Transdermal drug delivery quantitative evaluation method and device, electronic equipment and medium

Abstract

The invention discloses a transdermal drug delivery quantitative evaluation method and device, electronic equipment and a medium. The method comprises the following steps: extracting aperture fractal dimension based on a scanning electron microscope two-dimensional image of subcutaneous tissue, and extracting tortuosity fractal dimension based on a confocal laser scanning microscope three-dimensional image; constructing a dual fractal pore medium model, and calculating permeability and an effective diffusion coefficient; a temperature field-flow field-stress field-concentration field full coupling model is constructed through simulation, and a drug local permeation and absorption simulation result is solved; synchronously inverting the aperture fractal dimension, the tortuosity fractal dimension and the reference permeability by adopting a particle swarm algorithm to obtain the optimized aperture fractal dimension, tortuosity fractal dimension and reference permeability; and solving the optimized temperature field-flow field-stress field-concentration field full coupling model based on a multi-objective optimization algorithm to obtain an optimal drug administration scheme. The technical problem that an existing model can only represent a single feature and cannot restore a real penetration environment is solved.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a method, device, electronic device, and medium for quantitative evaluation of transdermal drug delivery. Background Technology

[0002] Transdermal drug delivery has become a research hotspot in the biopharmaceutical field due to its advantages such as being painless, having a long-lasting effect, and providing stable blood drug concentrations. Accurate prediction of drug percutaneous penetration efficiency is a core aspect of formulation development, and existing prediction methods are mainly divided into two categories: 1. Experimental methods: Represented by the Franz diffusion cell method, this method measures permeation through in vitro / in vivo experiments. However, it has problems such as "long cycle (24-72 hours per group of experiments), high cost (consumable cost of about 800-1200 yuan per group of experiments), and large individual differences", and it cannot achieve early virtual screening.

[0003] 2. Model prediction methods: 1. Traditional homogeneous model: Based on Fick's Law of Diffusion, this model assumes that the skin is a homogeneous medium. However, it ignores the porous structure of the skin and its prediction error generally exceeds 30%.

[0004] 2. Single fractal model: It only considers the fractal characteristics of pore size distribution, but does not take into account the fractal properties of the tortuosity of drug penetration pathways. The prediction error for large molecule drugs such as insulin is still 20%-40%.

[0005] 3. Partial Field Coupling Models: Existing models mostly focus on single-field or dual-field coupling (such as temperature-concentration, flow-concentration), failing to achieve full-dimensional integration of T (temperature field), H (flow field), M (stress field), and C (concentration field). For example, in ultrasound-induced permeation simulations, only the temperature field (T) and drug concentration field (C) induced by the acoustic field are coupled, neglecting the stress field (M) (such as stratum corneum pore expansion) and flow field (H) (such as drug convection driven by acoustic flow effects) generated by increased skin temperature, resulting in a permeability (k) prediction error exceeding 22%.

[0006] (II) Pain Points of Core Technologies 1. Lack of structural representation: The subcutaneous tissue pore system has a dual fractal characteristic of "multi-scale pore size distribution (10nm-100μm) + self-similar tortuosity of the permeation path". Existing models can only represent a single feature and cannot reproduce the real permeation environment. For example, the fractal dimension of pore size is calculated only from scanning electron microscopy (SEM) images without considering the tortuous characteristics of the three-dimensional path.

[0007] 2. Defects in the transmission model: Lack of coupling "aperture size fractal dimension (D)". a + Tortuosity Fractal Dimension (D) t The quantitative correlation model of "" leads to the permeability (k) and effective diffusion coefficient (D) e The calculation of key parameters such as ff showed significant deviations.

[0008] 3. Lack of full coupling of multiple physical fields: Current quantitative evaluation models for transdermal drug delivery are mostly partially coupled in single / dual fields and lack real-time bidirectional feedback between fields, resulting in large deviations in the calculation of key parameters such as permeability and effective diffusion coefficient, and making it difficult to adapt to new drug delivery scenarios.

[0009] 4. Limited Parameter Inversion: Traditional inversion methods only optimize 1-2 parameters (such as porosity φ), which easily leads to overfitting. For example, inverting only the diffusion coefficient can result in prediction errors of over 15% for drugs with different viscosities.

[0010] 5. Insufficient compatibility with novel formulations: The dual structural changes of "pore expansion + path alteration" in the skin after microneedle puncture cannot be modeled, which cannot meet the research and development needs of novel drug delivery technologies. Summary of the Invention

[0011] This invention provides a method, device, electronic device, and medium for quantitative evaluation of transdermal drug delivery, which solves the technical problem that existing models can only characterize a single feature and cannot reproduce the real penetration environment.

[0012] According to one aspect of the present invention, a method for quantitatively assessing transdermal drug delivery is provided, comprising: Aperture fractal dimension was extracted from two-dimensional images of subcutaneous tissue using scanning electron microscopy, and tortuosity fractal dimension was extracted from three-dimensional images of confocal laser scanning microscopy. A dual fractal porous medium model was constructed, and the permeability and effective diffusion coefficient were calculated based on the fractal dimension of pore size and the fractal dimension of tortuosity. A fully coupled model of temperature field, flow field, stress field, and concentration field is constructed through simulation, and the simulation results of local drug penetration and absorption are solved based on permeability and effective diffusion coefficient. The particle swarm optimization algorithm is used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability, resulting in optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability. The optimal dosing regimen is obtained by solving the fully coupled temperature field-flow field-stress field-concentration field model based on a multi-objective optimization algorithm.

[0013] Optionally, the extraction of aperture fractal dimension from two-dimensional scanning electron microscope images of subcutaneous tissue and the extraction of tortuosity fractal dimension from three-dimensional confocal laser scanning microscope images include: After Gaussian filtering and threshold segmentation of two-dimensional scanning electron microscope images of subcutaneous tissue, the number of pores under a preset box scale was counted using the box counting method, and the fractal dimension of the pore size was obtained by fitting. The pore structure of subcutaneous tissue was reconstructed using the region growing method from the three-dimensional images of the confocal laser scanning microscope. Multiple permeation paths were extracted, tortuosity was calculated, and the fractal dimension of tortuosity was obtained by fitting.

[0014] Optionally, the construction of a dual fractal porous medium model and the calculation of permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension include: A formula for calculating permeability k and effective diffusion coefficient D are constructed based on the fractal dimension of pore size and tortuosity. e The formula for calculating ff is:

[0015]

[0016] in, Porosity D represents the characteristic tortuosity, k0 represents the baseline permeability; a Let D be the fractal dimension of the aperture. t denoted as the tortuosity fractal dimension, and D0 as the free medium diffusion coefficient.

[0017] Optionally, the construction of the dual fractal porous medium model further includes: Set the boundary conditions for the model, including the boundary conditions for the skin-formulation interface, the dermis-subcutaneous tissue interface, and the pore walls.

[0018] Optionally, the governing equations of the coupled model are:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] in, It is a stress component. It is the skin shear modulus. It is the total strain component. Poisson's ratio for the skin For the Kronek Delta, The coefficient of thermal expansion, For skin volume modulus, Here, T is the skin bioratio, and T is temperature. Skin porosity, Let g be the fluid density, z be the gravity, and Q be the fluid flow rate. For heat flow, For fluid heat capacity, For fluid velocity, For skin thermal conductivity, For the body to adapt to strain, It is the bulk modulus of the skin matrix. It is the initial penetration rate. It is the initial porosity. Here, C represents the fitting coefficient, and C represents the drug concentration. This represents the concentration gradient, where t is time and p is the pore fluid pressure. D is the dynamic viscosity of the drug solution; k is the permeability, and D is the dynamic viscosity of the drug solution. e ff is the effective diffusion coefficient.

[0026] Optionally, the step of simultaneously inverting the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability using the particle swarm optimization algorithm to obtain optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability includes: The particle swarm optimization algorithm was used to simultaneously invert the fractal dimension of the aperture, the fractal dimension of the tortuosity, and the baseline permeability; the objective function used was:

[0027]

[0028]

[0029] Among them, the value of α was determined through orthogonal experiments; Q sim (t) i ) represents t i Simulates the cumulative drug penetration at any time, C sim (x) j ) represents x j Simulated drug concentration at depth; Q exp(t) i ) represents t i The cumulative permeation of the experimental drug at any given time; C exp (x) i ) represents x j Experimental drug concentration at depth; The optimal aperture fractal dimension, tortuosity fractal dimension, and baseline permeability are output after the number of iterations reaches the preset number.

[0030] Optionally, the step of solving the temperature field-flow field-stress field-concentration field-fully coupled model based on a multi-objective optimization algorithm to obtain the optimal dosing regimen includes: The multiple objectives include at least maximizing penetration efficiency, minimizing skin irritation, and minimizing drug retention. The constraints of the multi-objective optimization algorithm include at least drug molecular weight constraints, formulation particle size constraints, and patch replacement cycle constraints. Based on maximizing penetration efficiency, minimizing skin irritation, minimizing drug retention, and the aforementioned constraints, a non-dominated sorting genetic algorithm-III is used to solve for the Pareto optimal solution set, thereby obtaining the optimal dosing regimen.

[0031] According to another aspect of the present invention, a transdermal drug delivery quantitative assessment device is provided, comprising: The feature extraction unit is used to extract the aperture fractal dimension from two-dimensional images of subcutaneous tissue using scanning electron microscopy, and to extract the tortuosity fractal dimension from three-dimensional images of confocal laser scanning microscopy. The model building unit is used to construct a dual fractal porous medium model and calculate the permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension. The simulation unit is used to simulate and construct a fully coupled model of temperature field, flow field, stress field, concentration field, and solve for the simulation results of local drug penetration and absorption based on permeability and effective diffusion coefficient; The parameter optimization unit is used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability using the particle swarm optimization algorithm, and obtain the optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability. The multi-objective solution unit is used to solve the optimized temperature field-flow field-stress field-concentration field-fully coupled model based on the multi-objective optimization algorithm to obtain the optimal drug delivery plan.

[0032] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the transdermal drug delivery quantitative assessment method according to any embodiment of the present invention.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the transdermal drug delivery quantitative assessment method according to any embodiment of the present invention.

[0034] The technical solution of this invention constructs a dual fractal porous medium model based on the fractal dimension of pore size and the fractal dimension of tortuosity, thereby restoring the true structure of subcutaneous tissue and significantly reducing the error in drug penetration prediction. A fully coupled THMC model is constructed through simulation, and the drug penetration simulation results are solved based on permeability and effective diffusion coefficient, making the simulation results more accurate. The particle swarm optimization algorithm is used to simultaneously invert the fractal dimension of pore size, the fractal dimension of tortuosity, and the baseline permeability, obtaining optimized fractal dimensions of pore size, tortuosity, and baseline permeability, solving the overfitting problem of single-parameter inversion and greatly reducing the amount of experimentation.

[0035] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a transdermal drug delivery quantitative assessment method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a transdermal drug delivery quantitative evaluation method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a multilayer transdermal drug delivery model according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a transdermal drug delivery quantitative evaluation device provided according to Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device for implementing the transdermal drug delivery quantitative evaluation method of the present invention. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] Example 1 Figure 1 This is a flowchart illustrating a method for quantitative evaluation of transdermal drug delivery, as provided in Embodiment 1 of the present invention. Figure 1 As shown, the method includes: S101. Extracting aperture fractal dimension from two-dimensional images of subcutaneous tissue using scanning electron microscopy, and extracting tortuosity fractal dimension from three-dimensional images of confocal laser scanning microscopy.

[0041] The pore size fractal dimension characterizes the self-similarity of the scale distribution of pores in a two-dimensional plane. The pore size fractal dimension of normal skin is typically 2.3-2.5, while that after microneedle puncture is typically 2.6-2.8. The tortuosity fractal dimension characterizes the self-similarity of the curvature of the penetration path in three-dimensional space. The tortuosity fractal dimension of normal skin is typically 1.5-1.7, while that after microneedle puncture is typically 1.2-1.4.

[0042] In this embodiment, the aperture fractal dimension can be obtained by extracting from the two-dimensional image of the subcutaneous tissue using a scanning electron microscope, and the tortuosity fractal dimension can be obtained by extracting from the three-dimensional image of the confocal laser scanning microscope.

[0043] S102. Construct a dual fractal porous medium model, and calculate the permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension.

[0044] In this embodiment, a dual fractal porous medium model can be constructed based on the fractal dimension of pore size and the fractal dimension of tortuosity. Specifically, the construction can be based on Darcy's law for fractal porous media to establish the relationship between permeability and the fractal dimension of pore size and the fractal dimension of tortuosity, as well as the relationship between the effective diffusion coefficient and the fractal dimension of pore size and the fractal dimension of tortuosity. This makes the simulated dual fractal porous medium model closer to the actual drug penetration process in the skin.

[0045] For the constructed bifractal porous medium model, the permeability and effective diffusion coefficient corresponding to the pore size fractal dimension and tortuosity fractal dimension can be solved based on the extracted pore size fractal dimension and tortuosity fractal dimension, the relationship between permeability and pore size fractal dimension and tortuosity fractal dimension, and the relationship between effective diffusion coefficient and pore size fractal dimension and tortuosity fractal dimension. S103. Simulate and construct a fully coupled model of temperature field, flow field, stress field, and concentration field, and solve the simulation results of local drug penetration and absorption based on permeability and effective diffusion coefficient.

[0046] The fully coupled temperature-flow-stress-concentration field model used in this embodiment is a coupled model constructed from the equations of the temperature field, flow field, stress field, and concentration field. The concentration field model describes the transport process of matter in a fluid and is commonly used for seepage and mass transfer problems in complex porous media; the flow field equation describes macroscopic fluid motion; the temperature field equation controls the temperature changes in the model; and the stress field equation describes the deformation of the porous media.

[0047] In addition, in this embodiment, the coupling equation of the temperature field-flow field-stress field-concentration field fully coupled model is at least related to the permeability and the effective diffusion coefficient, that is, the permeation and absorption process of the drug in the skin can be analyzed by the permeability and the effective diffusion coefficient.

[0048] S104. The particle swarm optimization algorithm is used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability to obtain the optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability.

[0049] In this embodiment, a particle swarm optimization algorithm can be used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability parameters. Specifically, inversion parameters such as the number of particles, the number of iterations, the learning factor, and the inertia weight can be preset, along with a pre-defined objective function. This allows for iterative calculation of the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability, yielding optimized values ​​for these parameters.

[0050] S105. Solve the optimized temperature field-flow field-stress field-concentration field-fully coupled model based on the multi-objective optimization algorithm to obtain the optimal drug administration plan.

[0051] In this embodiment, a multi-objective function and corresponding constraints can be pre-set. Based on the optimized pore size fractal dimension, tortuosity fractal dimension, and baseline permeability parameters, the optimal drug delivery scheme is given for the validated temperature field-flow field-stress field-concentration field-fully coupled model.

[0052] The multi-objective function can be a target constraint on parameters such as drug permeation efficiency, skin irritation, and drug retention; the constraint function can be a constraint on dosing-related parameters, such as constraints on drug molecular weight, formulation particle size, and patch replacement cycle.

[0053] The technical solution of this invention constructs a dual fractal porous medium model based on the fractal dimension of pore size and the fractal dimension of tortuosity, thereby restoring the true structure of subcutaneous tissue and significantly reducing the error in drug penetration prediction. It constructs a fully coupled model of temperature field, flow field, stress field, concentration field, and calculates the simulation results of local drug penetration and absorption based on permeability and effective diffusion coefficient, making the simulation results more accurate. It employs a particle swarm optimization algorithm to simultaneously invert the fractal dimension of pore size, the fractal dimension of tortuosity, and the baseline permeability, obtaining optimized fractal dimensions of pore size, tortuosity, and baseline permeability, thus solving the overfitting problem of single-parameter inversion and greatly reducing the amount of experimentation.

[0054] Example 2 Figure 2 This is a flowchart illustrating a method for quantitative evaluation of transdermal drug delivery provided in Embodiment 2 of the present invention. Figure 2 As shown, the method includes: S201. After performing Gaussian filtering and threshold segmentation on the two-dimensional scanning electron microscope image of subcutaneous tissue, the number of pores under the preset box scale is counted using the box counting method, and the fractal dimension of the pore size is obtained by fitting.

[0055] Subcutaneous tissue layers (such as those from mice) were obtained using a scanning electron microscope (SEM). Two-dimensional images of the subcutaneous tissue layers were acquired, and Gaussian filtering was applied to the images for noise reduction. Otsu thresholding was used to distinguish between pores and matrix. A modified box-counting method was employed, and the box scale r=2 was statistically analyzed. n The number of pores N(r) for n=1-10 is obtained by fitting logN(r)-log(1 / r) to obtain the fractal dimension D of the pore size. a The obtained aperture fractal dimension is 1. <D a <2, goodness of fit R 2≥0.97 (R) 2 The coefficient of determination (R²) is a core indicator that measures how well the fitted curve matches the actual data. Its value ranges from 0 to 1. 2 The closer it is to 1, the better the fit.

[0056] S202. The pore structure of subcutaneous tissue is reconstructed using the region growing method from the three-dimensional images of the confocal laser scanning microscope. Multiple permeation paths are extracted, tortuosity is calculated, and the tortuosity fractal dimension is obtained by fitting.

[0057] Subcutaneous tissue was obtained by acquiring three-dimensional images of the subcutaneous tissue using a confocal laser scanning microscope (CLSM). 1000 penetration paths (starting point: skin surface, ending point: dermal-subcutaneous tissue interface) were randomly extracted from the three-dimensional images. The actual length L and straight-line distance L of each path were calculated to obtain the tortuosity τ = L / L0. The fitted logτ = D was then used. t log(L0) + C' (where C' is a constant), the slope is the tortuosity and fractal dimension D. t (1) <D t <3, R²≥0.96).

[0058] S203. Construct a dual fractal porous medium model and calculate the permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension.

[0059] Specifically, a formula for calculating permeability k and the effective diffusion coefficient D are constructed based on the fractal dimension of pore size and the fractal dimension of tortuosity. e The formula for calculating ff is:

[0060]

[0061] in, Porosity (typically 0.1-0.9). Characteristic tortuosity (typically 1.5-3.0), k0 is the baseline permeability (typically 1.0-1.5e). -15 m 2 ); D a Let D be the fractal dimension of the aperture. t D0 is the tortuosity fractal dimension; D0 is the free medium diffusion coefficient, such as D0 = 1.2e2 for lidocaine. -9 m 2 / s, insulin D0=0.8e -10 m 2 / s.

[0062] The derivation of this formula is based on porosity. A 10% increase in penetration rate (k) roughly doubles, and D... t Increasing it by 0.1 will decrease the penetration rate k by 15%.

[0063] It also includes setting boundary conditions for the model, including boundary conditions for the skin-formulation interface, the dermis-subcutaneous tissue interface, and the pore walls.

[0064] For example, the skin-formulation interface boundary condition can be set to C(x=0,t)=C0 (initial drug concentration, lidocaine C0=50mg / mL), which is the initial drug concentration at depth x=0. The dermal-subcutaneous tissue interface boundary condition can be set to... C / n(x=L,t)=0 (indicating no drug loss). The boundary condition of the pore wall can be set as: fluid velocity v(x,y,z,t)=0 at a no-slip boundary (fluid velocity is 0). S204. Simulate and construct a fully coupled model of temperature field, flow field, stress field, and concentration field, and solve the simulation results of local drug penetration and absorption based on permeability and effective diffusion coefficient.

[0065] In this embodiment, COMSOL Multiphysics 6.1 can be used, selecting the "Porous Medium Transport" + "Fluid Flow" + "Deformation Field" + "Temperature Field" modules to construct a fully coupled model of temperature field, flow field, stress field, and concentration field. The configurable model meshing parameters include: free tetrahedral mesh, minimum element size of 5μm, maximum element size of 20μm, and mesh quality ≥0.85 (mesh independence verification: element count from 1e...). 5 Increased to 2e 5 The prediction error variation is less than 1%, which meets the requirements of numerical simulation.

[0066] Specifically, the governing equations of the fully coupled temperature field-flow field-stress field-concentration field model are:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] in, It is a stress component. It is the skin shear modulus. It is the total strain component. Poisson's ratio for the skin For the Kronek Delta, The coefficient of thermal expansion, For skin volume modulus, Here, T is the skin bioratio, and T is temperature. Skin porosity, Let g be the fluid density, z be the gravity, and Q be the fluid flow rate. For heat flow, For fluid heat capacity, For fluid velocity, For skin thermal conductivity, For the body to adapt to strain, It is the bulk modulus of the skin matrix. It is the initial penetration rate. It is the initial porosity. Here, C represents the fitting coefficient, and C represents the drug concentration. This represents the concentration gradient, where t is time and p is the pore fluid pressure. D is the dynamic viscosity of the drug solution; k is the permeability, and D is the dynamic viscosity of the drug solution. e ff is the effective diffusion coefficient.

[0074] A transient solver can be used to solve the governing equations, with a time step of 1 hour and a total duration of 24 hours. This means that the cumulative drug penetration Q(t) and drug concentration C(x,t) at different depths are calculated once per hour, for a total of 24 calculations. The convergence tolerance can be set to 1e. -6 .

[0075] The final simulation model output includes the cumulative drug permeation Q(t) from 0 to 24 hours and the steady-state permeation flow rate J. stea The parameters include dy (μg / cm² / h), drug concentration C (x,t) at different depths of the skin, and fluid velocity v (x,y,z) within the pores. Among these, the cumulative drug penetration Q (t) and the drug concentration at different depths of the skin are directly related to permeability and effective diffusion coefficient.

[0076] It should be noted that during transdermal drug delivery, the drug passes sequentially through the stratum corneum, epidermis, dermis, and finally subcutaneous tissue. Therefore, when simulating the coupling model, it is necessary to extract the coupling model parameters for each layer and simulate the multilayer model of the skin individually. For example, it is necessary to extract parameters such as porosity, permeability, and diffusion coefficient for the stratum corneum, epidermis, and dermis separately, and construct a simulation model for each layer in the multilayer model. A schematic diagram of the multilayer model for transdermal drug delivery is shown below. Figure 3 As shown.

[0077] S205. The particle swarm optimization algorithm is used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability to obtain the optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability.

[0078] In this embodiment, experiments and simulations are required for each set of parameters. The drug concentration and cumulative drug penetration at different skin depths obtained from the experimental and simulation results are compared to invert the fractal dimension of the inverted pore size, the fractal dimension of the tortuosity, and the baseline permeability.

[0079] Specifically, the simulation results can be obtained using COMSOL Multiphysics 6.1. The final simulation results include the drug concentration C(x,t) at different skin depths and the cumulative drug penetration Q(t). Experimental results can be obtained by taking 0.5 mL samples every 2 hours (with an equal volume of receiving solution added), measuring the concentration using high-performance liquid chromatography (HPLC, Agilent 1260), and then calculating the cumulative amount Q(t). Additionally, a fluorescence labeling method (drug-labeled fluorescein isothiocyanate, FITC) is used, measuring the fluorescence intensity at different skin depths (0-200 μm) using a confocal laser scanning microscope (CLSM), and then converting this to a concentration distribution C(x,t).

[0080] Specifically, the particle swarm optimization algorithm is used to simultaneously invert the fractal dimension of the pore size, the fractal dimension of the tortuosity, and the baseline permeability. For example, the number of particles can be set to 20-50, the number of iterations to 80-150, and the learning factors c1=c2=1.5-2.5; the inertia weight decreases from 0.9 to 0.4. The final output simulation and experimental data include the cumulative drug penetration Q(t) over 24 hours and the drug concentration distribution C(x,t) along the skin depth direction.

[0081] The objective function used is:

[0082]

[0083]

[0084] Among them, the value of α was determined through orthogonal experiments; Q sim (t) i ) represents t i Simulates the cumulative drug penetration at any time, Csim (x) j ) represents x j Simulated drug concentration at depth; Q exp (t) i ) represents t i The cumulative permeation of the experimental drug at any given time; C exp (x) i ) represents x j Experimental drug concentration at depth; The optimal aperture fractal dimension, tortuosity fractal dimension, and baseline permeability are output after the number of iterations reaches the preset number.

[0085] S206. Based on maximizing penetration efficiency, minimizing skin irritation, minimizing drug retention, and the aforementioned constraints, the Pareto optimal solution set is obtained by using the non-dominated sorting genetic algorithm-III to obtain the optimal dosing regimen.

[0086] In this embodiment, a three-objective optimization algorithm can be used, including maximizing penetration efficiency, minimizing skin irritation, and minimizing drug retention. The constraints of the multi-objective optimization algorithm include at least drug molecular weight constraints, formulation particle size constraints, and patch replacement cycle constraints.

[0087] Specifically, the three objective functions include:

[0088] Where Jsteady represents penetration efficiency; S represents skin irritation, based on an in vitro cytotoxicity test score of 0-10; and R represents drug retention. To avoid local skin toxicity.

[0089] Constraints may include: drug molecular weight M∈[200, 10000] Da, formulation particle size d∈[50, 500] nm (measured by Malvern particle size analyzer), and patch replacement cycle T∈[8, 24] h.

[0090] This embodiment uses the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) with a population size of 100 and 200 iterations; it outputs a Pareto optimal solution set (containing 5-8 schemes), and the recommended scheme must satisfy: J stea dy≥8μg / cm 2 / h, S≤2, R≤20μg / cm 2 .

[0091] In one embodiment, the method further includes: dynamically increasing the fractal dimension of the pore size and dynamically decreasing the fractal dimension of the tortuosity after microneedle puncture or iontophoresis. For example, the fractal dimension of the pore size D... a Dynamically increase the value by 0.1-0.3 to improve the tortuosity fractal dimension D. t Dynamically reduce by 0.2-0.4.

[0092] The specific application example of this invention involves using SPF-grade SD rat samples (male, weighing 250±20g), removing the epidermis from the abdominal skin after excision; and preparing a 50mg / mL aqueous solution (μ=0.0012 Pa) using lidocaine hydrochloride (purity ≥99%, Sigma-Aldrich). (s, measured by NDJ-5S rotational viscometer) was used as the drug; and the fractal dimension D of the pore size of rat skin was obtained by SEM. a The tortuosity fractal dimension D of rat skin was obtained using CLSM. t The experimental results of drug concentration and cumulative permeation were measured using a Franz diffusion cell and HPLC.

[0093] Aperture fractal dimension D a Extraction was performed by excising rat abdominal skin, removing the epidermis to obtain subcutaneous tissue layer, freezing at -20℃ for 1 hour, cutting 5μm thick sections using a Leica CM1950 microtome, and placing them on silanized glass slides; fixing with 4% paraformaldehyde for 30 min, staining with 0.1% toluidine blue for 10 min, and rinsing three times with phosphate-buffered saline (PBS); using SEM at an accelerating voltage of 5kV, working distance of 8mm, and resolution of 5nm, acquiring 2D images of 5 fields of view (2048×2048 pixels each); using ImageJ 1.54f software, Gaussian filtering (σ=1.2) was used for noise reduction, and Otsu thresholding (threshold range 0-255) was used to distinguish pores from matrix; - CLSM images: using MATLAB 2023a, the 3D structure of pores was extracted based on the region growing method (seed point gray value ±5); the box scale was set to r=2. n (n=1,2,...,10, unit: pixels); count the number of boxes containing pores N(r) for each r; fit logN(r) = D a log(1 / r) + C (where C is a constant), the slope is D. a (1) <D a <2,R 2 ≥0.97).

[0094] Tortuousness and fractal dimension D t1000 penetration paths (starting point: skin surface, ending point: dermal-subcutaneous tissue interface) were randomly extracted from the CLSM 3D image; the actual length L and straight-line distance L0 of each path were calculated to obtain the tortuosity τ = L / L0; and logτ = D was fitted. t log(L0) + C' (where C' is a constant), the slope is D. t (1) <D t <3,R 2 ≥0.96). The final result is D. a =2.42 (R) 2 =0.987), D t =1.63 (R²=0.972). =0.32 (measured by ImageJ image analysis).

[0095] During the simulation model construction process, the COMSOL settings included: porous media module, 1.2e5 mesh elements, and a time step of 1 hour; initial parameters: k0 = 1.2e -15 m 2 D0=1.2e -9 m² / s (Sigma-Aldrich product manual); Analog output: Steady-state flow rate J stea dy = 13.2 μg / cm² / h, cumulative osmosis Q (24h) = 312 μg / cm².

[0096] During the parameter inversion process, the receiving solution for the Franz experiment was PBS (pH 7.4), at 32°C, with a stirring speed of 600 rpm, and samples were taken every 2 hours; Experimental results: J stea dy = 12.7 μg / cm 2 / h, Q (24h) = 301 μg / cm 2 PSO inversion parameter settings: particle number 30, iterations 100, optimized parameters: D a =2.45, D t =1.61, k0=1.18e-15 m²; final fitting result: R²=0.982, average error 3.9%.

[0097] The objective function used in the multi-objective optimization process was: J≥12μg / cm² / h, S≤2, R≤20μg / cm²; the final optimization results were: the recommended scheme was "drug load 2.3mg / cm², patch replacement cycle 12h", the measured J=12.5μg / cm² / h, S=1.8 (L929 cytotoxicity experiment), R=18.2μg / cm².

[0098] In another application example of microneedle puncture experiments, a polylactic acid microneedle array (500 μm in length and 100 μm in diameter) was used to perform subcutaneous tissue puncture experiments on rats. The microneedle patch was manually pressed for 30 seconds, and the puncture depth was 400 μm (measured with vernier calipers). The fractal dimension D of the pore size after puncture was... a =2.71 (0.29 more than without puncture), tortuosity fractal dimension D t =1.30 (0.33 less than without puncture), porosity =0.45; Simulated output steady-state permeation flow rate J stea dy = 9.8 μg / cm² / h (only 2.1 μg / cm² / h without puncture). Actual J stea The final error was 3.2%, which meets the clinical requirement of ≥8μg / cm² / h.

[0099] This invention utilizes scanning electron microscopy (SEM) for two-dimensional imaging and confocal laser scanning microscopy (CLSM) for three-dimensional reconstruction to simultaneously extract the fractal dimensions of pore size and tortuosity of subcutaneous tissue; based on fractal geometry and Darcy's law, it derives the coupled D... a D t The invention provides formulas for calculating permeability and effective diffusion coefficient; establishes a fully coupled THMC model in the simulation software COMSOL to solve the drug permeation and absorption process; employs particle swarm optimization (PSO) algorithm to simultaneously invert three core parameters, ensuring that the fitting error between simulation and experimental data is ≤8%; finally, uses non-dominated sorting genetic algorithm-III (NSGA-III) to output the optimal dosing regimen balancing "permeability efficiency-irritation-retention". This invention solves the problems of "partial structural characterization and low prediction accuracy" in existing models, reducing experimental workload by more than 60% and shortening the R&D cycle by 40%-50%. It is applicable to the R&D of conventional / novel transdermal formulations and personalized medicine, and has significant practical value.

[0100] Example 3 Figure 4 This is a schematic diagram of a transdermal drug delivery quantitative assessment device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: The feature extraction unit 401 is used to extract the aperture fractal dimension from two-dimensional images of subcutaneous tissue using scanning electron microscopy, and to extract the tortuosity fractal dimension from three-dimensional images of confocal laser scanning microscopy. Model building unit 402 is used to build a dual fractal porous medium model and calculate permeability and effective diffusion coefficient based on pore size fractal dimension and tortuosity fractal dimension; Simulation unit 403 is used to simulate and construct a fully coupled model of temperature field, flow field, stress field, concentration field, and solve for the simulation results of local drug penetration and absorption based on permeability and effective diffusion coefficient; The parameter optimization unit 404 is used to simultaneously invert the aperture fractal dimension, tortuosity fractal dimension, and baseline permeability using the particle swarm optimization algorithm, and obtain the optimized aperture fractal dimension, tortuosity fractal dimension, and baseline permeability. The multi-objective solving unit 405 is used to solve the optimized temperature field-flow field-stress field-concentration field-fully coupled model based on the multi-objective optimization algorithm to obtain the optimal drug delivery plan.

[0101] The transdermal drug delivery quantitative assessment device provided in the embodiments of the present invention can perform the transdermal drug delivery quantitative assessment device provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0102] Example 4 Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0103] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0104] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0105] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a transdermal drug delivery quantitative assessment method.

[0106] In some embodiments, a transdermal drug delivery quantification assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the transdermal drug delivery quantification assessment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a transdermal drug delivery quantification assessment method by any other suitable means (e.g., by means of firmware).

[0107] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0111] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0112] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0113] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0114] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for transdermal drug delivery quantitative assessment, characterized in that, The method comprises the following steps: extracting the pore size fractal dimension based on the two-dimensional scanning electron microscope image of the subcutaneous tissue, and extracting the tortuosity fractal dimension based on the three-dimensional confocal laser scanning microscope image; constructing a double fractal pore medium model, and calculating the permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension; simulating a full-coupling model of a temperature field-flow field-stress field-concentration field, and solving the local penetration and absorption simulation results of the drug based on the permeability and effective diffusion coefficient; synchronously inverting the pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability by using a particle swarm algorithm to obtain the optimized pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability; solving the full-coupling model of the temperature field-flow field-stress field-concentration field based on a multi-objective optimization algorithm to obtain the optimal drug administration scheme.

2. The transdermal delivery of a dose assessment method according to claim 1, wherein, The method for extracting the pore size fractal dimension based on the two-dimensional scanning electron microscope image of the subcutaneous tissue and the tortuosity fractal dimension based on the three-dimensional confocal laser scanning microscope image comprises the following steps: after the two-dimensional scanning electron microscope image of the subcutaneous tissue is subjected to Gaussian filtering and threshold segmentation, the pore quantity under a preset box scale is counted by using a box counting method, and the pore size fractal dimension is fitted; the pore structure of the three-dimensional confocal laser scanning microscope image of the subcutaneous tissue is reconstructed by using a region growing method, a plurality of penetration paths are extracted, the tortuosity is calculated, and the tortuosity fractal dimension is fitted.

3. The transdermal delivery dose uniformity assessment method of claim 1, wherein, The method for constructing a double fractal pore medium model and calculating the permeability and effective diffusion coefficient based on the pore size fractal dimension and the tortuosity fractal dimension comprises the following steps: The permeability k calculation formula and the effective diffusion coefficient D are constructed based on the aperture fractal dimension and the tortuosity fractal dimension e The calculation formula of ff is: wherein, is the porosity, is the characteristic tortuosity, k0is the baseline permeability; D a is the pore size fractal dimension, D t is the tortuosity fractal dimension, D0is the free medium diffusion coefficient.

4. The transdermal delivery of a drug for quantitative assessment method according to claim 1, wherein, The method for constructing a double fractal pore medium model further comprises the following steps: boundary conditions of the model are set, and the boundary conditions comprise a skin-preparation interface, a dermis-subcutaneous tissue interface and a pore wall boundary condition.

5. The transdermal delivery dose uniformity assessment method of claim 1, wherein, The method for simulating a full-coupling model of a temperature field-flow field-stress field-concentration field comprises the following steps: The control equation of the coupling model is: where, is the stress component, is the skin shear modulus, is the total strain component, is the Poisson ratio of the skin, is the Kronecker delta, is the thermal expansion coefficient, is the skin bulk modulus, is the skin Biot's coefficient, T is the temperature, is the skin porosity, is the fluid density, g is the gravity, z is the vertical coordinate, Q is the fluid flow rate, is the heat flow rate, is the fluid heat capacity, is the fluid velocity, is the skin thermal conductivity, is the bulk strain, is the skin matrix bulk modulus, is the initial permeability, is the initial porosity, is the fitting coefficient, C is the drug concentration, represents the concentration gradient, t is the time, p is the pore fluid pressure, is the drug solution dynamic viscosity; k is the permeability, D e ffis the effective diffusion coefficient.

6. The transdermal delivery of a drug for quantitative assessment method according to claim 1, wherein, The method for synchronously inverting the pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability by using a particle swarm algorithm to obtain the optimized pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability comprises the following steps: The pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability are synchronously inverted by using a particle swarm algorithm; the objective function used is: Wherein, the value of a is determined by orthogonal experiment optimization; Q sim (t i ) represents the cumulative penetration of the simulated drug at t i time; C sim (x j ) represents the concentration of the simulated drug at x j depth; Q exp (t i ) represents the cumulative penetration of the experimental drug at t i time; C exp (x i ) represents the concentration of the experimental drug at x j depth. Until the preset iteration number is reached, the optimal pore size fractal dimension, the tortuosity fractal dimension and the benchmark permeability are output.

7. The transdermal delivery method of claim 1, wherein, The method for solving the full-coupling model of the temperature field-flow field-stress field-concentration field based on a multi-objective optimization algorithm to obtain the optimal drug administration scheme comprises the following steps: The multi-objective optimization algorithm at least comprises maximizing the penetration efficiency, minimizing the skin irritation and minimizing the drug retention amount; The constraint conditions of the multi-objective optimization algorithm at least comprise a drug molecular weight constraint, a preparation particle size constraint and a patch replacement period constraint; Based on the constraint conditions of maximizing the penetration efficiency, minimizing the skin irritation and minimizing the drug retention amount, a non-dominated sorting genetic algorithm-III is used to solve a Pareto optimal solution set to obtain the optimal drug administration scheme.

8. A transdermal drug delivery delivery device for quantitative assessment, comprising: The method comprises the following steps: The feature extraction unit is configured to extract an aperture fractal dimension based on a scanning electron microscope two-dimensional image of subcutaneous tissue and to extract a tortuosity fractal dimension based on a confocal laser scanning microscope three-dimensional image; The model construction unit is configured to construct a dual fractal pore medium model and to calculate permeability and an effective diffusion coefficient based on the aperture fractal dimension and the tortuosity fractal dimension; The simulation unit is configured to simulate a temperature field-flow field-stress field-concentration field fully coupled model and to solve a drug local penetration and absorption simulation result based on the permeability and the effective diffusion coefficient; The parameter optimization unit is configured to simultaneously invert the aperture fractal dimension, the tortuosity fractal dimension, and a benchmark permeability by using a particle swarm algorithm to obtain optimized aperture fractal dimension, tortuosity fractal dimension, and benchmark permeability; The multi-objective solving unit is configured to solve an optimized temperature field-flow field-stress field-concentration field fully coupled model based on a multi-objective optimization algorithm to obtain an optimal drug administration scheme.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the transdermal drug delivery quantitative evaluation method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to perform the transdermal drug delivery quantitative evaluation method of any one of claims 1-7 when executed.

Images