Transport phenomena in polymeric nanoparticle drug delivery systems: linking soft-matter design to biological performance

1 Introduction

Polymeric nanoparticle-based drug delivery systems have become a key strategy for enhancing therapeutic efficiency and reducing adverse effects of many drugs by increasing stability, prolonging circulation time, and enabling targeted and controlled delivery. Several nanomedicine formulations have reached clinical use. Doxil®, the first FDA-approved nanomedicine, improved the therapeutic efficacy of doxorubicin against cancer while lowering cardiotoxicity (Barenholz, 2012). Likewise, Onpattro® (patisiran), the first approved lipid nanoparticle-based siRNA therapy, produced approximately 80% reductions in serum transthyretin and neurological improvement in patients with hereditary transthyretin-mediated amyloidosis (Adams et al., 2018). These successes demonstrate that rational nanoscale design can markedly enhance therapeutic performance. Nevertheless, relatively few polymeric nanoparticle systems have progressed to clinical application. This is largely because of incomplete understanding of limited ability to predict and optimize their transport through complex biological environments (Mitchell et al., 2021; Younis et al., 2022; Joyce et al., 2024).

Figure 1 shows a polymer nanoparticle structure with a typical structure. Nanogels and other soft polymeric nanoparticles have a lot to offer within these PNP systems and are especially interesting because of their high hydration, tunable network structure, deformability and stimulus induced behaviour (Mitchell et al., 2021; Beach et al., 2024).

Soft polymeric nanocarriers are hydrated deformable networks whose biological response is the result of an interplay between transport, mechanical and interfacial processes. The structure and mechanical state of nanoparticles change continuously during delivery due to interactions with blood components, extracellular matrices, cell membranes, and with intracellular compartments. Therefore single physical properties like particle size, surface charge, loading efficiency are not sufficient to provide a reliable prediction of in vivo behaviour. Nanogels are deformable poroelastic systems from a soft matter point of view where diffusion, polymer elasticity, swelling and interactions at interfaces are all intricately interconnected. The circulation behavior, protein corona formation, tissue penetration, cellular uptake, cellular trafficking, and drug release are all affected by parameters, such as the mesh size, cross linking density, swelling ratio, elastic modulus and viscoelastic relaxation. The biological fate of polymeric nanoparticles then arises from interplay among the soft-matter mechanics and cascades of biological transport barriers.

Although there is tremendous progress with synthesis of nanoparticles and their functionality, their translation to the clinic is limited. There are many systems that work well in vitro, but are not consistently successful in vivo when it comes to pharmacokinetics, tissue penetration or therapeutic efficacy (Sun et al., 2020; Younis et al., 2022; Joyce et al., 2024). This restriction is partly attributable to traditional formulation design approaches that focus on optimizing the formulation components with one set of transport conditions, but not fully considering the changing conditions that are apparent in biological systems. As the nanoparticles circulate through the vessels, they are subjected to continuous changes in transportation behaviour and their biological identity due to vascular flow, protein adsorption, steric limitations, immune surveillance and heterogeneity of tissue mechanics during delivery. The biological transport is a multimechanism, multiscale phenomenon. Convection and hydrodynamic interactions are the main modes of transport in blood circulation while hindered diffusion and deformation-assisted mobility are crucial in transport through extracellular matrices and mucus barriers. Membrane interaction, endosomal transport and biochemical transformation are other processes in cellular uptake and trafficking. Consequently the relative importance of diffusion, convection, deformation and reaction kinetics will vary from one stage to the next in the delivery process.

Hence, in this review, transport based-polymeric nanoparticle drug delivery systems have been explored from a “soft-matter” point of view. The discussion captures the link between transport phenomena and important soft-matter parameters that are involved in the mechanics, hydration, response/susceptibility, and biological interactions of nanoparticles over successive transport barriers. For the unification of these concepts, the Transport-Soft Matter Coupling Framework (TSMCF) is proposed, which regards soft polymeric nanoparticles as adaptive mechanical systems, whose transport properties change over time as they travel in biological environments. The TSMCF does not only cover the optimization of the formulation, but also considers transport-oriented and mechanics-sensitive design of clinically translatable soft nanomedicines.

2 Theoretical foundations of transport in soft polymeric nanoparticles

Coupled diffusion, polymer-network mechanics, swelling dynamics, and deformation are the key factors governing transport in polymeric nanoparticles. In contrast to rigid nanocarriers, nanogels are soft-matter systems that remain hydrated and continuously respond to biological conditions. As a result, drug transport and release depend not only on particle size and surface chemistry, but also on the time dependent mechanical and structural properties of the polymer network. In this context, coupled transport-mechanics models that capture the non-equilibrium structural and diffusional behaviour of soft polymeric nanoparticles are most appropriate for interpreting transport.

The following subsections discuss the fundamental theories and mechanistic principles underlying diffusion, swelling, deformation, and transport–reaction coupling in soft polymeric nanoparticle systems.

2.1 Coupled transport–mechanics framework

Transport processes in soft polymeric nanoparticles can be described using coupled continuum models that account for deformation, diffusion, solvent flow, and reactions. These processes are grounded in the classical transport framework of momentum, mass, and energy conservation (Bird et al., 2007).

For soft nanogels, a poroelastic coupled system provides a useful description of solute transport. In such systems, solvent flow and network deformation are both influenced by solute diffusion. Because physiological transport occurs under deforming conditions, changes in mesh permeability, solvent redistribution, and solute diffusion may all be affected by alterations in the network structure. Therefore, transport coefficients in soft nanogels should be treated as condition-dependent material properties rather than fixed constants.

A generalized transport balance may be expressed as:where C is solute concentration, v is local solvent velocity, D_eff is the emergent diffusivity dependent on mesh size (ξ), polymer volume fraction (φ), and deformation state, and R represents reaction or binding processes.

∂ C ∂ T + ∇ · Cv = ∇ · Deff ξ , φ , t ∇ C ) + R ( C , t

Mechanical equilibrium may be approximated as:

∇ · σ + f e x t = 0

Here, transport depends on an effective diffusivity (D_eff), which evolves with mesh size (ξ), polymer volume fraction (φ), and deformation state. This establishes a direct coupling between structure, mechanics, and transport.

Alternatively, soft nanogels can be considered within a poroelastic framework in which changes in solvent content within a deformable polymer network drive both mechanical and transport responses. In these systems, diffusion, fluid redistribution, and elastic deformation occur simultaneously, particularly when confinement or shear stress is present. This perspective provides insight into time dependent permeability, deformation enhanced transport, and mechanically controlled release in hydrated polymeric nanoparticles.

2.2 Diffusion in hydrated polymer networks

Classical Fickian diffusion applies primarily to idealized homogeneous systems:

J = ‐ D ∇ C

The diffusion of solutes in nanogels, however, is spatially and temporally variable due to mesh constraints, hydration, and solute–polymer interactions (Crank, 1975; Peppas et al., 2006). This gives rise to anomalous diffusion, particularly in restricted biological environments. Some additional sources of diffusional resistance in confined polymer networks include steric obstruction, hydrodynamic drag, and transient polymer-chain rearrangements. When the mesh size approaches the dimensions of the solute, transport increasingly deviates from ideal Brownian diffusion and becomes sensitive to local deformation and network heterogeneity.

2.3 Network structure, swelling, and mesh control

Soft materials with three-dimensional crosslinked hydrophilic polymer networks are commonly referred to as polymeric nanogels. They can absorb large amounts of water while maintaining their structural properties, which makes them highly suitable for numerous biomedical applications. The molecular structure of the polymer network (Alavarse et al., 2022) determines important material properties such as stiffness, viscoelasticity, and toughness. Proper design of the particle network, based on an understanding of its structure and swelling behaviour under physiologically relevant conditions, is essential for controlling both the amount and timing of drug release.

Swelling is commonly described by Flory-Rehner theory (Flory and Rehner, 1943), which combines polymer elasticity with solvent interaction effects and alters internal transport pathways. Peppas and Merrill, (1977) developed a mathematical model for the swelling and diffusional behaviour of nanopolymer systems, based on the equilibrium swelling theory of Flory and Rehner. Variations in Flory-Rehner parameters influence particle size, permeability, and diffusivity simultaneously (Li and Mooney, 2016). Thus, mesh size is a key regulator of transport and is closely related to polymer mechanics and solute mobility.

1 M c = 2 M n − υ V 1 ln 1 − υ 2 , s + υ 2 , s + χ 1 υ 2 , s 2 υ 2 , s υ 2 , s υ 2 , r 1 / 3 − υ 2 , s 2 υ 2 , r

where χ₁ is the Flory polymer-solvent interaction parameter,

• v is the specific volume of the copolymer defined as the reciprocal of the amorphous density of the polymer,
• V 1 is the molar volume of the water,
• v 2 s is the polymer volume fraction in swollen state,
• v 2 r is the polymer volume fraction in the relaxed state, and
• Mn is the number average molecular weight of the uncross linked (linear) polymer.

2.4 Viscoelastic relaxation and mechanical adaptation

Nanogels exhibit viscoelastic behavior characterized by relaxation time:where η is viscosity and G is elastic modulus. This determines the relative dominance of viscous and elastic responses during transport. In physiological environments, nanogels often operate in a transition regime (De ∼ 1), where deformation history directly influences transport pathways and barrier penetration.

τ = η / G

2.5 Dimensionless transport regimes

Transport across biological barriers is governed by three key dimensionless groups:describes diffusion–convection competition. In nanogels,D = D (ξ,ϕ,t), making transport inherently time-dependent.captures viscoelastic memory effects. Nanogels often operate in De ∼O (1), where both solid-like and liquid-like responses coexist.compares reaction (binding, degradation, uptake) to transport rates, defining transport-vs. reaction-limited regimes.

P é c l e t n u m b e r : P e = U L / D

D e b o r a h n u m b e r : D e = τ / t o b s

D a m k ö h l e r n u m b e r : D a = k r e a c t L 2 / D

Importantly, transitions between Pe-, De-, and Da-dominated regimes may occur sequentially during biological delivery as nanoparticles move between vascular flow, extracellular confinement, cellular uptake, and intracellular processing environments.

2.6 Transport–release coupling and model limitations

Transport and drug release are intrinsically coupled through the same evolving polymer network. Crosslink density, swelling ratio, mesh size, and relaxation dynamics jointly regulate mobility and payload liberation. Therefore, transport and release should not be treated as independent design variables.

Classical transport models remain limited because they assume constant diffusivity, rigid geometry, and equilibrium conditions. In contrast, nanogels exhibit non-Fickian transport, deformation-dependent permeability, and dynamic surface restructuring due to protein corona formation. These effects continuously alter Pe, De, and Da regimes during circulation, thereby limiting the usefulness of static transport assumptions.

Overall, transport in soft nanogels should be interpreted as a state-dependent process in which mechanical rearrangement, solvent redistribution, and physicochemical interactions continuously reshape transport behaviour.

2.7 Multiscale transport phenomena governing polymeric nanogel behaviour

The polymeric nanogels undergo a hierarchy of coupled transport and physicochemical processes at various spatial and temporal scales when introduced into a biological environment. Physiological fluid flow and pressure gradients, viscous stresses, convection, molecular diffusion, interfacial permeation, polymer-network swelling and deformation, polymer degradation, and drug–matrix interactions are among these processes. Relative contributions are different in various biological compartments. For example, blood flow sets the hydrodynamic conditions that affect systemic transport, while interstitial pressure and extracellular-matrix architecture and its permeability affect transport after extravasation. When the nanogel size goes down to the nanogel scale, the hydration and deformation of the nanogel mesh may change the mesh size and mobility of molecules, consequently affecting diffusion and drug release. Thus, a description of nanogel transport is typically not only given by a simple description of diffusion, but includes coupled fluid, mass-transport and soft-matter processes (Blanco et al., 2015; Scotti et al., 2022; Mitchell et al., 2021).

The multiscale transport view taken in this review is summarized in Table 1, where the major barriers in biology are connected to the primary mechanisms of transport and the important parameters associated with the material and physiology. In this context, macroscopic fluid mechanics is used to determine the pressure and velocity fields, in turn, the convective contribution to transport. Then, convection of the nanogels interacts with diffusion and interfacial permeability, and fluid and solute exchange may cause changes in nanogel swelling and mechanical properties. Effective diffusivity and permeability, and degradation/drug release can then change as a result of changes in network structure. This coupling is in line with the supporting evidence that the transport of nanoparticles in biological environments is driven by the interaction of physical and biological barriers, not just one of these (Mitchell et al., 2021; Sindhwani et al., 2020).

TABLE 1. Transport–Soft Matter Coupling Framework (TSMCF) illustrating how dominant transport constraints, soft-matter states, and failure mechanisms vary across sequential biological delivery stages.

2.7.1 Momentum transport, pressure and physiological flow

The blood and interstitial fluid movement drives the flow environment that nanogels are transported in at the physiological scale. Local velocity fields are defined by pressure gradients and viscous forces, while shear stresses caused by the flow of blood can affect the margination of nanoparticles, interactions with the vessel, endothelial adhesion and the time they can spend in the blood vessel. Pressure-driven convection has a particularly strong influence in tissue environments where interstitial pressure gradients can affect macromolecular transport and distribution, as seen in Baxter and Jain (1989), and Mitchell et al. (2021).

For an incompressible Newtonian fluid, conservation of momentum can be represented by:where ρ is fluid density, u is the velocity vector, p is pressure, τ is the viscous stress tensor, and f represents body forces.

ρ ∂ t / ∂ u + u · ∇ u = − ∇ p + ∇ · τ + f

For a Newtonian approximation:where μ is dynamic viscosity and D is the rate-of-deformation tensor.

τ = 2 μ D

This allows us to create these equations to fill the missing link between the pressure/viscous forces and the nanogel convection. The velocity field that results is then used in the mass-transport equation, which results in a coupling of momentum and mass transport and does not treat these as separate processes.

2.7.2 Convective and diffusive mass transport

After the local velocity field is obtained from the momentum-transport formulation, coupled convective and diffusive transport of nanogels and the released drug molecules occurs. The above coupling can be represented by a generalized advection–diffusion–reaction equation:

∂ t / ∂ c + u · ∇ c = ∇ · D eff ∇ c + R

Here, C represents the concentration, u is the local fluid velocity found in the momentum-transport problem, D_eff is the effective diffusivity and R is the reaction, degradation, or source/sink process. The equation thus gives the direct mathematical connection between pressure and viscosity dependence of the velocity field described in Section 2.7.1 and subsequent mass transport.

The relative contributions of convection and diffusion can be characterized using the Péclet number:

Pe = UL / D eff

The velocity, U, and length scale, L, are characteristic of the flow. If the Pe number is large, the transport is dominated by convection, otherwise diffusion dominates. However, for polymeric nanogels swelling, mesh size, deformation, the interactions between polymer and drug, and local tissue properties can affect the molecular mobility and thus D_eff is not necessarily constant. Therefore, the balance between convective and diffusive transport may change depending on the change of structure of the nanogel or the physiological conditions.

This coupling is hence considered as an intermediate transport layer in the TSMCF between physiological momentum transport and barrier penetration, deformation of the barrier soft matter, degradation and drug release. The model does not assume that convection and diffusion are separate processes, but rather the velocity field and effective transport properties can be modified based on the physiological and nanogel conditions.

2.7.3 Interfacial transport and biological permeability

Other barriers like endothelial barriers, extracellular matrices, mucus and cellular membranes restrict the transport of nanogels through biological systems. These interfaces may also have significant barriers, permeability and partitioning as in addition to bulk diffusion (Blanco et al., 2015; Mitchell et al., 2021).

A simplified permeability relationship can be expressed as:where J is interfacial flux, P is permeability coefficient, and C₁​ and C₂​ represent concentrations on the two sides of the interface.

J = P C 1 − C 2

Biological barriers can be represented as transport resistances in the framework. For instance, in tumour tissue, pressure-driven transport can also affect the distribution of macromolecules and nanoparticles, highlighting the need to include pressure-driven transport in addition to diffusion (Baxter and Jain, 1989; Sindhwani et al., 2020).

2.7.4 Nanogel swelling, deformation and soft-matter mechanics

The transport is coupled with mechanical behaviour in a bidirectional way in polymer networks. Nanogels can swell, shrink, change shape or undergo structural relaxation in response to a change of pH, temperature, ionic strength, redox conditions, or enzymatic activity. These responses can change the size of the mesh, permeability and molecular mobility (Zhang et al., 2021; Scotti et al., 2022).

The swelling response can be conceptually linked to transport through:where Q is swelling ratio and ξ is network mesh size.

Q → ξ → D eff

This correlation is significant since the softness and deformability of nanogels can impact the way they interact with biological systems and their transport characteristics (Scotti et al., 2022; Dave et al., 2022).

This bidirectional relationship is one of the principal soft-matter components of the proposed framework.

2.7.5 Reaction, degradation and drug release

Drug release from nanogels may be due to diffusion, degradation of the polymers, relaxation of the network, bond responsiveness and interaction between the drug and the polymer. Among these, the volume, water content, mechanical properties and network structure of nanogels can be modulated by stimulus and are very suitable for stimulus-responsive delivery (Zhang et al., 2021; Attama et al., 2022).

The coupled reaction–transport equation can be written as:where R (c,θ) represents reaction, degradation, binding or release processes.

∂ t / ∂ c + u · ∇ c = ∇ · D eff ∇ c + R c , θ

For a simplified first-order degradation process:where k_deg is the effective degradation rate constant.

R = − k deg c

Importantly, degradation can alter network structure and thus affect transport and release, in a feedback loop. Importantly, degradation can alter network structure and thus affect transport and release, in a feedback loop. Therefore, it is crucial to regard the drug release as an emergent property of coupled transport, soft-matter and reaction processes and not as a singular diffusion event (Dave et al., 2022; Attama et al., 2022).

3 Soft-matter design principles in polymeric nanoparticle drug delivery systems

Polymeric dispersions, which are the nanocarriers in soft polymeric materials, are controlled by the interactions between polymer-network structure, mechanics, hydration and biological environment. Nanogels are smaller than the rigid colloidal systems, and are hydrated and deformable networks that have properties which change continuously during biological delivery. Thus, even size and/or surface chemistry are not sufficient to describe the behaviour of nanoparticles.

The soft nanocarrier behavior is the consequence of interplay between the hygroscopic properties of the solvent, polymeric material deformability to the mechanical stresses, mesh structure of a gel and the mobility of the carrier component within the polymeric matrix. Permeability, diffusivity, interfacial interactions and drug release behavior are influenced by the various parameters, such as mesh size, crosslink density, swelling ratio, elastic modulus, and viscoelastic relaxation.

The remainder of the sections outline key soft-matter design elements and how they relate to each other in controlling the system transport and biological activity.

3.1 Hydrated polymer networks and dynamic architecture

In soft polymeric nanoparticles, water plays a central role in defining internal structure, mechanical compliance, and pathways for molecular transport. Hydration directly influences mesh size, permeability, effective particle diameter, and interfacial behaviour.

Polymer networks are not static; rather, they respond continuously to solvent redistribution, polymer-chain rearrangement, protein corona formation, and local ionic conditions. As a result, diffusional transport depends not only on the architecture of the internal network, but also on the surrounding biological environment.

3.2 Stimulus-responsive behavior

Stimulus-responsive nanogels undergo physical and/or chemical changes in response to external or internal stimuli. These changes may alter swelling behaviour, charge distribution, hydrophobicity, and mechanical stiffness.

Biological systems, however, rarely present uniform stimuli; instead, they exhibit heterogeneous signals in both space and time. For this reason, responsiveness should not be viewed solely as a release mechanism triggered by a stimulus, but also as a process that shapes transport through the stimulus environment. Structural changes induced by stimuli can modify diffusivity, interfacial interactions, and barrier permeability during circulation, tissue penetration, and cellular uptake.

3.3 Mechanical softness and deformability

The mechanical properties have a significant effect on the transport of nanoparticles through bio-bars. The hydrodynamic properties, ability to penetrate the extracellular matrix, ability to interact with membranes and cellular uptake, are all controlled by parameters including elastic modulus, viscoelastic relaxation and deformability.

The dominant transport mechanism is also a function of mechanical state: stiffness affects convection and margination in vascular flow, while deformability plays a growing role when cells are confined at cell–cell interfaces and within the extracellular matrix. Elasticity has been demonstrated to modulate the uptake of macrophages, their biodistribution, their penetration into tumors and their accumulation for therapy in experimental studies (Hui et al., 2020; Kong et al., 2021).

Importantly, mechanical softness and transport performance is not a linear relationship; if it’s too soft, the structure may be too floppy, but if it’s too hard, then it might not be as easy to penetrate and adjust materials to transport. The optimal performance is thus obtained with a balanced viscoelastic properties, between the extremes of the mechanical states.

3.4 Integrated soft-matter design and transport coupling

The hydration, responsiveness, and mechanics constitute a coupled transport system that can be described by the variables interdependently. When swelling alters the size of the mesh, it will also change the diffusivity; when deformation changes the shape of the interface, it will also change the degree of hydrodynamic and biological interaction; and protein adsorption will change the shape of the hydrodynamic and biological interactions. These feedback relationships progressively reshape the transport behaviour of nanoparticles during biological delivery. In summary, polymeric nanogels can be considered adaptive transport systems, whose biological performance is the result of the dynamic interactions between material state, transport conditions and biological environment. The key soft-matter parameters important for transport behavior, release characteristics and trade-offs are summarized in Table 2.

TABLE 2. Relationships between key soft-matter design parameters, transport behavior, release dynamics, and dominant transport-regime transitions in polymeric nanoparticle systems.

3.5 Unified design principle: transport–structure co-evolution

The main idea of soft-matter design is the unabridgable coupling between the performance of a nanoparticle and its individual variables. Rather, the biological transport is the outcome of the ongoing parallel evolution of structure, mechanics and interfacial chemistry. Therefore, polymeric nanogels can be considered “soft” materials, which are out of equilibrium, having different mechanical and interfacial states through different biological transport stages. This requires a shift to regime-aware engineering approaches based on predictions and involve mechanical state variables and transport coefficients that are evolving together.

During biological delivery, these coupled properties of soft matter are of great significance as they face a sequence of transport barriers, each of which have a distinct mechanical, hydrodynamic, and biochemical requirement.

4 Biological transport across sequential barriers

A series of successive barriers are responsible for the biological performance of polymeric nanoparticles, and each barrier has unique physical and biochemical mechanisms. Nanoparticle transport is achieved by passing between the convection-, diffusion-, deformation-, and reaction-dominated regimes. In the Transport-Soft Matter Coupling Framework (TSMCF), these stages correspond to different transport environments defined by the mechanics, hydration and interfacial interactions of the nanoparticles. Thus, the design of rational nanocarriers should be based on the assumption of their adaptable transport properties in various biological environments beyond simply optimizing individual physicochemical properties. Each biological transport barrier is described below, individually, and an example of how a soft matter property determines the pathway through which the nanoparticles will be transported, adapted, and perform a therapeutic effect is shown for each barrier (Figure 2).

5 Transport–soft matter coupling framework (TSMCF)

The multi-scale transport phenomena presented in Section 2 show that convection, diffusion, permeability, deformation, degradation, and drug delivery cannot be considered separately when studying polymeric nanogel delivery. Rather, these processes play a role on multiple scales, from physiology to transport and soft matter to biological ones. The proposed Transport–Soft Matter Coupling Framework (TSMCF) is therefore a system-level approach for the connection of physiological flow conditions and pressure gradients with nanogel transport, soft-matter response, biological interactions and therapeutic performance.

The framework assumes that the nanogel is a dynamic soft-matter system whose structural and mechanical state can change while it is transported in a biological milieu. The hydrodynamic environment at the physiological scale is determined by the pressure gradients, fluid viscosity, velocity fields and shear stresses. These variables affect the relative contributions of convection and diffusion and have effects on interactions with endothelial and tissue barriers. Then at the nanogel level, swelling, deformation, mesh size, permeability and degradation can alter the transport of molecules and their release of drugs. The framework therefore allows for bidirectional relationships between the physiology and the structure of the nanogel and the behaviour of the transport. The point is that the framework also includes the connections between the behaviour of the transport and the nanogel’s structure, and between the structure of the nanogel and the physiology. This systems perspective is aligned with established evidence that the fate of nanoparticles is influenced by the biological barriers to particles, and physiological transport pathways (Blanco et al., 2015; Mitchell et al., 2021; Sindhwani et al., 2020).

Figure 3 depicts the multiscale sequence of events leading to the TSMCF, from blood circulation to endothelial transport, extracellular-matrix penetration, cellular uptake, intracellular trafficking, to drug release. The framework also adds momentum transport explicitly, namely, under pressure and viscous effects, prior to including the resulting velocity field in the convection and mass transport. This gives rise to the physical link between macroscopic fluid mechanics and nanoscale transport which is needed for an engineering description of nanogel delivery.

5.1 Comparison of existing nanogel design frameworks and the proposed transport–soft matter coupling framework (TSMCF)

There are a number of conceptual and engineering strategies that have been put forward to inform the design of nanomedicines. The conventional nanoparticles design strategy based on physicochemical properties, the Quality-by-Design (QbD) approach, the mechanobiology-oriented approach and the pharmacokinetic or ADME model are existing strategies. Both offer a glimpse into various aspects of the development of nanomedicines, but have different primary goals and scales of modelling.

Factors that are typically considered in conventional nanoparticle design include particle size, surface charges, surface chemistry, targeting molecules, morphology and loading of drugs. The above parameters play significant roles in the stability, biodistribution, cellular interactions, therapeutic activity of nanoparticles. These methods are not always able to quantify the relationship between material architecture and coupled transport processes taking place through successive physiological barriers, however (Blanco et al., 2015; Mitchell et al., 2021).

Quality by Design (QbD) is a systematic approach to pharmaceutical development that is based on Critical Material Attributes (CMAs), Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs). QbD has great potential for optimization of formulation, manufacturing consistency and regulatory development of nanopharmaceuticals (Lionberger et al., 2008; Yu et al., 2014). Pressuredriven flow, convection, diffusion, permeability, deformation, and changing nanogel transport properties, however, are not explicitly solved for by QbD, and hence are not a part of the model.

The importance of material stiffness, extracellular-matrix mechanics, cellular forces, and viscoelasticity, on cellular behaviour and biological responses have been shown by mechanobiological approaches (Discher et al., 2005; Vogel and Sheetz, 2006; Wirtz et al., 2011). The techniques are very pertinent to soft nanomaterials as the deformation or mechanotransformation of nanogels can impact their biological interactions. However, the description for mechanobiology does not necessarily require a single nanogel engineering framework for the integration of momentum transport, mass transport, permeability, swelling, degradation and drug release.

Systemic absorption, distribution, metabolism and elimination, however, can be described with great power using pharmacokinetic models and models of ADME. They are extremely useful for giving information about drug disposition at the organism level but in general they reduce the level of detail of the detailed structural and mechanical behaviour of the nanocarrier. Therefore, they cannot simulate the changes in transport at tissue and cellular scales due to changes in mesh size, swelling, deformation and degradation of the nanogels.

This paper puts forward the TSMCF as an enabling framework that links these hitherto disparate viewpoints. It is not a substitute for QbD, mechanobiology or pharmacokinetics, or for the traditional approach to designing nanoparticles. Rather, it brings in an obvious transport–soft matter coupling layer between material design and biological performance. The framework integrates measurable material parameters with physiological transport variables and is then coupled to the biological and therapeutic outcomes. This separation is key to the suggested framework, and is explained briefly in Table 3.

TABLE 3. Comparison of existing nanogel design frameworks with the proposed TSMCF.

It is interesting to note that the unique value of the TSMCF is not that it introduces a new individual transport equation, or a new nanogel material. Instead, it is the way it brings together existing transport, soft-matter, biological and engineering concepts and principles into a coupled design. Existing approaches give us the individual components and the TSMCF explicitly links these components together with measurable variables like pressure, velocity, effective diffusivity, permeability, swelling ratio, mesh size, elastic properties and degradation rate (Blanco et al., 2015; Mitchell et al., 2021; Peppas et al., 2006; Li and Mooney, 2016).

5.2 Framework components

The TSMCF brings together the multiscale transport principles discussed in Section 2, and the comparison discussed in Section 5.1, and coordinates nanogel delivery into the interwoven physiological, transport, soft-matter, reaction, and biological components. The framework does not suggest that each stage of the process is dominated by one mechanism. Instead, every biological stage is a potentially “dominant” transport state of a coupled system, and the relative importance of convection, diffusion, deformation, reaction, release will vary depending on the material and the physiological environment.

Biological delivery stages are thus taken into account: (i) blood circulation and systemic transport, (ii) endothelial transport and extravasation, (iii) penetration of extracellular-matrix and tissues, (iv) cellular uptake, (v) intracellular trafficking and (vi) drug release. In all of these stages, velocity fields are created by physiological pressure and viscous forces which act to cause convective transport, and concentration gradients which act to cause diffusive transport. The permeability determines whether something can pass through a biological membrane and the swelling, deformation, degradation and relaxation of the nanogel changes the effective permeability of the polymer network. The bidirectional coupling outlined in Figure 3 is due to these relationships.

5.2.1 Blood circulation and immune interactions

After systemic injection, nanogels are carried by a dynamic fluid that results in a hydrodynamic behaviour of the nanogels, which can be influenced by pressure gradients, viscosity, velocity fields and shear stresses. Thus, the initial transport environment is set by blood flows and convection can play an important role in systemic transport of nanoparticles. Simultaneously, the plasma proteins and immune components bind to the surface of the nanoparticles leading to the formation of a layer of proteins referred to as protein-corona, which can alter the effective hydrodynamic size, surface properties and biological identity of the nanoparticles (Monopoli et al., 2012; Tenzer et al., 2013).

These processes are considered as coupled from the TSMCF point of view. The flow field affects particle transport and residence time and changes in the particle properties due to protein adsorption and biological interactions can alter the effective properties of the fluid in which the particles are moving. This can therefore impact the circulation time and biodistribution (Monopoli et al., 2012; Mitchell et al., 2021) via particle aggregation, opsonization and recognition by the mononuclear phagocyte system.

At this stage, the pertinent design aspects comprise hydrodynamic size, surface hydration, charge, colloidal stability, deformability and mechanical characteristics. For long circulating nanogels, therefore, it is crucial that it remains sufficiently stable in order to withstand physiological flow conditions, while retaining deformability.

5.2.2 Endothelial transport and extravasation

Nanogels then pass through endothelial barriers which control the passage of nanogels from the vascular compartment to the surrounding tissues after having been delivered systemically. Changes in the relative importance of convection, diffusion, adhesion and margination (plating) and barrier permeability occur during the transition from vascular to tissue transport. Extravasation and tissue distribution may be affected by the endothelial heterogeneity, the characteristics of the vascular pores and particle size, surface properties and deformability (Blanco et al., 2015; Cabral et al., 2011; Wilhelm et al., 2016).

The local hydrodynamic environment in the vicinity of the endothelial interface is controlled by pressure-driven and viscous flow within the TSMCF, while translocation across the barrier is controlled by permeability and interactions at the endothelial interface. Nanogel deformability allows an extra coupling since the mechanical adaptation can be helpful to pass through a spatially constrained environment.

Thus, the relevant relationship can be represented as:

flow / pressure → endothelial transpor t ↔ deformation / permeability

The coupling is a good example of the fact that the extravasation of particles is not solely dependent on size. The behavior of an adaptive surface deformable nanogel could be different than that of a rigid nanogel with similar hydrodynamic diameter (Cabral et al., 2011; Blanco et al., 2015).

5.2.3 Extracellular-matrix and mucus transport

After extravasation, the nanogels will come in contact with extracellular matrices, interstitial fluids, and in the case of a specified route of administration, mucus barriers. These environments may cause steric confinement, viscoelastic resistance, adhesive interactions and limited pore access. As a result, transport can transition from unrestricted diffusion to either hindered diffusion and/or deformation coupled transport (Lai et al., 2007; Lai et al., 2009; Jain and Stylianopoulos, 2010).

In the TSMCF, the extracellular matrix is considered as a transport barrier as well as a mechanical constraint. The movement of fluid may be affected by interstitial pressure and the extracellular-matrix architecture provides the pathways for the movement of nanoparticles. The swelling and deformation of nanogels can alter the effective size of the carrier, as well as interactions with the microenvironment.

Important parameters include:

• mesh size (ξ);
• swelling ratio (Q);
• elastic modulus (E);
• effective diffusion coefficient (Deff);
• permeability (P);
• degradation rate; and
• interfacial hydration and surface chemistry.

The resulting coupling can be expressed conceptually as:

Q , E , ξ → D eff , P → tissue penetration

Thus, optimizing tissue penetration requires simultaneous consideration of nanogel structure and the physical properties of the surrounding biological matrix.

5.2.4 Cellular uptake and intracellular transport

At the cellular level, the transport of the nanogel depends more and more on the nature of the interactions at the interface, the binding to receptors, the deformation of the membranes and the endocytosis. The uptake of nanoparticles may be membrane wrapping and receptor mediated internalization, which is dependent on the properties of the nanoparticles and the energetic interaction between the carrier and cell membrane (Smith et al., 2019; Hui et al., 2020).

The TSMCF therefore takes into consideration the biological, as well as mechanical, components at this level. The interactions between receptors and the surface chemistry and ligand presentation, or the elasticity and deformability of nanogels and their wrapping and internalization of membranes will be effected. The pH, enzymes, redox conditions and trafficking to and from membranes after internalization dictate the fate of the nanogel and the availability of the drug in the intracellular environment.

One problem with endosomal sequestration is that the internalized nanocarriers can be delivered to the lysosome where the nanocarrier itself is destroyed without the release of its contents at the site of interest. The stimuli for network relaxation, degradation, or endosomal escape could thus be a change in pH, redox status, or enzymatic activity and may be achieved through stimulus-responsive or degradable nanogels (Hajebi et al., 2019; Smith et al., 2019; Chatterjee et al., 2024).

5.2.5 Unified transport-regime perspective

The sequential biological stages described above should not be interpreted as completely independent transport regimes. Instead, the TSMCF considers nanogels as deformable soft-matter systems whose hydrodynamic, mechanical, structural, and biochemical states evolve throughout biological delivery.

For example, a nanogel may experience convection-dominated transport during systemic circulation, followed by coupled convection–diffusion near an endothelial barrier, hindered diffusion within tissue, deformation-coupled transport during cellular interaction, reaction-coupled transport within intracellular compartments, and diffusion/degradation-controlled release. The transition between these regimes is governed by the local physical environment and the evolving nanogel state rather than by biological stage alone.

The principal dimensionless indicators used to describe these transitions include the Péclet number, where U is characteristic velocity, L is characteristic length, D is diffusivity, λ is the characteristic relaxation time, t_obs​ is the observation time, and k is a characteristic reaction or degradation rate.

Pe = UL / D

the Deborah number , De = λ / t obs

and the Damk ö hler number , Da = kL / U

The dimensionless numbers should be treated as indicators of the regimes rather than as numbers in their own right for the following reasons: The value of each number depends on the nature of the nanogel, the type of payload, the biological compartment and the experimental conditions. The representative regimes in Figure 3 are thus indicative of the mechanisms that might be dominating, and not indicative of fixed values for all nanogel systems.

The central principle of the TSMCF is therefore:

Transport ↔ Soft − matter state ↔ Biological response

This reciprocal relationship distinguishes the TSMCF from approaches in which transport and material properties are treated as independent descriptors.

5.2.6 Quantitative parameters linking structure, transport and performance

The conceptual framework is backed by measurable parameters which can be utilized to link architecture of nanogels with the transport behavior and therapeutic efficacy. Summary of representative ranges from the literature is shown in Table 4. The values used are only indicative literature values, not a standard for all nanogels, since the measured values are determined by the polymer chemistry, the crosslinking method, the payload, the solvent, the pH, the ionic strength, the temperature and the method of measurement.

TABLE 4. Representative quantitative transport and soft matter parameters supporting the transport–soft matter coupling framework (TSMCF).

The transport behaviour is not influenced by one material parameter as shown in Table 4. On the contrary, the effective transport state is a result of interactions between the mesh size, swelling, mechanical properties, particle sizes, diffusion, and biological conditions. For instance, greater swelling will widen the network spacing and free mobility of molecules, while greater crosslinking will decrease the free mobility of molecules and hence reduce effective diffusivity as described by Peppas et al. (2006) and Li and Mooney (2016).

Therefore, the quantitative parameters given in Table 4 can serve as potential input variables for mathematical and computer simulations of the TSMCF. The variables are not considered as characterization results but are engineered parameters that are used to link the design of materials with transport behaviour and therapeutic performance.

5.3 Application of the transport–soft matter coupling framework to representative polymeric nanogels

In this section, we discuss representative polymeric nanogels in the context of the transport/soft matter coupling framework. In this section, we will discuss the representative polymeric nanogels in the context of the transport/soft matter coupling framework.

Representative polymeric nanogel systems were plotted based on their material architecture, stimulus responsiveness, dominant transport mechanism, biological interactions and therapeutic consequences, to illustrate the applicability of the TSMCF. These examples do not aim at being exhaustive case studies, nor is it the intention to apply these examples as experiments to validate the framework. Instead, they illustrate how the concepts can be applied to derive experimentally measurable properties in terms of transport–performance relationships.

5.3.1 Case study 1: PEG-based nanogels

The nanogels based on poly (ethylene glycol) (PEG) are extensively studied due to their high hydration, their biocompatibility and their ability to decrease the nonspecific interaction with the biological components (Oh et al., 2008; Vinogradov, 2010). In the TSMCF, the PEG-based systems show the correlation between polymer-network architecture, hydration, molecular diffusion and drug release.

More crosslinked polymer network results in more interconnected polymer network and thus the effective mesh size available for molecular transport can be reduced. This can reduce the effective diffusivity and increase the payload retention. On the other hand, an increase in swelling causes the network spacing and an increase in hydration which can increase the movement of molecules through the nanogel matrix (Peppas et al., 2006; Li and Mooney, 2016).

The TSMCF therefore maps the PEG nanogel relationship as:while simultaneously recognizing that swelling and environmental conditions can modify these relationships.

crosslink density → ξ → D eff → drug release

5.3.2 Case study 2: chitosan nanogels

Softer materials can make up a system that responds to the environment, such as the nanogels made with chitosan. The electrostatic interaction, hydration, swelling and network structure of the amino groups can be changed when they are protonated in acidic environments (Qiu and Park, 2012; Hamidi et al., 2008).

In the TSMCF, pH is therefore considered an external physiological parameter, which affects the soft matter properties of the nanogel. The subsequent changes in swelling can then change the size of the mesh and effective diffusivity and thus affect the drug release.

The coupling can be represented as:

pH → protonation → Q , ξ → D eff P → drug release .

In this example, environmental responsiveness is introduced to the design of a transport, and not viewed as just a characteristic of a formulation.

5.3.3 Case study 3: hyaluronic-acid nanogels

In the case of nanogels, hyaluronic-acid based nanogels are an example of material–biological interactions that are coupled to the transport and degradation. Hyaluronic acid may be able to bind to CD44 receptors, giving a potential mechanism for receptor mediated cellular recognition and uptake (Burdick and Prestwich, 2011; Mitchell et al., 2021).

Within the TSMCF, the relevant sequence involves:

HA architecture → CD 44 interaction → cellular uptake → intracellular transport → degradation / release .

The mobility of the payload can be affected by degradation by enzymes and/or changes in the polymer network, and receptor-mediated interactions can affect cellular internalization. This example shows that the biological recognition and transport problems do not necessarily have to be separated and can be modeled as parts of the therapeutic problem that are coupled together.

5.3.4 Computational implementation of the TSMCF

The present review is conceptual and does not include new numerical simulations but the structure of the TSMCF can be translated into computational models based on the measurable parameters of the TSMCF. On principle, fluid flow, mass transport, reaction/degradation and certain mechanical/swelling responses can be represented in a multiphysics framework such as COMSOL Multiphysics.

The fluid-flow equations can be used to calculate the pressure and velocity fields at the macroscopic scale. These fields can then be connected to convection–diffusion equations of transport of nanoparticles or drugs. At the nanogel scale it is possible to consider effective diffusivity, permeability, swelling behaviour, degradation rate and mechanical properties as material parameters or constitutive relationships. Reaction terms can then be added to describe degradation or binding or even stimulus responsive release (Bird et al., 2002, Cussler, 2009).

A simplified computational pathway can therefore be represented as:

∇ p , μ → u → Pe → D eff , P → Q , ξ , E → k deg → drug release .

This offers a possible path forward to numerically implement the TSMCF in the future. But most important it is not claimed that all these processes have to be solved at the same time in all applications. The degree of coupling will depend on the biological question, spatial scale, temporal scale, the parameters available, and from experimental validation data.

5.3.5 Engineering significance

Given that the TSMCF is a conceptual description rather than just an engineering methodology, the representative examples illustrate how it could be used as such. The framework enables measurable properties of the material to be related to the physiological transport variables, biological barriers and therapeutic outcome. As such, it may help in selecting materials, determine the most important transport parameters, develop computational speculations and establish experiments needed for the model validation.

5.4 Limitations and implementation challenges of the TSMCF

The TSMCF takes a holistic view of interpreting and designing polymeric nanogel systems, but there are a number of caveats to be aware of. First, biological transport is a very heterogeneous process. The composition of the extracellular matrix, interstitial pressure, immune activity, and disease status, as well as tissue permeability, may differ from organ to organ, disease to disease, and person to person. Thus, it is not possible to use the same set of transport parameters to characterize all physiological environments (Jain, 2005; Mitchell et al., 2021).

Second, there is a need for an accurate measurement of the parameters which relate material structure to transport behaviour in the framework. The effective diffusivity, permeability, mesh size, swelling ratio, elastic modulus, crosslink density, relaxation time and degradation kinetics can be sensitive to the environmental conditions and experimental procedure. Thus, it is necessary to standardize on physiologically relevant conditions prior to the reliability of using these parameters in predictive models (Peppas et al., 2006; Li and Mooney, 2016).

Third, it is difficult to implement on a multi-scale computational level. The spatial and temporal scales on which momentum transport, mass transport, soft-matter mechanics, reaction kinetics, and biological interactions can be in play are different. Thus, fully coupled simulations can be challenging to compute and need the correct constitutive equations, experimentally measured parameters and carefully defined boundary and initial conditions (Bird et al., 2002; Cussler, 2009).

Third, it is still difficult to implement a multiscale computational implementation. The spatial and temporal scales on which momentum transport, mass transport, soft-matter mechanics, reaction kinetics, and biological interactions can be in play are different. Fully coupled simulation thus may become difficult to compute and needs to be appropriately defined by different constitutive equations, experimentally measured parameters and careful definition of boundary and initial conditions (Bird et al., 2002; Cussler, 2009).

Another weakness is with respect to model identifiability. There can be multiple material parameters which affect the same observable phenomenon. Some of these changes can occur due to a change in the mesh size, swelling, degradation, drug–polymer affinity, and/or drug diffusivity, for example,. If this is the case, a release curve by itself may not allow the identification of the actual mechanism. The robust model calibration requires a structural, mechanical, transport and release characterization of the system in multimodal manner.

Last, the TSMCF should not be considered as a valid predictive model at this point. It is an approach that involves both a method and a concept, and which provides a set of variables and couplings that should be taken into account for the construction of predictive models. To be able to identify which of the coupling terms dominate in specific nanogel systems and physiological environments, experimental validation and forward computational work is needed.

Nevertheless, the framework can be a starting point for further integration with multiphysics modelling, artificial intelligence based material optimisation, digital-twin and patient-specific transport modelling. Transport-informed design of nanogels, in which individual physiological transport properties are embedded in the design along with the targeting and material properties, could be realized in the future (Mitchell et al., 2021).

5.5 Design implications of the TSMCF

The key design implication of the TSMCF is that the polymeric nanogel should not be optimized just based on the individual physicochemical properties, including particle size, surface charge and drug loading capacity. Rather, selection of design variables should be based on the variables’ anticipated impact on the coupled transport and biological processes that would be experienced during delivery.

This is the case, for instance, if a nanogel is intended to stay in the systemic circulation for a long period: the nanogel will need to be both high in its colloidal stability and sufficiently hydrated to pass the physiological flow and yet it must be sufficiently deformable. A nanogel targeting the penetration of the extracellular matrix might need to be a compromise between physical softness, size of the mesh, interactions with the surface, and swelling. On the other hand, a delivery system that operates in the cell must be able to respond to the environment with degradation and/or network relaxation to escape from the endosome and transport its cargo to the proper cellular compartment.

The TSMCF thus enables a stage-specific design approach where the properties of materials used in the design are adjusted to reflect the predominant transport constraints at each biological barrier. The mapping from biological stage to transport constraint, soft-matter state to governing parameters and soft-matter state to potential failure mode are given in Table 1; Figure 3 respectively.

The overall design principle can consequently be expressed as:

Material design → Transport state ↔ Soft − matter response → Biological interaction → Therapeutic performance

The bidirectional arrows emphasize that transport is not simply an outcome of nanogel design. Rather, physiological transport conditions can alter nanogel structure, while the evolving nanogel structure can subsequently alter transport. This reciprocal relationship represents the central methodological contribution of the TSMCF.

6 Coupling between soft-matter transport and drug release

Drug release in soft polymeric nanoparticles is intrinsically coupled to biological transport because both processes occur within the same evolving polymer-network architecture. Mesh size, crosslink density, swelling state, degradation kinetics, and viscoelastic relaxation all influence nanoparticle mobility as well as therapeutic release. Conventional release studies are often performed under dynamic conditions that differ substantially from the in vivo environment. During circulation and tissue penetration, nanogels are continuously exposed to shear flow, steric confinement, mechanical deformation, and intracellular processing, all of which alter network structure and the diffusion pathway (Liechty et al., 2010; Li and Mooney, 2016; Floyd et al., 2025).

Mechanical relationships between the transport process and the release of the drug are summarized and indicated that the changing properties of the polymer network can modulate the mobility of the nanoparticles as well as the release of the drug.

In diffusion-controlled systems, release depends on the mesh structure, the hydration, the interactions between the polymer and the solute as well as network tortuosity. Diffusivity in soft nanogels typically changes continuously as the swelling, deformation and environment conditions evolve. Stimulus-responsive systems add a further layer of coupling: changes in pH or enzymatic activity, redox state or temperature of the surroundings can cause changes in swelling, charge distribution and network integrity that affect the release properties (Peppas et al., 2006; Hajebi et al., 2019). The elasticity properties and dynamics of the network as well as mechanical stresses during circulation and tissue transport may further stimulate or inhibit the release. In the context of the TSMCF, a change in the polymer-network state is seen as a transport and mechanics-coupled process; drug release and the state of the polymer-network are not viewed as material property.

7 Experimental and computational models for transport-informed polymeric nanoparticle design

Experimental and computational approaches for designing polymeric nanoparticles for use in transport are described below.

To predict the in vivo behavior of soft polymeric nanoparticles, one should have models including the coupled mechanical, transport and biological processes in vivo. Adaptive nanocarriers are difficult to describe using conventional methods of static characterization. The next sections provide background on the main experimental and computational methods employed for characterizing the transport properties and predictive design of nanocarriers based on transport.

7.1 Characterization standards

Nanoparticle characterization should be based on biologically relevant environments as protein adsorption and protein hydration may significantly affect the size, surface charge and interfacial behavior of nanoparticles. The mechanical properties, like the elastic modulus and relaxation dynamics, are also highly condition dependent, and should be measured under defined physiological conditions. Therefore, there is a need for a multidisciplinary characterization of nanomedicines, including chemical, structural, mechanical, and biological characterization so that the design can be reproduced (Floyd et al., 2025).

7.2 Microfluidic and tissue-mimetic systems

Shear flow, endothelial transport, matrix confinement and stimulus-responsive release can be controlled in a microfluidic platform and/or hydrogel-based tissue models to investigate. These systems are also able to partially mimic the physiological transport environments, with predictive accuracy that varies greatly depending on the model fidelity and reporting of matrix properties and transport conditions (Li and Mooney, 2016; He et al., 2023).

7.3 Computational and AI-assisted modeling

Computational approaches link nanoscale polymer behavior with macroscopic transport and pharmacokinetic outcomes by integrating fluid dynamics, polymer mechanics, swelling, binding kinetics, and intracellular trafficking. AI-assisted approaches may help identify multivariable structure–transport relationships, although predictive reliability depends on standardized datasets and mechanistically meaningful descriptors.

7.4 Toward predictive transport-informed design

Realizing this predictive potential will depend on establishing standardized, mechanistically grounded datasets that link polymer architecture to transport behaviour across studies, enabling AI-assisted models to move from correlative pattern-matching toward genuinely predictive, transport-informed design of adaptive nanogels.

8 Translational challenges

Despite several clinical trials, the translation of soft polymeric nanocarriers is still limited due to biological heterogeneities and the limited predictive capabilities of these models, as well as because of the complexity of manufacturing. The transport and therapeutic effect of nanoparticles vary significantly from patient to patient and from disease to disease, due to differences in vascular permeability, immune activity, composition of the stromal matrix, composition of mucous secretions, and trafficking within the cytoplasm (Shi et al., 2017; Sun et al., 2020; Younis et al., 2022; Joyce et al., 2024). One of the biggest challenges in the field of preclinical nanomedicine is the use of surrogate indicators like the uptake of nanoparticles or nanoparticle localization in the cell, with inadequate characterization of the delivery of the intracellular payload and the therapeutic efficacy. The crucial integration of biological validation, manufacturability, quality control and clinical feasibility is, however, gaining importance in translational frameworks (Joyce et al., 2024). The key biological, manufacturing, and regulatory hurdles to be overcome in making it possible to clinically translate transport-informed soft nanomedicines are highlighted.

The production of soft nanogels is also challenging due to changes in swelling, mechanics, transport properties induced by slight changes in the polymer synthesis, cross-linking, purification or storage conditions. However, because these systems are adaptive and deformable, they are a challenge to evaluate from a regulatory perspective, and require standardized characterization methods, where these can be correlated with reproducible safety and efficacy outcomes (Bobo et al., 2016; Anselmo and Mitragotri, 2019; Younis et al., 2022).

9 Future directions and design recommendations

Future progress in polymeric nanogels will depend on the integration of transport engineering, soft-matter science, computational modelling, and artificial intelligence to produce predictable, clinically translatable nanomedicines. The ongoing development of adaptive polymer chemistry, predictive transport modelling and advanced physicochemical characterization of polymers will be key to redesigning nanogels in order to improve their therapeutic performance and clinical applicability (Blagojevic and Kamaly, 2025; Floyd et al., 2025). Routine characterization should extend beyond particle size, morphology, and surface charge to include particle transport-relevant properties like swelling behaviour, mesh size, crosslink density, mechanical stiffness, degradation kinetics, permeability and responsiveness to physiological stimuli. \Similarly, transport evaluation should move beyond the static release/cell uptake assays to dynamic and physiologically relevant experimental models such as microfluidic flow systems, endothelial barrier models, extracellular matrix mimics, organoids, and intracellular trafficking assays.

Computational technologies are emerging which present exciting opportunities for the transformation of the design of adaptive polymeric nanogels. Multiphysics simulation platforms (e.g., finite element modelling) can combine diffusion, convection, swelling, degradation and mechanical deformation within a single computational environment to predict the release and transport of the drug under physiologically relevant conditions. Artificial-intelligence and machine learning technologies have great potential to uncover complex interactions between polymer composition, transport coefficients and therapeutic efficacy, which can be used to speed up rational formulation optimization of nanogels. Achieving these attributes will, however, depend on the use of standardized descriptors that quantitatively relate the polymer mechanics, hydration, degradation behaviour and biological transport to enable robust predictive modelling and comparison between studies.

Manufacturability and regulatory readiness should be taken into account early on in the development of nanogels from a translational point of view. The transport behaviour, mechanical properties and therapeutic efficacy can significantly change as a result of small differences in polymer synthesis, crosslinking chemistry, purification techniques, and storage conditions. Thus, in future the development of the frameworks should include quality-by-design considerations together with an engineering approach based on the transportation data, so that it can be reproduced, scaled successfully and accepted by the regulators. Mechanistic computational models will also play an increasing role in supporting regulatory assessment, and in translating to the clinic, providing interpretable predictions.

Significant scientific challenges remain. In vivo nanoparticle trafficking is difficult to predict because transport varies across tissues, disease states, and patients. Experimentally derived transport parameters have limited transferability owing to the significant biological variability of parameters related to the architecture of the ECM, lymph drainage, activity of the immune system, intra- and extracellular biochemical conditions, and vascular permeability. Furthermore, few studies have succeeded in simultaneously measuring transport, swelling, deformability and biological interactions at multiple scales, making it difficult to experimentally validate coupled transport–mechanics behaviour in soft nanocarrier systems.

A key future direction is patient-specific, transport-informed nanomedicine. In addition to molecular targeting, Next-generation systems can incorporate individual transport profiles such as vascular permeability, stromal architecture and immune status, mucus barrier properties and tissue mechanics, to optimize nanogel design for individual patients. This combination of patient-derived biological information with computational modelling, digital twin technologies and transport-informed engineering can enable personalized prediction of drug distribution, release kinetics and therapeutic response. In this context, the proposed Transport–Soft Matter Coupling Framework (TSMCF) offers a flexible framework for the inclusion of transport phenomena and soft matter mechanics, and biological interactions, in future adaptive nanomedicine platforms and platforms for the rational design of clinically translatable polymeric nanogels.

10 Conclusion

This review has presented the Transport–Soft Matter Coupling Framework (TSMCF) as an integrated engineering strategy for the rational design of polymeric nanogels. By combining transport phenomena, soft matter mechanics, and biological interactions at a systems-level approach, TSMCF differs from traditional nanomedicine paradigms that treat material properties, biological targeting, or manufacturing issues in isolation. This holistic view systematically connect measurable engineering parameters (diffusivity, permeability, swelling behaviour, crosslink density, mechanical properties) to therapeutic performance.

Beyond its conceptual value, the TSMCF supplies a practical basis for computational modelling, transport-informed material optimization, and experimental validation. It is expected to accelerate the development of adaptive polymer nanogels and enhance their predictability, repeatability and clinical translation. The same principles can be extended beyond drug delivery to new fields like gene delivery, immunotherapy, tissue engineering and regenerative medicine, and theranostic systems. By bringing together transport engineering and soft-matter science, the framework provides a platform for interdisciplinary advances in transport science and precision nanomedicine.

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Footnotes

Edited by: Suman Basak, Dyson, United Kingdom

Reviewed by: Alina Rwei, Delft University of Technology, Netherlands

Amin Orash Mahmoudsalehi, Monterrey Institute of Technology and Higher Education (ITESM), Mexico

Footnotes

AI, artificial intelligence; ECM, extracellular matrix; EPR, enhanced permeability and retention; TSMCF, Transport-Soft Matter Coupling Framework.