Lung targeting mRNA delivery construction method based on particle size control

Abstract

The invention relates to the technical field of biological medicine, and discloses a lung targeting mRNA delivery construction method based on particle size control, which comprises the following steps: selecting a nano carrier material with biocompatibility and adjustable property; designing and modifying the nano-carrier material, and endowing the nano-carrier material with specific functional groups; and establishing a mathematical physical model, calculating the deposition behavior of the particles in the airflow based on fluid mechanics and particle dynamics theories, and calculating the deposition efficiency of the particles in the lung according to the particle size. The micro-fluidic technology and the temperature and pH response type polymer are combined, the particle size is accurately adjusted, the particle size can be controllably changed in different physiological environments, and the precision and flexibility of particle size distribution are improved. Adaptive optimization is carried out through simulated annealing and a genetic algorithm, lung deposition, cellular uptake and carrier stability are comprehensively considered, and the delivery efficiency is maximized. The model established by combining fluid mechanics and particle dynamics theories optimizes the relationship between the particle size and the cellular uptake efficiency.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a method for constructing lung-targeted mRNA delivery based on particle size control. Background Art

[0002] In the prior art, the design of mRNA delivery vectors usually relies on traditional chemical synthesis methods, and nanoparticles are prepared by polymer materials (such as PLGA, PNIPAM, etc.) to encapsulate mRNA and deliver it to target cells. The particle size of these vectors determines its behavior in the body to a certain extent. Especially in pulmonary delivery, the size of the particle size directly affects its deposition effect and cellular uptake ability. Although traditional methods control the particle size by adjusting the polymer type and synthesis conditions, these methods have certain limitations in the precise regulation of particle size, resulting in uneven distribution of carrier particle size, affecting its deposition effect and delivery efficiency in the lungs.

[0003] When it comes to particle size optimization, existing technologies typically rely on simple mathematical models or empirical formulas, failing to fully consider the complex interactions between multiple factors, including particle size, lung deposition, cellular uptake efficiency, and carrier stability. These optimization methods fail to achieve globally optimal particle size selection in actual biological environments, limiting delivery efficiency. Traditional optimization algorithms, such as gradient descent, are limited to local searches and inevitably fall into local optimal solutions, resulting in failure to maximize delivery system performance.

[0004] Currently, most optimization schemes in existing technologies rely on in vitro validation. However, these experiments cannot fully simulate the complex in vivo environment, especially the actual conditions of pulmonary delivery. The lack of in vivo validation makes it difficult to fully demonstrate the effects of optimized particle size, which in turn affects the delivery efficiency in real organisms.

[0005] Therefore, to address the deficiencies in precise particle size control, global optimization, and in vivo verification, the present invention proposes a precise particle size adjustment scheme that combines microfluidic technology with temperature- and pH-responsive polymers, and performs global optimization through simulated annealing and genetic algorithms, providing a more accurate and efficient technical means to improve mRNA delivery efficiency. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a method for constructing lung-targeted mRNA delivery based on particle size control, which solves the problem of precise adjustment and global optimization of the particle size of mRNA delivery vectors.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for constructing lung-targeted mRNA delivery based on particle size control, comprising the following steps:

[0008] S1. Selecting a nanocarrier material with biocompatibility and adjustable properties, wherein the material is a temperature-responsive or pH-responsive polymer capable of adjusting particle size under different environments;

[0009] S2. Designing and modifying the nanocarrier material to impart specific functional groups thereto to enhance its targeting and penetration in the lungs;

[0010] S3. Develop a mathematical physics model to calculate the deposition behavior of particles in the airflow based on fluid mechanics and particle dynamics theory, and calculate the particle deposition efficiency in the lungs based on particle size.

[0011] S4. Determine the cellular uptake efficiency at different particle sizes by establishing a relationship model between particle size and cellular uptake efficiency, and select the optimal particle size using an optimization algorithm;

[0012] S5. Particle size optimization based on an objective function that maximizes delivery efficiency, taking into account factors such as lung deposition, cellular uptake, and vector stability;

[0013] S6. Adaptive optimization of particle size using simulated annealing and genetic algorithms to achieve optimal delivery.

[0014] S7. Use microfluidics to precisely adjust particle size during carrier synthesis and controllably vary particle size under different conditions by adjusting temperature or pH-responsive polymers.

[0015] S8. Perform in vitro and in vivo experiments to verify mRNA carriers of different particle sizes and evaluate their lung deposition, cellular uptake efficiency, and mRNA delivery effect.

[0016] Preferably, the nanocarrier material is poly(lactic acid-co-glycolic acid) or poly(N-isopropylacrylamide), and the material can respond to temperature or pH changes in an in vivo environment, thereby achieving particle size regulation.

[0017] Preferably, the mathematical physics model includes using the Navier-Stokes equation to describe the movement of particles in the fluid, and the equation is used to calculate the deposition behavior of particles in the airflow, and the deposition behavior is related to factors such as particle size, airflow velocity, and fluid viscosity.

[0018] Preferably, the relationship between the particle size and the cellular uptake efficiency is described by the Langmuir adsorption model, which represents the dependence of the adsorption capacity between the particle and the cell surface on the particle size.

[0019] Preferably, the objective function of the particle size optimization is to maximize the delivery efficiency, which takes into account the lung deposition efficiency, cellular uptake efficiency and carrier stability, and is optimized by an adaptive algorithm, including a simulated annealing algorithm and a genetic algorithm.

[0020] Preferably, the adaptive particle size optimization algorithm is calculated by a multi-objective optimization method, which comprehensively considers the effects of particle size on lung deposition, cellular uptake, and immune escape factors, and dynamically adjusts the particle size to optimize the delivery effect.

[0021] Preferably, the microfluidic technology includes using a microfluidic chip to precisely adjust the particle size of the nanocarrier, and the microfluidic chip controls the speed and pressure of the fluid during the synthesis process to ensure the uniformity of the particle size.

[0022] Preferably, the experimental verification step includes an in vitro cell uptake experiment, using transmission electron microscopy and flow cytometry to evaluate the cell uptake efficiency of mRNA vectors of different particle sizes.

[0023] Preferably, the experimental verification step includes animal experiments, in which mRNA vectors of different particle sizes are injected into mouse or rat models to evaluate their deposition in the lungs and the mRNA transcription effect.

[0024] Preferably, the optimized range of the particle size is adjusted according to different physiological conditions and the needs of the lung area to ensure that the particle size achieves the best delivery effect in the target area, and the range is 10 nm to 500 nm.

[0025] The present invention provides a method for constructing lung-targeted mRNA delivery based on particle size control, which has the following beneficial effects:

[0026] 1. This invention utilizes microfluidic technology to precisely adjust particle size. Combined with the properties of temperature- and pH-responsive polymers, this allows for precise control of particle size during synthesis and controlled variations under varying physiological conditions, resulting in a highly tunable particle size distribution. Compared to existing approaches that impose less precise particle size adjustment, this invention, through the combination of microfluidic technology and responsive polymers, addresses the issues of uneven particle size and the difficulty of control, providing a delivery vehicle with greater precision and flexibility.

[0027] 2. This invention uses a simulated annealing algorithm and a genetic algorithm to adaptively optimize particle size. The optimization goal is to maximize delivery efficiency, taking into account factors such as lung deposition, cellular uptake, and carrier stability to achieve optimal delivery. Compared with traditional empirical and local optimization techniques, the global optimization algorithm of this invention significantly improves the optimization accuracy of particle size, avoids the limitations of local optimal solutions, and makes the carrier more efficient and stable in practical applications.

[0028] 3. This invention combines fluid mechanics and particle dynamics theory to establish a mathematical and physical model linking particle size and cellular uptake efficiency. Based on this model, optimization is performed to achieve optimal lung deposition and cellular uptake. Compared to existing approaches that rely primarily on a single theoretical model, this invention addresses the issues of low model prediction accuracy and inadequate consideration of multiple factors through the integration of multiple models, significantly improving the overall effectiveness of the mRNA delivery system.

[0029] 4. This invention comprehensively validates mRNA vectors of varying particle sizes through in vitro and in vivo experiments, ensuring optimal lung deposition, cellular uptake, and mRNA delivery. Compared to existing technologies that lack comprehensive validation processes, this invention optimizes vector design through precise experimental data feedback, ensuring that the final particle size selected achieves optimal results in actual biological systems, significantly enhancing the practical application of the delivery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] Example:

[0033] Please see the attached Figure 1 The present invention provides a method for constructing lung-targeted mRNA delivery based on particle size control, comprising the following steps:

[0034] S1. Selecting a nanocarrier material with biocompatibility and adjustable properties, wherein the material is a temperature-responsive or pH-responsive polymer capable of adjusting particle size under different environments;

[0035] In the present invention, the selection of nanocarrier materials is crucial. A reasonable carrier must not only meet the requirements of biocompatibility and adjustable properties, but also be able to regulate particle size under specific environmental conditions to achieve optimal mRNA delivery. Under different physiological conditions, changes in carrier particle size directly affect its deposition in the lungs, cellular uptake, and immune system clearance. Therefore, selecting the right nanocarrier material is a prerequisite for ensuring an efficient and accurate delivery system.

[0036] In this embodiment, the selected nanocarrier material should be a temperature-responsive or pH-responsive polymer. These polymers can adjust their particle size under different environmental conditions to adapt to changes in physiological conditions. Specifically, temperature-responsive polymers will undergo reversible swelling or contraction at a certain temperature, thereby changing the size of the carrier particles; while pH-responsive polymers can respond to changes in the body's pH and undergo corresponding structural changes to adjust the particle size. In this way, the carrier particle size can be precisely controlled to achieve optimal deposition and cellular uptake in the lungs.

[0037] Alternatively, the temperature-responsive polymer can be made of poly (N-isopropylacrylamide) (PNIPAM). PNIPAM has unique thermosensitive properties. When the temperature exceeds its critical swelling temperature (typically 32°C), the polymer chain undergoes a phase transition, resulting in a change in particle size. PNIPAM can adjust its particle size in response to temperature changes. Therefore, in the practice of the present invention, the temperature-responsive polymer can achieve particle size regulation under different physiological conditions by controlling temperature.

[0038] Specifically, the particle size regulation mechanism of PNIPAM can be described by the following relationship:

[0039] d(T)=d0·(1+α(TT c ))

[0040] Where d(T) is the particle size at temperature, d0 is the initial particle size, α is the temperature response coefficient, T c is the critical swelling temperature, and T is the actual ambient temperature.

[0041] In another embodiment, the pH-responsive polymer can be selected from poly(lactic-co-glycolic acid) (PLGA). PLGA is a common biodegradable polymer that can adjust its structure under different pH environments, thereby changing the particle size. In particular, when the pH value of PLGA is in an acidic or alkaline environment, its surface charge and hydration will change, thereby affecting the solubility and particle size of the particles. In an acidic environment, PLGA will shrink, resulting in a decrease in particle size, while in an alkaline environment, PLGA will swell, resulting in an increase in particle size.

[0042] In some embodiments, the pH response behavior of PLGA can be characterized by the following formula:

[0043] d(pH)=d0·(1+β(pH-pH0))

[0044] Where d(pH) is the particle size at the pH value, d0 is the initial particle size, β is the pH response coefficient, pH0 is the reference pH value, and pH is the actual environmental pH value.

[0045] In one possible implementation, the carrier material not only has temperature-responsive or pH-responsive properties but can also be combined with other biodegradable polymers. By combining multiple polymers, more flexible and controllable particle size adjustment can be achieved under different physiological environments.

[0046] For example, a composite material of a temperature- and pH-responsive polymer can respond to changes in temperature and pH at the same time, thereby achieving precise control of particle size under different physiological environments. Specifically, the particle size regulation behavior of a temperature- and pH-responsive material can be described by the following formula:

[0047] d(T,pH)=d0·(1+α(TT c )+β(pH-pH0))

[0048] Where d(T, pH) is the particle size under temperature and pH changes, α and β are the temperature and pH response coefficients respectively, T c is the critical swelling temperature, and pH0 is the reference pH value.

[0049] By selecting and optimizing different carrier materials, the particle size of the carrier can be flexibly controlled by adjusting the temperature or pH value under specific physiological conditions, thereby achieving the best mRNA delivery effect.

[0050] In this embodiment, some embodiments may select PNIPAM, PLGA, and composites thereof as the primary carrier, while other embodiments may select other similar biodegradable temperature-responsive or pH-responsive polymers, such as polyvinyl alcohol (PVA) and poly(2-vinylpyrrolidone) (PVP). The specific material selected can be optimized based on experimental requirements and the desired particle size range.

[0051] By selecting temperature-responsive or pH-responsive polymers, the particle size of the carrier can be precisely adjusted under different physiological environments, allowing the carrier to better deposit in the target area (such as the lungs) and improve cellular uptake efficiency. In addition, these materials are biocompatible and can be degraded in the body without producing long-term side effects or immune responses. Therefore, this choice not only optimizes the particle size of the carrier, but also improves the biosafety of the delivery system, ensuring the stability and safety of the treatment process.

[0052] By rationally selecting and adjusting the response characteristics of the material, a more precise and flexible carrier particle size control scheme can be provided for the implementation of the present invention, thereby achieving efficient lung-targeted mRNA delivery.

[0053] S2. Designing and modifying the nanocarrier material to impart specific functional groups thereto to enhance its targeting and penetration in the lungs;

[0054] In the implementation of the present invention, the design and modification steps of nanocarriers are crucial. By introducing specific functional groups on the surface of the carrier, its targeting and penetrability in the lungs can be significantly enhanced, thereby improving the delivery efficiency of mRNA. In order to ensure that the carrier can accurately reach the target cells and effectively enter the cells, the present invention optimizes the interaction between the carrier and the cells through the design and modification of functional groups, while reducing the possibility of non-specific targeting. Especially in lung-targeted delivery, considering the unique physiological environment of the lungs and its barrier function, the functional modification of the carrier is particularly important.

[0055] In this embodiment, specific functional groups are introduced onto the surface of the nanocarrier material. These functional groups primarily include, but are not limited to, targeting ligands, hydrophilic groups, and charged groups. The introduction of these functional groups not only improves the affinity of the carrier for target cells but also enhances its cell penetration ability. Specifically, the design of these functional groups needs to be tailored to the specific receptors or receptor families of lung-specific cells to ensure effective targeting of the nanocarrier within the lungs.

[0056] In general, the selection of targeting ligands can be based on lung-specific receptors. For example, for target cells such as lung epithelial cells or macrophages, ligands targeting specific receptors on these cell surfaces can be selected, such as transferrin receptor (TfR), surface glycoproteins, or enzymes on the surface of certain cells. In addition, hydrophilic groups can improve the stability of the vector in the blood and reduce clearance by the immune system, thereby increasing the concentration and retention time of the vector in the lungs.

[0057] Alternatively, the functional groups can be chemically covalently bonded to the surface of the carrier. Specifically, the targeting ligand and hydrophilic group are linked to the surface of the nanocarrier material through methods such as silanization, coupling, or surface self-assembly. In this way, the introduction of functional groups can impart enhanced targeting and penetrability to the carrier without altering its original physicochemical properties.

[0058] Specifically, assuming the transferrin receptor (TfR) is chosen as the targeting ligand, the transferrin (Tf) molecule will be bound to the surface of the nanocarrier as a functional group. Through this binding mode, the Tf molecule can bind with high affinity to the transferrin receptor on the surface of lung epithelial cells, promoting the cellular uptake of the nanocarrier. The coupling process between the functional group and the carrier can be described by the following chemical reaction formula:

[0059] Carrier-OH+Tf-NH2→Carrier-Tf+H2O

[0060] Carrier-OH represents a hydroxyl group on the carrier surface, and Tf-NH2 represents the amide of transferrin. The coupling of the carrier and transferrin produces a carrier with a targeting functional group. The density of the functional group attachment can be adjusted by controlling the reaction time and temperature.

[0061] In one possible implementation, the functional group can also be a hydrophilic group, such as polyvinyl alcohol (PEG) or polyvinylamine (PEI). This can be particularly effective in preventing nonspecific binding and immune recognition, particularly when the carrier needs to maintain long-term in vivo stability. PEG is a commonly used surface modification molecule that can significantly increase the carrier's blood half-life due to its excellent biocompatibility and immune evasion properties.

[0062] For example, the introduction of a PEG group can be described by the following reaction formula:

[0063] Carrier-NH2+PEG-COOH→Carrier-PEG

[0064] Carrier-NH2 is an amino group on the surface of the carrier, and PEG-COOH is a derivative of the PEG molecule with a carboxyl group. PEG is attached to the carrier surface through a chemical coupling reaction to form a stable PEG-carrier complex. By adjusting the molecular weight of PEG, the hydrophilicity and stability of the carrier can be precisely controlled.

[0065] In addition to targeting ligands and hydrophilic groups, in some embodiments, charged groups (such as amino groups, phosphate groups, etc.) can also be introduced. These groups can adjust the surface charge properties of the carrier, enhance the interaction between the carrier and the cell membrane, and further improve the cellular uptake rate. The introduction of charged groups can be performed by acid-base titration or charge adjustment methods.

[0066] For example, the introduction of an amino (-NH2) group can be carried out by the following reaction formula:

[0067] Carrier-COOH+NH2-R→Carrier-NH2-R+H2O

[0068] Among them, Carrier-COOH represents a carrier with a carboxyl group, and the amino (-NH2) functional group undergoes a chemical coupling reaction with the carrier surface to form a carrier with a positive charge.

[0069] Through this modification process, the vector not only achieves excellent lung targeting, but also improves delivery efficiency through enhanced cell permeability, ensuring effective mRNA expression in target cells. This design and modification method can significantly improve the nanocarrier's lung targeting and provide a more stable and efficient delivery method for mRNA delivery.

[0070] By modifying the surface of nanocarriers and introducing functional groups, the following technical effects can be achieved:

[0071] Enhanced targeting: By selecting specific targeting ligands (such as transferrin), the binding ability of the vector to lung target cells can be effectively increased, ensuring that the delivery system can accurately reach the target area.

[0072] Improved permeability: The introduction of hydrophilic groups reduces the immune clearance of the vector in the blood and enhances the cell permeability of the vector.

[0073] Enhanced stability: The introduction of hydrophilic groups such as PEG can effectively increase the stability of the carrier in the body, prolong its half-life in the blood, and thus improve delivery efficiency.

[0074] Improve cell uptake rate: The introduction of charged groups can change the surface charge characteristics of the carrier, further enhance the interaction between the carrier and the cell membrane, and promote cell uptake.

[0075] Through the above-mentioned design and modification, the present invention can effectively improve the targeting and penetration of nanocarriers in the lungs, thereby greatly improving the delivery efficiency of the lung-targeted mRNA delivery system and ultimately achieving better therapeutic effects.

[0076] S3. Develop a mathematical physics model to calculate the deposition behavior of particles in the airflow based on fluid mechanics and particle dynamics theory, and calculate the particle deposition efficiency in the lungs based on particle size.

[0077] In the present invention, the step of establishing a mathematical physics model plays a key role in optimizing particle size and improving mRNA delivery efficiency. The core of this step is to simulate and predict the motion trajectory, deposition behavior and deposition efficiency of particles in the airflow through fluid mechanics and particle dynamics theory. The deposition efficiency of particles directly affects their distribution in the lungs, thereby affecting the delivery effect of mRNA. Therefore, in the process of designing nanocarriers, it is crucial to accurately calculate and optimize the deposition behavior of particles in the airflow. By establishing a mathematical model, the deposition of carriers in the lungs can be systematically evaluated based on parameters such as particle size, fluid properties and airflow velocity.

[0078] In this example, a fluid dynamics model was first used to calculate the particle trajectory in the airflow, taking into account the interaction between the particles and the airflow. Subsequently, the particle deposition efficiency in the lungs was further derived based on particle size, airflow velocity, and other physical factors. These calculations not only help understand the deposition effects of different particle sizes but also provide a theoretical basis for particle size optimization.

[0079] In general, particle deposition behavior is influenced by particle size, airflow velocity, fluid viscosity, and the structure of the lung passages. Based on this, fluid mechanics and particle dynamics models provide a detailed analytical framework that accurately describes particle motion in airflow and calculates particle deposition efficiency in the lungs.

[0080] Alternatively, this embodiment uses the Stokes number to describe the interaction between particles and airflow. The Stokes number is a dimensionless quantity that is often used to describe the characteristics of particle motion in a fluid. The relationship between the Stokes number and particle size, fluid viscosity, and airflow velocity is shown below:

[0081]

[0082] Where d is the particle diameter, ρ p is the particle density, u is the air flow velocity, and μ is the viscosity of the fluid.

[0083] In this case, particle motion in an airflow can be divided into two types: inertia-dominated motion and viscosity-dominated motion. For inertia-dominated particles, the Stokes number is large, and the particles are propelled by the airflow, making them less likely to be captured by obstacles. For viscosity-dominated particles, the Stokes number is small, and the particles are easily deposited due to the viscosity of the airflow.

[0084] Specifically, variations in airflow velocity and particle size in different lung regions lead to differences in particle deposition. For example, airflow velocities are higher in the nasal cavity and trachea, while lower in the alveoli. Consequently, particle deposition efficiencies vary in these regions. Based on this phenomenon, mathematical and physical models can be used to optimize the particle size range, ensuring optimal deposition of nanocarriers in the deepest regions of the lungs, such as the alveoli.

[0085] In one possible implementation, the deposition efficiency of particles in an airflow can be predicted by the following formula:

[0086]

[0087] Where S is the deposition efficiency, U max is the maximum velocity of the airflow, τ is the time the particle stays in the lungs, d is the particle size, ρ p is the particle density and μ is the viscosity of the fluid.

[0088] In this example, based on fluid mechanics and particle dynamics theory, we were able to calculate the deposition behavior of particles of varying particle sizes in the airflow. By optimizing the particle size, we can maximize particle deposition efficiency in the lungs, thereby improving mRNA delivery.

[0089] In some embodiments, in addition to calculating the Stokes number and deposition efficiency, the Navier-Stokes equations can be used to further simulate the effect of airflow on particles. These equations can more accurately describe particle motion in airflow, taking into account factors such as airflow instability, turbulence, and the shape of the airflow channel. Using these equations, particle motion can be more accurately simulated, resulting in more accurate deposition efficiency data.

[0090] In some embodiments, a combination of multiple simulation techniques, including fluid dynamics simulation, particle dynamics simulation, and discrete element method (DEM), can be used to more comprehensively assess particle deposition and optimize particle size design. These methods can incorporate different physiological conditions (such as airflow velocity, lung passage structure, and breathing patterns) into the simulation process, thereby more accurately predicting the performance of particles in actual use.

[0091] By establishing these fluid dynamics and particle kinetic models, the present invention can provide systematic deposition predictions for nanocarriers of varying particle sizes. Based on this, the particle size can be optimized to achieve optimal lung deposition efficiency. Furthermore, particle size design optimized using these models can improve the delivery efficiency of mRNA to target cells, enhancing the effectiveness of gene therapy.

[0092] By establishing a mathematical physics model, we can effectively calculate the deposition behavior of particles in the airflow and optimize the deposition efficiency according to the particle size. Specifically:

[0093] Model calculations can predict the lung deposition effects of carriers of varying particle sizes, providing a theoretical basis for particle size design. By combining fluid mechanics and particle dynamics, we can achieve maximum particle deposition efficiency in the lungs, ensuring effective mRNA delivery to the target area. Using theoretical tools such as the Stokes number and the Navier-Stokes equations, we can meticulously simulate particle deposition behavior, providing a scientific basis for experimental design.

[0094] By establishing a mathematical and physical model, the present invention can provide precise guidance in particle size selection, ensuring the targeting and delivery effect of the mRNA delivery system in the lungs.

[0095] S4. Determine the cellular uptake efficiency at different particle sizes by establishing a relationship model between particle size and cellular uptake efficiency, and select the optimal particle size using an optimization algorithm;

[0096] In the present invention, a model for the relationship between particle size and cellular uptake efficiency is a key step in achieving optimal delivery. By establishing a quantitative relationship model between particle size and cellular uptake efficiency, the cellular uptake capacity of nanocarriers at different particle sizes can be predicted, thus providing a theoretical basis for subsequent particle size optimization. Cellular uptake efficiency directly affects the effectiveness of the delivery system. Therefore, accurately assessing and optimizing particle size to achieve optimal cellular uptake is an important step in improving mRNA delivery efficiency.

[0097] In this example, the relationship between particle size and cellular uptake efficiency was first established using experimental data or models from the literature. Based on this, an optimization algorithm was used to optimize the particle size to ensure that the nanocarrier particle size achieved optimal cellular uptake efficiency during pulmonary delivery. This process combines biological principles with mathematical modeling to maximize delivery efficiency.

[0098] In general, the effect of particle size on cellular uptake efficiency is nonlinear. If the particle size is too small, the carrier may be rapidly cleared by the immune system due to its strong surface charge. If the particle size is too large, the carrier may have difficulty passing through the pores of the cell membrane, resulting in low cellular uptake efficiency. Therefore, finding an appropriate particle size range is crucial. By establishing a precise mathematical model and utilizing optimization algorithms, it is possible to accurately predict cellular uptake efficiency at different particle sizes and optimize particle size design.

[0099] Alternatively, the relationship between particle size and cellular uptake efficiency can be described using the Langmuir adsorption model or the Michaelis-Menten model. Specifically, the cellular uptake of nanocarriers typically exhibits a saturation curve, where changes in particle size affect the cellular uptake rate. Assuming a relationship between uptake efficiency and particle size, it can be described by the following equation:

[0100]

[0101] Where R represents the cellular uptake efficiency, R max is the maximum uptake rate, d is the particle size, K d is the particle size at half-maximum uptake. This equation describes how uptake efficiency varies with particle size. By adjusting the particle size parameters, an optimal particle size can be found to maximize uptake efficiency.

[0102] Specifically, the present embodiment realizes the establishment of the relationship between particle size and cellular uptake efficiency by the following steps: First, through in vitro cell experiments, the uptake data of nanocarriers of different particle sizes in specific cell types (such as lung epithelial cells or macrophages) are collected. These data generally include the uptake rate under different particle sizes and the carrier concentration in the cell. According to the experimental data, the relationship between particle size and cellular uptake efficiency is fitted by nonlinear regression method. The quantitative relationship between particle size and uptake efficiency can be fitted by using models such as Langmuir adsorption model, Hill model, etc. After the relationship model between particle size and cellular uptake efficiency is established, the particle size is optimized using an optimization algorithm (such as gradient descent method, genetic algorithm or simulated annealing algorithm). The optimization goal is to maximize the cellular uptake efficiency, even if the particle size is within the optimal range, to ensure that the nanocarrier can efficiently enter the target cells.

[0103] As an implementation method, particle size optimization can be achieved through genetic algorithms. As a global optimization method, genetic algorithms can find the optimal solution in a multidimensional search space. The basic genetic algorithm process includes selection, crossover, mutation, and evaluation steps, and through repeated iterations, it searches for the optimal particle size that maximizes cellular uptake efficiency.

[0104] In some embodiments, the objective function for particle size optimization can be set as:

[0105] f(d)=-R(d)

[0106] Where f(d) is the optimization target for particle size d, and R(d) is the uptake efficiency at particle size d. The optimal particle size is found by maximizing the uptake efficiency. The selection operation in the optimization process selects individuals with higher fitness based on the uptake efficiency of each generation. The crossover operation exchanges information between superior individuals, and the mutation operation maintains population diversity.

[0107] In one possible implementation, the optimization algorithm is combined with experimental data for multiple iterations, each time adjusting the particle size and re-evaluating the uptake efficiency, and ultimately selecting the particle size that maximizes the uptake efficiency.

[0108] Furthermore, the relationship between particle size and cellular uptake efficiency is not simply a linear or nonlinear relationship. Factors such as cell type, differences in functional groups on the carrier surface, and particle surface charge all influence uptake efficiency. Therefore, these factors need to be comprehensively considered when building models. For example, in some embodiments, the choice of hydrophilic groups on the carrier surface or targeting ligands may further optimize uptake efficiency.

[0109] By establishing a relationship model between particle size and cellular uptake efficiency and selecting the optimal particle size through an optimization algorithm, the following technical effects can be achieved in the present invention:

[0110] Mathematical models and optimization algorithms enable accurate prediction of cellular uptake efficiency at varying particle sizes and selection of the optimal particle size, ensuring efficient nanocarrier entry into target cells. This optimized particle size not only improves cellular uptake but also prevents clearance by the immune system, enhancing the effectiveness of the mRNA delivery system. By comprehensively considering factors such as cell type and carrier surface modification, the particle size can be more precisely adjusted to suit diverse biological environments, further improving delivery efficiency. Optimization methods such as genetic algorithms can find the optimal solution within a multidimensional parameter space, avoiding the local optimal solution problem that can plague traditional optimization methods.

[0111] Through the above steps, the present invention can achieve precise particle size optimization, further improve the application effect of nanocarriers in lung-targeted mRNA delivery, and provide a more efficient and safe treatment plan.

[0112] S5. Particle size optimization based on an objective function that maximizes delivery efficiency, taking into account factors such as lung deposition, cellular uptake, and vector stability;

[0113] In the present invention, the core task of step S5 is to maximize the efficiency of mRNA delivery by optimizing the particle size based on the objective function. In order to ensure the maximization of delivery efficiency, multiple factors need to be considered, including the deposition efficiency of particles in the lungs, the uptake efficiency of cells, and the stability of the carrier. The deposition behavior of nanocarriers of different particle sizes in the lungs, the uptake capacity of cells, and the biological stability of the carrier in vivo all directly affect the delivery effect. Therefore, in this step, by accurately designing the objective function and combining the above factors, the particle size is optimized so that it shows the best effect during the delivery process.

[0114] In this example, the objective function was designed to consider three key factors: lung deposition efficiency, cellular uptake efficiency, and carrier stability. By quantifying these factors and integrating them into a comprehensive objective function, multi-objective optimization can be achieved to maximize delivery efficiency.

[0115] Generally speaking, lung deposition, cellular uptake efficiency, and carrier stability are mutually constrained. For example, nanocarriers with larger particle sizes may be more effective in lung deposition, but their large size may result in lower cellular uptake. Conversely, carriers with smaller particle sizes may be cleared by the immune system or fail to deposit deep within the lungs. Carrier stability also plays a crucial role in particle size optimization. Excessively unstable carriers may degrade prematurely in the body, resulting in decreased delivery efficiency. Therefore, the design of the objective function requires a trade-off between these mutually constraining factors to maximize delivery efficiency.

[0116] As an option, the objective function in this embodiment can be expressed as follows:

[0117]

[0118] Among them, F(d) is the objective function, which represents the delivery efficiency, d is the particle size, S(d) is the particle deposition efficiency in the lungs, R(d) is the cell uptake efficiency, τ(d) is the half-life of the carrier, w1, w2, and w3 are weight coefficients, which respectively represent the influence of deposition efficiency, uptake efficiency, and carrier stability on the delivery efficiency.

[0119] Specifically, S(d) can be calculated based on the fluid mechanics model and the particle dynamics model. As described in step S3, the deposition behavior of particles in the airflow is directly related to their particle size. R(d) is calculated based on the relationship model between particle size and cellular uptake efficiency established in step S4. τ(d) reflects the biodegradation rate and in vivo stability of the carrier, which is generally estimated through in vivo experiments or simulated annealing algorithms.

[0120] In one possible implementation, weight coefficients w1, w2, and w3 can be adjusted based on experimental data or experience to assign different importance to different factors according to actual needs. For example, if cellular uptake efficiency needs to be improved, the value of w2 can be appropriately increased; if lung deposition is more important, the value of w1 can be increased.

[0121] By setting the objective function, the particle size optimization problem is transformed into a mathematical optimization problem, the goal of which is to select the appropriate particle size d opt , so that the objective function F(d) reaches its maximum value. To find the optimal particle size, various optimization algorithms can be used. For example, genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms can effectively find the optimal solution in a multi-dimensional search space.

[0122] Specifically, the optimization algorithm searches within a given particle size range, gradually adjusting the particle size parameters, calculating the objective function value corresponding to each particle size, and iteratively selecting the particle size with the largest objective function value as the optimal solution. In some embodiments, the optimization process may include multiple iterative steps, with each step adjusting the particle size and re-evaluating the deposition efficiency, cellular uptake efficiency, and carrier stability to ultimately maximize delivery efficiency.

[0123] In some embodiments, a simulated annealing algorithm can be used for particle size optimization. Through the mechanism of simulated annealing, the algorithm can escape the trap of local optimal solutions and find the global optimal solution within a wider search space. The iterative process of the simulated annealing algorithm involves randomly selecting new particle size values, calculating the corresponding delivery efficiency through the objective function, and accepting the new solution with probability based on temperature control, thereby achieving global optimization.

[0124] In another embodiment, a genetic algorithm can also be applied to this optimization process. The algorithm generates a new generation of particle size solutions through operations such as selection, crossover, and mutation. By calculating the objective function value of each solution, the solutions with the highest fitness are selected for the next generation. Through repeated iterations, the optimal particle size is ultimately achieved.

[0125] Through particle size optimization based on the objective function, the following technical effects can be achieved:

[0126] By comprehensively considering factors such as lung deposition, cellular uptake, and vector stability, the objective function balances these factors to ensure the optimal overall delivery system. The optimization algorithm accurately identifies the optimal particle size that maximizes delivery efficiency, thereby improving mRNA delivery. Within the objective function, weight coefficients can be adjusted based on different experimental requirements, providing flexibility and adaptability to meet diverse therapeutic needs. Global optimization methods such as genetic algorithms and simulated annealing can avoid local optimal solutions and ensure global optimality in the particle size optimization process.

[0127] Through this step, the present invention can ensure that the selected particle size exhibits optimal performance during pulmonary delivery, improve the delivery efficiency of the nanocarrier, and provide effective technical support for lung-targeted mRNA delivery.

[0128] S6. Adaptive optimization of particle size using simulated annealing and genetic algorithms to achieve optimal delivery.

[0129] In step S6 of the present invention, the adaptive optimization of particle size is the key to ensure the best delivery effect. Through the previous steps (including the relationship model between particle size and cellular uptake efficiency, objective function design and multi-objective optimization), we have obtained multiple candidate particle sizes, and the goal of this process is to further accurately select the most suitable particle size by intelligent optimization algorithm to ensure the highest efficiency of mRNA delivery system. In this step, we introduced simulated annealing algorithm and genetic algorithm, these two global optimization methods, can find the global optimal solution in a wide search space, thereby avoiding the trap of local optimal solution, ensuring that the optimization of particle size can maximize the delivery effect.

[0130] In this example, simulated annealing and genetic algorithms were used for adaptive particle size optimization. Specifically, each algorithm offers distinct advantages: simulated annealing can escape local optima within the search space, while genetic algorithms can explore a wider range of potential solutions through population evolution. By combining these two optimization methods, we can more effectively explore the particle size space and find the optimal particle size that maximizes deposition efficiency while ensuring cellular uptake and carrier stability.

[0131] In general, the simulated annealing algorithm (SA) is based on the "annealing" process in physics. It uses a random search strategy to simulate the free motion of particles at high temperatures and then gradually cool down to reduce the energy of the system, thereby finding the global optimal solution. In this embodiment, the simulated annealing algorithm can be applied to particle size optimization by simulating the change of particle size within a certain range, gradually reducing the amplitude of the change in particle size, and calculating the delivery efficiency after each change according to the objective function. The algorithm decides whether to accept the current solution or jump to a new solution based on a certain probabilistic rule, so as to avoid falling into a local optimal solution.

[0132] As an option, the objective function of the simulated annealing algorithm can be set to the negative value of the delivery efficiency to minimize the objective function value, specifically:

[0133]

[0134] Among them, F SA (d) is the objective function of the simulated annealing algorithm, d is the particle size, S(d) is the lung deposition efficiency, R(d) is the cell uptake efficiency, τ(d) is the carrier stability (half-life), and w1, w2, and w3 are the weight coefficients of different factors, respectively.

[0135] In one possible implementation, the initial state of simulated annealing is usually set to a relatively high temperature, which results in a wider range of particle size variation. As the iteration proceeds, the temperature gradually decreases, the amplitude of the particle size variation decreases, and eventually converges to the optimal solution.

[0136] On the other hand, the genetic algorithm (GA) is an optimization algorithm that simulates natural selection and generates a new generation of solutions through operations such as selection, crossover, and mutation. For particle size optimization problems, the genetic algorithm can process multiple particle size solutions at the same time and gradually approach the optimal solution through generational updates. Specifically, the genetic algorithm process includes the following main steps: First, randomly select multiple particle size solutions from the initial population and calculate the objective function value (delivery efficiency) corresponding to each particle size; then, through the selection operation, select the particle size solution with higher fitness; then, use crossover and mutation operations to generate a new generation of solutions; finally, through repeated iterations, continuously optimize the particle size.

[0137] The fitness function in the genetic algorithm can directly adopt the objective function designed in step S5, that is:

[0138]

[0139] Among them, f GA (d) is the fitness function in the genetic algorithm, which represents the delivery efficiency under particle size d. The definitions of other parameters are consistent with the above.

[0140] In the implementation of genetic algorithms, the crossover operation usually generates a new solution by mixing part of the information of two particle size solutions, while the mutation operation generates a new solution by randomly adjusting the particle size, thereby increasing the diversity of the population and avoiding premature convergence to the local optimal solution.

[0141] In some embodiments, the particle size optimization process can utilize both a simulated annealing algorithm and a genetic algorithm. Through multiple alternating iterations, the two algorithms complement each other and fully explore the optimal solution in the particle size space. The simulated annealing algorithm can find potential optimal solutions within a broad search range, while the genetic algorithm can select and optimize among multiple solutions, ultimately providing a globally optimal solution.

[0142] By using simulated annealing algorithm and genetic algorithm to perform adaptive optimization of particle size, the present invention can achieve the following technical effects: simulated annealing algorithm and genetic algorithm are both global optimization algorithms, which can effectively avoid the dilemma of local optimal solutions and ensure that the particle size finally found is the global optimal solution. Through reasonable objective function design, comprehensive consideration of factors such as lung deposition, cell uptake and carrier stability, the optimized particle size can not only improve the delivery efficiency, but also ensure the stability and targeting of the carrier. The temperature control mechanism of the simulated annealing algorithm and the population evolution mechanism of the genetic algorithm make the particle size optimization process more flexible and able to adapt to different experimental requirements and conditions. By accurately optimizing the particle size, the mRNA delivery system can obtain the best deposition effect in the lungs, improve the cell uptake rate, and ultimately improve the delivery effect and therapeutic effect. Through the above steps, the combination of simulated annealing algorithm and genetic algorithm effectively solves the challenge of particle size optimization, makes the design of nanocarriers more precise, and ultimately achieves efficient and safe mRNA delivery.

[0143] S7. Use microfluidics to precisely adjust particle size during carrier synthesis and controllably vary particle size under different conditions by adjusting temperature or pH-responsive polymers.

[0144] In step S7 of the present invention, in the process of carrier synthesis using microfluidic technology, it is crucial to accurately adjust the particle size. By microfluidic technology, the flow rate, pressure, reaction time and other factors of the fluid can be accurately controlled during the synthesis process, thereby finely adjusting the particle size distribution of the carrier. This technology can significantly improve the uniformity of particle size, and makes the particle size control within the required range to optimize the delivery effect of mRNA. Especially when combined with temperature-responsive or pH-responsive polymers, the regulation of particle size can not only be accurately controlled during the synthesis process, but also can be controlled to change under different environmental conditions, thereby adapting to different physiological environment needs.

[0145] In this example, microfluidics technology was used to control the carrier synthesis process. First, on a microfluidic chip, the reaction liquid was precisely channeled into the reaction channel. By leveraging fluid dynamics and flow control techniques, the particle size of the nanoparticles during synthesis could be precisely adjusted. Specifically, by controlling factors such as fluid flow rate, the mixing rate of the fluid and reactants, temperature, and the properties of the solvent, the size of the nanoparticles could be adjusted at the microscale.

[0146] Generally speaking, microfluidic chips provide a highly precise fluid control environment, enabling high-throughput, high-efficiency mass production. By adjusting the flow time of the reaction liquid within the chip and the degree of contact with the reactants, the formation and growth of the particle nuclei can be precisely controlled. This control method avoids the wide particle size distribution problem of traditional methods, thereby significantly improving particle size uniformity.

[0147] As an option, in the present embodiment, microfluidic technology is also combined with temperature-responsive or pH-responsive polymers to further adjust the particle size of the carrier. During the carrier synthesis process, by changing the temperature or pH value, the physicochemical properties of the polymer can be caused to change, thereby affecting the size of the particle size. For example, temperature-responsive polymers (such as poly-N-isopropylacrylamide, PNIPAM) show an expanded state at low temperatures and shrink at high temperatures. By adjusting the temperature conditions during the synthesis process, the final particle size of the particles can be accurately controlled.

[0148] Specifically, the relationship between particle size and temperature can be expressed by the following formula:

[0149] d(T)=d0·(1+α(TT c ))

[0150] Where d(T) is the particle size under temperature conditions, d0 is the initial particle size, α is the temperature response coefficient, and T c is the critical swelling temperature, and T is the actual temperature. By adjusting the reaction temperature, the change in particle size can be precisely controlled.

[0151] In addition, pH-responsive polymers such as PLGA can undergo structural changes at specific pH values, thereby altering particle size. In an acidic environment, the PLGA polymer shrinks, resulting in a decrease in particle size; in an alkaline environment, the PLGA polymer expands, resulting in an increase in particle size. By adjusting the pH of the solvent used in the synthesis process, the particle size can be precisely adjusted.

[0152] In one possible implementation, the high-precision fluid control of microfluidics chips, combined with the tunability of temperature- and pH-responsive polymers, allows for the simultaneous regulation of particle size and the material's physicochemical properties during the same reaction process. This multi-modulation approach not only improves particle size accuracy but also provides flexibility for delivery under diverse physiological environments.

[0153] For example, by changing the pH value, the particle size of the PLGA material can be adaptively changed in different in vivo environments, increasing the particle size in acidic environments (such as certain tumor areas) and maintaining a moderate particle size in the neutral environment of the lungs, thereby enhancing the targeting and delivery effect of the particles.

[0154] By combining microfluidics and temperature- or pH-responsive polymers, the particle size can be precisely adjusted during carrier synthesis and ensured to undergo controllable changes under different environments. This step brings about the following technical effects: microfluidics can provide high-precision fluid control, precisely adjust the particle size, and avoid the problem of wide particle size distribution in traditional methods. Combined with temperature-responsive or pH-responsive polymers, the particle size can be adaptively changed under different environmental conditions, meeting physiological needs while optimizing the delivery effect. By precisely controlling the particle size, nanocarriers can be more effectively deposited in the lungs, increasing the cellular uptake rate, and thus improving the efficiency of mRNA delivery. Through dual regulation of temperature and pH, microfluidics not only adjusts the particle size, but also affects the physical and chemical properties of the carrier, making the carrier more environmentally adaptable.

[0155] In summary, the use of microfluidic technology to precisely adjust the particle size and combine it with temperature- or pH-responsive polymers for adaptive adjustment can not only achieve an efficient carrier synthesis process, but also improve the overall efficiency of the mRNA delivery system, providing a more reliable and flexible technical means for lung targeted delivery.

[0156] S8. Perform in vitro and in vivo experiments to verify mRNA carriers of different particle sizes and evaluate their lung deposition, cellular uptake efficiency, and mRNA delivery effect.

[0157] In the present invention, the key to step S8 is to evaluate the lung deposition, cellular uptake efficiency, and ultimate mRNA delivery efficacy of mRNA vectors of varying particle sizes through in vitro and in vivo experimental validation. This step provides experimental support for the aforementioned particle size optimization, vector synthesis, and delivery system design, ensuring that the designed vector can achieve the intended function in a real biological system. Experimental validation is crucial for confirming the accuracy of the theoretical model and evaluating the impact of different particle sizes on delivery efficacy.

[0158] In this example, in vitro cell uptake experiments were first conducted on mRNA vectors of different particle sizes to evaluate their uptake efficiency in lung-related cells (such as lung epithelial cells, macrophages, etc.). Subsequently, in vivo experiments were performed using small animal models (such as mice or rats) to verify the deposition efficiency of vectors of different particle sizes in the lungs and their mRNA delivery effects. Based on these experimental data, the particle size of the vector can be optimized to ensure its effectiveness in practical applications.

[0159] In vitro experiments typically use methods such as cell culture and flow cytometry to assess cellular uptake efficiency by measuring the intracellular accumulation of vectors of varying particle sizes. In vivo experiments, on the other hand, primarily use lung distribution experiments, combined with fluorescence microscopy or other imaging techniques, to assess the distribution and deposition efficiency of mRNA vectors of varying particle sizes in the lungs.

[0160] As an option, in vitro experiments can use lung epithelial cells (such as A549 cells) and macrophages (such as RAW264.7 cells) as model cells to evaluate the uptake of vectors of different particle sizes. The amount of mRNA vectors in the cells is quantitatively analyzed by flow cytometry (FACS), and the cellular uptake efficiency is further calculated. Specifically, the cellular uptake efficiency can be calculated according to the following formula:

[0161]

[0162] Among them, C eff is the cellular uptake efficiency, C cell is the intracellular carrier concentration, C input is the input vector concentration.

[0163] In in vivo experiments, mRNA vectors of varying particle sizes are first injected into mice or rats. Fluorescence microscopy or bioluminescence imaging (BLI) is then used to track the distribution and deposition of the vectors in the lungs. Through tissue sectioning and quantitative analysis, the amount of vectors of varying particle sizes deposited in the lungs can be determined, allowing the pulmonary deposition efficiency to be calculated. Specifically, the deposition efficiency can be described by the following formula:

[0164]

[0165] Among them, S lung is the lung deposition efficiency, m deposit is the mass of the carrier deposited in the lungs, m input is the total mass of the input carrier.

[0166] In some embodiments, RT-PCR can be used to further measure mRNA expression levels in lung tissue to assess the effectiveness of vector delivery. Quantitative PCR can be used to measure mRNA expression levels in the lungs of vectors of varying particle sizes to directly assess their functional performance in target cells. These experimental data will help further optimize vector particle size to ensure optimal in vivo delivery.

[0167] Through the combined validation of in vitro and in vivo experiments, the delivery effects of different particle sizes can be systematically evaluated. For example, a smaller particle size may have an advantage in cellular uptake, but its deposition in the lungs may be poor; whereas a larger particle size may have better deposition in the lungs, but may not be as efficient in cellular uptake as a smaller particle size. Therefore, in vitro and in vivo experimental validation can provide direct data support for the final selection of particle size.

[0168] Through in vitro and in vivo experiments, the following technical effects can be achieved:

[0169] In vitro experiments use flow cytometry and other techniques to quantitatively analyze the uptake efficiency of different particle size carriers in cells, providing a reference for subsequent particle size optimization. In vivo experiments use animal models and imaging techniques to accurately evaluate the deposition of different particle size carriers in the lungs, thereby optimizing the particle size to improve the deposition efficiency. Through methods such as RT-PCR, the expression level of mRNA delivered by different particle size carriers in the lungs is detected to evaluate the actual performance of its delivery effect. Combining the data from in vitro and in vivo experiments can provide a scientific basis for particle size selection and ensure the effectiveness and safety of the carrier in practical applications. Through this step, the present invention not only achieves a comprehensive verification of mRNA carriers of different particle sizes, but also further optimizes the carrier design and improves the delivery effect through feedback from experimental data.

[0170] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing lung-targeted mRNA delivery based on particle size control, characterized in that: The following steps are involved: S1. Selecting a nanocarrier material with biocompatibility and adjustable properties, wherein the material is a temperature-responsive or pH-responsive polymer capable of adjusting particle size under different environments; S2. Designing and modifying the nanocarrier material to impart specific functional groups thereto to enhance its targeting and penetration in the lungs; S3. Develop a mathematical physics model to calculate the deposition behavior of particles in the airflow based on fluid mechanics and particle dynamics theory, and calculate the particle deposition efficiency in the lungs based on particle size. S4. Determine the cellular uptake efficiency at different particle sizes by establishing a relationship model between particle size and cellular uptake efficiency, and select the optimal particle size using an optimization algorithm; S5. Particle size optimization based on an objective function that maximizes delivery efficiency, taking into account factors such as lung deposition, cellular uptake, and vector stability; S6. Adaptive optimization of particle size using simulated annealing and genetic algorithms to achieve optimal delivery. S7. Use microfluidics to precisely adjust particle size during carrier synthesis and controllably vary particle size under different conditions by adjusting temperature or pH-responsive polymers. S8. Perform in vitro and in vivo experiments to verify mRNA carriers of different particle sizes and evaluate their lung deposition, cellular uptake efficiency, and mRNA delivery effect.

2. A method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The nano-carrier material is polylactic acid-glycolic acid copolymer or poly N-isopropylacrylamide, and the material can respond to temperature or pH changes in an in vivo environment, thereby achieving particle size adjustment.

3. A method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The mathematical physics model includes using the Navier-Stokes equation to describe the movement of particles in the fluid. The equation is used to calculate the deposition behavior of particles in the airflow, and the deposition behavior is related to factors such as particle size, airflow velocity, and fluid viscosity.

4. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The relationship between the particle size and the cellular uptake efficiency is described by the Langmuir adsorption model, which represents the dependence of the adsorption capacity between particles and cell surfaces on the particle size.

5. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The objective function of the particle size optimization is to maximize the delivery efficiency, which takes into account lung deposition efficiency, cell uptake efficiency and carrier stability, and is optimized by an adaptive algorithm, including a simulated annealing algorithm and a genetic algorithm.

6. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The adaptive particle size optimization algorithm is calculated using a multi-objective optimization method that comprehensively considers the effects of particle size on lung deposition, cellular uptake, and immune escape factors, and dynamically adjusts the particle size to optimize delivery effects.

7. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The microfluidic technology includes using a microfluidic chip to precisely adjust the particle size of the nanocarrier. The microfluidic chip controls the speed and pressure of the fluid during the synthesis process to ensure the uniformity of the particle size.

8. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The experimental validation step includes an in vitro cell uptake experiment, and transmission electron microscopy and flow cytometry are used to evaluate the cell uptake efficiency of mRNA carriers with different particle sizes.

9. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The experimental verification step includes animal experiments, in which mRNA vectors of different particle sizes are injected into mouse or rat models to evaluate their deposition in the lungs and the mRNA transcription effect.

10. The method for constructing lung-targeted mRNA delivery based on particle size control according to claim 1, characterized in that: The optimized range of the particle size is adjusted according to different physiological conditions and the needs of the lung area to ensure that the particle size achieves the best delivery effect in the target area, and the range is 10 nm to 500 nm.

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