Barrier-to-Design Codes for Organ-Targeted Nanomedicine: A Framework Linking Barrier Phenotypes to Nano–Bio Interface Design and Translational Validation

Abstract

Organ-targeted nanomedicine is often defined by anatomical destination, yet delivery performance is determined by the biological barriers, disease-associated remodeling, and nano–bio interface interactions encountered before therapeutically relevant cells are reached. Whole-organ accumulation, tissue fluorescence, or bulk biodistribution can demonstrate tissue arrival, but stronger targeting claims require evidence matched to the intended delivery task, including barrier crossing or penetration, spatial localization, target-cell exposure, cargo release, target engagement, functional activity, and safety. We introduce the Barrier-to-Design Codes framework, a conceptual approach that links measurable barrier phenotypes to conditional material and nano–bio interface parameters, claim-matched validation evidence, failure boundaries, and translational gates. The framework complements existing reporting, delivery-system design, and translational frameworks by treating the barrier phenotype—rather than the organ label or material class—as the primary unit of analysis. It integrates five linked domains: barrier architecture, pathological remodeling, acquired nano–bio interface behavior, including protein adsorption and immune recognition, validation, and translation. Using the blood–brain barrier, lymph nodes and immune organs, liver and metabolic organs, skin and mucosal barriers, and cartilage/extracellular-matrix niches as representative systems, we examine how particle size, charge, stiffness, ligand density, coating, release, protein adsorption, and immune recognition can acquire different functional meanings across barrier contexts. The framework separates transferable reasoning principles from non-transferable platform assumptions and requires organ-level signals to be resolved through barrier-matched models, spatial and cell-resolved exposure, pharmacokinetic/pharmacodynamic interpretation, functional response, manufacturing control, and repeat-dose safety. The Barrier-to-Design Codes framework is not a nanomaterial taxonomy, scoring system, or universal design prescription. Its intended value lies in reducing unsupported targeting claims, improving alignment between design hypotheses and validation evidence, and supporting more interpretable and translation-aware decisions, while remaining open to prospective empirical testing and refinement.

Plain Language Summary

Nanomedicines are very small drug-delivery systems designed to carry medicines to specific organs or tissues. Reaching an organ is an important first step, but effective delivery also requires the medicine to cross or penetrate the relevant barrier, reach the intended cells, be released at the intended site, and produce a useful effect.

This review introduces “Barrier-to-Design Codes”, a practical way to connect the biological features of a barrier with the design and testing of a nanomedicine. The framework considers five linked questions: what limits access to the target tissue, how disease changes that barrier, how the nanomedicine interacts with biological fluids and cells, what evidence is needed to confirm successful delivery, and whether the product can be manufactured and used safely.

The review compares several barrier systems, including the blood–brain barrier, lymph nodes, the liver, skin and mucosal tissues, and cartilage. It explains that the same particle size, surface charge, coating, or targeting ligand can behave differently in different tissues. Each organ therefore requires design choices and testing methods tailored to its particular barrier.

The proposed framework encourages researchers to look beyond the total amount of a nanomedicine found in an organ and to assess where the particles are located, which cells receive the medicine, whether the treatment produces the intended effect, and whether repeated use is safe and consistent. This approach may support more reliable and clinically relevant development of organ-targeted nanomedicines.

Graphical Abstract

Introduction: From Organ Accumulation to Barrier-Informed Design and Translational Validation

Early organ-targeted nanomedicine was shaped by the premise that nanoscale size, prolonged circulation, and abnormal tissue vasculature could produce preferential accumulation at an anatomical destination. The 1986 report by Matsumura and Maeda provided the mechanistic basis for what became known as the enhanced permeability and retention (EPR) effect, while subsequent ligand-mediated active-targeting strategies sought to improve cellular recognition and uptake. Together with increasingly precise control of size, shape, charge, stiffness, and surface chemistry, these advances made the target organ an intuitive organizing unit for nanomedicine design, even as biological barriers increasingly emerged as determinants of nanoparticle transport and exposure.^1–3

Experience subsequently exposed the limits of this organ-centered view. EPR varies with vascular maturity, perfusion, interstitial pressure, matrix organization, lymphatic drainage, tumor type, and patient context.⁴ A widely cited analysis of the preclinical literature reported a median tumor-delivery efficiency of approximately 0.7% of the injected nanoparticle dose.⁵ Similar interpretive gaps occur outside oncology: brain signal may reflect vascular retention rather than parenchymal delivery; hepatic enrichment may represent Kupffer-cell or liver sinusoidal endothelial-cell clearance;⁶ and retention in lymph nodes, skin, mucosa, or cartilage does not by itself establish immune instruction, barrier-safe penetration, or deep-matrix exposure. Organ arrival is therefore an intermediate disposition event, not a complete targeting endpoint.

As the field shifted from nominal material identity toward nanoparticle–biological interactions, several frameworks addressed complementary needs. MIRIBEL strengthens reproducibility and cross-study comparability by defining minimum reporting expectations for material characterization, biological characterization, and experimental detail.⁷ The Poon–Chan framework organizes delivery-system design around disease biology, the delivery journey, nanoparticle–biological interaction data, and iterative optimization.⁸ DELIVER connects early design with manufacturing, preclinical development, regulation, and clinical implementation.⁹ None is primarily designed to provide a single cross-barrier analytical sequence that begins with a measurable disease-state barrier phenotype and proceeds through conditional parameter selection, claim-matched validation, explicit failure boundaries, and transfer assessment. The Barrier-to-Design Codes framework is positioned as a connective analytical layer across these complementary functions; their respective roles are summarized in Table 1.

Table 1.

Complementary Roles of Existing Nanomedicine Frameworks and Barrier-to-Design Codes

Framework	Primary Purpose	Principal Analytical Focus	Relationship to Barrier-to-Design Codes
MIRIBEL	Improve reporting completeness, reproducibility, and cross-study comparability	Material characterization, biological characterization, and experimental details	Provides the reporting foundation; it is not primarily designed to map disease-state barrier phenotypes to conditional design and validation requirements
Poon–Chan framework	Guide delivery-system design using nanoparticle–biological interaction data and iterative optimization	Delivery-system attributes, biological interactions, data integration, and computationally assisted design	Provides a design foundation; Barrier-to-Design Codes adds explicit barrier-phenotype definition, claim-matched evidence, and transfer boundaries
DELIVER	Connect nanomedicine design with clinical translation	Design, manufacturing, preclinical development, regulation, and clinical implementation	Provides the translational pathway; Barrier-to-Design Codes links barrier-specific design decisions to early translational gates
Barrier-to-Design Codes	Translate measurable barrier phenotypes into conditional design hypotheses and evidence requirements	Disease-state barrier phenotype, acquired interface behavior, target-cell exposure, failure boundaries, and transferability	Serves as a connective analytical layer; it is not a reporting checklist, validated score, or universal design prescription

Note: These frameworks are complementary rather than competing. Barrier-to-Design Codes is intended to connect barrier definition, design selection, validation, and translation without replacing established reporting, design, or development frameworks.

We therefore propose the Barrier-to-Design Codes framework as a conceptual analytical framework that connects, rather than replaces, these initiatives. Here, barrier phenotype denotes the disease- and stage-specific composite of baseline barrier architecture and pathological remodeling. A “code” is a structured conditional mapping across five linked domains: barrier architecture defines the dominant route or obstacle governing access; pathological remodeling specifies changes that create delivery opportunities and risks; nano–bio interface behavior captures the biological identity acquired through fluid-specific adsorption and immune recognition; validation resolves tissue arrival into spatial localization, target-cell exposure, cargo release, target engagement, function, and safety; and translation incorporates critical quality attributes, critical process parameters, pharmacokinetic/pharmacodynamic coupling, manufacturing controllability, repeat-dose behavior, and regulatory interpretability. Together, these domains connect biological observation to actionable design choices, claim-matched evidence, and explicit failure and transfer boundaries. The output is not a universally optimal nanoplatform, but a testable design hypothesis with defined evidence requirements and limits of applicability.

We examine the blood–brain barrier, lymph nodes and immune organs, liver and metabolic organs, skin and mucosal barriers, and cartilage/extracellular-matrix-dominated niches. These systems span controlled endothelial transport, lymphatic drainage and immune instruction, sinusoidal access versus reticuloendothelial clearance, local residence and barrier preservation, and matrix-limited exposure. Across them, the review asks when an organ-targeting claim is scientifically supportable, which design principles are transferable, and where extrapolation should stop. Figure 1 summarizes the historical transition from organ- or material-first narratives to barrier-informed nano–bio interface design and shows how barrier phenotypes are connected to conditional design parameters, validation requirements, and translational boundaries.

Figure 1.

Barrier-to-Design Codes for organ-targeted nanomedicine. (A) transition from organ- or material-first narratives to barrier-informed design; the “≠” symbol indicates that an organ label, material class, or organ-level signal is not equivalent to functional target-cell delivery. (B) five linked domains comprising barrier architecture, pathological remodeling, acquired nano–bio interface behavior, validation, and translation. (C) relative prominence of representative transport, clearance, immune, and matrix constraints across the blood–brain barrier, lymph nodes, liver, skin and mucosa, and cartilage/extracellular-matrix niches; larger and darker circles indicate greater relative prominence. (D) illustrative mappings between measurable barrier variables and conditional material or nano–bio interface parameters; connecting lines indicate potential relationships rather than fixed one-to-one rules. (E) progression from bulk tissue distribution to spatial localization, cell-resolved exposure, target engagement, functional response, and pharmacokinetic/pharmacodynamic and safety boundaries. (F) interface, manufacturing, and translational gates, together with representative failure boundaries including vascular trapping, non-productive immune uptake, clearance-dominated accumulation, barrier injury, and superficial matrix retention.

Review Scope and Evidence Selection

This focused critical review was informed by targeted searches of PubMed, Web of Science Core Collection, and Google Scholar through May 2026, supplemented by reference chaining and relevant reporting standards and regulatory documents. Search concepts combined nanomedicine or nanocarrier terms with biological barriers, nano–bio interface, biodistribution, cell-resolved delivery, immune recognition, manufacturing, and translation. Selection was purposive rather than systematic and prioritized evidence linking material attributes to barrier behavior, spatial or cellular exposure, mechanism, functional response, reproducibility, or development constraints.

The Barrier-to-Design Codes Framework

Barrier-to-Design Codes operationalizes the distinction established above through four linked decisions: defining the dominant barrier phenotype; mapping that phenotype to tunable material parameters and acquired nano–bio interface behavior; specifying the evidence required to support the intended delivery claim; and identifying failure, translational, and transfer boundaries. Here, barrier phenotype denotes the disease- and stage-specific composite of barrier architecture, pathological remodeling, local fluid and matrix conditions, immune-recognition environment, and clearance routes. The framework therefore asks how a defined barrier state constrains access, biological identity, target-cell reachability, pharmacological interpretation, and development risk, rather than whether a platform is intrinsically “brain-targeting” or “liver-targeting.”

Five Linked Codes

Barrier architecture defines the baseline route or obstacle governing access, including tight or fenestrated endothelium, lymphatic drainage, mucus, stratum corneum, extracellular matrix, and local flow. Pathological remodeling specifies disease- and stage-dependent changes in junctional integrity, receptor and transporter expression, immune-cell composition, pH, oxidative or enzymatic activity, matrix organization, and clearance. Together, these two codes define the principal structural and functional components of the barrier phenotype.

Nano–bio interface behavior describes the biological identity acquired when a platform encounters blood, interstitial fluid, mucus, synovial fluid, wound exudate, or other barrier-relevant media. These environments differ in protein composition, enzymes, lipids, polysaccharides, immune mediators, and cellular surveillance; interface behavior must therefore be evaluated in the fluid and disease context relevant to the intended route. Engineered biointerfaces can influence transport, cellular recognition, retention, clearance, and toxicity across heterogeneous barriers.^10,11 Protein adsorption may conceal targeting ligands or alter receptor engagement, while complement activation, opsonization, and phagocytic recognition can prevent target-cell exposure or, in selected immune-delivery settings, contribute to the intended delivery pathway.^12,13

The validation code separates tissue arrival from completion of the intended delivery task. BBB systems require evidence of transendothelial transport, vascular–parenchymal discrimination, relevant neural- or tumor-cell exposure, and neurovascular safety. Lymph-node systems require antigen-presenting-cell uptake, antigen presentation, and functional immune responses. Liver systems require resolution across Kupffer cells, liver sinusoidal endothelial cells, hepatocytes, stellate cells, and lesion-associated populations. Skin, mucosal, and cartilage platforms require claim-matched assessment of depth, residence, target-cell exposure, barrier integrity, and systemic-exposure boundaries. The translation code embeds critical quality attributes, critical process parameters, batch consistency, pharmacokinetic/pharmacodynamic coupling, immune safety, chemistry, manufacturing, and controls, and regulatory interpretability within design rather than treating them as late-stage documentation.¹⁴ The relationship between these five codes and the expanded operational workflow is summarizedin Supplementary Table S1.

Conditional Mapping from Barrier Inputs to Design Parameters

The framework proceeds through four operations: define the dominant barrier phenotype; map its components to tunable material and interface parameters; specify a validation chain matched to the intended claim; and state the principal failure and transfer boundaries. Barrier variables may include junctional integrity, endothelial fenestration, receptor availability, mucus viscoelasticity, matrix density, interstitial flow, local pH, oxidative or enzymatic activity, immune-cell composition, and clearance route. Corresponding parameters include size, shape, stiffness, charge, ligand affinity and density, coating, cargo release, and degradation. Representative phenotype variables and examples of phenotype-to-design mapping are provided in Supplementary Tables S2 and S3.

The mapping must remain conditional. Tight endothelium with a defined receptor-mediated entry route may favor controlled transcytosis, limited nonspecific adsorption, an appropriate ligand-binding window, abluminal release, and vascular safety. Lymphatic delivery requires alignment among injection-site release, drainage, antigen-presenting-cell access, antigen/adjuvant presentation, and immune function. In reticuloendothelial-system-dominated organs, whole-organ enrichment must be resolved to the cellular and functional levels before therapeutic targeting is claimed. Supplementary Table S4 details the minimum evidence chain required to distinguish organ accumulation from completion of the intended delivery task.

GBM/BBB delivery illustrates the full sequence. Contrast-enhancing tumor regions may show altered permeability, whereas infiltrative tumor compartments can retain a functionally consequential BBB; uniform blood–tumor-barrier leakiness should therefore not be assumed.¹⁵ When transferrin-receptor-mediated transport is selected, particle size, ligand density, affinity, valency, nonspecific adsorption, and release must be optimized jointly. High avidity may favor endothelial retention, while binding geometry can shift trafficking toward transcytosis or lysosomal sorting.^16–18 A supportable claim therefore requires preservation of barrier integrity, quantitative transendothelial transport, vascular–parenchymal discrimination, target-cell exposure, cargo release, target engagement, and neurovascular safety. Table 2 presents the expanded GBM/BBB worked example; Supplementary Material 1 provides the complete operational workflow.

Table 2.

Operational Template and Expanded GBM/BBB Worked Example

Framework Item	Core Question	Expected Output	Expanded GBM/BBB Example
Barrier phenotype	What structural and functional state governs access?	Define architecture, remodeling, immune context, and clearance	Tight polarized endothelium, low pinocytosis, regional blood–tumor-barrier heterogeneity, and intact BBB regions within infiltrative disease; do not assume uniform leakiness
Conditional design hypothesis	Which parameters address the defined phenotype?	State a testable, parameter-specific design rule	Use a receptor-mediated route only when receptor availability and trafficking are supported; tune particle size, ligand affinity, valency, density, nonspecific adsorption, and release rather than maximizing binding
Acquired nano–bio interface	How may biological media alter the proposed mechanism?	Define media-matched interface and immune assessments	Assess ligand accessibility, protein adsorption, aggregation, complement activation, endothelial uptake, and phagocytic recognition under blood- and disease-relevant conditions
Validation evidence	What demonstrates completion of the delivery task?	Build a claim-matched evidence chain	Confirm barrier integrity and transendothelial flux; distinguish vascular from parenchymal signal; demonstrate target-cell exposure, cargo release, target engagement, and functional response
Translational gates	Which attributes must remain controllable?	Connect CQAs and CPPs to delivery, safety, and PK/PD	Control ligand-display consistency, particle distribution, sterility, storage stability, batch potency, neurovascular safety, systemic exposure, and repeat-dose behavior
Failure and transfer boundaries	What invalidates the claim, and what may be transferred?	State the principal failure mode and scope of extrapolation	Endothelial association without parenchymal or target-cell exposure invalidates a BBB-targeting claim; transfer controlled-crossing and cell-resolved validation logic, not a specific TfR ligand, affinity, or formulation

Standardization for Cross-Barrier Comparison

Cross-barrier comparison is meaningful only when product attributes and acquired interfaces are reproducible under conditions relevant to the intended claim. Platforms with the same nominal composition may differ in size distribution, morphology, surface chemistry, ligand accessibility, endotoxin burden, protein adsorption, release, stability, degradation, and biological identity across batches, laboratories, and media. MIRIBEL and subsequent reproducibility initiatives establish minimum reporting expectations, whereas application-matched characterization prioritizes measurements that can alter barrier interaction, cellular exposure, potency, or safety.^7,19,20

Within Barrier-to-Design Codes, standardization serves three purposes: characterizing the product and acquired interface in relevant media; matching experimental models to the barrier phenotype and intended claim; and testing reproducibility across batches, models, and disease states. Table 2 is therefore an evidence-structuring and decision-support template, not a scoring system.

Representative Barrier Systems and Conditional Nano–Bio Interface Logic

Barrier-to-Design Codes become informative when applied to systems with distinct transport, immune-recognition, matrix, and clearance constraints. To maintain comparability without converting this review into a platform catalogue, each subsection follows the same analytical sequence: clinical context and barrier phenotype; representative platforms and targeting mechanisms; preparation and characterization priorities; and claim-matched validation and failure boundaries. The five systems considered here represent controlled endothelial transport, lymphatic drainage and immune instruction, sinusoidal access versus reticuloendothelial clearance, local residence with barrier preservation, and matrix-limited tissue exposure. Criteria for classifying studies as representative, supporting, background, or insufficiently validated evidence are provided in Supplementary Table S5.

Brain/BBB: Controlled Transport Without Neurovascular Disruption

The blood–brain barrier is formed by polarized brain endothelial cells, tight junctions, low vesicular transport, pericytes, basement membrane, and astrocytic end-feet. It maintains selective transport and neurovascular homeostasis rather than functioning as a passive wall. Ischemic stroke, neuroinflammation, neurodegeneration, and brain tumors can alter junctional integrity, receptor expression, endothelial trafficking, and perivascular immune activity, but these changes are spatially and temporally heterogeneous. Disease-associated permeability should therefore be treated as a locally defined barrier phenotype rather than evidence that the entire BBB is open.²¹

Representative strategies include receptor-targeted antibodies, ligand-functionalized polymeric or lipid nanoparticles, biomimetic carriers, and locally administered depots. Receptor-mediated transcytosis is appropriate when endothelial receptor availability and intracellular trafficking support abluminal transport, whereas direct intracranial administration, convection-enhanced delivery, or transient barrier opening should be described as barrier bypass or modulation rather than intrinsic BBB crossing. For transferrin-receptor systems, affinity, valency, binding geometry, ligand density, and cargo release must be optimized jointly because excessive avidity can increase endothelial retention or lysosomal routing rather than parenchymal exposure.^16–18

Preparation and characterization should therefore report size distribution, morphology, ligand conjugation and accessibility, serum stability, protein adsorption, payload retention and release, aggregation, endotoxin, and batch consistency. Minimum validation requires barrier-integrity measurements, quantitative transendothelial transport, vascular–parenchymal discrimination, relevant neural- or tumor-cell exposure, cargo release, target engagement, functional response, and neurovascular safety. Whole-brain fluorescence or bulk drug concentration is insufficient. The principal failure boundary is increased endothelial or vascular association without meaningful parenchymal and target-cell delivery.

Lymph Nodes and Immune Organs: Drainage Must Lead to Immune Instruction

Lymph-node delivery is relevant to prophylactic and therapeutic vaccination, cancer immunotherapy, chronic infection, and immune-tolerance strategies. Access is governed by injection-site release, interstitial flow, lymphatic entry, subcapsular-sinus filtering, and the spatial organization of macrophages, dendritic cells, and T- and B-cell zones. Tumor-draining lymph nodes and chronically inflamed nodes may show altered lymphatic flow, antigen-presenting-cell states, stromal architecture, and immunosuppression; accumulation alone therefore does not establish productive immune delivery.

Lipid, polymeric, inorganic, and protein-based particles can support lymphatic delivery, but platform selection should reflect the intended transport route and immune task. Smaller, well-dispersed particles may enter afferent lymphatics directly and distribute beyond the subcapsular sinus, whereas larger particles or depots may rely on cell-mediated transport or prolonged local release.^22,23 Size must be interpreted together with surface hydration, charge, injection route, protein adsorption, antigen/adjuvant ratio, and release kinetics. Importantly, the formulation that maximizes lymph-node accumulation may not produce the strongest cytotoxic T-cell response.²⁴ Primary studies of small lipid–calcium phosphate nanoparticles similarly support the contribution of size, PEGylation, and surface charge to lymphatic drainage.²⁵

Preparation and characterization should include particle-size distribution, antigen and adjuvant loading, co-encapsulation or conjugation efficiency, release at the injection site, surface stability, sterility, endotoxin, and batch-level immunological potency. Minimum validation requires injection-site kinetics, lymphatic entry, intranodal spatial localization, antigen-presenting-cell uptake, antigen presentation or cross-presentation, effector and memory responses, systemic cytokine effects, and repeat-dose immune safety. The principal failure boundary is lymph-node signal without effective antigen presentation or functional immune instruction.

Liver and Metabolic Organs: Therapeutic Delivery versus Clearance

The liver combines high blood exposure, fenestrated sinusoids, efficient filtration, and strong phagocytic clearance. Liver sinusoidal endothelial cells, Kupffer cells, hepatocytes, stellate cells, biliary pathways, and lesion-associated immune and tumor cells create multiple potential delivery destinations. Metabolic dysfunction-associated steatohepatitis, fibrosis and cirrhosis, acute liver injury, and hepatocellular carcinoma alter sinusoidal perfusion, endothelial fenestration, macrophage state, extracellular-matrix deposition, receptor expression, and hepatocyte accessibility. The appropriate target cell and entry mechanism therefore depend on disease phenotype and therapeutic objective.

Lipid nanoparticles are particularly relevant to nucleic-acid delivery, but they represent only one platform class. ApoE–LDLR interactions and GalNAc–ASGPR recognition can support hepatocyte entry, although functional expression may differ markedly among hepatocytes, Kupffer cells, and endothelial cells despite similar organ-level exposure.^26–28 Fibrosis and cirrhosis may instead require stellate-cell-directed delivery: vitamin A-coupled liposomes and vitamin A-decorated polymeric nanoparticles illustrate receptor-informed delivery of siRNA or nitric oxide to activated stellate cells.^29,30 Ligand-functionalized polymeric or inorganic carriers may be considered for hepatocellular carcinoma, whereas cell-derived vesicles or membrane-coated systems may be useful in inflammatory injury but introduce additional source, identity, purification, and potency constraints.

Platform-specific preparation variables must be connected to biological function. For LNPs, lipid composition, apparent pKa, mixing conditions, encapsulation, size, and storage can alter expression and cell tropism. Polymeric carriers require control of molecular weight, degradation, residual solvent, ligand coupling, and release. Inorganic carriers additionally require pore structure, surface silanol chemistry, dissolution, persistence, and long-term tissue-retention assessment. Across platforms, serum-corona formation, complement activation, endotoxin, and cell-type-resolved potency should be characterized. Minimum evidence includes organ kinetics, intrahepatic cell localization, functional cargo delivery, clearance, PK/PD coupling, liver-function safety, lesion or cell specificity, and repeat-dose immune effects. The principal failure boundary is interpreting RES capture or bulk liver concentration as hepatocyte-, stellate-cell-, or tumor-specific therapeutic delivery.

Skin and Mucosal Barriers: Residence–Penetration Balance with Barrier Preservation

Skin and mucosal tissues are local interfaces in which exposure is governed by residence, penetration, fluid turnover, immune surveillance, microbial ecology, and barrier repair. The stratum corneum constrains cutaneous entry, whereas chronic wounds, infection, inflammatory dermatoses, and burns introduce exudate, proteases, reactive oxygen species, biofilms, and disrupted extracellular matrix. Nasal, pulmonary, gastrointestinal, oral, and other mucosal surfaces differ in mucus thickness, ciliary or luminal clearance, epithelial permeability, enzymes, microbiota, and local immune organization. A single “mucosal delivery” rule is therefore inappropriate.

Microneedles, patches, hydrogels, lipid or polymeric particles, and stimulus-responsive systems should be selected according to whether the task is barrier bypass, local retention, deep penetration, antimicrobial delivery, or support of tissue repair. Microneedles can deliver particles or soluble cargo through the stratum corneum but require reliable insertion and dose transfer; hydrogels can provide prolonged local residence but may limit diffusion or alter wound drainage. Hollow microneedle delivery of polymeric vaccine nanoparticles has demonstrated that device-mediated intradermal administration can alter lymphatic transit, systemic exposure, and immune response, illustrating the need to evaluate device and formulation as a single delivery product.³¹ Mucosal systems require a conditional choice between mucoadhesion and mucus penetration: strong adhesion may prolong contact but promote mucus-mediated clearance, whereas low-fouling surfaces may improve diffusion but shorten residence or increase epithelial exposure.^32,33

Preparation and characterization should include particle stability in mucus or wound exudate, rheology and degradation of hydrogels, microneedle geometry and mechanical strength, insertion and dissolution efficiency, payload distribution, sterilization, storage, local pH responsiveness, and residual material. Minimum validation requires depth-resolved distribution, residence, release, barrier integrity, local inflammation, microbial or microbiota effects where relevant, systemic absorption, repair outcomes, and repeat-dose safety. The principal failure boundary is improved penetration or adhesion achieved at the cost of irritation, infection risk, delayed repair, barrier disruption, or uncontrolled systemic exposure.

Cartilage and ECM-Dominated Niches: Reversible Affinity, Deep-Matrix Exposure, and Joint Retention

Articular cartilage is an avascular, sparsely cellular tissue in which delivery is governed by synovial clearance and transport through a dense, negatively charged extracellular matrix. Osteoarthritis and cartilage injury alter proteoglycan content, collagen integrity, hydration, surface structure, synovial inflammation, and intra-articular clearance, thereby changing both carrier access and retention. Matrix degradation should not be interpreted as a uniformly favorable permeability window because loss of fixed negative charge can simultaneously weaken the electrostatic partitioning used by cationic carriers.³⁴

Representative approaches include cationic proteins or peptides, dendrimers, polymeric nanoparticles, affinity-functionalized carriers, and hydrogel or particulate depots. Platform selection should distinguish between rapid deep-matrix exposure and prolonged intra-articular release. Small or flexible carriers with moderate positive charge may improve cartilage partitioning and diffusion, whereas excessive affinity can immobilize particles near the articular surface. Partially PEGylated cationic dendrimers illustrate this balance: reversible electrostatic interactions increased cartilage uptake, penetration depth, joint residence, and sustained growth-factor delivery.³⁵

Preparation and characterization should assess hydrodynamic size, dispersity, branching or molecular weight, charge density, ligand or PEG density, payload coupling, degradation, and release under synovial-fluid-relevant conditions. Synovial proteins and hyaluronan may alter aggregation, apparent surface charge, and ligand accessibility. Minimum validation must distinguish synovial persistence from cartilage entry, quantify time- and depth-resolved distribution, demonstrate chondrocyte or other target-cell exposure, and connect cargo release to target engagement, function, cartilage integrity, and local safety. The principal failure boundary is joint-cavity or surface retention without sustained deep-matrix exposure. The transferable rule is to optimize reversible matrix affinity rather than maximize binding strength. Box 1 summarizes this cartilage-specific design logic, whereas Table 3 compares its clinical context, dominant constraints, platform rationale, validation requirements, and failure boundary with those of the other representative systems.

Box 1.

Cartilage as an ECM-Dominated Barrier Niche

Articular cartilage combines rapid joint-cavity clearance with restricted diffusion through a dense, negatively charged matrix. Effective delivery therefore requires a balance among synovial stability, cartilage partitioning, reversible matrix affinity, penetration depth, and controlled release. Small or flexible carriers with moderate cationic or matrix-binding properties may improve cartilage entry, but excessive affinity can trap a platform near the surface, whereas insufficient affinity permits rapid clearance. Validation should distinguish joint retention from cartilage penetration and should include depth-resolved distribution, target-cell exposure, payload release, local tissue integrity, and repeat-dose safety. Transferable rule: optimize reversible matrix affinity and depth-resolved exposure. Failure boundary: surface retention without sustained deep-matrix or cellular delivery.

Table 3.

Cross-Barrier Clinical Contexts, Platform Logic, Validation Requirements, and Failure Boundaries

Barrier System	Major Clinical Contexts	Dominant Constraint	Representative Platform and Design Logic	Minimum Validation	Main Failure Boundary
Brain/BBB	Stroke, neuroinflammation, neurodegeneration, brain tumors	Tight polarized endothelium and neurovascular safety	Receptor-targeted antibodies or ligand-functionalized lipid/polymeric carriers; optimize affinity, valency, corona behavior, and abluminal release	Barrier integrity; quantitative transendothelial flux; vascular–parenchymal distinction; target-cell exposure; cargo release; neurovascular safety	Vascular or endothelial signal without parenchymal and target-cell delivery
Lymph nodes	Vaccination, cancer immunotherapy, chronic infection, immune tolerance	Interstitial drainage, sinus filtering, and APC compartmentalization	Drainage-compatible particles or local depots; coordinate size, surface hydration, antigen/adjuvant ratio, release, and APC access	Injection-site kinetics; lymphatic entry; intranodal localization; APC uptake; presentation; effector and memory responses; immune safety	Accumulation without productive immune instruction
Liver/metabolic organs	MASH, fibrosis/cirrhosis, acute injury, hepatocellular carcinoma	Sinusoidal access coupled to RES clearance and disease-dependent cell accessibility	LNPs for nucleic-acid delivery; hepatocyte-entry ligands; stellate-cell-targeted lipid/polymeric carriers; lesion-directed polymeric, inorganic, or biomimetic systems	Cell-type localization; functional payload delivery; clearance; PK/PD; liver safety; lesion specificity; repeat-dose effects	RES capture or bulk liver concentration misread as therapeutic cell targeting
Skin/mucosa	Inflammatory dermatoses, wounds, infection, vaccination, mucosal disease	Stratum corneum or mucus, local clearance, microbial environment, and repair	Microneedles, hydrogels, patches, mucoadhesive or mucus-penetrating particles; balance residence, penetration, and barrier preservation	Depth distribution; residence; release; barrier integrity; local inflammation; microbial effects; systemic absorption; repair	Penetration or adhesion achieved at the cost of irritation, infection, delayed repair, or systemic exposure
Cartilage/ECM	Osteoarthritis, cartilage injury, intra-articular regenerative therapy	Dense anionic matrix, synovial dilution, and limited vascular access	Small or flexible cationic/affinity carriers for deep penetration; hydrogels or depots for prolonged release; optimize reversible matrix binding	Joint residence; synovial stability; depth-resolved penetration; target-cell exposure; cargo release; tissue integrity; local safety	Surface or joint-cavity retention without sustained deep-matrix exposure

Note: The systems share nano–bio interface and translational challenges, but their dominant constraints and acceptable delivery endpoints are not interchangeable. What can be transferred is the barrier-to-design reasoning process, not a specific material class, particle size, ligand, or organ-level readout.

The preceding analyses show that the same nominal material attribute can generate different delivery outcomes depending on transport, immune-recognition, clearance, fluid, and matrix context. Table 3 therefore provides a structured cross-barrier comparison rather than a ranking of platform classes.

Cross-Barrier Design Rules and Non-Transferable Boundaries

The representative systems above show that organ-targeted nanomedicine cannot be governed by organ labels, platform classes, or isolated physicochemical descriptors. A principle is transferable only when the relationships among barrier phenotype, acquired nano–bio interface, target-cell exposure, validation strategy, and product controllability remain testable in the new setting. Cross-barrier rules therefore define what may inform a new design hypothesis, what requires revalidation, and where extrapolation should stop.

Organ accumulation, receptor expression, and engineering complexity can all create unwarranted confidence. EPR heterogeneity, low tumor-delivery efficiency, hepatic clearance, and the clinical development of the prostate-specific membrane antigen-targeted docetaxel nanoparticle BIND-014 illustrate that a plausible targeting mechanism or altered pharmacokinetic profile does not alone establish target-cell delivery, therapeutic advantage, or translational readiness.^4–6,36,37 Each transferable principle is therefore paired below with an explicit boundary.

Figure 2 links dominant barrier constraints to conditional design parameters, claim-matched validation, process and interface considerations, and representative failure modes. It transfers a reasoning process, not a material platform, ligand, particle-size range, manufacturing protocol, or organ-level signal.

Figure 2.

Cross-barrier design rules and failure boundaries. (A) dominant transport, immune, clearance, and matrix constraints across the blood–brain barrier, lymph nodes, liver, skin and mucosa, and cartilage. (B) conditional interpretation of size, charge, stiffness, ligand density, coating, and release across barrier contexts. (C) claim-matched validation from tissue arrival to spatial and cell-resolved exposure, cargo release, target engagement, and function. (D) representative failure boundaries, including vascular trapping, ineffective immune instruction, reticuloendothelial-system-dominated uptake, barrier injury, and inadequate extracellular-matrix penetration. (E) transferable barrier-to-design and process–attribute reasoning together with non-transferable boundaries. The “≠” symbol indicates that the paired mechanisms, observations, or delivery contexts are not functionally equivalent and should not be directly extrapolated across barrier systems: blood–brain barrier receptor-mediated transcytosis is not equivalent to liver sinusoidal delivery; lymphatic drainage is not equivalent to cartilage extracellular-matrix transport; local residence is not equivalent to systemic circulation; enhanced-permeability-and-retention logic is not equivalent to universal organ targeting; and cationic uptake is not equivalent to safe barrier crossing.

Rule 1: Define the Barrier Before Selecting the Material

Tight endothelium, sinusoidal vasculature, lymphatic drainage, mucus, stratum corneum, and dense extracellular matrix impose different transport routes and failure modes. Size, charge, stiffness, ligand density, and coating become interpretable only after the dominant barrier phenotype and intended delivery task have been defined. A liver-active LNP cannot be assumed to retain its targeting logic at the BBB, mucosa, or cartilage; similarly, microneedle-mediated barrier bypass does not demonstrate that the encapsulated carrier is intrinsically skin-targeting. Cationic surfaces may enhance cartilage partitioning or cellular uptake while increasing serum adsorption, complement activation, mucus trapping, or phagocytic clearance. PEGylation may improve colloidal stability or mucus diffusion but can also reduce cell interaction, alter matrix affinity, or complicate repeat administration.^1,3

Transferable principle: define the barrier phenotype and delivery task before optimizing platform attributes.

Boundary: no material class or physicochemical parameter should be treated as universally favorable across barrier systems.

Rule 2: Treat Pathological Remodeling as a Conditional Window

Disease can alter permeability, receptor and transporter expression, immune composition, pH, oxidative or enzymatic activity, extracellular-matrix organization, perfusion, and clearance. These changes may create access routes or responsive triggers, but they are spatially heterogeneous, stage-dependent, and not inherently safe. Inflammation, acidity, protease activity, and oxidative stress also accompany injury and repair and may redirect uptake toward phagocytic or damaged compartments rather than the intended therapeutic cells.

Transferable principle: exploit pathological remodeling only when its spatial distribution, activation threshold, temporal stability, and safety margin are defined.

Boundary: altered permeability or inflammation does not justify nonspecific barrier disruption; a diseased BBB is not an open compartment, and fibrotic or inflamed tissues may increase sequestration as well as target access.

Rule 3: Evaluate the Acquired Nano–Bio Interface

A nanoplatform acquires a context-dependent interface through interactions with proteins, lipids, complement, polysaccharides, enzymes, mucus, extracellular matrix, and immune cells. This acquired identity can alter colloidal stability, ligand accessibility, receptor engagement, biodistribution, clearance, release, and toxicity. Protein-corona formation may conceal targeting ligands, while manufacturing, sterilization, storage, or reconstitution may change the surface that first encounters the biological environment.^12,13,38

Transferable principle: characterize the acquired interface in media matched to the route, disease state, and intended mechanism.

Boundary: measurements in water, buffer, or one standardized serum cannot be directly extrapolated to blood, mucus, synovial fluid, wound exudate, or pathological interstitial fluid.

Rule 4: Resolve Organ Accumulation into Functional Cellular Exposure

Whole-organ imaging, radiotracing, or tissue drug concentration can establish tissue arrival but not barrier crossing, target-cell exposure, intracellular release, or target engagement. Spatial and single-cell mapping can reveal substantial differences in uptake and functional cargo activity among cell populations within the same organ.³⁹

Transferable principle: decompose organ-level signal into spatial localization, cell-type exposure, intact-particle or cargo state, release, target engagement, and functional response.

Boundary: brain, lymph-node, liver, skin, mucosal, or cartilage signals cannot independently establish completion of the intended delivery task.

Rule 5: Match the Model to the Barrier Question

Model complexity is useful only when it reproduces the mechanism required by the targeting claim. BBB models should assess junctional integrity, polarity, transport, endothelial retention, and abluminal exposure; immune-delivery models should resolve antigen-presenting-cell function and T-cell responses; liver models should distinguish parenchymal and non-parenchymal cells; and local-delivery models should reproduce relevant mucus, stratum corneum, synovial fluid, extracellular matrix, or repair conditions.

Transferable principle: select models according to the dominant barrier mechanism, acquired interface, and intended claim.

Boundary: two-dimensional uptake does not demonstrate barrier crossing, and organoids or organ-on-chip systems do not replace whole-body distribution, clearance, immunological safety, and repeat-dose assessment.⁴⁰

Rule 6: Integrate Process–Attribute–Performance Relationships During Design

For nanomedicines, product performance can change when composition, mixing, purification, ligand conjugation, sterilization, lyophilization, reconstitution, or storage changes. Nominally identical materials may therefore represent biologically different products after scale-up or process transfer. Critical quality attributes, critical process parameters, batch reproducibility, potency, PK/PD, degradation products, immune responses, and regulatory identity should constrain candidate selection before efficacy claims are advanced.^7,9,20

Transferable principle: transfer process–attribute–performance reasoning and define which process variables control claim-linked product attributes.

Boundary: a laboratory preparation protocol, small-batch efficacy result, or nominal formulation composition cannot be directly extrapolated to another scale, manufacturing method, storage condition, route, or repeat-dose setting.

When is Cross-Barrier Translation Justified?

Cross-barrier translation is justified when five relationships remain testable in the new context: the barrier phenotype remains biologically relevant; the selected material or interface attribute retains a plausible mechanistic role; the validation endpoint still demonstrates completion of the delivery task; product identity is preserved after preparation and scale-up; and the principal safety and translational constraints remain controllable. Failure of any relationship requires redesign or revalidation. Typical failure modes, translational gates, and a structured template for deriving transferable rules and non-transferable boundaries are summarized in Supplementary Tables S6 and S7.

Barrier-to-Design Codes therefore does not seek a universally optimal nanoparticle or platform. It provides a discipline for testing whether a design rationale survives a change in barrier architecture, pathological state, biological fluid, target cell, route, model, or manufacturing context. A platform that succeeds in one system may generate a hypothesis for another, but it should not be transferred as a ready-made solution.

Figure 2 visualizes the cross-barrier reasoning sequence and representative failure modes, whereas Box 2 distills the corresponding transferable rules and non-transferable boundaries. Together, they emphasize that what can be carried into a new barrier context is the analytical and process–attribute reasoning, not a material platform, isolated parameter, laboratory preparation protocol, or organ-level signal. The following section concentrates the preparation, characterization, and translational controls needed to test whether a barrier-informed design remains a reproducible product.

Box 2.

Transferable rules and non-transferable boundaries

1. Transfer barrier-first reasoning, not organ labels. BBB transport, lymphatic drainage, hepatic access, mucosal residence, and cartilage penetration represent distinct delivery problems. 2. Transfer conditional design logic, not a platform or isolated parameter. Size, charge, stiffness, ligand density, coating, and release have no context-independent optimum. 3. Transfer acquired-interface evaluation, not nominal material identity. Biological performance depends on the interface formed in route-, tissue-, and disease-relevant fluids and biological environments. 4. Transfer cell-resolved validation, not bulk organ signals. Tissue arrival must be connected to spatial localization, target-cell exposure, cargo release, target engagement, and functional response. 5. Transfer model-matching discipline, not technological complexity. A model is informative only when it reproduces the barrier mechanism and biological context required by the intended claim. 6. Transfer process–attribute–performance reasoning, not a laboratory preparation protocol or preclinical formulation. Development readiness requires controlled product identity, CQAs, CPPs, manufacturing comparability, functional potency, immune safety, PK/PD, and repeat-dose behavior. Operational boundary: A principle may inform a new barrier context only when its biological mechanism, product attributes, validation endpoint, and translational controls remain testable. Otherwise, redesign or revalidation is required.

Translational Engineering and Standardized Evaluation

Translational requirements are not downstream administrative concerns; they determine whether a barrier-informed concept can become a reproducible product. A preclinical signal becomes development-relevant only when the formulation can be defined, manufactured consistently, scaled without uncontrolled attribute drift, and interpreted through coherent exposure–response and safety relationships. Organ accumulation or short-term efficacy is therefore insufficient when product identity, critical quality attributes, critical process parameters, target-cell potency, acquired interface, or repeat-dose behavior remains unresolved.

MIRIBEL, application-matched characterization, FDA guidance, and DELIVER converge on the need to connect material characterization, manufacturing, biological performance, and intended use rather than treating them as isolated stages.^7,9,14,20 FDA guidance specifically recognizes that nanoscale components may create formulation- and use-dependent attributes that require product-specific examination.

From Routine Descriptors to Claim-Linked CQAs

Size, polydispersity, morphology, charge, composition, loading, release, stability, impurities, and endotoxin are essential descriptors, but they do not by themselves establish targeting or translational readiness. An attribute becomes a candidate CQA when its variation can alter product identity, the intended barrier interaction, target-cell exposure, potency, or safety; it becomes development-relevant only when this relationship is experimentally supported.⁴¹

Three levels should be distinguished. Generic CQAs establish identity and baseline reproducibility. Barrier-linked CQAs govern interaction with a defined constraint, such as ligand affinity and density for receptor-mediated BBB transport, antigen/adjuvant ratio for lymph-node delivery, ionizable-lipid composition and apparent pKa for RNA–LNPs, microneedle mechanical strength for transcutaneous delivery, or reversible matrix affinity for cartilage penetration. Claim-linked CQAs directly support the proposed targeting mechanism and may include ligand accessibility after protein adsorption, target-cell functional potency, intracellular cargo release, depth-resolved exposure, or batch-level transcytosis activity.

This hierarchy prevents routine characterization from being mistaken for evidence of targeting. Platforms with similar nominal size or composition may differ in internal structure, surface heterogeneity, residual solvent, membrane source, ligand orientation, impurity burden, biological-media stability, or storage history and may consequently differ in biodistribution, potency, and immune response.

Platform Preparation and Process-Dependent Product Identity

For nanomedicines, preparation is part of product definition. The relevant question is not merely how a platform was produced, but which process variables determine the attributes that control barrier interaction, release, potency, and safety.

For lipid nanoparticles and liposomes, lipid identity and molar ratio, ionizable-lipid pKa, solvent composition, mixer geometry, total flow rate, aqueous-to-organic flow-rate ratio, temperature, solvent removal, encapsulation, filtration, buffer exchange, and storage can alter particle size, internal structure, encapsulation, surface composition, and biological activity. Comparative work has shown that microfluidic and turbulent-jet mixing can produce different size distributions, encapsulation efficiencies, and internal structures despite broadly matched compositions, illustrating why process changes require formal comparability assessment.⁴²

Polymeric nanoparticles require control of polymer identity, molecular weight and dispersity, nanoprecipitation or emulsification conditions, solvent and surfactant selection, residual solvent, purification, ligand coupling, degradation, and release. Inorganic systems additionally require control of nucleation and growth, crystalline phase, pore structure, surface chemistry, coating, dissolution, ion release, and long-term persistence. Across both classes, small changes in production may alter aggregation, payload localization, release kinetics, cellular uptake, or tissue retention.

Ligand-functionalized systems require characterization of conjugation chemistry, orientation, density, unbound-ligand removal, and ligand accessibility after exposure to biological media. Protein-, peptide-, or self-assembled carriers require control of sequence identity, folding, oligomerization, proteolytic stability, and cargo coupling. Biomimetic and extracellular-vesicle-based products require defined cell or tissue source, culture history, isolation, purification, cargo loading, identity, purity, contaminants, and functional potency. MISEV2023 provides updated guidance on EV production, separation, characterization, and reporting and is therefore an appropriate supporting standard for such platforms.⁴³

Hydrogels and local depots require control of polymer concentration, crosslinking, mesh structure, rheology, swelling, degradation, sterility, and spatial release. Microneedle and other device–drug systems require control of geometry, mechanical strength, insertion or dissolution efficiency, payload distribution, dose uniformity, packaging, sterilization, and storage. For these products, device performance and formulation performance cannot be evaluated independently.

Cross-Platform Characterization and Comparison

Cross-platform comparison should be organized around the delivery task rather than a universal hierarchy of materials. Relevant dimensions include cargo compatibility, control of size and surface properties, release and degradation, acquired interface, immune risk, manufacturing robustness, scalability, and the type of targeting claim that the platform can plausibly support. Table 4 summarizes representative process variables, claim-linked characterization needs, advantages, and dominant limitations.

Table 4.

Platform-Specific Preparation Variables, Claim-Linked Characterization, and Translational Constraints

Platform or Construct Class	Major Preparation Variables	Claim-Linked Characterization	Principal Strength	Main Limitation or Translational Risk
Lipid nanoparticles and liposomes	Lipid identity and ratio, apparent pKa, mixing method, flow conditions, solvent removal, encapsulation, filtration, buffer exchange, storage	Size and internal structure, encapsulation, lipid composition, surface PEG, release, endosomal activity, cell-resolved potency	Efficient encapsulation of nucleic acids or small molecules; substantial clinical and manufacturing precedent	Process- and composition-dependent tropism; storage sensitivity; complement and anti-carrier responses
Polymeric nanoparticles	Polymer molecular weight and dispersity, solvent, nanoprecipitation or emulsification, surfactant, purification, ligand coupling	Residual solvent, degradation, release, ligand accessibility, colloidal stability, cellular potency	Broad material tunability and sustained-release potential	Batch heterogeneity, burst release, residual solvents, scale-dependent precipitation and purification
Inorganic nanoparticles	Precursor ratio, nucleation, growth, crystalline phase, pore size, coating, surface modification	Composition, crystallinity, porosity, dissolution, ion release, persistence, long-term tissue fate	Imaging capability, structural stability, and high loading or surface-functionalization capacity	Limited biodegradation, long-term retention, dissolution-related toxicity, complex regulatory fate
Protein, peptide, and self-assembled carriers	Sequence, folding, assembly conditions, crosslinking, cargo coupling, purification	Conformation, oligomeric state, receptor binding, proteolytic stability, immunogenicity, potency	High biological recognition and potential biodegradability	Proteolysis, structural instability, immunogenicity, and demanding analytical control
Biomimetic and extracellular-vesicle-based systems	Cell source, culture conditions, isolation, purification, membrane coating, cargo loading	Identity, purity, source markers, membrane orientation, cargo, contaminants, functional potency	Natural multivalent interactions and potentially favorable cellular communication	Source-dependent heterogeneity, difficult purification, potency definition, scale-up, and storage
Hydrogels and particulate depots	Polymer concentration, crosslinking, mesh size, rheology, degradation, loading, sterilization	Mechanical and rheological behavior, swelling, local retention, spatial release, tissue compatibility	Prolonged local exposure and adaptable tissue residence	Diffusion limitation, incomplete dose release, sterilization effects, and difficult retrieval after administration
Microneedle and device–drug systems	Geometry, molding or fabrication, drying, loading, coating, packaging, sterilization	Mechanical strength, insertion depth, dissolution, dose uniformity, residual material, device–formulation compatibility	Controlled barrier bypass and localized dosing	Device variability, incomplete insertion or release, sterility requirements, and combination-product regulation

Note: Ligand functionalization is a cross-cutting modification rather than an independent platform. Regardless of carrier class, ligand density, orientation, conjugation stability, free-ligand removal, and biological accessibility require separate evaluation. The stated strengths are conditional and should not be interpreted as a ranking.

Acquired Interface and Immunological Safety

The acquired nano–bio interface is jointly determined by product attributes and host context. Protein adsorption, complement activation, opsonization, phagocytic recognition, mucus interaction, and extracellular-matrix binding may vary with species, disease state, route, dose, formulation, storage, and previous exposure. Interface studies should therefore evaluate both reproducibility and biological consequence rather than only cataloguing adsorbed proteins.

Complement activation can contribute to infusion reactions associated with some intravenous liposomal and nanoparticulate products, although the magnitude and mechanism are product- and species-dependent.⁴⁴ PEGylation may improve colloidal stability and circulation, but pre-existing or induced anti-PEG antibodies can, in some settings, promote complement activation, alter clearance, or compromise nanoparticle integrity. Estapé Senti et al showed that anti-PEG antibodies could trigger complement-mediated disruption of PEGylated liposomes and mRNA–LNPs, leading to premature drug release or cargo exposure.⁴⁵

The minimum immune-safety package should be route-, platform-, and indication-specific. Relevant assessments may include complement activation, cytokine release, innate-cell uptake, anti-carrier or anti-PEG antibodies, repeated-dose pharmacokinetic shifts, local inflammation, immunopathology, degradation products, and loss of functional potency. These endpoints should be interpreted jointly with target-cell exposure because immune recognition may alter both safety and delivery performance.

Barrier-Specific Evidence, Potency, PK/PD, and Staged Translation

Standardized evaluation should combine generic product characterization with claim-matched biological evidence. BBB systems require barrier integrity, quantitative transendothelial transport, vascular–parenchymal discrimination, and target-cell exposure. Lymph-node systems require lymphatic entry, antigen-presenting-cell uptake, presentation, and immune function. Liver platforms require intrahepatic cell resolution and functional cargo delivery. Skin and mucosal systems require depth, residence, barrier preservation, and systemic-exposure limits. Cartilage systems require synovial stability, matrix penetration, target-cell exposure, and tissue integrity.

PK/PD interpretation should extend beyond plasma concentration or total tissue burden. The relevant chain is:

dose → administered product state → acquired interface → tissue and cell exposure → cargo release → target engagement → functional response → safety boundary

Not every exploratory study must quantify every link, but stronger targeting and translational claims require fewer untested transitions. Particle and cargo kinetics may also diverge and should be measured separately when dissociation, degradation, or premature release is plausible. Repeated dosing is particularly informative because corona remodeling, anti-carrier immunity, altered clearance, tissue persistence, or degradation may change exposure over time.

Evaluation should be staged according to product maturity. Mechanistic studies should establish reproducible attributes and show that the selected model tests the intended barrier mechanism. In vivo efficacy studies should add quantitative disposition, cell-resolved exposure, functional potency, target engagement, and relevant safety. Translation-oriented candidates require defined CPP ranges, scale-up comparability, storage and reconstitution stability, validated or fit-for-purpose potency assays, repeated-dose evidence, PK/PD interpretation, and a coherent regulatory identity. Table 5 summarizes this staged evaluation.

Table 5.

Minimum Translational Evaluation Matrix for Barrier-Informed Nanomedicine

Evaluation Layer	Minimum Requirements	Strengthened Evidence	Main Translational Risk
Product identity and candidate CQAs	Composition, size/PDI, morphology, loading, release, stability, impurities, endotoxin	Ligand accessibility, stiffness, internal structure, degradation products, biological-media stability, functional potency	Nominally similar batches differ in biological performance
Manufacturing and CMC	Process description, purification, sterilization, storage, batch consistency	Defined CPP ranges, scale-up comparability, in-process controls, reconstitution stability, potency assay	Product attributes drift during scale-up, storage, or technology transfer
Acquired interface and immune safety	Stability in relevant media, protein adsorption, phagocytic uptake, complement and cytokine assessment	Corona reproducibility, anti-carrier antibodies, interface–potency relationships, repeated-dose PK shifts	Acquired interface changes targeting, safety, integrity, or release
Biodistribution and PK/PD	Organ distribution, time course, clearance, plasma and tissue exposure	Particle integrity, separate cargo kinetics, cell-resolved exposure, exposure–response modelling	Tissue burden is uncoupled from intact or functional cargo delivery
Barrier-specific mechanism and potency	Matched barrier model, spatial localization, target-cell exposure, target engagement	Mechanism perturbation or reversal, disease-stage testing, single-cell or spatial readouts	Bulk signal or phenotype is misattributed to targeted delivery
Safety and repeat dosing	Acute toxicity, organ pathology, local tolerance, immune response	Long-term retention, degradation, immunogenicity, repeated-dose exposure and toxicity	Single-dose studies fail to predict chronic or repeated use
Regulatory readiness	Product identity, route, intended use, quality controls, storage	Clinically relevant potency assay, device–drug classification, safety margin, early regulatory dialogue	Complex product lacks a clear identity, control strategy, or development pathway

Translational engineering is therefore not external to Barrier-to-Design Codes. It tests whether a barrier-informed hypothesis remains reproducible after manufacturing, biologically interpretable after interface remodeling, and controllable under scale-up and repeated use. A practical reporting checklist is provided in Supplementary Table S8.

Enabling Technologies for Barrier-to-Design Feedback

Artificial intelligence, organoids, organ-on-chip systems, and multi-organ microphysiological models are enabling tools within Barrier-to-Design Codes rather than independent design paradigms. Their value lies in integrating standardized formulation and biological data, testing predefined barrier hypotheses, and returning results to formulation, process, and candidate-selection decisions.

Standardized Product and Process Data Before AI-Assisted Optimization

AI and machine learning can support formulation screening, structure–activity analysis, biodistribution prediction, toxicity assessment, and candidate prioritization. Their usefulness depends on explicit, comparable, and sufficiently granular inputs. In addition to composition, size, charge, dose, route, biological model, disease state, and outcome definition, relevant datasets should include preparation method, CPPs, batch identity, ligand density, release, stability in biological media, cell-resolved potency, and assay context. Otherwise, models may learn laboratory-, process-, or batch-specific patterns rather than transferable nano–bio relationships.

Recent studies illustrate both the opportunity and the boundary. Mendes et al assembled experimental information from 745 preclinical studies of inorganic nanoparticles and used descriptive and explainable machine-learning approaches to identify design and study features associated with cancer outcomes.⁴⁶ Wang et al used AI and virtual screening to predict key properties of ionizable lipids before experimental evaluation of mRNA delivery.⁴⁷ These studies support data-assisted prioritization when features and outcomes are standardized; they do not establish transferability across unrelated materials, routes, barriers, or disease states.

Barrier-to-Design Codes can provide a structured input schema by separating barrier phenotype, formulation and process variables, acquired-interface measurements, validation endpoints, and translational constraints. AI should rank, refine, or identify uncertainty among biologically plausible candidates after data curation; it should not replace barrier definition, mechanistic reasoning, or experimental validation.

Barrier-Matched Models for Claim-Linked Testing

Organoids and organ-on-chip systems can reproduce selected features omitted by conventional uptake assays, including multicellular organization, tissue polarity, fluid flow, mechanical forces, extracellular matrix, and barrier integrity. Their relevance depends on whether these features correspond to the intended targeting claim. Flow rate, shear, channel geometry, membrane properties, medium composition, and cellular maturity can directly change nanoparticle transport and interaction and should therefore be treated as model variables rather than neutral background conditions.^40,48

A BBB model should test barrier integrity, transendothelial flux, endothelial retention, abluminal exposure, and relevant cell uptake. Immune-delivery systems should assess antigen-presenting-cell function and downstream responses. Liver and local-tissue models should resolve the relevant cell populations, biological fluids, matrix, and disease-stage conditions. These systems can also test whether a change in mixer, scale, ligand density, storage, or sterilization preserves claim-linked performance across product lots.

Greater model complexity is not inherently more informative. Microphysiological systems strengthen mechanism and comparability testing but cannot independently reproduce whole-body protein-corona formation, systemic clearance, organ competition, adaptive immunity, repeated-dose responses, or complete PK/PD.

Closed-Loop Feedback to Translational Selection

Multi-organ systems may extend local barrier models when first-pass metabolism, liver–spleen clearance, systemic inflammation, or inter-organ signaling materially affects delivery. Their principal value is controlled interrogation of selected inter-organ relationships rather than complete simulation of human pharmacology.⁴⁹

The proposed feedback sequence is: preparation and CPPs → product CQAs → acquired nano–bio interface → barrier-model transport and cell exposure → in vivo distribution, PK/PD, and safety → formulation or process refinement.

Standardized product and process data define the input; barrier phenotypes specify the biological question; AI or high-throughput methods prioritize candidates; barrier-matched models test transport, target-cell exposure, release, function, and local toxicity; in vivo studies establish whole-body disposition, immune effects, and repeat-dose behavior; and CMC and regulatory constraints return development evidence to candidate selection. Figure 3 therefore depicts a standardization–validation–translation loop rather than an AI-centered discovery pathway.

Figure 3.

Standardization–validation–translation feedback loop. (A) standardized formulation, process, barrier, and outcome data define the input space. (B) data curation, mechanistic filtering, high-throughput screening, and machine-learning-assisted prioritization identify candidates within predefined biological and manufacturing constraints. (C) barrier-matched Transwell, organoid, and organ-on-chip models evaluate transport, target-cell exposure, cargo release, functional response, and local safety. (D) in vivo studies establish biodistribution, particle and cargo kinetics, pharmacokinetic/pharmacodynamic relationships, immune safety, and repeat-dose behavior. (E) manufacturing comparability, potency, storage, regulatory requirements, and candidate refinement return translational evidence to formulation and process selection. The lower sequence depicts the iterative linkage among process parameters, product attributes, acquired nano–bio interfaces, model performance, in vivo evidence, and redesign.

Conclusions and Perspectives

Organ-targeted nanomedicine requires more than the continued expansion of materials, ligands, and disease applications. It requires an analytical framework that explains how barrier biology, pathological remodeling, and acquired nano–bio interfaces determine target-cell exposure, evidentiary requirements, and translational risk. Barrier-to-Design Codes is proposed here as a conceptual analytical framework that maps measurable barrier phenotypes to conditional design parameters, claim-matched validation, explicit failure and transfer boundaries, and translational gates. It is not a nanomaterial taxonomy, predictive score, or universal design prescription.

Across the blood–brain barrier, lymph nodes, liver, skin and mucosal tissues, and cartilage, the same nominal material attribute can produce different outcomes because transport routes, biological fluids, immune recognition, clearance, matrix constraints, and acceptable safety boundaries differ. What can be transferred across systems is therefore not a platform, ligand, particle-size range, laboratory preparation protocol, or organ-level signal, but the reasoning that connects barrier phenotype, process-dependent product attributes, acquired interface behavior, spatial and cell-resolved exposure, functional validation, and development control.

The framework now requires prospective and comparative testing. Future studies should report failed delivery mechanisms and boundary conditions alongside positive results; generate interoperable datasets that include barrier, formulation, process, interface, and cell-resolved outcome variables; and determine whether barrier-informed design rules remain reproducible across batches, models, disease stages, and repeated dosing. Barrier-to-Design Codes should remain empirically revisable. Its value will depend not on conceptual breadth alone, but on whether it reduces unsupported targeting claims, improves experimental and manufacturing decisions, and helps distinguish promising organ accumulation from reproducible, functional, and development-relevant delivery.

Funding Statement

This work was supported by the Military Key Discipline Construction Projects of China (HL21JD1206), the Shanghai Sailing Program (24YF2758300), the Shanghai Youth Program of Public Health Research (2024GKQ26), the Natural Science Research Project of Shanghai Jiading District (JDKW-2024-0013), the Project of Third Affiliated Hospital of Naval Medical University (2025QN09), and the Young Talent Cultivation Fund for Double First-Class Disciplines of Faculty of Naval Medicine, Naval Medical University (SYL2025XY04). The funders had no role in the conceptualization of the review, literature interpretation, manuscript preparation, or decision to submit the article for publication.

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

During manuscript preparation, the authors used the following AI tools: Microsoft Copilot (based on the GPT-5 chat model) and QuillBot AI Writing Assistant (no separate version number available). These tools were used solely for language editing, grammar and clarity improvement, structural refinement, consistency checking, and adaptation to journal formatting requirements. They were not used to determine the scientific scope of the review, select or verify the cited literature, interpret the evidence, or generate scientific conclusions.

The authors confirm that all manuscript content, including the text, data, analyses, and interpretations, is original and accurate. The authors take full responsibility for the integrity of the entire content of the manuscript, including the accuracy and completeness of all references. All AI-assisted outputs were independently reviewed, verified, and edited by the authors prior to submission.

The authors further confirm that the terms of use of Microsoft Copilot and QuillBot were reviewed prior to use, and that the use of these tools in the preparation of this manuscript is consistent with those terms and suitable for publication.

Abbreviations

AI, artificial intelligence; APC, antigen-presenting cell; ApoE, apolipoprotein E; ASGPR, asialoglycoprotein receptor; BBB, blood–brain barrier; CMC, chemistry, manufacturing, and controls; CPP, critical process parameter; CQA, critical quality attribute; ECM, extracellular matrix; EPR, enhanced permeability and retention; FDA, United States Food and Drug Administration; GalNAc, N-acetylgalactosamine; GBM, glioblastoma; LNP, lipid nanoparticle; LRP, low-density lipoprotein receptor-related protein; LSEC, liver sinusoidal endothelial cell; MIRIBEL, Minimum Information Reporting in Bio–Nano Experimental Literature; PEG, polyethylene glycol; PDI, polydispersity index; PK/PD, pharmacokinetics/pharmacodynamics; PSMA, prostate-specific membrane antigen; LDLR, low-density lipoprotein receptor; RES, reticuloendothelial system.

Data Sharing Statement

No new datasets were generated or analyzed in this review. All evidence discussed in the article is available from the cited publications. The operational workflow and reporting checklist are provided in Supplementary Material 1.

Ethics Approval and Informed Consent

Ethics approval and informed consent were not required because this article is a review of previously published literature and did not involve the collection or analysis of new data from human participants, human tissue, or animals.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.