Programmable immune biointerfaces for cell-state control through mechanotransduction, stimuli-responsive design, and AI-guided modeling in regeneration and cancer

1 Introduction from passive biocompatibility to immune-instructive interfaces

Biocompatibility is not defined by material inertness alone; it emerges from active interactions between material properties, immune cells, stromal components, and tissue-specific signals at the biomaterial–host boundary (Fernandez-Yague et al., 2026; Gao et al., 2025; Mahanty et al., 2025). These interactions occur across diverse material classes, including natural polysaccharides such as alginate, hyaluronic acid, and chitosan; protein-based matrices such as collagen, fibrin, and gelatin methacryloyl (GelMA); synthetic polymers such as polyethylene glycol (PEG), poly (lactic-co-glycolic acid) (PLGA), and polycaprolactone (PCL); and engineered surface systems such as zwitterionic coatings and RGD-functionalized hydrogels. Once introduced into a biological environment, these materials become part of a responsive interface where surface chemistry, stiffness, viscoelasticity, porosity, topography, ligand organization, degradation products, and local soluble factors are interpreted as regulatory cues (Dong et al., 2025; Maduka et al., 2024a; Mei et al., 2024; Ozcolak et al., 2024). These cues influence whether the surrounding tissue moves toward inflammation, fibrotic encapsulation, constructive remodeling, vascular integration, or regenerative repair.

This view marks an important shift in biomaterials science. Traditional approaches often evaluated compatibility through the absence of overt toxicity, excessive inflammation, or rejection-like responses (Fernandez-Yague et al., 2026; Mahanty et al., 2025; Wu et al., 2024). Although these criteria remain essential, they are insufficient for understanding how modern biomaterials shape biological outcomes. For example, alginate or hyaluronic acid hydrogels can be modified with adhesive peptides, degradation-sensitive linkers, or immunomodulatory cargo; PEG-based networks can be tuned for stiffness, ligand spacing, and stress relaxation; PLGA and PCL systems can regulate degradation kinetics, architecture, and release profiles; and zwitterionic coatings can alter protein adsorption and early innate immune recognition. A material may therefore appear tissue-compatible yet still promote chronic low-grade inflammation, fibrotic organization, poor integration, or ineffective repair (Fernandez-Yague et al., 2026; Mahanty et al., 2025; Wu et al., 2024). Conversely, a material designed with appropriate mechanical, biochemical, structural, and temporal cues can organize local responses in ways that support remodeling and functional tissue interaction.

Immune cells are central interpreters of these interface cues. Macrophages, dendritic cells, T cells, stromal cell networks, endothelial cells, and tissue-specialized immune-adjacent cells respond to the physical and chemical structure of their local environment (Fernandez-Yague et al., 2026; Mei et al., 2024; Chavda et al., 2026; Yan et al., 2026). Their behavior is not determined by a single signal, but by combinations of interfacial cues and inflammatory context. Through these combined inputs, biomaterial interfaces can shape immune activation, suppression, repair-associated remodeling, antigen presentation, fibrosis, and tissue integration (Chavda et al., 2026; Yan et al., 2026; Zhao et al., 2025).

Mechanotransduction is a major route through which material properties become immune-regulatory information. Cells convert physical cues into intracellular signals through adhesion complexes, cytoskeletal tension, ion flux, calcium signaling, and transcriptional programs (Mei et al., 2024; Kersey et al., 2024; Settle et al., 2024). These processes influence immune-cell phenotype, stromal activation, inflammatory tone, and tissue remodeling across both regenerative and cancer-relevant contexts (Fernandez-Yague et al., 2026; Mei et al., 2024; Tao et al., 2025).

The emergence of stimuli-responsive biointerfaces extends this principle from static material design to active signal control. Materials that respond to light, electrical or magnetic stimulation, mechanical actuation, ultrasound, and microenvironmental triggers such as pH, reactive oxygen species, or enzymes can deliver immune-regulatory cues with greater spatial and temporal precision (Song et al., 2025; Wang K. et al., 2026; Wu et al., 2026; Xu Y. et al., 2025). Instead of presenting a fixed surface or releasing factors passively, these systems can alter stiffness, expose ligands, release molecules, reorganize local structure, or modulate antigen presentation in response to defined triggers (Wu et al., 2026; Xu Y. et al., 2025; Mathes et al., 2025; Wang W. et al., 2025). This capability shifts the interface from a fixed material surface toward an actively regulated biological environment.

To define the central terminology used in this Review, a programmable immune biointerface is defined here as a biomaterial–host boundary whose mechanical, chemical, structural, degradative, and stimulus-responsive properties are intentionally designed to regulate immune-cell and stromal-cell state trajectories over space and time. This concept differs from conventional immunomodulatory biomaterials by emphasizing coordinated control of material cues, cellular state behavior, timing, spatial organization, and tissue-specific remodeling rather than the delivery of a single immune-regulatory signal. Predictive programmable biocompatibility is defined here as a design framework that links material variables, stimulus inputs, mechanotransductive pathways, immune-state readouts, and remodeling outcomes into testable, model-guided relationships between material properties and biological responses. In this framework, biocompatibility is treated not only as tissue tolerance or reduced adverse reaction, but as the capacity to direct measurable immune-state trajectories through experimentally defined and computationally interpretable design variables.

This article is a narrative Review rather than a systematic review. The literature was selected to support an integrative framework connecting biomaterials, mechanobiology, immunoengineering, stimuli-responsive materials, regenerative biointerfaces, tumor biomaterial models, and AI-guided biomaterial design. Priority was given to studies and reviews that directly linked material properties, biointerface structure, mechanotransductive signaling, immune-cell or stromal-cell state regulation, regenerative remodeling, cancer-associated matrix organization, or computational design strategies. Foundational studies were included when needed to define core concepts, while recent work was emphasized where it illustrated emerging material platforms, mechanistic readouts, responsive chemistries, tissue-model systems, or AI-guided design approaches. Advanced developmental-continuum and microphysiological platforms, including embryo-like structures, organoids, organ-on-chip systems, and engineered functional tissue interfaces, are also relevant testbeds because they allow material cues, tissue architecture, multicellular organization, and interface maturation to be examined under controlled conditions (Topal, 2026).

Despite rapid advances in mechanobiology, immunoengineering, and responsive biomaterials, a central gap remains: material design variables are not yet consistently linked to predictable immune-state outcomes across biological contexts. Regeneration and cancer expose this gap from opposite directions. Regenerative interfaces often require immune environments that limit persistent inflammation and fibrosis while supporting remodeling, vascularization, and tissue integration (Butenko et al., 2024; Mai et al., 2025; Xiao Y. et al., 2025). Cancer-associated interfaces, in contrast, often involve mechanically constrained and immunologically suppressive environments in which matrix architecture, antigen localization, and stromal organization shape local immune behavior (Callaway et al., 2025; Lu X. et al., 2025; Zeng et al., 2025). Both contexts depend on the same broader design question: how can material properties and stimulus inputs be organized to direct immune-state behavior at biological interfaces?

A predictive framework therefore connects material parameters, stimuli-responsive cues, mechanotransductive signaling, and measurable immune readouts into a single experimental structure. Such a framework clarifies how stiffness, ligand presentation, degradation, trigger timing, and matrix architecture influence macrophage behavior, stromal activation, antigen presentation, vascular organization, and tissue-specific remodeling while supporting AI-guided modeling of high-dimensional biomaterial datasets (Butenko et al., 2024; Riccio et al., 2025).

Figure 1 illustrates the conceptual shift from passive biocompatibility toward programmable immune biointerfaces by contrasting classic foreign body encapsulation with an engineered biomaterial–host interface that organizes macrophage state regulation, mechanotransduction, vascular ingrowth, matrix turnover, and tissue-specific remodeling outcomes.

Traditional passive biocompatibility frames an introduced material as a trigger of acute tissue response, macrophage recruitment, foreign body reaction, fibrotic encapsulation, and limited integration over time. Programmable immune biointerfaces instead treat the biomaterial–host boundary as an active regulatory site where material design inputs, degradation/release behavior, and stimuli-responsive cues shape integrin–focal adhesion signaling, mechanotransduction, calcium signaling, macrophage phenotype, cytokine signaling, and stromal activation. These interface processes can direct divergent remodeling outcomes, including pro-resolution, anti-fibrotic, vascular-supportive, and tissue-integrative responses in regeneration or suppressive, exclusionary, antigen-localizing, and dense tumor-like matrix organization in cancer-associated environments.

Programmable immune biointerfaces represent a shift from passive compatibility toward active biological instruction. Their defining value lies not in avoiding harmful reactions alone, but in organizing immune and stromal responses with specificity, timing, and reversibility. Biocompatibility is not safety or the absence of toxicity alone; it is the controllable capacity of a biointerface to shape immune states in space and time, organized through mechanotransduction, stimuli-responsive design, and predictive modeling.

2 Immune cell states at the biomaterial–host boundary

Biointerfaces regulate immune behavior by organizing the physical, biochemical, and temporal cues that cells encounter at material–tissue boundaries (Maduka et al., 2024b; Quartey et al., 2025; Zhou et al., 2025a). These cues do not act as isolated signals; they are interpreted within local environments shaped by injury, matrix turnover, inflammatory tone, vascular status, resident fibroblasts, pericytes, endothelial organization, stromal architecture, and disease-associated context (Maduka et al., 2024b; Quartey et al., 2025; Zhou et al., 2025a; Xie et al., 2025). Importantly, immune regulation at the interface begins before adaptive immune cells accumulate. Early cytokine gradients, oxygen and nutrient gradients, endothelial activation, pericyte-associated vascular stabilization, and fibroblast-derived matrix signals can shape macrophage recruitment, dendritic-cell positioning, and later lymphocyte access. As a result, the same material feature can support distinct cellular responses depending on where it is placed, how long it persists, which stromal and vascular cells are already present, and which immune populations are recruited to the local interface.

Macrophages are among the earliest and most influential responders to implanted or injected biomaterials (Zhou et al., 2025a; Liu D. et al., 2025). Their behavior cannot be fully captured by a simple inflammatory-versus-reparative binary. At material interfaces, macrophages occupy tissue- and matrix-sensitive states associated with acute inflammation, debris clearance, extracellular matrix turnover, foreign body reaction, fibrosis, vascular support, tissue reorganization, or immune suppression (Maduka et al., 2024b; Zhou et al., 2025a). These states are shaped by surface chemistry, degradation products, adsorbed proteins, local cytokines, oxygen availability, and interactions with stromal and endothelial cells (Dong et al., 2025; Xie et al., 2025; Liu D. et al., 2025). A macrophage response that supports matrix turnover in one setting may contribute to fibrotic encapsulation or suppressive immune organization in another, making macrophage state regulation a central design problem for immune-instructive biomaterials.

Dendritic cells provide an important link between material interfaces and adaptive immune engagement. Their interaction with biomaterials can influence antigen capture, maturation, cytokine production, migration, and antigen presentation (Chavda et al., 2026; Quartey et al., 2025; Choi and Ki, 2024). In immune-instructive systems, dendritic-cell behavior is especially relevant when materials localize antigens, present immunomodulatory ligands, release danger-associated signals, or structure the spatial relationship between antigen-presenting cells and lymphocytes (Gong et al., 2024; Wu et al., 2025; Zhou et al., 2025b). These processes determine whether the local immune environment favors activation, tolerance-like regulation, or limited adaptive engagement.

T cells respond to biomaterial-contact environments both directly and indirectly. Their activation, localization, persistence, and functional state are shaped by antigen availability, costimulatory cues, cytokine gradients, matrix architecture, and the behavior of antigen-presenting cells (Quartey et al., 2025; Gong et al., 2024; Li et al., 2025a; Lopez Ruiz et al., 2025). Depending on the surrounding cellular network, interface-localized T-cell states may include local activation, restricted migration, regulatory polarization, exhaustion-like dysfunction, or participation in localized immune reorganization (Lopez Ruiz et al., 2025; Zhang et al., 2025). These divergent T-cell programs illustrate how the same interface principles can support different biological outcomes across tissue contexts.

Resident fibroblasts and stromal cells help determine whether a biomaterial-contact niche becomes integrated, reorganized, or isolated. These cells deposit and remodel extracellular matrix, regulate local mechanics, produce cytokines and chemokines, communicate with macrophages and endothelial cells, and contribute to the persistence or resolution of inflammatory environments (Riccio et al., 2025; Anderson et al., 2025; Chen et al., 2026; Zhao et al., 2024). Because fibroblasts are often present before substantial adaptive immune infiltration, their early matrix-deposition and cytokine programs can shape the gradients that later guide dendritic-cell migration, T-cell access, vascular ingrowth, and fibrotic boundary formation. Their behavior can support vascular ingrowth, matrix turnover, and repair-associated tissue organization, but can also promote fibrotic barriers, contractile tissue remodeling, and chronic material separation (Zhao et al., 2024; Li L. et al., 2025). In this way, stromal cells act as structural and signaling intermediaries between material properties and immune-cell behavior.

Endothelial cells and pericytes participate in interface-associated immune regulation by controlling vascular access, leukocyte trafficking, barrier function, vascular stability, and nutrient exchange (Costa et al., 2025; Hirth et al., 2024; Jiang et al., 2024; Xiao J. et al., 2025). Vascular organization influences how immune cells enter, persist, and redistribute around biomaterials. Pericyte coverage and endothelial barrier state can shape local permeability, chemokine presentation, oxygen gradients, and the timing of leukocyte entry before strong adaptive immune infiltration develops. Productive vascular integration can support constructive tissue organization, whereas disordered or inflammatory vascular responses can reinforce immune imbalance, edema, hypoxia, or local dysfunction (Anderson et al., 2025; Li L. et al., 2025). Endothelial and pericyte-associated responses are therefore closely linked to macrophage behavior, stromal organization, and T-cell localization at material-contact sites.

In neural and neuroregenerative biomaterial contexts, glial cells function as immune-adjacent responders to material cues. Astrocytes, microglia, and other glial populations sense changes in matrix composition, stiffness, inflammatory tone, and injury-associated signals (Dryg et al., 2026; Li et al., 2024; Benincasa et al., 2024). Their responses can influence local inflammation, scar-associated organization, synaptic and metabolic support, progenitor environments, and the permissiveness of neural repair (Dryg et al., 2026; Li et al., 2025c; Shen J. et al., 2025; Zheng Y. et al., 2026). Glial responses shape nervous-system biomaterial interfaces by linking inflammatory signaling, scar-associated structure, metabolic support, and progenitor permissiveness.

A key temporal challenge is that cellular recruitment and material remodeling occur on different timescales. Neutrophil and macrophage recruitment, cytokine release, endothelial activation, and early stromal signaling can occur within hours to days, whereas hydrogel stress relaxation, scaffold degradation, enzymatic remodeling, matrix deposition, vascular maturation, and fibrotic or reparative tissue organization often evolve over days to weeks. This temporal mismatch means that early immune and stromal signals can shape the biological interpretation of a material before the scaffold has substantially degraded or mechanically relaxed. Programmable biointerface design therefore requires matching immune-cell recruitment kinetics, vascular and stromal gradients, material degradation, and remodeling tempo rather than treating immune response and scaffold remodeling as synchronized processes.

The immune state at a biomaterial boundary emerges from coordinated cellular interactions rather than from any single lineage. These interactions determine how cellular programs are distributed across the interface, which populations dominate the local response, and whether the material-contact zone becomes organized around activation, regulation, structural repair, fibrosis, or suppression. The design challenge is to organize this local cellular ecology so that immune behavior remains aligned with the intended material function.

Together, these cell populations define the major immune, vascular, stromal, and immune-adjacent states that biomaterial interfaces can regulate. Table 1 summarizes the principal interface cues, regulated responses, representative markers/readouts, functional consequences, and context-specific relevance of these cell types across regenerative and cancer-associated biomaterial environments.

TABLE 1. Immune and immune-adjacent cell states regulated at biomaterial interfaces.

3 Mechanotransduction as the interface-to-immune signaling axis

Mechanotransduction converts interfacial stiffness, viscoelasticity, ligand organization, matrix architecture, fluid shear, compressive loading, and cytoskeletal tension into immune-regulatory signals. At biomaterial–host boundaries, physical cues are not passive structural features; they are sensed by cells as information about tissue state, injury, constraint, and structural demand. These cues are processed through integrin clustering, focal adhesion kinase signaling, RhoA/ROCK-mediated actomyosin tension, mechanosensitive ion channels such as Piezo1 and TRPV4, nuclear envelope deformation, lamin A/C remodeling, and transcription-factor localization. The same material surface can therefore produce different immune effects depending on how its mechanical, architectural, adhesive, degradative, and dynamic loading properties are integrated by immune, stromal, vascular, and tissue-resident cells (Fish et al., 2025; Topal et al., 2019a; Topal et al., 2018; Topal et al., 2019b; Xu et al., 2026).

Stiffness is one of the most widely studied mechanical regulators of material-associated immune behavior. Cells encountering soft, intermediate, or rigid substrates generate different levels of adhesion, spreading, cytoskeletal tension, and intracellular signaling. These responses can influence macrophage activation, stromal cell contractility, endothelial behavior, and the formation or resolution of fibrotic material boundaries (Butenko et al., 2024; Chi et al., 2026; Coser et al., 2025; Ezzo et al., 2024). A rigid interface may amplify intracellular tension and promote inflammatory or fibrotic organization in some settings, whereas a more compliant or stress-relaxing interface can support different patterns of cellular attachment, extracellular matrix turnover, and tissue interaction. The immunological effect of stiffness is therefore not determined by stiffness alone, but by how mechanical resistance is integrated with biochemical signals, local inflammation, and tissue-specific cellular composition.

Viscoelasticity adds another layer of control by determining how materials respond to force over time. Unlike purely elastic substrates, viscoelastic materials can dissipate stress, relax under cellular traction, and change the mechanical experience of attached cells. This temporal dimension can influence cell spreading, migration, contractility, cytokine production, matrix deposition, and the stability of local immune responses (Tao et al., 2025; Mai et al., 2025; Han X. et al., 2026). In biomaterial design, viscoelastic behavior is especially important because many native tissues are not fixed mechanical surfaces; they deform, relax, and remodel under cellular and physiological forces. Materials that approximate these time-dependent properties can alter how immune and stromal cells interpret the interface.

Dynamic mechanical loading adds a further layer of immune regulation. Cyclic strain, substrate stretching, shear stress, compressive loading, and acoustic force can rapidly alter focal adhesion assembly, actomyosin contractility, cytoskeletal organization, calcium influx, and transcription-factor localization (Fish et al., 2025; Topal et al., 2019a; Topal et al., 2018; Topal et al., 2019b). In macrophages, these inputs can modify inflammatory activation, spreading morphology, cytokine production, and matrix-associated behavior. In T cells, mechanical confinement, substrate stiffness, shear exposure, and contact geometry can influence migration, immune-synapse formation, activation thresholds, and localization within dense or porous matrices. These examples emphasize that macrophage activation, T-cell localization, cytokine release, and transcription-factor movement depend not only on static stiffness, but also on the timing, direction, magnitude, and repetition of mechanical inputs.

Porosity and topography shape mechanotransduction by controlling cell infiltration, spatial confinement, surface contact, and local tissue architecture. Pore size, pore interconnection, fiber alignment, surface roughness, and nanoscale patterning influence how cells attach, migrate, cluster, and communicate within a material-contact zone (Dudaryeva et al., 2026; Hou et al., 2025; Jia et al., 2025; Wang G. et al., 2026). These structural features can modify macrophage morphology, stromal alignment, endothelial invasion, and leukocyte access without requiring a change in chemical composition. Architecture therefore becomes an immune-regulatory variable by determining which cells reach the interface and the physical geometry through which they sense it.

Ligand density and ligand organization define how cells physically couple to a material surface. Adhesive motifs, extracellular-matrix-derived ligands, and immobilized bioactive molecules regulate the number, spacing, mobility, and persistence of cell–material contacts (Feng J. et al., 2025; Liu et al., 2025b; Moghaddam et al., 2025; Wilson et al., 2022). Dense or spatially organized ligand presentation can strengthen cell attachment and traction, whereas sparse, mobile, or degradable ligand configurations can alter spreading, migration, and force transmission. These effects connect biochemical material design to mechanical signaling by defining the geometry and stability of cellular attachment.

Integrins and focal adhesions convert attachment geometry into intracellular signaling. Through integrin engagement, cells generate traction forces, organize actin networks, and activate focal adhesion kinase (FAK), Src-family signaling, and downstream pathways that regulate gene expression, cytokine production, metabolism, migration, and survival (Xu et al., 2026; Chi et al., 2026; Feng J. et al., 2025; Liu et al., 2025b; Moghaddam et al., 2025; Wilson et al., 2022). Focal adhesion complexes act as force-sensitive molecular assemblies that translate extracellular resistance into changes in cell behavior. Under cyclic strain, substrate stretching, shear, compression, or acoustic force, focal adhesions can reorganize rapidly, changing actomyosin contractility and downstream mechanotransductive signaling (Xu et al., 2026; Chi et al., 2026). In immune and stromal cells, these pathways can influence inflammatory activation, contractile behavior, antigen-presenting function, migration, and the persistence or resolution of material-associated responses.

RhoA/ROCK-mediated actomyosin tension links local adhesion to broader cellular state regulation. Integrin engagement and focal adhesion maturation activate Rho-family GTPase signaling, which regulates myosin contractility, actin stress-fiber organization, cytoskeletal force transmission, and nuclear deformation (Xu et al., 2026; Chi et al., 2026; Feng J. et al., 2025; Liu et al., 2025b; Moghaddam et al., 2025; Wilson et al., 2022). These processes influence transcriptional programs associated with inflammation, repair, fibrosis, and immune suppression. YAP and TAZ are important mechanosensitive transcriptional regulators in this process, connecting matrix mechanics, cell shape, and cytoskeletal tension to gene-expression programs that influence stromal activation, macrophage behavior, endothelial function, and tissue remodeling (Coser et al., 2025; Wilson et al., 2022). Their activity reflects how mechanical cues are converted into durable cellular decisions rather than transient surface responses.

Nuclear mechanics provides another mechanotransductive checkpoint. Spatial confinement, dense matrix architecture, substrate stretching, and cytoskeletal force transmission can deform the nucleus and alter nuclear envelope organization (Coser et al., 2025; Wilson et al., 2022). Lamin A/C contributes to nuclear stiffness, mechanical stability, and force-dependent transcriptional regulation (Wang et al., 2025b). Changes in lamin A/C organization can modify how mechanical signals are transmitted to chromatin and how transcription factors such as YAP/TAZ and NF-κB localize between cytoplasm and nucleus. At biomaterial interfaces, this nuclear layer is relevant because macrophages, stromal cells, endothelial cells, and T cells often encounter confined pores, aligned fibers, stiffness gradients, and mechanically active matrices that convert local geometry into transcriptional state changes.

Mechanosensitive ion channels connect mechanical force to rapid calcium-dependent signaling. Piezo1 and TRPV4 can respond to membrane tension, substrate deformation, fluid shear, compressive stress, and matrix stiffness, allowing mechanical inputs to regulate Ca²⁺ influx and downstream immune-cell behavior (Xu et al., 2026). In macrophages, Piezo1/TRPV4-associated signaling can influence inflammatory activation, cytokine production, phagocytic behavior, and matrix-associated responses. In T cells, mechanically regulated calcium signaling can influence activation, migration, immune-synapse behavior, and localization within confined tissue-like matrices. Calcium-dependent signaling also interacts with focal adhesion remodeling, actomyosin contractility, NF-κB signaling, and YAP/TAZ localization, positioning Piezo1/TRPV4-mediated Ca²⁺ influx as a bridge between material mechanics and immune-state regulation.

Mechanotransduction also couples immune cells to evolving stromal architecture. Macrophage-derived cytokines, fibroblast contractility, extracellular matrix deposition, endothelial invasion, and degradation-mediated changes in material structure can create feedback loops that modify the original interface (Butenko et al., 2024; Ezzo et al., 2024; Han X. et al., 2026; Wang G. et al., 2026). A biomaterial introduced with one mechanical profile may become part of a changing mechanical environment as cells deposit matrix, degrade material components, reorganize pores, or generate contractile forces. This feedback helps explain why early immune responses can stabilize into fibrotic organization, shift toward tissue integration, or contribute to suppressive tumor-like architecture.

Cellular mechanical sensing at the material-contact zone depends on combinations of material variables rather than any single design feature. Stiffness, viscoelasticity, porosity, ligand organization, degradability, shear, compression, cyclic strain, substrate stretching, and acoustic force together determine how cells sense the interface and how immune programs are stabilized over time. Similar materials can therefore generate distinct immune outcomes across tissues, injury states, regenerative environments, and cancer-associated matrices when their mechanical loading conditions, pore architecture, ligand spacing, or remodeling kinetics differ. Through this signaling axis, material design, dynamic force exposure, ion-channel activity, cytoskeletal tension, and nuclear transcriptional regulation are converted into immune instruction.

Figure 2 illustrates how material stiffness, viscoelasticity, porosity/topography, ligand density, degradation/remodeling, and dynamic mechanical inputs are translated into immune-state regulation through integrin clustering, focal adhesion signaling, FAK activation, RhoA/ROCK-mediated actomyosin tension, Piezo1/TRPV4-dependent Ca²⁺ influx, nuclear envelope deformation, lamin A/C remodeling, and YAP/TAZ- and NF-κB-associated transcriptional programs.

Material properties including stiffness, viscoelasticity, porosity/topography, ligand density, and degradation/remodeling behavior are sensed at the biomaterial–cell interface through integrin clustering, focal adhesion assembly, and FAK activation. These adhesion-dependent signals integrate with dynamic mechanical inputs, including cyclic strain, shear/compression, substrate stretch, and acoustic force, as well as mechanosensitive ion-channel activity through Piezo1/TRPV4-mediated Ca²⁺ influx. These pathways regulate RhoA/ROCK signaling, actomyosin tension, cytoskeletal force transmission, nuclear envelope deformation, lamin A/C remodeling, YAP/TAZ nuclear localization, and NF-κB transcriptional signaling. Together, these mechanisms connect material cues to immune-state outputs including inflammatory activation, repair-associated remodeling, fibrotic activation, suppressive immune states, T-cell localization/activation, and vascular or matrix organization.

Together, these material properties define the major mechanical, structural, adhesive, degradative, and chemical inputs through which biointerfaces shape immune behavior. Table 2 summarizes how these properties are sensed by cells and how they can influence immune-state outcomes.

TABLE 2. Material properties and mechanotransductive immune effects.

4 Stimuli-responsive materials for spatiotemporal immune-state control

Stimuli-responsive materials convert external or microenvironmental triggers into temporally controlled immune instructions. Unlike static biomaterial systems that present fixed cues or release molecules according to preset degradation profiles, responsive platforms alter local signals in response to external inputs or tissue-derived triggers (Gregorio et al., 2025; Quan et al., 2026; Yadav et al., 2025; Zheng H. et al., 2026). External inputs include light/NIR irradiation, magnetic fields, electrical stimulation, mechanical actuation, and ultrasound, whereas tissue-derived triggers include temperature or local heat, pH, reactive oxygen species, enzymes, and inflammatory or metabolic feedback. These triggers can activate material mechanisms such as photolabile linker cleavage, azobenzene switching, magnetic or NIR thermal activation, cavitation-assisted release, redox-sensitive bond cleavage, pH-sensitive hydrolysis, or protease-sensitive matrix remodeling (Zhou et al., 2024; Jain et al., 2024). This capability allows material signals to be timed, localized, repeated, withdrawn, or adjusted.

Externally activated systems provide one route for adjusting biointerface signals after placement. Magnetic-responsive systems use magnetic particles, field-sensitive scaffolds, or magnetically guided carriers to influence local release, material deformation, cellular localization, or immune-cell recruitment (Wang B. et al., 2025; Xuan X. et al., 2025; Yan et al., 2025). Their performance depends on particle retention, dispersion, magnetic responsiveness, heating behavior, and stability within the material-contact zone. Electrical-responsive materials deliver conductive cues, modulate charged surfaces, or support electrically regulated release in environments where immune behavior is influenced by ion movement, membrane activity, or electrically active tissue interfaces (Yadav et al., 2025; Du H. et al., 2025; Tang et al., 2025). Photonic and light-responsive systems provide spatially confined activation, ligand exposure, crosslinking, uncaging, or release of immunomodulatory molecules, allowing material outputs to be patterned with local precision (Gregorio et al., 2025; Liu et al., 2025c). However, optical systems must balance wavelength, penetration depth, exposure duration, phototoxicity, and thermal dose, particularly when NIR or photothermal activation is used to trigger gelation, molecular uncaging, or localized release.

Mechanical actuation and ultrasound-responsive systems allow biointerface properties to be modified after the local boundary has formed. Mechanically actuated materials change shape, stiffness, permeability, or release behavior after placement, creating externally timed changes in the material-contact zone (Xuan X. et al., 2025; Liu et al., 2025c; Bianco et al., 2024; Shu et al., 2025). Ultrasound-responsive systems can trigger molecular release, enhance transport, perturb local structure, or activate carriers within deeper tissue environments where direct optical access is limited (Wang K. et al., 2026; Qian et al., 2025; Xiao H. et al., 2025). These systems require careful control because acoustic attenuation, carrier stability, local heating, cavitation behavior, and tissue-specific acoustic properties can influence whether ultrasound produces localized release or nonspecific mechanical perturbation. For immune-instructive materials, acoustic parameters are therefore best treated as system-dependent design variables rather than universal thresholds, because the safe and effective range depends on frequency, intensity, duty cycle, exposure duration, carrier composition, microbubble or particle stability, and the surrounding tissue or matrix environment (Wang W. et al., 2025).

Microenvironment-sensitive systems operate through biological conditions present within the surrounding tissue. pH-responsive materials are especially relevant in inflammatory, ischemic, infected, or tumor-associated environments where acidity differs from adjacent tissue (Zheng H. et al., 2026; Cao et al., 2026; Nie et al., 2025). ROS-responsive systems respond to oxidative stress and release anti-inflammatory, immunomodulatory, or matrix-regulating signals in regions with elevated reactive oxygen species (Zheng H. et al., 2026; Shen et al., 2025b; Yang G. et al., 2025). Enzyme-responsive materials react to proteases, matrix-degrading enzymes, or disease-associated enzymatic activity, linking material behavior to local cellular restructuring and inflammatory state (Wu et al., 2026; Quan et al., 2026; Liang et al., 2025). These trigger-sensitive systems concentrate material outputs where the biological signal is present.

Temperature-sensitive platforms operate through thermal transitions. Thermoresponsive polymers, hydrogels, and carrier systems can change solubility, stiffness, gelation state, permeability, or release behavior in response to temperature shifts (Chen et al., 2025; Luo et al., 2025; Zhao et al., 2026). These properties influence material placement, local retention, molecular diffusion, and cue exposure. Within immune-instructive systems, thermal transitions mainly regulate signal availability, retention, and exposure duration rather than creating an independent immune mechanism.

Practical design constraints are central to responsive biointerface performance. Triggered systems must generate the intended material change without producing uncontrolled immune activation through nonspecific heating, phototoxicity, cavitation-associated damage, excessive oxidative byproducts, electrochemical side reactions, or off-target release. For ultrasound-responsive carriers, this requires matching acoustic intensity, frequency, duty cycle, exposure duration, carrier stability, and cavitation behavior to the target material system. For magnetic or NIR-responsive materials, particle retention, thermal dose, heat distribution, and reversibility influence whether activation remains localized. For light-responsive systems, wavelength selection, penetration depth, exposure time, and phototoxicity constrain how photolabile groups, azobenzene switches, or photothermal transitions are used. These constraints do not eliminate responsive control, but they define the operating window in which a stimulus can regulate immune-relevant material behavior without dominating the biological response.

Controlled-release systems remain central to time-dependent immune regulation. Release kinetics determine whether cells encounter a short pulse, gradual exposure, repeated signal, or sustained local concentration of cytokines, chemokines, antigens, nucleic acids, drugs, or matrix-modifying molecules (Han et al., 2025; Mantooth et al., 2025; Shen et al., 2025c). A transient early signal may support recruitment or activation, whereas prolonged exposure to the same molecule can stabilize a different immune state. Dose, sequence, and local concentration therefore become design variables rather than simple delivery parameters.

Feedback-responsive materials link biointerface outputs to the state of the surrounding environment. These systems respond to inflammatory mediators, oxidative stress, enzymatic activity, mechanical loading, or local metabolic changes and adjust release, stiffness, permeability, degradation, or ligand exposure accordingly (Fu et al., 2025; Wang et al., 2025d; Xu P. et al., 2025). This creates a regulatory loop in which the material not only delivers a signal, but modifies its behavior as the interface changes. Such feedback maintains immune cues within a desired range, reduces prolonged exposure, or concentrates activity where specific biological conditions are present.

Across regenerative and cancer-associated environments, responsive materials enable examination of how timing, localization, and trigger sensitivity alter immune-cell access, antigen exposure, and inflammatory persistence (Zhao et al., 2026; Shen et al., 2025c; Chao et al., 2025). The central principle is not that one class of responsive material is universally favorable, but that regulated cue delivery determines how immune cells encounter material-derived information.

Stimuli-responsive biointerfaces expand the design space of immune-instructive biomaterials by making material signals conditional, localized, and time-dependent. Their value lies in controlling the sequence, duration, and distribution of immune-regulatory cues rather than simply increasing biological activity. By linking external or microenvironmental triggers to changes in release, ligand exposure, structure, transport, or degradation, responsive materials provide a practical route for organizing immune behavior across space and time.

Figure 3 illustrates how external and microenvironmental triggers are converted into specific molecular and material switching mechanisms, including ortho-nitrobenzyl photolabile linker cleavage, azobenzene switching, ultrasound-associated microbubble oscillation and cavitation-assisted release, magnetic or NIR thermal activation, disulfide and thioketal ROS-responsive cleavage, hydrazone/acetal pH-sensitive cleavage, and MMP-cleavable peptide remodeling. These mechanisms regulate immune-relevant release, ligand exposure, antigen presentation, stiffness or permeability changes, surface remodeling, and matrix degradation/opening at the material-contact zone.

Stimuli-responsive biointerfaces convert external or microenvironmental triggers into localized material changes that regulate immune-cell access, molecular exposure, release kinetics, and interface remodeling. External inputs such as light/NIR, ultrasound, magnetic fields, electrical stimulation, and mechanical strain can activate photolabile linker cleavage, azobenzene switching, magnetic or NIR thermal activation, microbubble oscillation, cavitation-assisted release, acoustic transport, or thermoresponsive polymer transitions. Tissue-derived triggers such as pH, reactive oxygen species, enzymes, and local thermal changes can activate hydrazone or acetal linkages, disulfide or thioketal bond cleavage, oxidation-sensitive bond cleavage, MMP-cleavable peptide degradation, hydrogel swelling, or protease-sensitive matrix remodeling. These mechanisms produce immune-regulatory interface outputs including timed cytokine/drug release, ligand exposure, antigen exposure or presentation, stiffness or permeability changes, surface remodeling, and matrix degradation/opening, thereby shaping immune activation, immune suppression, repair-associated remodeling, anti-fibrotic regulation, and tumor-associated immune modulation.

The major responsive biointerface classes differ in the stimulus they detect, the trigger chemistry or mechanism they use, the material change they produce, and the immune-regulatory output they enable. Table 3 summarizes these systems according to trigger type, representative trigger chemistry or mechanism, material response, immune-regulatory mechanism, application context, and design constraint.

TABLE 3. Stimuli-responsive biointerface systems for immune regulation.

5 Regeneration and cancer as divergent material–immune contexts

Regeneration and cancer represent contrasting biological environments in which engineered material cues acquire different meanings. In regenerative settings, host–material boundaries are organized around resolution, repair, vascular support, matrix turnover, and tissue integration. In cancer-associated settings, similar interface principles operate within mechanically constrained, antigenically complex, and immunologically suppressive matrices. These settings diverge not because they involve entirely different signals, but because comparable material, stromal, vascular, and immune cues are directed toward different biological outcomes (Callaway et al., 2025; Anderson et al., 2025; Su et al., 2025).

5.1 Regenerative biointerface environments

In regenerative contexts, immune-instructive materials can bias local responses away from persistent inflammation and fibrotic encapsulation toward remodeling, vascular support, matrix integration, and progenitor-permissive repair environments. This shift depends on the ability of the interface to organize early inflammatory events without allowing them to stabilize into chronic material separation or dense fibrotic boundaries. A productive regenerative interface therefore does not eliminate immune activity; it shapes immune participation so that inflammation, clearance, stromal activation, vascularization, and matrix replacement occur in a coordinated sequence (Wang G. et al., 2026; Su et al., 2025; Nasiri B. et al., 2025).

Macrophage–stromal coordination is central to this process. Macrophages respond to material chemistry, stiffness, degradation products, adsorbed proteins, and local cytokines, while fibroblasts and other stromal cells remodel extracellular matrix, generate contractile forces, and define the architecture around the material-contact zone. When these interactions are balanced, macrophage activity can support debris clearance, matrix turnover, vascular support, and repair-associated tissue organization. When the same interactions remain unresolved, they can reinforce persistent inflammatory tone, excessive stromal contraction, and fibrotic isolation of the material (Nasiri B. et al., 2025; Spoddig et al., 2025).

Anti-fibrotic remodeling is therefore a major design challenge for regenerative biointerfaces. Materials that reduce mechanical mismatch, permit controlled degradation, support appropriate cell infiltration, and avoid excessive stromal compaction can limit the formation of dense encapsulating barriers. Conversely, rigid, poorly integrated, or non-remodelable interfaces can maintain local stress and sustain fibrotic organization even when acute inflammation has declined (Dryg et al., 2026; Han X. et al., 2026; Wang Q. et al., 2025). The relevant design question is not whether inflammation is present, but whether immune and stromal responses remain compatible with constructive remodeling.

Vascularization provides another essential component of regenerative material–immune organization. Endothelial invasion, macrophage-derived pro-vascular signals, stromal patterning, and material porosity influence whether the interface supports nutrient exchange, immune-cell trafficking, and living tissue incorporation (Anderson et al., 2025; Wang G. et al., 2026; El-Mallah et al., 2025). Poor vascular organization can prolong hypoxia, restrict material turnover, and reinforce inflammatory persistence. In contrast, organized vessel entry can distribute soluble signals and stabilize tissue architecture around the material-contact zone.

Matrix remodeling links immune regulation to long-term tissue integration. As cells deposit extracellular matrix, degrade material components, reorganize pores, and generate traction forces, the original material boundary becomes a changing hybrid environment composed of engineered and cell-derived structures. This evolving interface can support constructive integration when matrix turnover, degradation kinetics, and immune activation remain aligned. It can also become maladaptive when matrix deposition outpaces degradation, when contractile remodeling dominates, or when a mechanically restrictive boundary forms around the material (Nasiri B. et al., 2025; Lu Y. et al., 2025).

Progenitor-permissive repair environments depend on this broader immune–stromal organization. In tissues with active progenitor niches, repair requires local conditions that support cell survival, migration, differentiation, tissue architecture, and integration without imposing persistent inflammatory or fibrotic constraints. Biomaterial interfaces can contribute to these environments by regulating mechanical compliance, soluble cues, matrix composition, ligand presentation, degradation behavior, and immune-cell activity (Su et al., 2025; Huang et al., 2025; Yun et al., 2025). Neural stem and progenitor cell niches provide one example, because their regenerative capacity is influenced by stiffness, vascular proximity, inflammatory tone, glial reactivity, local metabolic support, and matrix architecture (Huang et al., 2025; Haki et al., 2025; Yokosawa et al., 2025; Naffaa et al., 2023; Naffaa and Yin, 2024). Defined 3D extracellular matrices, engineered fibronectin-based scaffolds, porous polymer matrices, and protein-engineered or synthetic hydrogel systems can provide controlled environments for neural progenitor survival, differentiation, spatial organization, and tissue-like architecture (Muniz et al., 2023). In these systems, matrix degradability, ligand availability, pore structure, and fluid-like or stress-relaxing behavior can influence whether progenitor-associated repair remains permissive or becomes restricted. Material-directed immune regulation is therefore relevant not only because it limits inflammatory injury, but because it shapes the scaffold and niche conditions that determine progenitor behavior.

In neural repair environments, glial and neuroimmune responses become important because they influence the trajectory of the material-contact zone after injury. A favorable neural interface limits prolonged glial reactivity while preserving structural support, vascular coordination, metabolic exchange, progenitor permissiveness, and defined matrix architecture. An unfavorable interface can extend inflammatory signaling, reinforce scar-associated barriers, restrict cellular migration, reduce progenitor survival or differentiation, or create a mechanically restrictive environment that prevents repair-associated remodeling from progressing (Dryg et al., 2026; Shen J. et al., 2025; Wan et al., 2026). Engineered neural biointerfaces therefore require coordinated control of immune tone, glial reactivity, scaffold porosity, matrix compliance, ligand presentation, and fluid-like or stress-relaxing microenvironmental behavior. Glial responses are part of the tissue-specific immune-adjacent landscape organized by neural biointerfaces.

Regenerative biointerfaces function by coordinating immune, stromal, vascular, and progenitor-associated processes around the material-contact zone. Their success depends less on imposing a single pro-repair phenotype than on maintaining a sequence of local responses that resolves excessive inflammation, limits fibrotic stabilization, and preserves repair-compatible tissue structure.

5.2 Cancer-associated matrix and immune-interface environments

Cancer-associated matrices provide a contrasting setting in which stiffness, collagen-rich architecture, antigen localization, stromal organization, and suppressive immune signaling can be modeled or redirected through engineered interfaces. Tumor environments are shaped by abnormal extracellular matrix deposition, altered stiffness, disorganized vascular structure, stromal activation, hypoxia, and immune-cell redistribution (Bai et al., 2025; Plesselova et al., 2026; Yang Y. et al., 2025). These features create interface-like conditions in which physical and biochemical constraints influence how immune cells enter, localize, persist, and function.

Matrix stiffness and architecture are central to this cancer-associated interface logic. Dense extracellular matrix, collagen-rich matrices, aligned fibers, altered porosity, stiffness gradients, and increased mechanical resistance can restrict immune-cell infiltration, alter macrophage and stromal behavior, and create spatial barriers around tumor regions (Bai et al., 2025; Hu et al., 2025). These matrix features do not only provide structural support; they shape the distribution of antigens, cytokines, metabolites, oxygen gradients, and immune cells. Importantly, macrophage-state changes do not map to one universal stiffness threshold or one fixed collagen-alignment density. Instead, macrophage behavior depends on matrix composition, tissue type, dimensionality, ligand availability, degradability, inflammatory context, oxygen tension, stromal-cell activity, and the specific readouts used to define the state. As a result, these material environments function as mechanically organized immune landscapes rather than soluble-factor environments alone.

Tumor-associated macrophage states are strongly influenced by this local architecture. Matrix stiffness, collagen density, fiber alignment, hypoxia, stromal-derived signals, degradability, and tumor-associated cytokines can support macrophage programs associated with immune suppression, matrix remodeling, angiogenic signaling, and restricted T-cell activity (Li W. et al., 2025; Liu et al., 2026; Li et al., 2026). In biomaterial systems designed to model tumor-like environments, hydrogels, collagen-rich matrices, stiffness-gradient systems, aligned fibrous scaffolds, and tumor–stroma co-culture platforms can reproduce selected features of macrophage-rich suppressive niches without reducing the tumor microenvironment to a single cell type or signal. These systems are most informative when they measure matrix mechanics, macrophage phenotype, spatial distribution, immune-cell invasion, stromal organization, and antigen or cytokine localization together rather than treating stiffness as an isolated determinant.

Antigen localization adds another layer of interface-level organization. Biomaterials can structure where antigens are retained, how long they remain available, which antigen-presenting cells encounter them, and whether antigen exposure is paired with inflammatory or regulatory cues (Feng K. et al., 2025; Wang N. et al., 2026). Neoantigen vaccine and delivery strategies further illustrate how antigen selection, presentation, and immune-cell access can be deliberately organized to shape tumor-directed immunity (Naffaa et al., 2025). For biomaterials science, the relevant features are spatial presentation, retention, release kinetics, and immune-cell access at the material boundary.

Tumor-mimicking biomaterial systems provide a way to study how matrix mechanics, immune suppression, antigen presentation, immune-cell invasion, and stromal architecture interact at controlled interfaces. Hydrogels, collagen-rich matrices, scaffold systems, organoid-associated matrices, microfluidic platforms, stiffness-gradient materials, aligned fiber systems, and engineered extracellular matrix models can reproduce selected features of tumor-associated environments, including matrix densification, altered degradability, protease activity, hypoxia-like conditions, macrophage enrichment, suppressive cytokine localization, and restricted immune-cell penetration (Plesselova et al., 2026; Isik et al., 2026; Nasiri R. et al., 2025; Ravi et al., 2025). These systems allow material variables to be adjusted while immune organization is observed within a structured matrix environment. They also clarify why exact kPa cutoffs should be interpreted cautiously: a stiffness range that promotes one macrophage or T-cell behavior in one hydrogel, tissue model, or ligand context may not produce the same outcome in another matrix composition or dimensionality (Sharma et al., 2026).

Within cancer-associated biointerfaces, immune-instructive delivery functions as localized organization of immune signals. Materials can organize antigen exposure, chemokine gradients, cytokine availability, checkpoint-relevant cues, matrix-modifying signals, or macrophage-directed inputs within defined spatial and temporal boundaries (Shen et al., 2025c; Feng K. et al., 2025; Wang N. et al., 2026). The central design issue is whether the interface alters immune-cell access, suppressive signaling, antigen presentation, or matrix constraint in a way that can be measured mechanistically.

Regeneration and cancer thus reveal two divergent uses of the same material-guided immune-control framework. In regeneration, the interface favors resolution, reparative remodeling, vascular coordination, tissue integration, and progenitor-permissive repair. In cancer-associated systems, the interface is used to model or reshape mechanically constrained, collagen-rich, macrophage-enriched, antigenically structured, and immunologically suppressive environments. Similar material variables can therefore produce different immune outcomes depending on matrix composition, dimensionality, ligand availability, stiffness gradients, fiber alignment, degradation behavior, stromal organization, and inflammatory context.

The divergent behavior of regenerative and cancer-associated biointerfaces can be summarized by comparing the dominant interface challenge, material cue, immune-state target, and design constraint in each context. Table 4 summarizes these tissue- and matrix-specific relationships, while Table 5 provides a focused matrix of tumor-mimicking biomaterial variables relevant to stiffness, collagen-rich architecture, fiber alignment, immune-cell invasion, and macrophage-rich suppressive niches.

TABLE 4. Regeneration and cancer as divergent engineered interface contexts.

TABLE 5. Tumor-mimicking biomaterial variables and immune-interface readouts.

Because matrix stiffness, fiber alignment, and macrophage polarization do not map to a universal threshold across material systems, Table 5 summarizes the main tumor-mimicking biomaterial variables, the immune processes they affect, and the readouts needed to interpret matrix- and readout-dependent shifts in macrophage state and immune-cell invasion.

6 Predictive programmable biocompatibility and AI-guided modeling

Biocompatibility is not safety or absence of toxicity alone; it is the controllable capacity of a biointerface to shape immune states in space and time. This definition shifts biomaterial design from passive tolerance toward regulated biological instruction. A material is not only judged by whether it avoids harmful inflammation, excessive cytotoxicity, or rejection-like responses, but by whether its properties organize immune behavior with appropriate timing, location, intensity, reversibility, and context specificity (Ding et al., 2025; Martkamjan et al., 2025; Sujeeun et al., 2025).

Programmable biocompatibility connects material inputs to measurable immune and remodeling outputs. The central sequence links material properties → stimulus input → mechanotransductive signaling → cellular readouts → regenerative or cancer-relevant outcome. Within this sequence, mechanical, structural, adhesive, chemical, degradative, and release-related properties define the starting conditions of the interface. Stimulus inputs modify when and where signals appear. Mechanotransduction converts physical and structural cues into cellular signaling, while immune-state regulation determines whether the local environment moves toward resolution, fibrosis, suppression, exclusion, antigen presentation, tissue integration, or tumor-like organization (Zhao et al., 2025; Hou et al., 2025; Sujeeun et al., 2025).

This perspective also clarifies why biocompatibility cannot be reduced to a single endpoint. A material that appears compatible at one time point may later promote fibrotic isolation, chronic inflammatory tone, immune suppression, or poor tissue integration. Conversely, an interface that induces a brief inflammatory phase may support repair if that response is followed by clearance, vascular coordination, matrix turnover, and structural integration. Programmable biocompatibility therefore requires attention to state trajectories rather than isolated measurements of toxicity or inflammation.

6.1 Design variables and immune-state outputs

Material variables become meaningful when they can be mapped to specific immune-state outcomes. Mechanical properties shape macrophage activation, stromal contractility, endothelial behavior, and fibrotic organization. Adhesive and chemical cues regulate cell attachment, receptor engagement, antigen-presenting behavior, and local immune activation. Architectural and degradative features influence immune-cell infiltration, vascular entry, molecular diffusion, material persistence, and clearance-associated responses. Release profiles determine whether cells encounter short pulses, sustained gradients, sequential signals, or localized concentrations of immune-active molecules (Martkamjan et al., 2025; Sujeeun et al., 2025; Pogostin et al., 2025).

These variables do not act independently. A stiff material with low porosity may restrict immune-cell entry differently from a stiff material that permits vessel ingrowth and matrix turnover. A degradable hydrogel with sparse ligand presentation may produce different macrophage markers, cytokine profiles, T-cell penetration, or stromal organization than one with similar degradation kinetics but dense adhesive cues. A stimulus-responsive system may alter the same payload depending on when it is activated, how long it remains active, and whether its output is spatially confined. Model-guided prediction therefore depends on combinations of stiffness, porosity, ligand density, degradation rate, release kinetics, and stimulus timing rather than isolated parameters.

Immune-state outputs require greater precision. General categories such as inflammatory, reparative, suppressive, or fibrotic are useful starting points, but biointerface design requires more specific state descriptions. Relevant outputs include macrophage state, stromal contractility, dendritic-cell activation, T-cell localization, regulatory polarization, glial reactivity, endothelial organization, and progenitor-niche permissiveness (Ding et al., 2025; Martkamjan et al., 2025; Zhu et al., 2025). These outputs become design targets only when they are linked to measurable changes in cell phenotype, spatial organization, cytokine profile, matrix structure, and temporal persistence.

A predictive interface map therefore connects each design variable to a defined biological readout. Stiffness is not simply high or low; it is interpreted through tissue context, cell type, inflammatory state, and time. Porosity is not only a structural feature; it determines access, diffusion, vascular entry, and spatial organization. Release kinetics are not only delivery parameters; they define the duration and sequence of cellular exposure. In this way, programmable biocompatibility transforms design features into testable immune-state hypotheses.

6.2 Controllability, reversibility, and timing

Controllability is central to programmable biocompatibility. An engineered interface is evaluated not only by the response it produces, but by whether that response can be tuned through material properties, stimulus intensity, trigger timing, ligand availability, degradation rate, or release schedule. Controllability links design to immune-state precision: the same material platform may produce different outcomes depending on the magnitude, duration, and spatial distribution of its signals (Ding et al., 2025; Pogostin et al., 2025; Zhu et al., 2025; Du F. et al., 2025).

Reversibility is equally important. At the cellular level, the persistence and reversibility of immune states can also depend on regulatory constraints that determine whether state-associated transcriptional programs remain accessible, reversible, or stabilized (Al-Ewaidat and Naffaa, 2026a). Some immune responses are useful only if they remain transient. Early inflammation can support recruitment, clearance, and initiation of remodeling, but persistent activation can stabilize fibrotic organization or chronic material separation. Suppressive immune states may be useful in some contexts but problematic when they restrict immune-cell access or antigen presentation. Responsive materials, degradable systems, reversible ligand exposure, and feedback-sensitive release provide routes for shifting immune states without permanently locking the interface into one biological program (Pogostin et al., 2025; Xuan Q. et al., 2025).

Timing determines whether the same cue supports adaptation or maladaptation. The importance of timing is also evident in cancer-associated systems, where transient state transitions can create temporally restricted biological configurations that differ from both preceding and subsequently stabilized states (Al-Ewaidat and Naffaa, 2026b). A cytokine, antigen, adhesive ligand, or mechanical signal can have different effects depending on whether it appears during early injury, active remodeling, vascular organization, matrix stabilization, or suppressive niche formation. Sequential delivery can separate early immune recruitment from later matrix organization, whereas poorly timed stimulation can produce competing signals at the same interface.

Spatial regulation adds another layer of specificity. A material cue confined to the material-contact zone may produce a different effect than the same cue distributed broadly through surrounding tissue. Localized antigen retention, patterned ligand exposure, scaffold gradients, and stimulus-triggered release determine which cells encounter the signal and in what order. Programmable biocompatibility therefore depends on aligning signal intensity, duration, and location with the biological phase of the interface.

6.3 AI-guided modeling of biointerface responses

AI-guided modeling can serve as the computational layer of programmable biocompatibility by converting material properties, stimulus schedules, mechanotransductive features, and immune-state readouts into testable design maps. Biointerface responses involve interdependent design variables, cellular composition, cytokine gradients, matrix structure, spatial organization, and time-dependent feedback. These relationships are difficult to interpret through one-variable-at-a-time experiments alone (Chen et al., 2027; Cheong et al., 2026; Han Z. et al., 2026).

The biological sections of this Review define the feature space for this modeling framework. Section 2 identifies immune-cell, stromal-cell, vascular-cell, glial-cell, and progenitor-associated states that can be translated into output labels or response classes, such as inflammatory activation, suppressive immune state, fibrotic activation, T-cell localization, vascular organization, and progenitor permissiveness. Section 3 defines mechanotransductive features, including stiffness, viscoelasticity, porosity/topography, ligand density, degradation/remodeling, integrin–focal adhesion signaling, RhoA/ROCK–actomyosin tension, Piezo1/TRPV4-mediated Ca²⁺ influx, lamin A/C remodeling, YAP/TAZ localization, and NF-κB signaling. Section 4 defines stimulus-related variables, including trigger type, stimulus timing, dose, duration, activation frequency, release kinetics, ligand exposure, surface remodeling, and matrix opening. Section 5 defines context-specific outcomes, including regeneration-associated resolution, vascular support, tissue integration, progenitor-permissive repair, tumor-associated immune exclusion, antigen localization, macrophage-rich suppressive niches, and tumor-like matrix organization.

In this structure, AI-guided modeling is not a separate theme added after the biological discussion; it is a method for organizing the variables reviewed in Sections 2–5 into measurable inputs, outputs, and design decisions. Useful input features include material formulation, stiffness, stress relaxation, porosity, fiber alignment, ligand density, degradation rate, trigger chemistry, stimulus schedule, imaging features, transcriptomic profiles, cytokine measurements, spatial cell distributions, matrix organization, and remodeling kinetics. Outputs include immune-cell phenotype, cytokine state, T-cell localization, antigen presentation, stromal contractility, vascular organization, glial reactivity, fibrotic boundary formation, suppressive niche structure, and repair-compatible tissue organization. Models can then be used to classify immune-state patterns, rank predictive material features, identify interacting variables, predict how stimulus timing alters biological outputs, and prioritize material configurations for experimental validation.

6.4 Representative AI-guided biomaterial design studies

Representative AI-guided biomaterial studies illustrate how computational models can contribute to material design at different stages of the workflow. In formulation-screening studies, machine-learning models can relate polymer composition, crosslinking density, degradation chemistry, peptide functionalization, nanoparticle content, or hydrogel stiffness to experimentally measured outputs such as gelation behavior, modulus, swelling, degradation rate, cell viability, cytokine release, or macrophage-associated markers (Cheong et al., 2026). In high-throughput hydrogel or scaffold arrays, models can identify combinations of matrix stiffness, ligand density, porosity, degradability, and soluble-factor presentation associated with desired cellular phenotypes or tissue-organization patterns (Chen et al., 2027; Cheong et al., 2026; Han Z. et al., 2026). In imaging-rich systems, computer vision and feature-extraction approaches can convert cell morphology, spatial clustering, matrix alignment, invasion depth, vessel-like organization, or fibrotic boundary formation into quantitative features for prediction (Chen et al., 2027; Han Z. et al., 2026; Mesfin et al., 2025).

AI-guided modeling is also relevant to immune-interface design because many desired outputs are multivariable rather than single-endpoint responses. For example, a material intended to support repair-compatible remodeling may need to reduce persistent inflammatory cytokine production while preserving macrophage-mediated matrix turnover, endothelial organization, and tissue integration. A tumor-mimicking matrix may need to reproduce macrophage enrichment, matrix densification, T-cell exclusion, antigen localization, and suppressive cytokine distribution in the same model. These outputs are difficult to optimize using a single material parameter. Predictive models can instead identify combinations of material variables and biological readouts that define favorable or unfavorable interface states (Chen et al., 2027).

Current AI-guided biomaterial design remains limited by cross-study inconsistency, small dataset size, batch effects, heterogeneous markers, variable material characterization, missing negative results, and limited causal interpretability (Chen et al., 2027; Cheong et al., 2026; Han Z. et al., 2026). Therefore, AI-generated predictions are best treated as experimentally testable hypotheses rather than established design rules. The most useful models are those linked to prospective validation, interpretable feature ranking, and clearly defined biological outputs. This distinction is important for programmable immune biointerfaces because relationships between stiffness, porosity, degradability, ligand density, and immune behavior may be plausible but remain context-dependent unless supported by specific experimental evidence.

Computational modeling is especially useful when immune states are defined across multiple scales. A macrophage phenotype, for example, may be described by molecular markers, secreted cytokines, morphology, spatial location, matrix association, and temporal persistence. A fibrotic boundary may require integration of cell composition, collagen organization, stiffness, vascular access, and material degradation. A tumor-mimicking immune interface may require simultaneous analysis of matrix architecture, antigen localization, macrophage enrichment, T-cell exclusion, and suppressive signaling. Predictive modeling integrates these layers into state maps that support programmable biointerface design (Han Z. et al., 2026; Mesfin et al., 2025; Sui et al., 2025).

A major modeling challenge is temporal integration across biological and material timescales. Hours-scale data may include Ca²⁺ signaling, early cytokine release, transcription-factor localization, and early transcriptomic responses. Days-scale data may include immune-cell recruitment, macrophage and T-cell migration, endothelial activation, stromal contraction, and early interface remodeling. Weeks-scale data may include scaffold degradation, stress relaxation, matrix deposition, fiber reorganization, vascular maturation, and fibrotic or reparative tissue organization. Months-scale data may include integration, remodeling stability, material persistence, chronic inflammation, fibrosis, or long-term immune-state trajectory. AI-guided models can address this mismatch by treating time as an explicit feature, separating early molecular readouts from later structural outcomes, and linking short-term predictors to longer-term remodeling endpoints through longitudinal datasets. This approach directly connects rapid cytokine and transcriptomic dynamics with slower scaffold degradation and matrix remodeling rather than treating them as synchronized processes.

Table 6 summarizes how the biological and material-design concepts reviewed in earlier sections can be translated into AI-guided modeling inputs, biological outputs, and experimentally testable design decisions for programmable immune biointerfaces.

TABLE 6. AI-guided modeling inputs, outputs, and design decisions for programmable immune biointerfaces.

Figure 4 summarizes predictive programmable biocompatibility as a closed-loop design cycle in which material design libraries, automated formulation/fabrication, biointerface testbeds, multiscale biological readouts, AI-guided feature extraction, predictive modeling, validation, and iterative redesign are connected to refine immune-state outcomes across regenerative and cancer-associated contexts.

Programmable immune biointerfaces can be developed through a closed-loop workflow that links material design libraries, automated formulation/fabrication, biointerface testbeds, multiscale biological readouts, AI-guided modeling, experimental validation, and iterative redesign. Material variables such as stiffness, viscoelasticity, porosity/topography, ligand density, degradation/release behavior, and stimulus-responsive chemistry can be tested in immune-cell assays, tissue-mimetic hydrogels, organoid or microphysiological systems, tumor–stroma models, and regenerative interface models. Readouts including transcriptomics, cytokine profiles, spatial imaging, cell-state markers, mechanical measurements, and degradation/remodeling kinetics provide data for feature extraction, multimodal integration, predictive modeling, interpretable feature ranking, and design-space mapping. Predicted material configurations are then evaluated against immune-state outputs such as pro-resolution remodeling, anti-fibrotic regulation, vascular integration, antigen presentation, reduced immune exclusion, and controlled suppressive states. The temporal integration bar highlights that AI-guided modeling must align rapid events, such as cytokine release, Ca²⁺ signaling, and early transcriptional responses, with slower processes including immune recruitment, scaffold degradation, matrix deposition, integration, remodeling stability, and long-term immune-state trajectories.

6.5 Closed-loop biointerface design

Closed-loop biointerface design connects material fabrication, immune-state measurement, computational modeling, prediction, validation, and iterative refinement. In this cycle, materials are not evaluated only after final formulation; they are repeatedly formulated, tested, measured, modeled, redesigned, and prospectively validated. Each experimental cycle links a defined material configuration to immune, stromal, vascular, mechanical, spatial, and temporal readouts, generating data that can guide the next design iteration (Chen et al., 2027; Cheong et al., 2026; Han Z. et al., 2026; Mesfin et al., 2025; Sui et al., 2025). Failed or unfavorable material configurations are also informative because they define boundaries of the design space and reduce overfitting to successful outcomes alone.

In vitro immune-cell assays provide controlled information about macrophage polarization, dendritic-cell activation, cytokine production, T-cell localization, stromal contraction, endothelial behavior, and glial reactivity. Tissue-mimetic platforms add matrix architecture, stiffness gradients, cell–cell interactions, vascular-like structures, oxygen gradients, and tumor-like constraints. Organoids and engineered tissue models capture more complex multicellular organization, including neural niche behavior, glial responses, tumor-like matrix environments, and immune-cell penetration. In vivo models then test whether material-directed immune patterns persist within full tissue environments (Quartey et al., 2025; Ravi et al., 2025; Mesfin et al., 2025; Skubal et al., 2025).

Feedback from these systems refines both material design and model structure. Persistent inflammatory activation can direct adjustments in stiffness, ligand density, degradation rate, or release timing. Poor immune-cell infiltration can guide changes in porosity, fiber architecture, or chemokine presentation. Incomplete modeling of immune exclusion can prompt revision of matrix density, stromal composition, or antigen localization. Prolonged glial reactivity at a neural interface can guide changes in degradation behavior, mechanical compliance, or anti-inflammatory signal timing.

Closed-loop design also changes how success is defined. Instead of asking whether a material is simply compatible or incompatible, this approach asks whether the biointerface produces the intended immune trajectory, whether that trajectory can be tuned, whether maladaptive states can be reversed, and whether material behavior remains aligned with the surrounding biological context. This creates a design cycle in which immune-state readouts become active inputs for material refinement.

Predictive programmable biocompatibility therefore unifies material science, mechanotransduction, stimuli-responsive design, immune-state biology, and AI-guided modeling into a single experimentally testable framework. A biointerface is programmable when its properties and stimulus responses are linked to defined immune-state trajectories. Predictive capacity emerges when those links are modeled, tested, prospectively validated, and refined across experimental systems. The framework does not assume that AI can replace mechanistic experimentation or establish universal design rules. Instead, AI-guided modeling functions as a structured way to organize complex biointerface datasets, identify candidate relationships, rank influential features, define uncertainty, and prioritize experimentally testable material designs. In this view, biocompatibility is not the absence of biological reaction; it is the capacity to organize biological reaction with specificity, timing, reversibility, and context dependence.

7 Conclusion

Biomaterials are not passive structures placed into biological environments; they are active immune-regulatory interfaces. Their biological effects emerge from the way material properties, tissue context, immune cells, stromal organization, vascular access, and local signals interact at the biomaterial–host boundary. Compatibility therefore cannot be defined by the absence of toxicity or overt inflammation alone. A biointerface is also defined by how it shapes immune-state behavior over time, across space, and within specific biological contexts.

Mechanical sensing links material design to immune behavior. Mechanical, architectural, adhesive, degradative, and chemical cues are interpreted by cells through adhesion, cytoskeletal tension, calcium signaling, transcriptional regulation, and matrix feedback. Through these pathways, material properties influence macrophage activation, stromal organization, endothelial behavior, glial reactivity, antigen presentation, fibrotic stabilization, and tissue integration. Physical design is therefore inseparable from immune regulation.

Stimuli-responsive materials extend this logic by allowing biointerfaces to deliver or modify signals with greater temporal and spatial control. External and microenvironmental triggers allow material outputs to be timed, localized, repeated, withdrawn, or adjusted. These systems shift immune regulation from a fixed material property to a sequence of controllable interfacial events.

Regeneration and cancer reveal different but related challenges in engineered immune control. In regenerative environments, immune-instructive materials are organized to support resolution, vascularization, matrix remodeling, tissue integration, and progenitor-permissive repair conditions. In cancer-associated environments, engineered interfaces can model or redirect suppressive, antigenically structured, and mechanically constrained immune states. The same material variables can therefore produce different immune outcomes depending on the biological context in which they operate.

Predictive programmable biocompatibility integrates these principles into a unified framework. Material properties, stimulus inputs, mechanotransductive signaling, immune-state regulation, and regenerative or cancer-relevant outcomes can be connected through measurable state trajectories and refined through feedback-guided experimentation. AI-guided modeling adds a computational layer for organizing high-dimensional biointerface data, identifying relationships among design variables and immune outputs, and supporting iterative material refinement.

The next-generation of biomaterials will be defined not by tissue tolerance alone, but by how precisely their immune-state trajectories can be designed, tested, and refined.

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Footnotes

Edited by: Christopher M. Madl, University of Pennsylvania, United States

Reviewed by: Supeng Ding, Johns Hopkins University, United States

Tugba Topal, Karadeniz Technical University, Türkiye