A method, system and electronic device for determining parameters of a tumor microenvironment-responsive biomimetic nanodrug delivery

Abstract

The application discloses a tumor microenvironment response bionic nanodrug delivery parameter determination method, system and electronic equipment. The method obtains microenvironment characteristic data of a target tumor of a patient; a relative distribution coefficient is calculated based on receptor protein abundance and uniformity, a density correction factor is determined in combination with an affinity constant, and the best ligand distribution density of a bionic camouflage layer and the best thickness of a shell are obtained; the stimulation weight and response potential of a sensitive key are calculated based on intracellular pH value and ROS concentration of the tumor, and the molar mass proportion of the pH-sensitive key and the ROS-sensitive key is determined; the single and double stimulation rupture rates are calculated based on the molar mass proportion, and the cross-linking density range of the carrier skeleton is determined; and the nanodrug delivery parameters containing the above parameters are output to guide the preparation of the bionic nanodrug. The application realizes explosive and accurate release of the drug in tumor cells, improves the killing efficiency of heterogeneous tumors and reduces the risk of drug resistance.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of artificial intelligence and medical data processing, specifically to a data-driven method, system, and electronic device for determining parameters of biomimetic nanodrug delivery based on tumor microenvironment response. Background Technology

[0002] Existing technologies disclose a ROS-responsive nanomedicine delivery system that relies solely on a single exogenous ROS stimulus (photoexcitation) to trigger drug release. Another existing technology discloses a fused vesicle nanomedicine delivery system that uses exogenous photothermal stimulation to trigger the disintegration of a single thermosensitive liposome to release the drug; its drug release behavior is highly dependent on externally applied light conditions. Human tumors generally exhibit high heterogeneity. During drug delivery, the slightly acidic environment of some patients' tumors may be insensitive to targeted drug delivery. That is, a single stimulus response through the tumor region often leads to slow and incomplete drug release. This delivery method cannot provide sustained and precise targeted drug administration and easily induces drug resistance in cancer cells, which is detrimental to tumor treatment. Summary of the Invention

[0003] This application discloses a method, system, and electronic device for determining tumor microenvironment-responsive biomimetic nanomedicine delivery parameters, aiming to solve the technical problems of insufficient sensitivity of existing single-response nanocarriers to the tumor microenvironment and low drug release efficiency.

[0004] To achieve the above objectives, the first aspect of this application provides a method for determining tumor microenvironment-responsive biomimetic nanomedicine delivery parameters, comprising: The microenvironmental characteristics data of the patient's target tumor are obtained, including the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor. Based on the absolute abundance and distribution uniformity of the receptor proteins on the surface of the tumor cells, and the average abundance and distribution uniformity of free receptors, the relative distribution coefficient of the receptor proteins is calculated; combined with the affinity constant between the ligand proteins and the receptors, the density correction factor of the ligand proteins is determined; based on the density correction factor, the density of the highest ligand protein is reduced to obtain the optimal ligand distribution density of the biomimetic camouflage layer. Based on the ratio of the optimal ligand distribution density to the highest ligand protein density, and combined with the minimum modification thickness of the biomimetic camouflage layer, the optimal shell thickness of the biomimetic camouflage layer is calculated. Based on the intracellular pH and ROS concentration of the tumor cells, the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds are calculated respectively; combined with the inherent degradation rates of the pH-sensitive bonds and ROS-sensitive bonds, their respective stimulation response potentials are calculated; based on the negative correlation between the stimulation response potentials, the molar mass ratios of the pH-sensitive bonds and ROS-sensitive bonds are calculated. Based on the molar mass ratio, the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation are calculated, and the lower and upper limits of the crosslinking density of the carrier skeleton are determined by combining the basic gelation critical density of unbroken bonds. The output includes nanomedicine delivery parameters such as the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range, to guide the preparation of biomimetic nanomedicines.

[0005] Optionally, calculating the relative distribution coefficient of the receptor protein based on the absolute abundance and distribution uniformity of the receptor protein on the surface of the tumor cells, and the average abundance and distribution uniformity of the free receptor, includes: Calculate the first ratio of the absolute abundance of tumor cell surface receptor proteins to the average abundance of free receptors, and the second ratio of the distribution uniformity of tumor cell surface receptor proteins to the distribution uniformity of free receptors. Multiplying the first ratio by the second ratio yields the relative distribution coefficient of the receptor protein; The step of determining the density correction factor of the ligand protein based on the affinity constant between the ligand protein and the receptor includes: Calculate the ratio of the relative distribution coefficient of the receptor protein to the affinity constant, and normalize it to obtain the density correction factor of the ligand protein with a value in the range of [0, 1]. The step of reducing the density of the highest ligand protein based on the density correction factor to obtain the optimal ligand distribution density of the biomimetic camouflage layer includes: The optimal ligand distribution density of the biomimetic camouflage layer is obtained by multiplying the highest ligand protein density by the density correction factor.

[0006] Optionally, the step of calculating the optimal shell thickness of the biomimetic camouflage layer based on the ratio of the optimal ligand distribution density to the highest ligand protein density, combined with the minimum modification thickness of the biomimetic camouflage layer, includes: The ratio of the difference between the optimal ligand distribution density and the highest ligand protein density is calculated as the degree of ligand density reduction. Based on the minimum modified thickness of the biomimetic camouflage layer, the degree of reduction in ligand density is used as an expansion weight to calculate the thickness increase, thereby obtaining the optimal shell thickness of the biomimetic camouflage layer, so that the shell thickness is positively correlated with the degree of reduction in ligand density.

[0007] Optionally, the step of calculating the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds based on the intracellular pH and ROS concentration of the tumor cells includes: The stimulation weight of pH-sensitive bonds is calculated based on the difference between the pH value of normal blood and the pH value of tumor tissue fluid. The stimulation weight of ROS-sensitive bonds is calculated based on the difference between the ROS concentration in tumor cells and the ROS concentration in normal cells. The step of calculating the respective stimulus response potential by combining the inherent degradation rates of the pH-sensitive bond and the ROS-sensitive bond includes: The stimulus response potential of the pH-sensitive bond is obtained by multiplying its stimulus weight by its normalized intrinsic degradation degree; the stimulus response potential of the ROS-sensitive bond is obtained by multiplying its stimulus weight by its normalized intrinsic degradation degree.

[0008] Optionally, the calculation of the molar mass ratio of the pH-sensitive bond to the ROS-sensitive bond based on the negative correlation between the stimulus response potential includes: The molar mass percentage of the pH-sensitive bond was calculated by taking the stimulus response potential of the ROS-sensitive bond as the numerator and the sum of the stimulus response potentials of the two sensitive bonds as the denominator. The molar mass percentage of ROS-sensitive bonds was calculated by using the stimulus response potential of the pH-sensitive bond as the numerator and the sum of the stimulus response potentials of the two sensitive bonds as the denominator, in order to inhibit carrier depolymerization under a single stimulus.

[0009] Optionally, calculating the maximum breakage rate of a single stimulus based on the molar mass percentage includes: Compare the molar mass ratios of the pH-sensitive bonds and the ROS-sensitive bonds, and take the maximum value of the two as the maximum breakage rate under single stimulation; The step of determining the lower limit of crosslinking density of the carrier skeleton based on the basic gelation critical density of the unbroken bonds includes: Based on the complement ratio between the basic gelation critical density and the maximum breakage rate under a single stimulus, the lower limit of the crosslinking density of the carrier skeleton is calculated to ensure that the crosslinking density of the remaining unbroken bonds under a single stimulus is greater than the basic gelation critical density.

[0010] Optionally, the calculation of the dual-stimulus synergistic breakage rate includes: The first product is obtained by multiplying the molar mass percentage of the pH-sensitive bond with its breaking efficiency in the tumor cell microenvironment; the second product is obtained by multiplying the molar mass percentage of the ROS-sensitive bond with its breaking efficiency in the tumor cell microenvironment. Adding the first product to the second product yields the dual-stimulus synergistic breakage rate; The step of determining the upper limit of the crosslinking density of the carrier skeleton based on the basic gelation critical density of the unbroken bonds includes: Based on the complement ratio between the basic gelation critical density and the dual-stimulation synergistic breakage rate, the upper limit of the crosslinking density of the carrier skeleton is calculated to ensure that the crosslinking density of the remaining unbroken bonds under dual stimulation is less than the basic gelation critical density.

[0011] Optionally, the output includes nanomedicine delivery parameters such as the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range to guide the preparation of biomimetic nanomedicines, including: Output the molar mass ratio of the pH-sensitive bond to the ROS-sensitive bond, as well as the crosslinking density range between the lower and upper limits of the crosslinking density; Based on the molar mass ratio and crosslinking density range, a drug preparation control instruction is generated. The control instruction is used to control the weighing of monomers containing pH-sensitive bonds, monomers containing ROS-sensitive bonds, and crosslinking agents to carry out copolymerization reactions and construct a nanomicelle framework. Based on the optimal ligand distribution density and the optimal shell thickness, a camouflage layer modification instruction is generated. This camouflage layer modification instruction is used to control the grafting of PEG with a specific molecular weight or to encapsulate homologous cancer cell membranes, thereby completing the assembly of biomimetic nanomedicines.

[0012] To achieve the above objectives, a second aspect of this application also provides a tumor microenvironment-responsive biomimetic nanomedicine delivery parameter determination system, comprising: The data acquisition module is used to acquire microenvironmental characteristic data of the patient's target tumor. The microenvironmental characteristic data includes the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor. The outer layer parameter calculation module is used to calculate the relative distribution coefficient of receptor proteins based on the absolute abundance and distribution uniformity of the receptor proteins on the surface of the tumor cells, as well as the average abundance and distribution uniformity of free receptors; determine the density correction factor of the ligand proteins by combining the affinity constant between the ligand proteins and the receptors; reduce the density of the highest ligand protein based on the density correction factor to obtain the optimal ligand distribution density of the biomimetic camouflage layer; and calculate the optimal shell thickness of the biomimetic camouflage layer based on the ratio of the optimal ligand distribution density to the highest ligand protein density and the minimum modification thickness of the biomimetic camouflage layer. The middle-layer parameter calculation module is used to calculate the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds based on the intracellular pH and ROS concentration of the tumor cells, respectively; calculate their respective stimulation response potentials by combining the inherent degradation rates of the pH-sensitive bonds and ROS-sensitive bonds; calculate the molar mass ratio of the pH-sensitive bonds and ROS-sensitive bonds based on the negative correlation mapping relationship of the stimulation response potentials; and calculate the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation based on the molar mass ratios, and determine the lower and upper limits of the crosslinking density of the carrier backbone by combining the basic gelation critical density of unbroken bonds. The output control module is used to output nanomedicine delivery parameters including the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range, so as to guide the preparation of biomimetic nanomedicines.

[0013] To achieve the above objectives, a third aspect of this application also provides an electronic device, comprising: at least one processor and a memory; The memory stores a computer program, and the at least one processor executes the computer program to implement the method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as previously provided.

[0014] This application discloses a method, system, and electronic device for determining parameters of tumor microenvironment-responsive biomimetic nanomedicine delivery, including: acquiring microenvironmental characteristic data of the target tumor in a patient, the microenvironmental characteristic data including the absolute abundance and distribution uniformity of receptor proteins on the tumor cell surface, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor; calculating the relative distribution coefficient of receptor proteins based on the absolute abundance and distribution uniformity of receptor proteins on the tumor cell surface and the average abundance and distribution uniformity of free receptors; determining the density correction factor of ligand proteins by combining the affinity constant between ligand proteins and receptors; reducing the density of the highest ligand protein based on the density correction factor to obtain the optimal ligand distribution density of the biomimetic camouflage layer; and determining the optimal ligand distribution density and the density of the highest ligand protein based on the optimal ligand distribution density and the density of the highest ligand protein. The optimal shell thickness of the biomimetic camouflage layer is calculated by combining the ratio with the minimum modification thickness of the biomimetic camouflage layer. The stimulation weights of pH-sensitive and ROS-sensitive bonds are calculated based on the intracellular pH and ROS concentration of the tumor cells. The inherent degradation rates of pH-sensitive and ROS-sensitive bonds are combined to calculate their respective stimulation response potentials. Based on the negative correlation between stimulation response potentials, the molar mass ratio of pH-sensitive and ROS-sensitive bonds is calculated. Based on the molar mass ratio, the maximum breakage rate for single stimulation and the synergistic breakage rate for dual stimulation are calculated. Combined with the basic gelation critical density of unbroken bonds, the lower and upper limits of the crosslinking density of the carrier framework are determined. The output includes nanomedicine delivery parameters containing the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range to guide the preparation of biomimetic nanomedicines. This application utilizes the tumor microenvironment to deliver nanomedicine molecules through a dual-layer stimulation response mechanism inside and outside tumor cells, achieving explosive and precise drug release within tumor cells, improving the killing efficiency against heterogeneous tumors and reducing the risk of drug resistance. The thickness of the carrier shell is adaptively determined based on individual patient tumor microenvironment characteristics data. This feature adaptively optimizes the ligand density of the nanocarrier by collecting the absolute abundance and distribution uniformity of receptors on the patient's tumor cells. This ensures the stability of the drug structure in the bloodstream and its targeted accumulation at the tumor site, guaranteeing the specific binding of the drug to the receptor. The molar ratio of the two sensitive bonds and the cross-linking density are adaptively determined based on the relative stimulation level and chemical degradation sensitivity of the tumor microenvironment. The optimal molar ratio of acidic sensitive bonds to ROS sensitive bonds is calculated and set using the actual concentration differences between the microacidity and highly reactive oxygen species (ROS) within the patient's tumor, and the cross-linking density is determined based on this optimal molar ratio. This allows the carrier to accurately sense and synergistically respond to dual endogenous stimuli in the tumor microenvironment, avoiding false releases caused by absolute dependence on a single stimulus and improving the burst precision of drug release. Attached Figure Description

[0015] Figure 1This is a flowchart illustrating an embodiment of the data-driven method for determining parameters of biomimetic nanomedicine delivery based on tumor microenvironment response in this application. Figure 2 This is a schematic diagram of an embodiment of the data-driven method for determining parameters of biomimetic nanomedicine delivery based on tumor microenvironment response in this application. Figure 3 This is a schematic diagram of the packaging model of the biomimetic nanomedicine provided in an embodiment of the data-driven tumor microenvironment response biomimetic nanomedicine delivery parameter determination method of this application.

[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0018] The main solution of this application embodiment is: based on the tumor microenvironment, nanomedicine molecules are delivered and released by setting a dual-layer stimulation response inside and outside tumor cells, thereby achieving explosive and precise release of drugs in tumor cells, improving the killing efficiency against heterogeneous tumors and reducing the risk of drug resistance. The thickness of the carrier shell is adaptively determined based on individual patient tumor microenvironment characteristics data. This feature is achieved by collecting the absolute abundance and distribution uniformity of receptors on the patient's tumor cells, and adaptively optimizing the ligand density of the nanocarrier to ensure the stability of the drug structure in blood circulation and its targeted accumulation ability at the tumor site, thereby ensuring the specific binding of the drug to the receptor.

[0019] The molar ratio of two sensitive bonds and the crosslinking density are adaptively determined based on the relative stimulation level and chemical degradation sensitivity of the tumor microenvironment. The optimal molar ratio of acidic sensitive bonds to ROS-sensitive bonds is calculated and set using the actual concentration differences between microacidity and highly reactive oxygen species (ROS) within the patient's tumor, and the crosslinking density is determined based on this optimal molar ratio. This allows the carrier to accurately sense and synergistically respond to dual endogenous stimuli in the tumor microenvironment, avoiding false releases caused by absolute dependence on a single stimulus and improving the burst precision of drug release.

[0020] In the prior art, patent application CN105617379B discloses a ROS-responsive nanomedicine delivery system, which relies solely on a single exogenous ROS stimulus to trigger drug release. When the endogenous ROS concentration is insufficient or unevenly distributed, drug release is slow and incomplete, easily inducing drug resistance in cancer cells. Patent application CN115919778A discloses a fused vesicle nanomedicine delivery system, which uses exogenous photothermal stimulation to trigger the disintegration and drug release of a single thermosensitive liposome. The drug release behavior is highly dependent on the externally applied light conditions, and is limited by issues such as light penetration depth and uneven distribution of photothermal agents at the tumor site, resulting in uncontrollable drug release. Furthermore, the single thermosensitive mechanism cannot cope with the heterogeneity of non-thermal factors in the tumor microenvironment. Existing single-response nanocarriers have insufficient sensitivity to the tumor microenvironment, low drug release efficiency, and cannot provide sustained and precise targeted drug delivery, easily inducing drug resistance in cancer cells.

[0021] refer to Figure 1 and Figure 2 The first embodiment of this application provides a method for determining tumor microenvironment-responsive biomimetic nanomedicine delivery parameters to solve the technical problems of insufficient sensitivity of existing single-response nanocarriers to the tumor microenvironment and low drug release efficiency mentioned in the background art. This method can be executed by a processor, which can be set in a terminal or a server. The execution process of the method for determining tumor microenvironment-responsive biomimetic nanomedicine delivery parameters can be as follows: Step S101: Obtain microenvironmental characteristic data of the patient's target tumor. The microenvironmental characteristic data includes the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor.

[0022] Step S102: Based on the absolute abundance and distribution uniformity of receptor proteins on the surface of tumor cells, and the average abundance and distribution uniformity of free receptors, calculate the relative distribution coefficient of receptor proteins; combine the affinity constant between ligand proteins and receptors to determine the density correction factor of ligand proteins; based on the density correction factor, reduce the density of the highest ligand protein to obtain the optimal ligand distribution density of the biomimetic camouflage layer.

[0023] Step S103: Based on the ratio of the optimal ligand distribution density to the highest ligand protein density, and combined with the minimum modification thickness of the biomimetic camouflage layer, the optimal shell thickness of the biomimetic camouflage layer is calculated.

[0024] Step S104: Based on the intracellular pH value and intracellular ROS concentration of the tumor cells, calculate the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds respectively; combine the inherent degradation degree of pH-sensitive bonds and ROS-sensitive bonds to calculate their respective stimulation response potentials; based on the negative correlation between stimulation response potentials, calculate the molar mass ratio of pH-sensitive bonds and ROS-sensitive bonds.

[0025] Step S105: Based on the molar mass ratio, calculate the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation, and combine the basic gelation critical density of unbroken bonds to determine the lower and upper limits of the crosslinking density of the carrier skeleton.

[0026] Step S106 outputs nanomedicine delivery parameters including optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range to guide the preparation of biomimetic nanomedicines.

[0027] In one embodiment of this application, to improve the targeted delivery and release efficiency of the drug, a dual stimulation response both inside and outside tumor cells is employed for delivery and release. The extracellular stimulation primarily addresses the problem of drug leakage before reaching the target, while the intracellular dual stimulation mainly improves the burst precision of drug release. Therefore, different carrier media are needed for intracellular and extracellular recognition. This embodiment sets up a biomimetic nanomedicine packaging model as follows: Figure 3 As shown.

[0028] Outer layer (bionic camouflage layer): The outermost layer of the model is wrapped with a bionic shell or cell membrane that is only sensitive to tumor cells and can evade immune clearance and achieve homologous targeting. When it enters the tumor cells, the shell detaches.

[0029] Middle layer (dual-response framework): The model carrier framework is a three-dimensional network composed of both pH-sensitive and ROS-sensitive bonds as cross-linking nodes. When both respond simultaneously, the carrier framework undergoes an "avalanche-like" depolymerization, releasing the drug.

[0030] Kernel (drug delivery pool): This refers to the drug itself and sets the physical encapsulation parameters for the chemical drug.

[0031] Based on the above model, the ability of a drug to achieve targeted and precise release depends primarily on the response of the tumor cell microenvironment to stimuli in the outer and middle layers of the model. Therefore, data collection based on the tumor cell microenvironment is necessary. For the outer layer, the absolute abundance and uniformity of tumor cell receptor proteins are obtained by sampling and monitoring patients. This determines whether the drug can target the tumor region and whether it can penetrate the tumor cells.

[0032] For the middle layer, since extreme triggering parameters and a leakage-proof safety baseline are required for the dual-response mechanism, the data to be collected mainly include: extreme pH values ​​of tumor intracellular lysosomes, abnormally high concentrations of reactive oxygen species (ROS) in tumor cells, the inherent degradation degree of the sensitive bonds themselves, and the basic gelation critical density. This ensures the controlled-release stability and targeted killing efficiency of nanomedicine formulations across batches.

[0033] This embodiment focuses on drug delivery effectiveness. By collecting patient-specific data, corresponding stimulus response modes are designed for different levels of adaptation based on this data. This improves the problems of drug delivery deviation and incomplete drug release that exist in the current single stimulus response, and guides and achieves explosive and precise drug release in tumor cells.

[0034] In one embodiment of this application, current nanomedicine release modes are generally based on a single stimulus response, resulting in poor drug penetration into tumors and hindering tumor treatment. Therefore, this solution sets up a dual-layer stimulus response mechanism to improve the accuracy of drug release. This step focuses on the outer biomimetic camouflage layer of the drug packaging model. The main function of the biomimetic camouflage layer is to achieve homologous targeting pairing and ensure that the drug enters the tumor cells, and then detaches to release the drug. Therefore, the relevant parameters need to be set appropriately.

[0035] To make drugs more sensitive to tumor areas and improve their hit rate, specific ligand proteins need to be incorporated into the biomimetic camouflage layer. These specific ligand proteins can match receptor proteins on the surface of tumor cells. However, once the drug enters the body, some free receptors bind to the drug ligands, prematurely consuming the ligands on the drug surface and failing to achieve targeted drug delivery. Therefore, specific modification of the receptor proteins is necessary.

[0036] Since the density of ligand proteins determines their adhesion to receptor proteins, we aim to set an appropriate ligand protein density to ensure strong adhesion when binding to tumor receptors, thereby improving the hit rate. Therefore, we can analyze the differences in receptor characteristics on the surface of patient tumor cells to adaptively determine the ligand protein density. Generally, the abundance of receptor proteins on the surface of tumor cells is greater than that of free receptors, and the uneven distribution of receptors in different regions of the tumor also differs from that of free receptors. Based on the above theory, the following holds true: ; In the formula This represents the relative distribution coefficient of receptor proteins on the surface of tumor cells. The absolute abundance of receptor proteins on the surface of the patient's tumor cells, The average abundance of free receptors. and The distribution uniformity of receptor proteins and free receptor proteins on the surface of tumor cells is determined by comparing their differences in abundance and distribution uniformity. A larger relative distribution coefficient indicates that the receptor proteins on tumor cells are more densely packed than the free receptor proteins, thus requiring a higher density of ligand proteins; conversely, a smaller relative distribution coefficient indicates that a higher density of ligand proteins is not required.

[0037] Meanwhile, since the modification of ligand proteins is done artificially, and there are many types of ligand proteins, the specific ligand protein to be used needs to be determined artificially. Furthermore, different ligand proteins have different affinities for receptor proteins on tumor cells. Therefore, when determining the density of ligand proteins, it is also necessary to consider the affinity of the ligand protein actually used. If the affinity is greater, a higher density is not required to generate a stronger adhesion force, and vice versa, a higher density is required to compensate for the difference in affinity.

[0038] Therefore, for the specific ligand proteins on the biomimetic camouflage layer, the following exists: ; In the formula It is a density correction factor for ligand proteins. This represents the relative distribution coefficient of receptor proteins on the surface of tumor cells. This is the affinity constant between the ligand protein and the receptor. Therefore, the greater the affinity of the ligand protein, the stronger its binding ability to the receptor protein, and the less dense the ligand protein needs to be. Simultaneously, by combining the relative distribution coefficient, which is positively correlated with the ligand density requirement, a density correction factor is obtained, and the result is normalized to the range [0, 1].

[0039] Therefore, for the biomimetic camouflage layer of this drug, the density correction factor of its ligand protein represents the degree of adaptation of this tumor cell receptor to the ligand density. It is important to note that the purpose of setting the ligand is to bind to tumor cells and thus help the drug enter the tumor cell interior. Therefore, a higher ligand density is not necessarily better. If the ligand density is too high, once the drug encounters the first layer of cells surrounding the tumor, it will be tightly bound and immediately engulfed due to the excessive "grabbing power." This will cause the drug to be completely consumed at the tumor edge, completely losing its ability to penetrate deeper. Therefore, in this embodiment, the ligand density is corrected based on the maximum density by reducing it using a density correction factor, thereby maximizing the hit rate. ; In the formula The optimal distribution density of the drug ligand protein is thus determined. This is the highest density of ligand proteins that the biomimetic camouflage layer of this drug can accommodate. It is a density correction factor for ligand proteins.

[0040] because The larger the receptor, the higher its requirement for ligand protein density; therefore, using this as the reduction weight, if... The larger it is, the more it is for The smaller the reduction, the higher the corresponding ligand protein density; conversely, if... The smaller the value, the better. The greater the reduction in density, the lower the density of the corresponding ligand protein. Therefore, the density distribution of ligand proteins in the biomimetic camouflage layer of the drug is adaptively determined based on the characteristics of the tumor cell receptor.

[0041] Then, for the biomimetic camouflage layer, it uses a biomimetic shell or cell membrane as the shell, and the ligand proteins exist in the shell in an embedded manner. Since the existence of the biomimetic camouflage layer needs to ensure that the inner layer material can completely enter the tumor cells, an appropriate particle size needs to be set, and in the biomimetic camouflage layer, the particle size is usually determined by the shell thickness.

[0042] Increasing shell thickness leads to an exponential increase in fluid friction and diffusion resistance within the dense matrix (ECM), hindering rapid drug entry into tumor cells. Conversely, higher ligand density results in stronger adhesion. Therefore, shell thickness (which determines particle size and fluid resistance) and ligand density (which determines adhesion) generally exhibit a negative synergistic relationship. Thus, the corresponding shell thickness can be determined based on the degree of ligand density reduction, as follows: ; In the formula Therefore, the optimal thickness of the shell for the biomimetic camouflage layer of this drug is required. The optimal distribution density of the drug ligand protein is thus determined. This is the highest density of ligand proteins that the biomimetic camouflage layer of this drug can accommodate. This represents the minimum modification thickness achievable for a biomimetic camouflage layer. That is, based on the minimum modification thickness, the degree of ligand density reduction... The thickness of the layer is increased to enhance the weighting. This allows the ligand density of the biomimetic camouflage layer to better match the variation in shell thickness, thereby improving the drug's penetration into tumor cells.

[0043] In one embodiment of this application, protected by the aforementioned biomimetic camouflage layer, the pH value of the biomimetic nanomedicine decreases after it penetrates the target cell and enters the interior. The lipid molecular structure on the outer shell undergoes a phase transition, and the outer shell subsequently dissolves, releasing the internal drug-carrying framework directly into the cytoplasm. It is important to note that the ligands in the biomimetic camouflage layer do not primarily target tumor cells. Therefore, the receptors binding to the biomimetic nanomedicine in vivo may not all be tumor cell receptors. Accidental triggering can also occur in the slightly acidic regions of normal tissues (such as ischemic tissue or inflammatory sites), leading to premature release of chemotherapy drugs in normal organs (such as the liver and heart), potentially causing systemic toxic side effects.

[0044] Therefore, this step employs a dual-stimulation response mechanism on the carrier backbone to ensure precise drug release. Once inside the target tumor cells, the backbone instantly depolymerizes, releasing all the drug within a short time, while other cells do not respond. In the tumor cell microenvironment, the Warburg effect unique to solid tumors and abnormal mitochondrial oxidative stress metabolism make the intracellular lysosomes the only orthogonal signal overlap region in the human body with both low pH and extremely high ROS, and the cleavage reactions of these two factors do not interfere with each other. Therefore, it is necessary to design a mechanism where, only when the pH is low and the ROS concentration is high, both sensitive chemical bonds on the carrier backbone break simultaneously (dual-stimulation response), causing an "avalanche" depolymerization of the backbone and the complete release of the drug within a short time.

[0045] To ensure the operation of the dual-stimulation response mechanism of the carrier backbone, adaptive modification of pH-sensitive bonds (such as acetal / hydrazone bonds) and ROS-sensitive bonds (such as thioacetals) on the carrier backbone is necessary. The more sensitive bonds present, the more intense the response to stimulation, and the faster the corresponding bonds break; conversely, fewer sensitive bonds result in less sensitivity. Since pH and ROS concentrations vary among tumor cells from different patients, the molar ratio of the two sensitive bonds in the carrier backbone must first be adaptively determined based on the pH to ROS concentration ratio within the patient's tumor microenvironment.

[0046] For the tumor cell microenvironment of a patient, the pH value and ROS concentration determine the molar ratio of the two sensitive bonds. Here, the corresponding stimulation weights are first calculated: ; ; In the formula and These represent the pH stimulation weights and ROS stimulation weights in the patient's tumor cells, respectively. and These represent the pH values ​​of normal blood and tumor tissue fluid, respectively. and These represent the ROS concentrations in tumor cells and normal cells, respectively. A higher weighting corresponds to a greater degree of stimulation of the sensitive bonds by the tumor cell microenvironment, making the sensitive bonds more prone to cleavage.

[0047] Furthermore, different sensitive bonds are broken at different rates. For example, under the same "10x stimulation," pH-sensitive bonds (such as hydrazone bonds) may inherently degrade faster than ROS-sensitive bonds (such as thioacetate bonds). Therefore, when determining the molar ratio, it is still necessary to consider the inherent degradation rate of the corresponding sensitive bonds. ,as follows: ; In the formula For a certain sensitive key The potential for stimulus response. For a certain sensitive key Stimulus weights. For a certain sensitive key The intrinsic degradation degree (normalized to the range [0, 1]). Therefore, the greater the intrinsic degradation degree of a sensitive bond, the more sensitive it is to the stimulus and the greater its stimulus response potential; conversely, the smaller the intrinsic degradation degree, the smaller its stimulus response potential.

[0048] To ensure the proper functioning of the dual-stimulation response, the sole determination of complete drug release is entrusted to the synergistic effect of the dual stimuli, rather than the release of the drug upon the rupture of a single sensitive bond under strong stimulation. For any sensitive bond, a higher stimulus-response potential corresponds to a more sensitive response to the tumor microenvironment, thus requiring a lower molar mass; conversely, a lower stimulus-response potential requires a higher molar mass, exhibiting a negative synergistic relationship. Therefore, the adaptive ratio for the corresponding sensitive bonds can be expressed as: ; ; In the formula and These represent the molar mass percentages of pH-sensitive bonds and ROS-sensitive bonds on the backbone of this carrier. and These represent the stimulus response potential of the corresponding sensitive bonds. In other words, to inhibit carrier depolymerization under a single stimulus, the molar copolymerization ratio of the sensitive bonds has an inverse relationship with their overall fracture potential, providing a basis for precise drug release.

[0049] Thus far, the molar ratio of sensitive bonds for the dual stimulus response mechanism has been determined for the middle layer carrier backbone. However, since the molar ratio of sensitive bonds only determines when the drug is fully released, and the drug release behavior requires the cleavage of the carrier backbone composed of sensitive bonds, the distribution characteristics of the carrier backbone in the three-dimensional structure determine the rate of drug release. Therefore, it is also necessary to determine the crosslinking density of the carrier backbone based on different molar ratios of sensitive bonds.

[0050] Determining the crosslinking density hinges on finding an "equilibrium point": this density must be high enough that even after a single stimulus damages part of the structure, the remaining framework can still hold up; simultaneously, it must be low enough that the network can easily break through the critical point and collapse under dual stimulation. Therefore, the lower limit of the crosslinking density is that, under a single stimulus, the crosslinking density of the remaining unbroken bonds must be greater than its basic gelation critical density. The upper limit is that, under dual stimulation, the crosslinking density of the remaining unbroken bonds must be less than the basic gelation critical density. (where is the intrinsic constant of the corresponding bond).

[0051] To determine the lower limit of crosslinking density, it's necessary to calculate the maximum bond breakage rate under a single stimulus. This maximum breakage rate is related to its molar ratio; a higher molar mass percentage of a bond results in more bonds breaking under that stimulus, leading to a higher maximum breakage rate. Therefore, to ensure no breakage under a single stimulus, the higher maximum breakage rate should be used as the maximum breakage rate under a single stimulus. As follows: ; In the formula The maximum fracture rate of the carrier skeleton under single-stress stimulation. and These represent the molar mass ratios of pH-sensitive bonds and ROS-sensitive bonds on the carrier backbone. Therefore, to ensure the backbone stability under a single stimulus, the lower limit of its crosslinking density can be expressed as: ; In the formula This is the lower limit of the crosslinking density of the carrier skeleton. The critical density for gelation of unbroken bonds. The maximum breaking rate under a single stimulus is used to determine the lower limit of crosslinking density.

[0052] To determine the lower limit of crosslinking density, it is necessary to calculate the co-fracture rate under dual stimulation, ensuring that the crosslinking density under dual stimulation is less than its basic gelation density. Therefore, the following holds: ; In the formula Therefore, the dual-stimulation synergistic fracture rate of the carrier skeleton ( ). and These represent the molar mass ratios of pH-sensitive bonds and ROS-sensitive bonds on the backbone of this carrier. and These represent the fragmentation efficiencies of the two components in the tumor cell microenvironment. Similarly, the upper limit of the corresponding crosslinking density can be expressed as: ; In the formula This is the upper limit of the crosslinking density of the carrier skeleton. The critical density for gelation of unbroken bonds. This represents a dual-stimulation synergistic fracture rate. Therefore, the above method can be used to determine the upper limit of the crosslinking density of the carrier backbone. and lower limit Within this range, relevant personnel select an appropriate cross-linking density to construct a carrier backbone through corresponding sensitive bonds, thereby achieving efficient drug delivery.

[0053] In one embodiment of this application, based on the drug packaging parameters determined by the above method, relevant personnel weigh monomers containing acetal / hydrazone bonds, monomers containing thioketal bonds, and crosslinking agents according to the output "molar ratio" and "total crosslinking density" to carry out copolymerization reactions, constructing a nanomicelle framework with specific mesh pore sizes. Then, PEG of a specific molecular weight is grafted onto the carrier surface or pre-treated homologous cancer cell membranes are encapsulated to complete the final camouflage assembly. When the drug enters the patient's body, it will produce a more efficient drug delivery effect, improving the killing efficiency against heterogeneous tumors and reducing the risk of drug resistance.

[0054] To address the aforementioned technical problems, the second embodiment of this application provides a tumor microenvironment-responsive biomimetic nanodrug delivery parameter determination system to solve the same technical problems as the method embodiment. The system may include the following modules: a data acquisition module, an outer layer parameter calculation module, a middle layer parameter calculation module, and an output control module.

[0055] The data acquisition module is used to acquire microenvironmental characteristic data of the patient's target tumor. The microenvironmental characteristic data includes the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor.

[0056] The outer parameter calculation module is used to calculate the relative distribution coefficient of receptor proteins based on the absolute abundance and distribution uniformity of receptor proteins on the surface of tumor cells, as well as the average abundance and distribution uniformity of free receptors; combined with the affinity constant between ligand proteins and receptors, the density correction factor of ligand proteins is determined; based on the density correction factor, the density of the highest ligand protein is reduced to obtain the optimal ligand distribution density of the biomimetic camouflage layer; and based on the ratio of the optimal ligand distribution density to the highest ligand protein density, combined with the minimum modification thickness of the biomimetic camouflage layer, the optimal shell thickness of the biomimetic camouflage layer is calculated.

[0057] The mid-layer parameter calculation module is used to calculate the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds based on the intracellular pH and ROS concentration of the tumor cells, respectively; to calculate the stimulation response potential of each bond by combining the inherent degradation degree of pH-sensitive bonds and ROS-sensitive bonds; to calculate the molar mass ratio of pH-sensitive bonds and ROS-sensitive bonds based on the negative correlation of stimulation response potential; and to calculate the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation based on the molar mass ratio. Combined with the basic gelation critical density of unbroken bonds, the lower and upper limits of the crosslinking density of the carrier backbone are determined.

[0058] The output control module is used to output nanomedicine delivery parameters including optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range to guide the preparation of biomimetic nanomedicines.

[0059] It is understood that this embodiment is the apparatus / system claim corresponding to the method claim, and therefore this embodiment has all the technical effects of the method claim, which will not be repeated here.

[0060] To address the aforementioned technical problems, the third embodiment of this application provides an electronic device to solve the same technical problems as the method embodiment. The electronic device may include at least one processor and a memory. The memory stores a computer program, and at least one processor executes the computer program to implement the method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as provided in the preceding embodiments.

[0061] It is understood that this embodiment is a device claim corresponding to the method claim, and therefore all the technical effects of the method embodiment are present in this embodiment, and will not be repeated here. The above are only preferred embodiments of this application, and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for determining parameters of tumor microenvironment-responsive biomimetic nanomedicine delivery, characterized in that, include: The microenvironmental characteristics data of the patient's target tumor are obtained, including the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor. The relative distribution coefficient of the receptor protein is calculated based on the absolute abundance and distribution uniformity of the tumor cell surface receptor protein, and the average abundance and distribution uniformity of the free receptor. By combining the affinity constants of ligand proteins with receptors, the density correction factor of ligand proteins is determined; based on the density correction factor, the density of the highest ligand protein is reduced to obtain the optimal ligand distribution density of the biomimetic camouflage layer. Based on the ratio of the optimal ligand distribution density to the highest ligand protein density, and combined with the minimum modification thickness of the biomimetic camouflage layer, the optimal shell thickness of the biomimetic camouflage layer is calculated. Based on the intracellular pH and ROS concentration of the tumor cells, the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds are calculated respectively; combined with the inherent degradation rates of the pH-sensitive bonds and ROS-sensitive bonds, their respective stimulation response potentials are calculated; based on the negative correlation between the stimulation response potentials, the molar mass ratios of the pH-sensitive bonds and ROS-sensitive bonds are calculated. Based on the molar mass ratio, the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation are calculated, and the lower and upper limits of the crosslinking density of the carrier skeleton are determined by combining the basic gelation critical density of unbroken bonds. The output includes nanomedicine delivery parameters such as the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range, to guide the preparation of biomimetic nanomedicines.

2. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 1, characterized in that, The calculation of the relative distribution coefficient of receptor proteins based on the absolute abundance and distribution uniformity of the tumor cell surface receptor proteins, and the average abundance and distribution uniformity of free receptors, includes: Calculate the first ratio of the absolute abundance of tumor cell surface receptor proteins to the average abundance of free receptors, and the second ratio of the distribution uniformity of tumor cell surface receptor proteins to the distribution uniformity of free receptors. Multiplying the first ratio by the second ratio yields the relative distribution coefficient of the receptor protein; The step of determining the density correction factor of the ligand protein based on the affinity constant between the ligand protein and the receptor includes: Calculate the ratio of the relative distribution coefficient of the receptor protein to the affinity constant, and normalize it to obtain the density correction factor of the ligand protein with a value in the range of [0, 1]. The step of reducing the density of the highest ligand protein based on the density correction factor to obtain the optimal ligand distribution density of the biomimetic camouflage layer includes: The optimal ligand distribution density of the biomimetic camouflage layer is obtained by multiplying the highest ligand protein density by the density correction factor.

3. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 1, characterized in that, The optimal shell thickness of the biomimetic camouflage layer is calculated based on the ratio of the optimal ligand distribution density to the highest ligand protein density, combined with the minimum modification thickness of the biomimetic camouflage layer, including: The ratio of the difference between the optimal ligand distribution density and the highest ligand protein density is calculated as the degree of ligand density reduction. Based on the minimum modified thickness of the biomimetic camouflage layer, the degree of reduction in ligand density is used as an expansion weight to calculate the thickness increase, thereby obtaining the optimal shell thickness of the biomimetic camouflage layer, so that the shell thickness is positively correlated with the degree of reduction in ligand density.

4. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 1, characterized in that, The calculation of stimulation weights for pH-sensitive and ROS-sensitive bonds based on the intracellular pH and ROS concentration of the tumor cells includes: The stimulation weight of pH-sensitive bonds is calculated based on the difference between the pH value of normal blood and the pH value of tumor tissue fluid. The stimulation weight of ROS-sensitive bonds is calculated based on the difference between the ROS concentration in tumor cells and the ROS concentration in normal cells. The step of calculating the respective stimulus response potential by combining the inherent degradation rates of the pH-sensitive bond and the ROS-sensitive bond includes: The stimulus response potential of the pH-sensitive bond is obtained by multiplying its stimulus weight by its normalized intrinsic degradation degree; the stimulus response potential of the ROS-sensitive bond is obtained by multiplying its stimulus weight by its normalized intrinsic degradation degree.

5. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 4, characterized in that, The calculation of the molar mass ratio of pH-sensitive bonds to ROS-sensitive bonds based on the negative correlation mapping relationship of the stimulus response potential includes: The molar mass percentage of the pH-sensitive bond was calculated by taking the stimulus response potential of the ROS-sensitive bond as the numerator and the sum of the stimulus response potentials of the two sensitive bonds as the denominator. The molar mass percentage of ROS-sensitive bonds was calculated by using the stimulus response potential of the pH-sensitive bond as the numerator and the sum of the stimulus response potentials of the two sensitive bonds as the denominator, in order to inhibit carrier depolymerization under a single stimulus.

6. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 5, characterized in that, The calculation of the maximum breakage rate of a single stimulus based on the molar mass ratio includes: Compare the molar mass ratios of the pH-sensitive bonds and the ROS-sensitive bonds, and take the maximum value of the two as the maximum breakage rate under single stimulation; The step of determining the lower limit of crosslinking density of the carrier skeleton based on the basic gelation critical density of the unbroken bonds includes: Based on the complement ratio between the basic gelation critical density and the maximum breakage rate under a single stimulus, the lower limit of the crosslinking density of the carrier skeleton is calculated to ensure that the crosslinking density of the remaining unbroken bonds under a single stimulus is greater than the basic gelation critical density.

7. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 6, characterized in that, The calculation of the dual-stimulation synergistic breakage rate includes: The first product is obtained by multiplying the molar mass percentage of the pH-sensitive bond with its breaking efficiency in the tumor cell microenvironment; the second product is obtained by multiplying the molar mass percentage of the ROS-sensitive bond with its breaking efficiency in the tumor cell microenvironment. Adding the first product to the second product yields the dual-stimulus synergistic breakage rate; The step of determining the upper limit of the crosslinking density of the carrier skeleton based on the basic gelation critical density of the unbroken bonds includes: Based on the complement ratio between the basic gelation critical density and the dual-stimulation synergistic breakage rate, the upper limit of the crosslinking density of the carrier skeleton is calculated to ensure that the crosslinking density of the remaining unbroken bonds under dual stimulation is less than the basic gelation critical density.

8. The method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in claim 1, characterized in that, The output includes nanomedicine delivery parameters such as the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range, to guide the preparation of biomimetic nanomedicines, including: Output the molar mass ratio of the pH-sensitive bond to the ROS-sensitive bond, as well as the crosslinking density range between the lower and upper limits of the crosslinking density; Based on the molar mass ratio and crosslinking density range, a drug preparation control instruction is generated. The control instruction is used to control the weighing of monomers containing pH-sensitive bonds, monomers containing ROS-sensitive bonds, and crosslinking agents to carry out copolymerization reactions and construct a nanomicelle framework. Based on the optimal ligand distribution density and the optimal shell thickness, a camouflage layer modification instruction is generated. This camouflage layer modification instruction is used to control the grafting of PEG with a specific molecular weight or to encapsulate homologous cancer cell membranes, thereby completing the assembly of biomimetic nanomedicines.

9. A tumor microenvironment-responsive biomimetic nanodrug delivery parameter determination system, characterized in that, include: The data acquisition module is used to acquire microenvironmental characteristic data of the patient's target tumor. The microenvironmental characteristic data includes the absolute abundance and distribution uniformity of tumor cell surface receptor proteins, the average abundance and distribution uniformity of free receptors, the intracellular pH value of the tumor, and the intracellular reactive oxygen species (ROS) concentration of the tumor. The outer parameter calculation module is used to calculate the relative distribution coefficient of the receptor protein based on the absolute abundance and distribution uniformity of the receptor protein on the surface of the tumor cells, and the average abundance and distribution uniformity of the free receptor. By combining the affinity constants of the ligand protein and the receptor, the density correction factor of the ligand protein is determined; based on the density correction factor, the density of the highest ligand protein is reduced to obtain the optimal ligand distribution density of the biomimetic camouflage layer; and based on the ratio of the optimal ligand distribution density to the highest ligand protein density, combined with the minimum modification thickness of the biomimetic camouflage layer, the optimal shell thickness of the biomimetic camouflage layer is calculated. The middle-layer parameter calculation module is used to calculate the stimulation weights of pH-sensitive bonds and ROS-sensitive bonds based on the intracellular pH and ROS concentration of the tumor cells, respectively; calculate their respective stimulation response potentials by combining the inherent degradation rates of the pH-sensitive bonds and ROS-sensitive bonds; calculate the molar mass ratio of the pH-sensitive bonds and ROS-sensitive bonds based on the negative correlation mapping relationship of the stimulation response potentials; and calculate the maximum breakage rate of single stimulation and the synergistic breakage rate of dual stimulation based on the molar mass ratios, and determine the lower and upper limits of the crosslinking density of the carrier backbone by combining the basic gelation critical density of unbroken bonds. The output control module is used to output nanomedicine delivery parameters including the optimal ligand distribution density, optimal shell thickness, molar mass ratio, and crosslinking density range, so as to guide the preparation of biomimetic nanomedicines.

10. An electronic device, characterized in that, include: At least one processor and memory; The memory stores a computer program, and the at least one processor executes the computer program to implement the method for determining tumor microenvironment-responsive biomimetic nanodrug delivery parameters as described in any one of claims 1-8.

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