Adaptive multiparameter predictive degradation nanostructured platform with closedloop feedback controlled molecular release architecture

Abstract

An adaptive nanostructured platform configured to modulate degradation kinetics based on multiparameter biological input signals and a timedependent predictive correction factor integrated within a closedloop feedback architecture. The system enables differential degradation and controlled molecular release under distinct biological microenvironments.

Description

[0001] INTERNATIONAL PATENT APPLICATION

[0002] (PCT)

[0003] TITLE OF THE INVENTION

[0004] Adaptive MultiParameter Predictive Degradation Nanostructured Platform with ClosedLoop FeedbackControlled Molecular Release Architecture

[0005] TECHNICAL FIELD

[0006] The present disclosure relates to adaptive nanostructured systems, bioresponsive material platforms, predictive degradation architectures, and multiparameter feedbackcontrolled molecular release systems.

[0007] More specifically, the invention concerns a nanostructured platform configured to dynamically modulate degradation kinetics based on weighted biological input signals and a timedependent predictive correction factor integrated within a closedloop control architecture.BACKGROUND ART

[0008] Conventional nanostructured delivery systems rely on:

[0009] singleparameter stimulus response (e.g., pHtriggered degradation), enzymesensitive linkers,

[0010] static hydrolytic breakdown,

[0011] preprogrammed release kinetics.

[0012] Although multistimuli responsive materials have been described, such systems:

[0013] operate reactively rather than predictively,

[0014] do not incorporate adaptive weighting of multiple biological parameters,

[0015] lack closedloop degradation recalibration,

[0016] do not integrate timedependent predictive correction factors governing material decomposition.

[0017] Existing Alassisted biomedical platforms are directed toward treatment optimization or signal prediction, but not toward realtime modulation of structural degradation kinetics of nanomaterials.

[0018] Accordingly, a technical gap exists for a nanostructured platform in which material degradation itself is governed by a predictive multiparameter adaptive function integrated within a feedbackcontrolled architecture.SUMMARY OF THE INVENTION

[0019] The invention provides an adaptive nanostructured platform (100) comprising:

[0020] a sensing interface (102),

[0021] a nanostructured matrix (104),

[0022] a release modulation layer (106),

[0023] a predictive degradation modulation module (204),

[0024] a predictive correction factor (208),

[0025] and a structural stability threshold control (210).

[0026] The degradation rate D(t) is governed by a multivariable adaptive function incorporating weighted biological input signals and a timedependent predictive correction term.

[0027] The system exhibits differential degradation kinetics under distinct biological microenvironments.

[0028] Unlike static systems, the present platform enables:

[0029] anticipatory degradation recalibration,

[0030] multisignal integration,

[0031] feedbackcontrolled release modulation,

[0032] structural stability enforcement.

[0033] The invention is not limited to drug release optimization but is directed to intrinsicdegradationgoverning architectures with predictive material parameter modulation.

[0034] BRIEF DESCRIPTION OF DRAWINGS

[0035] FIG. 1 illustrates a multilayer adaptive nanostructured platform (100) including sensing interface (102), nanostructured matrix (104), and release modulation layer (106).

[0036] FIG. 2 illustrates a predictive feedback control loop (200) including biological input signals (202), predictive degradation modulation module (204), predictive correction factor (208), dynamic degradation output (206), and structural stability threshold (210).

[0037] FIG. 3 illustrates comparative degradation response curves including static response (302) and adaptive response (304).

[0038] FIG. 4 illustrates comparative molecular release profiles including static release curve (402) and adaptive release curve (404).

[0039] FIG. 5 illustrates a layered structural embodiment (500) of the adaptive nanostructured platform, comprising an outer sensing layer (510), an intermediate modulation layer 520)), and a core degradation layer (530). Biological stimuli interact with the sensing interface, generating signals that propagate through the intermediate layer to regulate controlled molecular release from the core layer (106).

[0040] DETAILED DESCRIPTION OF THE INVENTION

[0041] I. Structural Platform ArchitectureReferring to FIG. 1:

[0042] The adaptive nanostructured platform (100) comprises:

[0043] A sensing interface (102) configured to detect at least two biological parameters. A nanostructured matrix (104) configured to undergo environmentally modulated degradation.

[0044] A release modulation layer (106) configured to regulate molecular release proportional to degradation kinetics.

[0045] The nanostructured matrix (104) may comprise:

[0046] biodegradable polymer composites,

[0047] hybrid inorganicorganic nanolaminates,

[0048] graphene derivatives,

[0049] transition metal carbides including MXene derivatives,

[0050] hydrogelinfused nanocomposites,

[0051] layered silicate structures,

[0052] conductive polymer networks.

[0053] No specific material composition is limiting.II. MultiParameter Biological Input

[0054] Referring to FIG. 2:

[0055] Biological input signals (202) may include:

[0056] oxygen concentration,

[0057] ATP levels,

[0058] calcium ion concentration,

[0059] pH,

[0060] enzymatic activity,

[0061] redox state,

[0062] temperature,

[0063] mechanical stress.

[0064] At least two parameters are processed simultaneously.

[0065] III. Predictive Degradation Modulation

[0066] The predictive degradation modulation module (204) calculates a dynamic degradation rate D(t) as a function of:baseline degradation constant,

[0067] weighted biological parameters,

[0068] timedependent predictive correction factor (208).

[0069] The predictive correction factor (208) may be generated via:

[0070] reinforcement learning,

[0071] neural differential equation modeling,

[0072] Bayesian adaptive inference,

[0073] rulebased adaptive modeling,

[0074] hybrid probabilisticdeterministic systems.

[0075] The module operates within predictive feedback control loop (200) and enforces structural stability threshold (210).

[0076] IV. ClosedLoop Architecture

[0077] The dynamic degradation output (206) is continuously recalibrated based on:

[0078] realtime biological fluctuation,

[0079] historical signal weighting,

[0080] stability enforcement constraints.This closedloop recalibration distinguishes the present invention from reactive degradation systems.

[0081] V. Technical Effect

[0082] The invention achieves:

[0083] controlled degradation within physiological ranges,

[0084] accelerated degradation under pathological conditions,

[0085] reduced premature release,

[0086] improved release precision,

[0087] structural preservation under nontriggering environments.

[0088] Measured simulations demonstrate differential degradation exceeding 20% under pathological microenvironments relative to baseline conditions.

[0089] VI. Alternative Embodiments

[0090] The platform may be:nanoparticulate,

[0091] layered slabbased,

[0092] implantcoating integrated,

[0093] scaffoldintegrated,

[0094] injectable composite,

[0095] microencapsulated matrix,

[0096] distributed modular network.

[0097] The predictive module may operate:

[0098] embedded within the material system,

[0099] externally via wearable biosensor interface,

[0100] cloudintegrated,

[0101] edgecomputing enabled.

[0102] All equivalent structural and computational variations are intended to fall within the scope of the claims.

[0103] VII. Industrial Applicability

[0104] The invention is applicable to:targeted therapeutic delivery,

[0105] regenerative material platforms,

[0106] smart implant coatings,

[0107] adaptive biomedical scaffolds,

[0108] bioelectronic interfaces,

[0109] controlled immunomodulatory systems.

[0110] The platform may be manufactured using conventional nanofabrication and composite synthesis techniques.

[0111] Physical Causality Integration

[0112] In certain embodiments, the predictive computational module modifies physical degradation kinetics by altering one or more of the following:

[0113] effective hydrolytic cleavage rate,

[0114] enzymatic susceptibility,

[0115] porosity expansion rate,

[0116] interlayer diffusion resistance,

[0117] polymer crosslink breakage probability,

[0118] electrochemical bond destabilization threshold.

[0119] The generated correction factor is not limited to signallevel modulation but results in measurable alteration of intrinsic material parameters governing degradation dynamics.

[0120] The physical modulation may occur via electrochemical stimulation, localized fieldvariation, surface potential adjustment, or other energy-mediated material parameter alteration mechanisms.

[0121] Example Implementation - Predictive Degradation Regulation

[0122] Athreelayer biodegradable polymeric nanostructure was configured with:

[0123] Outer layer degradation constant: k, = 0.04 hr1

[0124] Intermediate layer degradation constant: k2= 0.02 hr1

[0125] Core degradation constant: k3= 0.01 hr1

[0126] The sensing interface detected:

[0127] Inflammatory marker concentration (I)

[0128] Local pH level (P)

[0129] A predictive model computed:

[0130] Correction factor F

[0131]

[0132] (t) = aj(t) + a2AP(t) + |3 E(t1)

[0133] where E(t1) represents prior prediction error.

[0134] When inflammatory marker exceeded threshold T

[0135] k, dynamically increased from 0.04 hr1to 0.095 hr1Simultaneously:

[0136] Crosslink density reduced by 18%

[0137] Diffusion coefficient increased by 32%

[0138] Closedloop recalibration occurred every 15 minutes.

[0139] Measured degradation deviation reduced by 41 % compared to passive systems.

[0140] ULTRAEXPANDED DEFENSIVE EMBODIMENTS SECTION

[0141] This section is intentionally drafted to maximize scope defense, prevent designaround strategies, and strengthen inventive step resilience under international examination standards.

[0142] I. Material Variability Protection

[0143] The nanostructured matrix may comprise:

[0144] twodimensional materials

[0145] layered carbidesnitrides

[0146] oxides

[0147] sulfides

[0148] conductive polymers

[0149] biodegradable polymers

[0150] hybrid organicinorganic composites

[0151] crosslinked hydrogels

[0152] peptidebased frameworks

[0153] metalorganic frameworks (MOFs)

[0154] covalent organic frameworks (COFs)

[0155] Material substitution does not depart from the inventive concept.

[0156] II. Geometry Variability Protection

[0157] The platform may be:

[0158] spherical

[0159] rodshaped

[0160] layered slab

[0161] porous scaffold

[0162] microcapsulenanoflake

[0163] fiberintegrated

[0164] coatingbased

[0165] injectable suspension

[0166] implantbound

[0167] distributed network architecture

[0168] Geometry modification does not avoid infringement.

[0169] III. Computational Architecture Variability

[0170] Predictive computation may be:

[0171] embedded microcontrollerbased

[0172] ASICbased

[0173] FPGAbased

[0174] cloudbased

[0175] edgebased

[0176] wearableintegrated

[0177] externally computed

[0178] biologically embedded logicExecution location is nonlimiting.

[0179] IV. Algorithmic Variability

[0180] The predictive correction factor may be generated via:

[0181] supervised learning

[0182] unsupervised learning

[0183] reinforcement learning

[0184] neural ODE

[0185] stochastic modeling

[0186] deterministic modeling

[0187] hybrid symbolicAI

[0188] adaptive control theory

[0189] Bayesian inference

[0190] Kalman filtering

[0191] gradientbased optimization

[0192] evolutionary computation

[0193] Algorithm substitution does not depart from inventive concept.V. Signal Input Variability

[0194] Biological inputs may include:

[0195] biochemical markers

[0196] electrical signals

[0197] mechanical stress

[0198] thermal fluctuation

[0199] oxidative stress

[0200] inflammatory markers

[0201] metabolic indicators

[0202] electromagnetic signatures

[0203] At least two simultaneous inputs define the adaptive architecture.

[0204] VI. Spatial Adaptation

[0205] Degradation modulation may occur:

[0206] globally

[0207] locally

[0208] regionallyanisotropically

[0209] layerspecific

[0210] gradientbased

[0211] compartmentspecific

[0212] VII. MultiDomain Application Protection

[0213] The platform may apply to:

[0214] oncology

[0215] regenerative medicine immunotherapy

[0216] smart implants

[0217] tissue scaffolding

[0218] bioelectronics

[0219] controlled antimicrobial systems environmental bioresponsive materials

[0220] Application domain is nonlimiting.

[0221] VIII. FutureProof ClauseAny equivalent structural, computational, algorithmic, material, geometric, or signalprocessing modification that preserves the adaptive predictive degradation architecture is intended to fall within the scope of the claims.

Claims

AMENDED CLAIMSreceived by the International Bureau on 02 June 2026 (02.06.2026) AMENDED CLAIMS UNDER ARTICLE 19Claim : A self-adaptive nanostructured platform (100) configured to dynamically regulate its own degradation kinetics within a biological environment, the platform comprising:a multilayered degradable matrix (102, 104, 106);a multiparameter sensing interface configured to detect at least two distinct biological or physicochemical stimuli;a predictive computational module configured to generate a time-dependent degradation correction factor based on real-time and historical input data;wherein the predictive computational module is operatively coupled to the nanostructured matrix such that the degradation rate constant (k), crosslink density evolution, or diffusion coefficient within at least one structural layer is physically modulated in response to said correction factor;wherein the system operates in a closed-loop recalibration architecture that continuously updates degradation parameters based on deviation between predicted and measured environmental conditions.Claim 2 : The platform of claim 1, wherein the degradation rate D(t) is governed by a multivariable adaptive function incorporating:a baseline degradation constant,weighted biological parameters,and a time-dependent predictive correction term.Claim 3 :The platform of claim 1, wherein at least three biological parameters are processed simultaneously.Claim 4 :The platform of claim 1, further comprising a structural stability threshold configured to prevent premature systemic disintegration.Claim 5 :The platform of claim 1, wherein the predictive degradation modulation module comprises at least one of:reinforcement learning models,neural differential equation modeling,Bayesian adaptive inference,rule-based adaptive recalibration,hybrid probabilistic-deterministic systems.Claim 6 :The platform of claim 1, wherein degradation under pathological microenvironment conditions differs by at least 20% relative to baseline physiological degradation.Claim 7 : The platform of claim 1, wherein the nanostructured matrix comprises at least one of:biodegradable polymer composites,graphene derivatives,transition metal carbides,MXene derivatives,hydrogel-integrated nanocomposites,layered inorganic-organic hybrid structures.Claim 8 :The platform of claim 1, further comprising a release modulation layer configured to release a molecular payload proportional to the dynamically calculated degradation rate.Claim 9 :The platform of claim 1, wherein the predictive degradation modulation module operates in an external computational environment communicating with the nanostructured matrix.Claim 10 : The platform of claim 1, wherein the predictive degradation modulation module is embedded within the material platform.Claim 11 : The platform of claim 1 , wherein degradation kinetics are spatially differentiated across distinct regions of the nanostructured matrix.Claim 12 :The platform of claim 1, wherein adaptive weighting coefficients are updated based on longitudinal biological signal data.Claim 13 :A method of adaptive degradation modulation comprising:a) detecting at least two biological parameters;b) computing a weighted degradation function;c) applying a time-dependent predictive correction factor;d) modulating nanostructural degradation accordingly;e) recalibrating degradation kinetics via closed-loop feedback.Claim 14 : The method of claim 13, further comprising releasing a molecular payload proportionally to the dynamically modulated degradation rate.Claim 15 :A computing medium comprising a processor coupled to a memory, the processor configured to execute the dynamic predictive biological regulation model of any one of claims 1 to 14, wherein the processor computes a predictive degradation correction factor (k) based on a fusion of real-time sensory data and historical bio-information, and dynamically modulates predictive degradation-state variables of a nanostructured platform-including macromolecular matrix degradation kinetics, crosslink density evolution, and diffusion behavior-ratherthan a static responsive threshold, to maintain controlled degradation within physiological ranges and optimize molecular release precision.Claim 16 :The platform of claim 1, wherein degradation modulation is performed via a hybrid architecture combining embedded sensing and remote predictive computation.Claim 17 :The platform of claim 1, wherein the predictive correction factor incorporates anticipatory modeling based on projected environmental trends.Claim 18 : The platform of claim 5, wherein reinforcement learning updates degradation weighting coefficients iteratively to optimize stability and release precision.Claim 19 :The platform of claim 1, applicable to nanoparticulate systems, implant coatings, scaffolds, injectable composites, and layered structural constructs.Claim 20 :The platform of claim 1, wherein degradation behavior is governed by predictive adaptive integration rather than threshold-triggered reactive degradation alone.STATEMENT UNDER ARTICLE 19(1)1. IntroductionApplicant thanks the Honorable Examiner for the Written Opinion. While claims 1 -14 have been recognized as novel and possessing an inventive step, objection was raised against claim 15 based on document D1 (US20190374712). Claim 15 has been amended to more precisely define the physical degradation-state variables managed by the predictive model, fully aligning its scope with the inventive core of claims 1 -14 and the definitive structural elements of original Claim 1.

2. Inventive Step over D1 (IBM)Document D1 (IBM) discloses a reactive, threshold-triggered closed-loop drug delivery system that relies strictly on real-time sensor measurements (e.g., pH) to trigger an ON / OFF heating coil, altering a polymer's temperature to open a physical reservoir. D1 operates on standard reactive threshold logic: Measure -> Detect Threshold -> Release.In stark contrast, the present invention does not merely adjust a delivery threshold or trigger an ON / OFF thermal switch. As now explicitly specified in Claim 15, the computing medium processes a predictive degradation correction factor (k) through a predictive computational architecture using a neural differential equation modeling framework, which is fully disclosed in original Claims 1 and 5, as well as the Specification. This predictive factor dynamically modulates continuousdegradation-state variables of the nanostructured platform, specifically macromolecular matrix degradation kinetics, crosslink density evolution, and diffusion behavior, elements explicitly integrated within original Claim 1.Furthermore, as disclosed in the present specification, the generated correction factor results in a measurable alteration of intrinsic material parameters governing degradation dynamics, rather than signal-level modulation. D1 completely lacks any teaching or suggestion of predictive degradation-state modulation or intrinsicmacromolecular crosslink evolution. Therefore, the computing medium of Claim 15 is inextricably tied to the novel material kinetics of the platform and cannot be considered an obvious or routine extension of D1.

3. ConclusionThe limitation introduced into Claim 15 bridges the predictive processing of claims 1-14 with the unique physical architecture disclosed in Claim 1. Accordingly, Applicant respectfully submits that amended Claim 15 possesses novelty and an inventive step over D1 and therefore satisfies the requirements of PCT Article 33.

Images