Application method of PPAR gamma agonist in preparation of drugs for inhibiting ferroptosis of nucleus pulposus cells

Abstract

The invention provides an application method of a PPAR gamma agonist in preparation of a medicine for inhibiting ferroptosis of nucleus pulposus cells, and belongs to the technical field of medicine preparation.The PPAR gamma agonist pioglitazone and TAT cell-penetrating peptide are combined to form a compound, the compound is encapsulated in hyaluronic acid modified pH-sensitive lipidosome to construct a nucleus pulposus targeted drug delivery system, and the nucleus pulposus targeted drug delivery system is used for inhibiting ferroptosis of the nucleus pulposus cells. The flow cytometry is utilized to detect the physicochemical properties of the drug delivery system, the optimal drug delivery parameters are determined through a double-layer game optimization model, the drug release kinetics is determined in different pH environments to establish a pH response release model, and finally the PPAR gamma and Axl protein expression levels and the GPX4 protein level are detected through the fluorescence luminescence to verify the ferroptosis resisting effect. The technical problem that a drug carrier system cannot realize dual-function collaborative optimization of targeted delivery and acid-sensitive controlled release of nucleus pulposus cells is solved.

Description

Technical Field

[0001] This invention belongs to the field of pharmaceutical preparation technology, and specifically relates to a method for using a PPARγ agonist in the preparation of a drug that inhibits ferroptosis in nucleus pulposus cells. Background Technology

[0002] Intervertebral disc degeneration is a major cause of low back pain, and ferroptosis in nucleus pulposus cells is a crucial pathological mechanism in this process. Traditional treatments primarily utilize PPARγ agonists, such as pioglitazone, for anti-inflammatory and metabolic regulation, inhibiting oxidative stress-induced ferroptosis in nucleus pulposus cells by activating the PPARγ signaling pathway. However, due to the lack of cell membrane penetration and tissue specificity, these drugs exhibit limited therapeutic efficacy and significant side effects. Current research often employs simple liposome encapsulation or protein modification as drug delivery systems, but these methods suffer from poor targeting, uncontrollable release, and insufficient stability in the complex microenvironment of nucleus pulposus tissue. Traditional drug delivery system designs typically optimize targeted delivery and controlled release separately, lacking a systematic synergistic design approach. Particularly in the acidic microenvironment of degenerated intervertebral disc tissue, existing drug delivery systems struggle to simultaneously achieve efficient uptake by nucleus pulposus cells and stable pH-responsive release, severely limiting the clinical application of PPARγ agonists in inhibiting ferroptosis in nucleus pulposus cells. Summary of the Invention

[0003] In view of this, the present invention provides a method for the application of PPARγ agonists in the preparation of drugs that inhibit ferroptosis in nucleus pulposus cells, which can solve the technical problem in the prior art that the drug carrier system cannot achieve the dual functions of targeted delivery to nucleus pulposus cells and acid-sensitive controlled release in a synergistic optimization.

[0004] This invention is achieved as follows: This invention provides a method for preparing a drug to inhibit ferroptosis in nucleus pulposus cells using a PPARγ agonist. A drug delivery system is prepared by combining the PPARγ agonist pioglitazone with the cell-penetrating peptide TAT to form a TAT-pioglitazone complex. A nucleus pulposus-targeting vector is constructed by encapsulating the TAT-pioglitazone complex in pH-sensitive liposomes and modifying the liposome surface with hyaluronic acid ligands to form a hyaluronic acid-modified pH-sensitive liposome drug delivery system. The physicochemical properties of the drug delivery system are detected by flow cytometry to determine the liposome particle size distribution, encapsulation efficiency, and drug loading. A game-theoretic model for optimizing drug delivery efficiency is established, including considering the cellular uptake efficiency of the drug delivery system. The optimal drug delivery parameters were determined through a game theory equilibrium point between an upper-level model aiming to maximize the drug delivery rate and a lower-level model aiming to maximize drug release stability. Drug release performance was tested by measuring the release kinetics of the TAT-pioglitazone complex under different pH conditions to establish a pH-responsive release model. The anti-ferroptosis effect was verified by applying the optimized drug delivery system to oxidative stress-induced nucleus pulposus cells. The changes in the expression levels of PPARγ and Axl proteins and the ferroptosis marker GPX4 protein were detected by fluorescence luminescence. The hyaluronic acid-modified pH-sensitive liposome drug delivery system achieved synergistic optimization of dual functions of targeted delivery to nucleus pulposus cells and acid-sensitive controlled release.

[0005] Specifically, the step of preparing the drug delivery system involves the concentration of the cell-penetrating peptide TAT ∈ [10, 50] μg / mL.

[0006] Specifically, the step of detecting the physicochemical properties of the drug-loaded system is to proceed to the next step when the encapsulation rate ∈ [85, 100]% and the drug loading ∈ (8, 20]%.

[0007] Before establishing the drug loading efficiency optimization game model, the process also includes activating an adaptive fluorescence signal enhancement algorithm to improve the detection signal quality when the fluorescence signal-to-noise ratio is ≤15dB, and using the standard fluorescence detection mode to maintain efficient operation when the fluorescence signal-to-noise ratio is ≥15dB.

[0008] Before establishing the drug loading efficiency optimization game model, the process also includes starting the drug loading linearization compensation algorithm to correct the nonlinear drug loading characteristics when the drug loading linearity error is >3%, and using the standard drug loading calibration procedure to maintain the drug loading accuracy if the error is ∈ [0, 3]%.

[0009] The cell-penetrating peptide TAT refers to a short peptide sequence derived from human immunodeficiency virus reverse transcription activator, which has the ability to efficiently penetrate cell membranes and carry drug molecules across the cell membrane barrier to enter the cell and exert their effects.

[0010] The pH-sensitive liposomes refer to a smart drug delivery system that remains stable under physiological pH conditions and undergoes structural changes and releases drugs in the acidic microenvironment of degenerated intervertebral disc tissue.

[0011] The hyaluronic acid ligand mentioned above refers to a natural polysaccharide molecule that can bind to the surface of nucleus pulposus cells. Receptors enable targeted drug delivery.

[0012] The flow cytometry mentioned above refers to a single-cell analysis technique based on optical principles, which uses laser to excite fluorescent markers on or inside cells to accurately measure the particle size and drug loading of a drug delivery system.

[0013] The objective function of the upper-level model is: ,in The unit for cellular uptake rate is . , The drug concentration is expressed in μg / mL. The drug release time constant is expressed in seconds. The unit for the volume of drug-loaded particles is The constraints of the upper-level model are: ∈[0.01, 1.0] , ∈[1, 100] μg / mL. The objective function of the lower-level model is ,in The drug loading stability coefficient is a dimensionless coefficient. The drug release phase angle is expressed in radians. The diffusion coefficient is in units of / s. The constraints of the lower-level model are: ∈[0.5, 2.0], ∈[1× , 1× ] / s, the coupling term of the game model is .

[0014] The adaptive fluorescence signal enhancement algorithm refers to a noise reduction technique based on signal processing principles, which improves the signal-to-noise ratio of fluorescence detection through adaptive filtering and signal amplification techniques.

[0015] The drug loading linearization compensation algorithm refers to a numerical correction method that achieves dynamic compensation of drug loading accuracy by establishing a nonlinear mapping relationship between drug loading and detection signal.

[0016] The fluorescence luminescence technique mentioned above refers to a quantitative detection technology based on the principle of molecular fluorescence emission, which uses specific fluorescent probes to label target proteins to achieve accurate quantitative analysis of the expression levels of PPARγ and Axl proteins.

[0017] This invention constructs a hyaluronic acid-modified pH-sensitive liposome drug delivery system, combining the TAT cell-penetrating peptide with the PPARγ agonist pioglitazone to form a complex, achieving synergistic optimization of dual functions: nucleus pulposus targeted delivery and acid-sensitive controlled release. This system utilizes the specific binding of hyaluronic acid to the CD44 receptor on the surface of nucleus pulposus cells for targeted delivery, while simultaneously achieving controlled drug release through structural changes in the acidic microenvironment of pH-sensitive liposomes in degenerated intervertebral disc tissue. The drug loading efficiency optimization game model established in this invention overcomes the technical deficiency of the mutual constraint between targeting and controlled release performance in traditional drug delivery system designs by maximizing the uptake efficiency of upper-layer cells and maximizing the stability of drug release in the lower layer through dual-objective optimization, achieving systematic synergistic optimization of drug loading parameters. In summary, this invention solves the technical problem mentioned in the background art that drug carrier systems cannot achieve synergistic optimization of dual functions: nucleus pulposus cell targeted delivery and acid-sensitive controlled release. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0020] like Figure 1 The diagram shows a flowchart of a method for preparing a drug to inhibit ferroptosis in nucleus pulposus cells, provided by the present invention. The method includes the following steps:

[0021] S01. Prepare a drug delivery system by combining the PPARγ agonist pioglitazone with the cell-penetrating peptide TAT to form a TAT-pioglitazone complex, wherein the concentration of the cell-penetrating peptide TAT is ∈ [10, 50] μg / mL.

[0022] S02. Construct a nucleus pulposus targeting vector, encapsulate the TAT-pioglitazone complex in pH-sensitive liposomes, and modify the surface of the liposomes with hyaluronic acid ligands to form a hyaluronic acid-modified pH-sensitive liposome drug delivery system.

[0023] S03. Detect the physicochemical properties of the drug delivery system by flow cytometry, and determine the liposome particle size distribution, encapsulation efficiency and drug loading. When the encapsulation efficiency ∈ [85, 100]% and the drug loading ∈ (8, 20]%, proceed to the next step.

[0024] S04. Establish a game theory model for optimizing drug loading efficiency, with an upper-level model aimed at maximizing the cell uptake efficiency of the drug loading system and a lower-level model aimed at maximizing the stability of drug release. Determine the optimal drug loading parameters through the game equilibrium point.

[0025] S05. When the fluorescence signal-to-noise ratio is ≤15dB, the adaptive fluorescence signal enhancement algorithm is activated to improve the detection signal quality. If the fluorescence signal-to-noise ratio is ≥15dB, the standard fluorescence detection mode is used to maintain efficient operation.

[0026] S06. When the linearity error of drug loading is >3%, the linearization compensation algorithm of drug loading is started to correct the nonlinear drug loading characteristics. If the error ∈ [0, 3]%, the standard drug loading calibration procedure is used to maintain the drug loading accuracy.

[0027] S07. Conduct drug release performance tests, determine the release kinetics of the TAT-pioglitazone complex under different pH conditions, and establish a pH-responsive release model.

[0028] S08. To verify the anti-ferroptosis effect, the optimized drug delivery system was applied to oxidative stress-induced nucleus pulposus cells. The expression levels of PPARγ and Axl proteins were detected and quantitatively analyzed using fluorescence chemiluminescence. Changes in protein expression levels.

[0029] The cell-penetrating peptide TAT is a short peptide sequence derived from human immunodeficiency virus reverse transcription activator, possessing a high efficiency in penetrating cell membranes and capable of carrying drug molecules across the cell membrane barrier to exert their effects inside the cell. The pH-sensitive liposome is an intelligent drug delivery system that remains stable under physiological pH conditions and undergoes structural changes and releases drugs in the acidic microenvironment of degenerated intervertebral disc tissue.

[0030] The hyaluronic acid ligand is a natural polysaccharide molecule capable of binding to the surface of nucleus pulposus cells. Receptors enable targeted drug delivery. Flow cytometry is a single-cell analysis technique based on optical principles. It uses laser excitation to activate fluorescent markers on or inside cells, enabling precise measurement of particle size and drug loading in drug delivery systems.

[0031] The drug loading efficiency optimization game model comprises two layers: an upper-level model and a lower-level model. The upper-level model aims to maximize the cellular uptake efficiency of the drug loading system, and its objective function is expressed as follows: ,in The unit for cellular uptake rate is seconds (s). , The drug concentration is expressed in μg / mL. The drug release time constant is expressed in seconds. The volume of the drug-loaded particles is measured in nm. The constraints are: s , μg / mL.

[0032] The lower-level model aims to maximize drug release stability, and its objective function is expressed as follows: ,in The drug loading stability coefficient is a dimensionless coefficient. The drug release phase angle is expressed in radians. The diffusion coefficient is in units of / ; The constraint condition is , / s. The coupling terms of the game model are This affects the objective function values ​​of both the upper-level and lower-level models.

[0033] The objective function of the upper-level model is used to evaluate the uptake efficiency of the drug delivery system on nucleus pulposus cells, and the input includes the cell uptake rate. Drug concentration Drug release time constant and drug-loaded particle volume The output is a dimensionless value of the drug delivery system's cellular uptake efficiency. The objective function of the lower-level model is used to evaluate the stability of the drug release process, and the input includes the drug delivery stability coefficient. Drug release phase angle diffusion coefficient and drug release time constant The output is a dimensionless value for drug release stability.

[0034] The adaptive fluorescence signal enhancement algorithm is a noise reduction technique based on signal processing principles. It improves the signal-to-noise ratio of fluorescence detection through adaptive filtering and signal amplification. The drug loading linearization compensation algorithm is a numerical correction method that achieves dynamic compensation of drug loading accuracy by establishing a nonlinear mapping relationship between drug loading and the detection signal.

[0035] The fluorescence luminescence technique described is a quantitative detection method based on the principle of molecular fluorescence emission. It uses specific fluorescent probes to label target proteins, enabling precise quantitative analysis of the expression levels of PPARγ and Axl proteins. The ferroptosis biomarker GPX4 reflects the level of cellular ferroptosis activity; a decrease in its expression indicates activation of the ferroptosis process.

[0036] The specific implementation methods of the above steps are described in detail below.

[0037] The specific implementation of step S01 involves first covalently binding the PPARγ agonist pioglitazone to the cell-penetrating peptide TAT using chemical coupling technology. This step is based on the principle of intermolecular covalent bond formation, achieving a stable connection between the drug molecule and the carrier peptide through chemical bonding methods such as amide bonds or ester bonds. Specifically, pioglitazone is dissolved in a buffer solution to a concentration of 5–20 μg / mL, followed by the addition of a TAT solution to maintain a TAT concentration of 10–50 μg / mL. The carboxyl and amino reaction sites are activated using an EDC / NHS activation system, and the reaction is carried out at 4°C for 12–24 hours to form a stable TAT-pioglitazone complex. The purpose of this step is to construct a drug carrier complex with cell membrane penetration capability, utilizing the transmembrane transport properties of the TAT peptide to overcome the cell membrane barrier and achieve efficient intracellular drug delivery.

[0038] The specific implementation of step S02 involves preparing a pH-sensitive liposome drug delivery system using a thin-film hydration method. This method is based on the principle of lipid molecule self-assembly, forming pH-responsive drug-loaded vesicle structures by controlling lipid composition and preparation conditions. Specifically, DOPE, CHEMS, and cholesterol are mixed in a mass ratio of 6:2:2, dissolved in an organic solvent, and then rotary evaporated to form a lipid film. The film is then hydrated with a phosphate buffer containing a TAT-pioglitazone complex, and dispersed by ultrasonication to form liposomes with uniform particle size. Liposome surface modification involves chemically coupling hyaluronic acid ligands to the liposome surface, with the hyaluronic acid concentration controlled at 0.1–1.0 mg / mL. The purpose of this step is to construct an intelligent drug delivery system that utilizes the structural changes of pH-sensitive liposomes in an acidic environment to achieve controlled drug release, while simultaneously enabling targeted recognition and binding to nucleus pulposus cells via the hyaluronic acid ligands.

[0039] The specific implementation of step S03 involves using dynamic light scattering (DLS) and flow cytometry to jointly detect the physicochemical properties of the drug-loaded system. This detection method is based on the principles of light scattering and fluorescence detection, analyzing the physicochemical characteristics of the particles through the scattered light and fluorescence signals generated by laser irradiation. Specific operations include diluting the prepared liposome drug-loaded system to a suitable concentration, measuring the particle size distribution using a DLS spectrometer, requiring a polydispersity index of less than 0.3 to ensure particle size uniformity. Encapsulation efficiency is determined by separating free drug and drug-loaded liposomes using ultrafiltration centrifugation, and the drug concentration is quantitatively analyzed using high-performance liquid chromatography (HPLC) to calculate the encapsulation efficiency. Drug loading is determined by releasing all drug through liposome structure disruption, and the total drug content is quantitatively determined using ultraviolet spectrophotometry. The purpose of this step is to ensure that the drug-loaded system has suitable physicochemical properties; when the encapsulation efficiency reaches 85-100% and the drug loading is controlled within the range of 8-20%, the drug-loaded system possesses ideal drug loading capacity and stability.

[0040] The specific implementation of step S04 involves establishing a two-layer optimization model based on game theory to determine the optimal drug loading parameters. This model employs Stackelberg game theory, seeking a game equilibrium solution through the interaction of the upper and lower layers. The upper layer model aims to maximize cellular uptake efficiency, considering the combined effects of factors such as cellular uptake rate, drug concentration, release time constant, and particle volume. The lower layer model aims to maximize drug release stability, comprehensively evaluating parameters such as drug loading stability coefficient, release phase angle, and diffusion coefficient. The two models are coupled through the drug release time constant, and an iterative algorithm is used to solve for the game equilibrium point. The purpose of this step is to balance the cellular uptake capacity and drug release stability of the drug loading system through mathematical optimization methods, achieving coordinated optimization of drug loading efficiency and release performance.

[0041] The specific implementation of step S05 involves dynamically adjusting the fluorescence detection mode using an adaptive signal processing algorithm. This algorithm is based on the principle of real-time signal-to-noise ratio (SNR) monitoring and improves the quality of the detected signal through adaptive filtering technology and signal enhancement algorithms. Specifically, it involves real-time monitoring of the noise level of the fluorescence signal. When the SNR drops below 15 dB, a Wiener filter and wavelet denoising algorithm are automatically activated to preprocess the signal, while adaptive gain control technology amplifies the effective signal amplitude. Filter parameters are automatically adjusted according to noise characteristics, including setting the cutoff frequency to 1.5 times the signal bandwidth and controlling the gain coefficient within the range of 2 to 5 times. When the SNR remains above 15 dB, the system adopts a standard detection mode to maintain efficient operation. The purpose of this step is to ensure that the fluorescence detection system provides reliable measurement results under various environmental conditions, overcoming the impact of external interference on detection accuracy through intelligent signal processing technology.

[0042] The specific implementation of step S06 involves using a nonlinear correction algorithm to compensate for systematic errors in drug loading detection. This algorithm, based on interpolation theory and error compensation principles in numerical analysis, achieves accuracy correction by establishing a nonlinear mapping relationship between drug loading and the detection signal. Specifically, it involves pre-establishing a comparison curve between a standard drug loading and the detection signal, constructing a nonlinear correction model using a polynomial fitting method, with the model order determined between 3 and 5 based on the required fitting accuracy. When the detected drug loading linearity error exceeds 3%, the correction algorithm is automatically activated to correct the measurement results in real time, with the correction factor dynamically calculated based on the current detection conditions. When the error is controlled within 3%, a standard linear calibration procedure is used, employing a first-order linear model for simple calibration to maintain detection efficiency. The purpose of this step is to eliminate the influence of non-ideal characteristics of the drug loading system on quantitative detection, ensuring the accuracy and reproducibility of drug loading determination.

[0043] The specific implementation of step S07 involves evaluating the pH response performance of the drug delivery system using an in vitro release assay. This assay, based on diffusion theory and mass transfer principles, studies drug release kinetics by simulating pH changes in a physiological environment. Specifically, phosphate buffer solutions with pH values ​​of 7.4, 6.8, and 6.0 are prepared to simulate different physiological environments. Drug-loaded liposomes are placed in dialysis bags and immersed in the release medium, and the release assay is conducted under constant temperature and shaking conditions at 37°C. The release medium is collected at predetermined time points using a sampling method, and the cumulative release amount is quantitatively analyzed by high-performance liquid chromatography (HPLC). The release kinetic data are fitted using zero-order, first-order, and Higuchi models to determine the optimal fitting model and kinetic parameters. The purpose of this step is to verify the intelligent response characteristics of the drug delivery system under different pH environments and to establish a pH-responsive release model to provide a theoretical basis for clinical applications.

[0044] Step S08 involves using cell biology experimental techniques to verify the anti-ferroptosis effect of the drug delivery system. This verification process is based on cell culture technology and the principle of quantitative protein detection, assessing the therapeutic effect by detecting changes in the expression of key proteins. The specific procedures include culturing nucleus pulposus cells and establishing an oxidative stress-induced ferroptosis model using hydrogen peroxide or malondialdehyde. Subsequently, an optimized drug delivery system is added for intervention, with a treatment concentration set at 10–100 μg / mL and an action time of 12–48 hours. Fluorescence immunoassay is used to detect the expression levels of PPARγ and Axl proteins, and changes in fluorescence signal intensity are observed using a laser confocal microscope. The ferroptosis biomarker GPX4 is quantitatively analyzed using Western blot technology. Changes in protein expression levels. The aim of this step is to validate at the molecular level the biological effects of the drug delivery system in activating the PPARγ signaling pathway and regulating ferroptosis, confirming its protective effect against ferroptosis in nucleus pulposus cells.

[0045] It should be noted that the key technical ideas of this invention are mainly reflected in the following aspects. First, a two-layer optimization technique for determining drug delivery parameters based on game theory. This technique uses the cellular uptake efficiency and drug release stability of the drug delivery system as objective functions in a game-theoretic relationship, and finds the optimal balance between the two through a Stackelberg game model. Compared with traditional single-objective optimization methods, this technique can simultaneously consider the coordination relationship between drug delivery efficiency and release stability, avoiding the system performance imbalance problem caused by a single optimization objective. The game theory model quantifies the multiple constraints in the design of the drug delivery system through mathematical modeling, providing a theoretical basis for the scientific determination of drug delivery parameters.

[0046] Secondly, there is the intelligent detection technology featuring adaptive fluorescence signal enhancement and drug loading linearization compensation. This technology significantly improves the accuracy and stability of detecting the physicochemical properties of the drug-loaded system by dynamically adjusting the signal processing strategy and correction algorithm through real-time monitoring of changes in the detection environment. Compared to traditional fixed-parameter detection methods, this intelligent detection technology can effectively cope with the effects of environmental interference and system nonlinear characteristics, and achieves automatic optimization of detection accuracy through adaptive algorithms, avoiding the subjectivity and lag of manual adjustments.

[0047] Secondly, there is the synergistic drug delivery technology combining pH-sensitive liposomes with hyaluronic acid-targeted modification. This technology organically combines smart responsive materials with biotargeting molecules to construct a drug delivery system with dual selectivity. The structural changes of pH-sensitive liposomes in an acidic environment enable controlled drug release, while hyaluronic acid ligands bind to the surface of nucleus pulposus cells. Specific binding to the receptor enables targeted delivery. Compared to traditional passive diffusion drug delivery methods, this synergistic technology significantly improves drug accumulation and bioavailability in diseased tissues.

[0048] The synergistic effect of these key technologies forms a complete intelligent drug delivery system, achieving end-to-end technological innovation from drug delivery parameter optimization and quality control to targeted delivery and release. Game theory optimization models provide a scientific method for determining parameters in drug delivery system design, intelligent detection technology ensures precise quality control of the drug delivery system, and synergistic drug delivery technology achieves efficient targeted drug delivery. This multi-technology synergistic systemic innovation significantly improves the overall performance of the drug delivery system compared to existing technologies, providing a more scientific and effective technical solution for PPARγ agonist treatment of nucleus pulposus ferroptosis.

[0049] It should be noted that this invention also solves the following technical problems: unstable signal quality and insufficient accuracy in drug loading measurement during the detection of drug-loaded systems. In the characterization and performance evaluation of drug-loaded systems, traditional fluorescence detection methods are easily affected by background noise interference and signal attenuation, resulting in poor accuracy and reproducibility of the detection results. Especially in complex biological environments, the signal-to-noise ratio of fluorescence signals is often low, severely affecting the accurate determination of the physicochemical properties of the drug-loaded system. Simultaneously, there is a nonlinear response problem in the quantitative analysis of drug loading; traditional linear standard curve methods cannot accurately reflect the true relationship between drug concentration and detection signal in the drug-loaded system, leading to a large systematic error in drug loading measurement.

[0050] This invention solves the aforementioned technical problems by establishing an adaptive fluorescence signal enhancement algorithm and a drug loading linearization compensation algorithm. The adaptive fluorescence signal enhancement algorithm, based on signal processing theory, monitors the fluorescence signal-to-noise ratio in real time and automatically activates the signal enhancement function when the signal-to-noise ratio is below 15dB. It utilizes adaptive filtering technology to remove background noise and employs signal amplification technology to improve the effective signal strength, ensuring the stability and reliability of the detection process. The drug loading linearization compensation algorithm establishes a nonlinear mathematical model between the drug loading and the detection signal to achieve dynamic correction of the detection results. When the drug loading linearity error exceeds 3%, the compensation mechanism is automatically activated, and the nonlinear drug loading characteristics are corrected through numerical algorithms, significantly improving the accuracy and precision of drug loading measurement. The synergistic application of these two algorithms not only improves the technical level of drug loading system detection but also provides more accurate and reliable input data for the drug loading efficiency optimization game model, ensuring the scientific validity and effectiveness of the entire drug loading system design and optimization process.

[0051] Specifically, the principle of this invention is as follows: The core principle behind the technical solution of this invention in solving the problem of synergistic optimization of dual functions in drug delivery systems lies in the synergistic design and game-theoretic model optimization of multi-level drug delivery systems. First, the combination of TAT cell-penetrating peptide and the PPARγ agonist pioglitazone forms a drug complex with highly efficient cell penetration capability. The arginine and lysine residues of the TAT peptide bind to cell membrane phospholipid molecules through electrostatic interactions, realizing the transmembrane transport of drug molecules. Second, the hyaluronic acid-modified pH-sensitive liposome drug delivery system achieves an organic unity of targeting and controlled release. Hyaluronic acid ligands achieve active targeting through the specific recognition and binding of CD44 receptors on the surface of nucleus pulposus cells, while pH-sensitive liposomes undergo membrane structure rearrangement and permeability changes in the acidic microenvironment of degenerated intervertebral disc tissue, triggering the site-specific release of the drug. More importantly, the two-layer game theory optimization model established in this invention systematically solves the multi-objective optimization problem in drug delivery system design through mathematical modeling. The upper-layer model aims to maximize cellular uptake efficiency, considering the synergistic effects of key parameters such as cellular uptake rate, drug concentration, and drug-loaded particle volume. The lower-layer model aims to maximize drug release stability, comprehensively considering influencing factors such as drug delivery stability coefficient, diffusion coefficient, and release phase angle. The organic connection between the two layers is achieved through the drug release time constant as a coupling term. This game theory model design conforms to the inherent logic of drug delivery system optimization because cellular uptake and drug release are two key links in the therapeutic effect of drug delivery systems, and they have mutual influence and constraints. Traditional single-objective optimization methods cannot handle such complex interactions, while game theory can find the parameter combination that makes both objective functions optimal simultaneously by finding the Nash equilibrium point, thus achieving global optimization of the drug delivery system performance.

[0052] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0053] In this embodiment, the specific implementation of steps S01-S02 is the same as described above, and will not be repeated in detail here.

[0054] The specific implementation of step S03 involves detecting the physicochemical properties of the drug delivery system using flow cytometry. The encapsulation efficiency calculation formula is as follows:

[0055] ;

[0056] In the formula, Encapsulation ratio; Total drug mass, in units of ; The mass of free drug is expressed in units of... ; This is the drug quality standard value, taken as 10. The formula for calculating drug loading is as follows:

[0057] ;

[0058] In the formula, Drug loading; For the quality of the encapsulated drug, the unit is... ; This refers to the total mass of liposomes, in units of... ; This is a reference value for liposome quality, with a value of 1. The parameter acquisition method is as follows: The drug was obtained experimentally, including step 1: disrupting the lipid bilayer structure of the drug-loaded liposomes with methanol; and step 2: determining the total drug content released using high-performance liquid chromatography. The drug was obtained experimentally, including step 1: separating free drug and drug-loaded liposomes by ultrafiltration centrifugation; and step 2: collecting the ultrafiltrate and determining the drug concentration therein. The method used was experimental, including step 1: weighing the total lipid mass used to prepare liposomes by gravimetric method; step 2: correcting the actual liposome mass according to the loss rate during the liposome preparation process. Obtained through calculation. .

[0059] The specific implementation of step S04 is to establish a game model for optimizing drug loading efficiency. The objective function of the upper-level model is expressed as follows:

[0060] ;

[0061] In the formula, This represents the objective function value of the upper-level model. Cellular uptake rate, in units of ; Drug concentration, in units of ; This is the drug release time constant, in units of... ; This refers to the volume of the drug-loaded particles, in units of... ; The standard value for the uptake rate is 0.1. ; This is the standard value for drug concentration, taken as 10. ; The standard value for the time constant is 1000. ; The standard value for particle volume is [value to be filled in]. The objective function of the lower-level model is expressed as follows:

[0062] ;

[0063] In the formula, The objective function value of the lower-level model; This represents the drug loading stability coefficient. This is the phase angle of drug release, expressed in radians. This is the diffusion coefficient, in units of... ; The standard value for the stability coefficient is 1.0. The standard value of the diffusion coefficient is given by [variable name]. The game equilibrium point is solved using the Lagrange multiplier method, and the constraint optimization function is expressed as follows:

[0064] ;

[0065] In the formula, It is a Lagrange function; This is the weighting coefficient, with a value ranging from 0.3 to 0.7; and It is a Lagrange multiplier; This represents the upper limit of the ingestion rate, with a value of 1.0. ; This represents the upper limit of drug concentration, with a value of 100. The formula for iteratively solving for the equilibrium point in a game is expressed as follows:

[0066] ;

[0067] In the formula, For the first The parameter vector for the next iteration; The learning rate, ranging from 0.01 to 0.1; Let be the gradient of the Lagrange function. The parameters are obtained as follows: The drug was obtained using a cell uptake assay, which included step 1: co-culturing the drug delivery system with cultured nucleus pulposus cells; and step 2: detecting the slope of the change in intracellular fluorescence intensity over time using flow cytometry. The dynamic light scattering method was used to obtain the drug, including step 1: diluting the drug delivery system to a suitable concentration; step 2: using a particle size analyzer to determine the hydration dynamic diameter of the particles and calculate the volume. The diffusion experiment was used, including step 1: constructing a concentration gradient diffusion cell; and step 2: measuring the concentration distribution of the drug at different time points and fitting the diffusion equation. The release kinetics analysis was used, including step 1: determining the drug release curves under different pH conditions; and step 2: using Fourier transform to analyze the phase characteristics of the release process. Stability was obtained through a stability experiment, which included step 1: storing the drug-loaded system under different temperature and pH conditions; and step 2: measuring the change in drug loading over time and calculating the stability coefficient.

[0068] The specific implementation of step S05 is to use an adaptive fluorescence signal enhancement algorithm, and the signal-to-noise ratio calculation formula is expressed as follows:

[0069] ;

[0070] In the formula, Signal-to-noise ratio, unit: ; Effective signal strength; Noise signal strength; The signal strength standard value is 1000. The adaptive filter transfer function is expressed as follows:

[0071] ;

[0072] In the formula, The filter transfer function; Angular frequency, unit: ; The imaginary unit; and The time constant is expressed in units of 1. The value ranges from 0.01 to 0.1. ; This is the gain coefficient; The standard gain value is 2.0. The cutoff angular frequency, in units of The parameter acquisition method is as follows: The experimental method was used to obtain the signal amplitude, including step 1: measuring the intensity of the standard fluorescence signal in an interference-free environment; and step 2: extracting the effective signal amplitude using a peak detection algorithm. The noise signal was obtained experimentally, including step 1: measuring the background noise signal under the same conditions; and step 2: calculating the root mean square value of the noise signal using statistical analysis methods. The system identification method is used to obtain the time constant, including step 1: inputting a step signal to test the system response; step 2: fitting a first-order system model to determine the time constant. The frequency domain analysis is used to obtain the data, including step 1: measuring the frequency response characteristics of the system; and step 2: calculating the time constant based on the -3dB bandwidth. Determined based on signal bandwidth. ,in This refers to the signal bandwidth. Adjust adaptively based on signal-to-noise ratio ,when Enabled at any time.

[0073] The specific implementation of step S06 is to use a drug loading linearization compensation algorithm, and the nonlinear correction function is expressed as follows:

[0074] ;

[0075] In the formula, To correct for post-loaded drug concentration, the unit is... ; To measure drug loading concentration, the unit is... ; For correction factors; is the order of the polynomial, taking values ​​from 3 to 5; For reference drug loading concentration, a value of 50 is used. The formula for calculating linearity error is as follows:

[0076] ;

[0077] In the formula, This refers to linearity error; Number of measurement points; For the first The measured value at each measurement point, in units of ; For the first The actual value of each measurement point, in units of The parameter acquisition method is as follows: The least squares method was used to obtain the results, including step 1: preparing standard samples with different drug loading concentrations; and step 2: establishing a nonlinear regression equation between the measured values ​​and the actual values ​​and determining the correction coefficients. The standard method is used to obtain the drug, including step 1: accurately preparing a standard solution with a known drug loading concentration using the gravimetric method; and step 2: verifying the actual drug loading concentration using standard analytical methods.

[0078] The specific implementation of step S07 involves conducting a drug release performance test. The formula for calculating the cumulative release rate is as follows:

[0079] ;

[0080] In the formula, for Cumulative release rate over time; for The cumulative amount of drug released over time, in units of ; The total amount of drug in the drug delivery system, in units of ; This is the standard value for drug dosage, taken as 100. The release kinetics were fitted using the Higuchi model, expressed by the following formula:

[0081] ;

[0082] In the formula, Higuchi release constant, in units of ; Release time, in units of ; The time standard value is 1. The parameter acquisition method is as follows: The sampling method is used to obtain the drug, including step 1: taking a sample from the release medium at a predetermined time point; and step 2: determining the drug concentration in the sample and calculating the cumulative release amount using high performance liquid chromatography. The data was obtained by fitting, including step 1: collecting cumulative release rate data at different time points; step 2: fitting the Higuchi model using the least squares method and determining the release constant.

[0083] The specific implementation of step S08 is to verify the anti-ferroptosis effect. The quantitative analysis formula for PPARγ protein expression level is expressed as follows:

[0084] ;

[0085] In the formula, The relative expression level of PPARγ protein; The fluorescence intensity of the sample; The fluorescence intensity is for the control group. Background fluorescence intensity; The standard value for fluorescence intensity is 10000; The normalization coefficient is set to 1.0. The formula for quantitative analysis of Axl protein expression levels is as follows:

[0086] ;

[0087] In the formula, This represents the relative expression level of Axl protein; This represents the absorbance value of the sample. The absorbance value is for the control group. Background absorbance value; The absorbance standard value is 1.0; The standardization coefficient is set to 1.0. The formula for quantitative analysis of the expression level of the ferroptosis marker GPX4 protein is as follows:

[0088] ;

[0089] In the formula, for Protein expression levels; This represents the expression level of GPX4 protein. The reference protein expression level is the β-actin protein expression level. The standard value for protein expression level is 100. The parameter was obtained as follows: The fluorescence was acquired using fluorescence microscopy, including step 1: labeling cells with PPARγ antibody and fluorescent secondary antibody; and step 2: detecting the fluorescence signal intensity using laser confocal microscopy. The protein was obtained using enzyme-linked immunosorbent assay (ELISA), including step 1: extracting total cellular protein and determining its concentration; and step 2: performing immunoassay with Axl antibody and determining the absorbance. The protein was obtained using Western blotting, including step 1: separating the protein using sodium dodecyl sulfate polyacrylamide gel electrophoresis; step 2: using... Antibody detection and quantitative analysis of protein band gray values.

[0090] It should be explained that the formula for calculating the drug loading efficiency of the cell-penetrating peptide TAT is as follows:

[0091] ;

[0092] In the formula, For TAT drug loading efficiency; Intracellular drug uptake, in units of ; The total amount of drug applied is expressed in units of... ; This is the standard value for drug dosage, with a value of 5. ; For the time taken, the unit is... ; The time standard value is 60. .

[0093] The kinetic equation for the release of pH-sensitive liposomes is expressed as follows:

[0094] ;

[0095] In the formula, The cumulative amount of drug released is expressed in units of... ; For time, the unit is ; The release rate constant is expressed in units of . pH refers to the ambient pH value. The standard pH value is 7.4. Total drug amount, in units of ; The volume of the released medium is expressed in units of... ; For standard volume, the value is 10. .

[0096] The formula for calculating the binding affinity of hyaluronic acid ligands is as follows:

[0097] ;

[0098] In the formula, The dissociation constant is expressed in units of 1000 ppm. ; This represents the concentration of free hyaluronic acid, in units of... ; For free Receptor concentration, in units of ; Hyaluronic acid Complex concentration, in units of ; The concentration standard value is 1. The parameter acquisition method is as follows: The fluorescence polarization method was used to obtain the hyaluronic acid probe, which included step 1: preparing a fluorescently labeled hyaluronic acid probe; and step 2: measuring the change in fluorescence polarization before and after binding and calculating the free concentration. Obtained using flow cytometry, including step 1: using Step 2: Quantitatively analyze the number of unbound receptors by fluorescence intensity using antibody labeling of cell surface receptors. The method of obtaining the sample using surface plasmon resonance includes step 1: ... The receptor is fixed on the surface of the sensor chip; Step 2: Measure the change in resonance angle after hyaluronic acid binding and calculate the concentration of the complex.

[0099] The encapsulation efficiency calculation formula is based on the principle of mass conservation. It eliminates the influence of dimensions through dimensionless processing, accurately reflects the encapsulation ability of liposomes for drugs, and improves detection accuracy and reproducibility compared with traditional single measurement methods.

[0100] ;

[0101] The drug delivery efficiency optimization game model uses cell uptake efficiency and drug release stability as objective functions in a game between the two. Through mathematical optimization, it achieves a coordinated balance between the two, which avoids performance bias problems compared with single-objective optimization methods and significantly improves the overall performance of the drug delivery system.

[0102] ;

[0103] The logarithmic term in this formula This reflects the diminishing marginal effect of drug concentration on uptake efficiency, the square root term. This demonstrates the influence of particle size on diffusion resistance. The game equilibrium iterative solution algorithm is based on the gradient descent principle, using the gradient information of the Lagrange function to guide the parameter update direction.

[0104] ;

[0105] The adaptive filter transfer function combines low-pass filtering and adaptive gain control, and achieves coordinated processing of the frequency and time domains through complex domain analysis. Compared with fixed parameter filters, it has stronger environmental adaptability and noise suppression effect.

[0106] ;

[0107] Pole-zero placement terms in the transfer function To achieve phase compensation of the signal, a low-pass filter term is used. Suppressing high-frequency noise. The nonlinear correction function uses a high-order polynomial to approximate the nonlinear characteristics, and dimensionless processing ensures numerical stability, significantly improving the accuracy and linearity of drug loading detection compared to linear correction methods.

[0108] ;

[0109] The Higuchi release kinetic model describes the diffusion-controlled release process of matrix-based drug delivery systems. It reflects the drug diffusion mechanism through the square root of time and more accurately describes the release characteristics of liposome-based drug delivery systems compared to zero-order or first-order kinetic models.

[0110] ;

[0111] The formula for quantifying the relative expression level of PPARγ protein is based on the principle of fluorescence intensity ratio analysis. It eliminates systematic errors by comparing the relative fluorescence intensity of the sample and the control group, thus improving the accuracy and reproducibility of detection compared to absolute quantification methods.

[0112] ;

[0113] The pH-sensitive liposome release kinetic equation is based on diffusion theory and chemical kinetics principles, and uses a pH-dependent term. and volume correction term It describes the complexity of the release process and predicts drug release behavior under different physiological conditions more accurately than a simple first-order kinetic model.

[0114] ;

[0115] The formula for hyaluronic acid ligand binding affinity is based on the thermodynamic principle of ligand-receptor interaction. It quantitatively assesses the targeting binding ability through the dissociation constant, providing a quantitative target efficiency evaluation index compared to qualitative evaluation methods.

[0116] .

[0117] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2:

[0118] A technical team, while developing a treatment for intervertebral disc degeneration, encountered the problem of disc function loss caused by ferroptosis of nucleus pulposus cells. The team decided to construct a targeted drug delivery system using the PPARγ agonist pioglitazone to improve the symptoms of intervertebral disc degeneration by inhibiting ferroptosis of nucleus pulposus cells.

[0119] The technical team first prepared the drug delivery system. In Experiment 1, 20 μg / mL of the cell-penetrating peptide TAT was reacted with 5 mg / mL of pioglitazone at 4°C for 2 hours to form a TAT-pioglitazone complex. In Experiment 2, the TAT concentration was adjusted to 35 μg / mL, while other conditions remained constant. In Experiment 3, the TAT concentration was set to 45 μg / mL for a comparative experiment. Comparative Example 1 used unmodified pioglitazone as a control, and Comparative Example 2 used a conventional polymer carrier PLGA to encapsulate pioglitazone. The preparation parameters of the drug delivery system are shown in Table 1.

[0120] Table 1 Comparison of Preparation Parameters for Drug Delivery Systems

[0121] The technical team then constructed a nucleus pulposus-targeting vector system. pH-sensitive liposomes were prepared using a thin-film hydration method, with a lipid composition of phosphatidylcholine:cholesterol:pH-sensitive lipid in a molar ratio of 7:2:1. The liposome surface was then modified with hyaluronic acid ligands via EDC / NHS chemical coupling, with hyaluronic acid having a molecular weight of [missing information]. Da, the modified density is Lipids. The liposome preparation parameters differed among the different experimental groups. Experiment 1 used the basic formula, while Experiment 2 increased the hyaluronic acid modification density to... Lipids, Experiment Example 3: The molecular weight of hyaluronic acid was adjusted to... Da.

[0122] The physicochemical properties of each drug delivery system were detected by flow cytometry. The technical team used dynamic light scattering to determine the liposome particle size distribution, ultrafiltration centrifugation to determine the encapsulation efficiency, and high-performance liquid chromatography (HPLC) to determine the drug loading. The results showed that the liposomes in Example 1 had an average particle size of 185 nm, a polydispersity index of 0.23, an encapsulation efficiency of 91.2%, and a drug loading of 12.8%. The corresponding values ​​for Example 2 were 168 nm, 0.19, 94.7%, and 15.3%, respectively. For Example 3, the values ​​were 192 nm, 0.28, 88.6%, and 11.4%, respectively. The physicochemical property results are shown in Table 2.

[0123] Table 2. Results of physicochemical property tests of the drug delivery system

[0124] The technical team established a game-theoretic model to optimize drug loading efficiency and determine the optimal drug loading parameters. The upper-level model aims to maximize cellular uptake efficiency by measuring cellular uptake rates under different conditions. Drug concentration Drug release time constant and drug-loaded particle volume Calculate the objective function value. Example 2 in... , , , Under the given conditions, the maximum upper-level objective function value of 0.0847 was obtained. The lower-level model aims to maximize drug release stability. Experimental Example 2 was conducted under these conditions. , radian, , Obtain the maximum lower-level objective function value under the given conditions The optimization results of the game theory model are shown in Table 3:

[0125] Table 3 Comparison of Game Theory Model Optimization Results

[0126] Regarding signal quality optimization, the technical team monitored changes in the fluorescence signal-to-noise ratio (SNR). When the SNR of Experiment 1 dropped to 12.8 dB, the system automatically activated the adaptive fluorescence signal enhancement algorithm, boosting the SNR to 18.6 dB through Kalman filtering and adaptive gain control. The SNRs of Experiments 2 and 3 remained at 16.4 dB and 17.2 dB, respectively, using the standard fluorescence detection mode. Drug loading linearity error monitoring showed that the drug loading linearity error of Experiment 1 was 4.2%, triggering the drug loading linearization compensation algorithm. After three iterations, the error decreased to 2.1%. The drug loading linearity errors of Experiments 2 and 3 were 2.8% and 1.9%, respectively, both within acceptable ranges.

[0127] The technical team conducted drug release performance tests, determining the release kinetics of the TAT-pioglitazone complex in buffer solutions with pH values ​​of 7.4, 6.8, and 5.5. The results showed that at physiological pH 7.4, the cumulative drug release rate was only 18.3% over 24 hours, while in an acidic environment at pH 5.5, the cumulative release rate reached 78.6% within the same time period, demonstrating good pH response characteristics. The release kinetic parameters under different pH conditions were fitted using the Korsmeyer-Peppas model, with the release index n ranging from 0.43 to 0.89, indicating that the drug release mechanism is a combined process of Fickian diffusion and non-Fickian diffusion. The drug release performance test results are shown in Table 4.

[0128] Table 4. Results of Drug Release Performance Tests

[0129] To verify the anti-ferroptosis effect, the technical team applied the optimized drug delivery system to oxidative stress-induced nucleus pulposus cells. An oxidative stress model was established by treating nucleus pulposus cells for 6 hours, followed by treatment with different concentrations of the drug delivery system. The expression levels of PPARγ and Axl proteins, as well as autophagy markers, were detected by fluorescence chemiluminescence. Changes in ratios. Results showed that after oxidative stress treatment, the expression level of PPARγ protein in nucleus pulposus cells decreased by 62.4% compared to the normal control group, and the expression of Axl protein decreased by 58.7%. Protein expression levels decreased. After treatment with the drug delivery system in Experiment 2, PPARγ protein expression increased by 156% compared to the oxidative stress group, Axl protein expression increased by 142%, and GPX4 protein expression level increased, indicating that the drug delivery system effectively activated the PPARγ signaling pathway and inhibited ferroptosis.

[0130] Cell viability was assessed using the MTT assay. Results showed that nucleus pulposus cell viability decreased to 45.2% after oxidative stress treatment, but recovered to 78.9% after treatment with the drug delivery system in Experiment 2. Ferrapholysis markers were detected, including the measurement of malondialdehyde (MDA), a lipid peroxidation product, and glutathione peroxidase (GPx) activity. In the oxidative stress group, MDA content increased by 238% and GPx activity decreased by 69% compared to the normal group. After treatment with the drug delivery system, MDA content decreased by 51% and GPx activity increased by 89%. The results of the anti-ferropholysis effect are shown in Table 5.

[0131] Table 5. Validation results of anti-ferromorbidity effect

[0132] The technical team also conducted stability evaluations and in vitro release behavior studies of the drug-loaded system. The system was stored at 4°C, 25°C, and 37°C, and particle size changes, encapsulation retention, and drug leakage were monitored over 30 days. Results showed that the drug-loaded system exhibited the best stability under 4°C storage conditions, with particle size increase of less than 8% and encapsulation retention above 86% after 30 days. The drug release from the system in a simulated intervertebral disc degeneration microenvironment (pH 6.2, 37°C) exhibited a clear burst release characteristic, releasing approximately 45% of the drug within the first 4 hours, followed by a slow release phase, with a cumulative release rate of 85.7% over 24 hours.

[0133] Studies on cellular uptake mechanisms have revealed that drug delivery systems primarily enter nucleus pulposus cells via receptor-mediated endocytosis. Hyaluronic acid ligands and cell surface... Receptor binding triggers endocytosis, causing the drug delivery system to undergo structural changes and release the drug in the acidic intracellular environment. Confocal microscopy revealed that the fluorescently labeled drug delivery system exhibited a typical endosome-lysosome distribution pattern within the cell. Free fluorescent signals were detected in the cytoplasm after 2 hours, indicating that the drug had been successfully released from the carrier.

[0134] Compared to traditional drug delivery methods, the PPARγ agonist drug delivery system of this invention achieves significant improvements in several aspects. Regarding cellular uptake efficiency, the hyaluronic acid-modified liposome drug delivery system exhibits a 17.2% increase in uptake compared to the unmodified carrier, primarily due to… Receptor-mediated active targeting mechanism. Regarding drug bioavailability, the intracellular drug concentration of the TAT-pioglitazone complex was 14.8% higher than that of the free drug, indicating that the transmembrane peptide effectively promoted transmembrane drug transport. In terms of therapeutic efficacy, the drug delivery system was 18.6% more effective than traditional administration methods in inhibiting ferroptosis in nucleus pulposus cells, manifested in higher cell viability and a more significant antioxidant effect. pH-sensitive release characteristics increased drug release efficiency in diseased tissues by 16.3% compared to normal tissues, achieving precise lesion-targeted therapy. The long circulation characteristics and sustained-release behavior of the drug delivery system prolonged the duration of drug action, reduced the frequency of administration, and improved patient compliance and treatment convenience.

[0135] It should be noted that the variables involved in Embodiment 1 of the present invention are explained in detail in Tables 6 and 7 below.

[0136] Table 6. Variable Explanation Table (Part 1)

[0137] Table 7. Variable Explanation Table (Part Two)

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of using a PPARy agonist for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The preparation of drug delivery system combines PPAR gamma agonist pioglitazone with cell penetrating peptide TAT to form TAT-pioglitazone complex; the construction of nucleus pulposus targeting carrier encapsulates TAT-pioglitazone complex in pH-sensitive liposomes and modifies hyaluronic acid ligand on the surface of the liposomes to form hyaluronic acid modified pH-sensitive liposome drug delivery system; the physical and chemical properties of the drug delivery system are detected by flow cytometry to determine the particle size distribution, encapsulation efficiency and drug loading capacity of the liposomes; the drug loading efficiency optimization game model is established, including the upper model with the maximum cell uptake efficiency of the drug delivery system as the target and the lower model with the maximum drug release stability as the target, and the optimal drug loading parameters are determined by the game equilibrium point; the drug release performance test is carried out to determine the release kinetics of TAT-pioglitazone complex in different pH environments and establish a pH-responsive release model; the anti-ferroptosis effect is verified by applying the optimized drug delivery system to the nucleus pulposus cells induced by oxidative stress, and the protein expression levels of PPAR gamma and Axl and the changes of ferroptosis marker GPX4 protein level are detected by fluorescence emission method, wherein the hyaluronic acid modified pH-sensitive liposome drug delivery system realizes the synergistic optimization of nucleus pulposus cell targeted delivery and acid-sensitive controlled release dual functions.

2. The use of a PPARy agonist according to claim 1 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The preparation of the drug delivery system specifically includes the concentration of the cell penetrating peptide TAT ∈ [10, 50] μg / mL.

3. The use of a PPARy agonist according to claim 2 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The detection of the physical and chemical properties of the drug delivery system specifically includes when the encapsulation efficiency ∈ [85, 100]% and the drug loading capacity ∈ (8, 20]%, the next step is entered.

4. The use of a PPARy agonist according to claim 3 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, Before the step of establishing the drug loading efficiency optimization game model, it further includes activating the adaptive fluorescence signal enhancement algorithm to improve the detection signal quality when the fluorescence signal-to-noise ratio ≤ 15 dB, and using the standard fluorescence detection mode to maintain high efficiency if the fluorescence signal-to-noise ratio ≥ 15 dB.

5. The use of a PPARy agonist according to claim 4 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, Before the step of establishing the drug loading efficiency optimization game model, it further includes starting the drug loading linearization compensation algorithm to correct the nonlinear drug loading characteristics when the drug loading linearity error > 3%, and using the standard drug calibration program to maintain the drug loading accuracy if the error ∈ [0, 3]%.

6. The use of a PPARy agonist according to claim 5 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The cell penetrating peptide TAT refers to a short peptide sequence derived from human immunodeficiency virus reverse transcriptase, which has the ability to efficiently penetrate the cell membrane and can carry drug molecules across the cell membrane barrier into the cell interior to play a role.

7. The use of a PPARy agonist according to claim 6 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The pH-sensitive liposome refers to an intelligent drug delivery system that remains stable under physiological pH conditions and undergoes structural changes and releases drugs in the acidic microenvironment of intervertebral disc degeneration tissue.

8. The use of a PPARy agonist according to claim 7 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The drug loading linearization compensation algorithm realizes dynamic compensation of drug loading accuracy by establishing a nonlinear mapping relationship between drug loading capacity and detection signal.

9. The use of a PPARy agonist according to claim 8 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The adaptive fluorescence signal enhancement algorithm improves the signal-to-noise ratio of fluorescence detection through adaptive filtering and signal amplification technology.

10. The use of a PPARy agonist according to claim 9 for the preparation of a medicament for inhibiting ferroptosis in nucleus pulposus cells, characterized in that, The changes of the ferroptosis marker GPX4 protein level.

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