Microcirculation dynamics modeling-based drug delivery optimization method

Abstract

The invention relates to a drug delivery optimization method based on microcirculation dynamics modeling, which comprises the following steps: establishing a comprehensive model coupled with hemodynamics and drug delivery based on microcirculation characteristics including blood vessel distribution, blood flow velocity and blood vessel elasticity of an individual patient; according to the comprehensive model, interaction modeling is carried out by combining physicochemical properties, including solubility, molecular weight and hydrophilicity, of drug molecules and a microenvironment, including blood flow shear force and capillary permeability, of blood flow; a sensor array integrating a blood flow sensor, a tissue oxygenation sensor and a pH sensor is used for monitoring changes of local microcirculation in real time; sensor data is combined with pathological information of abnormal microvessels of a tumor area or an inflammation area of a patient, the state of microcirculation is dynamically decoded, and the permeability of the microvessels, the oxygenation effect of local tissue and the instantaneous change of blood flow are evaluated in real time; and real-time feedback of drug delivery is provided by using a state decoding technology.

Description

Technical Field

[0001] The present invention relates to a method for optimizing drug delivery, specifically a method for optimizing drug delivery based on microcirculation dynamics modeling. Background Art

[0002] At present, although certain progress has been made in the research and application of methods for optimizing drug delivery, there are still many deficiencies and drawbacks in actual operation, which restrict its effectiveness and wide application. First of all, traditional drug delivery systems often rely on the passive transfer of drugs through the blood circulation system to the target area. The main problem with this method is that the drugs are too widely distributed throughout the body, resulting in the inability to achieve local high-concentration drug accumulation during the treatment process. The diffusion of drugs in the body is affected by factors such as biological barriers, hemodynamics, and tissue structure. Therefore, it is very difficult to precisely control the drug concentration in the lesion area, often resulting in insufficient drug efficacy or significant side effects.

[0003] Although there are currently some targeted drug delivery systems for specific lesion areas, most of these systems rely on drug carriers and specific targeting molecules to achieve precise delivery. However, this method still has problems such as uncontrollable drug release rate, insufficient drug targeting, and toxicity of the carrier, which greatly reduces the actual application effect of the drug. In addition, in existing drug delivery systems, due to the lack of a dynamic feedback mechanism, the drug release process often cannot be adjusted in real time, and the drug release strategy cannot be optimized in a timely manner according to the specific situation of the patient, resulting in low efficiency during drug delivery. In the drug diffusion model, it is usually assumed that the diffusion of drugs in the blood is uniform. However, the actual situation is complex, and different components in the blood (such as red blood cells, white blood cells, plasma proteins, etc.) and the different structures and functions of blood vessels will greatly affect the diffusion speed and distribution of drugs. Traditional drug delivery methods often ignore these factors, resulting in uneven distribution of drugs in different vascular regions, and even being unable to reach certain regions at all, thus reducing the treatment effect. Additionally, another major problem in drug delivery is the blood-brain barrier and the tumor microenvironment. Especially in tumor treatment, due to the complex and irregular vascular structure of tumor tissues, changes in vascular permeability, blood flow velocity, and local oxygenation will directly affect the drug delivery efficiency and treatment effect. Existing drug delivery methods often lack precise regulation mechanisms when dealing with these complex microenvironments, resulting in the inability of drugs to accumulate sufficient concentrations in lesion areas such as tumors, thus affecting the treatment effect. In actual applications, factors such as the absorption, metabolism, and excretion processes of drugs may also cause the drug concentration in the body to fluctuate violently over time, making it difficult to maintain a stable treatment level.

[0004] Although drug delivery systems can delay drug release in some cases, the drug concentration still cannot be precisely controlled, and the distribution of drugs in tissues is also difficult to predict, resulting in the inability to adjust the drug dosage and release rate in a timely manner according to the changes in the condition. In addition, most of the existing drug delivery methods rely on a single treatment mode and lack a comprehensive dynamic regulation mechanism. During the treatment process, factors such as the microcirculation state, immune response, and tissue metabolism of the patient will continuously change, and these changes have a direct impact on drug delivery and efficacy. However, traditional drug delivery methods usually do not have a real-time monitoring and feedback mechanism, so they cannot adjust the drug release strategy in a timely manner to cope with the changes in the patient's state. This lack of dynamic adaptation in the treatment method greatly reduces the accuracy and efficacy of drug delivery and fails to fully exert the therapeutic potential of drugs. Moreover, the complexity and diversity of drug delivery systems have led to technical and operational problems. Currently, many drug delivery systems require the use of complex carrier systems and technologies. The preparation, implementation, and operation of these systems require high-precision equipment and technical support, and often require professional operators, increasing the difficulty and cost of treatment. At the same time, the long-term effects of these systems are not fully clear. Especially in clinical applications, the drug delivery efficiency may fluctuate due to individual differences, further increasing the uncertainty of clinical treatment. Summary of the Invention

[0005] The object of the present invention is to provide an optimized method for drug delivery based on microcirculation dynamics modeling, so as to solve some of the drawbacks and deficiencies pointed out in the background technology.

[0006] The technical solutions adopted by the present invention to solve its above technical problems include the following steps:

[0007] S1. Microcirculation blood flow dynamic modeling and drug transport mechanism:

[0008] S1.1. Based on the microcirculation characteristics of the patient individual, including blood vessel distribution, blood flow velocity, and blood vessel elasticity, establish a comprehensive model that couples hemodynamics and drug transport;

[0009] S1.2. For the comprehensive model, establish an interaction model by combining the physical and chemical properties of drug molecules, including solubility, molecular weight, and hydrophilicity, with the microenvironment of blood flow, including blood flow shear stress and microvascular permeability;

[0010] S2. Real-time monitoring and state decoding of the local microcirculation microenvironment:

[0011] S2.1. Use a sensor array integrating sensors including blood flow sensors, tissue oxygenation sensors, and pH sensors to real-time monitor the changes in the local microcirculation;

[0012] S2.2. Combine the sensor data with the pathological information of the patient's microvascular abnormalities including the tumor area or the inflammatory area, dynamically decode the state of the microcirculation, and real-time evaluate the microvascular permeability, the oxygenation of the local tissue, and the instantaneous changes in blood flow;

[0013] S2.3. Use the state decoding technology to provide real-time feedback on drug delivery;

[0014] S3. Dynamic adaptation of the drug release rate to the local microcirculation feedback:

[0015] S3.1. Introduce a dynamic regulation mechanism based on the state of the microcirculation, and associate the drug release rate with the changes in the microcirculation;

[0016] S3.2. When the local blood flow increases, increase the drug release rate; while when the local blood flow decreases, adjust the release rate to avoid excessive drug accumulation;

[0017] S4. Optimization of drug delivery based on microvascular permeability:

[0018] S4.1. Develop a permeability prediction model, combine the pathological information including the angiogenesis characteristics of the tumor and the changes in vascular permeability in the inflammatory area, and predict the permeability of the drug through the microvessels;

[0019] S4.2. Based on the permeability prediction model, optimize the drug delivery path and adjust the distribution of the drug in different microvascular structures to increase the local drug concentration;

[0020] S5. Spatiotemporal analysis of drug interactions in the microcirculation:

[0021] S5.1. Analyze the spatiotemporal distribution characteristics of the drug in the microcirculation, including the interactions between the drug and the vascular wall and blood components;

[0022] S5.2. Conduct a temporal analysis of the distribution of the drug in the blood flow, and track the propagation path and diffusion speed of drug molecules under different vascular types and different blood flow velocities.

[0023] Furthermore, the real-time monitoring and state decoding method for the local microcirculation microenvironment includes:

[0024] Use local three-dimensional positioning technology to enable each sensor to synchronously acquire data within the same microvascular area; the sensor array combines optical imaging technology and bioelectric sensor technology; enable penetration of tissue layers to collect parameters within the microvessels in real time; perform weighted processing on the signals of different sensors through the following integral formula:

[0025]

[0026] Where:

[0027] S(t) represents the comprehensive signal output by the sensor array, reflecting the comprehensive information of multiple microcirculation-related parameters; F(t) is the signal of real-time blood flow, representing the blood flow volume in the microvessels; O(t) is the signal of real-time tissue oxygenation, representing the oxygenation level of the tissue, and the change of oxygenation directly affects the drug absorption ability; pH(t) is the signal of real-time pH value, and the pH change is associated with tissue metabolism, which is a key parameter for evaluating tissue metabolism and microvascular permeability; β 1 , β 2 , β 3 are weighting coefficients used to adjust the influence degree of each physiological parameter on the comprehensive signal; the integration interval τ 1 to τ 2 means accumulating the sensor data within the unit time range to obtain periodic monitoring results.

[0028] Furthermore, the real-time monitoring and status decoding method for the local microcirculation microenvironment includes:

[0029] Dynamically decoding the real-time sensor data and the patient's pathological information to evaluate the local microcirculation status in real time; taking the microvascular structure change in the tumor area as the key factor for decoding; by combining the sensor data and the pathological data, the microcirculation status of different pathological areas is identified and decoded in real time; and the personalized decoding function can be expressed as:

[0030]

[0031] Where:

[0032] Φ(X, P) represents the personalized microcirculation status decoding function, which dynamically evaluates the microcirculation status according to the real-time data and the patient's pathological information; X is the patient's pathological data, including the microvascular abnormality information in the tumor or inflammation area, reflecting the changes in the vascular structure and function of the pathological area; P is the real-time sensor data, including physiological parameters such as blood flow, oxygenation, and pH value, reflecting the real-time status of the local microcirculation; F(t) is the real-time blood flow, O(t) is the real-time oxygenation, and pH(t) is the real-time pH value, corresponding to different physiological parameters respectively; V local is the volume of the local microvascular area, representing the blood volume or spatial size of this area; C local is the coefficient of local oxygenation, representing the oxygenation ability of this area; M local is the local metabolic parameter, reflecting the metabolic activity level of this area; α 1 , α 2 , α 3 are weighting coefficients used to adjust the weights of blood flow, oxygenation, and pH value in the decoding process.

[0033] Further, the real-time monitoring and state decoding method for the local microcirculation microenvironment includes:

[0034] By combining real-time blood flow data, oxygenation data, and pH value data, a multi-dimensional dynamic prediction model is developed to evaluate the interaction between microvascular permeability, oxygenation, and blood flow in real time; capture the changing trend of microvascular permeability over time, and evaluate the drug absorption capacity of tissues; the dynamic formula for predicting the local tissue drug absorption capacity is expressed as:

[0035]

[0036] Where:

[0037] P local (t) is the drug absorption capacity of local tissues, representing the efficiency or ability of tissues to absorb drugs; F(t) is the real-time blood flow, reflecting the speed and volume of blood flow; O(t) is the real-time oxygenation, representing the oxygenation level of tissues; pH(t) is the real-time pH value, and the change in pH value is associated with tissue metabolism and is also an indicator for evaluating tissue metabolic status and microvascular permeability; γ 1 ,γ 2 ,γ 3 is a weighting coefficient, representing the influence degree of different physiological parameters on the drug absorption capacity; γ 4 is an attenuation coefficient, controlling the attenuation rate of microvascular permeability; e -δt represents the attenuation trend of vascular permeability over time.

[0038] Further, the spatio-temporal analysis method for the microcirculation-drug interaction includes:

[0039] By combining real-time data, analyze the changes in vascular wall permeability, oxygenation, and pH, and dynamically simulate the interaction between drugs and vascular walls and blood components; including in the tumor region, due to abnormal angiogenesis and permeability changes, drug molecules will have different diffusion and adsorption behaviors; the interaction force F between the drug and the vascular wall is described by the following model:

[0040]

[0041] Where:

[0042] F is the interaction force between the drug molecule and the vascular wall, representing the intensity of the drug molecule attaching to or penetrating the vascular wall; γ is the coefficient of contact between the drug molecule and the vascular wall, reflecting the affinity between the drug molecule surface and the vascular wall; β is the influence coefficient of blood components on drug diffusion, comprehensively including the influence of red blood cells, white blood cells, and plasma proteins on drug diffusion; v is the local blood flow velocity, reflecting the influence of blood flow velocity on the movement of drugs in blood vessels.

[0043] Furthermore, the spatio-temporal analysis method for the microcirculation drug interaction includes:

[0044] By establishing a dynamic microfluidic model and a time series analysis technique, combining real-time blood flow velocity, blood vessel shape, and drug diffusion parameters, simulating the distribution of drugs in different types of blood vessels; including the influence of different blood vessel types such as arteries, capillaries, and veins and different blood flow velocities such as high-flow arteries and low-flow capillaries on the drug diffusion characteristics; real-time tracking the propagation path of drug molecules in the microcirculation, dynamically predicting the time and concentration of the drug reaching the target tissue; the change of the drug concentration distribution in the microvessels with blood flow velocity and blood vessel type is described by the following equation:

[0045]

[0046] Wherein,

[0047] C(r,t) is the drug concentration at time t and position r, indicating the distribution of drug molecules in the blood vessel at a certain time point; C 0 is the initial concentration, indicating the concentration of the drug when it enters the bloodstream; r is the blood vessel position, indicating the propagation position of drug molecules in the microvessels; v is the blood flow velocity, reflecting the influence of blood flow velocity on the drug propagation path; k is a constant related to blood vessel permeability and drug diffusion, describing the drug diffusion characteristics in a specific blood vessel type; t is the time, and the drug distribution changes over time.

[0048] Furthermore, the spatio-temporal analysis method for the microcirculation drug interaction includes:

[0049] By real-time monitoring of multi-dimensional data such as blood flow velocity, permeability, and oxygenation of the microvessels, dynamically adjusting the drug diffusion model, predicting the drug diffusion rate and distribution characteristics according to the changes in the local environment; molecular dynamics simulation simulates the movement of drug molecules in the blood, and the dynamic process of drug diffusion is tracked in real-time, and feedback is provided for the drug delivery system; the dynamic velocity during the drug diffusion process is represented by the following model:

[0050] D(t) = D 0 ·e -αt

[0051] Wherein:

[0052] D(t) is the drug diffusion coefficient at time t, indicating the drug diffusion ability in the microvessels; D 0 is the initial diffusion coefficient, indicating the diffusion rate of the drug when it first enters the microcirculation; α is the attenuation coefficient related to blood vessel permeability and blood flow velocity, reflecting the influence of local blood vessel characteristics on drug diffusion; t is the time, and as time passes, the drug diffusion rate in the microvessels changes.

[0053] The drug delivery optimization method based on microcirculation dynamics modeling of the present invention combines real-time monitoring and dynamic adjustment technologies, and can precisely control the drug diffusion process at the microvascular level, thereby achieving more efficient and precise drug delivery. The beneficial effects of this method are reflected in the following aspects:

[0054] By real-time monitoring multi-dimensional data of microcirculation (such as blood flow velocity, vascular permeability, oxygenation, etc.) and precisely regulating drug diffusion based on a dynamic microcirculation model, it is possible to significantly increase the drug concentration in the target area, ensure the accumulation of drugs in the tumor or lesion area, and thus improve the treatment effect. Dynamically adjusting the drug delivery strategy according to the microcirculation characteristics of individual patients (such as abnormal hyperplasia of blood vessels in the tumor area, different blood flow velocities, tissue oxygenation, etc.) avoids the neglect of individual differences in traditional drug treatment regimens. Each patient can obtain a customized treatment plan based on their specific pathological data and microcirculation status, thereby improving the accuracy of treatment.

[0055] By real-time monitoring and dynamically adjusting the drug diffusion process, the present invention can capture changes in the microcirculation environment in real time during drug delivery and make rapid responses and optimizations. For example, in the tumor area, due to changes in vascular permeability and blood flow velocity, the drug diffusion rate and distribution may deviate. Based on the spatio-temporal analysis and feedback mechanism of this method, the drug release strategy or diffusion model can be adjusted in a timely manner to maximize the therapeutic effect of the drug. Brief Description of the Drawings

[0056] Figure 1 It is a flowchart of the drug delivery optimization method based on microcirculation dynamics modeling of the present invention.

[0057] Figure 2 It is a flowchart of the real-time monitoring and state decoding method for the local microcirculation microenvironment of the present invention.

[0058] Figure 3 It is a flowchart of the spatio-temporal analysis method for the microcirculation drug interaction of the present invention. Detailed Embodiment

[0059] The following will give a detailed description of the specific embodiments of the present invention in conjunction with the drawings.

[0060] In conjunction with the attached Figure 1As shown in the process, for the dynamic modeling of microcirculation blood flow and drug transport mechanism, first, starting from the microcirculation characteristics of the patient's individual, such as blood vessel distribution, blood flow velocity, and blood vessel elasticity, a comprehensive model coupling hemodynamics and drug transport is constructed. In this comprehensive model, the hemodynamic part mainly considers the flow characteristics of blood in the microcirculation, including blood flow velocity, flow pattern, and non-linear effects of flow, etc.; while the drug transport mechanism involves how drug molecules are transported, diffused in the blood, and the exchange process between the blood vessel wall and tissues. The characteristics of drug molecules in the model include their physicochemical properties, such as solubility, molecular weight, hydrophilicity, etc., which directly affect the transport ability, diffusion rate, and final targeted delivery effect of drugs in the body. At the same time, the hemodynamic part also needs to consider the effects of blood flow shear stress, microvascular permeability, etc. on the drug molecule transport process. Especially in the microvessels, the shear stress of blood flow will affect the interaction, adsorption, and penetration process of drug molecules with the blood vessel wall.

[0061] Combined with the microscopic environment of drug molecules and blood flow, through the model, the transport path and diffusion law of drugs in the microcirculation can be analyzed. This modeling process not only involves the hydrodynamic equations in hemodynamics but also needs to consider factors such as the diffusion equation of drugs in microvessels, the interaction force with the blood vessel wall, and the influence of blood components on drugs. In addition, the shear stress of blood flow and the permeability of microvessels directly affect the penetration ability of drugs. Especially in pathological states such as tumors or inflammation, changes in vascular permeability will significantly affect the transport efficiency of drug molecules. Through this comprehensive model, the distribution and delivery process of drugs in the patient's body can be simulated in real-time and dynamically, and optimized according to specific blood flow conditions, vascular characteristics, and drug molecule properties.

[0062] For the real-time monitoring and state decoding of the local microcirculation microenvironment, first, by integrating multiple sensors, such as blood flow sensors, tissue oxygenation sensors, and pH sensors, a sensor array is formed to monitor the changes in the local microcirculation in real-time. These sensors work together in the same system and can simultaneously collect multiple physiological parameters related to microcirculation, including blood flow velocity, tissue oxygenation level, local pH value, etc. These parameters can not only reflect the health status of microvessels but also reveal the flow and distribution of drugs in the local microcirculation. The blood flow sensor is used to capture the velocity and flow of local blood flow, which is crucial for understanding the transport of drug molecules; the tissue oxygenation sensor is used to evaluate the oxygenation status of the local area, and the oxygenation level directly affects the distribution and efficacy of drug molecules in tissues; the pH sensor can monitor the acid-base balance of the local area, and this parameter is usually closely related to tissue metabolism and microvascular permeability. Especially in diseased areas such as tumors or inflammation areas, changes in pH can indicate microvascular permeability and the possibility of drug delivery.

[0063] By collecting these sensor data in real time and combining the individual differences of patients and local pathological information (such as microvascular abnormalities in tumor or inflammation areas), the system can dynamically decode the state of microcirculation. The dynamic decoding process not only includes the real-time assessment of microvascular permeability and local oxygenation, but also involves the detection of instantaneous changes in blood flow, comprehensively capturing the health and pathological changes of microcirculation and reflecting the state of local tissues in real time during drug treatment. This state decoding technology can provide continuous and real-time feedback information to help doctors grasp the distribution and effect of drugs in local microcirculation in real time, providing a scientific basis for the adjustment of drug delivery strategies. By combining sensor data with pathological data, the system can provide personalized optimization suggestions for the treatment plans of different patients, realizing precise drug delivery.

[0064] The dynamic adaptation of drug release rate to local microcirculation feedback first introduces a dynamic regulation mechanism based on the state of microcirculation, which can adjust the drug release rate according to the real-time microcirculation environment. The drug release rate is closely related to factors such as local blood flow, hemodynamic state, and microvascular permeability. Changes in local microcirculation directly affect the drug transmission efficiency and distribution range. When the local blood flow increases, it usually means that the drug transmission demand in this area increases, and drugs are more likely to be quickly transported through microvessels to the target tissue. In this case, the system can automatically adjust the drug release rate according to the increase in blood flow, ensuring that the drug can effectively reach the target area quickly and improving the therapeutic effect of the drug. On the contrary, when the local blood flow decreases, it means that the blood circulation efficiency in this area decreases, and drug molecules may be retained or cannot be quickly transported to the target tissue, resulting in a decrease in drug efficacy or drug accumulation in the local area, which may cause side effects. To avoid this situation, the system will adjust the drug release rate and reduce the drug release amount to prevent excessive drug accumulation in inappropriate areas. This mechanism ensures the personalized regulation of drug release, making drug delivery more precise and effective, and able to respond to changes in the microcirculation state at any time, improving the safety and effect of treatment.

[0065] Optimization of drug delivery based on microvascular permeability. First, a permeability prediction model is developed. This model precisely predicts the permeability of drugs in microvessels by integrating pathological information, especially data such as angiogenesis characteristics in tumor regions and changes in vascular permeability in inflammatory regions. Blood vessels in tumor or inflammatory regions usually have abnormal vascular structures. For example, the common phenomenon of excessive angiogenesis in tumor regions leads to significant changes in the permeability of microvessels. These changes have a crucial impact on the diffusion, penetration, and absorption processes of drug molecules. Therefore, accurately predicting the permeability of drugs in these regions is the key to optimizing drug delivery. In the design of the model, factors such as specific biomarkers in tumor or inflammatory regions, morphological changes in microvessels, and hemodynamic parameters are combined, enabling the prediction model to simulate and quantify the penetration of drug molecules through different microvascular structures. Through this prediction model, the distribution path of drugs in the microcirculation can be adjusted in real time, optimizing the drug delivery strategy to ensure that drugs can better penetrate into the target tissue. Specifically, the system can adjust the distribution of drugs in different types of microvessels according to the prediction results, increasing the local concentration of drugs in the diseased area, thereby improving the efficacy of drugs. For tumor regions, by optimizing the drug delivery path, the accumulation of drugs in the tumor microenvironment can be enhanced, thus overcoming the delivery challenges brought by the abnormal structure of tumor blood vessels; for inflammatory regions, the drug delivery method can be flexibly adjusted according to changes in microvascular permeability, reducing the impact of drugs on normal tissues and enhancing the therapeutic effect of drugs.

[0066] Traditional drug distribution studies often overlook the complexity of the microcirculation. Through spatio-temporal analysis, it is possible to more accurately predict the diffusion and transmission of drugs in microvessels. First, the interaction between drug molecules and the blood vessel wall is an important factor affecting drug delivery efficacy. The physicochemical properties of drug molecules, such as size, solubility, and hydrophilicity, and the physiological characteristics of the blood vessel wall, such as permeability and elasticity, act together to determine the adsorption, penetration, and diffusion processes of drugs. The structural differences in the blood vessel wall, hemodynamic status, and components in the blood (such as red blood cells, white blood cells, plasma proteins, etc.) may all affect drug distribution. Therefore, it is necessary to analyze in detail the interaction between drugs and the blood vessel wall as well as blood components to understand the dynamic process of drug attachment, release, and penetration on the surface of the blood vessel wall.

[0067] Secondly, the distribution of drugs in the bloodstream is also a key factor in optimizing drug delivery. In the microcirculation, due to differences in blood vessel types (such as arteries, capillaries, veins) and blood flow velocities, the propagation path and diffusion rate of drugs will also change. For example, in arteries, due to the relatively fast blood flow velocity, drug molecules will rapidly pass through the blood vessels. In capillaries, however, the blood flow velocity is slower, and the diffusion of drugs is strongly affected by the local blood flow and the blood vessel wall, resulting in different drug distribution patterns. Through real-time time-series analysis techniques, it is possible to track the propagation path of drug molecules under different blood vessel types and blood flow velocities, calculate the diffusion rate of drugs in the blood, and comprehensively evaluate the spatio-temporal distribution characteristics of drugs by combining factors such as blood flow velocity, blood vessel type, and blood composition. By dynamically simulating these factors, it is possible to accurately predict the distribution of drugs in different microvascular structures, thereby achieving personalized optimization of the drug delivery path, enhancing the local concentration of drugs and the therapeutic effect. Through time-series analysis, the dynamic changes of drug molecules in the microcirculation can be captured, the drug release strategy can be optimized, enabling the drug to reach the target area more precisely, avoiding drug waste, and reducing side effects on healthy tissues.

[0068] Example 1:

[0069] Combined with the attached Figure 2 As shown in the process, the purpose of optimizing the drug delivery treatment for a certain cancer patient is to increase the local concentration of the drug in the tumor area while avoiding excessive accumulation of the drug in healthy tissues. The local microcirculation of the patient is monitored in real time using an integrated sensor array and three-dimensional positioning technology. The sensor array is used, including a blood flow sensor, an oxygenation sensor, a pH sensor, etc. Through local three-dimensional positioning technology, it is ensured that these sensors can synchronously and accurately obtain the microcirculation data of the same area. In this solution, the sensor array combines optical imaging technology and bioelectric sensor technology, enabling the penetration of tissue layers to collect key parameter data in the microvasculature in real time. Optical imaging technology can provide structural information of the microvasculature, while bioelectric sensor technology can directly capture real-time physiological data such as blood flow, oxygenation, and pH value.

[0070] During the specific operation process, through the integral formula:

[0071]

[0072] The output signals of multiple sensors are weighted and integrated to generate a comprehensive signal S(t) for real-time evaluation of the state of the microcirculation. Specifically, S(t) is the comprehensive signal output by the sensor array, which reflects the combined influence of the three key parameters: blood flow F(t), oxygenation O(t), and pH value pH(t).

[0073] F(t): Blood flow signal, representing the amount of blood flow in the microvessels, which directly affects the distribution of drugs in the blood vessels. O(t): Oxygenation signal, representing the oxygenation level of tissues. Areas with insufficient oxygenation are usually accompanied by changes in vascular permeability, affecting drug absorption. pH(t): pH signal, related to the metabolic conditions of tissues. Areas with high metabolic levels are often accompanied by high vascular permeability, affecting the diffusion rate and distribution of drugs.

[0074] To more accurately reflect the influence of each physiological parameter on the comprehensive signal, the weighting coefficients β 1 , β 2 and β 3 are introduced. Their value ranges are 0.1 ≤ β 1 , β 2 , β 3 ≤ 1.0, and these coefficients are adjusted according to experimental data and clinical responses. For example, if the oxygenation parameter has a greater influence on drug absorption, the weight of β 2 can be set to a higher value.

[0075] By integrating the sensor data over the time interval τ 1 to τ 2 , the comprehensive state of microcirculation over a period of time can be obtained, which provides dynamic feedback for the subsequent adjustment of drug release strategies. Assuming the set time interval is 5 minutes (τ 1 = 0, τ 2 = 5 minutes), within this time range, the changes in blood flow, oxygenation level, and pH value are weighted and accumulated to obtain the comprehensive signal S(t). For example, assuming that within a 5-minute monitoring period, the blood flow F(t) is 5 mL / s, the oxygenation O(t) is 80%, the pH value pH(t) is 7.4, and at the same time assuming the weighting coefficients β 1 = 0.5, β 2 = 0.3, β 3 = 0.2, the calculated comprehensive signal is:

[0076]

[0077] Therefore, within the 5-minute monitoring period, the comprehensive signal S(t) = 139.9, representing the dynamic changes in the local microcirculation state. In different local pathological regions (such as tumor regions or inflammation regions), due to the differences in blood flow, oxygenation, and pH value, the comprehensive signal will change significantly, thus providing a basis for real-time adjustment of drug release strategies.

[0078] By real-time monitoring of the microcirculation state and combining comprehensive signals, the state of the microcirculation can be dynamically decoded, and according to different microcirculation parameters, the drug release rate can be adjusted in real time. For example, when the local blood flow increases, the comprehensive signal will rise, and at this time the system automatically increases the drug release rate; while when the local blood flow decreases, the comprehensive signal will drop, and the system will adjust the release rate to avoid excessive accumulation of drugs in a certain area, thus achieving precise optimization of drug delivery. The key to this method lies in its personalized and real-time feedback characteristics. By dynamically decoding the specific microcirculation state of each patient, the drug release strategy can be continuously optimized during the treatment process to ensure that the drug can accurately and effectively reach the target area, improve the treatment effect, and reduce side effects.

[0079] In this embodiment, it is assumed that the patient's tumor is located in the liver area and the tumor is accompanied by obvious microvascular abnormalities. An integrated sensor array is used to real-time monitor the local microcirculation of the patient. These sensors can synchronously obtain physiological parameters such as blood flow, oxygenation, and pH value. During this process, combined with the patient's pathological data, including the microvascular structure changes in the tumor area, personalized decoding of the microcirculation state is performed.

[0080] To achieve personalized microcirculation decoding, the following decoding function is adopted:

[0081]

[0082] In this formula:

[0083] Φ(X,P) represents the individualized microcirculation state decoding function for dynamically evaluating the state of the microcirculation; X is the patient's pathological data, including microvascular abnormality information in the tumor or inflammation area, reflecting the changes in the vascular structure and function of the pathological area; P is the real-time sensor data, including blood flow F(t), oxygenation O(t), pH value pH(t), reflecting the real-time state of the local microcirculation; V local is the volume of the local microvascular area, representing the blood volume or spatial size of this area;

[0084] C local is the coefficient of local oxygenation, representing the oxygenation ability of this area; M local is the local metabolic parameter, reflecting the metabolic activity level of this area; α 1 ,α 2 ,α 3 are weighting coefficients used to adjust the weights of blood flow, oxygenation, and pH value in the decoding process.

[0085] In this example, it is assumed that detailed monitoring has been carried out on the liver tumor area, and the monitoring interval is 10 minutes (τ 1 =0, τ 2= 10 minutes). The following are the actual data of some key parameters:

[0086] Blood flow: In the tumor region, the blood flow is approximately F(t) = 6.5 mL / s within 10 minutes. This value is increased compared to the normal region, reflecting the characteristics of angiogenesis and increased permeability in the tumor region.

[0087] Oxygenation: Assuming a low level of oxygenation in the tumor region, the oxygenation is O(t) = 60% during the monitoring period.

[0088] pH value: Due to the strong metabolic activity of the tumor, the pH value is low, assumed to be pH(t) = 6.8.

[0089] Local microvascular region volume: The microvascular volume in the tumor region is V local = 10 mL.

[0090] Local oxygenation coefficient: For the tumor region with weak oxygenation ability, set C local = 0.5.

[0091] Local metabolic parameter: The tumor region is metabolically active, M local = 1.2.

[0092] Based on these parameters, they can be substituted into the personalized decoding function for calculation. First, calculate each integral term:

[0093]

[0094] Next, set the weighting coefficients as:

[0095] α 1 = 0.4

[0096] α 2 = 0.3

[0097] α 3 = 0.3

[0098] Therefore, the result of the personalized decoding function is:

[0099] Φ(X,P) = 0.4×6.5 + 0.3×1200 + 0.3×56.7 = 2.6 + 360 + 17.01 = 379.61

[0100] Based on the calculation result, the personalized decoding function Φ(X,P) = 379.61. This value reflects the microcirculation state of the tumor region. A higher decoding value indicates the characteristics of increased blood flow and decreased oxygenation in the tumor region, which are usually related to angiogenesis and malignant metabolic activities of the tumor. Therefore, based on this decoding result, the system can decide to increase the drug release rate to meet the high metabolic demand and abnormal vascular permeability in the tumor region.

[0101] During the treatment of the patients in this embodiment, an integrated sensor array is used to monitor their local microcirculation data in real time, including blood flow, oxygenation, and pH value in the tumor area. These data provide real-time feedback and can help optimize drug delivery. A multi-dimensional dynamic prediction model is developed to evaluate the drug absorption capacity of local tissues. The model considers the interactions between physiological parameters such as blood flow, oxygenation, and pH value, and captures the changing trend of microvascular permeability over time. The prediction formula is expressed as:

[0102]

[0103] P local (t) represents the drug absorption capacity of local tissues, referring to the efficiency or ability of tissues to absorb drugs; F(t) is the real-time blood flow, reflecting the speed and volume of blood flow; O(t) is the real-time oxygenation, indicating the oxygenation level of tissues; pH(t) is the real-time pH value. The change in pH value is associated with tissue metabolism and is a key indicator for evaluating tissue metabolic status and microvascular permeability;

[0104] γ 1 ,γ 2 ,γ 3 are weighting coefficients, respectively representing the influence degrees of blood flow, oxygenation, and pH value on drug absorption capacity; γ 4 is the attenuation coefficient, controlling the attenuation rate of microvascular permeability; e -δt represents the attenuation trend of vascular permeability over time, reflecting the dynamic changes of drug absorption.

[0105] In this example, based on the aforementioned monitoring data, the following specific parameters are selected:

[0106] Blood flow: Assume that the blood flow in the tumor area is F(t) = 7 mL / s;

[0107] Oxygenation: The oxygenation level in the tumor area is relatively low, and O(t) = 50% is set;

[0108] pH value: Since the tumor metabolism is active, the pH value is relatively low, and pH(t) = 6.9 is set;

[0109] Weighting coefficients: Assume that the weighting coefficients are set according to pathological data and physiological research as:

[0110] γ 1 = 0.5 (blood flow has a greater influence on drug absorption)

[0111] γ 2 = 0.3 (oxygenation has a medium influence on drug absorption)

[0112] γ3 = 0.2 (The influence of pH value on drug absorption is relatively small);

[0113] Attenuation coefficient: Set the attenuation rate of microvascular permeability to γ 4 = 0.1, this value reflects the attenuation rate of the drug in the microvessels over time; Time attenuation parameter: Set the time attenuation parameter to δ = 0.05.

[0114] Now substitute the above data into the prediction formula to calculate the drug absorption capacity P local (t) of the local tissue. First, calculate the numerator part:

[0115] γ 1 ·F(t) + γ 2 ·O(t) + γ 3 ·pH(t) = 0.5·7 + 0.3·50 + 0.2·6.9 = 3.5 + 15 + 1.38 = 19.88

[0116] Next, calculate the attenuation part:

[0117] 1 + γ 4 ·e -δt = 1 + 0.1·e -0.05·t

[0118] Assume the calculation is carried out at the 5th minute (t = 5 minutes) of monitoring:

[0119] e -0.05·5 = e -0.25 ≈ 0.7788

[0120] So the attenuation term is:

[0121] 1 + 0.1·0.7788 = 1 + 0.07788 = 1.07788

[0122] Finally, calculate the drug absorption capacity of the local tissue:

[0123]

[0124] Through calculation, the drug absorption capacity of the local tissue is obtained as 18.47. This value indicates that the microvascular permeability, blood flow, oxygenation, and pH value in the tumor region have a significant impact on drug absorption. During the treatment process, the drug absorption capacity changes dynamically with time. When the blood flow in the tumor region increases, oxygenation improves, or the pH value is regulated, the drug absorption capacity may increase accordingly.

[0125] Example 2:

[0126] Combined with the attached Figure 3As shown in the process, for a patient with lung cancer, there is abnormal angiogenesis in the tumor area of the patient, resulting in increased microvascular permeability in this area. It is hoped to optimize drug delivery to ensure that the drug can accurately reach the tumor area and exert its therapeutic effect. To achieve this goal, a spatio-temporal analysis method of microcirculation drug interaction is adopted, combined with real-time blood flow, oxygenation and pH data, and the interaction between drug molecules and blood vessel walls and blood components is dynamically simulated.

[0127] The interaction force (F) between drug molecules and blood vessel walls is described by the following formula:

[0128]

[0129] The meanings of each parameter in the formula:

[0130] F is the interaction force between drug molecules and blood vessel walls, indicating the intensity of drug molecules attaching to or penetrating the blood vessel walls. It reflects whether drug molecules can effectively penetrate the blood vessel walls or attach to the inner wall of blood vessels, thereby affecting drug absorption. γ is the coefficient of contact between drug molecules and blood vessel walls, reflecting the affinity between the surface of drug molecules and blood vessel walls. This coefficient is usually based on the physicochemical properties of drugs, such as molecular weight, surface charge, etc. β is the influence coefficient of blood components on drug diffusion, comprehensively considering the influence of components such as red blood cells, white blood cells, and plasma proteins in blood on drug diffusion. This coefficient can describe the viscosity of blood and its influence on drug diffusion. v is the local blood flow velocity, reflecting the influence of blood flow velocity on the movement of drugs in blood vessels. Blood flow velocity affects the diffusion and transport speed of drugs, and thus determines the distribution of drugs in microvessels.

[0131] Based on the real-time data of the patient, the following monitoring results are assumed to be obtained:

[0132] Blood flow velocity: The local blood flow velocity in the tumor area of the patient is v = 1.2 cm / s (according to the data of the blood flow velocity monitoring device). Coefficient of contact between drug and blood vessel wall: Since the drug molecule is hydrophilic, it is assumed that the affinity coefficient between the drug and the blood vessel wall is γ = 0.8 (assumed value, based on the physicochemical properties of the drug).

[0133] Influence coefficient of blood components on drug diffusion: According to the blood components in the tumor area, β = 0.5 is set (assumed value, indicating medium influence of plasma proteins and red blood cells on drug diffusion).

[0134] Based on the above data, these values are substituted into the interaction force model for calculation:

[0135]

[0136] First, calculate the denominator part:

[0137] 1 + 0.5·1.2 = 1 + 0.6 = 1.6

[0138] Then calculate the interaction force:

[0139]

[0140] Through calculation, the interaction force between the drug molecule and the blood vessel wall is obtained as 1.3. This value indicates that there is a medium-strength interaction between the drug molecule and the blood vessel wall, and the drug can effectively penetrate the blood vessel wall, but this penetration effect is subject to certain limitations. In the tumor area, the abnormal proliferation and permeability changes of blood vessels may cause changes in the interaction force between the drug molecule and the blood vessel wall, thereby affecting the absorption and diffusion of the drug. This result provides valuable information for optimizing drug delivery. During the treatment process, the drug release rate can be adjusted according to the interaction force between the drug and the blood vessel wall. For example, when the local blood flow velocity is slow, the drug release amount can be increased to ensure that the drug can penetrate the blood vessel wall in the tumor area and reach an effective concentration in the tumor tissue.

[0141] In this embodiment, in order to improve the therapeutic effect of the drug, the doctor needs to optimize the drug delivery plan through microcirculation dynamics modeling to ensure that the drug can efficiently cross the microvessels and accumulate in the tumor area. By combining a dynamic microfluidic model to simulate the distribution of the drug in different types of blood vessels such as arteries, capillaries, and veins, the concentration change of the drug in the tumor area can be predicted, and the drug release strategy can be adjusted in real time. To simulate the distribution of the drug in the microvessels, the following formula is used to describe the change of the drug concentration with time and position:

[0142]

[0143] The meanings of the parameters in the formula:

[0144] C(r,t) is the concentration of the drug at time t and position r, indicating the distribution of drug molecules in the blood vessel at a certain time point. C 0 is the initial concentration, indicating the concentration of the drug when it enters the bloodstream. For example, assuming the drug concentration is 10 mg / ml, it represents the concentration of the drug when it is just injected into the blood. r is the blood vessel position, indicating the propagation position of the drug molecule in the microvessel. Here, r is the distance relative to the center of the blood vessel. v is the local blood flow velocity, reflecting the influence of the blood flow velocity on the propagation path of the drug. For example, the blood flow velocity of an artery may be v = 5 cm / s, while the blood flow velocity of a capillary may be v = 0.1 cm / s. k is a constant related to blood vessel permeability and drug diffusion, describing the diffusion characteristics of the drug in a specific type of blood vessel. Different types of blood vessels will have different k values. For example, the k values of arteries and capillaries are different. Assume that k in the artery is 0.2 cm 2 / s, and in the capillaries, k = 0.05 cm 2 / s. t is the time, and the drug distribution changes over time. The drug concentration will continuously decay over time.

[0145] In this example, it is assumed that the patient's tumor is located in the central part of the right lung. Through a real-time monitoring device, the following data was obtained:

[0146] Initial drug concentration: C 0 = 10 mg / ml.

[0147] Vessel position: The vessel position in the tumor area is set to r = 1 mm (1 millimeter away from the center of the vessel).

[0148] Blood flow velocity: The arterial blood flow velocity in the tumor area is v = 5 cm / s, and the blood flow velocity in the capillary area is v = 0.1 cm / s. Drug diffusion constant: The drug diffusion constant in the arterial area is set to k = 0.2 cm 2 / s, and the drug diffusion constant in the capillary area is set to k = 0.05 cm 2 / s. Time: The calculated time is set to t = 10 min = 600 seconds.

[0149] Substitute these parameters into the drug concentration distribution formula for calculation. First, calculate the drug concentration distribution in the arterial area:

[0150]

[0151] Calculate the denominator part:

[0152]

[0153] Then calculate the drug concentration:

[0154]

[0155] This result shows that in the artery, after 600 seconds, the drug concentration has almost completely decayed.

[0156] Next, calculate the drug concentration distribution in the capillary area:

[0157]

[0158] Calculate the denominator part:

[0159]

[0160] Then calculate the drug concentration:

[0161]

[0162] The calculation results show that the concentration of the drug in the capillaries is almost zero.

[0163] From the calculation results of the drug concentration in these two regions, it can be seen that the drug decays rapidly in the artery, while the drug concentration in the capillaries is almost zero. This is because the blood flow velocity in the artery is relatively fast, which greatly limits the diffusion of the drug in the artery, and the blood flow velocity in the capillaries is slower, making it almost impossible to maintain the drug concentration in the capillaries.

[0164] In actual treatment, doctors can adjust the drug release strategy based on these results. For example, at the capillaries in the tumor region, the drug release rate needs to be increased to ensure that the drug can effectively diffuse in the slow blood flow and reach the target area. In the artery region, since the drug passes through quickly, the drug concentration may drop significantly before entering the tumor region. Therefore, more precise delivery methods need to be adopted, such as local high-concentration drug delivery or enhancing drug absorption by changing vascular permeability.

[0165] In this embodiment, to improve the efficiency of drug treatment, doctors hope to adjust the drug release strategy in real time through a dynamic drug diffusion model based on microcirculation to ensure that the drug can quickly diffuse in the target area and achieve the maximum therapeutic effect.

[0166] The dynamic process of drug diffusion can be represented by the following equation:

[0167] D(t) = D 0 ·e -αt

[0168] The meanings of the parameters in the formula are as follows:

[0169] D(t) is the diffusion coefficient of the drug at time t, indicating the diffusion ability of the drug in the microvessels. As time goes by, the diffusion rate of the drug will gradually change.

[0170] D 0 is the initial diffusion coefficient, indicating the diffusion rate of the drug when it first enters the microcirculation. For example, assume that the initial diffusion coefficient D 0 is 1.0 cm 2 / s, which means that the drug molecules diffuse very rapidly when they first enter the blood vessels.

[0171] α is the attenuation coefficient, reflecting the influence of factors such as vascular permeability and blood flow velocity on drug diffusion. Different types of blood vessels have different diffusion attenuation characteristics. Assume that the vascular permeability in the liver region is relatively high and the blood flow velocity is slower, then α = 0.031 / s can be set, indicating that the attenuation rate of drug diffusion is slower. t is the time, representing the time for the drug to diffuse in the blood vessels, with the unit of seconds.

[0172] Through real-time monitoring devices, the doctor obtained the following data:

[0173] Initial drug diffusion coefficient: D 0 = 1.0 cm 2 / s.

[0174] Influence coefficient of vascular permeability and blood flow velocity: α = 0.031 / s.

[0175] Time of the target treatment area: The set time is t = 300 seconds (after 5 minutes).

[0176] Substitute these data into the drug diffusion model for calculation. First, calculate the diffusion coefficient of the drug in the microvessels of the liver area. The diffusion coefficient D(t) after 5 minutes:

[0177] D(300) = 1.0·e -0.03·300

[0178] Calculate the exponential part:

[0179] e -0.03·300 = e -9 ≈ 1.234×10 -4

[0180] Then calculate the diffusion coefficient:

[0181] D(300) = 1.0·1.234×10 -4 ≈ 1.234×10 -4 cm 2 / s

[0182] Through calculation, it is found that after 5 minutes, the diffusion ability of the drug in the microvessels has decreased significantly, from the initial D 0 = 1.0 cm 2 / s decreased to D(300) ≈ 1.234×10 -4 cm 2 / s. This change indicates that over time, the diffusion ability of the drug in the microvessels gradually weakens, mainly due to changes in vascular permeability and the influence of blood flow velocity.

[0183] Based on this calculation result, the doctor can monitor the diffusion process of the drug in real time and adjust the drug release strategy according to the actual situation of drug diffusion. If the diffusion ability of the drug decreases too quickly in some areas, it may be necessary to adjust the drug release rate or improve the local vascular permeability through a drug enhancer. In addition, if the diffusion speed significantly slows down in the microvessel area near the tumor, the doctor can consider optimizing the drug distribution through local microfluidic techniques, such as enhancing blood flow or changing vascular permeability.

[0184] If more data in the patient's body are further collected, doctors can simulate different vascular and tissue states by adjusting the attenuation coefficient α. For example, assuming that the permeability of certain blood vessels is particularly high, a smaller attenuation coefficient α can be set so that the drug diffuses more slowly in these areas, thereby maintaining the drug concentration for a longer time. For those vascular regions with slower blood flow and poor permeability, the attenuation coefficient α may increase, thus accelerating the weakening of the drug diffusion ability.

Claims

1. Drug delivery optimization method based on microcirculatory dynamics modeling, characterized by The following steps are involved: S1. Microcirculatory blood flow dynamic modeling and drug delivery mechanism: S1.

1. Establish a comprehensive model of coupled hemodynamics and drug delivery based on the microcirculatory characteristics of individual patients, including vascular distribution, blood flow velocity, and vascular elasticity; S1.2, the comprehensive model combines the physicochemical properties of drug molecules including solubility, molecular weight, and hydrophilicity with the microenvironment of blood flow including blood flow shear force and microvascular permeability to model the interaction; S2. Real-time monitoring and status decoding of local microcirculatory microenvironment: S2.1, using an integrated sensor array including a blood flow sensor, a tissue oxygenation sensor, and a pH sensor to monitor changes in local microcirculation in real time; S2.2, combining sensor data with pathological information of microvascular abnormalities in the patient, including tumor areas or inflammatory areas, dynamically decoding the state of microcirculation, and evaluating microvascular permeability, local tissue oxygenation, and transient changes in blood flow in real time; S2.3, using state decoding technology to provide real-time feedback of drug delivery; S3. Dynamic adaptation of drug release rate to local microcirculatory feedback: S3.

1. Introduce a dynamic regulation mechanism based on the microcirculatory state, and the drug release rate is associated with the changes in microcirculation; S3.

2. When the local blood flow increases, the drug release rate is increased; when the local blood flow decreases, the release rate is adjusted to avoid excessive drug accumulation; S4. Drug delivery optimization based on microvascular permeability: S4.

1. Develop a permeability prediction model that combines pathological information including tumor angiogenesis characteristics and changes in vascular permeability in inflammatory areas to predict the permeability of drugs through microvessels. S4.

2. Based on the permeability prediction model, optimize the drug delivery pathway and adjust the distribution of drugs in different microvascular structures to increase the local concentration of drugs; S5. Spatiotemporal analysis of microcirculatory drug interactions: S5.

1. Analyze the spatiotemporal distribution characteristics of drugs in the microcirculation, including the interaction between drugs and the vascular wall and blood components; S5.

2. Perform time-series analysis on the distribution of drugs in the bloodstream and track the propagation paths and diffusion speeds of drug molecules in different blood vessel types and at different blood flow velocities.

2. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 1, characterized in that The real-time monitoring and state decoding method of the local microcirculation microenvironment includes: Through local three-dimensional positioning technology, each sensor can synchronously acquire data in the same microvascular area; the sensor array combines optical imaging technology with bioelectric sensor technology; it can penetrate the tissue layer and collect parameters in the microvessel in real time; the signals of different sensors are weighted by the following integral formula: in: S(t) is the integrated signal output by the sensor array, reflecting the integrated information of multiple microcirculation related parameters; F(t) is the real-time blood flow signal, indicating the blood flow in the microvessels; O(t) is the real-time tissue oxygenation signal, indicating the oxygenation level of the tissue, and the change of oxygenation directly affects the absorption capacity of the drug; pH(t) is the real-time pH value signal, and the pH change is related to tissue metabolism and is a key parameter for evaluating tissue metabolism and microvascular permeability; β1, β2, and β3 are weighting coefficients used to adjust the degree of influence of each physiological parameter on the integrated signal; the integration interval τ1 to τ2 represents the accumulation of sensor data within a unit time range to obtain periodic monitoring results.

3. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 2, characterized in that The method for real-time monitoring and state decoding of the local microcirculation microenvironment includes: dynamically decoding real-time sensor data and the patient's pathological information to evaluate the local microcirculation state in real time; taking the changes in the microvascular structure of the tumor area as the key factor for decoding; and combining the sensor data with the pathological data to identify and decode the microcirculation state of different pathological areas in real time.

4. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 3, characterized in that The real-time monitoring and state decoding method of the local microcirculation microenvironment includes: By combining real-time blood flow data, oxygenation data and pH data, a multi-dimensional dynamic prediction model is developed to evaluate the interaction between microvascular permeability, oxygenation and blood flow in real time; capture the changing trend of microvascular permeability over time and evaluate the tissue's absorption capacity of drugs; the dynamic formula for predicting the local tissue drug absorption capacity is expressed as: in: P local (t) is the drug absorption capacity of local tissues, indicating the efficiency or ability of tissues to absorb drugs; F(t) is the real-time blood flow, reflecting the speed and amount of blood flow; O(t) is the real-time oxygenation, indicating the oxygenation level of tissues; pH(t) is the real-time pH value, and the change of pH value is related to tissue metabolism, and is also an indicator for evaluating tissue metabolic status and microvascular permeability; γ1, γ2, and γ3 are weighting coefficients, indicating the degree of influence of different physiological parameters on drug absorption capacity; γ4 is the attenuation coefficient, which controls the attenuation rate of microvascular permeability; e -δt Indicates the attenuation trend of vascular permeability over time.

5. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 1, characterized in that The spatiotemporal analysis method of microcirculatory drug interactions comprises: By combining real-time data, analyzing the permeability, oxygenation and pH changes of the vascular wall, the interaction between the drug and the vascular wall and blood components is dynamically simulated. Including in the tumor area, due to the abnormal proliferation and permeability changes of the blood vessels, the drug molecules will have different diffusion and adsorption behaviors. The interaction force F between the drug and the vascular wall is described by the following model: in: F is the interaction force between the drug molecule and the blood vessel wall, which indicates the strength of the drug molecule's attachment or penetration into the blood vessel wall; γ is the contact coefficient between the drug molecule and the blood vessel wall, which reflects the affinity between the drug molecule surface and the blood vessel wall; β is the influence coefficient of blood components on drug diffusion, which comprehensively includes the effects of red blood cells, white blood cells, and plasma proteins on drug diffusion; v is the local blood flow velocity, which reflects the effect of blood flow velocity on the movement of drugs in blood vessels.

6. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 5, characterized in that The spatiotemporal analysis method of microcirculatory drug interactions comprises: By establishing a dynamic microfluidic model and timing analysis technology, combined with real-time blood flow velocity, blood vessel shape and drug diffusion parameters, the distribution of drugs in different types of blood vessels is simulated; including the influence of different blood vessel types such as arteries, capillaries and veins and the different blood flow velocities including high-flow arteries and low-flow capillaries on the diffusion characteristics of drugs; real-time tracking of the propagation path of drug molecules in the microcirculation, and dynamic prediction of the time and concentration of drugs reaching the target tissue.

7. The drug delivery optimization method based on microcirculation dynamics modeling according to claim 6, characterized in that The spatiotemporal analysis method of microcirculatory drug interactions comprises: By real-time monitoring of microvascular blood flow velocity, permeability, oxygenation and other multi-dimensional data, the drug diffusion model is dynamically adjusted to predict the drug diffusion velocity and distribution characteristics according to changes in the local environment; molecular dynamics simulation simulates the movement of drug molecules in the blood, and the dynamic process of drug diffusion is tracked in real time and provides feedback for the drug delivery system; the dynamic velocity of drug diffusion is represented by the following model: D(t)=D0·e -αt in: D(t) is the drug diffusion coefficient at time t, which indicates the diffusion ability of the drug in the microvessels; D0 is the initial diffusion coefficient, which indicates the diffusion rate of the drug when it just enters the microcirculation; α is the attenuation coefficient related to vascular permeability and blood flow velocity, which reflects the influence of local vascular characteristics on drug diffusion; t is time, and the diffusion rate of the drug in the microvessels changes with the passage of time.

Images