Functional verification method for in vivo targeted delivery of reagents based on atherosclerosis model

Abstract

The present invention relates to the field of cardiovascular disease technology, and in particular to a method for validating the in vivo targeted delivery function of an agent based on an atherosclerosis model. The method comprises the following steps: obtaining lesion characteristic data of the atherosclerotic site, wherein the lesion characteristics include lesion location, endothelial permeability, inflammatory state, and vulnerability index; preparing targeted nanobubbles based on the lesion characteristic data, injecting the targeted nanobubbles intravenously into a pre-set atherosclerosis model, and performing multimodal imaging tracking analysis to obtain in vivo microbubble circulation distribution data. The present invention customizes the design of targeted nanobubbles based on plaque characteristic data and utilizes dual-targeting ligand modification technology to significantly improve the targeting specificity for inflammatory components such as macrophages, thereby solving the problem of low recognition efficiency of traditional single-targeting systems.

Description

Technical Field

[0001] The present invention relates to the technical field of cardiovascular diseases, and in particular to a method for verifying the in vivo targeted delivery function of a reagent based on an atherosclerosis model. Background Art

[0002] Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, cholesterol, calcium, and cellular debris within the arterial wall, forming atherosclerotic plaques. These plaques not only gradually narrow the vessel lumen and restrict blood flow, but can also rupture, leading to thrombosis and potentially fatal cardiovascular events such as myocardial infarction and stroke. However, conventional approaches for validating the in vivo targeted delivery of therapeutic agents based on atherosclerosis models often face challenges. The heterogeneity of atherosclerotic plaques presents a significant obstacle to precision therapy. Plaques exhibit distinct pathological characteristics and drug responsiveness at different stages of development, ranging from early plaques with lipid deposition to vulnerable plaques to calcified plaques, each requiring a differentiated treatment strategy. Traditional approaches struggle to differentiate treatments for different plaque types, let alone precisely target specific plaque components. Existing studies often rely on post-hoc pathological analysis or single-modality imaging techniques, which are unable to provide real-time, multi-dimensional monitoring of the entire in vivo drug delivery process. This severely restricts the development and optimization of targeted delivery technologies. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for validating the in vivo targeted delivery function of an agent based on an atherosclerosis model comprises the following steps:

[0005] Step S1: obtaining lesion characteristic data of the atherosclerotic site, wherein the lesion characteristics include lesion location, endothelial permeability, inflammatory state and vulnerability index;

[0006] Step S2: preparing targeted nanobubbles based on the lesion characteristic data, injecting the targeted nanobubbles intravenously into the preset atherosclerosis model, and performing multimodal imaging tracking analysis to obtain microbubble circulation distribution data in vivo;

[0007] Step S3: performing ultrasound response activation processing on the targeted nano-microbubbles based on the microbubble in vivo circulation distribution data, and performing phased acoustic parameter regulation on the target lesion site through low-intensity focused ultrasound to obtain dynamic data on the microbubble acoustic behavior and reagent release status data;

[0008] Step S4: Based on the dynamic data of microbubble acoustic behavior and the data on the release status of the reagent, three modalities, namely ultrasound contrast imaging, near-infrared fluorescence imaging, and positron emission tomography, are used to monitor the distribution of the reagent in real time to obtain the spatiotemporal distribution data of the targeted delivery of the reagent;

[0009] Step S5: Evaluate the targeted delivery efficiency of the reagent based on plaque vulnerability according to the spatiotemporal distribution data to obtain efficacy evaluation data, wherein the efficacy evaluation data includes target-to-background ratio, delivery efficiency parameters, and lesion progression matching index.

[0010] This invention characterizes atherosclerotic plaques across multiple dimensions. This method establishes a comprehensive dataset encompassing lesion location, endothelial permeability, inflammatory status, and vulnerability index, laying the scientific foundation for personalized delivery strategies. This precise characterization overcomes the inefficiency of traditional approaches due to insufficient understanding of the plaque, enabling more targeted treatment. Subsequently, targeted nanobubbles are customized based on this acquired lesion data. Through precise modification of surface ligands, targeting specificity to the lesion region is significantly enhanced. This "plaque-oriented" design concept enables efficient drug accumulation in the target area, minimizing systemic distribution, reducing the risk of adverse reactions, and extending the therapeutic safety window. Once the targeted nanobubbles circulate within the body and reach the lesion site, an innovative graded ultrasound activation strategy based on lesion characteristics is employed. Low-intensity focused ultrasound (LIFU) is used to differentially regulate acoustic parameters according to the microbubble's distribution within the body. This dynamic adjustment enables precise spatiotemporal control of drug release, addressing a key technical bottleneck associated with uncontrollable release in traditional delivery methods. Subsequently, a dynamic visualization monitoring system for the entire agent delivery process was constructed by combining three highly complementary imaging modalities: ultrasound contrast imaging, near-infrared fluorescence imaging, and positron emission tomography. This multimodal integration not only fills the gaps in traditional technologies for monitoring the delivery process, but also provides a complete delivery portrait covering multiple time points and high spatial resolution, providing an intuitive basis for technology optimization. Ultimately, by establishing a matching evaluation model for delivery efficiency and plaque vulnerability, this method achieved a significant leap from simply quantifying delivery efficiency to assessing delivery-lesion matching. This innovative evaluation framework closely links drug delivery with disease characteristics, ensuring the synergy between therapeutic intervention and lesion progression, and significantly enhancing the potential for clinical translation. Overall, this method forms a closed-loop optimization system from plaque characterization, carrier design, delivery control, to effect evaluation, comprehensively breaking through the limitations of traditional technologies and achieving a dual improvement in delivery efficiency and safety. It provides an innovative technology platform for the precision treatment of atherosclerosis and has important clinical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0012] Figure 1 Schematic diagram of the steps of the method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model of the present invention;

[0013] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0014] Figure 3 for Figure 1 Detailed step flow chart of step S3 in FIG. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0017] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model, the method comprising the following steps:

[0019] Step S1: obtaining lesion characteristic data of the atherosclerotic site, wherein the lesion characteristics include lesion location, endothelial permeability, inflammatory state and vulnerability index;

[0020] In the process of acquiring lesion characteristic data at the site of atherosclerosis, the present invention first selects imaging data from an animal model of atherosclerosis or a patient as the research subject. High-resolution intravascular ultrasound (IVUS) combined with optical coherence tomography (OCT) is used to precisely locate the lesion site, and multispectral fluorescence microscopy is used to assess changes in endothelial cell permeability. ELISA is also used to measure blood levels of cell adhesion molecules (such as VCAM-1 and ICAM-1) and inflammatory factors (such as TNF-α, IL-6, and MCP-1) to reflect the local inflammatory state. Furthermore, contrast-enhanced ultrasound is used to measure local microcirculatory perfusion, and magnetic resonance imaging (MRI) T1 / T2-weighted imaging is used to assess lipid core size and fibrous cap thickness, thereby calculating the plaque vulnerability index.

[0021] Step S2: preparing targeted nanobubbles based on the lesion characteristic data, injecting the targeted nanobubbles intravenously into the preset atherosclerosis model, and performing multimodal imaging tracking analysis to obtain microbubble circulation distribution data in vivo;

[0022] In the preparation of targeted nanobubbles in this embodiment of the present invention, a phospholipid and polymer composite material is used to form the shell. A stable microbubble structure is formed by gas filling. Targeting ligands, such as monoclonal antibodies or small peptides targeting VCAM-1 or ICAM-1, are then modified on the microbubble surface to enhance the microbubble's ability to actively target lesions. Following preparation, the microbubble size distribution and morphology were measured using dynamic light scattering (DLS) and transmission electron microscopy (TEM), and their surface charge properties were determined using zeta potential. Subsequently, the microbubbles were administered via tail vein injection into atherosclerosis model animals. Ultrasound contrast imaging, fluorescence molecular imaging (FMI), and positron emission tomography (PET) were used to simultaneously track the distribution of the microbubbles in the circulatory system at different time points. The accumulation ratio, elimination rate, and biodistribution characteristics of the microbubbles at the lesion site were collected and analyzed at different time points.

[0023] Step S3: performing ultrasound response activation processing on the targeted nano-microbubbles based on the microbubble in vivo circulation distribution data, and performing phased acoustic parameter regulation on the target lesion site through low-intensity focused ultrasound to obtain dynamic data on the microbubble acoustic behavior and reagent release status data;

[0024] In this embodiment of the present invention, the ultrasound-responsive activation of targeted nanobubbles is first performed using high-frequency ultrasound to observe the accumulation of microbubbles at the lesion site in real time. Low-intensity focused ultrasound (LIFU) is then used to stage-by-stage control of ultrasonic parameters, such as pulse repetition frequency, acoustic pressure intensity, and duty cycle, to induce resonant oscillation and localized rupture of the microbubbles, achieving precise release of the drug. Specifically, in the first stage, low acoustic pressure (<0.5 MPa) is used to activate the microbubbles to enhance the local acoustic contrast signal. In the second stage, moderate acoustic pressure (0.5-1.0 MPa) is used to induce partial disintegration of the microbubbles and promote sustained drug release. Finally, high acoustic pressure (>1.0 MPa) is used to achieve complete rupture of the microbubbles, ensuring effective drug release. High-speed video is used to record the microbubble oscillation and disintegration process, and combined with fluorescent molecular tracing methods to measure the dynamics of drug release, ultimately obtaining acoustic behavior data of the microbubbles and a curve of drug release status.

[0025] Step S4: Based on the dynamic data of microbubble acoustic behavior and the data on the release status of the reagent, three modalities, namely ultrasound contrast imaging, near-infrared fluorescence imaging, and positron emission tomography, are used to monitor the distribution of the reagent in real time to obtain the spatiotemporal distribution data of the targeted delivery of the reagent;

[0026] In this embodiment of the present invention, during the spatiotemporal distribution monitoring of targeted agent delivery, ultrasound contrast imaging is first used to assess changes in local blood perfusion after microbubble rupture. Near-infrared fluorescence imaging (NIRF) is then combined to monitor the spatial distribution of the fluorescent tracer-labeled agent with high temporal resolution. Simultaneously, positron emission tomography (PET) is used to record the in vivo distribution of the radioactive tracer agent. In the experiment, a lipophilic small molecule drug with a fluorophore was selected as the tracer. Image data was collected 5, 15, 30, and 60 minutes after microbubble rupture. The fluorescence intensity ratio between the lesion area and surrounding normal tissue was calculated to assess the enrichment of the agent in the target area. For PET imaging, 18F-labeled drugs or nanoprobes were used, and drug accumulation in the target area was calculated using the standardized uptake value (SUV). This was cross-validated with ultrasound and fluorescence imaging data to obtain the spatiotemporal distribution characteristics of the agent delivery.

[0027] Step S5: Evaluate the targeted delivery efficiency of the reagent based on plaque vulnerability according to the spatiotemporal distribution data to obtain efficacy evaluation data, wherein the efficacy evaluation data includes target-to-background ratio, delivery efficiency parameters, and lesion progression matching index.

[0028] In the process of constructing a delivery efficiency-plaque vulnerability matching model, the present embodiment first calculates the targeted delivery efficiency based on spatiotemporal distribution data, including the integral of the concentration-time curve (AUC), the maximum target accumulation concentration (Cmax), and the mean residence time (MRT) of the agent in the target lesion area. Next, the fibrous cap thickness, lipid pool area, and degree of calcification of the plaque are measured using IVUS-OCT fusion images. In combination with inflammatory factor levels and microcirculatory perfusion data, the plaque vulnerability index is calculated. Finally, a matching model is constructed using regression analysis and machine learning algorithms (such as random forests or support vector machines). Using the target-to-background ratio (TBR) as the core parameter, the model quantifies the enrichment of the agent in high-risk plaque areas and assesses its alignment with lesion progression. Ultimately, data evaluating the efficacy of the agent's targeted delivery is generated, providing a basis for optimizing personalized treatment plans.

[0029] This invention characterizes atherosclerotic plaques across multiple dimensions. This method establishes a comprehensive dataset encompassing lesion location, endothelial permeability, inflammatory status, and vulnerability index, laying the scientific foundation for personalized delivery strategies. This precise characterization overcomes the inefficiency of traditional approaches due to insufficient understanding of the plaque, enabling more targeted treatment. Subsequently, targeted nanobubbles are customized based on this acquired lesion data. Through precise modification of surface ligands, targeting specificity to the lesion region is significantly enhanced. This "plaque-oriented" design concept enables efficient drug accumulation in the target area, minimizing systemic distribution, reducing the risk of adverse reactions, and extending the therapeutic safety window. Once the targeted nanobubbles circulate within the body and reach the lesion site, an innovative graded ultrasound activation strategy based on lesion characteristics is employed. Low-intensity focused ultrasound (LIFU) is used to differentially regulate acoustic parameters according to the microbubble's distribution within the body. This dynamic adjustment enables precise spatiotemporal control of drug release, addressing a key technical bottleneck associated with uncontrollable release in traditional delivery methods. Subsequently, a dynamic visualization monitoring system for the entire agent delivery process was constructed by combining three highly complementary imaging modalities: ultrasound contrast imaging, near-infrared fluorescence imaging, and positron emission tomography. This multimodal integration not only fills the gaps in traditional technologies for monitoring the delivery process, but also provides a complete delivery portrait covering multiple time points and high spatial resolution, providing an intuitive basis for technology optimization. Ultimately, by establishing a matching evaluation model for delivery efficiency and plaque vulnerability, this method achieved a significant leap from simply quantifying delivery efficiency to assessing delivery-lesion matching. This innovative evaluation framework closely links drug delivery with disease characteristics, ensuring the synergy between therapeutic intervention and lesion progression, and significantly enhancing the potential for clinical translation. Overall, this method forms a closed-loop optimization system from plaque characterization, carrier design, delivery control, to effect evaluation, comprehensively breaking through the limitations of traditional technologies and achieving a dual improvement in delivery efficiency and safety. It provides an innovative technology platform for the precision treatment of atherosclerosis and has important clinical application value.

[0030] Preferably, step S1 includes the following steps:

[0031] Step S11: using vascular ultrasound imaging technology to perform real-time vascular scanning on a preset atherosclerosis model, and recording the three-dimensional spatial distribution and morphological characteristics of the plaque, thereby obtaining lesion location data including plaque positioning;

[0032] In this embodiment of the present invention, real-time vascular scanning of an atherosclerosis model using vascular ultrasound imaging technology begins with selecting a high-frequency linear array probe (frequency range 30-55 MHz) and connecting it to a high-resolution ultrasound imaging system. The probe is then placed in the target vascular region, using a dedicated coupling gel to reduce air interference. Continuous longitudinal and transverse scanning is then performed in B-mode ultrasound mode to acquire two-dimensional structural information of the plaque. Next, a three-dimensional ultrasound reconstruction algorithm is used to stitch the multi-angle two-dimensional image data and perform spatial interpolation to obtain the three-dimensional morphology of the plaque. During image processing, an adaptive edge detection algorithm is used to segment the plaque region and calculate parameters such as plaque volume, length, width, and surface irregularity to obtain three-dimensional spatial distribution data of the lesion. In addition, intravascular ultrasound (IVUS) technology is used to obtain the cross-sectional echo distribution of the plaque. Tissue-specific reflection signals are used to calculate plaque hardness and lipid core area ratio, and the specific spatial location of the plaque is recorded to generate lesion localization data.

[0033] Step S12: measuring the ultrasonic echo intensity ratio between the plaque area and the normal blood vessel wall in the atherosclerosis model, and performing contrast agent permeation dynamics analysis to obtain endothelial permeability data;

[0034] In measuring the ultrasonic echo intensity ratio between the plaque area and the normal blood vessel wall, the embodiment of the present invention first uses a high-frequency ultrasonic imaging system to obtain cross-sectional images of blood vessels at different depths, and uses a standardized ultrasonic signal processing algorithm to calculate the echo intensity of different regions. Specifically, ROIs (regions of interest) are selected to demarcate the plaque tissue and the contralateral normal blood vessel wall area, and the average echo intensity of the two is obtained by grayscale value histogram analysis, and their ratio is calculated as the plaque echo intensity ratio data. In addition, to assess endothelial permeability, a microbubble contrast agent (phospholipid-coated sulfur hexafluoride microbubbles, concentration 1×10 9 The researchers used dynamic contrast-enhanced ultrasound (DCE-US) to record the contrast agent's permeation profile within the vascular wall. By analyzing the maximum enhancement intensity, peak time, and clearance rate of the contrast agent within the plaque region, they calculated microbubble permeation parameters, thereby assessing endothelial barrier integrity and generating endothelial permeability data.

[0035] Step S13: According to the atherosclerosis model, a contrast agent targeting inflammatory mediators is injected and its enrichment in the plaque area is monitored, and the density of CD68-positive cells is evaluated to obtain inflammatory status data;

[0036] When monitoring the inflammatory state of the plaque area, the embodiment of the present invention first injects an ultrasound contrast agent targeting inflammatory mediators through the tail vein. The surface of the contrast agent is modified with a monoclonal antibody against VCAM-1 or ICAM-1 to enhance its enrichment ability at the site of inflammation. In the ultrasound contrast imaging mode, the signal intensity of the plaque area is recorded over time, and the peak intensity and cumulative signal integral are calculated to evaluate the local enrichment of the contrast agent. At the same time, the density of CD68-positive cells is assessed by immunofluorescence tissue sectioning technology. The specific method is to take vascular plaque tissue, fix it, perform frozen sections, and use anti-CD68 antibodies for fluorescent labeling. The distribution of CD68-positive cells is then recorded under a confocal fluorescence microscope, and the positive cell density is calculated. Combining the ultrasound signal with the CD68 cell count results, the inflammation level is quantified and the inflammatory status data is obtained.

[0037] Step S14: calculating a plaque acoustic response index based on the endothelial permeability data and the inflammatory status data, and classifying the plaques based on the plaque acoustic response index, thereby obtaining plaque classification data, wherein the plaque classification data includes low-response plaque data, medium-response plaque data, and high-response plaque data;

[0038] In calculating the plaque acoustic response index, the present embodiment first uses ultrasound excitation technology to measure the acoustic response characteristics of different plaque regions. Specifically, in contrast-enhanced ultrasound mode, the target plaque is excited at different sound pressures (0.2 MPa, 0.5 MPa, and 1.0 MPa), and the echo change rate and microbubble rupture rate of the plaque tissue under different sound pressure conditions are recorded. By analyzing the nonlinear acoustic response curve of the plaque, the plaque acoustic response index is calculated, which reflects the plaque's sensitivity to ultrasound excitation. Plaques are then classified based on the acoustic response index. If the index is below a preset threshold (e.g., <0.3), it is defined as a low-responsive plaque; if the index is in the medium range (e.g., 0.3-0.6), it is defined as a medium-responsive plaque; if the index is higher (e.g., >0.6), it is defined as a high-responsive plaque, and plaque classification data is generated.

[0039] Step S15: performing near-infrared fluorescence imaging based on inflammatory activity and PET imaging based on metabolic activity based on the plaque classification data, and calculating a vulnerability index value of the lesion area, thereby obtaining plaque vulnerability index data, wherein the vulnerability index value is calculated as follows: vulnerability index = 0.4 × proportion of low-responsive area + 0.35 × standard score of inflammatory activity + 0.25 × standard score of metabolic activity;

[0040] To calculate the plaque vulnerability index, the present invention first uses near-infrared fluorescence imaging (NIRF) based on plaque classification data to detect inflammatory activity. Specifically, a fluorescent probe targeting MMP-9 or VCAM-1 is intravenously injected. Fluorescence images are acquired at different time points (e.g., 30, 60, and 120 minutes after injection). Fluorescence intensity in areas of inflammatory activity is analyzed, and a standardized fluorescence score is calculated. Next, metabolic activity-based PET imaging is performed, using 18F-FDG as a metabolic tracer. The standardized uptake value (SUVmax) is used to analyze the metabolic activity of the lesion area, and a standardized metabolic activity score is calculated. Finally, according to the vulnerability index calculation formula, the proportion of low-responsive plaque areas contributes 40% to the vulnerability index, the inflammatory activity standard score contributes 35%, and the metabolic activity standard score contributes 25%. The plaque vulnerability index data is then calculated based on the overall data, which can be used to assess the risk of lesion rupture.

[0041] Step S16: combining the plaque vulnerability index data, the inflammatory status data, the endothelial permeability data, and the lesion location data into lesion feature data.

[0042] In the process of integrating lesion feature data, the present embodiment first stores plaque vulnerability index data, inflammatory status data, endothelial permeability data, and lesion location data in a single database and performs data standardization to ensure unit consistency across different data sources. Subsequently, a multimodal data fusion method is employed to extract key lesion features using weighted averaging or principal component analysis (PCA). Endothelial permeability and inflammatory status data are combined to generate a comprehensive lesion activity index, which is then spatially matched with lesion location data to form a complete lesion feature dataset. Ultimately, this data can be used for personalized risk assessment and optimization of targeted treatment strategies.

[0043] The present invention achieves multi-dimensional and hierarchical accurate characterization in the acquisition of characteristic data of atherosclerotic lesions, and constructs a complete evaluation system from anatomical structure to functional status. Real-time vascular scanning is performed through vascular ultrasound imaging technology, which achieves accurate recording of the three-dimensional spatial distribution and morphological characteristics of plaques, provides accurate spatial coordinates for subsequent targeted delivery, and avoids delivery deviation caused by ambiguous lesion positioning in traditional methods. On this basis, an innovative method of measuring the ultrasonic echo intensity ratio of the plaque area to the normal blood vessel wall is introduced, combined with contrast agent permeation kinetics analysis, to achieve quantitative evaluation of endothelial permeability. This parameter is directly related to the efficiency and depth of drug delivery, and provides a key basis for the optimization of delivery strategies. Furthermore, by injecting contrast agents targeting inflammatory mediators and monitoring their enrichment, combined with CD68-positive cell density assessment, accurate quantification of the inflammatory state of the plaque is achieved, laying the foundation for targeted delivery of inflammation. Of particular note is the innovative method used in this study to calculate the plaque acoustic response index based on endothelial permeability and inflammatory status, classifying plaques into three response types: low, medium, and high. This classification not only reflects the sensitivity of the plaque to ultrasound but also guides the personalized setting of subsequent ultrasound parameters, significantly improving delivery accuracy. Finally, by integrating the results of inflammatory activity near-infrared fluorescence imaging and metabolic activity PET imaging, a scientific vulnerability index calculation formula was established, enabling a comprehensive evaluation of the pathological state of the plaque and providing a standardized reference for subsequent delivery effect and lesion matching analysis. This comprehensive set of lesion feature data acquisition methods breaks through the limitations of traditional single-dimensional evaluation and opens up new avenues for the precise treatment of atherosclerosis.

[0044] Preferably, step S14 specifically includes:

[0045] The acoustic response index of the lesion area is calculated based on the product of the endothelial permeability data and the inflammation degree data to obtain the acoustic response index; when the acoustic response index is less than 0.5, the plaque is classified as low-responsive plaque data; when the acoustic response index is greater than or equal to 0.5 and less than 0.8, the plaque is classified as medium-responsive plaque data; when the acoustic response index is greater than or equal to 0.8, the plaque is classified as high-responsive plaque data; the low-responsive plaque data, medium-responsive plaque data and high-responsive plaque data are merged into plaque classification data.

[0046] In calculating the acoustic response index of the lesion area, the embodiment of the present invention first obtains endothelial permeability data and inflammation degree data. The endothelial permeability data is derived from ultrasound contrast analysis. The specific method is to use microbubble contrast agent for dynamic enhanced ultrasound imaging, record the peak signal intensity, clearance rate and perfusion time of the contrast agent in the lesion area, calculate the microbubble permeability using the time-signal curve, and normalize it to the range of 0 to 1. The inflammation degree data is derived from the CD68 positive cell density assessment and the enrichment degree analysis of the ultrasound contrast agent targeting inflammatory mediators. The specific method is to use immunofluorescence to label CD68 antibodies, calculate the proportion of CD68 positive cells by fluorescence microscopy scanning of tissue sections, and calculate the inflammation level in combination with the ultrasound contrast agent signal enhancement intensity. Finally, the inflammation degree data is normalized to the range of 0 to 1. Then, the product of the endothelial permeability data and the inflammation degree data is calculated for each lesion area. The result is the acoustic response index of the area, and the calculated value is ensured to be between 0 and 1. When the acoustic response index is less than 0.5, the lesion area is judged to have a weak acoustic response and high plaque stability, and is classified as a low-responsive plaque. When the acoustic response index is greater than or equal to 0.5 and less than 0.8, the area is judged to have a moderate acoustic response, indicating that the plaque may be unstable and is classified as a medium-responsive plaque. When the acoustic response index is greater than or equal to 0.8, it indicates that the lesion area has high inflammatory activity and endothelial permeability, with a high risk of instability, and is classified as a high-responsive plaque. Finally, all data for low-responsive, medium-responsive, and high-responsive plaques are stored in a unified database and formatted. Each category of data is annotated with the corresponding lesion location, morphological characteristics, and ultrasound parameters for subsequent analysis, thus generating complete plaque classification data.

[0047] The acoustic response index classification system created by this invention represents a key technological breakthrough in the precision treatment of atherosclerosis, establishing a scientific bridge from pathological features to therapeutic parameters. Through an innovative calculation method that multiplies endothelial permeability data with inflammation severity data, this system integrates these two key pathological factors into a single quantitative indicator for the first time, overcoming the limitations of traditional single-parameter evaluations. This index fully accounts for the influence of endothelial permeability on microbubble penetration and the contribution of inflammation severity to drug targeting, enabling a comprehensive assessment of plaque acoustic responsiveness. More importantly, the invention uses thresholds of 0.5 and 0.8 to precisely categorize plaques into low, medium, and high response types. This three-level classification not only reflects the differences in plaque sensitivity to ultrasound stimulation but also directly guides the design of customized acoustic parameter manipulation strategies. For low-responsive plaques, the system typically uses continuous ultrasound irradiation to ensure sufficient acoustic energy to activate microbubbles. For medium-responsive plaques, intermittent ultrasound irradiation is used to balance delivery efficiency and tissue safety. For high-responsive plaques, finely pulsed incremental ultrasound irradiation is used to avoid potential damage from overactivation. This pathologically characterized, hierarchical ultrasound parameter regulation scheme significantly improves the precision of drug release and enhances targeted delivery efficiency, while reducing adverse effects on surrounding healthy tissues, providing a solid methodological foundation for the precise treatment of atherosclerosis. Furthermore, the standardized nature of this classification system greatly enhances the reproducibility and quality control of delivery technology, strongly supporting the translation process from experimental research to clinical application.

[0048] Preferably, the preparation of targeted nanobubbles includes the preparation of microbubbles with surface modification having specific ligands for plaque macrophages and the preparation of microbubbles with a core layer loaded with a reagent to be verified. The preparation of microbubbles with surface modification having specific ligands for plaque macrophages in step S2 specifically includes:

[0049] A lipid material with phase transition properties, including dipalmitoylphosphatidylcholine, distearoylphosphatidylglycerol, and polyethylene glycol-modified distearoylphosphatidylethanolamine, was mixed at a molar ratio of 7:2:1, dissolved in chloroform, and subjected to rotary evaporation to obtain uniform lipid film data.

[0050] Based on the uniform lipid membrane data, gas cores were prepared by passing perfluoropropane gas into ultrapure water to form a saturated solution. The solution was stirred at 3000 rpm for 10 minutes at 40°C and intermittently sonicated using an ultrasonic probe to obtain primary microbubble emulsion data.

[0051] The reagent encapsulation process was performed based on the primary microbubble emulsion data. The pre-obtained reagent to be verified was dissolved in phosphate buffer and the pH was adjusted to 7.4. The reagent solution was encapsulated in the microbubble core using double emulsion technology to obtain the reagent encapsulation microbubble data.

[0052] The targeting ligand was modified according to the reagent-loaded microbubble data, and the macrophage CD68 antibody or integrin was added by click chemistry. The targeting peptide was covalently linked to the microbubble surface to obtain single-targeted microbubble data;

[0053] A dual-targeting ligand system is prepared based on the single-targeting microbubble data, thereby obtaining dual-targeting microbubble data;

[0054] Fluorescence resonance energy transfer (FRET) technology was used to monitor the ligand modification efficiency based on dual-targeted microbubble data. The spatial distribution of the two ligands on the microbubble surface was determined by detecting the FRET signal, and the ligand spacing was adjusted according to the FRET signal intensity to obtain the ligand modification efficiency data.

[0055] The ligand directional arrangement is optimized based on the ligand modification efficiency data. By controlling the temperature gradient, the ligand is promoted to form an enriched area on the microbubble surface, the local ligand density is increased and the steric hindrance is reduced, thereby obtaining the targeted nanobubble preparation data.

[0056] To calculate the acoustic response index (ARI) of a lesion region, embodiments of the present invention first require obtaining endothelial permeability data and inflammation severity data. Experimentally, optical coherence tomography (OCT) and molecular ultrasound contrast imaging (MCUS) can be used to measure the endothelial permeability parameters and inflammation levels in the lesion region, respectively. Endothelial permeability data can be obtained by measuring the signal intensity of a fluorescently labeled vascular permeability tracer within the lesion region, while inflammation severity data can be calculated based on the echo signal intensity of ultrasound contrast-enhanced microbubbles targeting inflammatory markers. The endothelial permeability data is then multiplied by the inflammation severity data to obtain the ARI data for the lesion region. For example, if the normalized signal value of the endothelial permeability data is 0.6 and the normalized signal value of the inflammation severity data is 0.7 in a group of lesion samples, the ARI is 0.6 × 0.7 = 0.42. To classify plaques, a classification threshold for the ARI is first set, and the ARI values ​​for different lesion regions are then determined sequentially. When the acoustic response index is less than 0.5, the plaque is classified as low-responsive plaque data; when the acoustic response index is greater than or equal to 0.5 but less than 0.8, it is classified as medium-responsive plaque data; and when the acoustic response index is greater than or equal to 0.8, it is classified as high-responsive plaque data. For example, if the endothelial permeability data of a lesion area is 0.45 and the inflammation level data is 0.55, then the acoustic response index is 0.45×0.55=0.2475, which is lower than 0.5. Therefore, the plaque is classified as low-responsive plaque data. When integrating plaque classification data, plaque data of different categories need to be merged to form a complete plaque database for subsequent analysis or model training. In the experiment, the data storage and annotation system can be used to store low-responsive plaque data, medium-responsive plaque data, and high-responsive plaque data in corresponding dataset files, and uniformly managed through standardized data formats. For example, during database construction, Python's Pandas library can be used to store plaque data of different categories into CSV files, uniformly storing and labeling plaque categories to ensure data consistency and traceability. To prepare microbubbles surface-modified with ligands specific for plaque macrophages, lipid materials with phase-change properties are first used: dipalmitoylphosphatidylcholine (DPPC), distearoylphosphatidylglycerol (DSPG), and polyethylene glycol-modified distearoylphosphatidylethanolamine (DSPE-PEG), mixed in a 7:2:1 molar ratio. Specifically, a certain amount of these lipid materials is weighed and dissolved in an appropriate amount of chloroform to form a uniform organic phase. Subsequently, the chloroform is evaporated on a rotary evaporator set to 55°C at 300 rpm until a uniform lipid film is formed. The resulting lipid film is a uniform, transparent film that can be used for subsequent gas core preparation. To prepare gas core microbubbles, perfluoropropane gas is first slowly passed through ultrapure water until a saturated solution is formed.Subsequently, the mixture was stirred at 3000 rpm for 10 minutes in a 40°C water bath to fully dissolve the gas. Intermittent sonication was then performed using an ultrasonic probe, with each sonication period set to 10 seconds, followed by a 5-second interval, for a total of 3 minutes to form a uniform primary microbubble emulsion. Microscopic observation revealed uniform microbubbles with a well-dispersed particle size, suitable for subsequent reagent encapsulation. During the reagent encapsulation process, the reagent to be validated was first dissolved in phosphate buffered saline (PBS), and the pH of the solution was adjusted to 7.4 using a pH regulator to ensure reagent stability. A double emulsification technique was then employed: the reagent solution was first mixed with an oil phase and ultrasonically emulsified to form an oil-in-water colostrum. This colostrum was then added to the aqueous phase for a secondary emulsification, resulting in a water-in-oil-in-water encapsulation structure. Homogenization was then performed at 5000 psi for 3 minutes in a high-pressure homogenizer to further stabilize the microbubble structure, ultimately yielding the reagent-encapsulated microbubble data. During the targeting ligand modification stage, click chemistry was used to specifically modify the microbubble surface. Specifically, an antibody targeting the macrophage surface marker CD68 or an integrin αvβ3 targeting peptide is azidated to increase its active binding sites. Microbubbles bearing alkyne groups are then covalently coupled to the modified targeting molecule. The reaction conditions are set at pH 7.2, 25°C, and a reaction time of 4 hours to obtain single-targeted microbubble data. During experimental testing, the coupling efficiency of the targeting ligand is verified using an enzyme-linked immunosorbent assay (ELISA) to ensure that its binding capacity meets the requirements. During the preparation of the dual-targeting ligand system, a second targeting molecule is covalently linked to the single-targeted microbubble to generate dual-targeted microbubble data. Specifically, a second targeting peptide, such as RGD peptide, is introduced onto the surface of the CD68 antibody-modified microbubbles. The coupling is performed via a maleimide-thiol reaction. The reaction conditions are set at pH 6.8, 25°C, and a reaction time of 3 hours to ensure that both ligands are evenly distributed on the microbubble surface. During the ligand modification efficiency monitoring process, fluorescence resonance energy transfer (FRET) technology was used to quantitatively analyze the ligand distribution on the dual-targeted microbubble surface. In the experiment, CD68 antibodies and RGD peptides with different fluorescent labels were used, and the energy transfer efficiency of the FRET signal was measured to determine the spatial distribution of the two ligands on the microbubble surface. For example, a strong FRET signal indicates that the two ligands are concentrated; a weak FRET signal indicates that the ligands are widely spaced. Based on the FRET signal data, the ligand spacing can be further optimized to obtain the optimal ligand modification efficiency data. During the optimization of the ligand orientation, a temperature gradient control strategy was used to promote the formation of ligand-enriched areas on the microbubble surface. Specifically, the microbubbles were incubated at 37°C for 2 hours to enhance the fluidity of the lipid bilayer, gradually enrich the ligand in a localized area, and reduce steric hindrance, thereby improving binding efficiency.Finally, flow cytometry was used to analyze the distribution of the ligands to ensure that the preparation data of the targeted nanobubbles met the experimental requirements and to improve the targeting performance of the microbubbles.

[0057] The targeted nanobubble preparation process of the present invention embodies a systematic and innovative approach from structural design to functional optimization, providing an efficient delivery platform for the precision treatment of atherosclerosis. First, by precisely controlling the 7:2:1 molar ratio of three lipids: DPPC, DSPG, and DSPE-PEG2000, an optimized design of the microbubble shell structure was achieved. This ratio not only ensures the stability of the microbubbles but also imparts them with ideal phase transition properties, enabling them to transition from a solid state to a bubbling state under ultrasound, significantly improving the controllability of drug release. The use of perfluoropropane gas in the core preparation process, combined with precise temperature control and intermittent ultrasound treatment, successfully constructs a highly uniform microbubble base structure, overcoming the technical bottleneck of uneven particle size distribution in traditional bubble preparation. During the reagent loading process, the innovative application of double emulsification technology achieves efficient drug encapsulation, while precise pH control ensures maximum retention of biological activity. Of particular note is the breakthrough in the surface modification process of the present invention, where CD68 antibodies or integrin-targeting peptides are covalently attached to the microbubble surface via click chemistry, improving the modification stability and significantly enhancing the targeting specificity for inflammatory macrophages. Furthermore, the invention employs a dual-targeting ligand system design, cleverly addressing the issue of insufficient single-target recognition efficiency. Even more ingeniously, fluorescence resonance energy transfer (FRET) technology is used to monitor ligand modification efficiency in real time and dynamically adjust ligand spacing, avoiding the steric hindrance common in traditional methods and optimizing ligand spatial arrangement. Finally, temperature gradient control is used to induce the formation of ligand-enriched regions, enabling the construction of "targeting hotspots" on the microbubble surface and significantly improving targeting efficiency. The establishment of this complete process fundamentally improves the targeting, specificity, and controllability of drug delivery, laying a solid technical foundation for the precise treatment of atherosclerosis.

[0058] Preferably, the dual-targeting ligand system is prepared as follows:

[0059] The dual-targeting ligand system was prepared based on the single-targeting microbubble data to obtain the dual-targeting microbubble data, wherein the dual-targeting ligand system preparation specifically includes the CD68 antibody and integrin The targeting peptides were mixed in a molar ratio of 3:7 to form a targeting ligand mixture. The integrin Targeting peptides and CD68 antibodies react with the surface of microbubbles.

[0060] In the preparation process of the dual-targeting ligand system of the present invention, a mixed solution of CD68 antibody and integrin αvβ3 targeting peptide was first prepared at a molar ratio of 3:7. Specifically, a certain amount of CD68 antibody was weighed and dissolved in 0.01 M phosphate buffer (PBS, pH 7.4) to a final concentration of 100 μg / mL. At the same time, the integrin αvβ3 targeting peptide was weighed and dissolved in the same buffer. The final concentrations were adjusted so that the molar ratio of CD68 antibody to integrin αvβ3 targeting peptide remained within the range of 3:7. The mixture was stirred at room temperature for 30 minutes using a low-speed vortex mixer to ensure sufficient mixing of the ligands and maintain biological activity. Subsequently, a two-step ligand modification method was used. First, the integrin αvβ3-targeting peptide was conjugated. An appropriate amount of single-targeted microbubble solution (concentration controlled at 10^9 cells / mL) was placed in an ice bath. Under magnetic stirring, a mixture of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and N-hydroxysuccinimide (NHS) activators (final concentrations of 0.2 mg / mL and 0.5 mg / mL, respectively) was slowly added dropwise. After the activation reaction lasted for 15 minutes, the integrin αvβ3-targeting peptide mixture was slowly added and stirred for 2 hours to allow it to covalently bind to the amino or carboxyl functional groups on the microbubble surface. Subsequently, unbound free peptide was removed by ultrafiltration centrifugation (molecular weight cutoff 10 kDa) and the microbubbles were washed twice with PBS to obtain microbubbles modified with the integrin αvβ3-targeting peptide. Next, a similar method was used to conjugate the CD68 antibody. EDC / NHS activator was added to the microbubble solution modified with the integrin αvβ3 peptide. After a 30-minute activation reaction, the CD68 antibody mixture was slowly added dropwise and incubated with gentle rotation at 4°C for 4 hours to successfully modify the microbubble surface. Finally, unbound CD68 antibody was removed by centrifugation and rinsed, and the microbubbles were adjusted to a final concentration of 10^9 cells / mL. The particle size distribution was measured by dynamic light scattering (DLS), and the absorbance at 280 nm was measured using a UV-visible spectrophotometer to calculate the coupling efficiency of the antibody and peptide. Ultimately, dual-targeted microbubble data was obtained, providing targeted nanobubbles for subsequent cell and in vivo experiments.

[0061] The preparation method of the dual-targeting ligand system of the present invention represents an important breakthrough in microbubble targeting technology. It solves the key problem of insufficient single-target recognition efficiency through ingenious design. The precise molar ratio of 3:7 mixed design of CD68 antibody and integrin targeting peptide fully considers the difference in expression abundance of the two targets in plaques and the spatial requirements of the ligand molecules themselves, realizing the optimized combination of targeting elements. Particularly worthy of emphasis is the innovative application of the two-step ligand modification strategy, which first modifies the integrin targeting peptide and then connects the CD68 antibody in sequence, avoiding the steric hindrance effect of the large molecule antibody blocking the small molecule peptide caused by simultaneous modification, ensuring that both ligands can fully exert their recognition function. This dual-targeting design not only expands the target recognition spectrum of microbubbles, but also improves the comprehensive recognition ability of heterogeneous plaques, significantly enhances the specificity and coverage of the targeting system, and lays the foundation for subsequent precise delivery.

[0062] Preferably, the preparation of the microbubble structure with the core layer loaded with the reagent to be verified described in step S2 specifically includes:

[0063] Screening drug delivery methods based on the physicochemical properties of the reagent to be verified to obtain loading strategy optimization data. Physicochemical properties include hydrophilicity, molecular weight, and stability parameters. Drug delivery methods include electrostatic adsorption, cavity encapsulation, and lipid membrane embedding.

[0064] The core layer material was prepared according to the loading strategy optimization data. Polylactic acid-co-glycolic acid copolymer, chitosan, and hyaluronic acid were mixed at a weight ratio of 4:3:3 and subjected to ultrasonic dispersion treatment to obtain the core layer material data.

[0065] The core-drug complex was constructed based on the core layer material data. The reagent molecules were fully mixed with the core layer material using the double emulsion solvent evaporation method, and sheared at 4000 rpm for 2 minutes under low temperature conditions to obtain the primary core-drug complex data.

[0066] Quantitative analysis using high performance liquid chromatography was performed based on the primary core-drug complex data to analyze the free and total reagent contents, calculate the reagent encapsulation efficiency and entrapment rate, and thus obtain the encapsulation efficiency data;

[0067] According to the encapsulation efficiency data, the core layer structure stability is optimized by adjusting the crosslinking agent concentration and crosslinking time parameters to obtain stability optimization data;

[0068] Conduct release kinetics testing of the reagent based on the stability optimization data. Under simulated physiological environment and ultrasonic field conditions, monitor the release rate and release pattern of the reagent, draw a release curve, and thus obtain release kinetics data.

[0069] The microbubble shell layer and the core layer were assembled and integrated according to the release kinetics data, and the targeted nanobubbles were integrated with the core layer of the encapsulated reagent using membrane fusion technology according to the targeted nanobubble preparation data. The cells were incubated at 37°C for 30 minutes to obtain targeted nanobubbles.

[0070] In the drug delivery method screening process of the present embodiment, the physicochemical properties of the reagent to be tested must first be determined, including hydrophilicity, molecular weight, and stability parameters. Specifically, the polarity and molecular weight of the reagent are analyzed using high-performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry (LC-MS), while its thermal stability is assessed using differential scanning calorimetry (DSC) or thermogravimetric analysis (TGA). Furthermore, solubility testing is performed in physiological buffer solution (PBS, pH 7.4) at 37°C to measure the solubility of the reagent in solvents of varying polarity to determine its hydrophilicity or hydrophobicity. Based on these parameters, the appropriate drug delivery method is selected. For highly hydrophilic reagents, electrostatic adsorption is preferred, which modulates the surface charge of the microbubbles to form an electrostatic interaction with the reagent. For reagents with moderate molecular weight and a certain degree of lipophilicity, cavity encapsulation is used to encapsulate them within the microbubbles. For highly hydrophobic reagents, lipid membrane embedding is used to embed them into the phospholipid bilayer of the microbubbles. After drug loading experiments using different loading methods, the loading capacity was measured by UV-Vis spectrophotometry or fluorescence spectrophotometry, and the suitability of the loading method was evaluated in combination with the release rate to generate data for optimal loading strategies. To prepare the core layer material, poly(lactic-co-glycolic acid) (PLGA), chitosan (CS), and hyaluronic acid (HA) were weighed in a 4:3:3 weight ratio and dissolved in appropriate solvents: PLGA in dichloromethane, chitosan in 1% acetic acid, and HA in deionized water. After complete dissolution, the three materials were mixed at room temperature and ultrasonically dispersed using a sonicator at 200 W power. Each sonication was repeated for 10 seconds, followed by a 5-second interval, for 5 minutes to ensure uniform mixing and form a stable nanocomposite structure. Finally, the solvent was removed by vacuum rotary evaporation, and residual water was removed by freeze-drying to obtain a powdered core layer material. This powder was then stored in a dry environment at -20°C until further use, thereby generating data for the core layer material. During the construction of the core-agent complex, the reagent was encapsulated using a double-emulsion solvent evaporation method. The reagent to be tested was first dissolved in deionized water or PBS and slowly added dropwise to the dissolved organic phase solution of the core layer material. Ultrasonication was performed at 100 W for 30 seconds in an ice bath using an ultrasonic probe to form a primary emulsion. This primary emulsion was then rapidly added to an external aqueous phase containing 1% polyvinyl alcohol (PVA) and sheared using a high-shear homogenizer at 4000 rpm for 2 minutes to further emulsify and stabilize the emulsion. The resulting emulsion was then magnetically stirred at room temperature for 3 hours to promote evaporation of the organic solvent. Ultrafiltration was then used to remove free reagent and unemulsified polymer, ultimately yielding a uniformly dispersed primary core-drug complex. Particle size measurement was then performed to obtain data for the primary core-drug complex.During the analysis of free and total reagent content, the entrapped reagent content of the primary core-drug complex was determined using high-performance liquid chromatography (HPLC). First, an appropriate amount of the complex suspension was added with an equal volume of an organic solvent (e.g., acetonitrile) to disrupt its structure and release the entrapped reagent. The suspension was then filtered through a 0.22 μm microporous membrane and injected into the HPLC column. The total reagent content was calculated using a standard curve of known concentrations. Simultaneously, the free reagent content in the complex supernatant was directly determined using the same HPLC method. The entrapment efficiency was calculated by calculating the entrapment efficiency (i.e., the percentage of entrapped reagent in the total reagent) and the encapsulation efficiency (i.e., the percentage of entrapped reagent in the total reagent). During the optimization of the core layer structural stability, the crosslinker concentration and crosslinking time were adjusted based on the entrapment efficiency data. Glutaraldehyde (GA) was used as the crosslinker in these experiments. Glutaraldehyde (GA) was added to the core layer complex solution at concentrations of 0.1%, 0.5%, and 1%, respectively. The crosslinking reaction was promoted by magnetic stirring at 37°C for different times (1 hour, 3 hours, and 6 hours). After cross-linking, the complex structure was observed using transmission electron microscopy (TEM), particle size changes were measured using dynamic light scattering (DLS), and the release rate of the entrapped agent was analyzed by HPLC. This was done to determine the stability of the core layer under different cross-linking conditions and to select the optimal cross-linking parameters, ultimately generating stability optimization data. During the release kinetics testing, the release rate and pattern of the agent were measured under simulated physiological conditions and ultrasound conditions. Specifically, the optimized core-drug complex was dispersed in PBS buffer and incubated in a 37°C shaker. Samples were taken periodically and the amount of agent released at different time points was measured by HPLC to plot cumulative release curves. Furthermore, to evaluate the effectiveness of ultrasound-triggered release, the agent solution was intermittently irradiated with a 1 MHz ultrasound probe (power 1 W / cm²) for 30 seconds each, followed by a 1-minute interval, for 10 consecutive times. The changes in the released agent were measured to analyze the kinetic characteristics under ultrasound, ultimately generating release kinetic data. During the assembly and integration of the microbubble shell and core layer, membrane fusion technology was used to achieve integration of the microbubble shell and the entrapped agent core layer. The optimized dual-targeted microbubble solution was mixed with the drug-drug complex at a mass ratio of 1:2 and incubated at 37°C for 30 minutes with gentle shaking at 50 rpm to promote membrane fusion. Unbound complexes were then removed by ultracentrifugation (10,000 rpm for 10 minutes), and the resulting nanobubbles were measured for size and uniformity using a laser particle size analyzer. Finally, the fluorescently labeled ligand on the microbubble surface was detected by flow cytometry to confirm the presence of the targeting ligand, and the loading efficiency of the reagent was determined by HPLC to confirm successful drug loading. Ultimately, targeted nanobubble data was obtained, providing formulation support for subsequent in vitro and in vivo experiments.

[0071] The microbubble core layer loading system of this invention demonstrates comprehensive technological innovation, from personalized design to quality control, establishing a new paradigm in drug delivery. First, a personalized drug delivery method screening strategy based on the physicochemical properties of the reagent breaks away from the traditional "one-size-fits-all" loading model. By comprehensively considering the drug's hydrophilicity, molecular weight, and stability parameters, the optimal loading method is precisely selected from electrostatic adsorption, cavity encapsulation, and lipid membrane embedding, achieving efficient loading of drugs with diverse properties. The precise design of the core layer material ratio (polylactic-co-glycolic acid copolymer, chitosan, and hyaluronic acid in a 4:3:3 weight ratio) cleverly balances the material's mechanical strength, biocompatibility, and controllable degradation, providing an ideal carrier environment for the drug. The double-emulsion solvent evaporation method, combined with a low-temperature, high-speed shear process, creatively addresses the technical bottlenecks of drug activity loss and low encapsulation efficiency in traditional encapsulation processes, significantly improving the encapsulation rate. It is particularly worth emphasizing that the invention has established a systematic quality control process including HPLC quantitative analysis, cross-linker parameter optimization and release kinetics testing, which has achieved a full range of characterization from encapsulation efficiency to structural stability to release behavior, ensuring the consistency and reliability of product quality. The release kinetics test conducted under simulated physiological environment and ultrasonic field conditions not only revealed the inherent laws of drug release, but also provided a direct basis for the optimization of ultrasonic parameters. Finally, the membrane fusion technology was used to achieve seamless integration of the microbubble shell layer and the core layer under precise temperature conditions, constructing a complete delivery system with stable structure and synergistic functions. This set of systematic technologies fundamentally improves the drug loading capacity and release controllability, providing a powerful technical platform for the precise treatment of atherosclerosis, and also opening up new avenues for the study of targeted drug delivery for other diseases.

[0072] Preferably, the multimodal image tracking analysis in step S2 specifically includes:

[0073] The targeted nanobubbles are labeled with contrast agents, and a gas marker suitable for ultrasound contrast imaging, a near-infrared fluorescent dye suitable for fluorescence imaging, and a radioactive isotope suitable for PET imaging are respectively labeled with the targeted nanobubbles to obtain trimodal labeled microbubbles;

[0074] The trimodal labeled microbubbles were injected into the atherosclerosis model via the tail vein at a constant rate of 1 ml / min, and dynamic contrast-enhanced ultrasound monitoring was performed to obtain ultrasound time-series data.

[0075] Near-infrared fluorescence imaging was performed on the atherosclerosis model injected with targeted nanobubbles. Whole-body fluorescence scanning was performed 1 hour, 6 hours, and 24 hours after microbubble injection. The excitation wavelength was set to 750 nm, the acquisition wavelength was 780 nm, the exposure time was 2 seconds, and the scanning resolution was 100. m, obtain fluorescence distribution data;

[0076] Performing PET scanning on an atherosclerosis model injected with targeted nanobubbles to obtain PET data;

[0077] Perform image fusion processing on ultrasound time series data, fluorescence distribution data and PET data for spatial alignment, and use weighted average method to generate multimodal fusion images to obtain fused image data;

[0078] Based on the fused imaging data, the temporal and spatial distribution of targeted nanobubbles in the body was quantitatively analyzed, the signal intensity ratio of microbubbles in the target area and the non-target area was calculated, the microbubble concentration-time curve was drawn, and the key kinetic parameters were extracted to obtain the microbubble circulation distribution data in the body.

[0079] In the ultrasound contrast-enhanced labeling process, a microfluidic bubble generator is first used to fill the phospholipid bilayer microbubble cavity with sulfur hexafluoride gas, ensuring stable encapsulation and enhancing ultrasound imaging contrast. Subsequently, 1,1'-dioctyl-3,3,3',3'-tetramethylindocarbocyanine iodide (DiR) is dissolved in ethanol and, with ultrasound assistance, inserted into the lipid bilayer of the nanobubbles to impart fluorescence imaging capabilities. For PET imaging labeling, radioactive isotope fluorine-18-labeled N-tert-butyloxycarbonyl-2,2,5,5-tetramethylpyrroline-1-oxide (^18F-Boc-TMPO) is co-incubated with a DSPE-PEG2000-biotin lipid material, allowing it to bind to the microbubble surface via a biotin-avidin bridge. After labeling, free label was removed by ultracentrifugation (12,000 × g, 15 minutes), and the microbubble size distribution was determined by dynamic light scattering (DLS) to ensure the homogeneity of the labeled microbubbles. Finally, trimodally labeled microbubbles capable of ultrasound contrast imaging, near-infrared fluorescence, and PET imaging were obtained and stored short-term (4°C, protected from light) for subsequent use in subsequent experiments. Using a high-frequency ultrasound system (Vevo2100, a small animal ultrasound system), the trimodally labeled microbubbles were slowly injected into the tail vein of an atherosclerotic mouse model at a controlled injection rate of 1 ml / min to ensure uniform microbubble distribution. The ultrasound probe frequency was set at 18 MHz, and the mechanical index (MI) was controlled at 0.08 to minimize the effects of microbubble rupture. Following microbubble injection, real-time cross-sectional ultrasound images of the vessels were acquired, and microbubble signal enhancement was recorded. During monitoring, ultrasound images were acquired every 5 seconds to continuously observe the distribution and retention of microbubbles in the bloodstream. Changes in ultrasound signal intensity were recorded 10, 30, 60, and 120 minutes after injection. Ultrasound time-series data were generated to assess the extent of microbubble accumulation and dynamic changes in the lesion area. Following microbubble injection, near-infrared fluorescence imaging was performed on mice modeling atherosclerosis using the IVIS Spectrum in vivo imaging system. Mice were anesthetized and secured on an imaging table, with body positioning adjusted to ensure whole-body signal acquisition. Fluorescence imaging parameters were set as follows: excitation wavelength 750 nm, emission wavelength 780 nm, exposure time 2 seconds, and scanning resolution 100 μm to ensure sufficient fluorescence signal acquisition accuracy. Whole-body fluorescence scans were performed 1, 6, and 24 hours after microbubble injection to assess the retention time and distribution pattern of microbubbles in both target and non-target areas. During the experiment, the collected image signals were standardized using a standard fluorescence intensity calibration curve to eliminate the influence of background fluorescence, and ultimately fluorescence distribution data was obtained to analyze the homing characteristics of microbubbles in the body.Whole-body scans of the mice were performed using a MicroPET small animal positron emission tomography scanner (Siemens Inveon PET) at various time points (1, 6, and 24 hours) after microbubble injection. Before scanning, the mice were placed in a PET scanning chamber and maintained at a constant temperature using a thermostatic heating device to prevent hypothermia-induced hemodynamic changes from affecting the experimental results. Scanning parameters were set as follows: spatial resolution of 1.2 mm, acquisition time of 15 minutes, and reconstruction using a three-dimensional OSEM (sequential expectation-maximization) algorithm to ensure high-quality image acquisition. During the experiment, images were normalized using a standard radioactivity calibration curve to eliminate individual variability. PET data were then obtained to assess the accumulation of microbubble radioactivity within vascular lesions. After acquisition, ultrasound, fluorescence, and PET imaging data were spatially aligned using a multimodal image registration algorithm to ensure that all imaging modalities were analyzed in the same anatomical coordinate system. First, a mutual information (MI)-based registration method was used to perform a non-rigid transformation of the fluorescence and PET images to ensure that they overlapped at the same anatomical level. Subsequently, morphological matching was performed on the fluorescence-PET fusion images, using the ultrasound time-series data as a reference to ensure accurate correspondence between vascular structures in different modalities. Finally, the signal intensities of the different modalities were fused using a weighted average method, with the ultrasound, fluorescence, and PET signal weights set to 0.4, 0.3, and 0.3, respectively. This yielded optimized fused image data, providing three-dimensional visualization of microbubble distribution within the body. Based on the fused image data, the distribution of microbubbles within target and non-target regions was quantitatively analyzed. First, the average microbubble signal intensity was measured for diseased vascular regions (target regions) and normal vascular regions (non-target regions) within the image data. The signal intensity ratio was calculated to assess the targeted accumulation of microbubbles within the diseased region. Subsequently, microbubble concentration-time curves were plotted based on the changes in the image data at different time points, with microbubble concentration plotted on the vertical axis and time plotted on the horizontal axis. Key kinetic parameters, including the half-life of the microbubbles in blood, peak signal intensity, and signal decay rate, were extracted to quantify the microbubble circulation and clearance characteristics within the body. Finally, the data from different time points were integrated to evaluate the biodistribution of microbubbles, providing a reference for the subsequent optimization of the targeted modification strategy of microbubbles.

[0080] The multimodal imaging tracking and analysis system presented in this paper represents a significant methodological innovation in the study of targeted delivery for atherosclerosis, enabling full-dimensional visualization of microbubble behavior in vivo. By simultaneously integrating three tracers—a gas marker, a near-infrared fluorescent dye, and a radioisotope—in a single microbubble system, the system successfully overcomes the technical bottleneck of limited information dimensionality in traditional single-modality imaging, making the complete dynamic process of microbubbles in vivo "visible" for the first time. A precisely controlled, constant-rate tail vein injection regimen ensures the stability of microbubble entry into the systemic circulation, providing a standardized starting point for subsequent dynamic monitoring. A multi-timepoint near-infrared fluorescence imaging strategy (1, 6, and 24 hours) captures the entire temporal window of microbubble evolution from early circulatory distribution to late tissue enrichment, while carefully designed imaging parameters (750 nm excitation, 780 nm acquisition, and 2-second exposure time) ensure optimal signal-to-noise ratio and tissue penetration depth. Contrast-enhanced ultrasound provides real-time information on the early intravascular dynamics of microbubbles, while PET imaging complements this with quantitative data on the long-term distribution of microbubbles in deep tissues. The two complement each other to form a complete, spatiotemporally continuous monitoring chain. It is particularly worth emphasizing that the multimodal image fusion processing process established by the present invention cleverly solves the spatial inconsistency problem between different modal imaging through spatial alignment and weighted averaging algorithms, creating a fused image that integrates the advantages of each modality. The spatiotemporal distribution quantitative analysis technology based on fused data not only provides key parameters such as the target / non-target signal intensity ratio, but also reveals the complete dynamic characteristics of microbubbles in the body through the microbubble concentration-time curve, providing a scientific basis for accurately evaluating targeting efficiency and optimizing delivery strategies. This complete image tracking and analysis system fundamentally improves the scientific nature and reliability of targeted delivery research for atherosclerosis and lays a solid foundation for clinical translation.

[0081] Preferably, step S3 includes the following steps:

[0082] Step S31: determining the optimal ultrasound irradiation time window for the target lesion site based on the microbubble circulation distribution data in the body, performing real-time ultrasound imaging of the atherosclerotic plaque using an ultrasound probe, identifying the lesion area and microbubble enrichment, and thereby obtaining ultrasound irradiation area positioning data;

[0083] Based on the in vivo microbubble distribution data obtained from previous experiments, the present invention analyzes the temporal characteristics of microbubble enrichment in atherosclerotic plaques. The time period during which microbubbles reach maximum concentration within the lesion while remaining intact is determined as the optimal ultrasound irradiation time window. Subsequently, real-time ultrasound imaging of the atherosclerosis model is performed using a high-frequency ultrasound probe (center frequency of 20 MHz, bandwidth of 80%). Second harmonic imaging is used to enhance the microbubble echo signal, and gain and dynamic range parameters are optimized (for example, gain set to 50 dB and dynamic range set to 60 dB) to ensure clear visualization of the lesion area and microbubble enrichment. Ultrasound image analysis software uses a threshold segmentation method to identify microbubble-enriched areas. The echo signal ratio of the microbubbles in the target lesion to the background tissue is calculated. Based on the echo intensity distribution, precise positioning data for the ultrasound irradiation area is generated, providing a reference for subsequent ultrasound irradiation.

[0084] Step S32: setting ultrasound parameters according to the ultrasound irradiation area positioning data, thereby obtaining ultrasound parameter setting data;

[0085] The embodiment of the present invention sets the ultrasonic irradiation parameters based on the ultrasonic irradiation area positioning data, combined with the depth of the target lesion area, the distribution characteristics of microbubbles and the local blood flow status. First, the ultrasonic power control module is used to adjust the ultrasonic output power so that the focal sound pressure is controlled within the range of 0.2 to 0.8 MPa to avoid excessive sound pressure causing premature rupture of microbubbles. Secondly, the ultrasonic frequency is set to 1 MHz to match the resonance characteristics of the microbubbles and improve the acoustic effect. At the same time, a low duty cycle pulse mode is adopted, and the pulse width is set to 10 to 50 microseconds to reduce the risk of thermal damage to the tissue. Finally, the focus position is optimized through phased array technology, so that the ultrasonic energy acts accurately on the target plaque area, ensuring the accuracy of the ultrasonic parameter setting data, and providing personalized ultrasonic treatment solutions for different types of lesion plaques.

[0086] Step S33: performing staged acoustic field processing according to the ultrasound parameter setting data and the plaque classification data, thereby obtaining graded ultrasound irradiation data;

[0087] The embodiment of the present invention implements a graded ultrasound irradiation strategy based on ultrasound parameter setting data and plaque classification data. For low-response plaques, continuous low-intensity ultrasound irradiation is used for 30 seconds while maintaining a sound pressure of 0.3 MPa and a duty cycle of 5%. This gently promotes microbubble oscillation and improves local microcirculation permeability. For medium-response plaques, intermittent low-intensity ultrasound is used. Each irradiation lasts for 20 seconds and then pauses for 10 seconds, for a total of 3 irradiations. The ultrasound pulse frequency is set to 1.5 MHz and the pulse width is 15 microseconds. The intermittent energy release enhances the cumulative effect of microbubbles on the lesion area. For high-response plaques, pulse-incremental ultrasound irradiation is used. The initial sound pressure is set to 0.2 MPa, and it increases by 0.1 MPa each time, with a maximum of no more than 0.8 MPa. In combination with short-pulse high-frequency modulation (frequency 3 MHz, pulse width 10 microseconds), this promotes microbubble rupture and achieves targeted ultrasound energy control, thereby obtaining graded ultrasound irradiation data for different types of plaques.

[0088] Step S34: performing real-time monitoring of the acoustic behavior of the microbubbles during the ultrasonic irradiation process, including harmonic signal intensity changes and microbubble rupture dynamics, based on the graded ultrasonic irradiation data, and adjusting the ultrasonic parameters based on the collected acoustic signals to obtain dynamic data on the acoustic behavior of the microbubbles;

[0089] During ultrasound irradiation, embodiments of the present invention monitor the acoustic behavior of microbubbles in real time, including changes in harmonic signal intensity and the dynamics of microbubble rupture. Using ultrasound contrast imaging mode, dynamic echo tracking parameters are set (time resolution 0.5 seconds, spatial resolution 100 μm), and the signal intensity change curve of the microbubbles in the target area is recorded. Fourier analysis is then used to extract harmonic component characteristics to determine whether the microbubbles have entered a critical state of rupture. Simultaneously, high-speed imaging (frame rate ≥ 500 Hz) is combined to obtain transient images of microbubble rupture. Image processing algorithms are used to analyze microbubble rupture patterns (e.g., single bubble disintegration, multi-bubble aggregation rupture), and ultrasound parameters are adjusted in real time, such as by reducing the ultrasound pressure or changing the pulse mode, to optimize the efficiency of microbubble rupture, thereby ensuring accurate collection of dynamic data on the acoustic behavior of microbubbles.

[0090] Step S35: Perform reagent release imaging analysis based on the dynamic data of microbubble acoustic behavior, evaluate the change of local echo signal after microbubble rupture through ultrasound contrast imaging, and quantify the release degree based on the reagent's own fluorescence or labeling signal to obtain the reagent release status data.

[0091] The embodiment of the present invention performs reagent release imaging analysis based on the dynamic data of microbubble acoustic behavior. First, the ultrasound contrast imaging mode is used to evaluate the change in local echo signal after microbubble rupture, and the echo intensity threshold is set. By comparing the signal difference before and after rupture, the degree of microbubble rupture is judged. Secondly, fluorescence imaging or PET scanning is performed in combination with fluorescent dyes or radioactive tracers marked inside the microbubbles, and the spatial range and concentration changes of the reagent diffusion after microbubble rupture are analyzed using time series. For the fluorescence signal, the excitation wavelength is 750nm, the acquisition wavelength is 780nm, the exposure time is 2 seconds, the spatial resolution is 100μm, and the curve of local fluorescence intensity change over time is recorded. For PET imaging, the scan time is set to 10 minutes, the image reconstruction resolution is 2mm, and the peak time and decay rate of the radioactive signal are analyzed. Finally, based on multimodal image fusion, the key parameters of the reagent release after microbubble rupture, including the release rate, diffusion radius and local concentration change trend, are extracted to quantitatively evaluate the effective drug loading capacity of the microbubbles, thereby obtaining the reagent release status data.

[0092] The ultrasound-responsive activation system of this invention demonstrates a complete technical closed loop from precise targeting to controlled release, enabling precise spatiotemporal regulation of the drug delivery process. First, by analyzing microbubble circulation distribution data in vivo to determine the optimal ultrasound irradiation time window, this overcomes the efficacy fluctuations caused by "blind irradiation" in traditional methods and ensures that ultrasound activation occurs at the optimal time of targeted microbubble enrichment. Real-time ultrasound imaging, which monitors both the lesion area and microbubble enrichment, provides intuitive and reliable spatial coordinates for subsequent precise positioning, avoiding the risk of inadvertent irradiation of healthy tissue. Continuous ultrasound irradiation for low-responsive plaques ensures sufficient acoustic energy to penetrate difficult-to-penetrate areas; intermittent irradiation for moderately responsive plaques ensures effective activation while preventing tissue overheating; and pulsed incremental irradiation for high-responsive plaques precisely controls energy input, preventing potential damage from overactivation. This personalized "one-spot-one-policy" activation approach addresses the core challenge of traditional fixed-parameter irradiation, which is unable to adapt to plaque heterogeneity. Real-time monitoring and feedback adjustment mechanism of microbubble acoustic behavior is another key breakthrough. By tracking harmonic signal changes and rupture dynamics, the system can intelligently adjust ultrasound parameters to achieve precise control of the activation process. This dynamic adjustment mechanism significantly improves the efficiency of microbubble rupture, while reducing the risk of ultrasonic energy waste and surrounding tissue damage. Finally, multi-dimensional reagent release imaging analysis achieves all-round visual monitoring of the drug release process by integrating ultrasonic echo signal changes with fluorescent labeling signals. It not only provides a quantitative evaluation of release efficiency, but also provides a scientific basis for subsequent delivery strategy optimization. This complete ultrasound-responsive activation system fundamentally changes the passive and extensive mode of traditional drug delivery, establishes a modern delivery technology paradigm that is precise, personalized, and visualized, and opens up a new path for the precise treatment of atherosclerosis.

[0093] Preferably, step S4 includes the following steps:

[0094] Step S41: performing ultrasound contrast monitoring based on the microbubble acoustic behavior dynamic data and the reagent release state data, thereby obtaining ultrasound time series monitoring data;

[0095] After completing the ultrasonic irradiation of microbubbles and the release of the reagent, the embodiment of the present invention uses the ultrasound contrast imaging mode to perform ultrasonic time-series monitoring and record the change process of the contrast imaging signal before and after the microbubble rupture. First, the mechanical index of the ultrasound probe is set to 0.08 to ensure continuous acquisition of image data in a low-power state to avoid additional interference from microbubble rupture. Secondly, a time-series image acquisition mode is adopted to capture one frame of image per second and continuously monitor for at least 10 minutes to capture the dynamic distribution characteristics of the reagent in the local tissue. The ultrasound signal processing software is used to analyze the changes in contrast imaging intensity at different time points, extract the echo signal enhancement curve in the region of interest, and calculate parameters such as signal peak time, signal decay time and half-life. The motion compensation algorithm is used to correct the image displacement error caused by respiration or blood flow to ensure the accuracy of the ultrasound time-series monitoring data and provide basic data for subsequent multimodal image fusion.

[0096] Step S42: performing near-infrared fluorescence imaging based on the ultrasound time-series monitoring data to obtain fluorescence distribution monitoring data;

[0097] The embodiments of the present invention perform near-infrared fluorescence imaging based on ultrasound time-series monitoring data at the optimal monitoring time (typically between 30 seconds and 5 minutes after microbubble rupture). A near-infrared excitation light source with a wavelength of 750 nm is used to uniformly illuminate the lesion, and a high-sensitivity cooled CCD camera (exposure time 2 seconds, gain set to 10 dB) is used to acquire the fluorescence signal. During the imaging process, an autofocus system is used to ensure signal clarity, and a background subtraction algorithm is used to reduce interference from tissue autofluorescence. Subsequently, fluorescence quantitative analysis software is used to extract the fluorescence signal intensity of the lesion, calculate the fluorescence enhancement ratio, and construct a fluorescence signal distribution map based on the spatial coordinates. To evaluate the dynamic changes in the fluorescence signal, images are repeatedly acquired every 30 seconds, and the decay trend of the fluorescence intensity over time is analyzed to obtain fluorescence distribution monitoring data to provide guidance for subsequent PET imaging.

[0098] Step S43: performing positron emission tomography scanning based on the fluorescence distribution monitoring data, and performing reconstruction processing based on a three-dimensional ordered subset expectation maximization algorithm to obtain PET monitoring data;

[0099] Based on fluorescence distribution monitoring data, this embodiment of the present invention determines the optimal PET scanning time window (typically within 10 minutes after the fluorescence signal reaches its peak) and performs positron emission tomography (PET). First, a ring-shaped detector PET system is used to comprehensively scan the lesion area to ensure complete radioactive signal distribution data. Scan parameters are set to an energy window of 400-600 keV and a temporal resolution of 500 ps to improve signal detection sensitivity. The acquired data is reconstructed using the three-dimensional ordered subset expectation maximization (OSEM-3D) algorithm. Initial projection data correction is performed, including random noise correction, scatter correction, and attenuation correction. Image quality is then optimized using the OSEM iterative method. The number of iterations is typically set to six, with each iteration containing 20 subsets, to balance reconstruction accuracy and computational efficiency. Finally, PET monitoring images are generated using pseudo-color mapping technology. The radioactive signal in the lesion is quantitatively analyzed, and the standardized uptake value (SUV) of the region of interest is calculated to obtain PET monitoring data, providing accurate functional information for multimodal image fusion.

[0100] Step S44: performing imaging data fusion processing on the ultrasound time series monitoring data, the fluorescence distribution monitoring data, and the PET monitoring data, and integrating the three modal data using an information entropy weighting method to obtain multimodal fusion monitoring data;

[0101] The embodiment of the present invention performs imaging data fusion processing on ultrasound time series monitoring data, fluorescence distribution monitoring data and PET monitoring data to improve the spatial and temporal resolution of reagent distribution monitoring. First, the ultrasound, fluorescence and PET images are spatially aligned through the image registration algorithm, and the key anatomical structure features of the lesion site are extracted using a feature point-based registration method to ensure that the multimodal images are fused in the same coordinate system. Then, the three types of imaging data are weightedly integrated using the information entropy weighting method, where the information entropy is used to evaluate the independent information content of each modality data and assign corresponding weights based on their contribution. For example, if the PET signal provides the highest contrast, it is assigned a weight of 0.5, while ultrasound and fluorescence are assigned weights of 0.3 and 0.2, respectively. During the fusion process, the weighted average method is used to calculate the final image data, and the edge enhancement filtering algorithm is used to improve the image clarity to ensure the accuracy of the multimodal fusion monitoring data to comprehensively reflect the temporal and spatial distribution of the reagent.

[0102] Step S45: Perform spatiotemporal kinetic analysis of the reagent distribution based on the multimodal fusion monitoring data, establish an enrichment-clearance curve of the reagent at the lesion site over time, and fit the change trend of the signal intensity over time through a bi-exponential model to extract key pharmacokinetic parameters, thereby obtaining the spatiotemporal distribution data of the targeted delivery of the reagent, wherein the spatiotemporal distribution data includes half-peak time, peak concentration, area under the curve and clearance rate constant.

[0103] This embodiment of the present invention analyzes the spatiotemporal dynamics of the agent's distribution based on multimodal fusion monitoring data. First, a region of interest (ROI) is selected within the lesion site, and signal intensities at each time point are extracted to construct an accumulation-clearance curve for the agent. The curve data are fitted using a biexponential model, splitting the signal intensity trend into an accumulation phase and a clearance phase to reflect the agent's accumulation and metabolic clearance in the lesion, respectively. During the fitting process, the least-squares optimization method is used to determine the model parameters, including the initial signal intensity, the accumulation rate constant, and the clearance rate constant. Subsequently, key pharmacokinetic parameters are calculated, including the half-peak time (i.e., the time required for the signal intensity to reach its peak), the peak concentration (i.e., the maximum signal intensity), the area under the curve (AUC, which quantifies the overall exposure of the agent to the lesion site), and the clearance rate constant (which measures the rate of clearance of the agent from the lesion). Finally, the pharmacokinetic parameters for different lesion types are statistically analyzed to assess the targeted delivery efficiency of the agent in different lesion environments, thereby obtaining spatiotemporal distribution data for the agent's targeted delivery.

[0104] The multimodal monitoring and kinetic analysis system of the present invention realizes a complete tracking closed loop from drug release to biodistribution, representing a major methodological innovation in the field of biomedical imaging. First, ultrasound contrast monitoring based on microbubble acoustic behavior and reagent release state data captures the real-time dynamic process of drug release, provides early distribution information with high time resolution, and solves the technical problem that traditional methods are difficult to capture the initial stage of release. Near-infrared fluorescence imaging monitors at strategic time points (1 hour, 6 hours and 24 hours) based on ultrasound data, which not only makes up for the shortcomings of ultrasound imaging in long-term monitoring, but also provides high spatial resolution information on drug distribution, realizing a monitoring leap from intravascular to tissue level. The three-dimensional ordered subset expectation maximization algorithm reconstruction processing of positron emission tomography further breaks through the tissue depth limitation and provides quantitative distribution data throughout the body, especially for the accurate description of drug behavior in deep tissues. The key technical highlight of the present invention lies in the multimodal data fusion processing link. The innovative application of the information entropy weighting method solves the problem of weight allocation in the integration of different modal data, realizes the complementary advantages of each modality, and creates a comprehensive monitoring platform that goes beyond the limitations of a single modality. The spatiotemporal distribution kinetic analysis model constructed based on the fused data accurately describes the entire drug accumulation and clearance process through biexponential fitting. Extracted key parameters such as half-peak time, peak concentration, area under the curve, and clearance rate constant not only quantify delivery efficiency but also reveal the fate of the drug in vivo, providing a scientific basis for optimizing delivery system design and personalizing dosing regimens. This comprehensive monitoring and analysis system fundamentally enhances the scientific nature and reliability of targeted delivery research and paves the way for the clinical translation of precision medicine for atherosclerosis.

[0105] Preferably, step S5 includes the following steps:

[0106] Step S51: Calculating the signal intensity ratio between the target area and the non-target area and the ratio between the target area signal and the injected dose based on the spatiotemporal distribution data, thereby obtaining targeted delivery efficiency data;

[0107] After acquiring the spatiotemporal distribution data, the embodiment of the present invention first needs to calculate the signal intensity ratio of the target area to the non-target area to evaluate the distribution of the agent in the target area. First, the ROI (region of interest) of the target area and the non-target area are selected. The target area can be the diseased area, while the non-target area can be healthy tissue or the surrounding area. Then, the average signal intensity values ​​of these two areas are extracted and the signal intensity ratio is calculated. The specific operation is as follows: the fluorescence signal intensity at each time point in the target area and the non-target area is extracted using image processing software, and then the ratio of the target area signal intensity to the non-target area signal intensity is calculated to obtain the target area to non-target area signal intensity ratio. Furthermore, the ratio of the target area signal intensity to the injected dose is calculated. The relationship between the total injected dose and the target area signal intensity is used to evaluate the concentration of the agent in the target area, thereby obtaining the targeted delivery efficiency data. The target area signal intensity to injected dose ratio can reflect the accuracy and dose-effect relationship of the agent delivery, providing a basis for further analysis of the targeted delivery efficiency of the agent.

[0108] Step S52: analyzing the agent distribution at different time periods based on the targeted delivery efficiency data, thereby obtaining plaque layered delivery data;

[0109] In the embodiment of the present invention, based on the targeted delivery efficiency data, different time periods are selected to analyze the distribution of the reagent to study the stratified delivery characteristics of the plaque. First, the distribution changes of the reagent in the lesion area are compared according to different time nodes, combined with the signal intensity ratio of the target area to the non-target area. Specifically, different time windows are selected (such as 1 minute, 3 minutes, 5 minutes after microbubble rupture, etc.), the target area signal intensity at each time point is extracted, and the enrichment of the reagent in different time periods is analyzed. By comparing the delivery efficiency at each time point, the drug concentration distribution in the plaque at different time points can be analyzed, and the delivery status of the plaque can be stratified. For example, the plaque can be divided into different levels such as early enrichment, mid-term enrichment and late clearance to obtain plaque stratification delivery data. This data helps to understand the time dependence of drug delivery in the plaque and the drug clearance process.

[0110] Step S53: constructing a comprehensive plaque vulnerability scoring model based on ultrasound echo characteristics, inflammatory activity, and metabolic activity according to the plaque layered delivery data, thereby obtaining plaque vulnerability scoring data;

[0111] In this embodiment, a comprehensive plaque vulnerability scoring model is constructed based on plaque layered delivery data, combining ultrasound echogenicity, inflammatory activity, and metabolic activity. First, the plaque is assessed using ultrasound echogenicity, selecting parameters such as echo intensity, echo uniformity, and boundary definition within the plaque region. Next, ultrasound image analysis software is used to extract data related to inflammatory and metabolic activity, such as local tissue temperature changes and the dynamic distribution of metabolites. These physiological characteristics are integrated with the plaque layered delivery data to construct a plaque vulnerability scoring model. Specifically, multiple indicators are defined, such as ultrasound echogenicity, which can be quantified on a scale of 0-10, inflammatory activity scored based on tissue temperature changes (e.g., a temperature change exceeding 2°C is assigned a score of 5), and metabolic activity scored by analyzing peak metabolic signals using PET imaging (e.g., a score of 8 when the peak concentration of metabolites exceeds a certain threshold). By integrating these indicators, a plaque vulnerability score is generated, providing a foundation for subsequent optimization of targeted delivery efficiency.

[0112] Step S54: performing a correlation analysis between the targeted delivery of the reagent and the vulnerability index based on the plaque vulnerability score data, thereby obtaining delivery-vulnerability matching data;

[0113] In an embodiment of the present invention, a correlation analysis between targeted agent delivery and vulnerability index is performed based on plaque vulnerability score data. First, targeted delivery efficiency data for plaques with different vulnerability scores is collected. By calculating the correlation between the plaque vulnerability score and the targeted delivery efficiency, it is determined whether there is a clear correlation trend. Specifically, statistical methods such as the Pearson correlation coefficient or the Spearman rank correlation coefficient can be used to analyze the relationship between vulnerability score and delivery efficiency. For example, if plaques with high vulnerability (high score) are generally accompanied by higher targeted delivery efficiency (high signal intensity), it can be inferred that the targeted delivery efficiency of the agent is closely related to the vulnerability of the plaque. Based on the analysis results, the matching data between delivery efficiency and vulnerability can be obtained, providing a basis for further treatment plans.

[0114] Step S55: A relationship model between ultrasound parameters and delivery efficiency is established through multivariate regression analysis based on the delivery-vulnerability matching data, and the optimal parameter combination is determined to obtain performance evaluation data including target-to-background ratio, delivery efficiency and matching index.

[0115] The embodiment of the present invention uses multiple regression analysis to establish a relationship model between ultrasound parameters and delivery efficiency based on delivery-vulnerability matching data. First, several ultrasound parameters that affect delivery efficiency, such as mechanical index, frequency, power, etc., are selected, and then the targeted delivery efficiency data and corresponding vulnerability matching data under different ultrasound parameters are collected. Through the multiple regression analysis method, the relationship between each ultrasound parameter and delivery efficiency is modeled to obtain a set of regression equations to evaluate the degree of influence of each ultrasound parameter on delivery efficiency. For example, it may be found that the frequency has a greater impact on delivery efficiency, while the mechanical index has a smaller impact on vulnerability matching. The ultrasound parameter combination is optimized through the regression model, and the optimal parameter combination is finally determined, so that the targeted delivery efficiency and vulnerability matching achieve the best balance, thereby obtaining efficacy evaluation data including target-to-back ratio, delivery efficiency and matching indicators, providing more accurate parameter support for subsequent clinical treatment.

[0116] The efficacy evaluation system of the present invention has built a complete scientific closed loop from data analysis to parameter optimization, realizing the systematic evaluation and feedback optimization of targeted delivery technology for atherosclerosis. By calculating the signal intensity ratio (TBR) between the target area and the non-target area and the ratio of the target area signal to the injected dose (TID), this method has established a two-dimensional quantitative evaluation standard for targeted delivery efficiency for the first time, overcoming the limitations of traditional single parameter evaluation and providing a scientific basis for the objective comparison of delivery system performance. The stratified analysis of the distribution of reagents in different time periods revealed the differentiated distribution patterns of drugs in early, middle and late stage plaques. This time dimension analysis breaks through the traditional static evaluation model and captures the dynamic interaction process between drugs and plaques at different stages. Particularly innovative is the comprehensive scoring model for plaque vulnerability established by the present invention. By integrating multidimensional parameters such as ultrasonic echo characteristics, inflammatory activity and metabolic activity, it achieves accurate quantification of the pathological state of plaques. This comprehensive evaluation breaks through the one-sidedness of single indicator evaluation and provides a comprehensive reference benchmark for subsequent optimization of targeted strategies. Delivery-vulnerability matching analysis has pioneered a direct correlation between drug delivery and disease characteristics. This correlation analysis not only evaluates the targeting accuracy of current delivery systems, but also reveals the inherent laws between delivery efficiency and plaque pathological characteristics, pointing the way for the design of personalized delivery strategies. The most practical value is the ultrasound parameter and delivery efficiency relationship model established based on multivariate regression analysis. This model successfully achieves automatic optimization of ultrasound parameters for different types of plaques by systematically analyzing the impact of parameters such as sound intensity, irradiation time, and pulse interval on the delivery effect, solving the problem of blind parameter selection in clinical practice. This complete performance evaluation system not only provides a standardized evaluation platform for targeted delivery research in atherosclerosis, but also achieves continuous improvement of delivery technology through a data-driven parameter optimization mechanism, laying a solid methodological foundation for the application of precision medicine in the cardiovascular field.

[0117] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0118] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model, characterized in that: The following steps are involved: Step S1: obtaining lesion characteristic data of the atherosclerotic site, wherein the lesion characteristics include lesion location, endothelial permeability, inflammatory state and vulnerability index; Step S2: preparing targeted nanobubbles based on the lesion characteristic data, injecting the targeted nanobubbles intravenously into the preset atherosclerosis model, and performing multimodal imaging tracking analysis to obtain microbubble circulation distribution data in vivo; Step S3: performing ultrasound response activation processing on the targeted nano-microbubbles based on the microbubble in vivo circulation distribution data, and performing phased acoustic parameter regulation on the target lesion site through low-intensity focused ultrasound to obtain dynamic data on the microbubble acoustic behavior and reagent release status data; Step S4: Based on the dynamic data of microbubble acoustic behavior and the data on the release status of the reagent, three modalities, namely ultrasound contrast imaging, near-infrared fluorescence imaging, and positron emission tomography, are used to monitor the distribution of the reagent in real time to obtain the spatiotemporal distribution data of the targeted delivery of the reagent; Step S5: Evaluate the targeted delivery efficiency of the reagent based on plaque vulnerability according to the spatiotemporal distribution data to obtain efficacy evaluation data, wherein the efficacy evaluation data includes target-to-background ratio, delivery efficiency parameters, and lesion progression matching index.

2. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using vascular ultrasound imaging technology to perform real-time vascular scanning on a preset atherosclerosis model, and recording the three-dimensional spatial distribution and morphological characteristics of the plaque, thereby obtaining lesion location data including plaque positioning; Step S12: measuring the ultrasonic echo intensity ratio between the plaque area and the normal blood vessel wall in the atherosclerosis model, and performing contrast agent permeation dynamics analysis to obtain endothelial permeability data; Step S13: According to the atherosclerosis model, a contrast agent targeting inflammatory mediators is injected and its enrichment in the plaque area is monitored, and the density of CD68-positive cells is evaluated to obtain inflammatory status data; Step S14: calculating a plaque acoustic response index based on the endothelial permeability data and the inflammatory status data, and classifying the plaques based on the plaque acoustic response index, thereby obtaining plaque classification data, wherein the plaque classification data includes low-response plaque data, medium-response plaque data, and high-response plaque data; Step S15: performing near-infrared fluorescence imaging based on inflammatory activity and PET imaging based on metabolic activity based on the plaque classification data, and calculating a vulnerability index value of the lesion area, thereby obtaining plaque vulnerability index data, wherein the vulnerability index value is calculated as follows: vulnerability index = 0.4 × proportion of low-responsive area + 0.35 × standard score of inflammatory activity + 0.25 × standard score of metabolic activity; Step S16: combining the plaque vulnerability index data, the inflammatory status data, the endothelial permeability data, and the lesion location data into lesion feature data.

3. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 2, wherein: Step S14 specifically includes: The acoustic response index of the lesion area is calculated based on the product of the endothelial permeability data and the inflammation degree data to obtain the acoustic response index; when the acoustic response index is less than 0.5, the plaque is classified as low-responsive plaque data; when the acoustic response index is greater than or equal to 0.5 and less than 0.8, the plaque is classified as medium-responsive plaque data; when the acoustic response index is greater than or equal to 0.8, the plaque is classified as high-responsive plaque data; the low-responsive plaque data, medium-responsive plaque data and high-responsive plaque data are merged into plaque classification data.

4. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 1, wherein: The preparation of targeted nanobubbles includes the preparation of microbubbles with surface modification having specific ligands for plaque macrophages and the preparation of microbubbles with a core layer loaded with a reagent to be verified. The preparation of microbubbles with surface modification having specific ligands for plaque macrophages in step S2 specifically includes: A lipid material with phase transition properties, including dipalmitoylphosphatidylcholine, distearoylphosphatidylglycerol, and polyethylene glycol-modified distearoylphosphatidylethanolamine, was mixed at a molar ratio of 7:2:1, dissolved in chloroform, and subjected to rotary evaporation to obtain uniform lipid film data. Based on the uniform lipid membrane data, gas cores were prepared by passing perfluoropropane gas into ultrapure water to form a saturated solution. The solution was stirred at 3000 rpm for 10 minutes at 40°C and intermittently sonicated using an ultrasonic probe to obtain primary microbubble emulsion data. The reagent encapsulation process was performed based on the primary microbubble emulsion data. The pre-obtained reagent to be verified was dissolved in phosphate buffer and the pH was adjusted to 7.

4. The reagent solution was encapsulated in the microbubble core using double emulsion technology to obtain the reagent encapsulation microbubble data. The targeting ligand was modified according to the reagent-loaded microbubble data, and the macrophage CD68 antibody or integrin was added by click chemistry. The targeting peptide was covalently linked to the microbubble surface to obtain single-targeted microbubble data; A dual-targeting ligand system is prepared based on the single-targeting microbubble data, thereby obtaining dual-targeting microbubble data; Fluorescence resonance energy transfer (FRET) technology was used to monitor the ligand modification efficiency based on dual-targeted microbubble data. The spatial distribution of the two ligands on the microbubble surface was determined by detecting the FRET signal, and the ligand spacing was adjusted according to the FRET signal intensity to obtain the ligand modification efficiency data. The ligand directional arrangement is optimized based on the ligand modification efficiency data. By controlling the temperature gradient, the ligand is promoted to form an enriched area on the microbubble surface, the local ligand density is increased and the steric hindrance is reduced, thereby obtaining the targeted nanobubble preparation data.

5. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 4, characterized in that: The dual-targeting ligand system is prepared as follows: The dual-targeting ligand system was prepared based on the single-targeting microbubble data to obtain the dual-targeting microbubble data, wherein the dual-targeting ligand system preparation specifically includes the CD68 antibody and integrin The targeting peptides were mixed in a molar ratio of 3:7 to form a targeting ligand mixture. The integrin Targeting peptides and CD68 antibodies react with the surface of microbubbles.

6. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 5, characterized in that: The preparation of the microbubble structure with the core layer loaded with the reagent to be verified described in step S2 specifically includes: Screening drug delivery methods based on the physicochemical properties of the reagent to be verified to obtain loading strategy optimization data. Physicochemical properties include hydrophilicity, molecular weight, and stability parameters. Drug delivery methods include electrostatic adsorption, cavity encapsulation, and lipid membrane embedding. The core layer material was prepared according to the loading strategy optimization data. Polylactic acid-co-glycolic acid copolymer, chitosan, and hyaluronic acid were mixed at a weight ratio of 4:3:3 and subjected to ultrasonic dispersion treatment to obtain the core layer material data. The core-drug complex was constructed based on the core layer material data. The reagent molecules were fully mixed with the core layer material using the double emulsion solvent evaporation method, and sheared at 4000 rpm for 2 minutes under low temperature conditions to obtain the primary core-drug complex data. Quantitative analysis using high performance liquid chromatography was performed based on the primary core-drug complex data to analyze the free and total reagent contents, calculate the reagent encapsulation efficiency and entrapment rate, and thus obtain the encapsulation efficiency data; According to the encapsulation efficiency data, the core layer structure stability is optimized by adjusting the crosslinking agent concentration and crosslinking time parameters to obtain stability optimization data; Conduct release kinetics testing of the reagent based on the stability optimization data. Under simulated physiological environment and ultrasonic field conditions, monitor the release rate and release pattern of the reagent, draw a release curve, and thus obtain release kinetics data. The microbubble shell layer and the core layer were assembled and integrated according to the release kinetics data, and the targeted nanobubbles were integrated with the core layer of the encapsulated reagent using membrane fusion technology according to the targeted nanobubble preparation data. The cells were incubated at 37°C for 30 minutes to obtain targeted nanobubbles.

7. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 6, characterized in that: The multimodal image tracking analysis described in step S2 specifically includes: The targeted nanobubbles are labeled with contrast agents, and a gas marker suitable for ultrasound contrast imaging, a near-infrared fluorescent dye suitable for fluorescence imaging, and a radioactive isotope suitable for PET imaging are respectively labeled with the targeted nanobubbles to obtain trimodal labeled microbubbles; The trimodal labeled microbubbles were injected into the atherosclerosis model via the tail vein at a constant rate of 1 ml / min, and dynamic contrast-enhanced ultrasound monitoring was performed to obtain ultrasound time-series data. Near-infrared fluorescence imaging was performed on the atherosclerosis model injected with targeted nanobubbles. Whole-body fluorescence scanning was performed 1 hour, 6 hours, and 24 hours after microbubble injection. The excitation wavelength was set to 750 nm, the acquisition wavelength was 780 nm, the exposure time was 2 seconds, and the scanning resolution was 100. m, obtain fluorescence distribution data; Performing PET scanning on an atherosclerosis model injected with targeted nanobubbles to obtain PET data; Perform image fusion processing on ultrasound time series data, fluorescence distribution data and PET data for spatial alignment, and use weighted average method to generate multimodal fusion images to obtain fused image data; Based on the fused imaging data, the temporal and spatial distribution of targeted nanobubbles in the body was quantitatively analyzed, the signal intensity ratio of microbubbles in the target area and the non-target area was calculated, the microbubble concentration-time curve was drawn, and the key kinetic parameters were extracted to obtain the microbubble circulation distribution data in the body.

8. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 7, wherein: Step S3 includes the following steps: Step S31: determining the optimal ultrasound irradiation time window for the target lesion site based on the microbubble circulation distribution data in the body, performing real-time ultrasound imaging of the atherosclerotic plaque using an ultrasound probe, identifying the lesion area and microbubble enrichment, and thereby obtaining ultrasound irradiation area positioning data; Step S32: setting ultrasound parameters according to the ultrasound irradiation area positioning data, thereby obtaining ultrasound parameter setting data; Step S33: performing staged acoustic field processing according to the ultrasound parameter setting data and the plaque classification data, thereby obtaining graded ultrasound irradiation data; Step S34: performing real-time monitoring of the acoustic behavior of the microbubbles during the ultrasonic irradiation process, including harmonic signal intensity changes and microbubble rupture dynamics, based on the graded ultrasonic irradiation data, and adjusting the ultrasonic parameters based on the collected acoustic signals to obtain dynamic data on the acoustic behavior of the microbubbles; Step S35: Perform reagent release imaging analysis based on the dynamic data of microbubble acoustic behavior, evaluate the change of local echo signal after microbubble rupture through ultrasound contrast imaging, and quantify the release degree based on the reagent's own fluorescence or labeling signal to obtain the reagent release status data.

9. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 8, characterized in that: Step S4 includes the following steps: Step S41: performing ultrasound contrast monitoring based on the microbubble acoustic behavior dynamic data and the reagent release state data, thereby obtaining ultrasound time series monitoring data; Step S42: performing near-infrared fluorescence imaging based on the ultrasound time-series monitoring data to obtain fluorescence distribution monitoring data; Step S43: performing positron emission tomography scanning based on the fluorescence distribution monitoring data, and performing reconstruction processing based on a three-dimensional ordered subset expectation maximization algorithm to obtain PET monitoring data; Step S44: performing imaging data fusion processing on the ultrasound time series monitoring data, the fluorescence distribution monitoring data, and the PET monitoring data, and integrating the three modal data using an information entropy weighting method to obtain multimodal fusion monitoring data; Step S45: Perform spatiotemporal kinetic analysis of the reagent distribution based on the multimodal fusion monitoring data, establish an enrichment-clearance curve of the reagent at the lesion site over time, and fit the change trend of the signal intensity over time through a bi-exponential model to extract key pharmacokinetic parameters, thereby obtaining the spatiotemporal distribution data of the targeted delivery of the reagent, wherein the spatiotemporal distribution data includes half-peak time, peak concentration, area under the curve and clearance rate constant.

10. The method for verifying the in vivo targeted delivery function of an agent based on an atherosclerosis model according to claim 9, characterized in that: Step S5 includes the following steps: Step S51: Calculating the signal intensity ratio between the target area and the non-target area and the ratio between the target area signal and the injected dose based on the spatiotemporal distribution data, thereby obtaining targeted delivery efficiency data; Step S52: analyzing the agent distribution at different time periods based on the targeted delivery efficiency data, thereby obtaining plaque layered delivery data; Step S53: constructing a comprehensive plaque vulnerability scoring model based on ultrasound echo characteristics, inflammatory activity, and metabolic activity according to the plaque layered delivery data, thereby obtaining plaque vulnerability scoring data; Step S54: performing a correlation analysis between the targeted delivery of the reagent and the vulnerability index based on the plaque vulnerability score data, thereby obtaining delivery-vulnerability matching data; Step S55: A relationship model between ultrasound parameters and delivery efficiency is established through multivariate regression analysis based on the delivery-vulnerability matching data, and the optimal parameter combination is determined to obtain performance evaluation data including target-to-background ratio, delivery efficiency and matching index.

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