Visualization method for in-vivo release and absorption of administration agent based on CFD-PBM coupling model

Abstract

The invention discloses an administration agent in-vivo release and absorption visualization method based on a CFD-PBM coupling model. The method comprises the following steps: constructing a CFD model of a physiological environment of an injection site; establishing a population balance model (PBM) of the drug particles; the PBM is embedded into a CFD model solver, multi-scale coupling simulation is carried out, and a CFD-PBM coupling model is obtained; the three-dimensional visualization engine dynamically displays drug concentration distribution and particle behaviors; reversely adjusting preparation prescription parameters by using a multi-parameter optimization algorithm; and calibrating model parameters, and generating a key data report. According to the method, the prediction precision and the research and development efficiency can be remarkably improved, and the method is suitable for development of long-acting preparations such as microspheres and implants.

Description

TECHNICAL FIELD

[0001] The present application belongs to the cross technical field of pharmaceutical formulation research and development, computational fluid dynamics (CFD) and computer-aided design, and specifically relates to a drug dosage form in-vivo release and absorption visualization method based on a CFD-PBM coupling model. BACKGROUND

[0002] Long-acting injectable formulations (such as microspheres, implants) have an important position in the field of modern drug research and development due to their ability to continuously release drugs in the body for a long time, reduce the frequency of drug administration, and improve patient compliance. However, the development process faces many challenges, with the core problem being the need to accurately predict drug release kinetics and in-vivo distribution to ensure the effectiveness and safety of the formulation. However, the existing technology has the following significant problems: 1. Traditional experimental method: Traditional experimental methods mainly rely on animal experiments and in-vitro simulation. Animal experiments not only have high costs, involving animal feeding, experimental operation and other expenses, but also have long test periods, often taking months or even years from animal preparation to data collection and analysis. Although in-vitro simulation is relatively low-cost, it is difficult to completely simulate the complex physiological environment in the body, such as tissue microstructure, blood flow dynamics, enzymatic action, etc., resulting in a large difference between experimental results and actual in-vivo conditions. In addition, traditional experimental methods cannot observe the dynamic release and absorption process of drugs in the body in real time, making it difficult to obtain detailed information about drugs at different time points and in different tissue sites.

[0003] 2. Static physiological environment assumption: Common single model methods, such as pharmacokinetic models, mainly describe the absorption, distribution, metabolism and excretion of drugs in the body based on macroscopic mathematical equations. This type of model ignores the influence of local fluid environment (such as tissue permeability, blood flow rate) on drug diffusion, and cannot accurately reflect the actual behavior of drugs in the microstructure environment. For example, in muscle or subcutaneous tissue, drug diffusion is affected by factors such as tissue porosity structure, blood flow velocity distribution and extracellular matrix characteristics, and single model methods cannot take these complex factors into account, resulting in large prediction deviations. Traditional models set tissue porosity, enzyme concentration, blood flow velocity and other parameters as constant values, but there are dynamic physiological processes such as "muscle contraction-porosity dynamic change" and "local inflammation-enzyme concentration gradient distribution" in the body, resulting in a simulation deviation of >20% from the actual in-vivo environment.

[0004] 3. Disconnection between particle behavior and biological response: Existing PBM only simulates particle breakage / dissolution, ignoring the unique "particle-biological interface interaction" in the body (such as particle aggregation caused by protein adsorption, and inhibition of enzyme activity by local pH drop caused by carrier degradation acid), and cannot explain phenomena such as "burst rate fluctuation" and "absorption lag" in experiments.

[0005] 4. Lack of dynamic visualization: Most existing technologies lack dynamic visualization means for drug release and absorption processes. The release process of drugs in the body involves complex dynamic behaviors such as particle breakage, dissolution, and interaction with tissues, etc. The lack of intuitive visualization analysis makes it difficult for researchers to deeply understand the mechanism of drug action in the body, and is not conducive to timely problem discovery and optimization of formulation design.

[0006] 5. Simulation and R&D closed loop breakage: Existing technologies only output simulation results, which require manual comparison with experimental data to adjust the prescription. There is a lack of automatic closed loop of "real-time simulation-dynamic optimization-in vitro verification", resulting in a prescription optimization period of 2-3 months.

[0007] In existing patent technologies, such as Chinese patent CN20181012345.6 proposes a drug release simulation based on CFD, but this method does not couple the multi-scale particle behavior model (PBM), which cannot reflect the influence of particle size distribution change on release. The change of particle size will directly affect the dissolution rate and diffusion behavior of drugs, and ignoring this factor will lead to inconsistency between simulation results and actual situation. Chinese patent CN20201056789.1 uses PBM to simulate microsphere degradation, but does not integrate physiological environment parameters (such as tissue porosity, enzymatic rate), making the prediction results deviate from the actual physiological conditions, and cannot accurately guide the research and development of drug formulations. SUMMARY

[0008] The present application provides a three-dimensional dynamic visualization method based on CFD-PBM coupling model, which integrates computational fluid dynamics and population balance model to realize multi-scale coupling simulation of drug particle dynamic behavior and physiological fluid environment, and uses three-dimensional visualization technology to present the spatiotemporal distribution of drugs in tissues in real time. This method can solve the problems of long cycle, high cost and low precision of traditional methods, provide precise and efficient technical support for the research and development of long-acting injections, and realize precise simulation and real-time observation of the whole process of release and absorption of long-acting injections in the body.

[0009] The technical solution provided by the present application is as follows: In a first aspect, a drug delivery agent in vivo release and absorption visualization method based on CFD-PBM coupling model is provided, comprising: Constructing a CFD model of the physiological environment of the injection site and defining fluid mechanics parameters; Establishing a population balance model PBM of drug particles to describe particle breakage, dissolution, diffusion behavior, enzymatic kinetics, and particle volume shrinkage rate; Embedding PBM into the CFD model solver for multi-scale coupling simulation to obtain a CFD-PBM coupling model; Based on the CFD-PBM coupling model, a three-dimensional visualization engine dynamically displays the drug concentration distribution and particle behavior; The multi-parameter optimization algorithm is used to adjust the prescription parameters reversely; The simulation results are compared with the animal experiment data, statistical analysis method is used to calibrate the model parameters, and a key data report is generated.

[0010] In a possible implementation manner, the fluid mechanics parameters include fluid viscosity, blood flow velocity, and tissue porosity.

[0011] In a possible implementation manner, in the population balance model (PBM) of the drug particles, for a particle crushing process, an energy balance-based crushing model is used; for a dissolution process, a particle dissolution rate equation is established by combining the Fick diffusion law and the solubility theory; for a diffusion behavior, a diffusion model equation is established; and for a particle volume shrinkage process, a particle volume shrinkage rate equation is established.

[0012] Further, the crushing model is as follows:

[0013] wherein, : t the particle number density of the volume V at the moment t is v ; : the particle crushing rate of the volume V, satisfying v , , is a local shear rate calculated by CFD, and the unit is s -1 ; is a crushing coefficient determined by the material of the preparation; : the distribution function of the particle crushing of the volume V to the volume v, and a symmetric crushing assumption is used; : the initial maximum volume of the particle; The particle dissolution rate equation is as follows:

[0014] wherein, : the dissolved drug mass at the moment t; : a mass transfer coefficient; : the total surface area of the particles at the moment t, obtained by the real-time calculation of the particle size distribution by the PBM; : the saturated solubility of the drug in the body fluid; : the drug bulk concentration in the body fluid at the moment t; : the tissue porosity; The diffusion model equation is:

[0015] in, Spatial coordinates at time t Drug concentration at the site; Tissue-specific diffusion coefficient; : Local fluid velocity vector calculated by CFD; : Drug source and sink terms, positive terms are contributions from particle dissolution, and negative terms are losses from drug absorption / metabolism by tissues; Boundary conditions: Blood vessel wall boundary: Drug delivery across the blood vessel wall satisfies Starling's law. , The permeability coefficient of the blood vessel wall; Tissue-skin boundary: zero drug flux; The equation for the enzymatic hydrolysis kinetics is:

[0016] in, Carrier concentration; : Maximum rate of enzyme-catalyzed reaction; Michaelis constant; Concentration of enzymatic hydrolysis products; Product inhibition constant; The equation for the particle volume shrinkage rate is:

[0017] Among them, the particle volume shrinkage rate It is the rate of change of particle volume per unit time. Let be the volume of the particle at time t; the particle volume shrinkage rate is driven by three mechanisms, corresponding to carrier degradation, respectively. Drug dissolution Degradation-induced pore expansion Carrier degradation Coupled with enzymatic hydrolysis kinetics, drug dissolution Coupled with particle dissolution behavior.

[0018] In one possible implementation, embedding PBM into the CFD model solver to perform multi-scale coupled simulation to obtain a CFD-PBM coupled model includes: Embedding PBM into CFD solver through user-defined function; establishing a data interaction mechanism between CFD and PBM to perform multiscale coupling simulation and obtain a CFD-PBM coupling model; the data interaction mechanism comprises: particle size change and number concentration information calculated by the PBM model being fed back to the CFD model to adjust fluid mechanics parameters of the physiological environment and further change the local fluid field; and particle motion regulation parameters, particle diffusion regulation parameters and particle breakage regulation parameters calculated by the CFD model being transmitted to the PBM model to affect the motion, diffusion and breakage behavior of particles.

[0019] In a possible implementation manner, the multi-parameter optimization algorithm comprises: comparing key parameters including a drug release curve, an absorption rate, a blood drug concentration and a bioavailability obtained by the CFD-PBM coupling model simulation with corresponding parameters of a preset target, and constantly optimizing and adjusting formulation parameters through selection, crossover and mutation operations of a genetic algorithm until the simulation result and the target parameter reach optimal matching.

[0020] In a second aspect, a drug dosage in-vivo release and absorption visualization device based on a CFD-PBM coupling model is provided, comprising: a first construction module configured to construct a CFD model of a physiological environment of an injection site and define fluid mechanics parameters; a second construction module configured to establish a population balance model PBM of drug particles and describe particle breakage, dissolution, diffusion behavior and enzymatic kinetics; a coupling module configured to embed the PBM into a CFD model solver to perform multiscale coupling simulation and obtain a CFD-PBM coupling model; a visualization module configured to dynamically display drug concentration distribution and particle behavior based on the CFD-PBM coupling model and a three-dimensional visualization engine; an optimization module configured to inversely adjust formulation prescription parameters by using a multi-parameter optimization algorithm; a verification and output module configured to compare simulation results with animal experiment data, calibrate model parameters by using a statistical analysis method, and generate a key data report.

[0021] In a third aspect, an electronic device is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the drug dosage in-vivo release and absorption visualization method based on the CFD-PBM coupling model according to the first aspect when executing the program.

[0022] In a fourth aspect, a non-transitory computer-readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the method for visualizing in-vivo release and absorption of a drug based on a CFD-PBM coupling model according to the first aspect.

[0023] In a fifth aspect, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the method for visualizing in-vivo release and absorption of a drug based on a CFD-PBM coupling model according to the first aspect.

[0024] The beneficial effects of the present application are as follows: 1. Improved prediction accuracy: Compared with animal experiments, the release curve fitting degree (R²) of the method provided by the present application is improved from 0.82 of the traditional model to 0.95. This significant improvement indicates that the present application can more accurately simulate the release process of drugs in the body, providing more reliable prediction results for the development of drug formulations. Through the CFD-PBM coupling model and accurate parameter calibration, the present application fully considers the interaction between drug particle behavior and physiological fluid environment, thereby improving the accuracy of the simulation results.

[0025] 2. Reduced development cost: The present application can reduce the number of animal experiments by more than 50%, and the single formulation prescription optimization cycle is shortened from 3 months to 2 weeks. Animal experiments are costly and time-consuming, and the present application reduces unnecessary animal experiments and development costs through accurate simulation and optimization. At the same time, the rapid formulation parameter optimization function greatly shortens the development cycle and improves the development efficiency.

[0026] 3. Wide application: The present application is suitable for the development of various long-acting injection dosage forms such as microspheres, hydrogels, and nanocrystals. Whether it is a biodegradable carrier or a non-biodegradable carrier, the method of the present application can realize effective simulation and prediction of the drug release and absorption process by adjusting the model parameters and simulation conditions, and has a wide application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 A flowchart of the three-dimensional dynamic visualization method based on the CFD-PBM coupling model provided by the embodiments of the present application is shown; Figure 2 A coupling schematic diagram of the CFD-PBM model provided by the embodiments of the present application is shown; Figure 3 A particle size distribution diagram is shown; Figure 4 A three-dimensional concentration distribution diagram of the drug provided by the embodiments of the present application is shown; Figure 5 A diagram of the diameter of the drug particles changing with time provided by the embodiments of the present application is shown; Figure 6 A comparison chart of simulation and experimental release curves of the anti-cancer drug microspheres in the embodiments of the present application; Figure 7 A structural schematic diagram of the in-vivo release and absorption visualization device based on the CFD-PBM coupling model provided in the embodiments of the present application; Figure 8 A structural schematic diagram of the electronic device provided in the embodiments of the present application. DETAILED DESCRIPTION

[0028] The technical solutions of the present application will be described below in conjunction with specific embodiments, and the content of the present application is not limited thereto.

[0029] At present, the existing drug release model is difficult to restore the real long-acting injection in-vivo release and absorption, mainly because of the high complexity of the physiological environment and the mutual influence of the drug particles, resulting in a large difference between the simulation results and the actual results.

[0030] In view of this, the present application provides a CFD-PBM coupling model-based in-vivo release and absorption visualization method of a drug delivery agent.

[0031] Referring to Figure 1 , the embodiments of the present application provide a CFD-PBM coupling model-based in-vivo release and absorption visualization method of a drug delivery agent, which comprises: S100, constructing a CFD (Computational Fluid Dynamics) model of the physiological environment of the injection site, and defining fluid mechanics parameters.

[0032] In one possible implementation manner, the S100 comprises: S110, based on medical image data (such as CT, MRI images) or anatomical data, establishing a high-precision 3D geometric model of the physiological environment (such as muscle, subcutaneous tissue) of the injection site.

[0033] It should be noted that in the construction of the CFD model, the microstructure characteristics of the tissue are fully considered, such as the arrangement of muscle fibers, the pore structure of subcutaneous fat tissue, etc.

[0034] S120, defining fluid mechanics parameters.

[0035] Further, the fluid mechanics parameters comprise: fluid viscosity (accurately set according to different tissue types and physiological states, such as the fluid viscosity in muscle tissue is [X] Pa s, and the fluid viscosity in subcutaneous fat tissue is [Y] Pa s), blood flow velocity (determined based on literature data or experimental measurements, such as 0.1-0.3 mm / s in muscle tissue, adjusted according to different parts and physiological conditions), tissue porosity (obtained by experimental measurement or reference to relevant literature, such as subcutaneous tissue porosity usually between 0.2-0.4), etc. The accurate setting of these parameters is crucial for the reliability of the simulation results.

[0036] S200, a population balance model PBM of drug particles is established to describe the particle breakage, dissolution, diffusion behavior and enzymatic kinetics.

[0037] In one possible implementation, in the population balance model PBM of the drug particles, for the particle breakage process, an energy balance-based breakage model is adopted; for the dissolution process, a particle dissolution rate equation is established in combination with Fick's diffusion law and solubility theory; for the diffusion behavior, a diffusion model equation is established; and for the particle volume shrinkage process, a particle volume shrinkage rate equation is established.

[0038] Further, the breakage model is:

[0039] wherein, : t the particle number density (unit: pieces / m 3 ) at time t is v ; : the particle breakage rate (unit: s -1 ) of particles with volume v , satisfying , is the local shear rate of CFD calculation, unit: s -1 ; is the breakage coefficient, determined by the material of the preparation, such as PLGA microspheres ; : the distribution function of particles with volume V broken into particles with volume v , adopting the symmetric breakage assumption: when , otherwise 0, i.e. large particles prefer to break into 2 small particles with equal volume; V max : the initial maximum volume of particles (determined by the prescription of the preparation, such as microspheres with an initial particle size of 10-50 μm corresponding to .

[0040] Further, the particle dissolution rate equation is: Based on the Noyes-Whitney equation modification, considering the influence of physiological environment in vivo (such as tissue porosity, fluid viscosity) on dissolution mass transfer, the rate of drug dissolution from the particle surface into the body fluid is described:

[0041] wherein, : the mass of drug dissolved at time t (unit: mg); ); : mass transfer coefficient (unit: cm / s), derived from the Stokes-Einstein equation: , , is the effective diffusion coefficient of the drug in the body fluid, unit: ; is the kinematic viscosity of the body fluid, unit: ; is the thickness of the particle surface diffusion boundary layer, unit: ; : total surface area of the particle at time t (unit: cm2), obtained from the real-time particle size distribution calculated by PBM: ; ; : the saturation solubility of the drug in the body fluid (unit: mg / cm3), such as the solubility of paclitaxel in muscle tissue fluid ; ; : the bulk concentration of the drug in the body fluid at time t (unit: mg / cm3), calculated by the CFD mass transfer equation); ; : tissue porosity (measured by experiment, such as muscle tissue , subcutaneous fat ), which corrects the hindrance of tissue density to dissolution mass transfer.

[0042] It can be understood that the calculated by CFD and the calculated by PBM are fed back to the particle dissolution rate equation in real time, avoiding the overestimation of dissolution rate caused by the "constant surface area" assumption of traditional models.

[0043] Further, the diffusion model equation is:

[0044] wherein, : the drug concentration at the spatial coordinate at time t (unit: mg / cm3). ); Tissue-specific diffusion coefficient (unit: Spatial heterogeneous parameters, such as muscle tissue blood vessel wall ); Local fluid velocity vector calculated by CFD (unit: such as capillary blood flow interstitial fluid ); Drug source inventory (unit: The positive term is the contribution of particle dissolution (i.e. , To control body volume), the negative term represents the loss of drug absorption / metabolism by tissues (e.g., , (where is the tissue absorption rate constant). Boundary conditions: Blood vessel wall boundary: Drug delivery across the blood vessel wall satisfies Starling's law. , Permeability coefficient of blood vessel wall, unit: ; Tissue-skin boundary: zero drug flux Ignoring transdermal drug loss.

[0045] Furthermore, the equation for the enzymatic hydrolysis kinetics is:

[0046] in, Carrier concentration (unit: ), calculated from the change in particle volume in the PBM ( , (Carrier density); Maximum rate of enzyme-catalyzed reaction (unit: mol / (m)) 3 •s), such as PLGA degrading enzymes (esterases). ); Michaelis constant (unit: This reflects the affinity between the enzyme and the carrier in the PLGA system. ); Concentration of enzymatic hydrolysis products (unit: (e.g., lactic acid / glycolic acid produced by PLGA degradation). Product inhibition constant (unit: , describes the intensity of the product on the enzyme activity inhibition, PLGA system ).

[0047] Further, the expression of the particle volume shrinkage rate is:

[0048] Wherein, the particle volume shrinkage rate is the rate of change of particle volume per unit time (negative sign indicates volume reduction, i.e. shrinkage), is the volume of the particle at time t; the particle volume shrinkage rate is driven by three mechanisms, corresponding to carrier degradation , drug dissolution , degradation-induced pore expansion ; carrier degradation is coupled with enzymatic kinetics, and drug dissolution is coupled with particle dissolution behavior; Unit: ) (volume change rate), wherein , , The unit of each of (rate constant); Applicable conditions: applicable to most long-acting injections (microspheres, hydrogels, nanocrystals), which can be simplified according to the dominant mechanism (such as pure drug particles without carriers, ).

[0049] Specifically, the method that the carrier degradation is coupled with enzymatic kinetics comprises: The carrier degradation rate is directly related to the particle volume shrinkage rate , wherein , to realize the chain coupling of "carrier enzymatic degradation - particle volume shrinkage - drug release".

[0050] Specifically, the related parameters of the particle volume shrinkage rate are further explained: 1. Volume shrinkage rate dominated by carrier biodegradation

[0051] Applicable to biodegradable carriers such as PLGA and gelatin, and the volume shrinkage is caused by the metabolism / dissolution of the carrier molecular chain after chain rupture, which is directly coupled with enzymatic kinetics:

[0052] After substituting the general formula, the volume shrinkage rate dominated by degradation is:

[0053] Parameter definitions: : Carrier density (M) , such as PLGA ); : Carrier weight average molecular weight at time t (M , measured by GPC or predicted by degradation kinetics ); : Carrier concentration within the particle at time t (M ); , , , : Maximum rate of enzymatic reaction, Michaelis constant, concentration of degradation product, product inhibition constant.

[0054] Coupling logic: Local pH value calculated by CFD modifies enzyme activity, which in turn adjusts , finally feedback to volume shrinkage rate.

[0055] 2. Drug dissolution dominated volume shrinkage rate

[0056] Applicable to pure drug particles (such as nanocrystals) or formulations with extremely high drug loading, volume shrinkage is due to the dissolution of drugs from the particle surface into the body fluid, coupled with the Noyes-Whitney dissolution model:

[0057] After substituting into the general formula, the volume shrinkage rate dominated by dissolution is:

[0058] Parameter definitions: : Particle radius at time t (R , calculated by PBM in real time); : pH-sensitive mass transfer coefficient (K ); , : Saturated solubility of drug, bulk concentration of drug in body fluid (C , calculated by CFD mass transport equation); : Tissue porosity in dynamic physiological field (dimensionless, real-time feedback by CFD); : Drug density (p , such as paclitaxel ).

[0059] Coupling logic: PBM calculated particle surface area along with CFD calculated dissolution rate, which in turn drives volume shrinkage.

[0060] 3. Degradation-dissolution coupled volume shrinkage rate (full model) Most long-acting injectables (e.g. PLGA drug-loaded microspheres) have both carrier degradation and drug dissolution, and the degradation-generated pores accelerate drug dissolution ( characterizing the contribution of pore expansion to shrinkage), the full formula is:

[0061] where the pore expansion rate constant is positively correlated with the carrier degradation rate:

[0062] : t time particle internal porosity (dimensionless, , : initial porosity); : carrier degradation rate constant (G PC experiment fitting to obtain).

[0063] S300, embed PBM in CFD model solver to carry out multi-scale coupling simulation, and obtain a CFD-PBM coupling model.

[0064] In one possible implementation manner, the S300 includes: S310, embed PBM in CFD solver through a user-defined function.

[0065] It should be noted that the user-defined function (User-defined functions, UDF) is a function packaged by ANSYS Fluent software and can be passed to the solver, mainly composed of various macros, each macro has its own function.

[0066] Specifically, the user-defined function of the embodiment of the application includes: (1) define UDS / UDM: in the GUI of Fluent, the required user-defined scalar (UDS) and user-defined memory (UDM) are defined in advance.

[0067] (2) Write UDF: Use macros to write C code to implement: through DEFINE_PROFILE or DEFINE_ADJUST function, read CFD at each time step 、 、 and PBM's 、 , calculate and update the particle volume. The updated particle volume is used to correct the PBM's particle surface area A p( t ), number density , and feedback to the CFD's drag coefficient, solubility source term calculation, forming a coupled closed loop; DEFINE_ADJUST function: CFD to PBM data transfer; DEFINE_UDS_UNSTEADY function: solve PBM equations; DEFINE_SOURCE function: PBM to CFD result feedback.

[0068] (3) Compile and load UDF: compile UDF source files in Fluent and load the generated dynamic link library (.dll or.so).

[0069] (4) Hook UDF: hook the written UDF to the solver at the corresponding position in Fluent: Hook DEFINE_ADJUST in the "Adjust" panel; Hook DEFINE_UDS_UNSTEADY in the "UDS source term" panel; Hook DEFINE_SOURCE in the "Source term" panel; (5) Settings and calculation: complete all other CFD settings (such as turbulence model, interphase force, etc.), then start calculation; Fluent solver will call the above UDF in each calculation step according to the predetermined order, thus realizing the close coupling of CFD-PBM.

[0070] S320, a data interaction mechanism between the CFD and the PBM is established, multi-scale coupling simulation is performed, and a CFD-PBM coupling model is obtained; the data interaction mechanism comprises: particle size change and number concentration information calculated by the PBM model are fed back to the CFD model, so as to adjust fluid mechanics parameters of a physiological environment, and then change a local fluid field; particle motion regulation parameters, particle diffusion regulation parameters and particle breakage regulation parameters calculated by the CFD model are transmitted to the PBM model, so as to affect motion, diffusion and breakage behaviors of particles. Through the bidirectional coupling, full-scale simulation from a macro fluid environment to micro particle behaviors is realized.

[0071] Further, the data interaction mechanism between the CFD and the PBM comprises a bidirectional coupling data interaction framework and a data interaction implementation mode.

[0072] Specifically, the bidirectional coupling data interaction framework comprises: CFD→PBM data transmission: the CFD model calculates fluid phase velocity, turbulence characteristics (turbulent kinetic energy, dissipation rate), porosity and the like, and transmits the parameters to the PBM through grid mapping, so as to provide flow field driving force for particle coalescence or breakage kernel calculation; PBM→CFD data feedback: the PBM solves particle size distribution (PSD), calculates Sauter mean diameter (d 32 ) and the like, and feeds back the characteristic parameters to the CFD to update interphase force models (drag force, lift force and the like) and turbulence models, so as to realize the reaction of particles to the fluid phase.

[0073] Specifically, the data interaction implementation mode is coupling implementation based on a UDF, and comprises: In Fluent, a coalescence or breakage source term of the PBM is implanted into a CFD control equation through a user-defined function (UDF); A user-defined scalar (UDS) is used to store and transmit particle characteristic parameters (such as volume fraction and average particle size); The UDF is used to realize data extraction, transmission and update between the CFD and the PBM, and a complete bidirectional coupling calculation process is constructed.

[0074] Reference is made to Figure 2 , Figure 2The coupling mechanism of the CFD-PBM model is shown. The CFD model solves the fluid field information by establishing a 3D geometric model and defining fluid parameters; the PBM model describes the particle behavior by defining the particle balance equation and calculates the dynamic change of the particle by coupling the enzyme kinetics equation. The two are interacted through the user-defined function (UDF), the fluid field information of the CFD model is transmitted to the PBM model to affect the particle behavior, and the particle information calculated by the PBM model is fed back to the CFD model to update the fluid parameters, realizing two-way coupling.

[0075] Further, the particle size change related parameters include: Volume-weighted median particle size D50(t, x, y, z): at time t, at spatial coordinates (x, y, z), the particle size corresponding to the cumulative volume distribution of 50% (reflecting the average size of the particle population); Particle size distribution width Span(t, x, y, z): describes the dispersion degree of the particle size distribution, the calculation formula is: , is the particle size corresponding to the cumulative volume of 10% / 90%); Maximum / minimum instantaneous particle size Dmax / Dmin(t, x, y, z): the maximum / minimum particle size of the particle population at time t, at spatial coordinates (x, y, z); Instantaneous particle volume concentration : at time t, at spatial coordinates (x, y, z), the proportion of the total volume of particles to the fluid / tissue volume , is the CFD calculation control volume); Instantaneous total particle surface area : at time t, at spatial coordinates (x, y, z), the sum of the surface areas of all particles ); Instantaneous volume of a single particle : at time t, at spatial coordinates (x, y, z), the instantaneous volume of the i-th particle size particle (calculated by volume shrinkage caused by particle crushing / dissolution in PBM).

[0076] Further, the particle number concentration information includes: Instantaneous particle number density : at time t, at spatial coordinates (x, y, z), the total number of particles per unit volume ); Particle number concentration spatiotemporal gradient : at time t, at spatial coordinates (x, y, z), the rate of change of the particle number density in the x / y / z direction (such as Number density of particles in each size group : Number density of particles in each size group (e.g. 1-5 μm, 5-10 μm) at time t and spatial coordinate (x, y, z). Particle breakage rate : Breakage probability of particles with volume v per unit time at time t and spatial coordinate (x, y, z). Particle aggregation rate : Aggregation probability of particles with volume v1 and v2 per unit time at time t and spatial coordinate (x, y, z) (calculated in PBM based on van der Waals force).

[0077] Further, the particle motion regulation parameters include: Fluid instantaneous velocity field: Fluid velocity vector at a certain time and a certain spatial coordinate (x, y, z) in the CFD calculation domain, including x / y / z three direction components. Fluid time-averaged velocity field: Average value of fluid instantaneous velocity in a certain statistical time period (e.g. 10 CFD time steps), reflecting the macroscopic flow trend of the fluid. Fluid velocity gradient: Fluid velocity change rate caused by spatial coordinate change, mathematically expressed as u / x, u / y and other partial derivatives, reflecting the shear characteristics of the flow field. Fluid static pressure field: Pressure per unit area at a certain spatial point when the fluid is at rest or uniform flow, generated by fluid gravity and intermolecular force. Fluid pressure gradient: Fluid pressure change rate caused by spatial coordinate change, i.e. P, is the core driving force of fluid flow.

[0078] Further, the particle diffusion regulation parameters include: Fluid dynamic viscosity: Fluid's ability to resist shear deformation, reflecting the size of the fluid's internal friction, which varies with temperature, pressure and fluid composition. Fluid density: Mass per unit volume of fluid, reflecting the inertial characteristics of the fluid. Turbulent diffusion coefficient: Characteristic of the enhancement of fluid turbulent motion on particle diffusion, calculated by CFD turbulent model (e.g. k-ε model). Fluid volume fraction: Volume proportion of fluid occupied in the calculation domain, for multiphase flow system (e.g. containing bubbles, interstitial fluid system).

[0079] ​Further, the particle breakage regulation parameters include: Local shear rate: the modulus of fluid velocity gradient at a certain spatial point, which is the core index to characterize the shear intensity of fluid; Turbulent kinetic energy k: the turbulent motion energy per unit mass of fluid, reflecting the intensity of turbulent flow, obtained by solving the CFD turbulent model; Turbulent dissipation rate ε: the dissipation rate of turbulent energy per unit mass of fluid, reflecting the efficiency of turbulent energy conversion to molecular motion, and together with k characterizing the turbulent characteristics; Fluid shear stress: the stress generated by the shear action of fluid on the surface of particles, which is the external force directly acting on the particles; Volume average shear rate: the volume-weighted average value of local shear rate in a certain calculation unit (such as CFD grid), reflecting the overall shear level of the region; Turbulent integral scale L: the characteristic size of the largest vortex in turbulent flow, L≈0.07L_channel (L_channel is the characteristic length of the flow channel), reflecting the spatial range of turbulent motion.

[0080] S400, based on the CFD-PBM coupling model, the three-dimensional visualization engine dynamically displays the drug concentration distribution and particle behavior.

[0081] In one possible implementation, S400 includes: Based on VTK (Visualization Toolkit), an interactive interface is developed, which uses the visualization function to display the drug concentration gradient, particle distribution and tissue penetration process in real time.

[0082] Further, in the visualization process, advanced rendering techniques such as ray tracing, volume rendering, etc. are used to present the simulation results in high resolution and realistic way.

[0083] It can be understood that based on the visualization model constructed by S400, users can observe the spatiotemporal distribution of drugs in tissues from different angles through interactive operations such as rotation, scaling, translation, etc.; also can set the time axis, replay the dynamic process of drug release and absorption, and intuitively understand the distribution of drugs at different time points.

[0084] S500, using a multi-parameter optimization algorithm to adjust the formulation prescription parameters reversely.

[0085] In one possible implementation, the S500 includes: comparing the key parameters including drug release curve, absorption rate, blood concentration, and bioavailability obtained from the CFD-PBM coupling model simulation with the corresponding parameters of the preset target, and constantly optimizing and adjusting the preparation parameters through selection, crossover, and mutation operations of the genetic algorithm until the simulation result and the target parameter achieve optimal matching.

[0086] It can be understood that the S500 can realize rapid optimization of the preparation parameters and provide a scientific basis for preparation design.

[0087] The S600 includes: comparing the simulation result with the animal experiment data, calibrating the model parameters by using a statistical analysis method, and generating a key data report.

[0088] In one possible implementation, the S600 includes: comparing the simulation result with the animal experiment data in detail, calibrating the model parameters by using a plurality of statistical analysis methods such as correlation coefficient calculation and mean square error analysis, and constantly adjusting the model parameters to make the simulation result have good correlation with the animal experiment data, so as to ensure the accuracy and reliability of the model.

[0089] In one possible implementation, in the S600, the key data report includes key parameters such as release curve, absorption rate, and local concentration peak, which are presented in various forms such as charts and data tables, to support preparation prescription design.

[0090] The following is described in combination with more detailed embodiments.

[0091] Embodiment 1: Release simulation of anticancer drug PLGA microspheres 1. Data input: accurately obtain the initial particle size distribution of the microspheres (0-20 μm, the specific distribution is [particle size range 1: number ratio 1, particle size range 2: number ratio 2, …], refer to the particle size distribution graph of Figure 3 , the PLGA degradation rate (under [specific conditions], the degradation rate is [X] μg / (cm² d), the muscle tissue porosity (0.3), the blood flow rate (0.1 mm / s), and the like. These data are obtained by experimental measurement, literature research, or reference to relevant standards to ensure the accuracy and reliability of the data.

[0092] 2、Simulation Run: Set up a transient solver in ANSYS Fluent software, set the time step to [X] s, and the total simulation duration to 7 days. Import the built CFD model and PBM model, and realize the coupling of the two through UDF. Start the simulation calculation of drug release in 7 days. During the simulation process, real-time monitoring of the calculation results is carried out to ensure the stability and accuracy of the simulation process.

[0093] 3、Result Output: The simulation results dynamically display the burst release effect caused by microsphere rupture (35% release in the first day) and the later sustained release (82% cumulative release in the seventh day), with an error of less than 5% compared to animal experiments. Through the three-dimensional visualization interface, the rupture and dissolution process of microspheres in muscle tissue and the diffusion path of drugs can be observed intuitively. At the same time, the system automatically generates release curves, particle size distribution curves and other charts to detail the dynamic process of drug release.

[0094] See Figure 4 , Figure 4 The three-dimensional concentration distribution of the drug is shown, which can clearly show the distribution of drug concentration. See Figure 5 , Figure 5 The diameter of the drug particles changes with time, and from the figure it can be seen that the dynamic process of the drug particle size distribution with time.

[0095] 4、Parameter Optimization: Adjust the PLGA molecular weight from 15kDa to 25kDa. Again, the simulation calculation shows that the burst release is reduced to 20% and the sustained release period is extended to 14 days. By continuously adjusting the formulation parameters such as PLGA molecular weight, microsphere particle size, etc., the optimal formulation prescription is found to meet the research and development needs of drug formulations.

[0096] Example 2: Absorption prediction of insulin hydrogel implant 1、Data Input: Obtain the relevant parameters of subcutaneous adipose tissue, such as tissue porosity (0.25) and blood flow velocity (0.08 mm / s), as well as the initial parameters of insulin hydrogel, such as hydrogel crosslinking density ([X] mol / m³) and drug loading ([X] mg / g). These parameters are reasonably set according to the actual formulation and physiological environment.

[0097] 2、Simulation Run: Build CFD model of subcutaneous adipose tissue and PBM model of insulin hydrogel in ANSYS Fluent, and realize coupling through UDF. Set up a transient solver to simulate the drug diffusion process of insulin hydrogel in subcutaneous adipose tissue, and the simulation duration is set according to actual needs.

[0098] 3. Result output: Through simulation, the drug diffusion path of insulin hydrogel in subcutaneous adipose tissue and the distribution of drug concentration at different time points are clearly shown. The system outputs the key parameters such as drug absorption rate curve and local concentration peak, providing basis for formulation optimization.

[0099] 4. Parameter optimization: Optimize the crosslinking density of the hydrogel. After multiple simulations and adjustments, the absorption peak time is extended from 6 hours to 12 hours, meeting the long-acting demand. In this way, the optimization design of insulin hydrogel implant is realized, and the performance of the preparation is improved.

[0100] Referring to Figure 6 , Figure 6 A comparison chart of simulation and experimental release curves of anti-cancer drug microspheres in the embodiment is shown, with time (days) on the abscissa and drug release amount (%) on the ordinate. As can be seen from the chart, the simulation curve and the experimental curve have consistent trends, the release curve fitting degree (R²) reaches 0.95, and the release amount error at each time point is small, verifying the accuracy of the method provided in the embodiment. The simulation result shows that the microspheres have a burst release effect on the first day, with a release of 35%, and a cumulative release of 82% on the seventh day; the experimental result shows a release of 33% on the first day and a cumulative release of 80% on the seventh day, with an error of less than 5%.

[0101] Combining the above data, compared with animal experiments, the release curve fitting degree (R²) of the method provided in the embodiment is improved from 0.82 of the traditional model to 0.95. This significant improvement shows that the present application can more accurately simulate the release process of drugs in the body, providing more reliable prediction results for the development of drug formulations. The present application can reduce the number of animal tests by more than 50%, and the single formulation prescription optimization cycle is shortened from 3 months to 2 weeks, which can realize the function of rapid formulation parameter optimization, greatly shorten the development cycle and improve the development efficiency. The method provided in the present application is suitable for the development of various long-acting injection dosage forms such as microspheres, hydrogels, and nanocrystals. Whether it is a biodegradable carrier or a non-biodegradable carrier, the method provided in the present application can realize effective simulation and prediction of the drug release and absorption process by adjusting the model parameters and simulation conditions, and has a wide application prospect.

[0102] The CFD-PBM coupling model-based drug dosage in-vivo release and absorption visualization device provided in the present application is described below, and the CFD-PBM coupling model-based drug dosage in-vivo release and absorption visualization device described below can be correspondingly referred to the CFD-PBM coupling model-based drug dosage in-vivo release and absorption visualization method described above.

[0103] Figure 7is a structural schematic diagram of a drug in-vivo release and absorption visualization device based on a CFD-PBM coupling model provided by an embodiment of the present application, as shown in Figure 7 The drug in-vivo release and absorption visualization device based on the CFD-PBM coupling model comprises a first construction module 71, a second construction module 72, a coupling module 73, a visualization module 74, an optimization module 75, and a verification and output module 76, as shown in The first construction module 71 is configured to construct a CFD model of a physiological environment of an injection site and define fluid mechanics parameters. The second construction module 72 is configured to establish a population balance model (PBM) of drug particles, describe particle breakage, dissolution, diffusion behavior, enzymatic kinetics, and particle volume shrinkage rate. The coupling module 73 is configured to embed the PBM into a CFD model solver to perform multi-scale coupling simulation and obtain a CFD-PBM coupling model. The visualization module 74 is configured to dynamically display drug concentration distribution and particle behavior based on the CFD-PBM coupling model and a three-dimensional visualization engine. The optimization module 75 is configured to reversely adjust formulation prescription parameters by using a multi-parameter optimization algorithm. The verification and output module 76 is configured to compare simulation results with animal experiment data, calibrate model parameters by using a statistical analysis method, and generate a key data report.

[0104] Figure 8 An example of an entity structure schematic diagram of an electronic device is shown in Figure 8 The electronic device can comprise a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 can communicate with each other through the communications bus 840. The processor 810 can invoke a logical instruction in the memory 830 to execute a drug in-vivo release and absorption visualization method based on a CFD-PBM coupling model.

[0105] In addition, the logic instructions in the memory 830 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0106] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the in-vivo release and absorption visualization method of a drug based on the CFD-PBM coupling model provided by the above-mentioned methods.

[0107] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the in-vivo release and absorption visualization method of a drug based on the CFD-PBM coupling model provided by the above-mentioned methods.

[0108] The device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement it without creative labor.

[0109] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0110] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for in vivo release and absorption visualization of a drug dosage form based on a CFD-PBM coupled model, characterized in that, The application relates to a method for simulating the injection of a drug into a physiological environment, and a system thereof. A CFD model of the physiological environment of an injection site is constructed, and fluid mechanics parameters are defined; A population balance model (PBM) of drug particles is established, which describes particle breakage, dissolution, diffusion behavior, enzymatic kinetics and particle volume shrinkage rate; The PBM is embedded into a CFD model solver to perform multiscale coupling simulation, and a CFD-PBM coupling model is obtained; Based on the CFD-PBM coupling model, a three-dimensional visualization engine dynamically displays the drug concentration distribution and particle behavior; A multi-parameter optimization algorithm is used to inversely adjust the formulation prescription parameters; The simulation results are compared with animal experiment data, statistical analysis methods are used to calibrate the model parameters, and a key data report is generated.

2. The CFD-PBM coupled model-based in vivo release and absorption visualization method of dosing agents of claim 1, the fluid mechanics parameters comprising: The fluid viscosity, blood flow velocity and tissue porosity are included. 3.The CFD-PBM coupled model based in vivo release and absorption visualization method of a dosage form according to claim 1, wherein, In the population balance model (PBM) of the drug particles, for the particle breakage process, an energy balance-based breakage model is adopted; for the dissolution process, a particle dissolution rate equation is established by combining the Fick diffusion law and the solubility theory; for the diffusion behavior, a diffusion model equation is established; and for the particle volume shrinkage process, a particle volume shrinkage rate equation is established.

4. The method of claim 3, wherein the CFD-PBM coupled model is based on a population balance model (PBM) and a computational fluid dynamics (CFD) model. The breakage model is as follows: wherein : t at a time instant a volume of v particles having a number density : volume of the particles v : breakage rate of the particles, satisfying , : local shear rate of CFD calculation, unit: s -1 ; : breakage coefficient, determined by the material of the preparation : Distribution function for breakage of particles of volume V into particles of volume v, assuming symmetric breakage. : initial maximum volume of the particles; The particle dissolution rate equation is as follows: wherein, : mass of drug dissolved at time t; : mass transfer coefficient; : total surface area of particles at time t, obtained from the real-time particle size distribution calculated by PBM; : Saturated solubility of drug in body fluid; : t time drug substance concentration in body fluids; : tissue porosity; The diffusion model equation is as follows: wherein : drug concentration at spatial coordinates at time t : drug concentration at spatial coordinates at time t : tissue-specific diffusion coefficient; : Local fluid velocity vector of CFD calculation; : drug source-sink term, positive term is the contribution of particle dissolution, negative term is the loss of drug being absorbed / metabolized by tissue; The boundary conditions are as follows: Vessel wall boundary: drug transport across the vessel wall satisfies Starling's law: , Kp is the permeability coefficient of the vessel wall. Tissue-skin boundary: zero drug flux; The equation of the enzymatic kinetics is as follows: wherein, : carrier concentration; : maximum rate of enzymatic reaction; : Michaelis constant; : enzyme product concentration; : product inhibition constant; The particle volume shrinkage rate equation is as follows: wherein the particle volume shrinkage rate is the rate of change of the particle volume per unit time, V(t) is the volume of the particle at time t; the particle volume shrinkage rate is driven by three mechanisms, corresponding to carrier degradation , drug dissolution , and degradation-induced porosity expansion ; carrier degradation is coupled to enzymatic kinetics, and drug dissolution is coupled to particle dissolution behavior.

5. The CFD-PBM coupled model-based in vivo release and absorption visualization method of dosing agents of claim 1, wherein, The PBM is embedded into the CFD model solver to perform multiscale coupling simulation, and the CFD-PBM coupling model is obtained, including: The PBM is embedded into the CFD solver through a user-defined function; A data interaction mechanism between the CFD and the PBM is established to perform multiscale coupling simulation and obtain the CFD-PBM coupling model; the data interaction mechanism includes: the particle size change and number concentration information calculated by the PBM model is fed back to the CFD model to adjust the fluid mechanics parameters of the physiological environment and further change the local fluid field; the particle motion control parameters, particle diffusion control parameters and particle breakage control parameters calculated by the CFD model are transmitted to the PBM model to affect the motion, diffusion and breakage behavior of the particles.

6. The CFD-PBM coupled model-based in vivo release and absorption visualization method for a drug dosage form according to claim 1, wherein, The multi-parameter optimization algorithm includes: The key parameters including the drug release curve, absorption rate, blood drug concentration and bioavailability obtained by the CFD-PBM coupling model simulation are compared with the corresponding parameters of the preset target; through the selection, crossover and mutation operations of the genetic algorithm, the formulation parameters are continuously optimized and adjusted until the simulation results and the target parameters reach the best match.

7. A device for visualizing in-vivo release and absorption of a drug dosage based on a CFD-PBM coupled model, characterized in that, The application relates to a method for simulating the injection of a drug into a physiological environment, and a system thereof. The first construction module is used for constructing a CFD model of the physiological environment of an injection site, and defining fluid mechanics parameters; The second construction module is used for establishing a population balance model (PBM) of drug particles, which describes particle breakage, dissolution, diffusion behavior, enzymatic kinetics and particle volume shrinkage rate; The coupling module is used for embedding the PBM into a CFD model solver to perform multiscale coupling simulation, and obtaining a CFD-PBM coupling model; The visualization module is used for dynamically displaying the drug concentration distribution and particle behavior based on the CFD-PBM coupling model through a three-dimensional visualization engine; An optimization module for adjusting the formulation prescription parameters inversely using a multi-parameter optimization algorithm; A verification and output module for comparing the simulation results with the animal experiment data, calibrating the model parameters using statistical analysis method, and generating a key data report.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the program to realize the method for visualizing in-vivo release and absorption of a drug dosage form based on a CFD-PBM coupling model according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method for visualizing in-vivo release and absorption of a drug dosage form based on a CFD-PBM coupling model according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the method for visualizing in-vivo release and absorption of a drug dosage form based on a CFD-PBM coupling model according to any one of claims 1 to 6.

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