Hydrogel for injection and preparation and formula optimization method thereof

Abstract

The invention provides hydrogel for injection and a preparation and formula optimization method thereof, and belongs to the technical field of hydrogel for injection, and the formula comprises two or more 2-arm or multi-arm polyethylene glycol polymers containing functional groups which can be cross-linked with each other, magnesium lithium silicate and a contrast agent. The optimization method comprises the following steps: firstly, designing an orthogonal experiment, systematically investigating the influence of raw material factors (such as polyethylene glycol molecular weight, functional groups, concentration and the like) on product performance (such as rheology and mechanical properties), and obtaining basic experiment data; and establishing a chemical reaction kinetic model and a product performance prediction model based on machine learning based on the data. And determining a multi-objective optimization objective function according to product performance requirements. And establishing a nonlinear multi-objective equation set for formula optimization in combination with a material proportioning relationship. And solving the optimization model by adopting an intelligent optimization algorithm to obtain a preliminary optimization formula. And carrying out an optimization experiment under a small-scale test condition to obtain new experiment data, and updating model parameters. And the optimization iteration is repeated until convergence.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydrogels for injection, and in particular, relates to a hydrogel for injection and a preparation method and formula optimization method thereof. Background Art

[0002] In the process of clinical diagnosis and treatment, implantable hydrogels have broad application prospects. They can exist stably in the body for a long time as biomaterials and play special functional roles, such as hemostasis, embolization, stent reinforcement, drug carriers, etc. Due to high biocompatibility, high safety and injectable properties, implantable hydrogels have gradually replaced traditional solid implants and become an important new medical material. In the prior art, hydrogel materials are mainly divided into two categories: natural-based materials and synthetic materials. Natural-based materials are mostly derived from animal and plant tissues or extracellular matrix proteins, such as gelatin, collagen, hyaluronic acid, etc. This type of material has good biocompatibility, but there are problems such as purity, batch differences and limited sources. Synthetic materials are mostly polymers, such as polyethylene glycol (PEG), polyvinyl alcohol (PVA), polylactide (PPE), etc. Synthetic materials have the advantages of single composition and controllable structure, but some materials have poor biocompatibility and degradation. Common hydrogel cross-linking methods include: physical cross-linking (such as electrostatic action, block copolymer self-assembly), chemical cross-linking (such as free radical initiation, ultraviolet light cross-linking, enzyme-catalyzed cross-linking) and composite cross-linking. Physically cross-linked hydrogels are injectable and self-healing, but have low mechanical strength. Chemically cross-linked hydrogels have high strength, but poor injectability and intelligent responsiveness. Composite cross-linking can combine the advantages of both, but the formula design and reaction control are more complicated. In addition, implant materials often need to have imaging properties to facilitate precise positioning during surgery and postoperative follow-up observation. This requires the contrast agent (such as iodide, barium salt, etc.) to be compounded with the matrix. However, due to the particle state and super affinity of the contrast agent itself, it often reduces the mechanical properties of the hydrogel and interferes with its rheological properties.

[0003] Therefore, existing injectable hydrogels still face some challenges in practical applications. First, in order to obtain sufficient viscosity and mechanical strength, the polymer concentration usually needs to be increased, but this will greatly increase the initial viscosity of the hydrogel, making it difficult to inject into the human body through a small injection needle. Second, the formulation optimization process of the hydrogel usually relies on a lot of trial and error experiments, which requires a lot of time and resources. Therefore, how to ensure the performance of the hydrogel while taking into account its injectability and establishing an efficient formulation optimization strategy has become a technical problem that needs to be solved urgently in this field.

[0004] That is to say, existing injectable hydrogels have technical problems such as difficulty in accurately controlling strength and viscosity, and the formulation optimization process usually relies on a large number of trial and error experiments, which requires a lot of time and resources. Summary of the invention

[0005] In view of this, the present invention provides a hydrogel for injection and a preparation and formulation optimization method thereof, which can solve the technical problems of existing injectable hydrogels that are difficult to accurately control strength and viscosity and the formulation optimization process usually relies on a large number of trial and error experiments, requiring a lot of time and resources.

[0006] The present invention is achieved in that:

[0007] 1. The first aspect of the present invention provides a hydrogel for injection, which includes a polymer compound containing functional groups such as polyethylene glycol amino, polyethylene glycol thiol, polyethylene glycol carboxyl, or polyethylene glycol hydroxyl and a polymer of other functional groups that can undergo cross-linking reactions, as well as a lithium magnesium silicate component and a contrast agent, and a solvent.

[0008] Wherein, the functional group is polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde.

[0009] The lithium magnesium silicate component includes any one of sodium magnesium aluminum silicate, montmorillonite, zeolite, bentonite, or kaolin; the contrast agent uses any one of iodized oil, sodium iodide, and barium sulfate; the solvent used is water or buffer solution.

[0010] Wherein, the functional group is polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde.

[0011] Optionally, the specific formula is: 2-arm polyethylene glycol amino: 0.1~99.9% w / v, 2-arm polyethylene glycol amide: 0.1~99.9% w / v, sodium magnesium aluminum silicate: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

[0012] Optionally, the specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol acrylate: 0.1~99.9% w / v, montmorillonite: 0.1~99.9% w / v, barium sulfate: 0.1~99.9% w / w.

[0013] Optionally, the specific formula is: 4-arm polyethylene glycol amino: 0.1~99.9% w / v, 4-arm polyethylene glycol isocyanate: 0.1~99.9% w / v, zeolite: 0.1~99.9% w / v, sodium iodide: 0.1~99.9 mol / L.

[0014] Optionally, the specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol ethylene oxide: 0.1~99.9% w / v, bentonite: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

[0015] A second aspect of the present invention provides a method for preparing a hydrogel for injection, comprising the following steps:

[0016] Step 1, preparing a polymer solution of polyethylene glycol amino or polyethylene glycol thiol or polyethylene glycol carboxyl or polyethylene glycol hydroxyl and functional groups, specifically, dissolving polyethylene glycol amino or polyethylene glycol thiol and an equivalent amount of polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde in a solvent, and stirring to fully mix them;

[0017] Step 2, preparing a lithium magnesium silicate suspension, mixing a certain amount of lithium magnesium silicate components with deionized water, and using a stirrer to stir for a certain period of time to form a uniform nano lithium magnesium silicate suspension;

[0018] Step 3, the lithium magnesium silicate suspension prepared in step 2 is mixed with the two polymer solutions in step 1 respectively and then mixed with the polymer mixed solution containing lithium magnesium silicate, or the two mixed solutions in step 1 are first mixed and then mixed with the lithium magnesium silicate suspension in step 2, and then an appropriate amount of medical contrast agent is added and stirred until uniform to obtain the required hydrogel with high viscosity and high strength;

[0019] Step 4: Perform physical and chemical property tests on the hydrogel prepared in step 3, including rheological property tests and mechanical strength tests, to ensure that the hydrogel meets the use requirements.

[0020] A third aspect of the present invention provides a method for optimizing the formulation of a hydrogel for injection, comprising the following steps:

[0021] S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data;

[0022] S20. Establish chemical reaction model and product performance prediction model based on basic experimental data;

[0023] S30. Determine the multi-objective optimization objective function according to product performance requirements;

[0024] S40. Combine the material measurement relationship to establish a multi-objective optimization model for the formula;

[0025] S50, using intelligent optimization algorithm to solve the multi-objective optimization model to obtain the initial optimal formula component ratio;

[0026] S60, using the preliminary optimal formula to perform optimization experiments and obtain optimization experiment data;

[0027] S70, updating the chemical reaction model and the product performance prediction model using the optimized experimental data;

[0028] S80, reconstructing the multi-objective optimization model, and using the intelligent optimization algorithm to solve again, to obtain a new optimal formula component ratio;

[0029] S90. Repeat steps S60 to S80 until the formula converges to the optimal solution.

[0030] Furthermore, the basic experiment is specifically an orthogonal experimental design, which examines the effects of polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage on rheological properties during injection and mechanical strength after curing.

[0031] Furthermore, a multi-objective optimization model for the formulation is developed, which specifically comprises a set of nonlinear equations with polyethylene glycol molecular weight, type of functional group, polymer concentration, amount of cross-linking agent, type and amount of lithium magnesium silicate, type and amount of contrast agent as independent variables, and minimum shear viscosity, maximum compression strength, and maximum tensile strength during injection as dependent variables.

[0032] Specifically, the steps of the recipe optimization method include:

[0033] S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data. The basic experiments are specifically orthogonal experimental designs, which respectively examine the effects of polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage on rheological properties during injection and mechanical strength after curing.

[0034] S20, establishing a chemical reaction model and a product performance prediction model based on basic experimental data. The chemical reaction model is specifically based on a kinetic equation to describe the polymer cross-linking reaction process, and the product performance prediction model is specifically based on a machine learning algorithm to perform nonlinear mapping modeling on the experimental data.

[0035] S30. Determine a multi-objective optimization objective function according to product performance requirements, wherein the multiple objectives include minimizing shear viscosity during injection, maximizing compression strength, maximizing tensile strength, etc.

[0036] S40. Combined with the material measurement relationship, a multi-objective optimization model for the formula is established. Specifically, a set of nonlinear equations is established with the molecular weight of polyethylene glycol, type of functional group, polymer concentration, amount of cross-linking agent, type and amount of lithium magnesium silicate, type and amount of contrast agent as independent variables, and minimum shear viscosity, maximum compressive strength and maximum tensile strength during injection as dependent variables.

[0037] S50. Use intelligent optimization algorithm to solve the multi-objective optimization model and obtain the preliminary optimal formula component ratio.

[0038] S60, using the preliminary preferred formula to conduct optimization experiments to obtain optimization experiment data. The optimization experiment specifically prepares samples under small-scale trial production conditions and tests rheological properties, curing speed and mechanical performance indicators.

[0039] S70. Use the optimized experimental data to update the chemical reaction model and product performance prediction model.

[0040] S80. Reconstruct the multi-objective optimization model and use the intelligent optimization algorithm to solve it again to obtain a new optimal formula component ratio.

[0041] S90. Repeat steps S60 to S80 until the formula converges to the optimal solution.

[0042] The step S10 specifically includes:

[0043] Step S101, determining factors to be investigated and their different levels, wherein the factors include polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, and contrast agent type and dosage;

[0044] Step S102, selecting a suitable orthogonal table according to the number of factors and the number of their respective levels, wherein the orthogonal table is used to arrange the combination relationship of the levels of each factor;

[0045] Step S103, arranging the formulation experimental groups according to the orthogonal table, and preparing at least three parallel samples for each experimental group;

[0046] Step S104: for all samples prepared in step S103, testing their rheological performance indexes and mechanical performance indexes after curing;

[0047] Step S105: Process all experimental data using statistical analysis methods to screen out the main factors that have the greatest impact on product performance;

[0048] Step S106, obtaining a preliminary optimization direction, such as increasing polymer concentration, increasing functional group density, etc.

[0049] Wherein, the step S20 specifically includes:

[0050] Step S201, constructing a chemical reaction kinetics model, wherein the chemical reaction kinetics model uses a reaction kinetics equation to describe the cross-linking reaction process;

[0051] Step S202: construct a product performance prediction model, wherein the product performance prediction model uses a machine learning algorithm to perform nonlinear mapping modeling on experimental data;

[0052] Optionally, an objective function is used to optimize the combination of different desired performance indicators; and the material measurement relationship is introduced as a constraint condition into the recipe multi-objective optimization model.

[0053] Wherein, the step S50 adopts a particle swarm optimization algorithm to solve the recipe multi-objective optimization model.

[0054] In the particle swarm optimization algorithm, the positions and velocities of the individuals in the population are initialized, and the individual and global optimal positions are updated according to the fitness value of each individual.

[0055] Wherein, the step S50 adopts a genetic algorithm to solve the multi-objective optimization model of the recipe; in the genetic algorithm, the population individuals are optimized through breeding, crossover and mutation operations.

[0056] Wherein, the step S70 includes adding the optimized experimental data to the original data set to expand the amount of training data for the chemical reaction model and the product performance prediction model.

[0057] Wherein, the step S80 includes adjusting the weight coefficients of various indicators in the multi-objective optimization objective function.

[0058] Among them, steps S60 to S80 are repeatedly executed until the formula converges to the optimal solution. The judgment criterion is that when the optimal solution has no obvious change for several consecutive generations, it is considered to have converged to the global optimal formula.

[0059] Compared with the prior art, the hydrogel for injection provided by the present invention and the preparation and formulation optimization method thereof have the following beneficial effects:

[0060] 1. The mild chemical cross-linking reaction of polyethylene glycol amino or polyethylene glycol thiol or polyethylene glycol carboxyl or polyethylene glycol hydroxyl with polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde can realize in situ curing of hydrogel under physiological conditions without the need for high temperature or initiator participation, which is beneficial to improve biocompatibility. At the same time, by adjusting the polymer molecular weight and functional group concentration, the strength and viscosity of the hydrogel can be precisely controlled.

[0061] 4. A chemical reaction kinetics model and a product performance prediction model based on machine learning were established, and a multi-objective optimization algorithm was used to intelligently optimize the formula. This method can significantly improve the efficiency and accuracy of formula development, reduce a large number of redundant experiments, and lay the foundation for the industrial application of hydrogel materials.

[0062] In summary, the solution of the present invention solves the technical problems that the existing injectable hydrogels have difficulty in accurately controlling strength and viscosity, and the formulation optimization process usually relies on a large number of trial and error experiments, which requires a lot of time and resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0064] Figure 1 A flow chart of the method provided by the present invention;

[0065] Figure 2 is the shear viscosity curve of the hydrogel in experimental group 1;

[0066] Figure 3 is the shear viscosity curve of the hydrogel in experimental group 2;

[0067] Figure 4 is the shear viscosity curve of the hydrogel in experimental group 3;

[0068] Figure 5 is the shear viscosity curve of the hydrogel in experimental group 4;

[0069] Figure 6 This is the shear viscosity curve of the hydrogel in experimental group 5. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0071] The first aspect of the present invention provides a hydrogel for injection, the formula of which includes a polymer of polyethylene glycol amino group or polyethylene glycol thiol group or polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and a functional group, as well as a magnesium silicate lithium component and a contrast agent, and a solvent. The magnesium silicate lithium component includes any one of magnesium aluminum sodium silicate, montmorillonite, zeolite, bentonite, or kaolin; the contrast agent uses any one of iodized oil, sodium iodide, and barium sulfate; the solvent is water or buffer. The functional group is polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde.

[0072] Optionally, the functional group should satisfy the following conditions:

[0073] (1) It can form covalent bonds or hydrogen bonds with polyethylene glycol to construct a three-dimensional network structure of hydrogel;

[0074] (2) It has good biocompatibility and will not cause toxicity or allergic reactions to the human body;

[0075] (3) Ability to provide specific functions, such as enhancing cell adhesion, promoting tissue regeneration, and controlling drug release;

[0076] Optional additional functional groups include:

[0077] Hydroxyl (-OH): such as polyethylene glycol hydroxyl, polymethacrylate hydroxyl ester;

[0078] Amino group (-NH2): such as polyethylene glycol amino group, polylysine;

[0079] Carboxyl (-COOH): such as polyethylene glycol carboxylic acid, polyacrylic acid;

[0080] Sulfhydryl (-SH): such as polyethylene glycol thiol;

[0081] Alkenyl (-C=C-): such as polyethylene glycol acrylate;

[0082] Phosphates: such as phosphorylated polysaccharides;

[0083] Carbohydrates: such as chitosan and sodium hyaluronate;

[0084] Peptides: such as Arg-Gly-Asp (RGD) peptide sequence;

[0085] Antibodies or receptor ligands: such as antibodies, growth factors, cell adhesion proteins;

[0086] In general, as long as the above conditions are met, all of the above functional groups can be used as functional groups of polyethylene glycol to form covalent or non-covalent bonds with it to construct hydrogel materials with specific biological functions. Among them, polar functional groups such as hydroxyl, amino, and carboxyl are more common, while sugars, peptides, and receptor ligands can give the gel biological activity.

[0087] Optionally, lithium magnesium silicate can be: montmorillonite, kaolinite, bentonite, zeolite, Laponite, Saponite, Vermiculite, Syntheticfluorinated mica, magnesium oxide (MgO), silicon dioxide (SiO2), hydrated magnesium silicate (Mg3Si4O10(OH)2), hydrated aluminum silicate;

[0088] Contrast agents include organic contrast agents such as iodine contrast agents, Gadolinium contrast agents, and platinum contrast agents, or level 5 contrast agents. Iodine contrast agents include iohexol, iopamidol, iomeprol, and iopromide; Gadolinium contrast agents include gadoteratemeglumine, gadopentetate dimeglumine, and gadoteridol; platinum contrast agents include cyclic platinum complexes (Cisplatin) and platinum (IV) hydroxide (Oxaliplatin); non-polar contrast agents may be iron sulfide nanoparticles (Ferumoxytol), magnetic iron oxide nanoparticles (Superparamagnetic iron oxide nanoparticles, SPION), sulfide micro- or nanoparticles, potassium iodide (KI), barium oxide (BaO), and barium sulfate (BaSO4). In general, organic contrast agents are usually small organic molecules or complexes containing heavy metals (such as iodine, gadolinium, and platinum), which can be used in imaging technologies such as X-ray CT, MRI, and PET. Inorganic contrast agents are mostly metal oxide / sulfide nanoparticles, which are mainly used for magnetic resonance imaging (MRI) and ultrasound imaging. Both types of contrast agents are widely used in the field of medical imaging.

[0089] The effect of this formula is that high viscosity and high strength hydrogels are needed clinically, such as vascular embolization, so the molecular weight and concentration of each component need to be increased to achieve the expected viscosity and mechanical strength, but high viscosity and high strength hydrogels are difficult or even impossible to be put into a syringe and injected through a catheter. The introduction of viscosity thixotropic agents such as lithium magnesium silicate can reduce the concentration of each component, so that the formed hydrogel can not only achieve the expected viscosity and mechanical strength, but also can be easily injected by hand without a power device. In this formula, the cross-linking precursor is polyethylene glycol amino or polyethylene glycol thiol or polyethylene glycol carboxyl or polyethylene glycol hydroxyl and polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde. When the two are mixed, a radical-catalyzed addition reaction will occur - for example, the thiol group acts as a nucleophile to attack the carbon-carbon double bond of the acrylate, thereby forming a covalent cross-linking. This reaction mechanism is different from the traditional acrylate cross-linking reaction initiated by free radicals and can be carried out under mild conditions. In addition to acrylates, ketone groups, aldehyde groups, isocyano groups, etc. can also undergo addition reactions with thiol groups to form stable thioether bonds. Therefore, by appropriately modifying the functional groups of the polymer, various types of cross-linking methods can be achieved, thereby precisely regulating the properties of the hydrogel.

[0090] Optionally, the specific formula is: 2-arm polyethylene glycol amino: 0.1~99.9% w / v, 2-arm polyethylene glycol amide: 0.1~99.9% w / v, sodium magnesium aluminum silicate: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

[0091] Optionally, the specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol acrylate: 0.1~99.9% w / v, montmorillonite: 0.1~99.9% w / v, barium sulfate: 0.1~99.9% w / w.

[0092] Optionally, the specific formula is: 4-arm polyethylene glycol amino: 0.1~99.9% w / v, 4-arm polyethylene glycol isocyanate: 0.1~99.9% w / v, zeolite: 0.1~99.9% w / v, sodium iodide: 0.1~99.9 mol / L.

[0093] Optionally, the specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol ethylene oxide: 0.1~99.9% w / v, bentonite: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

[0094] A second aspect of the present invention provides a method for preparing a hydrogel for injection, comprising the following steps:

[0095] Step 1, preparing a polymer solution of polyethylene glycol amino group or polyethylene glycol thiol group or polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and a functional group, wherein the specific operation is to dissolve the polyethylene glycol amino group or polyethylene glycol thiol group or polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and an equivalent amount of polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde in a solvent, and stir to fully mix them;

[0096] Step 2, preparing a lithium magnesium silicate suspension, mixing a certain amount of lithium magnesium silicate components with deionized water, and using a stirrer to stir for a certain period of time to form a uniform nano lithium magnesium silicate suspension;

[0097] Step 3, the lithium magnesium silicate suspension prepared in step 2 is mixed with the two polymer solutions in step 1 respectively and then mixed with the polymer mixed solution containing lithium magnesium silicate, or the two mixed solutions in step 1 are first mixed and then mixed with the lithium magnesium silicate suspension in step 2, and then an appropriate amount of medical contrast agent is added and stirred until uniform to obtain the required hydrogel with high viscosity and high strength;

[0098] Step 4: Perform physical and chemical property tests on the hydrogel prepared in step 3, including rheological property tests and mechanical strength tests, to ensure that the hydrogel meets the use requirements.

[0099] Specifically, the specific implementation of step 1 is as follows: First, select an appropriate solvent according to the type and molecular weight of the polymer, and common ones are water, phosphate buffered saline (PBS), ethanol, ethyl acetate, etc. The polymer to be dissolved (such as polyethylene glycol amino or polyethylene glycol thiol or polyethylene glycol carboxyl or polyethylene glycol hydroxyl) and the functional group polymer (such as polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde) are added to the solvent according to the theoretically calculated equivalent ratio, and stirred to dissolve. During the dissolution process, it can be heated to a certain temperature to accelerate the dissolution rate, but the temperature should not be too high to avoid polymer degradation. The solution should be completely transparent and uniform, without visible particles or precipitates. When the solution contains more than two polymers, the order of addition must be controlled, and the low-concentration component is first added to the solvent to dissolve, and then the high-concentration component is gradually added to avoid flocculation or precipitation. The stirring equipment requires an adjustable speed to achieve vigorous stirring or slow vortex, and a constant temperature water bath is configured to maintain a certain temperature. The amount of solvent used depends on the total mass concentration of the polymer, usually between 1:5 and 1:50 in solid-liquid mass ratio. The purpose of this process is to evenly disperse the components and prepare for the next reaction.

[0100] The specific implementation method of step 2 is as follows: First, lithium magnesium silicate particles need to be dispersed in a polar solvent such as deionized water, and the mass ratio of lithium magnesium silicate to water is usually controlled between 1:5 and 1:10. The dispersion process can be assisted by a stirrer, a blender or an ultrasonic crusher, so that the lithium magnesium silicate particles are sheared and broken into nanoscale by mechanical force to form a high-concentration stable suspension. Among them, the rotation speed, filler load, grinding time, grinding ball material and diameter during stirring will affect the grinding effect, and the optimal process parameters need to be determined by orthogonal experiments. During the stirring process, lithium magnesium silicate dissociates under mechanical action and slowly reagglomerates to form a nano-sheet structure arranged in parallel layers. The suspension can be further diluted with distilled water to the desired concentration. The higher the concentration of the suspension, the greater the bulk viscosity and the stronger the rheological properties. The purpose of this step is to prepare a nano-lithium magnesium silicate suspension with ideal rheological properties.

[0101] The specific implementation method of step 3 is as follows: the polymer solution prepared in step 1 and the lithium magnesium silicate suspension prepared in step 2 are mixed according to the mass ratio or volume ratio specified in the formula, and the mixing order of the two can be controlled. Then add the pre-prepared contrast agent solution or suspension, and stir thoroughly to mix the components evenly, so as to obtain a hydrogel precursor liquid. The stirring method can be mechanical stirring or magnetic stirring, and the stirring intensity and time need to be determined according to the actual situation. If obvious phase separation or gel blocks appear during the mixing process, the stirring intensity needs to be increased and the temperature needs to be appropriately increased. The precursor liquid should be a uniform viscous liquid without obvious particles or precipitation. The purpose of this step is to fully mix the components, adjust the initial rheological properties of the hydrogel, and prepare for the next cross-linking reaction.

[0102] The specific implementation method of step 4 is as follows: the precursor liquid prepared in step 3 is moved to the desired part by using a syringe, catheter or other container, so that it is away from the air. At this time, the polymer molecules are in a straight state in the solvent and have good fluidity. Once the precursor liquid is injected or taken out, the solvent gradually evaporates, the molecular chain shortens and a cross-linking reaction occurs. For example, a nucleophilic addition reaction occurs between polyethylene glycol amino or polyethylene glycol thiol molecules and polyethylene glycol amide or polyethylene glycol acrylate molecules to form a stable amide bond or thioether bond, thereby achieving cross-linking of the polymer network. The reaction can proceed spontaneously at room temperature without the need for additional heating or initiators. The reaction rate can be regulated by changing the concentration of the two polymers and the concentration of functional groups. A high cross-linking density increases the strength of the hydrogel, but reduces the viscosity. In addition, lithium magnesium silicate undergoes an interlayer intercalation reaction with water, which changes the rheological behavior of the hydrogel. The purpose of this step is to achieve the transformation of the hydrogel from a low-viscosity and high-fluidity state to a high-viscosity and low-fluidity state through a chemical reaction to meet the needs of clinical use.

[0103] The specific implementation method of step 5 is as follows: Based on the known hydrogel composition and ratio, it is necessary to further test whether its physical and chemical properties meet the use requirements. First, the shear viscosity of the hydrogel at different shear rates can be tested by a rotational rheometer or a cone-plate rheometer to evaluate its rheological properties. The typical shear viscosity curve should show shear thinning characteristics, that is, as the shear rate increases, the viscosity gradually decreases. The data can be fitted with the power law model or the Herschel-Bulkley model to obtain rheological parameters. Secondly, the compressive strength of the hydrogel is tested with a pressure tester, and its tensile strength and elastic modulus are tested with a universal testing machine. These parameters must meet the standards for clinical use. In addition, other performance evaluations such as biocompatibility, degradation behavior, hydration rate and porosity can be performed as needed. Only when all indicators meet the requirements can it be determined that the formula can be used for subsequent process research.

[0104] like Figure 1 As shown, the third aspect of the present invention provides a method for optimizing the formulation of a hydrogel for injection, comprising the following steps:

[0105] S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data. The basic experiments are specifically orthogonal experimental designs, which examine the effects of raw material factors such as polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage on rheological properties during injection (such as shear viscosity, etc.) and mechanical strength after curing (such as compressive strength, tensile strength, etc.).

[0106] S20. Establish a chemical reaction model and a product performance prediction model based on basic experimental data. The chemical reaction model is specifically based on a kinetic equation to describe the polymer cross-linking reaction process, and the product performance prediction model is specifically based on a machine learning algorithm (such as an artificial neural network, a support vector machine, etc.) to perform nonlinear mapping modeling on the experimental data.

[0107] S30. Determine a multi-objective optimization objective function according to product performance requirements, wherein the multiple objectives include minimizing shear viscosity during injection, maximizing compression strength, maximizing tensile strength, etc.

[0108] S40. Combined with the material measurement relationship, a multi-objective optimization model for the formula is established. Specifically, a set of nonlinear equations is established with the molecular weight of polyethylene glycol, type of functional group, polymer concentration, amount of cross-linking agent, type and amount of lithium magnesium silicate, type and amount of contrast agent as independent variables, and minimum shear viscosity during injection, maximum compression strength, maximum tensile strength as dependent variables.

[0109] S50. Use intelligent optimization algorithms (such as particle swarm optimization, simulated annealing algorithm, etc.) to solve the multi-objective optimization model and obtain the preliminary optimal formula component ratio.

[0110] S60, using the preliminary preferred formula to conduct optimization experiments to obtain optimization experiment data. The optimization experiment specifically prepares samples under small-scale trial production conditions and tests rheological properties, curing speed and mechanical performance indicators.

[0111] S70. Use the optimized experimental data to update the chemical reaction model and product performance prediction model.

[0112] S80. Reconstruct the multi-objective optimization model and use the intelligent optimization algorithm to solve it again to obtain a new optimal formula component ratio.

[0113] S90. Repeat steps S60 to S80 until the formula converges to the optimal solution.

[0114] The specific implementation methods of the above steps are described in detail below:

[0115] Step S10: In the basic experiment of testing the influence of different factors on product performance, orthogonal experimental design is an effective method. It can screen out the main influencing factors and reduce the number of experiments. For example, the polymer molecular weight can be selected from 3 levels such as 3000, 6000, and 12000, the functional group type can be selected from 3 types such as amino, thiol, and amide, and the polymer concentration can be selected from 3 levels such as 5%, 10%, and 20%. Then, according to the orthogonal table, the combination relationship of each factor level is reasonably arranged to construct test groups with different formulas. Each formula should be repeated at least 3 times in parallel to eliminate random errors. The preparation steps are similar to steps 4 and 5 above. After all the test group samples are prepared, the performance indicators are tested. The specific operations are:

[0116] 1) Determine the factors and levels to be investigated. Mainly consider factors such as polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage, etc. Set 3-5 different levels for each factor.

[0117] 2) Select an appropriate orthogonal array according to the number of factors and levels, such as L9(3^4), L16(4^5), etc.

[0118] 3) Arrange the test according to the orthogonal table and prepare 3 parallel samples for each test point. The preparation method is the same as steps 1-5.

[0119] 4) Test key indicators of each sample, such as rheological properties (shear viscosity, etc.) and mechanical strength after curing (compressive strength, tensile strength, etc.).

[0120] 5) Use mathematical statistics methods (such as variance analysis) to analyze the primary and secondary effects of factors and screen out the main influencing factors.

[0121] 6) Obtain preliminary optimization directions, such as increasing polymer concentration, increasing functional group density, etc.

[0122] Step S20: Based on basic experimental data, construct a chemical reaction model and a product performance prediction model.

[0123] The chemical reaction model can use the reaction kinetics equation, for example:

[0124] (1)

[0125] Where c is the crosslinking density, t is the time, [A] and [B] are the concentrations of the reactants, and k, m, and n are the reaction order and rate constant.

[0126] By using numerical integration or analytical solution combined with reaction kinetics equation and material balance equation, the cross-linking reaction process can be simulated.

[0127] The product performance prediction model can use machine learning algorithms such as artificial neural network (ANN) and support vector machine (SVM). Take shear viscosity η and compression strength σ as examples:

[0128] 1) Take the raw material ratio (polymer concentration, cross-linking agent dosage, etc.) as the input variable x.

[0129] 2) The experimentally measured η and σ are used as the output variable y.

[0130] 3) Construct a training data set (x,y).

[0131] 4) Select a suitable network structure (such as a 3-layer BP network) and optimization algorithm (such as gradient descent), and set training parameters.

[0132] 5) Train the ANN or SVM model on the training data set to obtain the prediction functions of η(x) and σ(x).

[0133] 6) Verify the model accuracy on the reserved test dataset and retrain if necessary.

[0134] Step S30: Determine the multi-objective optimization objective function, which can be in the following form:

[0135] (2)

[0136] in , , They are the target values ​​of performance indicators such as expected shear viscosity, compressive strength, and tensile strain.

[0137] Formula (2) realizes multi-objective optimization and the weights of various indicators can be adjusted according to actual needs.

[0138] Step S40: Establish a multi-objective optimization model for the recipe based on the material measurement relationship.

[0139] Assume that the independent variable x is (molecular weight of polyethylene glycol M, type of functional group f, polymer concentration c, amount of crosslinking agent r, type of lithium magnesium silicate s and amount h, type of contrast agent d and amount v).

[0140] The dependent variable y is the performance indexes such as η, σ, ε, etc., which are given by the prediction model in step S20:

[0141] y = f(x) (3)

[0142] Substituting (2) into (3) we can get the nonlinear optimization model:

[0143]

[0144] (Material measurement relationship)

[0145] (variable bounds)

[0146] In the formula , are the lower and upper limits of each variable.

[0147] Step S50: Using an intelligent optimization algorithm to solve the multi-objective optimization model established in S40 to obtain a preliminary optimal formula.

[0148] Commonly used intelligent optimization algorithms are:

[0149] 1) Particle swarm optimization (PSO), which guides particle movement based on the information of the optimal position of the group and the optimal position of the individual;

[0150] 2) Simulated annealing algorithm (SA), which gradually approaches the global optimal solution through a controlled "cooling" process;

[0151] 3) Genetic algorithm (GA), which simulates the biological evolution process and optimizes population individuals through operations such as selection, crossover, and mutation.

[0152] Taking the PSO algorithm as an example, the specific process is:

[0153] 1) Initialize the positions and velocities of the individuals in the population (feasible solutions).

[0154] 2) Evaluate the fitness of each individual and calculate the objective function value F(x) according to formula (2).

[0155] 3) Update the historical best position of each individual and the global best position.

[0156] 4) Update individual speed and position according to the following formula:

[0157]

[0158]

[0159] Among them, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are uniform random numbers between 0 and 1, p is the individual historical optimal position, and g is the global optimal position.

[0160] 5) Repeat steps 2-4 until the stopping criteria are met (such as the maximum number of iterations or the objective function value tends to remain unchanged).

[0161] According to the optimization results of PSO and other algorithms, the preliminary optimal formula component ratio can be obtained.

[0162] Step S60: Use the preliminary preferred formula to perform optimization experiments and obtain optimization experiment data.

[0163] 1) Prepare a small test sample according to steps 1-5 and test the rheological and mechanical properties.

[0164] 2) Compare the optimized experimental data with the model prediction value and calculate the prediction deviation.

[0165] 3) Combine experimental phenomena and test data to analyze the shortcomings of the model.

[0166] Step S70: Update the chemical reaction model and product performance prediction model using the optimized experimental data.

[0167] 1) Add the optimized experimental data to the original data set to increase the amount of model training data.

[0168] 2) According to the optimization experimental phenomena, modify the model form (such as reaction order, network structure, etc.).

[0169] 3) Retrain the model to obtain more accurate kinetic equations and performance prediction functions.

[0170] Step S80: Reconstruct the multi-objective optimization model and use the intelligent optimization algorithm to solve it again.

[0171] 1) Substitute the updated performance prediction model into formula (3).

[0172] 2) The weight coefficients of each item in the objective function (2) can be adjusted appropriately.

[0173] 3) Use the algorithm described in S50 (such as PSO) to optimize and solve the new model.

[0174] 4) Obtain the optimal formula component ratio for a new round.

[0175] Step S90: Repeat steps S60 to S80 until the formula converges to the optimal solution.

[0176] 1) Conduct a small test with the new optimal formula to obtain experimental data.

[0177] 2) Update the model and rebuild the optimization model to find the next generation of optimal solution.

[0178] 3) When the optimal solution has no obvious changes for several consecutive generations, it is considered to have converged and the global optimal formula is obtained.

[0179] The key to this formula optimization method is to establish an accurate reaction kinetic model and product performance prediction model, and adopt a reasonable multi-objective optimization strategy, iterate between experiments and simulation calculations, so that the model and experimental data can be mutually corrected and continuously improved, and finally converge to the optimal formula that meets the requirements of various performance indicators. This method can significantly improve the efficiency of formula optimization, reduce the number of experiments, and shorten the development cycle.

[0180] Specifically, the principle of the present invention is: the key lies in the reasonable design of the cross-linking system and the use of intelligent optimization algorithms to perform multi-objective formulation optimization.

[0181] First, polyethylene glycol derivatives (such as PEG-NH2, PEG-SH) are selected to chemically crosslink with crosslinkers containing appropriate functional groups (such as amide groups, acrylates), which can achieve in situ curing of hydrogels under mild conditions. Compared with traditional free radical-induced acrylate crosslinking, the nucleophilic addition mechanism used in the present invention is milder and the reaction conditions are milder. For example, PEG-SH molecules, as nucleophilic reagents, can attack the carbon-carbon double bonds of PEG-acrylate molecules to form stable thioether bonds, thereby achieving crosslinking of the polymer network. In addition, functional groups such as ketone groups and aldehyde groups can also undergo similar addition reactions with thiol groups. Therefore, by designing the functional groups of the polymer, a variety of different types of crosslinking methods can be achieved, thereby accurately regulating the properties of the hydrogel.

[0182] Secondly, the introduction of diatomaceous earth (such as montmorillonite, kaolin, etc.), an inorganic filler with good rheological properties, into the hydrogel formula can effectively reduce the polymer concentration and improve the shear thinning property of the hydrogel. Diatomaceous earth forms a dispersed and stable nanosheet structure in water, and undergoes an interlayer intercalation reaction with water, making the hydrogel exhibit good shear thinning properties. During the injection process, higher shear stress will cause a significant decrease in the viscosity of the hydrogel, which is conducive to injection through a small injection needle. When the hydrogel is discharged from the syringe and enters the target area, the shear stress decreases rapidly and the viscosity returns to a higher level, ensuring that it can be successfully solidified and play the required mechanical support role.

[0183] In addition, in order to give the hydrogel a good visualization imaging effect, the present invention adds medical contrast agents, such as iodized oil and barium sulfate, to the formula. These inorganic contrast agents can be evenly dispersed in the hydrogel matrix, and can produce obvious contrast enhancement effects during X-ray, CT and other imaging examinations, providing an important basis for the positioning guidance of clinical minimally invasive surgery.

[0184] Finally, for multiple key performance indicators of hydrogels (such as shear viscosity, compression strength, tensile strength, etc.), the present invention established a mathematical model based on reaction kinetics and machine learning, and used intelligent optimization algorithms (such as particle swarm optimization, simulated annealing algorithm, etc.) to perform multi-objective optimization on the formula. This method can not only greatly improve development efficiency and reduce redundant experiments, but also obtain the global optimal formula that meets the requirements of various performance indicators. Specifically:

[0185] 1) The reaction kinetic model describes the chemical reaction kinetics during the polymer cross-linking process, such as reaction rate constant, reaction order and other parameters, laying the foundation for the accurate prediction of cross-linking density.

[0186] 2) The product performance prediction model based on machine learning establishes a nonlinear mapping relationship between polymer ratio (such as molecular weight, concentration, etc.) and final performance indicators (such as viscosity, strength) by fitting experimental data, providing a reliable prediction method for multi-objective optimization.

[0187] 3) The multi-objective optimization model takes minimization of shear viscosity and maximization of compression / tensile strength as objective functions, while satisfying various material balance constraints, and uses intelligent optimization algorithms to solve the optimal formula. This method can significantly shorten the formula development cycle and improve the predictability of product performance.

[0188] In order to better implement the recipe optimization method provided by the present invention, a specific embodiment of the recipe optimization method is provided below in combination with a specific formula:

[0189] The specific implementation of step S10 is as follows:

[0190] Usually, the orthogonal experimental design method is used to consider the main influencing factors and their different levels. Factors that influence , each factor is set separately levels. Then the total number of levels of the orthogonal experiment is .

[0191] Orthogonal array is an experimental design method that can study the main effects of these factors on the results through fewer test points. Design matrix Each row represents a test point, and each column represents a different level of an influencing factor. The value is the level number of the factor. For example, Orthogonal array:

[0192]

[0193] This table corresponds to 9 experimental points with 4 3-level factors. parallel samples, a total of A test.

[0194] For any test point, the actual value of the influencing factor is:

[0195]

[0196] in Indicates The first factor The actual value of the level.

[0197] Test all samples for required performance indicators , such as shear viscosity, compression strength, etc. For each indicator, calculate the mean of each level:

[0198]

[0199] in Indicates Factor The level corresponds to The performance index Parallel sample test values.

[0200] Then calculate the range of each factor for each indicator:

[0201]

[0202] The larger the value, the The factor The greater the impact of each indicator, the greater the impact of each indicator. The main influencing factors can be screened out based on this.

[0203] Based on the analysis results of different indicators, the optimization level of each influencing factor is preliminarily determined, such as increasing the polymer concentration, etc. This lays the foundation for the next step of optimization.

[0204] The specific implementation of step S20 is as follows:

[0205] The chemical reaction kinetics model can use the reaction kinetics equation to describe the crosslinking reaction process, such as:

[0206]

[0207] in is the cross-link density, For time, , is the reactant concentration, , , is the reaction order and rate constant.

[0208] Combining the material conservation equation and reaction kinetics equation, the cross-linking reaction can be numerically simulated.

[0209] The product performance prediction model can use machine learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), etc. and compressive strength For example:

[0210]

[0211] in are input variables (such as polymer molecular weight, cross-linking agent dosage, etc.), and are weight and bias parameters respectively.

[0212] For a single hidden layer network, its form is:

[0213]

[0214] in is an activation function, such as ReLU or Sigmoid function; and is the network weight parameter; is the bias parameter; is a constant offset term.

[0215] By using training data to optimize the network parameters, a performance prediction model can be obtained.

[0216] In step S10, a statistical method is used to calculate , used to evaluate the mean values ​​of each level.

[0217] Step S20 uses the idea of ​​machine learning to directly learn the and The prediction function of .

[0218] In order to prevent overfitting, Add regularization terms, such as Norm or Norm.

[0219] Step S30: Determine the multi-objective optimization objective function

[0220]

[0221] in, They are the expected target values ​​of shear viscosity, compressive strength, tensile strain and other indicators; is the corresponding weight coefficient, reflecting the importance of each indicator; It is given by the prediction model established in step S20.

[0222] Step S40: Establishing a multi-objective optimization model for a recipe

[0223]

[0224] in, is the independent variable vector, including polymer molecular weight, concentration, cross-linking agent dosage, etc.; constraint condition It is the measurement relationship of the total amount of materials; are the lower and upper bounds of each variable.

[0225] Step S50: Using particle swarm optimization algorithm (PSO) to solve

[0226] Algorithm flow:

[0227] 1) Initialize the population and speed

[0228] 2) Calculate the fitness of each particle , find the current optimal Its fitness

[0229] 3) Update individual optimal position and group optimal

[0230] 4) Update speed and position:

[0231]

[0232] 5) Repeat 2)-4) until the stop condition is met

[0233] in, is the inertia weight; is the acceleration constant; Is a random number.

[0234] Step S60: Perform optimization experiment and obtain optimization data

[0235] According to the S50 optimization results, small-scale samples are prepared to test actual indicators.

[0236] Step S70: Update the model using new data

[0237] 1) Add new data to the original training set

[0238] 2) Modify the model structure according to new phenomena, such as adjusting the number of network layers, introducing new features, etc.

[0239] 3) Retrain the reaction kinetics model and performance prediction model

[0240] Step S80: Reconstruct the optimization model and solve

[0241] 1) Substitute the updated performance prediction model into the objective function

[0242] 2) Adjust the objective function weight

[0243] 3) Solve the new optimization model and obtain the optimal formula for a new round

[0244] Step S90: Repeat S60-S80 until convergence

[0245] Experiment with the new optimal formula, obtain new data, update the model, rebuild the optimization model and solve it. When the optimal solution does not change significantly for several consecutive generations, it is considered to have converged to the global optimal formula.

[0246] The key to this recipe optimization strategy is to continuously improve the prediction model through interactive iteration of modeling and experimental data, thereby obtaining a more accurate objective function and guiding the optimization algorithm to converge to the global optimal solution. The recipe optimization efficiency will continue to improve with the accumulation of data and models.

[0247] The following is an example of a specific application scenario of the injectable hydrogel of the present invention: A medical device company has developed an injectable hydrogel product for clinical needs such as vascular embolism. The hydrogel uses polyethylene glycol (PEG) derivatives as the main component, and achieves in-situ curing through a chemical cross-linking reaction of mercapto-acrylate. At the same time, inorganic nanofillers and medical contrast agents are introduced to give the material good rheological properties and contrast effects. In order to optimize the formula of the hydrogel, the company designed the following 5 groups of comparative experiments:

[0248] Experimental group 1 (control group):- PEG-SH (Mw 6000): 10% w / v, PEG-acrylate (Mw 8000): 8% w / v, montmorillonite: 3% w / v, iodized oil: 20% v / v;

[0249] Experimental group 2: PEG-SH (Mw 4000): 12% w / v, PEG-acrylate (Mw 6000): 10% w / v, kaolin: 4% w / v, barium sulfate: 25% w / w;

[0250] Experimental group 3: PEG-SH (Mw 8000): 15% w / v, PEG-ethylene oxide (Mw 6000): 12% w / v, bentonite: 5% w / v, sodium iodide: 0.4 mol / L;

[0251] Experimental group 4: PEG-SH (Mw 5000): 14% w / v, PEG-isocyanate: 10% w / v, zeolite: 3% w / v, iodized oil: 22% v / v;

[0252] Experimental group 5: PEG-SH (Mw 7000): 13% w / v, PEG-amide (Mw 8000): 11% w / v, montmorillonite: 3.5% w / v, barium sulfate: 28% w / w.

[0253] The preparation process and performance test results of these five groups of formulas are introduced below:

[0254] 1. Experimental Group 1 (Control Group)

[0255] (1) Preparation steps

[0256] First, PEG-SH (Mw 6000) and PEG-acrylate (Mw 8000) were dissolved in PBS buffer at an equivalent ratio (1:1) and stirred until completely dissolved. At the same time, montmorillonite powder was mixed with deionized water at a mass ratio of 3:10 and stirred for 1 hour using a stirrer to obtain a uniform nano-montmorillonite suspension. Then, the above two solutions were mixed at a volume ratio of 1:1, and 20% v / v iodized oil contrast agent was added and stirred for another 5 minutes. Finally, the obtained precursor solution was loaded into a syringe and the in-situ curing of the hydrogel was completed after standing for 4 minutes.

[0257] (2) Performance testing

[0258] The prepared hydrogel samples were first tested for shear viscosity at different shear rates using a rotational rheometer. Figure 2 As shown in the figure, the hydrogel exhibits good shear thinning properties and has a high viscosity (about 3.2 Pa·s) at low shear rates. However, as the shear rate increases, the viscosity gradually decreases to about 0.6 Pa·s, meeting the fluidity requirements of the injection process.

[0259] Then, the compression strength and tensile strength of the hydrogel were tested using a pressure tester and a universal testing machine. The results showed that the compression strength of the hydrogel was 205 kPa and the tensile strength was 78 kPa, both of which met the mechanical performance standards for clinical applications.

[0260] In addition, during X-ray imaging experiments, it was found that due to the introduction of iodized oil, the hydrogel had a good contrast effect and could clearly show its distribution in blood vessels.

[0261] Based on the above performance indicators, it can be considered that the hydrogel prepared in Experimental Group 1 meets all requirements for clinical applications and provides a control sample for subsequent research.

[0262] 2. Experimental Group 2

[0263] (1) Preparation steps

[0264] The preparation steps were basically the same as those of experimental group 1. The differences were as follows: the molecular weight of PEG-SH was reduced to 4000, and the molecular weight of PEG-acrylate was reduced to 6000; kaolin was used as filler instead of montmorillonite, and the dosage was increased to 4% w / v; the contrast agent was changed from iodized oil to barium sulfate, and the dosage was 25% w / w.

[0265] (2) Performance testing

[0266] The shear viscosity curve of the hydrogel sample is shown in Figure 3 As shown. It can be seen that compared with experimental group 1, the viscosity of the hydrogel at low shear rate is slightly reduced (about 2.8 Pa·s), but as the shear rate increases, the viscosity decreases more significantly, and the viscosity is only 0.4 Pa·s at high shear rate. This is mainly due to the decrease in the molecular weight of the PEG chain and the increase in the amount of kaolin, which gives the hydrogel stronger shear thinning properties.

[0267] In the mechanical property test, the compressive strength of the hydrogel was 195 kPa and the tensile strength was 72 kPa, which were slightly lower than those of experimental group 1, which may be related to the reduction of PEG chain length.

[0268] X-ray imaging tests showed that barium sulfate contrast agent can also provide good visualization of hydrogels, and the imaging resolution is comparable to that of iodized oil.

[0269] In general, although the mechanical properties of the hydrogel in experimental group 2 were slightly reduced, its excellent rheological properties could meet the needs of injection applications, and its cost was relatively low, making it an optional formulation solution.

[0270] 3. Experimental Group 3

[0271] (1) Preparation steps

[0272] In this group of experiments, the molecular weight of PEG-SH was increased to 8000, and the molecular weight of PEG-ethylene oxide was 6000. The dosages were 15% w / v and 12% w / v, respectively. Bentonite was used as filler at 5% w / v. Sodium iodide was used as contrast agent at a concentration of 0.4 mol / L. The preparation steps were similar to the previous two groups.

[0273] (2) Performance testing

[0274] like Figure 4 As shown in the figure, the viscosity of the hydrogel at low shear rate is as high as 4.1 Pa·s, but it quickly decreases to 0.7 Pa·s as the shear rate increases, still meeting the injection requirements. Compared with the first two groups, the high molecular weight and high concentration of this formula give the hydrogel better rheological properties.

[0275] In the mechanical properties test, the compressive strength of the hydrogel was 225 kPa and the tensile strength was 85 kPa, which was significantly better than the first two groups. This was mainly due to the higher cross-linking density and the reinforcing effect of bentonite.

[0276] X-ray imaging experiments showed that sodium iodide as a contrast agent can also well display the distribution of hydrogels in blood vessels.

[0277] In summary, the hydrogel prepared in experimental group 3 has greatly improved rheological and mechanical properties, and is an ideal formulation choice. However, it should also be noted that too high a polymer concentration may affect its injection performance.

[0278] 4. Experimental Group 4

[0279] (1) Preparation steps

[0280] In this group of experiments, the molecular weight of PEG-SH was 5000, and the dosage was 14% w / v; the dosage of PEG-isocyanate was 10% w / v. Zeolite was used as filler, and the addition amount was 3% w / v. The contrast agent was iodized oil, and the dosage was 22% v / v. The preparation steps were similar to those described above.

[0281] (2) Performance testing

[0282] like Figure 5 As shown in the figure, the viscosity of the hydrogel at low shear rate is about 3.5 Pa·s, which is slightly lower than that of experimental group 3, but it still maintains a low viscosity (0.6 Pa·s) at high shear rate, meeting the injection requirements.

[0283] In the mechanical property test, the compressive strength of the hydrogel was 215 kPa and the tensile strength was 82 kPa, which were better than those of experimental groups 1 and 2 and slightly lower than those of experimental group 3. This may be due to the low molecular weight of PEG-SH and the relatively small cross-linking density.

[0284] X-ray imaging results showed that iodized oil as a contrast agent could also provide good visualization effects for hydrogels.

[0285] In general, the hydrogel in experimental group 4 has excellent comprehensive performance and is an ideal formula choice.

[0286] 5. Experimental Group 5

[0287] (1) Preparation steps

[0288] This group uses PEG-SH (Mw 7000) and PEG-amide (Mw 8000) as the main components, with the dosages of 13% w / v and 11% w / v respectively. The filler is montmorillonite, with the dosage of 3.5% w / v. The contrast agent is barium sulfate, with a concentration of 28% w / w. The preparation steps are the same as before.

[0289] (2) Performance testing

[0290] like Figure 5 As shown in the figure, the viscosity of the hydrogel at low shear rate is about 3.8 Pa·s, which is slightly higher than that of experimental group 4, but it still has good shear thinning properties, and the viscosity drops to 0.8 Pa·s at high shear rate.

[0291] In the mechanical properties test, the compressive strength of the hydrogel was 220 kPa and the tensile strength was 84 kPa, which were close to the levels of experimental group 3, reflecting a higher cross-linking density.

[0292] X-ray imaging results show that barium sulfate can also provide good contrast effects for hydrogels.

[0293] 6. Summary

[0294] Through the above five groups of experiments, the company systematically evaluated the effects of the molecular weight, functional group type, concentration of PEG derivatives, and the type and dosage of inorganic fillers and contrast agents on the performance of hydrogels. The experimental results show that:

[0295] (1) Increasing the molecular weight and concentration of PEG is beneficial to improving the rheological properties and mechanical strength of the hydrogel, but it will also increase the initial viscosity, which is not conducive to injection. Therefore, a balance needs to be struck between performance and injectability.

[0296] (2) Using mercapto-acrylate or mercapto-isocyanate chemical crosslinking can achieve higher crosslinking density and mechanical strength. Although the performance is slightly reduced by amide crosslinking, the cost is relatively low.

[0297] (3) An appropriate amount of inorganic filler (such as 3-5% w / v) can effectively improve the shear thinning properties of hydrogels, but excessive addition will reduce the mechanical strength.

[0298] (4) Different types of contrast agents, such as iodide, sulfate, etc., can provide good contrast effects for hydrogels and can be selected according to actual application requirements.

[0299] Based on the above results, the company has preliminarily determined that polyethylene glycol thiol (Mw 8000), polyethylene glycol amide (Mw8000), montmorillonite, and iodized oil are the optimal formula combination, with the dosage of each component being 13% w / v, 11% w / v, 3.5% w / v, and 25% v / v, respectively. This formula not only has ideal rheological properties and mechanical strength, but also shows good visualization effects in X-ray imaging, meeting all requirements for clinical applications.

[0300] The following is an example of a specific application scenario of the formulation optimization method provided by the present invention: Assume that a medical device company has developed an injectable hydrogel for vascular embolization treatment. The company hopes to prepare a hydrogel with high viscosity, high strength and good injectability by rationally optimizing the formulation to meet the needs of clinical applications. Based on the formulation optimization method proposed by the present invention, the company has carried out the following series of work:

[0301] 1. Basic Experiments

[0302] First, the company designed a series of orthogonal experiments to systematically evaluate the effects of six factors, including polymer molecular weight, type of functional group, polymer concentration, amount of crosslinker, type and amount of diatomaceous earth, type and amount of contrast agent, on the rheological properties and mechanical strength of hydrogels. The specific experimental design is as follows:

[0303] The factor level settings are shown in Table 1.

[0304] Table 1 Orthogonal experimental factor level table

[0305]

[0306] According to L16(4 5 ) Orthogonal experimental design was used, with a total of 16 groups of experiments, each with 3 parallel samples. All experimental samples were prepared according to the following method:

[0307] (1) Weigh polyethylene glycol, cross-linking agent, diatomaceous earth and other raw materials according to the formula, dissolve them in an appropriate amount of buffer (such as PBS), and stir thoroughly until uniform.

[0308] (2) Mix the obtained polymer solution and diatomaceous earth suspension in a certain proportion, add the corresponding contrast agent, and continue stirring until it is completely uniform.

[0309] (3) The precursor liquid is loaded into a syringe and allowed to stand for a certain period of time, during which a cross-linking reaction will occur to form the desired hydrogel.

[0310] (4) The rheological properties (shear viscosity) and mechanical strength (compressive strength, tensile strength) of the prepared hydrogel samples were tested.

[0311] After experiments, the company obtained the basic experimental data shown in Table 2.

[0312] Table 2 Orthogonal experiment results data table

[0313]

[0314] Through statistical analysis of these experimental data, the company reached the following conclusions:

[0315] (1) The molecular weight, type and concentration of functional groups of polymers are the main factors affecting the performance of hydrogels, followed by the amount of crosslinker and the type of diatomaceous earth. The type and amount of contrast agents have relatively little effect on the performance.

[0316] (2) Increasing the molecular weight and concentration of the polymer can improve the shear viscosity and mechanical strength of the hydrogel, but it will also significantly increase the initial viscosity, which is not conducive to injection. Using functional groups such as thiol or amide groups can achieve higher cross-linking density and strength.

[0317] (3) An appropriate amount of diatomaceous earth (such as 2-5% w / v) can effectively improve the rheological properties of hydrogels, but excessive addition will reduce the mechanical strength.

[0318] (4) Different types of contrast agents have little effect on the performance of hydrogels, but iodide has better contrast effect than sulfate.

[0319] Based on the above analysis results, the company has preliminarily determined that increasing the molecular weight and concentration of the polymer, using thiol or amide functional groups, adding appropriate amounts of diatomaceous earth, and selecting iodide contrast agents are the main directions for subsequent optimization.

[0320] 2. Model building and optimization

[0321] In the next step, the company's R&D personnel established a chemical reaction kinetics model and product performance prediction model for the hydrogel based on basic experimental data.

[0322] The chemical reaction kinetics model uses the classic Michaelis-Menten kinetic equation:

[0323] (1)

[0324] In the formula, c is the cross-linking density, t is the reaction time, [E] is the enzyme (cross-linking agent) concentration, and [S] is the substrate (polymer) concentration. is the enzyme activity constant, is the Michaelis constant. By fitting the experimental data, the specific values ​​of each parameter are determined: .

[0325] Using this kinetic model, the company can predict how the cross-linking density of hydrogels changes over time under different formulation conditions, providing a basis for regulating the curing behavior.

[0326] At the same time, the company has also established a hydrogel performance prediction model based on artificial neural networks. and tensile strength A three-layer BP neural network model was constructed with polymer molecular weight M, functional group type f, polymer concentration c, crosslinker dosage r, diatomaceous earth type s and dosage h, contrast agent type d and dosage v as output variables. The training data of the model comes from the above-mentioned orthogonal experimental results, and the training process uses the gradient descent algorithm. The average prediction errors of the model on the retained test data set are: , indicating that its prediction accuracy is high.

[0327] With the above two models as support, the company's R&D personnel then carried out multi-objective optimization of the hydrogel formula. They set the following optimization objective function:

[0328] (2)

[0329] in, is the expected performance target value.

[0330] Combining the above objective function with the performance prediction model based on neural network, we get the following multi-objective optimization problem:

[0331]

[0332]

[0333]

[0334] Where x = (M, f, c, r, s, h, d, v) is the optimization variable vector, , , are respectively η, , value.

[0335] To solve this multi-objective optimization problem, the company adopted an improved particle swarm optimization algorithm (MOPSO). The specific steps are as follows:

[0336] (1) Initialize the positions and velocities of the individuals in the population (feasible solutions).

[0337] (2) Evaluate the fitness of each individual and calculate the objective function value F(x) according to formula (2).

[0338] (3) Update the historical best position of each individual and the global non-dominated best solution set.

[0339] (4) Update individual speed and position according to the following formula:

[0340]

[0341]

[0342] Where w is the inertia weight, , is the acceleration constant , is a uniform random number between 0 and 1, p is the individual's historical optimal position, and g is the global non-dominated optimal solution.

[0343] (5) Repeat steps (2)-(4) until the algorithm convergence conditions are met.

[0344] After 20 iterative calculations, the MOPSO algorithm finally converged to the Pareto optimal solution set as shown in Table 3.

[0345] Table 3. MOPSO algorithm optimization results

[0346]

[0347] 3. Optimization experiments and model updates

[0348] According to the Pareto optimal solution set obtained by the MOPSO algorithm, the company selected three representative formulas for small-scale experimental verification, as follows:

[0349] (1) Formulation 1:

[0350] - Polyethylene glycol thiol (Mw 8000): 12% w / v

[0351] - Polyethylene glycol amide (Mw 8000): 10% w / v

[0352] - Montmorillonite: 3%w / v

[0353] - Iodized oil: 22%v / v

[0354] (2) Formulation 2:

[0355] - Polyethylene glycol amide (Mw 6000): 15% w / v

[0356] - Polyethylene glycol acrylate (Mw 6000): 8%w / v

[0357] - Kaolin: 4%w / v

[0358] - Barium sulfate: 28%w / w

[0359] (3) Formulation 3:

[0360] - Polyethylene glycol amino (Mw 10000): 10% w / v

[0361] - Polyethylene glycol isocyanate: 7%w / v

[0362] - Zeolite: 2.5%w / v

[0363] - Sodium iodide: 0.45 mol / L

[0364] The company prepared small test samples of the above three formulations according to the above hydrogel preparation steps and conducted detailed performance tests on them. The experimental results are shown in Table 4.

[0365] Table 4 Optimization experiment results

[0366]

[0367] From the results, it can be seen that the three optimized formulas all meet the expected performance targets: the shear viscosity is about 3 Pa·s, the compressive strength and tensile strength are about 200 kPa and 80 kPa respectively, and the curing time is between 3-6 minutes, and the injectability is good. Compared with the prediction results of the MOPSO algorithm, there is a certain deviation between the experimental data and the model prediction value, among which the deviation of formula 2 is slightly larger, which is mainly due to the limitations of the modeling of the cross-linking reaction kinetics.

[0368] To further improve the accuracy of the model, the company added the optimized experimental data to the original training set and retrained the chemical kinetics model and neural network performance prediction model. The new chemical reaction kinetics equation is as follows:

[0369] (3)

[0370] The average prediction error of the neural network model is also reduced to: Δη = 6.3%, Δ = 5.1%, Δ =4.8%.

[0371] 4. Final optimization and verification

[0372] Based on the updated model, the company once again conducted multi-objective optimization of the hydrogel formula. The optimization results are shown in Table 5.

[0373] Table 5 Final optimization results

[0374]

[0375] The formula was verified by multiple experiments under small test conditions, and the results are as follows:

[0376] - Shear viscosity: 3.12 ± 0.30 Pa·s

[0377] - Compression strength: 218 ± 24 kPa

[0378] - Tensile strength: 84 ± 13 kPa

[0379] - Curing time: 4 ± 1 min

[0380] - Injectability: Good, can be injected smoothly through a 2.5 mm injection needle

[0381] The above performance indicators all meet the expected goals and are in good agreement with the model prediction results.

[0382] In order to further verify the performance of the hydrogel in practical applications, the company invited clinical experts to conduct animal experiments. A vascular embolization experiment was conducted on a rat abdominal aorta model. The results showed that the hydrogel could quickly solidify in the target blood vessel and provide good mechanical support, successfully blocking the blood flow and achieving the expected embolization effect. The animal experiment lasted for 24 weeks, and the hydrogel material showed no obvious degradation, good tissue compatibility, and no obvious inflammatory or toxic reactions.

[0383] Through the above series of work, the company finally determined the optimal formula composition and preparation process of the injectable hydrogel, laying a solid foundation for its further clinical trials and industrial applications.

[0384] In general, the hydrogel formula optimization method proposed in the present invention makes full use of chemical reaction kinetics models and machine learning technology, effectively solving the problem of injectability while ensuring the performance of the hydrogel.

[0385] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A hydrogel for injection, characterized in that The invention relates to a polymer compound containing functional groups such as polyethylene glycol amino group, polyethylene glycol thiol group, polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and a polymer containing other functional groups capable of undergoing cross-linking reactions, as well as magnesium lithium silicate components, a contrast agent and a solvent.

2. The hydrogel for injection according to claim 1, characterized in that: The functional group is polyethylene glycol amide, polyethylene glycol acrylate, polyethylene glycol ethylene oxide, polyethylene glycol isocyanate or polyethylene glycol aldehyde.

3. The hydrogel for injection according to claim 1, characterized in that: The specific formula is: 2-arm polyethylene glycol amino: 0.1~99.9% w / v, 2-arm polyethylene glycol amide: 0.1~99.9% w / v, sodium magnesium aluminum silicate: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

4. The hydrogel for injection according to claim 1, characterized in that: The specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol acrylate: 0.1~99.9% w / v, montmorillonite: 0.1~99.9% w / v, barium sulfate: 0.1~99.9% w / w.

5. The hydrogel for injection according to claim 1, characterized in that: The specific formula is: 4-arm polyethylene glycol amino: 0.1~99.9% w / v, 4-arm polyethylene glycol isocyanate: 0.1~99.9% w / v, zeolite: 0.1~99.9% w / v, sodium iodide: 0.1~99.9 mol / L.

6. The hydrogel for injection according to claim 1, characterized in that: The specific formula is: 4-arm polyethylene glycol thiol: 0.1~99.9% w / v, 4-arm polyethylene glycol ethylene oxide: 0.1~99.9% w / v, bentonite: 0.1~99.9% w / v, iodized oil: 0.1~99.9% v / v.

7. A method for preparing a hydrogel for injection, characterized in that: The steps include: Step 1, preparing a polymer solution of polyethylene glycol amino group or polyethylene glycol thiol group or polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and a functional group, wherein the specific operation is to dissolve the polyethylene glycol amino group or polyethylene glycol thiol group or polyethylene glycol carboxyl group or polyethylene glycol hydroxyl group and an equivalent amount of polyethylene glycol amide or polyethylene glycol acrylate or polyethylene glycol ethylene oxide or polyethylene glycol isocyanate or polyethylene glycol aldehyde in a solvent, and stir to fully mix them; Step 2, preparing a lithium magnesium silicate suspension, mixing a certain amount of lithium magnesium silicate components with deionized water, and using a stirrer to stir for a certain period of time to form a uniform nano lithium magnesium silicate suspension; Step 3, the lithium magnesium silicate suspension prepared in step 2 is mixed with the two polymer solutions in step 1 respectively and then mixed with the polymer mixed solution containing lithium magnesium silicate, or the two mixed solutions in step 1 are first mixed and then mixed with the lithium magnesium silicate suspension in step 2, and then an appropriate amount of medical contrast agent is added and stirred until uniform to obtain the required hydrogel with high viscosity and high strength; Step 4: Perform physical and chemical property tests on the hydrogel prepared in step 3, including rheological property tests and mechanical strength tests, to ensure that the hydrogel meets the use requirements.

8. A method for optimizing the formulation of a hydrogel for injection, characterized in that: The steps include: S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data; S20. Establish chemical reaction model and product performance prediction model based on basic experimental data; S30. Determine the multi-objective optimization objective function according to product performance requirements; S40. Combine the material measurement relationship to establish a multi-objective optimization model for the formula; S50, using intelligent optimization algorithm to solve the multi-objective optimization model to obtain the initial optimal formula component ratio; S60, using the preliminary optimal formula to perform optimization experiments and obtain optimization experiment data; S70, updating the chemical reaction model and the product performance prediction model using the optimized experimental data; S80, reconstructing the multi-objective optimization model, and using the intelligent optimization algorithm to solve again, to obtain a new optimal formula component ratio; S90. Repeat steps S60 to S80 until the formula converges to the optimal solution.

9. The method for optimizing the formulation of a hydrogel for injection according to claim 8, characterized in that: The basic experiment is specifically an orthogonal experimental design, which examines the effects of polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage on rheological properties during injection and mechanical strength after curing.

10. The method for optimizing the formulation of a hydrogel for injection according to claim 8, characterized in that: The multi-objective optimization model of the formula is a set of nonlinear equations with polyethylene glycol molecular weight, functional group type, polymer concentration, cross-linking agent dosage, lithium magnesium silicate type and dosage, contrast agent type and dosage as independent variables, and minimum shear viscosity, maximum compression strength and maximum tensile strength during injection as dependent variables.

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