1. INTRODUCTION:

Nanomedicine, generally defined as the medical application of materials with at least one dimension below approximately 1000 nm, has emerged over the past two decades as one of the most active interfaces between pharmaceutical science, materials chemistry and translational medicine ^1–3. By engineering carriers at a length scale comparable to that of cellular and sub-cellular structures, formulators can address several of the most persistent limitations of conventional dosage forms, including poor aqueous solubility of new chemical entities, rapid plasma clearance, narrow therapeutic windows and limited access to anatomically protected compartments such as the central nervous system ^4,5.

Clinically approved nanomedicines now span a broad therapeutic landscape. Liposomal formulations of doxorubicin, daunorubicin and amphotericin B, polymeric micelle formulations of paclitaxel, iron-oxide-based imaging agents, lipid nanoparticle (LNP) carriers of small interfering RNA, and most recently the lipid-nanoparticle messenger RNA platforms that underpin several COVID-19 vaccines collectively illustrate the breadth and clinical relevance of the field ^[6–8]. The marketing approvals of these products have nevertheless been accompanied by repeated regulatory observations that the behaviour of nano-sized products in vivo is exquisitely sensitive to subtle variations in physicochemical attributes such as particle size, surface charge, lipid composition, polymorphic form of the encapsulated drug and the identity of adsorbed plasma proteins, often referred to collectively as the protein corona ^9,10.

Consequently, the quality control of nanomedicines is conceptually distinct from that of conventional small-molecule dosage forms. Whereas a tablet or solution can usually be adequately characterised by assay, content uniformity, dissolution and impurity testing, a nano-sized carrier requires an integrated analytical strategy that captures particle-level attributes, payload-level attributes, and bio-interaction-relevant attributes ^11,12. The complexity is further compounded by the heterogeneity inherent to most nanoparticle populations: a single batch may comprise particles of varying size, morphology, crystallinity and surface density of functional ligands, and no single technique can adequately describe all of these dimensions simultaneously ¹³.

Recognising this complexity, regulators including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have issued nanotechnology-specific guidance documents that emphasise the need to identify critical quality attributes (CQAs) on a product-by-product basis and to control them through a Quality-by-Design (QbD) framework grounded in ICH Q8, Q9 and Q10 ^14–16. Implementation of these expectations places considerable demand on analytical laboratories, which must combine ensemble techniques such as dynamic light scattering (DLS) with single-particle approaches such as nanoparticle tracking analysis (NTA) and electron microscopy, and integrate them with classical pharmaceutical methods such as high-performance liquid chromatography (HPLC), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) ^17,18.

The present review provides a structured overview of the analytical toolbox now considered standard for the quality control of nanomedicines. It begins with a brief survey of the principal nanocarrier classes and the CQAs that determine their clinical performance, before discussing the underlying principles, typical readouts, advantages and limitations of each major characterisation technique. Regulatory expectations, stability testing strategies, current challenges and likely future directions are then considered. The intent is not to enumerate every available method, but to convey the rationale by which a fit-for-purpose battery of techniques is assembled for a given nanomedicine product.

2. Types of Nanomedicine and Nanocarriers:

Nanocarriers can be classified in several complementary ways: by chemical composition (lipidic, polymeric, inorganic, hybrid), by architecture (vesicular, matrix, core–shell, dendritic), by surface functionalisation (PEGylated, ligand-targeted, stimuli-responsive) and by the nature of the cargo (small molecule, biologic, nucleic acid, imaging agent) ^[19]. For the purpose of quality control, classification by composition and architecture is most useful because these features determine which analytical methods are appropriate and how their readouts should be interpreted. Table 1 summarises the principal classes considered in this review, together with their typical size ranges and representative examples.

2.1 Liposomes:

Liposomes are spherical vesicles formed by the self-assembly of amphiphilic phospholipids in an aqueous environment. The constituent lipids organise into one or more bilayers, each typically 4–5 nm thick, that surround an internal aqueous compartment ^20,21. According to lamellarity, liposomes are categorised as unilamellar (small unilamellar vesicles, SUVs, 30–100 nm; large unilamellar vesicles, LUVs, 100–1000 nm) or multilamellar (MLVs, up to several micrometres) ²². The bilayer can accommodate hydrophobic drugs within the lipid phase, whereas hydrophilic drugs partition into the aqueous core. Surface decoration with polyethylene glycol (PEG) confers prolonged circulation by reducing opsonisation, and conjugation of targeting ligands such as antibodies or peptides enables active targeting²³. Approved liposomal products such as Doxil®, AmBisome® and Onpattro® illustrate both the clinical maturity and the analytical demands of this class.

Fig 1. Types of liposomes

2.2 Polymeric Nanoparticles:

Polymeric nanoparticles are colloidal carriers fabricated from biodegradable polymers such as poly(lactic-co-glycolic acid) (PLGA), poly(ε-caprolactone) (PCL), chitosan, alginate or albumin ^24,25. Depending on the preparation method, they may be obtained as nanospheres, in which the drug is dispersed throughout a polymer matrix, or as nanocapsules, in which a liquid core is surrounded by a thin polymer shell. Polymeric carriers offer high formulation flexibility: drug release can be tuned by varying molecular weight, lactide-to-glycolide ratio in PLGA, or by introducing stimuli-sensitive moieties. They are particularly attractive for sustained or controlled release of small molecules, peptides and nucleic acids, and have been investigated for parenteral, pulmonary, ocular and oral administration ^26,27.

2.3 Solid Lipid Nanoparticles (SLNs):

Solid lipid nanoparticles consist of a matrix of physiological lipids that remain solid at both ambient and body temperature, stabilised by one or more surfactants in an aqueous dispersion ^28,29. They combine several advantages of liposomes, emulsions and polymeric nanoparticles: physiological tolerability of the lipid matrix, feasibility of large-scale production by high-pressure homogenisation, avoidance of organic solvents, and the possibility of controlled release through the lipid phase ³⁰. However, the crystalline order of the lipid matrix may lead to drug expulsion during storage, which limits the drug-loading capacity of conventional SLNs and motivates the use of more disordered systems such as NLCs ³¹.

2.4 Nanostructured Lipid Carriers (NLCs):

Nanostructured lipid carriers represent the second generation of lipid nanoparticles. The matrix consists of a blend of solid and liquid lipids that, after solidification, forms an imperfect crystalline lattice with greater capacity to accommodate drug molecules and reduced tendency toward drug expulsion during storage ³². NLCs typically display higher entrapment efficiency, better physical stability and more flexible release profiles than first-generation SLNs, and have been explored for dermal, oral and parenteral delivery of poorly soluble drugs.

Fig 2. SLN and NLC

2.5 Metallic and Inorganic Nanoparticles:

Metallic nanoparticles, particularly those based on gold, silver, iron oxide and silica, have attracted attention for imaging, photothermal therapy, antimicrobial applications and as platforms for surface functionalisation^33,34. Gold nanoparticles exhibit a characteristic surface plasmon resonance in the visible region, whose wavelength is sensitive to size, shape and aggregation, providing a convenient spectroscopic handle for quality control. Iron-oxide nanoparticles are routinely used as magnetic resonance contrast agents and as magnetic-targeting vectors, while mesoporous silica nanoparticles can host high payloads of small molecules within their well-defined pore structure. The persistence of inorganic cores in vivo raises specific safety considerations that must be addressed during quality and biocompatibility testing ³⁵.

3. Critical Quality Attributes (CQAs) of Nanomedicines:

Within the ICH Q8(R2) framework, a CQA is a physical, chemical, biological or microbiological property or characteristic that must be maintained within an appropriate range to ensure the desired product quality ³⁶. For conventional small-molecule products the candidate CQAs are well established (assay, content uniformity, dissolution, impurities, microbial limits). For nanomedicines, several additional product-specific attributes assume critical importance because they determine the colloidal stability, the in vivo fate and the biological identity of the carrier.

A practical CQA identification exercise for a nanomedicine begins with definition of the Quality Target Product Profile (QTPP), proceeds through systematic risk assessment using tools such as failure mode and effects analysis (FMEA) or Ishikawa diagrams, and concludes with a justified list of attributes whose ranges are then linked to formulation and process parameters through the design space ³⁷. Table 2 summarises the CQAs commonly considered for nanomedicines, together with the clinical relevance of each.

3.1 Particle Size and Polydispersity Index (PDI):

Particle size is arguably the single most influential CQA of any nanomedicine. It governs biodistribution (renal clearance below approximately 5–6 nm; splenic and hepatic uptake of larger particles), tumour accumulation via the enhanced permeability and retention (EPR) effect, cellular uptake pathways, and the release rate of encapsulated drug ^38,39. The polydispersity index, a dimensionless number derived from cumulant analysis of DLS data, quantifies the breadth of the size distribution; values below 0.1 indicate a near-monodisperse system, values between 0.1 and 0.3 are typical of well-defined pharmaceutical nanoparticles, and values above 0.3 suggest a broad or multimodal population that may behave unpredictably in vivo ⁴⁰.

3.2 Zeta Potential:

The zeta potential is the electrostatic potential at the hydrodynamic plane of shear that surrounds a particle moving relative to the dispersion medium. It is determined by the surface charge density and the composition of the surrounding ionic atmosphere, and it serves as the principal predictor of colloidal stability ^41,42. Absolute values above approximately 30 mV (either positive or negative) are commonly regarded as indicative of electrostatically stabilised dispersions, whereas values closer to zero indicate a tendency toward aggregation unless steric stabilisation is provided. The zeta potential also influences cellular uptake: cationic particles are taken up more efficiently by most cell types but tend to interact non-specifically with serum proteins, while neutral or slightly anionic particles generally exhibit longer circulation times ⁴³.

3.3 Morphology and Surface Chemistry:

Morphology encompasses shape (spherical, rod-like, cubic, worm-like), surface topography and lamellarity. Non-spherical particles such as filomicelles can circulate longer than their spherical counterparts and may show altered cellular uptake, while surface roughness influences protein adsorption ^[44]. Surface chemistry, including the density and orientation of PEG chains and the presence of targeting ligands, determines both the protein-corona profile and the receptor-mediated interactions of the particle. Surface attributes are therefore CQAs in their own right, even though they are rarely captured by a single technique.

3.4 Drug Loading and Entrapment Efficiency:

Drug loading (the mass of drug per unit mass of total nanoparticle) and entrapment efficiency (the fraction of the input drug recovered within the nanoparticle) jointly determine the dose volume required for therapy and the economic viability of the formulation ⁴⁵. Both attributes are typically quantified by HPLC or UV–Vis assay after separation of free drug by ultracentrifugation, ultrafiltration, size-exclusion chromatography or dialysis. Entrapment efficiencies of 60–90% are commonly reported for liposomal and lipid-nanoparticle formulations of small molecules; reproducible quantification requires rigorous mass-balance accounting and validation of the separation step.

3.5 In Vitro Drug Release Profile:

The release profile reflects the rate and extent at which the encapsulated drug becomes available from the carrier. For most parenteral nanomedicines no compendial release method exists; release is typically studied in dialysis, sample-and-separate or flow-through configurations under sink conditions ^[46]. The choice of medium, surfactant content and agitation can profoundly influence the apparent release rate, and harmonisation of methodology between development sites remains an unresolved challenge.

3.6 Physical and Chemical Stability:

Nanomedicines are thermodynamically metastable systems and may undergo aggregation, fusion, Ostwald ripening, lipid oxidation, polymer hydrolysis or polymorphic transition during storage and shipping ^[47]. Stability is therefore not a single attribute but a collection of related observations on size, PDI, zeta potential, drug content, related substances and visual appearance, monitored as a function of time under defined conditions.

4. Analytical Techniques for Quality Control of Nanomedicines:

No single analytical platform can adequately characterise a nanomedicine. Modern quality control therefore relies on an orthogonal combination of ensemble and single-particle techniques, supported by classical pharmaceutical methods for assay, impurities and solid-state characterisation. The following subsections describe each technique in turn, emphasising the type of information obtained, the sample requirements and the limitations that determine its appropriate use. Tables 3 and 4 provide a comparative summary.

4.1 Dynamic Light Scattering (DLS):

DLS, also known as photon correlation spectroscopy, measures the time-dependent fluctuations in the intensity of laser light scattered by particles undergoing Brownian motion. The diffusion coefficient extracted from the autocorrelation function is converted, through the Stokes–Einstein relation, into a hydrodynamic diameter ⁴⁸. DLS yields an intensity-weighted Z-average diameter and a polydispersity index in minutes, requires only microlitre volumes of dilute dispersion, and is therefore the workhorse technique for routine size determination. Its principal limitations are a strong bias toward larger particles (intensity scales with the sixth power of diameter for particles much smaller than the wavelength), limited ability to resolve multimodal distributions, and sensitivity to dust and aggregates ⁴⁹. Where high resolution of complex populations is required, DLS is best complemented by single-particle techniques.

4.2 Nanoparticle Tracking Analysis (NTA):

In NTA, individual nanoparticles in dispersion are visualised through a microscope by the light they scatter, and their Brownian trajectories are recorded by a sensitive CMOS or sCMOS camera. The diffusion coefficient of each tracked particle is converted into a hydrodynamic diameter, providing a number-weighted distribution and an absolute particle concentration in the range of approximately 10⁸–10¹⁰ particles per mL ^50,51. NTA is particularly informative for polydisperse populations and for extracellular vesicles and viral vectors, where particle counting is itself a CQA. Limitations include a lower detection limit of about 30–40 nm for poorly scattering materials, sensitivity to operator-defined camera and threshold settings, and limited throughput compared with DLS.

4.3 Zeta Potential Analysis:

Zeta potential is most commonly measured by electrophoretic light scattering, in which the velocity of charged particles in an applied electric field is determined from the Doppler shift of scattered laser light and converted to a zeta potential by Henry’s equation ⁵². The measurement is highly sensitive to ionic strength, pH and the presence of small amounts of multivalent ions, so the dispersion medium must be carefully specified. Modern instruments combine size and zeta-potential measurement in a single cuvette, allowing both attributes to be tracked during stability studies on a common sample.

4.4 Scanning Electron Microscopy (SEM):

SEM produces three-dimensional images of nanoparticle surface topography by scanning a focused electron beam across a sample and detecting secondary or backscattered electrons. Resolution down to a few nanometres can be achieved with modern field-emission instruments, and the technique is well suited to imaging dried nanoparticles, aggregates and surface coatings ⁵³. Sample preparation typically involves deposition on a conductive substrate and, for non-conductive materials, sputter coating with a thin layer of gold or platinum. Because the sample must be dry and placed under high vacuum, soft nanocarriers such as liposomes and polymeric micelles may collapse, and the apparent morphology should always be interpreted in light of these artefacts.

4.5 Transmission Electron Microscopy (TEM):

TEM provides higher resolution than SEM by transmitting electrons through an ultrathin specimen and is therefore the technique of choice for visualising internal structure. Negative staining with uranyl acetate or phosphotungstic acid is widely used for soft nanoparticles; for liposomes and lipid nanoparticles, cryogenic TEM (cryo-TEM) preserves the native hydrated state by vitrifying the sample in liquid ethane and provides definitive evidence of vesicle lamellarity, polymeric micelle architecture and the internal structure of lipid nanoparticles used for mRNA delivery ^54,55. The principal limitations are cost, the need for highly trained operators, and the small number of particles examined per session, which constrains statistical inferences about a batch.

4.6 Atomic Force Microscopy (AFM):

AFM probes the surface of nanoparticles with an oscillating cantilever tip and produces three-dimensional topographic maps with sub-nanometre vertical resolution. It can be operated under ambient or aqueous conditions, and therefore allows imaging of hydrated nanocarriers without the artefacts associated with vacuum-based electron microscopy ⁵⁶. Beyond morphology, AFM provides mechanical information such as stiffness and adhesion, which are increasingly recognised as relevant to cellular uptake and intracellular trafficking. The technique is, however, slow, sensitive to tip–sample convolution, and limited to small fields of view.

4.7 Ultraviolet–Visible (UV–Vis) Spectroscopy:

UV–V is spectroscopy is used for the rapid quantification of drugs that possess suitable chromophores, for monitoring the surface plasmon resonance of gold and silver nanoparticles, and for detecting aggregation through changes in baseline scattering ^[57]. It is inexpensive, robust and easy to validate, but offers limited selectivity in the presence of interfering excipients and does not resolve the molecular environment of the analyte.

4.8 Fourier-Transform Infrared (FTIR) Spectroscopy

FTIR spectroscopy probes the vibrational modes of chemical bonds and is widely used to confirm the chemical identity of the carrier, to detect chemical interactions between drug and excipients, and to monitor surface functionalisation. The appearance, disappearance or shift of characteristic absorption bands provides a sensitive indication of hydrogen bonding, esterification or polymorphic transition ⁵⁸. Attenuated total reflectance (ATR) accessories enable measurement of solid, semi-solid and liquid samples with minimal preparation.

4.9 High-Performance Liquid Chromatography (HPLC):

HPLC remains the gold standard for quantitative analysis of drug content, entrapment efficiency, related substances and in vitro release samples. Reversed-phase methods are most common, although ion-exchange and size-exclusion variants are useful for biologics and for separating free drug from nanoparticle-bound drug ^[59]. Modern ultra-high-performance liquid chromatography (UHPLC) systems, particularly when coupled with diode-array, fluorescence or mass-spectrometric detection, deliver the sensitivity, selectivity and throughput required to support stability programmes and quality control release testing.

4.10 Differential Scanning Calorimetry (DSC)

DSC measures the heat flow associated with thermal transitions such as melting, crystallisation and glass transition. For lipid nanoparticles, DSC reveals the crystalline order of the lipid matrix and any drug-induced disordering, both of which influence drug loading and release ^[60]. For polymeric nanoparticles, the glass-transition temperature of the matrix is a key indicator of physical stability. The technique requires only a few milligrams of dried or freeze-dried material and is particularly useful in conjunction with XRD.

4.11 X-Ray Diffraction (XRD):

XRD distinguishes crystalline from amorphous material on the basis of the Bragg reflections produced when monochromatic X-rays scatter from periodic lattice planes. It is used to confirm the polymorphic form of the encapsulated drug, to detect crystallisation of an initially amorphous payload during storage, and to characterise the lipid lattice in SLNs and NLCs ^[61]. Small-angle X-ray scattering (SAXS) extends the technique to the structural characterisation of nanoparticle assemblies in dispersion.

4.12 Raman Spectroscopy:

Raman spectroscopy is complementary to FTIR in that it probes vibrational modes through inelastic light scattering and is sensitive to symmetric bonds and to the molecular environment within nanoparticles ^[62]. Surface-enhanced Raman scattering (SERS) on metallic nanoparticles offers extraordinary sensitivity for trace detection. Confocal Raman microscopy can map the spatial distribution of drug and excipients within individual particles or within tissues following administration, providing a powerful link between in vitro characterisation and in vivo behaviour.

4.13 Inductively Coupled Plasma Mass Spectrometry (ICP-MS):

ICP-MS provides ultra-trace elemental analysis and is the technique of choice for quantifying metallic nanoparticles, monitoring residual metal catalysts and tracking iron-oxide or gold nanoparticles in biological matrices⁶³. Single-particle ICP-MS (sp-ICP-MS) extends the method to count individual nanoparticles and to determine their elemental composition, offering sensitivity well below the detection limit of optical methods.

6. Stability Studies of Nanomedicines:

Stability testing of nanomedicines is conducted according to ICH Q1A(R2) but is broader in scope than that of conventional dosage forms because it must capture physical changes of the colloidal system in addition to the chemical changes of the drug substance ^[72]. A typical programme follows long-term (25 °C / 60% RH or 5 ± 3 °C for refrigerated products), intermediate (30 °C / 65% RH) and accelerated (40 °C / 75% RH) conditions, with appropriate time points up to the proposed shelf life. The analytical battery at each time point includes appearance, pH, osmolality, particle size and PDI, zeta potential, assay, related substances, in vitro release, sterility, endotoxin and, where relevant, drug leakage from the carrier.

Lyophilisation is frequently employed to overcome the limited solution-state stability of lipid and polymeric nanoparticles, and the choice of cryoprotectant (sucrose, trehalose, mannitol) and the parameters of the freeze-drying cycle become themselves CMAs and CPPs. Post-reconstitution stability and the time within which the reconstituted dispersion must be used are then established as part of the in-use stability programme.

7. Nano–Bio Interactions and Translational Considerations:

The clinical performance of a nanomedicine is determined not by its as-manufactured properties alone, but by the biological identity that the particle acquires after contact with body fluids. Within seconds of intravenous administration, plasma proteins adsorb onto the nanoparticle surface to form the protein corona, a dynamic layer whose composition reflects both the surface chemistry of the particle and the individual proteome of the patient ^[73,74]. The corona modulates opsonisation, cellular uptake and biodistribution and may unmask cryptic epitopes that elicit hypersensitivity reactions, complicating the prediction of in vivo behaviour from in vitro characterisation alone.

For nanomedicines intended for central-nervous-system delivery, additional translational challenges arise from the blood–brain barrier (BBB). The BBB is constituted by capillary endothelial cells sealed by tight junctions, embedded in a basement membrane and surrounded by pericytes and astrocytic end-feet ^[75,76]. Strategies to cross the BBB include receptor-mediated transcytosis (transferrin, low-density-lipoprotein receptor, glucose transporter), adsorptive-mediated transcytosis using cationic ligands, cell-penetrating peptides, transient osmotic or focused-ultrasound opening, and intranasal delivery exploiting the olfactory pathway ^[77,78]. Each of these strategies introduces additional CQAs (ligand density, ligand orientation, surface charge) that must be quantified and controlled.

Targeted drug delivery more generally relies on three complementary mechanisms: passive accumulation through size-dependent extravasation, active targeting through ligand–receptor recognition, and stimuli-responsive release triggered by pH, enzymes, redox potential, temperature, light or magnetic fields ^[79]. The successful translation of any of these mechanisms requires rigorous physicochemical characterisation: ligand-mediated targeting, for example, is critically dependent on the number and orientation of ligands per particle, both of which must be characterised by techniques such as ELISA, isothermal titration calorimetry or surface plasmon resonance.

8. Challenges in the Quality Control of Nanomedicines:

Despite considerable progress, several challenges remain. First, no single analytical platform captures all CQAs simultaneously, and harmonisation of results obtained on different instruments and in different laboratories remains imperfect. Second, the lack of universally accepted reference materials for size, zeta potential and concentration limits inter-laboratory comparability. Third, there is no compendial in vitro release method for most nanomedicine classes, which complicates the development of bioequivalence approaches for generic nano-medicines ^[80]. Fourth, the protein-corona-dependent biological identity of a nanoparticle is difficult to mimic in vitro, leading to a persistent gap between physicochemical characterisation and in vivo behaviour. Fifth, scale-up from laboratory to commercial manufacture frequently alters CQAs in ways that are difficult to predict, requiring iterative process redesign.

9. Future Perspectives:

Several technological developments are likely to reshape the analytical landscape of nanomedicine quality control over the coming decade. Microfluidic platforms enable in-line size and concentration monitoring during continuous manufacturing of lipid nanoparticles and may eventually support real-time release testing. Machine-learning approaches are increasingly applied to electron-microscopy images, DLS correlograms and Raman spectra to extract richer information from existing measurements and to detect anomalies that escape conventional thresholds. Single-particle techniques such as sp-ICP-MS, mass-photometry and resistive-pulse sensing are extending the boundary of what can be measured on individual particles, while advances in cryo-TEM and cryo-electron tomography are providing unprecedented insight into the internal structure of lipid nanoparticles carrying nucleic acids.

In parallel, regulatory science is moving toward harmonised reference materials and toward greater acceptance of model-based comparability assessment. The development of standardised, accessible reference materials for size and zeta potential, and the publication of consensus protocols by international bodies such as the International Organization for Standardization (ISO) and the National Institute of Standards and Technology (NIST), are expected to gradually close the gap between physicochemical characterisation and biological performance. Finally, the integration of QbD principles with continuous manufacturing and process analytical technology promises a quality assurance paradigm in which CQAs are controlled in real time rather than verified at release.

10. CONCLUSION:

Nanomedicines offer powerful means of improving the solubility, stability, biodistribution and selectivity of therapeutic agents, but their clinical translation depends on a level of analytical rigour that exceeds that traditionally applied to small-molecule dosage forms. A clear definition of the QTPP, a science- and risk-based identification of CQAs, and a strategically chosen battery of complementary analytical techniques are essential to ensure that the safety, efficacy and reproducibility of these products are maintained from early development through commercial manufacture. Dynamic and electrophoretic light scattering, nanoparticle tracking analysis, electron and atomic-force microscopy, vibrational and elemental spectroscopies, chromatography and thermal analysis each contribute a distinct piece of the puzzle, and their integration within a QbD framework, guided by FDA, EMA and ICH expectations, provides the foundation for robust nanomedicine quality control. Continued harmonisation of methodology, development of reference materials and adoption of advanced single-particle and in-line techniques will be central to the next phase of nanomedicine development.

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Received on 26.05.2026 Revised on 09.06.2026

Accepted on 23.06.2026 Published on 04.07.2026

Available online from July 30, 2026

Asian J. Research Chem.2026; 19(4):335-346.

DOI: 10.52711/0974-4150.2026.00052

This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. Creative Commons License.

Nanocarrier	Composition	Typical size (nm)	Main advantages	Representative example
Liposome	Phospholipid bilayer with aqueous core	30–1000	Biocompatible; can carry hydrophilic and hydrophobic drugs; clinically proven	Doxil® (PEGylated liposomal doxorubicin)
Polymeric nanoparticle	Biodegradable polymer (e.g., PLGA, chitosan)	50–500	Tunable, sustained release; high formulation flexibility	Eligard® (PLGA-based leuprolide)
Solid lipid nanoparticle (SLN)	Solid physiological lipid + surfactant	50–500	Solvent-free production; controlled release	Tretinoin-loaded SLNs (preclinical)
Nanostructured lipid carrier (NLC)	Solid + liquid lipid blend	50–500	Higher drug loading; less expulsion during storage	Cyclosporine A NLC (preclinical)
Metallic nanoparticle	Au, Ag, Fe₃O₄, SiO₂, etc.	5–100	Imaging, theranostics, surface functionalisation	Feraheme® (iron oxide)
Polymeric micelle	Amphiphilic block copolymers	10–100	Solubilisation of hydrophobic drugs	Genexol®-PM (paclitaxel micelles)
Dendrimer	Hyperbranched polymer	1–10	Defined architecture; multivalent surface	PAMAM-based platforms (investigational)

CQA	Typical acceptance range	Why it matters clinically
Particle size (Z-average)	Product-specific; commonly 50–200 nm for systemic use	Governs biodistribution, EPR-mediated tumour uptake and renal clearance
Polydispersity index (PDI)	≤ 0.3 (often ≤ 0.2)	Indicates batch homogeneity and predictability of in vivo behaviour
Zeta potential	\|ζ\| > 20–30 mV preferred for colloidal stability	Predicts aggregation tendency and cellular uptake; affects protein corona
Morphology	Defined by intended design (spherical, rod, etc.)	Influences circulation time and cellular internalisation pathway
Drug loading	Product-specific (often 1–20% w/w)	Determines dose volume and economic feasibility
Entrapment efficiency	Generally > 60–80%	Reflects formulation robustness and manufacturing yield
In vitro release	Comparable to reference product	Surrogate for in vivo release; required for similarity assessment
Residual solvents/surfactants	ICH Q3C limits	Patient safety, especially for parenteral products
Endotoxin and sterility	Compendial limits	Mandatory for parenteral nanomedicines
Drug-related impurities	ICH Q3A/B limits	Patient safety and product shelf life

Technique	Principle	Attribute(s) assessed
DLS	Brownian motion analysed via scattered-light fluctuations	Hydrodynamic size; PDI
NTA	Single-particle tracking under dark-field microscopy	Size distribution; particle concentration
Zeta potential (ELS)	Electrophoretic mobility under applied field	Surface charge; colloidal stability
SEM	Secondary electron imaging	Surface morphology; topography
TEM / cryo-TEM	Electron transmission through thin/vitrified specimen	Internal structure; lamellarity
AFM	Cantilever probe in contact or tapping mode	3-D topography; mechanics
UV–Vis	Electronic absorption	Drug assay; SPR; aggregation
FTIR / ATR-FTIR	Mid-IR vibrational absorption	Functional groups; interactions
HPLC / UHPLC	Liquid chromatographic separation	Assay; entrapment; impurities; release
DSC	Heat flow versus temperature	Crystallinity; melting; Tg
XRD / SAXS	Diffraction of monochromatic X-rays	Polymorphic form; lattice structure
Raman / SERS	Inelastic light scattering	Molecular identity; mapping
ICP-MS / sp-ICP-MS	Mass spectrometry after plasma ionisation	Elemental content; metallic NP counting

Technique	Advantages	Limitations
DLS	Rapid; non-destructive; small sample; widely available	Intensity-weighted bias; poor multimodal resolution
NTA	Number-weighted; gives concentration; sees down to ~30 nm	Operator-dependent settings; limited throughput
Zeta potential	Quick predictor of colloidal stability	Strongly affected by ionic strength and pH
SEM	Direct image of surface; large field of view	Vacuum drying may distort soft particles
TEM / cryo-TEM	Highest resolution; native state with cryo-TEM	Costly; small sampling; staining artefacts
AFM	Hydrated imaging; mechanical data	Slow; tip artefacts; small field of view
UV–Vis	Simple, inexpensive, easily validated	Limited selectivity in complex matrices
FTIR	Non-destructive; identifies interactions	Overlapping bands; semi-quantitative
HPLC	Quantitative; specific; compendial	Method development can be lengthy
DSC	Sensitive to crystallinity and Tg	Requires dry sample; bulk technique
XRD	Definitive on crystalline form	Insensitive to amorphous content at low levels
Raman	Mapping capability; aqueous compatible	Fluorescence interference
ICP-MS	Ultra-trace elemental sensitivity	Costly; not suitable for organic-only carriers

Authority / Body	Document / Guideline	Principal scope
U.S. FDA	Liposome Drug Products (CMC, PK, Labelling) Guidance, 2018	CMC and clinical expectations for liposomal products
U.S. FDA	Drug Products Containing Nanomaterials Guidance, 2022	General expectations for nanotechnology-based drugs
U.S. FDA	21 CFR 210/211 (cGMP)	Legally enforceable manufacturing standards
EMA	Reflection paper on intravenous liposomal products, 2013	Quality and comparability of liposomes
EMA	Reflection paper on block copolymer micelle products	Polymeric micelle characterisation
EMA	Reflection paper on nano-colloidal iron preparations	Iron-carbohydrate nanomedicines
ICH	Q8(R2) Pharmaceutical Development	QbD; design space; QTPP/CQA framework
ICH	Q9 Quality Risk Management	Risk-based decision making
ICH	Q10 Pharmaceutical Quality System	Lifecycle quality management
ICH	Q1A(R2) Stability Testing	ICH stability programme requirements
ICH	Q2(R1) / Q14 Analytical Procedures	Method validation and development
USP / Ph. Eur.	General chapters on particulate matter and DLS	Pharmacopoeial methods relevant to nano-sized products