Method and device for obtaining a modified value of the diffusion coefficient of nanomedicine particles

Abstract

A method for obtaining a corrected value for the diffusion coefficient of nanoparticles can be used to assist in the development of tumor drugs. The method disclosed herein includes the following steps: collecting tumor tissue samples; reconstructing the internal morphology of the samples using electron microscopy or nuclear magnetic resonance to obtain the three-dimensional structure of the samples; and analyzing and statistically analyzing the three-dimensional structure of the samples to obtain the porosity ε of the tumor tissue and the average diameter r of the tumor tissue cytoplasmic matrix fibers. f The average diameter r of nanoparticles within the cytoplasm of tumor tissue cells was obtained using a particle size analysis device. s A predictive model for the diffusion coefficient of nanoparticles was constructed, taking into account the porosity ε of the tumor tissue and the average diameter r of the tumor tissue cytoplasmic matrix fibers. f and the average diameter r of nanoparticles s Substitute the values ​​into the prediction model to obtain the predicted value of the diffusion coefficient of the nanoparticles; then correct the predicted value of the diffusion coefficient of the nanoparticles to obtain the corrected value of the diffusion coefficient of the nanoparticles.

Description

[0001] Cross-references

[0002] This application claims the benefit of Chinese application No. 2021113464926, filed on November 15, 2021, with the State Intellectual Property Office of the People's Republic of China (CNIPA), entitled "A method and apparatus for predicting the diffusion coefficient of nanomedicine particles in tumors," the entire contents of which are incorporated herein by reference.

[0003] For relevant English academic literature related to this application, please refer to the following two documents:

[0004] The relevant author, Di Tian et al., published their work in 2022 in PhysicalChemistry Chemical Physics (2022), 24, 24394-24403, doi: 10.1039 / D2CP03397F, titled: A prediction model for nanoparticle diffusion behavior in fibrous materials considering steric and hydrodynamic resistances;

[0005] Reference 2, authored by Di Tian et al., who are also related to this application, was published in 2022: Electronic Supplementary Material (ESI) for Physical Chemistry Chemical Physics.

[0006] This journal is The Owner Societies 2022

[0007] https: / / www.rsc.org / suppdata / d2 / cp / d2cp03397f / d2cp03397f1.pdf Technical Field

[0008] This disclosure belongs to the field of biomedical technology, specifically relating to a method and apparatus for obtaining a correction value of the diffusion coefficient of nanoparticles. Background Technology

[0009] In the field of drug delivery, the following patent CN101578520B, which was applied for and granted by Harvard, proposes a lateral flow based on patterned porous media and a passing through biological assay device, and its preparation method and use method, which includes a porous, hydrophilic medium; a fluid-impermeable barrier comprising a polymeric photoresist, the barrier substantially penetrating the thickness of the porous, hydrophilic medium and defining a boundary of an assay region within the porous, hydrophilic medium; and an assay reagent in the assay region. It is explicitly disclosed that in some aspects, the porous, hydrophilic medium is patterned with a hydrophobic barrier to provide a class of low-cost, portable, and technically simple platforms for running multiplexed biological assays on biological fluids. One example of a useful hydrophilic medium for biological assays is paper, which is inexpensive, readily commercially available, disposable, rapidly absorbs liquid by capillary action, and does not require careful handling as some conventional platforms do. The paper or other porous, hydrophilic medium is patterned with a hydrophobic barrier that provides spatial control of the biological fluid and enables transport of the fluid due to capillary action within the region defined by the barrier. The hydrophobic barrier can be polymeric, such as a curable polymer or photoresist, and provides a substantially impermeable barrier through the thickness of the porous, hydrophilic medium within the defined region. Unlike conventional microfluidic devices that include empty fluidic channels or pores in a polymer or glass, the regions bounded by these barriers are not empty, but are composed of and contain the porous, hydrophilic medium.

[0010] As the above-mentioned Harvard granted patent considers "spatial control", the prior art mostly only considers the spatial steric hindrance of the porous medium, thereby overestimating the diffusion coefficient of the nanopharmaceutical particles in the fibrous porous medium.

[0011] In the field, the drug with a size ranging from 1-1000 nm is generally called nanopharmaceutical, which has greater improvement in solubility, absorption and targeting compared with traditional drugs, and has important significance in the field of drug delivery. Tumor is a vascular-dependent pathological proliferation of abnormal cell proliferation in the body. Tumor can be divided into solid tumor and non-solid tumor, and the death caused by solid tumor accounts for more than 85% of the total tumor death. After entering the body by oral, intravenous injection and other ways, nanopharmaceutical particles need to pass through vascular transmission and tumor porous interstitial diffusion to reach tumor cells. The structure of tumor interstitial matrix is complex, which is composed of a highly adhesive network structure of various biological macromolecules, and the pore skeleton is formed by the adhesion of fibrous protein and glycoprotein. The porous viscoelastic structure of tumor interstitial matrix can greatly reduce the diffusion capacity of nanopharmaceutical particles, causing the local aggregation of nanopharmaceutical particles around the tumor tissue vascular wall, resulting in the preservation of some tumor cells due to the difficulty in contacting drugs, thereby causing the metastasis and recurrence of cancer. Therefore, in order to achieve immediate and effective treatment of tumor, it is necessary to predict the diffusion coefficient of nanopharmaceutical in tumor to guide the design of nanopharmaceutical.

[0012] Due to the coupling of complex structure and multiple dynamic mechanisms in the porous interstitial matrix of tumor, it is a great challenge to predict the effective diffusion coefficient of nanomedicine particles and the macroscopic transport phenomenon thereof in the porous medium. The structure of tumor tissue matrix has strong specificity, and different types of tumor tissue matrix structures have great differences.

[0013] Therefore, there is a need in the art to develop a technical solution for accurately predicting and calculating the diffusion coefficient of nanomedicine particles in the cytoplasmic matrix of tumor tissue without overestimating the diffusion coefficient of nanomedicine particles in the fibrous porous medium due to only considering the space steric hindrance of the porous medium. SUMMARY

[0014] In view of the deficiencies in the prior art, the purpose of the present disclosure is to provide a method and device for obtaining a corrected value of the diffusion coefficient of nanomedicine particles, which can accurately predict and calculate the diffusion coefficient of nanomedicine particles in the cytoplasmic matrix of tumor tissue without experiments, and which is in good agreement with the values obtained by experiments. The technical solution of the present disclosure can be used to assist drug development.

[0015] To achieve the above-mentioned purpose, the present disclosure provides the following technical solution:

[0016] A method for obtaining a corrected value of the diffusion coefficient of nanomedicine particles, comprising the following steps:

[0017] S100: Collecting a tumor tissue sample, reconstructing the internal morphology of the sample by electron microscopy scanning or nuclear magnetic resonance to obtain the three-dimensional structure of the sample, and analyzing and counting the three-dimensional structure of the sample to obtain the porosity ε of the tumor tissue and the average diameter r of the cytoplasmic matrix fibers of the tumor tissue f ;

[0018] S200: Obtaining the average diameter r of the nanomedicine particles in the cytoplasmic matrix of the tumor tissue by a particle size detection device s ;

[0019] S300: Constructing a prediction model of the diffusion coefficient of nanomedicine particles, and substituting the porosity ε of the tumor tissue, the average diameter r of the cytoplasmic matrix fibers of the tumor tissue f and the average diameter r of the nanomedicine particles s into the prediction model to obtain a predicted value of the diffusion coefficient of nanomedicine particles;

[0020] S400: Correcting the predicted value of the diffusion coefficient of nanomedicine particles to obtain a corrected value of the diffusion coefficient of nanomedicine particles.

[0021] Preferably, in step S300, the prediction model of the diffusion coefficient of nanomedicine particles is represented as follows:

[0022]

[0023] In the formula, D eff represents the effective diffusion coefficient of the nano-drug particle in the cytoplasmic matrix of the tumor tissue; D0represents the free diffusion coefficient of the nano-drug particle in water; k represents a correction coefficient; and the correction coefficient is related to the type of tumor tissue, and the correction coefficients are different for different tumor tissues; D ste , D hd respectively represent the diffusion coefficients of the nano-drug particle in the cytoplasmic matrix of the tumor tissue under the influence of steric hindrance and hydrodynamic resistance;

[0024] and

[0025]

[0026] In the above formula, a represents a dimensionless structure parameter of the tumor tissue; and ε represents the porosity of the tumor tissue.

[0027] Preferably, the step S400 comprises the following steps:

[0028] S401: calculating the steric hindrance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0029] S402: calculating the hydrodynamic resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0030] S403: calculating the correction value of the diffusion coefficient of the nano-drug particle based on the steric hindrance and the hydrodynamic resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue.

[0031] Preferably, in the step S401, the steric hindrance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula:

[0032]

[0033] In the formula, D ste (t) represents the diffusion coefficient of the nano-drug particle in the cytoplasmic matrix of the tumor tissue under the influence of steric hindrance; D0represents the free diffusion coefficient of the nano-drug particle in water; N represents the number of simulated nano-drug particles; Δr i represents the diffusion distance of the i-th nano-drug particle at time t; and <Δr> represents the average diffusion distance of the i-th nano-drug particle at time t.

[0034] Preferably, in the step S402, the hydrodynamic resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula:

[0035]

[0036] wherein D hd represents the diffusion coefficient of the nano-drug particles in the tumor tissue cytoplasm matrix under the influence of hydrodynamic resistance, D0represents the free diffusion coefficient of the nano-drug particles in water, and K represents the permeability of the tumor tissue.

[0037] Preferably, in step S403, the modified value of the diffusion coefficient of the nano-drug particles is obtained by the following formula:

[0038]

[0039] wherein D0represents the free diffusion coefficient of the nano-drug particles in water, and ε represents the porosity of the tumor tissue.

[0040] The present disclosure also provides a device for obtaining a modified value of the diffusion coefficient of nano-drug particles, comprising:

[0041] a collecting module configured to collect a tumor tissue sample;

[0042] a reconstructing module configured to reconstruct the internal morphology of the sample to obtain a three-dimensional structure of the sample, and to analyze and count the three-dimensional structure of the sample to obtain the porosity ε of the tumor tissue and the average diameter r f of the tumor tissue cytoplasm matrix fibers;

[0043] a particle size detecting module configured to detect the nano-drug particles in the tumor tissue cytoplasm matrix to obtain the average diameter r s of the nano-drug particles;

[0044] a predicting module configured to predict the diffusion coefficient of the nano-drug particles according to a prediction model of the diffusion coefficient of the nano-drug particles to obtain a predicted value;

[0045] a modifying module configured to modify the predicted value of the diffusion coefficient of the nano-drug particles to obtain a modified value of the diffusion coefficient of the nano-drug particles.

[0046] Preferably, the prediction model of the diffusion coefficient of the nano-drug particles is represented as follows:

[0047]

[0048] In the formula, D eff represents the effective diffusion coefficient of the nano-drug particles in the tumor tissue cytoplasm matrix; D0represents the free diffusion coefficient of the nano-drug particles in water; k represents a correction coefficient; and the correction coefficient is related to the type of tumor tissue, and the correction coefficients are different for different tumor tissues; D ste , and D hd respectively represent the diffusion coefficients of the nano-drug particles in the tumor tissue cytoplasm matrix under the influence of steric hindrance and hydrodynamic resistance.

[0049] and

[0050]

[0051] In the above formula, a represents a dimensionless structure parameter of the tumor tissue; ε represents the porosity of the tumor tissue; and K represents the permeability of the tumor tissue.

[0052] Preferably, the correction module comprises:

[0053] a first calculation unit configured to calculate the steric hindrance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0054] a second calculation unit configured to calculate the hydrodynamic resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0055] a third calculation unit configured to calculate the corrected value of the diffusion coefficient of the nanodrug particle based on the steric hindrance and the hydrodynamic resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue.

[0056] Compared with the prior art, the present disclosure has the beneficial effects that the effective diffusion coefficient of the nanodrug particle can be accurately predicted without experiments, and can be widely applied in the fields of nanodrug design, filter membrane design, porous electrode regulation and design, etc. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flowchart of a method for obtaining a corrected value of a diffusion coefficient of a nanodrug particle according to an embodiment of the present disclosure;

[0058] Figure 2 is a comparison chart of an experimental and a prediction model for predicting a diffusion coefficient of a nanodrug in a tumor according to another embodiment of the present disclosure;

[0059] Figure 3 is a comparison chart of an experimental and a prediction model for predicting a diffusion coefficient of a nanodrug in a tumor according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] The specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments of the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art. Figures 1 to 3 The specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments of the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0061] It should be noted that some terms are used in the description and claims to refer to certain components. Those skilled in the art will understand that the same component can be referred to by different terms. The description and claims do not distinguish components by the difference in the name, but by the difference in the function. As mentioned throughout the description and claims, "including" or "comprising" is an open term, which should be interpreted as "including but not limited to". The subsequent description is a preferred embodiment for implementing the present disclosure, and is for the purpose of illustrating the general principles of the description, but not to limit the scope of the present disclosure. The scope of protection of the present disclosure is defined by the appended claims.

[0062] In order to facilitate the understanding of the embodiments of the present disclosure, the following will be further explained and described with specific examples combined with the accompanying drawings, and each drawing does not constitute a limitation to the embodiments of the present disclosure.

[0063] In one embodiment, as shown in Figure 1 The present disclosure provides a method for obtaining a corrected value of the diffusion coefficient of a nano-drug particle, comprising the following steps:

[0064] S100: Collecting a tumor tissue sample, reconstructing the internal morphology of the sample by electron microscope scanning or nuclear magnetic resonance to obtain the three-dimensional structure of the sample, and analyzing and counting the three-dimensional structure of the sample to obtain the porosity ε of the tumor tissue and the average diameter r of the cytoplasmic matrix fibers of the tumor tissue f ;

[0065] S200: Obtaining the average diameter r of the nano-drug particles in the cytoplasmic matrix of the tumor tissue by a particle size detection device s ;

[0066] S300: Constructing a prediction model of the diffusion coefficient of the nano-drug particle, and substituting the porosity ε of the tumor tissue, the average diameter r of the cytoplasmic matrix fibers of the tumor tissue, and the average diameter r of the nano-drug particle into the prediction model to obtain a predicted value of the diffusion coefficient of the nano-drug particle; f s

[0067] S400: Correcting the predicted value of the diffusion coefficient of the nano-drug particle to obtain a corrected value of the diffusion coefficient of the nano-drug particle.

[0068] In this step, even if a large number of sample statistics results are obtained, due to the specificity of the tumor tissue, the prediction model may deviate from the actual value in some cases. Therefore, in order to solve this detail problem, the prediction model needs to be corrected.

[0069] Therefore, in step S300, the prediction model of the diffusion coefficient of the nano-drug particle is represented as follows:​​

[0070]

[0071] In the formula, D eff represents the effective diffusion coefficient of the nano-drug particle in the cytoplasmic matrix of the tumor tissue; D0represents the free diffusion coefficient of the nano-drug particle in water; k represents a correction coefficient, and the correction coefficient is related to the type of tumor tissue, and the correction coefficients are different for different tumor tissues; D ste , D hd respectively represent the diffusion coefficients of the nano-drug particle in the cytoplasmic matrix of the tumor tissue under the influence of steric hindrance and hydrodynamic resistance;

[0072] and

[0073]

[0074] In the above formula, a represents a dimensionless structure parameter of the tumor tissue; ε represents the porosity of the tumor tissue; K represents the permeability of the tumor tissue.

[0075] For the above embodiments, the effective diffusion coefficient of the nano-drug particle can be predicted without experiments, only by constructing a prediction model of the diffusion coefficient of the nano-drug particle, and the predicted value is highly consistent with the experimental value, which saves time and labor, saves cost, and can be widely applied in the fields of nano-drug design, filter membrane design, porous electrode regulation and design, etc. The above embodiments fully consider that the resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue includes hydrodynamic resistance and steric hindrance, a sufficient number of tissue fiber matrices are established by random reconstruction, and then the hydrodynamic resistance and steric hindrance values of the nano-drug particle in the tissue fiber matrix are obtained by simulation calculation, and the above prediction model can be obtained by linear regression. As for the constants in the model, they can be obtained by linear regression of a large number of samples, which belongs to a typical mathematical means in the prior art, and the present application does not propose a new linear regression means, so it is not described here.

[0076] In another embodiment, step S400 includes the following steps:

[0077] S401: based on the three-dimensional structure of the tumor tissue sample, calculating the steric hindrance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue;

[0078] In this step, the steric hindrance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula:

[0079]

[0080] In the formula, D ste(t) represents the diffusion coefficient of the nanodrug particle under the influence of steric hindrance in the cytoplasmic matrix of the tumor tissue; D0 represents the free diffusion coefficient of the nanodrug particle in water; N represents the number of simulated nanodrug particles; Δr i represents the diffusion distance of the i-th nanodrug particle at time t; <Δr> represents the average diffusion distance of the i-th nanodrug particle at time t.

[0081] S402: Based on the three-dimensional structure of the tumor tissue sample, the hydrodynamic resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue is calculated;

[0082] In this step, the hydrodynamic resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula:

[0083]

[0084] where D hd represents the diffusion coefficient of the nanodrug particle under the influence of hydrodynamic resistance in the cytoplasmic matrix of the tumor tissue, D0 represents the free diffusion coefficient of the nanodrug particle in water, and K represents the permeability of the tumor tissue.

[0085] S403: Based on the steric hindrance and hydrodynamic resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue, the correction value of the diffusion coefficient of the nanodrug particle is calculated.

[0086] In this step, the correction value of the diffusion coefficient of the nanodrug particle is obtained by the following formula:

[0087]

[0088] where D0 represents the free diffusion coefficient of the nanodrug particle in water, and ε represents the porosity of the tumor tissue.

[0089] In this embodiment, the resistance of the nanodrug particle in the cytoplasmic matrix of the tumor tissue includes hydrodynamic resistance and steric hindrance, and most of the existing methods only consider steric hindrance, thereby overestimating the diffusion coefficient of the nanodrug particle. The present embodiment comprehensively considers the hydrodynamic resistance and steric hindrance and the interaction between them, so as to more scientifically and effectively evaluate the diffusion coefficient of the nanodrug particle in the cytoplasmic matrix of the tumor tissue.

[0090] In order to further understand the technical effects of the scheme of the present disclosure, the following will be described in detail in combination with Figure 2 and Figure 3

[0091] Figure 2 ​is a comparison chart of experimental and predicted model for predicting the diffusion coefficient of nanomedicine in tumor provided by one embodiment of the present disclosure, wherein the circle, square and angle points represent the experimental values of the dimensionless diffusion coefficient of different molecular weight of fluorescent loaded dextran particles (FDX10, FDX20, FDX20) in different volume fractions of polymethyl acrylate alginate (MALG) and polymer solutions prepared by sodium alginate (ALG) (MALG, ALG, ALG, representing tumor tissue matrix), and the curve is the predicted value of the model, which is obtained by Figure 2 It can be seen that the predicted value of the model is in good agreement with the experimental value, and the correlation coefficient of the two is greater than 95% and the average relative error is less than 5% through statistics.

[0092] Figure 3 is a comparison chart of experimental and predicted model for predicting the diffusion coefficient of nanomedicine in tumor provided by another embodiment of the present disclosure, wherein the circle, square and angle points represent the experimental values of the dimensionless diffusion coefficient of different size of fluorescent loaded alprazolam mesylate, vitamin B12 and lysozyme particles (AM, Vit B 12 , LYZ) in different volume fractions of hydroxypropyl methyl cellulose (HPMC) and polymer solutions prepared by polyvinyl acetate (PVA) (HPMC, PVA, PVA, representing tumor tissue matrix), and the curve is the predicted value of the model, which is obtained by Figure 3 It can be seen that the predicted value of the model is in good agreement with the experimental value, and the correlation coefficient of the two is greater than 95% and the average relative error is less than 5% through statistics.

[0093] Figure 2 and Figure 3 It can be seen that the effective diffusion coefficient of nanomedicine particles can be accurately predicted without experiment by using the method of the present disclosure, and the prediction result is basically consistent with the experimental value.

[0094] In another embodiment, the present disclosure further provides a device for obtaining a corrected value of the diffusion coefficient of nanomedicine particles, comprising:

[0095] The acquisition module is used for acquiring a tumor tissue sample.

[0096] The reconstruction module is used for reconstructing the internal morphology of the sample to obtain a three-dimensional structure of the sample, and analyzing and counting the three-dimensional structure of the sample to obtain the porosity ε of the tumor tissue and the average diameter r f of the cytoplasmic matrix fibers of the tumor tissue.

[0097] The particle size detection module is used for detecting the nanomedicine particles in the cytoplasmic matrix of the tumor tissue to obtain the average diameter r s of the nanomedicine particles.

[0098] ​a prediction module configured to predict the diffusion coefficient of the nano-drug particle according to a prediction model of the diffusion coefficient of the nano-drug particle to obtain a predicted value;

[0099] a correction module configured to correct the predicted value of the diffusion coefficient of the nano-drug particle to obtain a corrected value of the diffusion coefficient of the nano-drug particle.

[0100] In another embodiment, the correction module comprises:

[0101] a first calculation unit configured to calculate the steric hindrance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0102] a second calculation unit configured to calculate the hydrodynamic resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample;

[0103] a third calculation unit configured to calculate the corrected value of the diffusion coefficient of the nano-drug particle based on the steric hindrance and the hydrodynamic resistance of the nano-drug particle in the cytoplasmic matrix of the tumor tissue.

[0104] Although the embodiments of the present application are described above with reference to the drawings, the present application is not limited to the above-described specific embodiments and application fields, and the above-described specific embodiments are merely illustrative and instructive, but not restrictive. Those skilled in the art can make many forms under the guidance of the present specification and without departing from the scope of the claims of the present application, which are all included in the protection of the present application.

Claims

1. A method for obtaining a corrected value of a diffusion coefficient of a nanoparticle, comprising the steps of: S100: collect tumor tissue samples, reconstruct the internal morphology of the samples by electron microscopy scanning or nuclear magnetic resonance to obtain the three-dimensional structure of the samples, and analyze and count the three-dimensional structure of the samples to obtain the porosity of the tumor tissue and the average diameter of the cytoplasmic matrix fibers of the tumor tissue ; S200: obtaining the average diameter of the nanodrug particles in the cytoplasmic matrix of the tumor tissue cells by the particle size detection device ; S300: constructing a prediction model of the nanodrug particle diffusion coefficient, inputting the porosity of the tumor tissue , the average diameter of the tumor tissue cytoplasmic matrix fibers and the average diameter of the nanodrug particles into the prediction model to obtain a predicted value of the nanodrug particle diffusion coefficient; S400:correcting a predicted value of the diffusion coefficient of the nanoparticle to obtain a corrected value of the diffusion coefficient of the nanoparticle; wherein To solve the specificity of tumor tissue that causes the predicted model to deviate from the actual value in some cases, the predicted model needs to be corrected: In step S300, the predicted model of the diffusion coefficient of the nanoparticle is represented as follows: ， In the formula, Dn,eff represents the effective diffusion coefficient of the nanodrug particles in the cytoplasmic matrix of the tumor tissue; Dn,free represents the free diffusion coefficient of the nanodrug particles in water; Dn,corr represents the correction coefficient, and the correction coefficient is related to the type of tumor tissue, and the correction coefficients of different tumor tissues are different; , Dn,eff and Dn,eff represent the diffusion coefficients of the nanodrug particles in the cytoplasmic matrix of the tumor tissue under the influence of steric hindrance and hydrodynamic resistance, respectively. and ， ， ， In the above formula, denotes a dimensionless structure parameter of the tumor tissue; denotes a porosity of the tumor tissue; denotes a permeability of the tumor tissue; wherein Step S400 comprises the following steps: S401:calculating the steric hindrance of the nanoparticle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample; S402:calculating the hydrodynamic resistance of the nanoparticle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample; S403:calculating the corrected value of the diffusion coefficient of the nanoparticle based on the steric hindrance and the hydrodynamic resistance of the nanoparticle in the cytoplasmic matrix of the tumor tissue; In step S401, the steric hindrance of the nanoparticle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula: ， wherein, Df(t) represents the diffusion coefficient of the nanodrug particle under the influence of steric hindrance within the cytosolic matrix of the tumor tissue cell; Df,free represents the free diffusion coefficient of the nanodrug particle in water; N(t) represents the number of simulated nanodrug particles; t represents the simulated time; Df(t) represents the diffusion distance of the nanodrug particle at time t; Df(t) represents the average diffusion distance of the nanodrug particle at time t; In step S402, the hydrodynamic resistance of the nanoparticle in the cytoplasmic matrix of the tumor tissue is obtained by the following formula: ， wherein, D represents the diffusion coefficient of the nanodrug particle in the cytosol matrix of the tumor tissue under the influence of hydrodynamic resistance; D represents the diffusion coefficient of the nanodrug particle in the cytosol matrix of the tumor tissue under the influence of hydrodynamic resistance; In step S403, the corrected value of the diffusion coefficient of the nanoparticle is obtained by the following formula: ， wherein, Dfrepresents the free diffusion coefficient of the nanoparticulate drug in water, represents the porosity of the tumor tissue. 2.A device for implementing the method for obtaining a corrected value of a diffusion coefficient of a nanoparticle according to claim 1, comprising: a collection module for collecting a tumor tissue sample; A reconstruction module is configured to reconstruct the internal morphology of the sample to obtain a three-dimensional structure of the sample, and analyze and count the three-dimensional structure of the sample to obtain the porosity of the tumor tissue and the average diameter of the cytoplasmic matrix fibers of the tumor tissue ; a particle size detection module for detecting the nano-drug particles in the cytoplasmic matrix of the tumor tissue cells to obtain their average diameter ; a prediction module for predicting the diffusion coefficient of the nanoparticle to obtain a predicted value according to a predicted model of the diffusion coefficient of the nanoparticle; a correction module for correcting the predicted value of the diffusion coefficient of the nanoparticle to obtain a corrected value of the diffusion coefficient of the nanoparticle; wherein, to solve the specificity of tumor tissue that causes the predicted model to deviate from the actual value in some cases, the predicted model needs to be corrected: the predicted model of the diffusion coefficient of the nanoparticle is represented as follows: ， wherein Dn,eff represents the effective diffusion coefficient of the nanoparticulate drug in the cytosol matrix of the tumor tissue; Dn,free represents the free diffusion coefficient of the nanoparticulate drug in water; Cn,corr represents a correction factor; and the correction factor is related to the type of tumor tissue, and the correction factor is different for different tumor tissues; , Dn,eff and Dn,eff represent the diffusion coefficients of the nanoparticulate drug in the cytosol matrix of the tumor tissue under the influence of steric hindrance and hydrodynamic resistance, respectively. and ， ， ， In the above formula, denotes a dimensionless structure parameter of the tumor tissue; denotes a porosity of the tumor tissue; denotes a permeability of the tumor tissue.

3. The apparatus of claim 2, wherein, the correction module comprises: a first calculation unit for calculating the steric hindrance of the nanoparticle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample; a second calculation unit for calculating the hydrodynamic resistance of the nanoparticle in the cytoplasmic matrix of the tumor tissue based on the three-dimensional structure of the tumor tissue sample; a third calculation unit for calculating the corrected value of the diffusion coefficient of the nanoparticle based on the steric hindrance and the hydrodynamic resistance of the nanoparticle in the cytoplasmic matrix of the tumor tissue.

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