Assessment method for screening drug compatibility

Abstract

The invention discloses an evaluation method for screening drug compatibility, and particularly relates to the field of drug compatibility in a simulated vein environment, which comprises the following steps: constructing a bionic vein environment platform based on haemodynamic variables and blood chemical indexes collected clinically; the input variables of the bionic vein environment platform comprise the temperature, the pH buffer area, the flow velocity and the ion concentration; in a bionic vein environment platform, chemical stability and / or reaction characteristics among drug molecules are evaluated by monitoring drug concentration gradient, drug solubility, reaction kinetics and oxidation-reduction potential. According to the method, precipitation generation under the intravenous infusion condition is monitored, the defect that only physical and chemical parameters are relied on is overcome, a precipitation formation mechanism can be recognized, early warning is conducted in advance, and the infusion blockage risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of drug compatibility under simulated venous environment. More specifically, the present invention relates to an evaluation method for screening drug compatibility. Background Art

[0002] Drug compatibility refers to the ability of two or more drugs to maintain physical, chemical, and biological stability during mixing, storage, or infusion, ensuring no precipitation, decomposition, inactivation, or generation of harmful by-products; compatibility involves chemical reactions between drugs, pH changes, solubility effects, and interactions with infusion device materials; clinically, drug compatibility assessment is crucial for intravenous infusions, compound preparations, and combination drug regimens, ensuring stable drug efficacy, infusion safety, and reducing risks such as drug adverse reactions and particulate blockages;

[0003] The traditional drug compatibility assessment system mainly focuses on basic physical and chemical parameters, lacking systematic analysis of precipitate formation after drug mixing during intravenous infusion, thus being unable to relatively accurately predict the mechanism of precipitate formation and its impact on infusion safety. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an evaluation method for screening drug compatibility, which makes up for the deficiency of relying solely on physical and chemical parameters by monitoring precipitate formation under intravenous infusion conditions to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution: An evaluation method for screening drug compatibility, comprising:

[0006] Based on hemodynamic variables and blood chemistry indicators collected clinically, a bionic venous environment platform is constructed, and the input variables of the bionic venous environment platform include temperature, pH buffer, flow rate, and ion concentration;

[0007] In the bionic venous environment platform, by monitoring drug concentration gradient, drug solubility, reaction kinetics, and redox potential, the chemical stability and / or reaction characteristics between drug molecules are evaluated;

[0008] Scattering spectroscopy or electron imaging or potential analysis is used to perform multi-angle detection on the particle size distribution, morphology, aggregation rate, and surface charge of the precipitate;

[0009] Immediately afterwards, in combination with biological variables such as cytotoxicity, immune activation, thrombotic tendency, and microcirculation status, the impact of the precipitate and drug mixture on biocompatibility is evaluated;

[0010] Based on variable characteristics, a risk grading model is constructed, and real-time early warning and decision support for drug compatibility screening are realized based on threshold judgment and dynamic intervention variables.

[0011] In a preferred embodiment, clinical hemodynamic variables and blood chemical indicators are extracted, an input variable mapping relationship is established, and temperature, pH buffer, flow rate, and ion concentration data are received and parsed through a bionic venous environment platform;

[0012] A data processing pipeline for input variables is constructed, and the input variables are adjusted in real time through dynamic data stream conversion to match the simulation requirements of the venous environment;

[0013] In the bionic venous environment platform, a temperature control unit, a pH buffer regulation unit, a fluid shear force regulation unit, and an ion concentration regulation unit are configured to regulate the input variables at the physical and chemical levels;

[0014] Through an integrated sensor network, the temperature, pH value, flow rate, and ion concentration in the bionic venous environment platform are monitored in real time, and the input variables are dynamically adjusted through a feedback control mechanism;

[0015] Immediately afterwards, a hydrodynamic model is constructed to make the input variables act on the fluid structure of the simulated vascular lumen;

[0016] A mathematical mapping between the input variables and the environmental response parameters is established, and a computational model is used to predict the diffusion, dissolution, and interaction characteristics of drugs in the bionic venous environment;

[0017] The environmental change characteristics within the fluctuation range of the input variables are analyzed on the bionic venous environment platform, and the control strategy is optimized based on this;

[0018] A boundary condition constraint system for the input variables is constructed to make the bionic venous environment platform consistent under different experimental conditions.

[0019] In a preferred embodiment, a drug concentration gradient measurement model is established, the drug diffusion rate is analyzed in the bionic venous environment platform, and a mathematical mapping of the concentration distribution changing with time is constructed;

[0020] A solubility evaluation model is constructed, and by dynamically monitoring the solubility changes of drugs in different flow rates, ion concentrations, and pH buffers, a solubility function curve is generated;

[0021] An integrated reaction kinetics analysis unit is integrated, and based on the real-time reaction rate measurement data, the interaction strength between drug molecules and the chemical equilibrium shift are calculated;

[0022] An oxidation-reduction potential monitoring unit is deployed to analyze the oxidation state changes of drugs in the venous environment and calculate the electron transfer rate within the oxidation-reduction system;

[0023] Construct a chemical stability prediction model, and use the combined variables of drug concentration gradient, solubility, reaction kinetics, and redox potential to evaluate the molecular structure integrity and degradation trend;

[0024] Establish a classification system for intermolecular interactions of drug molecules, and summarize and generate a probability matrix for precipitation, complexation, redox, or catalytic degradation according to different reaction types;

[0025] Construct a chemical stability risk assessment mechanism, and generate a decision on the chemical compatibility level after drug mixing through variable deviation analysis and trend prediction;

[0026] Construct a chemical stability feedback regulation model, and optimize the drug mixing conditions in the bionic venous environment platform through input variable adjustment strategies.

[0027] In a preferred embodiment, obtain a precipitate sample to be measured from the bionic venous environment platform;

[0028] Use scattering spectroscopy technology to measure the particle size distribution of the precipitate and establish a particle size distribution data model;

[0029] Or use electronic imaging technology to collect images of the precipitate and perform numerical processing on the images to analyze the morphological parameters;

[0030] Or use potentiometric analysis technology to measure the surface charge of the precipitate and record the potential value;

[0031] Monitor the precipitation aggregation process and construct a quantitative relationship between the aggregation rate and time.

[0032] In a preferred embodiment, perform an evaluation based on the bionic venous environment platform, and collect, analyze, and intervene in data in combination with hydrodynamic, blood chemistry, and biological variables;

[0033] Measure the cytotoxicity and analyze the change trend of cell viability under the action of the precipitate and the drug mixture;

[0034] Monitor the immune activation signal and analyze how the release of inflammatory mediators affects the cytotoxicity level;

[0035] Evaluate the thrombosis risk and correlate the degree of immune activation with the platelet aggregation rate;

[0036] Observe the microcirculation state and analyze the influence of thrombosis tendency on the local blood flow pattern and vascular permeability;

[0037] Establish an association matrix between biological variables and precipitate characteristics and quantify the biocompatibility risk level;

[0038] Set a safety threshold and trigger a corresponding intervention mechanism when the corresponding variable exceeds the preset target range.

[0039] In a preferred embodiment, a coupled formula of a bionic venous environment fluid model and a drug concentration is constructed based on Equation 1-1;

[0040] Equation 1-1 is expressed as:

[0041]

[0042] where u(x, t) is a three-dimensional velocity field vector, and the three-dimensional velocity field vector represents the fluid velocity at position and time t; represents the incompressible condition; is the fluid density; is the partial derivative with respect to time t; is the convection term; P(x, t) is the pressure scalar field; μ is the dynamic viscosity coefficient; is the Laplacian operator of the velocity field;

[0043] A convection-diffusion equation for drug concentration is constructed based on Equation 1-2;

[0044] Equation 1-2 is expressed as:

[0045]

[0046] where C(x, t) is the concentration scalar of the drug at position x and time t; is the convection term of the concentration field; is the diffusion coefficient; is the Laplacian term of the concentration field.

[0047] In a preferred embodiment, based on multi-component reaction kinetics and precipitation formation mechanism, on the basis of the known fluid and drug concentration distributions, considering the precipitation formation between drugs and between drugs and the environment, the multi-component reaction rate is expressed based on Equation 2-1;

[0048] Equation 2-1 is expressed as:

[0049]

[0050] where R i is the rate of generation or consumption of the i-th component; C j is the concentration of the j-th component in the fluid; K i is the pre-exponential factor; E i represents the apparent activation energy related to the i-th component; Θ is the reaction energy scale constant; T sys is the reaction environment temperature; α ij is the reaction order of the i-th type of reaction with respect to the j-th component; M represents the total number of components in the reaction system;

[0051] Construct a precipitation formation rate model based on Equation 2-2, and use Γ solid to represent the precipitation formation rate;

[0052]

[0053] where Λ is a constant related to the formation of precipitation nuclei; Ω ion is the ion product or saturation value index; Ω crit represents the threshold for precipitation generation. When Ω ion > Ω crit , precipitation forms; (X) + = max{X, 0}, indicating that if X < 0, then 0 is taken, and no precipitation is generated when not supersaturated; ζ is an exponent used to describe the sensitivity of the precipitation formation rate to supersaturation.

[0054] In a preferred embodiment, in the quantification of precipitation particle size and aggregation behavior, after the precipitation appears in solid phase, its particle size, aggregation rate and morphological distribution are quantified. Based on this, the dynamics of the particle size distribution function is described by Equation 3-1;

[0055] Equation 3-1 is expressed as:

[0056]

[0057] where G(r, t) represents the number density function of precipitation particles with particle size r at time t; is the particle growth rate; is the nucleation function; is the effective rate of decrease or increase in the number of particles near size r due to aggregation or agglomeration;

[0058] Construct Equation 3-2 to describe the integral form of the aggregation effect;

[0059]

[0060] where κ col (r ′ , r - r ′ ) is the collision kernel function, which describes the probability of particles with particle size r ′ and particle size r - r ′ colliding and merging per unit time; G9r ′ , t) and G(r - r ′ , t) are the number densities of particles with particle sizes r ′ and r - r ′ , respectively.

[0061] In a preferred embodiment, in the biological variable coupling model, after quantitatively describing the particle generation and distribution, its comprehensive effects on cells, the immune system, thrombosis, and microcirculation are evaluated;

[0062] Based on Equation 4-1, the coupling of cell viability and immune activation is described;

[0063]

[0064] where X cell (t0 is the cell viability index; Y immune (t) is the immune activation index; S tox (t) is the toxicity stimulating factor, which is obtained by weighted integration of G(r,t) in Equation 3-1 and is expressed as ω(r) is the weight function for measuring the toxicity of particles of different sizes; r max and r min represent the minimum and maximum values of the particle size, respectively; β 1 , β 2 represent the sensitivity coefficients for regulating cell viability decay and immune activation enhancement, respectively; Υ 1 , Υ 2 are the background recovery or inhibition rate constants;

[0065] By constructing Equation 4-2, the thrombosis and microcirculation patency are described;

[0066] Equation 4-2 is expressed as:

[0067]

[0068] where Z thromb (t) is the thrombosis factor accumulation index; β 3 is the influence coefficient of immune activation on thrombosis factor generation; Γ flow is the baseline microcirculation flux; W micro (t) is the microcirculation patency, and the microcirculation patency represents the ratio of the actual throughput to the baseline, and its range is in [0, Γ flow .

[0069] In a preferred embodiment, a risk grading and dynamic intervention mechanism is constructed, and the comprehensive risk function is described by Equation 5-1;

[0070] Equation 5-1 is expressed as:

[0071]

[0072] where R(t) is the comprehensive risk degree; τ is the intermediate integration variable, from 0 to the current time t; Ξ chem (τ is the chemical-level risk index; Ξ chem(τ) is described by Equation 5-2; Ξ bio (τ) is a biological risk indicator; Ξ bio (τ) is described by Equation 5-3; Ω 1 , Ω 2 is the chemical and biological risk weight;

[0073] Equation 5-2 is:

[0074]

[0075] Equation 5-3 is expressed as:

[0076] Ξ bio (τ) = 1 - X cell (τ) + Y immune (τ) + Z thromb (τ

[0077] where Γ solid (C 1 (τ), …, C M (τ)) represents the precipitation formation rate; is used to measure the distribution of precipitation particle size, and G(r, τ) is the particle size distribution function;

[0078] where 1 - X cell (τ) represents the proportion of cell damage; Y immune (τ) represents the degree of inflammation; Z thromb (τ) represents the accumulation of thrombus factors;

[0079] The intervention trigger condition is constructed by Equation 5-4;

[0080] Equation 5-4 is expressed as:

[0081]

[0082] where R crit is the risk threshold; is the intervention function.

[0083] Technical effects and advantages of the present invention:

[0084] 1. By monitoring the precipitation formation under intravenous infusion conditions, this solution makes up for the deficiency of relying only on physical and chemical parameters, which is conducive to identifying the precipitation formation mechanism and early warning, and reducing the risk of infusion blockage;

[0085] 2. By integrating temperature, pH, flow rate and ion concentration through a bionic venous environment platform, the reaction of drugs under simulated vascular conditions is closer to the clinical situation, realizing multi-parameter coupling monitoring and real-time regulation;

[0086] 3. Based on the multi-component reaction kinetics and precipitation formation model, reveal the potential chemical reaction paths between drugs, predict the particle size and morphology, and assist in judging the precipitation formation conditions;

[0087] 4. The present invention quantitatively evaluates the impact of precipitation on the biological system by integrating variables such as cytotoxicity, immune activation, thrombus risk, and microcirculation status, and identifies potential coagulation hazards;

[0088] 5. By constructing a risk grading model and a dynamic intervention mechanism, using the threshold determination as the trigger point, coordinate parameters such as temperature, pH, and flow rate, and real-time regulate the mixing conditions to reduce the risk of infusion blockage or immune overreaction. Description of the Drawings

[0089] Figure 1 is the flowchart of the present invention. Detailed Embodiment

[0090] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0091] Referring to the attached Figure 1 description, an evaluation method for screening drug compatibility according to an embodiment of the present invention includes:

[0092] Based on the hemodynamic variables and blood chemical indicators collected clinically, construct a bionic venous environment platform, and the input variables of the bionic venous environment platform include temperature, pH buffer, flow rate, and ion concentration;

[0093] In the bionic venous environment platform, evaluate the chemical stability and / or reaction characteristics between drug molecules by monitoring the drug concentration gradient, drug solubility, reaction kinetics, and redox potential;

[0094] Use scattering spectroscopy, electron imaging, or potential analysis to perform multi-angle detection on the particle size distribution, morphology, aggregation rate, and surface charge of the precipitate;

[0095] Immediately afterwards, combine the biological variables of cytotoxicity, immune activation, thrombus tendency, and microcirculation status to evaluate the impact of the precipitate and drug mixture on biocompatibility;

[0096] Based on the above variable characteristics, construct a risk grading model, and based on the threshold judgment and dynamic intervention variables, realize real-time early warning and decision support for drug compatibility screening.

[0097] Extract clinical hemodynamic variables and blood chemistry indicators, establish the mapping relationship of input variables, and receive and analyze temperature, pH buffer, flow rate, and ion concentration data through a bionic venous environment platform;

[0098] Construct a data processing pipeline for input variables, and adjust the input variables in real time through dynamic data stream conversion to match the simulation requirements of the venous environment;

[0099] In the bionic venous environment platform, configure a temperature control unit, a pH buffer regulation unit, a fluid shear force regulation unit, and an ion concentration regulation unit to regulate the input variables at the physical and chemical levels;

[0100] Through an integrated sensor network, monitor the temperature, pH value, flow rate, and ion concentration in the bionic venous environment platform in real time, and dynamically adjust the input variables through a feedback control mechanism;

[0101] Immediately construct a hydrodynamic model to make the input variables act on the fluid structure of the simulated vascular lumen;

[0102] Establish a mathematical mapping between input variables and environmental response parameters, and use a computational model to predict the diffusion, dissolution, and interaction characteristics of drugs in the bionic venous environment;

[0103] Analyze the environmental change characteristics within the fluctuation range of input variables on the bionic venous environment platform, and optimize the control strategy based on this;

[0104] Construct a boundary condition constraint system for input variables to make the bionic venous environment platform consistent under different experimental conditions.

[0105] Establish a drug concentration gradient measurement model, analyze the drug diffusion rate in the bionic venous environment platform, and construct a mathematical mapping of the concentration distribution changing with time;

[0106] Construct a solubility evaluation model, and generate a solubility function curve by dynamically monitoring the solubility changes of drugs at different flow rates, ion concentrations, and pH buffers;

[0107] Integrate a reaction kinetics analysis unit, and calculate the interaction strength between drug molecules and the chemical equilibrium shift based on real-time reaction rate measurement data;

[0108] Deploy a redox potential monitoring unit to analyze the oxidation state changes of drugs in the venous environment and calculate the electron transfer rate in the redox system;

[0109] Construct a chemical stability prediction model, and evaluate the molecular structure integrity and degradation trend using the combined variables of drug concentration gradient, solubility, reaction kinetics, and redox potential;

[0110] Establish a classification system for intermolecular interactions of drugs, and generate a probability matrix for precipitation, complexation, redox, or catalytic degradation according to different reaction types;

[0111] Construct a chemical stability risk assessment mechanism, and generate a decision on the chemical compatibility level after drug mixing through variable deviation analysis and trend prediction;

[0112] Construct a chemical stability feedback regulation model, and optimize the drug mixing conditions in the bionic venous environment platform by adjusting the input variable strategy.

[0113] Obtain a sample of the precipitate to be measured from the bionic venous environment platform;

[0114] Use scattering spectroscopy technology to measure the particle size distribution of the precipitate and establish a particle size distribution data model;

[0115] Or use electronic imaging technology to collect images of the precipitate and perform numerical processing on the images to analyze the morphological parameters;

[0116] Or use potentiometric analysis technology to measure the surface charge of the precipitate and record the potential value;

[0117] Monitor the aggregation process of the precipitate and construct a quantitative relationship between the aggregation rate and time.

[0118] Perform an assessment based on the bionic venous environment platform, and collect, analyze, and intervene in data in combination with hydrodynamic, blood chemistry, and biological variables;

[0119] Measure cytotoxicity and analyze the change trend of cell viability under the action of the precipitate and the drug mixture;

[0120] Monitor immune activation signals and analyze how the release of inflammatory mediators affects the cytotoxicity level;

[0121] Evaluate the risk of thrombosis and correlate the degree of immune activation with the platelet aggregation rate;

[0122] Observe the microcirculation state and analyze the influence of thrombotic tendency on local blood flow patterns and vascular permeability;

[0123] Establish a correlation matrix between biological variables and precipitate characteristics to quantify the biological compatibility risk level;

[0124] Set a safety threshold and trigger a corresponding intervention mechanism when the corresponding variable exceeds the preset target range.

[0125] Construct a coupled formula for the bionic venous environment fluid model and drug concentration based on Equation 1-1. Equation 1-1 includes the Navier-Stokes equation and the incompressible condition. Based on the fluid mechanics theory, simulate the blood flow motion state in the blood vessel. The incompressible condition conforms to the approximation of normal blood within the physiological pressure range. Through the numerical solution of the equation set of Equation 1-1, obtain the velocity distribution and pressure distribution in the real blood vessel on the bionic venous environment platform.

[0126] Equation 1-1 is expressed as:

[0127]

[0128] where u(x, t) is the three-dimensional velocity field vector, and the three-dimensional velocity field vector represents the fluid velocity at position and time t. means that x is a point in a three-dimensional Euclidean space, that is, x has three coordinate components, which is written as represents the incompressible condition, which is also that the volume of the fluid does not change with pressure during the flow process. is the fluid density; is the partial derivative with respect to time t, and the partial derivative with respect to time t represents the instantaneous change rate of the velocity field; is the convection term, and the convection term represents the influence of the fluid's own motion on the velocity field distribution; P(x, t) is the pressure scalar field; μ is the dynamic viscosity coefficient, and the dynamic viscosity coefficient is used to characterize the fluid resistance. is the Laplace operator of the velocity field, is used to describe the viscous diffusion effect; in addition is the gradient operator, which is used to represent the spatial derivative operation of a vector or scalar field.

[0129] Construct the convection-diffusion equation for drug concentration based on Equation 1-2.

[0130] Equation 1-2 is expressed as:

[0131]

[0132] where C(x, t) is the concentration scalar of the drug at position x and time t; is the convection term of the concentration field, and the convection term of the concentration field represents the migration effect of the fluid motion on the drug distribution; is the diffusion coefficient; is the Laplace term of the concentration field, is used to describe the diffusion phenomenon driven by the concentration gradient; represents the partial derivative with respect to time t, that is, the instantaneous change rate; in Equation 1-2, couple the drug concentration field and the blood flow field to simulate the spatial distribution of the drug in the blood vessel and its change over time. On the bionic venous environment platform, According to the input variables such as temperature, pH, and ionic strength, real-time adjustments are made. In addition, equation 1-2 is numerically solved together with equation 1-1 to obtain a three-dimensional drug concentration distribution diagram that evolves over time.

[0133] Based on the multi-component reaction kinetics and precipitation formation mechanism, on the basis of known fluid and drug concentration distribution, considering the precipitation formation between drugs and between drugs and the environment, the multi-component reaction rate is expressed based on formula 2-1;

[0134] Formula 2-1 is expressed as:

[0135]

[0136] Where R i is the rate of generation or consumption of the i-th component; C j is the concentration of the jth component in the fluid; K i is the pre-exponential factor, which is used to characterize the benchmark value of the reactivity of the i-th component; E i represents the apparent activation energy associated with the ith component; Θ is the reaction energy scaling constant; T sys is the reaction environment temperature; α ij is the reaction order of the i-th type of reaction to the j-th component; M represents the total number of components in the reaction system;

[0137] Formula 2-1 is based on the comprehensive expansion of Arrhenius theory and the law of mass action and is used for multi-component systems. In addition, in practical applications, different α ij The value can be calibrated in in vitro titration tests or microfluidics experiments. When the value is realized, R i It will be coupled with the concentration field C(x,t) in Equation 1-2 to update the concentration distribution of each component as time changes;

[0138] The precipitation rate model is constructed based on formula 2-2, and the precipitation rate model is constructed by Γ solid represents the precipitation generation rate;

[0139]

[0140] Where Λ is the constant related to the formation of precipitation nuclei; Ω ion It is the ion product or saturation value index, which is composed of the concentration C of each component. j And its chemical equilibrium constant is calculated; Ω crit Indicates the threshold for precipitation. When Ω ion >Ω crit When precipitation can be formed; (X) + =max{X,0}, which means that if X<0, it is taken as 0, and no precipitation is generated when there is no supersaturation; ζ is an index used to describe the sensitivity of the precipitation generation rate to supersaturation;

[0141] The model construction of Equation 2-2 is based on the theory of supersaturated precipitation. When the ion concentration product in the system exceeds the critical value, solid phase precipitation is induced. Additionally, in the bionic vein environment, Ω ion varies with time and space, so Γ solid also changes with time. After combining Equation 2-2 and Equation 2-1, the dynamic transformation process of each component between the solution and the precipitate can be obtained, laying the foundation for subsequent particle size analysis.

[0142] In the quantification of precipitation particle size and aggregation behavior, after the precipitate appears in solid phase form, its particle scale, aggregation rate, and morphological distribution are quantified. Based on this, the kinetics of the particle size distribution function is described by Equation 3-1;

[0143] Equation 3-1 is expressed as:

[0144]

[0145] where G(r,t) represents the number density function of precipitate particles with particle size r at time t; is the particle growth rate, which indicates how the particle size increases with time and can be deduced from the precipitation growth mechanism or local supersaturation in practical applications; is the nucleation function, and the nucleation function in Equation 3-1 represents the rate of new particle generation near size r; is the effective rate of particle number reduction or increase near size r due to aggregation or agglomeration, which can be distinguished by a positive or negative sign to indicate whether absorption or merger occurs;

[0146] Equation 3-1 represents a one-dimensional particle size distribution model in particle population dynamics, which consists of growth, nucleation, and aggregation; functions such as are calculated based on Γ in Equation 2-2 solid and diffusion-reaction conditions; after numerical solution of the above equation, the particle size distribution at different times is obtained, which is conducive to evaluating its impact on blood vessel channel obstruction and subsequent biology;

[0147] The integral form of the aggregation effect is described by constructing Equation 3-2;

[0148]

[0149] where κ col (r ′ ,r-r ′ ) is the collision kernel function, which describes the probability of particles with particle size r ′ and particle size r-r ′ colliding and merging per unit time, and is related to factors such as medium viscosity and particle surface charge; G(r ′ ,t) and G(r-r ′, t) are the number density of particles with particle sizes r ′ and r - r ′ ;

[0150] The above equation is constructed based on the Smoluchowski aggregation model, which is used to describe the binary collision and coalescence process between particles. Additionally, κ col is calibrated by combining experimental data such as scattering spectra and potential analysis to reflect the influence of particle surface characteristics and hydrodynamic environment; by substituting Equation 3-2 into Equation 3-1, the continuous evolution simulation of particle size distribution is realized.

[0151] In the biological variable coupling model, after quantitatively describing the generation and distribution of particles, their comprehensive effects on cells, the immune system, thrombosis, and microcirculation are evaluated;

[0152] Based on Equation 4-1, the coupling of cell viability and immune activation is described;

[0153]

[0154] where X cell (t) is the cell viability index, which includes but is not limited to cell survival rate, and its value is in the range of [0, 1] or expressed as an absolute quantity; Y immune (t) is the immune activation index, such as the level of inflammatory mediators or the activation degree of immune cells, and its value is also characterized in the range of [0, 1] or measured as an absolute quantity; S tox (t) is the toxicity stimulating factor, which is related to the characteristics of precipitated particles. The toxicity stimulating factor is obtained by weighted integration of G(r, t) in Equation 3-1, and it is expressed as ω(r) is the weight function for measuring the toxicity of particles with different sizes; r max and r min represent the minimum and maximum values of the particle size, respectively; β 1 , β 2 represent the sensitivity coefficients for regulating the attenuation of cell viability and the enhancement of immune activation, respectively; Υ 1 , Υ 2 is the background recovery or inhibition rate constant, and Υ 1 , Υ 2 is used to describe the cell repair mechanism and the self-limitation of the immune system; represents the instantaneous change rate of X cell (t) with respect to time t; represents the instantaneous change rate of Y immune (t) with respect to time t;

[0155] In Equation 4-1, the dual effects of "precipitated particles" on cells and the immune system are combined, that is, the more or larger the particles, the greater the S tox(t) is higher, which further exacerbates the decline in cell viability and the increase in immune activation. In addition, the decline in cell viability can further affect the immune environment. When a large number of cells die, it may instead weaken the release of immune factors (the Υ term in Equation 4-1), which can be determined according to the actual physiological mechanism. The numerical solution of Equation 4-1 can dynamically track the changes in the proportion of live cells and the degree of immune activation over time; 2 X cell (t) term), and the numerical solution of Equation 4-1 can dynamically track the changes in the proportion of live cells and the degree of immune activation over time;

[0156] By constructing Equation 4-2, the formation of thrombus and the microcirculation patency are described;

[0157] Equation 4-2 is expressed as:

[0158]

[0159] where Z thromb (t) is the thrombus factor accumulation index, such as the degree of platelet aggregation. The thrombus factor accumulation index can measure the process from no obstruction to complete obstruction within the range of [0, 1]; β 3 is the influence coefficient of immune activation on thrombus factor generation. The larger the value of β 3 , the more the immune response can promote thrombus formation; Γ flow is the baseline microcirculation flux. In practical applications, the meaning of the baseline microcirculation flux includes but is not limited to the blood flow through the microvessels per unit time. The baseline microcirculation flux can correspond to the flow rate control of the bionic venous environment platform; W micro (t) is the microcirculation patency. The microcirculation patency represents the ratio of the actual throughput to the baseline, and its range is in [0, Γ flow ;

[0160] In the construction of Equation 4-2, thrombus formation is often affected by immune and inflammatory responses. The increase in Z thromb (t) will reduce W micro (t); when Z thromb is close to 1, it indicates severe or complete obstruction. At this time, W micro (t) ≈ 0, and the local tissue will face ischemia. Equation 4-2 and the cytotoxicity and immune activation of Equation 4-1 together constitute the description of the biological effects.

[0161] Construct a risk grading and dynamic intervention mechanism, and describe the comprehensive risk function through Equation 5-1;

[0162] Equation 5-1 is expressed as:

[0163]

[0164] where R(t) is the comprehensive risk degree. The larger the value of the comprehensive risk degree, the more serious the risk accumulation; τ is the intermediate integration variable, from 0 to the current time t; Ξ chem(τ) is a chemical-level risk indicator, which is composed of measures such as precipitation formation rate, reaction supersaturation, or particle size; Ξ chem (τ) is described by Equation 5-2; Ξ bio (τ) is a biological risk indicator, and the biological risk indicator is associated with Equation 4-1 and Equation 4-2; Ξ bio (τ) is described by Equation 5-3; Ω 1 , Ω 2 are chemical and biological risk weights, Ω 1 , Ω 2 used to balance the relative contributions in the comprehensive risk degree;

[0165] Equation 5-2 is:

[0166]

[0167] Equation 5-3 is expressed as:

[0168] Ξ bio (τ) = 1 - X cell (τ) + Y immune (τ) + Z thromb (τ)

[0169] where Γ solid (C 1 (τ), …, C M (τ)) represents the precipitation formation rate, and C 1 (τ), …, C M (τ) represents the concentration distribution of M drugs or reactants in the system at the concentration at time τ; used to measure the distribution of precipitation particle size, G(r, τ) is the particle size distribution function, indicating the number density of particles with particle size r at time τ; rG(r, τ) is the weighted particle contribution term, indicating that larger particles have a greater impact on the precipitation risk;

[0170] where 1 - X cell (τ) represents the proportion of cell damage; Y immune (τ) represents the degree of inflammation; Z thromb (τ) represents the accumulation of thrombus factors;

[0171] It should be noted that for the above formula, the comprehensive risk degree R(t) realizes the description of "cumulative damage" by performing time integration on the risk signals at the chemical and biological levels. In practical applications, different studies or different clinical needs can adjust Ω 1 , Ω 2 as well as Ξ chem (τ) and Ξ bioThe more specific the form of (τ) is, the larger the finally obtained value of R(t) is, indicating that the system is more likely to be in an incompatible or high-risk state;

[0172] Construct the intervention trigger condition through Equation 5-4;

[0173] Equation 5-4 is expressed as:

[0174]

[0175] where R crit is the risk threshold; is the intervention function. Once the risk exceeds the threshold, the intervention function adjusts control variables such as the temperature ΔT, pH value ΔpH, ion concentration ΔIon, and flow rate ΔU;

[0176] When the system monitors that R(t) exceeds R crit , operations such as cooling, adjusting the pH, diluting the ion concentration, or changing the flow rate are automatically or manually performed to inhibit the further deterioration of precipitation or thrombus, thereby providing real-time early warning and decision support.

[0177] Regarding the above-mentioned solution, it should be overall explained that:

[0178] In Equations 1-1 and 1-2, in the bionic venous environment, the incompressible Navier-Stokes equation and the convection-diffusion equation are coupled to simulate the blood flow field and the drug concentration field;

[0179] In Equations 2-1 and 2-2, based on multi-component reaction kinetics, the drug interaction and precipitation formation rate are calculated, and the ion product and the critical value are introduced to describe the supersaturation mechanism to ensure the quantification of precipitation formation;

[0180] In Equations 3-1 and 3-2, the scale distribution, nucleation, and aggregation processes of precipitation particles are further characterized, and partial differential equations in the form of distribution functions are used in combination with the Smoluchowski aggregation model;

[0181] In Equations 4-1 and 4-2, the particle characteristics are coupled with biological variables to construct a model of the mutual influence of cell viability, immune activation, thrombus factors, and microcirculation;

[0182] In Equations 5-1, 5-2, Equation 5-3, and Equation 5-4, the risk signals at the chemical and biological levels are accumulated through R(t), and the threshold is set to trigger the intervention to complete the real-time monitoring and response to the drug compatibility screening.

[0183] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating drug compatibility screening, characterized in that: include: Based on the hemodynamic variables and blood chemistry indicators collected clinically, a bionic venous environment platform was constructed. The input variables of the bionic venous environment platform included temperature, pH buffer, flow rate and ion concentration. In the biomimetic intravenous environment platform, the chemical stability and / or reaction characteristics of drug molecules are evaluated by monitoring drug concentration gradient, drug solubility, reaction kinetics and redox potential; Use scattering spectroscopy, electron imaging or potential analysis to perform multi-angle detection of the particle size distribution, morphology, aggregation rate and surface charge of the precipitate; Next, the effects of precipitation and drug mixture on biocompatibility were evaluated in combination with biological variables of cytotoxicity, immune activation, thrombotic tendency, and microcirculatory status; Based on variable characteristics, a risk grading model is constructed, and based on threshold judgment and dynamic intervention variables, real-time early warning and decision support for drug compatibility screening are achieved.

2. The method for evaluating drug compatibility screening according to claim 1, characterized in that: Extract clinical hemodynamic variables and blood chemistry indicators, establish input variable mapping relationships, and receive and parse temperature, pH buffer, flow rate, and ion concentration data through the bionic venous environment platform; Build a data processing pipeline for input variables and adjust input variables in real time through dynamic data flow conversion to match the needs of venous environment simulation; In the bionic vein environment platform, a temperature control unit, a pH buffer adjustment unit, a fluid shear force adjustment unit and an ion concentration adjustment unit are configured to control the physical and chemical aspects of the input variables; Through the integrated sensor network, the temperature, pH value, flow rate and ion concentration in the bionic vein environment platform are monitored in real time, and the input variables are dynamically adjusted through the feedback control mechanism; Next, a fluid dynamics model is constructed so that the input variables act on the fluid structure simulating the lumen of the blood vessel; Establish a mathematical mapping between input variables and environmental response parameters, and use computational models to predict the diffusion, dissolution, and interaction characteristics of drugs in a bionic venous environment; Analyze the environmental change characteristics within the fluctuation range of input variables on the bionic vein environment platform and optimize the control strategy based on this; Construct a boundary condition constraint system for input variables to ensure consistency of the bionic venous environment platform under different experimental conditions.

3. The method for evaluating drug compatibility screening according to claim 2, characterized in that: Establish a drug concentration gradient determination model, analyze drug diffusion rate in a biomimetic venous environment platform, and construct a mathematical mapping of concentration distribution over time; Construct a solubility evaluation model to generate a solubility function curve by dynamically monitoring the solubility changes of drugs in different flow rates, ion concentrations and pH buffers; Integrated reaction kinetics analysis unit, which calculates the interaction strength and chemical equilibrium shift between drug molecules based on real-time reaction rate measurement data; Deploy a redox potential monitoring unit to analyze changes in the oxidation state of drugs in the intravenous environment and calculate the electron transfer rate within the redox system; Construct a chemical stability prediction model to evaluate the molecular structural integrity and degradation trend using the combined variables of drug concentration gradient, solubility, reaction kinetics and redox potential; Establish a classification system for drug-molecule interactions, and generate a probability matrix of precipitation, complexation, redox or catalytic degradation based on different reaction types; Construct a chemical stability risk assessment mechanism to generate chemical compatibility level decisions after drug mixing through variable deviation analysis and trend prediction; A chemical stability feedback control model was constructed to optimize the drug mixing conditions within the bionic venous environment platform by adjusting the strategy through input variables.

4. The method for evaluating drug compatibility screening according to claim 3, characterized in that: Obtaining sediment samples to be tested from the bionic vein environment platform; The particle size distribution of the sediment was measured using scattering spectroscopy technology, and a particle size distribution data model was established; Or use electronic imaging technology to collect sediment images and digitize the images to analyze morphological parameters; Or use the potential analysis technique to measure the surface charge of the precipitate and record the potential value; Monitor the precipitate aggregation process and construct a quantitative relationship between the aggregation rate and time.

5. The method for evaluating drug compatibility screening according to claim 4, characterized in that: Perform assessments based on a biomimetic venous environment platform, combining fluid dynamics, blood chemistry, and biological variables for data collection, analysis, and intervention; Determine cytotoxicity and analyze the changing trend of cell viability under the action of precipitation and drug mixture; Monitor immune activation signals and analyze how the release of inflammatory mediators affects cytotoxicity levels; Assess the risk of thrombosis and correlate the degree of immune activation with platelet aggregation rate; Observe the microcirculatory status and analyze the impact of thrombotic tendency on local blood flow pattern and vascular permeability; Establish a correlation matrix between biological variables and precipitation characteristics to quantify the biocompatibility risk level; Set safety thresholds and trigger corresponding intervention mechanisms when the corresponding variables exceed the preset target range.

6. The method for evaluating drug compatibility screening according to claim 5, characterized in that: Based on formula 1-1, the coupling formula of bionic venous environment fluid model and drug concentration is constructed; Formula 1-1 is expressed as: Where u(x,t) is the three-dimensional velocity field vector, which represents the position and the fluid velocity at time t; It is expressed as an incompressible condition; is the fluid density; is the partial derivative at time t; is the convection term; P(x,t) is the pressure scalar field; μ is the dynamic viscosity coefficient; is the Laplace operator of the velocity field; Construct the drug concentration convection-diffusion equation based on equation 1-2; Formula 1-2 is expressed as: Where C(x,t) is the concentration scalar of the drug at position x and time t; is the convection term of the concentration field; is the diffusion coefficient; is the Laplace term of the concentration field.

7. The method for evaluating drug compatibility screening according to claim 6, characterized in that: Based on the multi-component reaction kinetics and precipitation formation mechanism, on the basis of known fluid and drug concentration distribution, considering the precipitation formation between drugs and between drugs and the environment, the multi-component reaction rate is expressed based on formula 2-1; Formula 2-1 is expressed as: Where R i is the rate of generation or consumption of the i-th component; C j is the concentration of the jth component in the fluid; K i is the pre-exponential factor; E i represents the apparent activation energy associated with the i-th component; Θ is the reaction energy scaling constant; T sys is the reaction environment temperature; α ij is the reaction order of the i-th type of reaction to the j-th component; M represents the total number of components in the reaction system; The precipitation rate model is constructed based on formula 2-2, and the precipitation rate model is constructed by Γ solid represents the precipitation generation rate; Where Λ is the constant related to the formation of precipitation nuclei; Ω ion Is the ion product or saturation value index; Ω crit Indicates the threshold for precipitation. When Ω ion >Ω crit When a precipitate is formed; (X) + =max{X,0}, which means that if X<0, it is taken as 0, and no precipitation is generated when it is not oversaturated; ζ is an index used to describe the sensitivity of the precipitation generation rate to supersaturation.

8. The method for evaluating drug compatibility screening according to claim 7, characterized in that: In the quantification of the particle size and aggregation behavior of the precipitate, when the precipitate appears in the solid phase, its particle size, aggregation rate and morphological distribution are quantified, based on which the particle size distribution function dynamics is described by equation 3-1; Formula 3-1 is expressed as: Where G(r,t) represents the number density function of precipitated particles with a particle size of r at time t; is the particle growth rate; is the kernel generating function; The effective rate at which the number of particles around size r decreases or increases due to aggregation or agglomeration; The integral form of the aggregation effect is described by constructing formula 3-2; where κ col (r ′ ,rr ′ ) is the collision kernel function, which describes the particle size r ′ and particle size rr ′ The probability of particles colliding and merging in a unit time; G(r ′ ,t) and G(rr ′ ,t) are the particle sizes r ′ With rr ′ The particle number density.

9. The method for evaluating drug compatibility screening according to claim 8, characterized in that: In the biological variable coupling model, after quantitatively describing the particle generation and distribution, its comprehensive effects on cells, immune system, thrombosis and microcirculation are evaluated; Based on Equation 4-1, the coupling between cell viability and immune activation is described; Where X cell (t) is the cell viability index; Y immune (t) is the immune activation index; S tox (t) is the toxic stimulation factor, which is obtained by weighted integration of G(r,t) in formula 3-1, and is expressed as ω(r) is the weight function for measuring the toxicity of particles of different sizes; r max and r min Respectively represent the minimum and maximum values ​​of particle size; β1 and β2 represent the sensitivity coefficients for regulating cell viability attenuation and immune activation enhancement; Υ1 and Υ2 are background recovery or inhibition rate constants; By constructing formula 4-2, we can describe the relationship between thrombosis and microcirculatory patency; Formula 4-2 is expressed as: Where Z thromb (t) is the accumulation index of thrombotic factors; β3 is the influence coefficient of immune activation on the generation of thrombotic factors; Γ flow is the baseline microcirculatory flux; W micro (t) is the patency of microcirculation, which indicates the ratio of actual flow rate to baseline, and its range is [0,Γ flow ].

10. The method for evaluating drug compatibility screening according to claim 9, characterized in that: Construct risk grading and dynamic intervention mechanism, and describe the comprehensive risk function through formula 5-1; Formula 5-1 is expressed as: Where R(t) is the comprehensive risk; τ is the intermediate integral variable, from 0 to the current time t; chem (τ) is the risk index at the chemical level; chem (τ) is described by equation 5-2; bio (τ) is the biological risk index; bio (τ) is described by formula 5-3; Ω1, Ω2 are chemical and biological risk weights; Formula 5-2 is: Formula 5-3 is expressed as: X bio (τ)=1-X cell (t)+Y immune (t)+Z thromb (t) where Γ solid (C1(τ),…,C M (τ)) represents the precipitation generation rate; It is used to measure the distribution of precipitation particle size. G(r,τ) is the particle size distribution function; Where 1-X cell (τ) represents the proportion of cell damage; Y immune (τ) represents the degree of inflammation; Z thromb (τ) represents the accumulation of thrombotic factors; Construct the intervention trigger condition through formula 5-4; Formula 5-4 is expressed as: Where R crit is the risk threshold; is the intervention function.

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