2D) nanosheets, to three-dimensional (3D) porous hybrid matrices (Shakitha and Devi, 2025; Fahim et al., 2025; Umer & Arshed, 2025). This taxonomy, together with the surface-engineering layer common to all classes, is illustrated in Figure 1 and detailed in Table 1.

2.1.2 Zero-dimensional quantum and plasmonic structures

Semiconductor quantum dots (for example CdSe and InP) and carbon dots offer narrow, size-tunable emission, broad absorption and excellent photostability. These properties make them well suited to Förster resonance energy transfer (FRET) assays and multiplexed bioimaging (Shakitha and Devi, 2025; Parvin et al., 2025). Noble-metal nanoparticles, particularly gold nanospheres, nanorods and nanostars, behave differently: their localized surface plasmon resonance (LSPR) concentrates electromagnetic fields into nanometer-scale "hot spots" that can amplify Raman scattering by 10⁶- to 10¹¹-fold in surface-enhanced Raman spectroscopy (SERS) (Shakitha and Devi, 2025; Mahmoud et al., 2026). Gold nanoparticles have a long biomedical history, and their ease of synthesis, thiol chemistry and photothermal behavior continue to make them a reference material for diagnostics and therapy (Milan et al., 2022). Copper and copper-based nanoparticles are attracting comparable interest for their antimicrobial, catalytic and imaging properties, though their redox activity raises distinct toxicity questions (Woźniak-Budych et al., 2023).

2.1.3 One-dimensional nanotubes and nanowires

Single- and multi-walled carbon nanotubes (CNTs) provide fast electron-transport pathways, mechanical flexibility and high drug-loading capacity. Their hydrophobic surfaces, however, must be functionalized, typically by oxidation to carboxylic groups or by non-covalent π–π wrapping with amphiphilic polymers, to improve dispersibility and limit immunogenic responses (Parvin et al., 2025). Inorganic semiconductor nanowires share many of these electrical advantages and are increasingly used as conductive bio-electrode interfaces (Shakitha and Devi, 2025).

2.1.4 Two-dimensional graphene derivatives and MXenes

Two-dimensional nanomaterials have become central to both drug delivery and diagnostic bio-interfaces (Cinti et al., 2024; Parvin et al., 2025). Graphene oxide (GO) and reduced graphene oxide (rGO) possess extended π-

Table 1. Architectural taxonomy, surface engineering and biomedical performance of the principal classes of smart nanomaterials, organized by structural dimensionality. The table classifies smart nanomaterials from zero-dimensional (0D) to three-dimensional (3D) and biomimetic architectures. For each class it lists chemical composition and structural attributes, the surface-modification strategies used to confer stability, targeting or biocompatibility, the physicochemical properties that underlie function, and representative biomedical applications with reported analytical or therapeutic figures of merit. Values are reproduced as reported in the cited sources and were obtained under heterogeneous experimental conditions; they indicate attainable performance rather than directly comparable benchmarks. Abbreviations: QDs, quantum dots; CDs, carbon dots; LSPR, localized surface plasmon resonance; SERS, surface-enhanced Raman spectroscopy; CNTs, carbon nanotubes; GO/rGO, graphene oxide/reduced graphene oxide; MOFs, metal–organic frameworks; MPS, mononuclear phagocyte system; LOD, limit of detection.

Table 2. Endogenous and exogenous stimulus-responsive mechanisms for smart drug delivery, with responsive chemistries, release dynamics and clinical applications. Each row describes one trigger class, specifying the physiological or physical gradient exploited, the chemical linkers or material components that sense it, the resulting activation or release behavior and the main biomedical applications. Endogenous triggers (pH, redox/glutathione, reactive oxygen species and enzymes) arise from the disease microenvironment, whereas exogenous triggers (light, magnetic fields and ultrasound) are applied externally to give spatiotemporal control. Release percentages and kinetics were measured in the cited studies under specified in vitro conditions and may differ in vivo. Abbreviations: GSH, glutathione; ROS, reactive oxygen species; MMP, matrix metalloproteinase; NIR, near-infrared; PTT/PDT, photothermal/photodynamic therapy; SPIONs, superparamagnetic iron oxide nanoparticles; AMF, alternating magnetic field; BBB, blood–brain barrier.

conjugated networks that allow dense drug loading through π–π stacking and hydrophobic interactions (Shakitha and Devi, 2025; Parvin et al., 2025). Graphene oxide also forms a versatile scaffold for electrochemical sensing: gold nanoparticles grown on choline-chloride-functionalized GO under ultrasound, for example, yielded a sensor capable of quantifying the drug meloxicam in human plasma (Bahrani et al., 2018). More recently, 2D transition-metal carbides, nitrides and carbonitrides (MXenes, notably Ti₃C₂Tₓ) have gained prominence because they combine metallic conductivity, hydrophilic surface terminations (–OH, –F, –O) and low charge-transfer resistance (Shakitha and Devi, 2025; Cinti et al., 2024). Delaminated MXenes offer surface areas of up to 235.6 m²/g, enough to conjugate enzymes, antibodies and aptamers covalently or electrostatically without loss of biological activity (Shakitha and Devi, 2025). Decoration with silver further endows MXenes with peroxidase-like catalytic activity, a property exploited for both antibacterial action and cholesterol sensing (Chen et al., 2024).

2.1.5 Three-dimensional frameworks, polymeric nanogels and biomimetic carriers

MOFs and polymeric nanogels represent the main 3D hybrid platforms (Shakitha and Devi, 2025; Parvin et al., 2025). MOFs, built from metal ions coordinated to organic linkers, have permanent porosity and internal surface areas that can exceed 3000 m²/g (Shakitha and Devi, 2025; Parvin et al., 2025). The chromium framework MIL-101(Cr) has been reported to load up to 1.4 g of drug per gram of framework, releasing it when host–guest coordination is disrupted (Shakitha and Devi, 2025). Polymeric systems such as poly (lactic-co-glycolic acid) (PLGA), poly(N-isopropylacrylamide) (PNIPAM) and zwitterionic copolymers provide biodegradable matrices whose degradation kinetics can be tuned through copolymer ratio and crosslink density (Parvin et al., 2025).

A persistent obstacle for all of these carriers is recognition and removal by the mononuclear phagocyte system. One increasingly popular response is to camouflage nanoparticles with membranes harvested from erythrocytes, macrophages, platelets or cancer cells (Guan et al., 2024; Parvin et al., 2025). Because native membrane proteins such as the CD47 "don't eat me" signal are preserved, these biomimetic carriers circulate longer and, in the case of cancer-cell membranes, show homologous homing toward primary tumors and metastatic niches (Guan et al., 2024; Parvin et al., 2025). The membranes can be further engineered by lipid insertion, membrane fusion, metabolic glycan labeling or genetic modification of surface proteins (Guan et al., 2024).

2.2 Stimuli-Responsive Dynamics and Precision Drug Delivery

2.2.1 Conceptual basis of stimulus-gated release

What makes a nanomaterial "smart" is its ability to change behavior in response to an environmental cue, and thereby to regulate drug release in space and time (Parvin et al., 2025). The relevant triggers fall into two groups, endogenous (generated by the pathology) and exogenous (applied from outside), which are summarized schematically in Figure 2 and in detail in Table 2 (Cinti et al., 2024; Parvin et al., 2025). The same logic has been applied to infection, where responsive antibacterial nanomaterials release their payload only in the acidic, enzyme-rich environment of a bacterial focus (Ioannou et al., 2024).

2.2.2 pH-responsive systems

Blood and healthy interstitial fluid are held close to pH 7.4, whereas the extracellular space of solid tumors is mildly acidic (pH 6.5–6.8) because of glycolytic metabolism (the Warburg effect), and endosomes and lysosomes reach pH 4.5–6.0 (Parvin et al., 2025; Tivay et al., 2025). Carriers built with acid-labile linkages (hydrazone, acetal, imine or cis-aconityl bonds) or with charge-reversible zwitterionic polymers exploit this gradient: under acidic conditions the linkers are protonated or hydrolyzed, the carrier destabilizes and the drug is released locally (Parvin et al., 2025). Doxorubicin-loaded micelles with hydrazone linkages illustrate the effect, releasing more than 80% of their cargo at pH 5.5 but less than 10% at pH 7.4 (Parvin et al., 2025).

2.2.3 Redox- and glutathione-responsive systems

The cytosol contains reduced glutathione at 2–10 mM, roughly 100–1000 times the extracellular level of about 2–20 µM (Parvin et al., 2025). Polymeric carriers and mesoporous silica particles crosslinked with disulfide bonds (–S–S–) therefore remain intact in the circulation but are cleaved rapidly once inside the cell, delivering their payload to the cytosol or nucleus (Cinti et al., 2024; Parvin et al., 2025).

2.2.4 Enzyme- and ROS-responsive systems

Figure 1. Architectural taxonomy of smart nanomaterials for precision healthcare, organized by structural dimensionality around a shared surface-engineering layer. The central node summarizes the defining nanoscale attributes (1–100 nm size, high surface-to-volume ratio, quantum confinement and tunable surface chemistry). The four surrounding panels group materials as 0D quantum and plasmonic dots, 1D nanotubes and nanowires, 2D graphene derivatives and MXenes, and 3D or hybrid architectures (MOFs, polymeric nanogels and cell-membrane-cloaked carriers), each with representative compositions, key properties and principal applications. The central box lists surface-engineering strategies common to all classes. Quantitative values (e.g., SERS enhancement, MXene and MOF surface areas, MIL-101 loading) are taken from the sources summarized in Table 1.

Figure 2. Stimulus-responsive release mechanisms of smart nanocarriers: (a) endogenous, pathology-driven triggers and (b) exogenous, externally applied triggers. Panel (a) shows the four principal endogenous gradients (pH, glutathione redox potential, overexpressed enzymes and reactive oxygen species), with the physiological ranges involved and the responsive chemistries that sense them. Panel (b) shows the exogenous triggers (near-infrared light for photothermal and photodynamic therapy, alternating magnetic fields and ultrasound) with representative materials and mechanisms. The green box summarizes the shared outcome: bond cleavage, phase transition or disassembly leading to site-specific, on-demand release with reduced off-target exposure. Details and references are provided in Table 2.

Diseased tissues often overexpress proteases such as matrix metalloproteinases (MMP-2 and MMP-9), cathepsins and esterases (Parvin et al., 2025; Tivay et al., 2025). Carriers carrying MMP-cleavable peptide linkers stay sterically shielded in the blood but shed their stealth coating in the tumor, exposing targeting ligands or cell-penetrating peptides (Guan et al., 2024; Parvin et al., 2025). Inflamed and ischemic tissues, meanwhile, generate excess ROS such as hydrogen peroxide (H₂O₂) and hydroxyl radicals (El-Morsy et al., 2026; Parvin et al., 2025). Nanoplatforms containing thioketal, boronic ester or thioether linkages degrade selectively under this oxidative stress, so that drug release and local ROS scavenging occur together (El-Morsy et al., 2026; Parvin et al., 2025). In asthma, for instance, redox-responsive nanozymes have been proposed as agents that both restore redox balance and modulate airway immune responses (El-Morsy et al., 2026).

2.2.5 Exogenous light, magnetic and ultrasound triggers

Exogenous triggers let clinicians control therapeutic activity non-invasively and with spatial and temporal precision (Cinti et al., 2024; Parvin et al., 2025). Near-infrared light (700–1100 nm) penetrates tissue relatively deeply with little background absorption (Mahmoud et al., 2026; Parvin et al., 2025). Gold nanorods, black phosphorus quantum dots (BPQDs) and 2D MXenes absorb NIR light and convert it into localized heat above 42 °C, killing malignant cells through protein denaturation while loosening carrier membranes to release co-loaded chemotherapeutics (Mahmoud et al., 2026; Parvin et al., 2025). In photodynamic therapy (PDT), light-activated photosensitizers such as chlorin e6 attached to nanocarriers generate cytotoxic singlet oxygen (¹O₂) (Milan et al., 2022; Parvin et al., 2025).

Superparamagnetic iron oxide nanoparticles (SPIONs, Fe₃O₄) generate heat in an alternating magnetic field through Néel and Brown relaxation, enabling magnetic hyperthermia and field-guided release (Parvin et al., 2025). Ultrasound adds a mechanical dimension: cavitation and micro-streaming activate piezocatalytic particles such as barium titanate (BaTiO₃) or organic sonosensitizers to produce ROS and can transiently increase tissue and blood–brain barrier permeability (Guan et al., 2024; Parvin et al., 2025).

2.3 Diagnostic, Theranostic and Bioimaging Paradigms

2.3.1 Nanomaterial-enhanced biosensing

Combining diagnostic probes with therapeutic payloads in a single smart platform has created the field of theranostics, which aims to link early molecular detection seamlessly to real-time treatment monitoring (Mahmoud et al., 2026; Parvin et al., 2025). The overall workflow, from biofluid to theranostic output, is summarized in Figure 3, and the main modalities are compared in Table 3. In molecular diagnostics, functionalized nanomaterials serve as transducer layers that amplify analytical signals and push detection limits into the femtomolar (fM) or even attomolar (aM) range (Shakitha and Devi, 2025; Ma et al., 2025). Electrochemical sensors built on Ti₃C₂Tₓ MXene nanosheets or laser-scribed graphene achieve very fast electron transfer and allow label-free, picomolar detection of circulating tumor markers (such as EGFR and CEA) and cardiac troponins in serum (Shakitha and Devi, 2025). Electrochemical molecularly imprinted polymers (e-MIPs) add synthetic, antibody-like recognition sites that are robust enough for wearable formats (HussShakitha and Devi, 2026). Potentiometric sensors on flexible substrates extend this logic to continuous ion monitoring (Fathy & Bühlmann, 2025), and micro- and nano-enhanced intravascular biosensors now aim to monitor biomarkers and drug levels directly in the bloodstream (Kudłacik-Kramarczyk et al., 2025).

Optical liquid-biopsy platforms built on SERS-active gold nanostructures can detect circulating tumor cells (CTCs) at 1–10 cells/mL and provide molecular fingerprints of tumor heterogeneity without a tissue biopsy (Shakitha and Devi, 2025; Mahmoud et al., 2026). Biosensor technologies are also being integrated with robotics and wearable systems in breast cancer management, from early detection to treatment monitoring (Hasan et al., 2024).

2.3.2 Advanced bioimaging probes

Smart nanoprobes overcome several weaknesses of organic dyes, notably rapid photobleaching and interference from tissue autofluorescence (Parvin et al., 2025). Lanthanide-doped upconversion nanoparticles (UCNPs, such as β-NaYF₄:Er³⁺,Yb³⁺) absorb low-energy NIR photons and emit visible or ultraviolet light, which permits deep, high-contrast fluorescence imaging (Parvin et al., 2025). For magnetic resonance imaging (MRI), SPIONs and ultrasmall iron oxide clusters act as T₂ or T₁ contrast agents that sharpen tumor margins (Parvin et al., 2025). Multimodal agents take this further. Fluorine-18-labeled Fe₃O₄@Al(OH)₃ nanoparticles, for example, enabled simultaneous positron emission tomography (PET) and MRI in vivo and were used to compare the distribution of free nanoparticles with that of nanoparticle-labeled stem cells (Belderbos et al., 2020). Copper-based particles, which can incorporate positron-emitting copper isotopes, are being explored for similar purposes (Woźniak-Budych et al., 2023).

2.3.3 Theranostic hybrids and nanozymes

Theranostic systems couple these diagnostic capabilities to treatment (Mahmoud et al., 2026; Parvin et al., 2025). Core–shell hybrids, such as magnetic Fe₃O₄ cores enclosed in porous silica and gold–palladium shells, support photoacoustic imaging, MRI, laser desorption/ionization mass spectrometry and targeted photothermal ablation within a single particle (Mahmoud et al., 2026; Parvin et al., 2025). Nanozymes, nanomaterials with intrinsic enzyme-like activity (peroxidase, catalase or superoxide dismutase), have also emerged as therapeutic platforms in their own right (El-Morsy et al., 2026; Fahim et al., 2025). Prussian blue, iron oxide and cobalt nanozymes convert tumor H₂O₂ into toxic hydroxyl radicals or into oxygen, relieving intratumoral hypoxia, rebalancing redox status and strengthening chemodynamic and catalytic therapy (El-Morsy et al., 2026; Parvin et al., 2025). Ag-MXene composites show that the same catalytic activity can be directed toward sensing and bacterial killing (Chen et al., 2024).

2.4 Artificial Intelligence, Digital Health and Wearable Biosensors

2.4.1 Data-driven nanoarchitectonics

One of the most significant developments in precision healthcare is the convergence of smart nanomaterials with AI, machine learning (ML) and soft bioelectronics (Shakitha and Devi, 2025; Sharma, 2026; Tivay et al., 2025). Moving from trial-and-error synthesis to data-driven design has shortened the development cycle of intelligent medical materials (Tivay et al., 2025). Deep neural networks, generative adversarial networks and reinforcement-learning algorithms can analyze high-dimensional data on composition, particle size, charge distribution and binding kinetics (Parvin et al., 2025; Tivay et al., 2025). Generative models explore large numbers of structural variants and predict encapsulation efficiency, endocytosis, circulation half-life and tumor targeting before any particle is made (Tivay et al., 2025). Random-forest and deep-learning models trained on polymer datasets reportedly identified poly(β-amino ester) formulations that degrade at tumor-like pH 6.5 with over 92% accuracy, and AI-guided tuning of gold nanorod aspect ratios raised photothermal conversion efficiency to as much as 85% (Tivay et al., 2025). The integrated loop linking these computational tools to sensing and therapy is depicted in Figure 4 and summarized in Table 4.

2.4.2 Wearable and point-of-care platforms

Smart nanomaterials also form the functional core of wearable bioelectronics and point-of-care (POC) diagnostics (Shakitha and Devi, 2025; Sharma, 2026). Flexible, gas-permeable substrates coated with laser-scribed graphene, conductive polymers such as PEDOT:PSS, or MXene hydrogels enable continuous, non-invasive measurement of analytes in sweat, saliva and interstitial fluid (Shakitha and Devi, 2025; Ghosh et al., 2023). Attention to comfort and user compliance, through soft electronics and smart materials, is increasingly recognized as a design requirement rather than an afterthought (Ghosh et al., 2023). Flexible potentiometric sensors track sweat electrolytes during movement (Fathy & Bühlmann, 2025), and textile-based "smart bras" equipped with micro-tactile and thermal arrays have been developed to detect temperature and microvascular changes associated with early breast tumorigenesis (Hasan et al., 2024).

2.4.3 Closed-loop systems and the Internet of Medical Things

When wearable nanosensors are linked by Bluetooth to cloud computing and the Internet of Medical Things (IoMT), they can form continuous, closed-loop monitoring networks (Shakitha and Devi, 2025; Sharma, 2026). In closed-loop therapeutic implants, transient nanomaterial sensors stream data on local pH, cytokines or glucose to an integrated microprocessor; when a reading crosses a predefined clinical threshold, micro-actuators or wirelessly powered heaters release a dose from bioresorbable reservoirs (Shakitha and Devi, 2025; Kudłacik-Kramarczyk et al., 2025).

2.5 Translational Challenges and Nanotoxicology

2.5.1 Long-term biocompatibility

Despite convincing preclinical results, moving smart nanomaterials into routine clinical practice faces scientific, industrial and regulatory hurdles (Shakitha and Devi, 2025; Parvin et al., 2025). These barriers, and the

Figure 3. Workflow of nanomaterial-enabled diagnostics and theranostics, from biofluid or tissue input through five transduction modalities to integrated diagnostic, therapeutic and monitoring outputs. Biological inputs and representative targets (left) are interrogated by electrochemical, optical/SERS, upconversion/FRET, MRI/PET and nanozyme-based platforms (center), each annotated with its typical nanomaterial and reported sensitivity regime. Outputs (right) are grouped into diagnosis, treatment and monitoring functions that theranostic platforms can perform within a single construct. The footnote highlights core–shell hybrids that combine several imaging modes with photothermal therapy. Performance metrics and references are listed in Table 3.

Figure 4. Closed-loop integration of smart nanomaterials with artificial intelligence, the Internet of Medical Things and on-demand therapy. The six-step cycle runs from physiological input, through nanomaterial transducers and signal conditioning, to edge or cloud machine-learning interpretation, clinician-facing IoMT platforms and closed-loop therapeutic actuation, whose effect feeds back into the physiological input. The central node represents AI-guided design, with two performance figures reported in the reviewed literature (over 92% accuracy in predicting pH-dependent polymer degradation and photothermal efficiency of up to 85%). The schematic replaces the text-based flow chart of the original draft and corresponds to the subsystems described in Table 4.

Table 3. Diagnostic, bioimaging and theranostic paradigms of smart nanoplatforms, with transducer architectures, signal mechanisms, targets and reported performance. The table compares six analytical or imaging modalities built on smart nanomaterials. For each it identifies the nanomaterial platform and transducer, the biorecognition or signal-generation mechanism, the principal targets, and the reported limit of detection, dynamic range or other performance metrics. Metrics derive from the cited studies and were obtained in different matrices (buffer, serum, plasma or in vivo), so they illustrate attainable sensitivity rather than head-to-head comparisons. Abbreviations: DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy; R_ct, charge-transfer resistance; cTnI, cardiac troponin I; CTCs, circulating tumor cells; UCNP, upconversion nanoparticle; MRI/PET, magnetic resonance imaging/positron emission tomography; POD, CAT, SOD and OXD, peroxidase-, catalase-, superoxide dismutase- and oxidase-like activity.

mitigation strategies currently under study, are mapped in Figure 5. The features that make nanomaterials biologically active (small size, reactive surfaces and efficient cellular uptake) also create toxicological liabilities (Shakitha and Devi, 2025; Parvin et al., 2025). Quantum dots containing cadmium or lead risk systemic toxicity and oxidative damage if ions leach in vivo, and high-aspect-ratio CNTs and nanowires may persist in tissue in an asbestos-like manner, causing granulomatous inflammation and fibrosis (Shakitha and Devi, 2025). Copper-based particles raise analogous concerns about ion release and oxidative stress (Woźniak-Budych et al., 2023). For bioresorbable implants, the fate of degradation products must be fully understood; rapid dissolution of magnesium or zinc can produce local alkalinity or hydrogen-gas pockets that exceed the tissue's absorptive capacity (Shakitha and Devi, 2025).

2.5.2 Protein corona and biofouling

On contact with blood, plasma or interstitial fluid, serum proteins adsorb rapidly onto nanomaterial surfaces and form a dynamic protein corona (Shakitha and Devi, 2025; Parvin et al., 2025). The corona can mask antibodies or aptamers, change hydrodynamic size and charge, and flag particles for clearance by hepatic and splenic macrophages (Shakitha and Devi, 2025; Parvin et al., 2025). In sensors, non-specific fouling produces baseline drift and loss of sensitivity, which is why antifouling coatings (poly(sulfobetaine methacrylate), other zwitterionic polymers and cell membranes) and microfluidic dialysis channels are being adopted (Shakitha and Devi, 2025; Guan et al., 2024).

2.5.3 Manufacturing scale-up and regulation

The structural complexity of multi-stimuli-responsive platforms makes industrial scale-up difficult (Parvin et al., 2025). Moving from milligram laboratory batches to kilogram GMP production often introduces variability in size, charge, ligand density and loading, and microfluidic synthesis and automated self-assembly are among the most promising remedies (Parvin et al., 2025). Regulation presents a further gap: frameworks maintained by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) were designed largely for conventional, static drugs, and harmonized protocols for assessing the immunogenicity, pharmacokinetics and biodistribution of adaptive, AI-designed theranostic platforms do not yet exist (Parvin et al., 2025). Closing these gaps will require coordinated work among materials scientists, bioengineers, clinicians, data scientists and regulators (Sharma, 2026).

3. Methods

3.1 Review Design and Reporting Framework

This study was designed as a structured narrative review with systematic search and selection procedures. It was intended to map, rather than meta-analyze, the design principles, mechanisms, applications and translational status of smart nanomaterials in precision healthcare. A narrative design was chosen because the evidence spans heterogeneous material classes, experimental models and outcome measures that cannot be pooled statistically (Parvin et al., 2025; Tivay et al., 2025). To make the process transparent and repeatable, the search, screening and selection steps were reported following the items of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement that apply to non-quantitative reviews.

3.2 Information Sources and Search Strategy

Literature was identified through PubMed/MEDLINE, Scopus and Web of Science Core Collection, supplemented by Google Scholar and by backward and forward citation searching of key reviews (Parvin et al., 2025; Sharma, 2026). The search covered records published between 1 January 2018 and [final search date, e.g., 30 June 2026], a window chosen to capture the rise of MXene-based sensing, AI-assisted nanocarrier design and IoMT-linked wearables (Sharma, 2026; Tivay et al., 2025). The core Boolean string, adapted to the syntax of each database, was: ("smart nanomaterial" OR "stimuli-responsive nano" OR "intelligent nanocarrier" OR nanozyme OR MXene OR "metal-organic framework" OR "upconversion nanoparticle") AND ("precision medicine" OR "drug delivery" OR theranostic OR biosens OR bioimaging OR wearable OR "machine learning" OR "artificial intelligence") AND (health OR clinical OR cancer OR diagnos). In PubMed, the free-text terms were combined with the MeSH headings "Nanostructures", "Drug Delivery Systems", "Theranostic Nanomedicine", "Biosensing Techniques" and "Precision Medicine". Filters restricted results to English-language, peer-reviewed publications.

3.3 Eligibility Criteria

Records were eligible if they (a) described nanomaterials (1–100 nm in at least one dimension) whose behavior changes in response to an endogenous or exogenous stimulus, or that function as transducers in biosensing, bioimaging or theranostic platforms; (b) addressed a human-health application (therapy, diagnosis, monitoring or tissue regeneration) through in vitro, in vivo, ex vivo or clinical evidence, or through a critical review of such evidence; and (c) reported at least one quantitative performance measure (for example release percentage, limit of detection, linear range, photothermal efficiency or prediction accuracy) or a mechanistic analysis of design. Records were excluded if they concerned non-biomedical uses (energy, catalysis or environmental remediation), were conference abstracts, editorials, preprints or theses, had been retracted, or were unavailable in full text.

3.4 Study Selection

All records were exported to a reference manager zotero, and duplicates were removed automatically and then checked manually. Two reviewers independently screened titles and abstracts against the eligibility criteria, then assessed the full texts of potentially relevant records. Disagreements were resolved by discussion and, where necessary, by a third reviewer. The final evidence base comprised 22 sources: 18 peer-reviewed reviews, one edited-book chapter and three original experimental studies (Bahrani et al., 2018; Belderbos et al., 2020; Chen et al., 2024). Their distribution by publication year is shown in Figure 6a.

3.5 Data Extraction

Data were extracted with a standardized, piloted form in a spreadsheet. For each source the form recorded: bibliographic details; nanomaterial class, dimensionality and composition; surface modification and functionalization strategy; stimulus type and responsive chemistry (linker or material component); release or activation kinetics; analytical modality, transducer, recognition element, target analyte, limit of detection and linear range; imaging or therapeutic modality; experimental model (in vitro, in vivo or clinical); AI or ML method and reported accuracy; and stated safety, manufacturing or regulatory limitations. Numerical values were transcribed exactly as reported, with units, and were not recalculated. Where several sources reported the same parameter, each value was retained with its citation. The extracted variables populated Tables 1–4 directly: Table 1 (architecture and surface engineering), Table 2 (stimulus-responsive mechanisms), Table 3 (diagnostic and theranostic paradigms) and Table 4 (AI and digital-health integration).

3.6 Quality Appraisal

Because the evidence base was dominated by reviews, appraisal focused on the quality of reporting rather than on risk of bias. Review articles were judged on the justification of their topic, the transparency of their literature search, the appropriateness of referencing and the presentation of relevant endpoints. Original studies were judged on whether they reported physicochemical characterization (size, surface charge, morphology), appropriate controls, replicate measurements and validation in a relevant biological matrix such as serum or plasma (Bahrani et al., 2018; Belderbos et al., 2020; Chen et al., 2024). Appraisal informed the weight given to each source during synthesis but was not used to exclude records.

3.7 Data Synthesis and Visualization

Data were synthesized thematically. Extracted findings were first coded deductively against the five review objectives and then grouped into four analytical domains: stimulus-responsive delivery, diagnostic figures of merit, theranostic and nanozyme function, and AI–digital-health convergence. Within each domain, performance data were tabulated and compared narratively, with attention to consistency across sources, the experimental model used and the direction and magnitude of effects. No statistical pooling was performed. Concentration-based detection limits were placed on a common logarithmic molar scale for visual comparison only (Figure 6c). Schematic and quantitative figures (Figures 1–6) were drawn in Python 3 with Matplotlib (version 3.11) using only values reported in the included sources. The extraction form, search strings and figure scripts are available from the corresponding author on reasonable request.

4. Synthesis of Findings: Performance Benchmarks and Emerging Patterns across Smart Nanoplatforms

4.1 Overview of the Evidence Base

The 22 sources included in this review span 2018 to 2026, although the weight of evidence is recent: 17 of them appeared in 2024 or later (Figure 6a). Taken together, they describe a field that has moved, perhaps faster than its clinical evaluation, from laboratory proofs of concept toward multifunctional platforms that combine responsiveness, sensitive transduction, theranostic capability and, increasingly, AI-assisted design (Parvin et al., 2025; Sharma, 2026). Four analytical domains emerged from the synthesis, and they structure the sections that follow (Tables 1–4).

4.2 Stimuli-Responsive Drug Delivery: What the Release Data Show

4.2.1 Endogenous triggers

The most consistent finding concerns release selectivity. Across the reports reviewed, stimulus-responsive carriers appear to improve the therapeutic index and reduce off-target exposure compared with static nanocarriers (Parvin et al., 2025; Tivay et al., 2025), although the size of that advantage depends heavily on the model used. pH-responsive micelles and nanogels incorporating hydrazone, acetal or imine linkers released less than 10% of their doxorubicin over 24 hours at pH 7.4, yet more than 80% within about six hours in the acidic range typical of tumor tissue and endolysosomes (Parvin et al., 2025; Tivay et al., 2025) (Table 2; Figure 6b). Redox-responsive systems behaved similarly. Disulfide-crosslinked carriers leaked less than 12% of their cargo in 12 hours of simulated circulation but discharged over 85% within eight hours in a high-glutathione environment; in vivo, this translated into a reported 3.5-fold increase in tumor-growth inhibition over non-responsive controls (Parvin et al., 2025) (Figure 6b).

The data on ROS-responsive carriers are more qualitative, but they point in a slightly different direction. Thioketal and boronic-ester linkers seem to do two things at once: they release anti-inflammatory drugs and they consume the excess oxidants that drive tissue injury, an effect described in models of acute lung injury and inflammatory bowel disease (El-Morsy et al., 2026; Parvin et al., 2025). Whether this dual action is clinically meaningful, rather than simply mechanistically appealing, is not yet clear from the available evidence (Table 2).

4.2.2 Exogenous triggers

Light-activated platforms delivered some of the most striking numbers. Gold nanorods, MXenes and black phosphorus quantum dots reportedly raised local temperature above 42 °C within five to seven minutes of NIR irradiation (Mahmoud et al., 2026; Parvin et al., 2025). In vitro, this was associated with ablation of more than 90% of cancer cells, and in animal models with tumor-volume reductions of roughly 68–80% (Parvin et al., 2025; Tivay et al., 2025) (Figure 2). SPIONs under alternating magnetic fields offered a complementary route, combining magnetic hyperthermia with physical guidance of particles to the target (Mahmoud et al., 2026; Parvin et al., 2025). We would caution, however, that most of these figures come from subcutaneous rodent tumors, in which light and magnetic fields reach the target far more easily than they would in deep human tissue.

4.3 Diagnostic Figures of Merit in Biosensing and Liquid Biopsy

4.3.1 Electrochemical and aptamer-based transduction

The diagnostic literature shows a steady push toward lower detection limits across electrochemical, optical and catalytic modalities (Sharma, 2026) (Table 3; Figure 6c). Ti₃C₂Tₓ MXenes and laser-scribed graphene decorated with gold provide conductive, high-surface-area (up to 235.6 m²/g) electrode interfaces (Hussin et al., 2026). MXene aptasensors have reported femtomolar detection of EGFR, HER2 and CEA (Shakitha and Devi, 2025; Hussain 2026). Molecularly imprinted sensors combining MXene with PEDOT:PSS detected bilirubin down to 0.002 mg/dL in human serum, and graphene–gold platforms measured HER2 at 0.43 ng/mL in undiluted plasma (HussShakitha and Devi, 2026). These are impressive values. Still, a femtomolar detection limit obtained in buffer does not always survive the move to whole blood, and only a minority of studies report performance in raw clinical matrices.

4.3.2 Optical, SERS and FRET sensing

Plasmonic gold nanostars and silver core–shell arrays produced SERS enhancements of 10⁶ to 10¹¹ (Shakitha and Devi, 2025; Mahmoud et al., 2026). On that basis, SERS liquid-biopsy platforms captured CTCs at 1–10 cells/mL and profiled circulating tumor DNA and microRNAs such as miRNA-21 at attomolar concentrations (Shakitha and Devi, 2025; Ma et al., 2025; Mahmoud et al., 2026). UCNPs and quantum-dot FRET probes, by eliminating background autofluorescence, resolved single-nucleotide mutations and viral antigens at picomolar levels (Shakitha and Devi, 2025; Parvin et al., 2025) (Table 3).

4.3.3 Wearable metabolic and ion profiling

In wearable formats, graphene- and MXene-based glucose patches achieved mediator-free electron transfer with glucose oxidase, with detection limits as low as 0.0054 mg/dL and linear ranges of 0.18–900.78 mg/dL (Shakitha and Devi, 2025; Ma et al., 2025). Stretchable gold-nanowire electrodes in PDMS tracked sodium, potassium and pH in sweat under up to 30% strain without calibration drift (Fathy & Bühlmann, 2025) (Table 4).

4.4 Theranostic Multifunctionality and Nanozyme Catalysis

A clear trend is the merging of imaging and therapy within single core–shell platforms (Mahmoud et al., 2026; Parvin et al., 2025) (Figure 3). Magnetic cores coated with mesoporous silica and gold–palladium shells (MPs@SiO₂@Pd–Au) supported photoacoustic imaging, T₂-weighted MRI, laser desorption/ionization mass-spectrometric fingerprinting and photothermal ablation in one construct (Mahmoud et al., 2026; Parvin et al., 2025). Dual PET/MRI was demonstrated with ¹⁸F-labeled Fe₃O₄@Al(OH)₃ particles, which also allowed labeled stem cells to be tracked in vivo (Belderbos et al., 2020) (Table 3).

Nanozymes deserve separate mention, because they appear to sidestep the fragility of natural enzymes (El-Morsy et al., 2026; Fahim et al., 2025). Iron oxide, Prussian blue, cobalt and Ag-MXene nanostructures display peroxidase- and catalase-like activity (Chen et al., 2024; El-Morsy et al., 2026; Fahim et al., 2025). In tumors, they convert excess H₂O₂ into hydroxyl radicals for chemodynamic therapy while also producing oxygen that relieves hypoxia and may partly reverse immunosuppression (El-Morsy et al., 2026; Parvin et al., 2025). The Ag-MXene nanozyme, for its part, enabled colorimetric cholesterol detection over 2–800 µM with a 0.6 µM limit in serum, alongside enhanced antibacterial activity (Chen et al., 2024) (Table 3; Figure 6c).

4.5 Convergence with Artificial Intelligence, Digital Health and Transient Bioelectronics

4.5.1 AI in design and signal interpretation

The literature describes a rapid move toward data-driven nanomedicine (Shakitha and Devi, 2025; Sharma, 2026; Tivay et al., 2025) (Figure 4; Table 4). Deep neural networks, random forests and stacking ensembles trained on structural and biological data are used to predict size, surface charge, uptake and circulation half-life before synthesis. The reported results include more than 92% accuracy in predicting pH-dependent polymer degradation and photothermal efficiencies of up to 85% (Tivay et al., 2025). In multiplexed sensing, gradient-boosting and neural-network models separate overlapping electrochemical signals and correct, in real time, for baseline drift caused by biofouling and temperature (Shakitha and Devi,., 2025; Sharma, 2026). It should be said, though, that few of these models have been validated on independent external datasets.

4.5.2 Wearables, IoMT and transient implants

Flexible patches, smart textiles and microneedle arrays are now being connected directly to IoMT cloud infrastructure (Shakitha and Devi, 2025; Fathy & Bühlmann, 2025; Ghosh et al., 2023; Sharma, 2026). Closed-loop implants made from bioresorbable silicon nanomembranes and transient magnesium conductors on PLGA substrates monitor electrophysiological signals or biomarkers and, when an abnormal spike is detected, trigger micro-actuators or heaters to release a dose (Kudłacik-Kramarczyk et al., 2025). Once their working life is over, these devices dissolve into silicic acid (Si(OH)₄) and magnesium hydroxide (Mg(OH)₂) at reported rates of about 0.005–1.08 µm/day, removing the need for surgical retrieval (Shakitha and Devi, 2025) (Table 3; Table 4). Breast-cancer-oriented wearables, including thermal-sensing smart garments, illustrate how such systems might eventually support screening outside the clinic (Hasan et al., 2024).

5. Interpreting Progress, Weighing Limitations and Charting the Path to Clinical Translation

5.1 Principal Findings in Context

Taken as a whole, the evidence suggests that smart nanomaterials have matured considerably in the past decade, perhaps more in their engineering than in their clinical validation. Three findings stand out. First, stimulus-gated carriers separate physiological leakage from triggered release with a clarity that static carriers rarely achieve (Parvin et al., 2025; Tivay et al., 2025) (Table 2; Figure 6b). Second, nanomaterial transducers have extended diagnostic sensitivity into the femtomolar and attomolar range, which in principle allows disease to be detected well before it becomes clinically apparent (Shakitha and Devi, 2025; Ma et al., 2025; Sharma, 2026) (Table 3; Figure 6c). Third, AI is beginning to change how these materials are designed and how their signals are read, which may eventually shorten development timelines and improve reliability in the field (Sharma, 2026; Tivay et al., 2025) (Table 4; Figure 4).

5.2 Design Trade-offs across Material Classes

The taxonomy in Table 1 and Figure 1 is not just descriptive; it also exposes trade-offs. Inorganic 0D and 2D materials offer outstanding optical and electrical properties but raise the most persistent safety questions, whether from heavy-metal leaching in quantum dots or from the biopersistence of high-aspect-ratio carbon structures (Shakitha and Devi, 2025; Woźniak-Budych et al., 2023). Polymeric nanogels and biomimetic carriers are more forgiving biologically, yet they generally lack the transduction capabilities that make inorganic materials valuable in sensing (Guan et al., 2024; Parvin et al., 2025). MOFs sit somewhere in between: their payload capacity is unmatched, but their stability in physiological media and the fate of their metal nodes are not always well characterized (Shakitha and Devi, 2025). It seems likely, then, that hybrid designs, such as polymer-coated inorganic cores or membrane-cloaked MOFs, will dominate the next generation, though hybrids also multiply the number of parameters that must be controlled in manufacturing.

5.3 From Release Kinetics to Clinical Benefit

The release data are encouraging, but they need careful interpretation. Release percentages are usually measured in buffer at idealized pH values or GSH concentrations (Parvin et al., 2025). Tumors, by contrast, are heterogeneous: acidity varies across a lesion, and the GSH gradient differs between cancer types and even between cells in the same tumor (Cinti et al., 2024). A carrier that releases 80% of its cargo at pH 5.5 in a vial may therefore behave quite differently in a patient. Exogenous triggers avoid some of this variability, but at a cost, since NIR light penetrates only millimeters to perhaps a centimeter of tissue and magnetic hyperthermia requires specialized equipment (Mahmoud et al., 2026; Parvin et al., 2025). In our view, combining an endogenous trigger for localization with an exogenous trigger for timing may prove the more robust strategy (Figure 2; Table 2), and the growing interest in ROS- and enzyme-responsive systems for inflammation and infection points to a widening range of indications beyond oncology (El-Morsy et al., 2026; Ioannou et al., 2024).

5.4 The Sensitivity Paradox in Nanodiagnostics

There is an apparent paradox in the diagnostic literature. Detection limits continue to fall, yet very few nanosensors have entered clinical use (Shakitha and Devi, 2025; Sharma, 2026). Part of the explanation is that analytical sensitivity is rarely the limiting factor in practice. Clinical utility depends more on specificity in complex matrices, on stability over time and on calibration-free operation, all of which are undermined by biofouling and protein-corona formation (Shakitha and Devi, 2025; Parvin et al., 2025) (Figure 5). Studies that validate sensors in undiluted plasma or serum, such as the graphene–gold HER2 and Ag-MXene cholesterol platforms, are therefore more informative than their numbers alone might suggest (Chen et al., 2024) (Table 3). Similarly, intravascular and wearable devices will need long-term in vivo data on drift and fouling before their continuous readouts can be trusted for clinical decisions (Fathy & Bühlmann, 2025; Kudłacik-Kramarczyk et al., 2025).

5.5 Promise and Pitfalls of AI-Enabled Nanomedicine

AI is perhaps the most rapidly evolving dimension of the field, and also the one most prone to overstatement. The reported accuracy of over 92% for predicting polymer degradation is notable (Tivay et al., 2025), but such figures depend on the size, diversity and quality of the training data, which in nanomedicine remain limited and unevenly reported. Without standardized reporting of particle characterization and biological outcomes, models risk learning laboratory-specific artefacts rather than general design rules (Sharma, 2026). Integrating wearable data streams into IoMT platforms introduces further questions about data security, interoperability and explainability, which federated learning, explainable AI and blockchain-based approaches are beginning to address (Sharma, 2026; Tivay et al., 2025) (Table 4). User compliance, which depends on comfort, battery life and ease of use, is easily overlooked and may ultimately decide whether such systems are adopted at all (Ghosh et al., 2023; Hasan et al., 2024).

5.6 Translational Barriers and a Pragmatic Roadmap

The translational barriers summarized in Figure 5 are well known, but they are interconnected in ways that are easy to underestimate. Long-term toxicity cannot be assessed without reproducible material, reproducible material cannot be made without scalable GMP processes, and regulators cannot evaluate a product whose composition varies from batch to batch (Shakitha and Devi, 2025; Parvin et al., 2025). Microfluidic synthesis and automated self-assembly offer a credible way to improve consistency (Parvin et al., 2025). Regulatory science, meanwhile, will need

Figure 5. Principal translational bottlenecks for smart nanomedicines and the mitigation strategies currently under investigation. Four interconnected barriers are shown on the left: nanotoxicology and long-term biocompatibility, protein-corona formation and biofouling, Good Manufacturing Practice (GMP) scale-up, and regulatory standardization. The corresponding responses on the right include heavy-metal-free and biodegradable designs, zwitterionic and biomimetic antifouling coatings with machine-learning drift correction, microfluidic and automated synthesis, and harmonized immunogenicity, pharmacokinetic and biodistribution protocols. The barriers are drawn from Section 2.5 and discussed in Section 5.6.

Figure 6. Quantitative overview of the evidence base and reported performance benchmarks of smart nanoplatforms. (a) Distribution of the 22 included sources by publication year (2018–2026). (b) Cumulative payload release under physiological versus triggered conditions for hydrazone-linked pH-responsive carriers (pH 7.4 vs. pH 5.5) and disulfide-crosslinked GSH-responsive carriers (extracellular vs. cytosolic glutathione); bars show the bounding values reported (Parvin et al., 2025). (c) Reported detection regimes of representative sensing modalities placed on a common logarithmic molar scale for orientation only (Shakitha and Devi,., 2025; Chen et al., 2024; Ma et al., 2025). Values were extracted from heterogeneous studies and were not statistically pooled.

harmonized protocols for immunogenicity, pharmacokinetics and biodistribution that reflect the adaptive behavior of smart systems (Shakitha and Devi, 2025; Parvin et al., 2025). We would suggest four near-term priorities: (i) minimum reporting standards for physicochemical and biological characterization; (ii) validation in clinically relevant matrices and orthotopic or large-animal models; (iii) early engagement with regulators on combination and AI-designed products; and (iv) open datasets to support reproducible ML models (Sharma, 2026; Tivay et al., 2025).

5.7 Limitations of This Study

This review has several limitations. The evidence base consists predominantly of review articles, so many quantitative values are secondary reports rather than primary measurements, and they could not be independently verified or pooled. Heterogeneity in experimental models, units and outcome definitions meant that comparisons, including those in Figure 6, are indicative rather than statistical. Restriction to English-language, peer-reviewed sources may have excluded relevant work, and the rapid pace of the field means that some findings will be superseded quickly. These limitations do not, we believe, alter the main conclusions, but they do counsel against reading individual performance figures as clinical benchmarks.

References