An immune effect evaluation system, method and apparatus for an immunosuppressant

Abstract

The present application belongs to the technical field of medical drug effect evaluation, and particularly relates to an immune effect evaluation system, method and device of an immunosuppressant. The present application can more comprehensively reflect the immune activation characteristics of T cells, reduce the omission of key immune information, and improve the accuracy of immune effect evaluation of the immunosuppressant by collecting dynamic secretion data of cytokines in the T cell activation process in real time, extracting multi-dimensional functional state indicators, and compared with the traditional single-time-point sampling detection method. Through the iterative evaluation method, the drug dosage can be gradually adjusted combined with each evaluation result, which helps to quickly obtain the drug administration scheme suitable for the individual patient in the clinic, ensures the immunosuppression effect, avoids various adverse reaction risks caused by excessive dosage, provides reliable quantitative evaluation basis for the clinical application of immunosuppressants for diseases such as organ transplantation and autoimmune diseases, and helps to improve the treatment prognosis of patients.

Description

Technical Field

[0001] This invention belongs to the field of medical drug efficacy evaluation technology, specifically relating to an immunosuppressant efficacy evaluation system, method and equipment. Background Technology

[0002] In the clinical diagnosis and treatment of diseases such as organ transplantation and autoimmune diseases, the rational use of immunosuppressants is a key link in ensuring treatment effectiveness and improving patient prognosis. If the dosage of immunosuppressants is insufficient, the ideal immunosuppressive effect cannot be achieved, which may easily trigger transplant rejection or autoimmune attack, leading to treatment failure. If the dosage is too high, it will excessively suppress the patient's immune function, greatly increasing the risk of serious adverse reactions such as infection and tumor development.

[0003] Current methods for detecting immune function mostly collect immune indicators at a single time point, which cannot reflect the dynamic secretion characteristics of cytokines during T cell activation. This can easily lead to the omission of key immune information, resulting in insufficient accuracy of the assessment results and failing to provide a reliable basis for adjusting the dosage of immunosuppressants. Therefore, this solution provides a method for evaluating the immune efficacy of immunosuppressants to address the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a system, method, and device for evaluating the immune efficacy of immunosuppressants, which can extract multi-dimensional functional state indicators by collecting dynamic secretion data of cytokines during T cell activation, thereby achieving precise quantitative evaluation of the immune efficacy of immunosuppressants.

[0005] The specific technical solution adopted by this invention is as follows: A method for evaluating the immunosuppressive effect, comprising: Peripheral blood samples were obtained from subjects receiving immunosuppressant therapy, and peripheral blood mononuclear cells were isolated from them; Peripheral blood mononuclear cells were dispersed and cultured, and then activated using T-cell-specific stimulants in an environment containing immunosuppressants. Real-time acquisition of cytokine secretion data from T cells during the activation process, and determination of T cell functional status indicators based on cytokine secretion data; The immune efficacy assessment results of immunosuppressants are generated based on functional status indicators, and a deviation term between the immune efficacy assessment results and the target results is generated based on the immune efficacy assessment results. Based on the deviation, the dosage was administered, and peripheral blood samples were collected from the subjects again for iterative evaluation until the immune effect evaluation results reached the target results.

[0006] In a preferred embodiment, the step of obtaining an ex vivo peripheral blood sample from a subject receiving immunosuppressant therapy and isolating peripheral blood mononuclear cells therefrom includes: Collect venous blood from the subject to form a whole blood sample, and place it in a blood collection tube containing anticoagulant, then invert and mix thoroughly. Add a whole blood sample to the top of an equal volume of lymphocyte separation medium and centrifuge at room temperature; Peripheral blood mononuclear cells were obtained by aspirating the intermediate white membrane layer, and then washed with phosphate buffer and resuspended for later use.

[0007] In a preferred embodiment, the step of dispersing and culturing peripheral blood mononuclear cells and activating them using T-cell-specific stimulants in an environment containing immunosuppressants includes: The resuspended peripheral blood mononuclear cells were adjusted to the preset cell density with complete culture medium, seeded in culture plates, and immunosuppressants were added to the target concentration. T cell-specific stimulants were added to the culture plate and incubated in an incubator to activate T cells and induce them to secrete cytokines. The cell density in each well of the culture plate was consistent, and the immunosuppressant concentration gradient covered the clinical therapeutic window.

[0008] In a preferred embodiment, the step of real-time acquisition of cytokine secretion data of T cells during the activation process, and determining the functional status indicators of T cells based on the cytokine secretion data, includes: The culture period was divided into multiple sub-time windows, and cell culture supernatant was collected at equal intervals during the activation process within each sub-time window. The concentrations of various cytokines were then determined based on the cell culture supernatant. The secretion peak, secretion rate, and cumulative secretion amount of each cytokine within each sub-time window were statistically analyzed. The secretion peak of all cytokines was organized into a peak feature vector, the secretion rate of all cytokines was organized into a rate feature vector, and the cumulative secretion amount of all cytokines was organized into a cumulative feature vector. The peak eigenvector, rate eigenvector, and cumulative eigenvector are standardized respectively to form a standardized data matrix; Calculate the covariance matrix of the corresponding features under each dimension, and perform eigenvalue decomposition on the covariance matrix to extract the eigenvalues ​​and corresponding eigenvectors; Calculate and output the cumulative contribution rate of each covariance matrix, and select the main feature dimensions from the peak eigenvector, rate eigenvector and cumulative eigenvector respectively based on the cumulative contribution rate; The main feature dimensions of the peak feature vector, the main feature dimension of the rate feature vector, and the main feature dimension of the cumulative feature vector are used together as indicators of the functional state of T cells.

[0009] In a preferred embodiment, the step of generating the immunosuppressant efficacy evaluation result based on functional status indicators includes: The functional status indicators and the functional status indicators obtained from historical assessments are arranged in chronological order of assessment time to form a sequence of functional status changes across rounds. Based on the functional state change sequence, the change amplitude of functional state indicators between two adjacent assessments was extracted, and the correlation coefficient between the change amplitude and the immunosuppressant concentration gradient was calculated. The correlation coefficient was used to quantify the strength of the association between the concentration of immunosuppressants and the immunization effect, and an evaluation value of the immunosuppressive effect was obtained.

[0010] In a preferred embodiment, the step of quantifying the correlation strength between the concentration of the immunosuppressant and the immunization effect based on a correlation coefficient to obtain an immunosuppressive effect assessment value includes: Collect the statistical distribution of correlation coefficients in historical assessments, and use the median of the historical correlation coefficient distribution as the first threshold and the upper quartile of the distribution as the second threshold, wherein the first threshold is less than the second threshold. The absolute value of the correlation coefficient is compared with the first threshold and the second threshold; When the correlation coefficient is less than the first threshold, it indicates that the current dose adjustment has not caused a discernible change in immune effect, and it is determined that there is no significant correlation between the change in the concentration of immunosuppressant and the change in functional status indicators. When the correlation coefficient is between the first threshold and the second threshold, it is determined that there is a weak correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. When the correlation coefficient is greater than the second threshold, it is directly determined that there is a strong correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. The sign of the correlation coefficient indicates the consistency between the direction of concentration changes and changes in functional status indicators. If the correlation coefficient is negative, it means that the functional status index decreases when the concentration of immunosuppressant increases, which is a normal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the negative correlation strength between the immunosuppressant concentration and the functional status index. If the correlation coefficient is positive, it means that the functional status index increases synchronously when the concentration of immunosuppressant increases, which is an abnormal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the positive correlation strength value between the immunosuppressant concentration and the functional status index. Different weighting coefficients were assigned to the correlation coefficient ranges corresponding to weak and strong associations, and the positive and negative association strength values ​​were weighted and corrected to obtain the final immunosuppressive effect evaluation value.

[0011] In a preferred embodiment, the step of generating a deviation term between the immune efficacy evaluation result and the target result includes: The immunosuppression effect assessment value is compared with the target assessment value, and the difference between the immunosuppression effect assessment value and the target assessment value is calculated and output as the assessment factor. Obtain the difference between all immunosuppressive effect assessment values ​​and target assessment values ​​in historical data, and calculate their mean and standard deviation. Construct a deviation threshold interval with the mean as the center and the standard deviation as the scale. The evaluation factor is compared with a preset deviation threshold range. If the evaluation factor is not within the deviation threshold range, the concentration of the corresponding immunosuppressant is marked as a deviation item; otherwise, it is marked as a normal item.

[0012] In a preferred embodiment, the step of administering the dosage based on the deviation and iteratively evaluating the subject's peripheral blood samples again until the immune effect evaluation result reaches the target result includes: Based on the sign and magnitude of the deviation, determine the direction and magnitude of adjustment for the dosing regimen: When the deviation is positive, it means that the current direction of immunosuppression is correct but there is still a gap with the target. The dosage of immunosuppressant will be reduced proportionally according to the amount of deviation. When the deviation is negative, it indicates that the current immunosuppression is insufficient, and the dosage should be increased proportionally. After each dose adjustment, peripheral blood samples were collected from the subjects again, and the immunosuppressive effect was repeatedly evaluated based on the newly collected peripheral blood samples until the difference between the immunosuppressive effect evaluation result and the target evaluation value fell within the deviation threshold range. If the number of repeated evaluations of the immunosuppressive effect reaches the iteration limit, the iteration will stop and a warning signal indicating that the adjustment scheme has failed will be issued.

[0013] The present invention also provides a system for evaluating the immune efficacy of immunosuppressants, using the above-described method for evaluating the immune efficacy of immunosuppressants, comprising: An initialization module is used to obtain peripheral blood samples from subjects receiving immunosuppressant therapy and isolate peripheral blood mononuclear cells from them; The cell culture module is used for the dispersion culture of peripheral blood mononuclear cells and the activation treatment using T cell-specific stimulants in an environment containing immunosuppressants. The indicator extraction module is used to collect cytokine secretion data of T cells in real time during the activation process, and to determine the functional status indicators of T cells based on the cytokine secretion data. The immune assessment module is used to generate immune efficacy assessment results of immunosuppressants based on functional status indicators, and to generate deviation items between the immune efficacy assessment results and the target results. The iterative optimization module is used to administer the dosage based on the deviation and to re-collect peripheral blood samples from the subjects for iterative evaluation until the immune effect evaluation results reach the target results.

[0014] And, an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for evaluating the immune efficacy of immunosuppressants.

[0015] The technical effects achieved by this invention are as follows: This invention collects dynamic secretion data of cytokines during T cell activation in real time and extracts multi-dimensional functional state indicators. Compared with traditional single-time-point sampling detection methods, it can more comprehensively reflect the immune activation characteristics of T cells, reduce the omission of key immune information, and improve the accuracy of immunosuppressant efficacy assessment. Through iterative evaluation, the dosage can be gradually adjusted based on the results of each evaluation, helping clinicians to quickly obtain a dosing regimen suitable for individual patients. While ensuring immunosuppressive efficacy, it avoids the risk of various adverse reactions caused by excessive dosage. It provides a reliable quantitative assessment basis for the clinical application of immunosuppressants in organ transplantation, autoimmune diseases, and other diseases, and helps improve patient treatment prognosis. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system modules of the present invention; Figure 3 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0020] Please see Figure 1 As shown, the present invention provides a method for evaluating the immunogenicity of immunosuppressants, comprising: S1. Obtain peripheral blood samples from subjects receiving immunosuppressant therapy and isolate peripheral blood mononuclear cells from them; In step S1, immunosuppressants, as drugs used in the medical industry to suppress the body's immune response, are widely used in organ transplant rejection and autoimmune disease treatment. By collecting peripheral blood for in vitro evaluation, the actual response of the subject's immune cells to the drug can be directly reflected. Specifically, venous blood is collected from the subject to form a whole blood sample, which is placed in a blood collection tube containing an anticoagulant and mixed by inversion to prevent blood clotting, thereby ensuring the activity and purity of subsequent cell separation. Then, the whole blood sample is added to the upper layer of an equal volume of lymphocyte separation medium and centrifuged at room temperature. After centrifugation, the blood is separated into three layers: a plasma layer, a white membrane layer, and a red blood cell layer. The white membrane layer is the interface rich in peripheral blood mononuclear cells. Peripheral blood mononuclear cells are obtained by aspirating the middle white membrane layer, washing with phosphate buffer, and resuspending for later use. This is used as the basic cell sample for subsequent immune cell function detection and drug sensitivity analysis.

[0021] S2. Peripheral blood mononuclear cells were dispersed and cultured, and then activated using T cell-specific stimulants in an environment containing immunosuppressants. In step S2, after obtaining peripheral blood mononuclear cells, they are dispersed and cultured to form multiple parallel experimental groups. This also provides a sufficient cell sample base for subsequent treatments with different concentrations of immunosuppressants. Specifically, the steps of dispersing and culturing peripheral blood mononuclear cells and activating them using T-cell-specific stimulants in an environment containing immunosuppressants include: Resuspended peripheral blood mononuclear cells were adjusted to the preset cell density using complete culture medium, seeded into culture plates, and immunosuppressants were added to the target concentration. The cell density was set according to experimental requirements, typically 1 × 10⁻⁶. 6 Cells / mL to 2×10 6 Cells / mL were seeded in 96-well or 48-well plates, with cell suspension added to provide the necessary nutrient environment for cell growth. The cell density in each well of the culture plate was consistent, and the immunosuppressant concentration gradient covered the clinical therapeutic window. This limitation was intended to allow subsequent experiments to systematically evaluate the effects of different drug concentrations on T cell function, thereby more accurately reflecting the correlation between immunosuppressive effect and dose. T-cell-specific stimulants were added to the culture plates to induce proliferation and functional activation, thus mimicking the regulatory effect of immunosuppressants on activated T-cell function. This facilitates subsequent evaluation of the inhibitory effect of drugs on the immune response. The plates were then incubated in an incubator for activation. To ensure sufficient experimental data, multiple parallel culture plates were placed in the incubator simultaneously, and a negative control group (without immunosuppressants) and a positive control group (with known effective concentrations of immunosuppressants) were set up to verify the reliability of the experimental system. By inducing T-cell activation and secretion of cytokines, the effect of immunosuppressants on T-cell function could be quantitatively detected, and the inhibitory effect at different drug concentrations could be evaluated.

[0022] S3. Real-time acquisition of cytokine secretion data of T cells during the activation process, and determination of T cell functional status indicators based on cytokine secretion data. In step S3, after activating the T cells, their functional status needs to be monitored in real time. This is achieved by collecting cytokine secretion data, as detailed below: The culture cycle was divided into multiple sub-time windows, and cell culture supernatant was collected at equal intervals during the activation process within each sub-time window. The concentrations of various cytokines were then measured based on the cell culture supernatant. Specifically, at the end of each sub-time window, the culture supernatant was collected, and multiplex cytokine detection technology was used to measure the concentrations of various cytokines, including but not limited to key immunomodulatory factors such as IL-2, IFN-γ, and TNF-α. By continuously monitoring changes in cytokine concentrations, the functional activation level of T cells at different time points could be dynamically tracked, which would facilitate subsequent assessment of the temporal effects of immunosuppressants on cytokine secretion. The secretion peak, secretion rate, and cumulative secretion amount of each cytokine within each sub-time window were statistically analyzed. The secretion peak is the highest concentration value reached by each cytokine within the sub-time window, reflecting the maximum activation intensity of T cells. The secretion rate is the slope of the cytokine concentration change over time, reflecting the dynamic response speed of T cell functional activation. The cumulative secretion amount is the area under the curve of each cytokine within the sub-time window, comprehensively reflecting the total secretory capacity of T cells. The secretion peak of all cytokines was organized into a peak feature vector, the secretion rate of all cytokines was organized into a rate feature vector, and the cumulative secretion amount of all cytokines was organized into a cumulative feature vector. This allows for a comprehensive quantification of the functional state of T cells within different time windows. The peak eigenvector, rate eigenvector, and cumulative eigenvector are standardized separately to form a standardized data matrix. The purpose of standardizing these eigenvectors is to eliminate the dimensional influence caused by differences in concentration magnitudes between different cytokines, ensuring that all eigenvectors are on the same comparable scale. Specifically, this involves subtracting the mean of each eigenvector from its elements and then dividing by its standard deviation—a process known as Z-score standardization. This yields the standardized data matrix. The covariance matrix of the corresponding features under each dimension is calculated. The covariance matrix is ​​used to measure the degree of linear correlation between different cytokine features and reflects the synergistic change relationship between each feature dimension. Specifically, the covariance of each feature dimension is calculated separately. That is, the inner product operation is performed on the corresponding column vectors of the two feature dimensions in the standardized data matrix, and then divided by the number of samples minus one. The covariance calculation results of all feature dimensions are arranged in rows and columns to form a complete covariance matrix. Then, the covariance matrix is ​​decomposed into eigenvalues ​​to extract eigenvalues ​​and corresponding eigenvectors. The purpose is to identify the direction of the largest variance in the data. Calculate and output the cumulative contribution rate of each covariance matrix. The cumulative contribution rate reflects the degree to which the principal components in the covariance matrix explain the variance of the original data. Specifically, after sorting the eigenvalues ​​from largest to smallest, calculate the variance contribution rate of each principal component (i.e., the proportion of that eigenvalue to the sum of all eigenvalues). Then, sum them up from the beginning to the end to obtain the cumulative contribution rate. Based on the cumulative contribution rate, select the main feature dimensions from the peak eigenvector, rate eigenvector, and cumulative eigenvector respectively. This process can effectively reduce data dimensionality and eliminate redundant information. Specifically, the selection is done by setting a contribution threshold. The contribution threshold is usually set to stop accumulating when the cumulative contribution rate reaches 85% to 95%. This is because... Each inhibitor is still in the testing phase and lacks sufficient clinical validation data. Therefore, 85% to 95% is preferred as the contribution threshold. When sufficient clinical validation data is available, the contribution threshold can be further determined based on the distribution characteristics of historical data. Specifically, the cumulative contribution rate curve is compared with the inflection point distribution of historical data, and the number of principal components corresponding to the inflection point is selected as the threshold. This preserves key information while compressing the data dimensionality to the greatest extent. Finally, the principal feature dimensions of the peak feature vector, the rate feature vector, and the cumulative feature vector are used together as indicators of T cell functional status to facilitate a comprehensive assessment of T cell functional status.

[0023] S4. Generate the immune efficacy assessment results of the immunosuppressants based on the functional status indicators, and generate the deviation items between the immune efficacy assessment results and the target results based on the immune efficacy assessment results. In step S4, after the functional status indicators of the immunosuppressant are determined, the immune effect can be evaluated based on them, specifically as follows: The functional state indicators and the functional state indicators obtained from historical assessments are arranged in chronological order of assessment time to form a sequence of functional state changes across rounds. As mentioned above, multiple sub-time windows have been set during the cell culture process, so it is possible to form a sequence of functional state indicators arranged in chronological order across rounds, which makes it easier to track the evolution trend of T cell functional state as the immunosuppressant effect time progresses. Based on the functional state change sequence, the amplitude of change between two adjacent assessments of functional state indicators is extracted. This amplitude reflects the response strength of T cell functional state under the action of immunosuppressants; that is, the larger the amplitude, the more significant the regulatory effect of immunosuppressants on T cell functional state, and vice versa. Then, the correlation coefficient between the amplitude of change and the immunosuppressant concentration gradient is calculated. The output of the correlation coefficient quantifies the degree of influence of immunosuppressant concentration changes on T cell functional state. Specifically, the Pearson correlation coefficient is used for calculation, and the specific calculation formula is as follows: ; In the formula, This represents the correlation coefficient between the magnitude of change and the immunosuppressant concentration gradient. The value ranges from -1 to 1, with positive values ​​indicating positive correlation and negative values ​​indicating negative correlation. Indicates the first The magnitude of change at the time of the next assessment Indicates the first The immunosuppressant concentration at the time of the next assessment and These represent the magnitude of change and the mean concentration, respectively. For the number of evaluations.

[0024] Then, the correlation coefficient can be used to quantify the strength of the relationship between the concentration of immunosuppressants and the immunization effect, thereby outputting an assessment value of the immunosuppressive effect. The specific process is as follows: The statistical distribution of correlation coefficients in historical assessments is collected, and the median of the historical correlation coefficient distribution is used as the first threshold, i.e., the mean of the historical correlation coefficients, to distinguish between high and low levels of correlation coefficients. The upper quartile of the distribution is used as the second threshold, which represents the value at the 75th percentile of the historical correlation coefficient distribution, to define the high-level range of correlation coefficients. The first threshold is less than the second threshold. By comparing the absolute value of the correlation coefficient with the first and second thresholds, the strength of the association between the concentration of immunosuppressants and the immune effect can be determined. For the first assessment scenario without prior historical data accumulation, a commonly used statistical threshold in the field is used for level classification, or the mean of the correlation coefficients of immunosuppressants with the same function is used as the first threshold, and the upper quartile of the statistical distribution of the correlation coefficients of drugs with the same function is used as the second threshold.

[0025] When the absolute value of the correlation coefficient is less than the first threshold, it indicates that the current dose adjustment has not caused a discernible change in the immune effect, and it is determined that there is no significant correlation between the change in the concentration of the immunosuppressant and the change in the functional status index. When the absolute value of the correlation coefficient is between the first threshold and the second threshold, it is determined that there is a weak correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. When the absolute value of the correlation coefficient is greater than the second threshold, it is directly determined that there is a strong correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. The difference between weak and strong associations is that a weak association indicates that although the effect of changes in immunosuppressant concentration on T cell functional status can be detected, it has not yet reached a significant regulatory level. That is, the regulatory effect of immunosuppressants is still in a reversible or non-dominant stage. On the other hand, a strong association indicates that changes in immunosuppressant concentration have a significant and stable regulatory effect on T cell functional status. That is, changes in immunosuppressant concentration can dominate changes in T cell functional status. The sign of the correlation coefficient is used to determine the consistency between the direction of concentration change and the direction of functional status index change, thereby determining the positive or negative direction of the immunosuppressive effect assessment value. If the correlation coefficient is negative, it means that the functional status index decreases when the concentration of immunosuppressant increases. This indicates that the immunosuppression effect is good and it is a normal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the negative correlation strength between the immunosuppressant concentration and the functional status index. If the correlation coefficient is positive, it means that the functional status index increases synchronously when the concentration of immunosuppressant increases, which is an abnormal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the positive correlation strength value between the immunosuppressant concentration and the functional status index. Different weight coefficients are assigned based on the correlation coefficient ranges corresponding to weak and strong associations. In this process, the correlation coefficient corresponding to a strong association is assigned a weight coefficient of at least 0.5, while the correlation coefficient corresponding to a weak association is assigned a weight coefficient lower than 0.5. Of course, when the correlation coefficient is negative, the positive association strength value is recorded as 0; conversely, if the correlation coefficient is positive, the negative association strength value is recorded as 0. Specifically, in quantification, the difference between the correlation coefficient under a weak association and the first threshold is first output and recorded as the weak association difference. Then, the weak association difference is input into the weight allocation function to calculate the weight coefficient corresponding to the weak association. The specific calculation formula is as follows: ; In the formula, This represents the weighting coefficient corresponding to the weak correlation difference. Indicates the difference in weak correlation. Indicates the first threshold. This represents the second threshold. It should be noted that if the calculation result... If the value is ≥0.5, then the weighting coefficient will be forcibly set to 0.49. If the value is less than 0.5, the calculated weight coefficient will be output directly. For strong correlations, the weight coefficient will be 1- ; Subsequently, the positive and negative correlation strength values ​​can be weighted and corrected based on the output weighting coefficients to obtain the final immunosuppressive effect evaluation value. This is because, within the same immunosuppressive regimen, the strength of the correlation between different indicators directly affects the reliability and accuracy of the evaluation results. Therefore, by introducing weighting coefficients, the contribution of different correlations to the evaluation results can be effectively distinguished. The weighted summation method can directly adopt linear weighting, i.e. In the formula, This indicates the assessment value of the immunosuppressive effect. Indicates the strength of the positive correlation. The weighting coefficients represent the strength of negative association. This represents the negative correlation strength value.

[0026] Secondly, the steps for generating a deviation term between the immunization efficacy assessment results and the target results include: The immunosuppression effect assessment value is compared with the target assessment value, and the difference between the immunosuppression effect assessment value and the target assessment value is calculated and output as the assessment factor. Obtain the difference between all immunosuppressive effect assessment values ​​and target assessment values ​​in historical data, and calculate their mean and standard deviation. Construct a deviation threshold interval with the mean as the center and the standard deviation as the scale. The evaluation factor is compared with a preset deviation threshold range. If the evaluation factor is not within the deviation threshold range, the concentration of the corresponding immunosuppressant is marked as a deviation item; otherwise, it is marked as a normal item. In this implementation, after the immunosuppressive effect assessment is output, the deviation between the immunosuppressive effect and the target effect at different immunosuppressant concentrations can be visually displayed. First, the difference between the output immunosuppressive effect assessment value and the preset target assessment value is used to obtain an assessment factor. The target assessment value is determined by a preset standard inhibitory effect value within the domain. For customized immunosuppressants adapted to specific receptor genotypes, the medical center presets this value based on the receptor's immune tolerance target. Then, a deviation threshold interval is constructed based on the statistical characteristics of historical assessment data. The deviation threshold interval is set based on the mean and standard deviation of historical data, effectively reflecting the distribution characteristics of the overall assessment results. Specifically, the mean is used as the center, and... The standard deviation is multiplied by a preset factor (such as 1.5 or 2) to form the interval radius, thus forming the upper and lower limits. Of course, to ensure the conservatism of the experimental process, the factor of the standard deviation is generally 1.5. This can identify obvious deviations and anomalies without misjudging normal fluctuations as deviations due to an overly narrow threshold. This allows for the rapid screening of immunosuppressant concentrations that deviate from the expected effect, providing clear direction for subsequent dose optimization, avoiding ineffective repeated validation, and improving the overall efficiency of the evaluation process. For the first evaluation scenario where there is no historical evaluation data accumulation, a preset fixed deviation threshold interval is directly used. The interval range can be set with reference to the general statistical standards for the evaluation of similar immunosuppressants to ensure that the deviation identification process can adapt to different evaluation scenarios.

[0027] S5. Administer the drug dosage according to the deviation item, and re-collect peripheral blood samples from the subjects for iterative evaluation until the immune effect evaluation results reach the target results; In step S5, after the deviation is determined, it indicates a significant deviation between the immunosuppressive effect at the given immunosuppressant concentration and the target effect, requiring further adjustment. The adjustment aims to bring the immunosuppressant concentration closer to the normal value, i.e., by adjusting the immunosuppressant concentration to bring the evaluation factor back into the deviation threshold range. The adjusted immunosuppressant concentration will not be the same as the concentration value corresponding to the normal value. The purpose is to find an immunosuppressant concentration that more closely approximates the target effect during the adjustment process. Specifically, the process involves: Based on the sign and magnitude of the deviation, determine the direction and magnitude of the adjustment to the dosing regimen; the sign of the deviation indicates the direction of adjustment. When the deviation is positive, it means that the current direction of immunosuppression is correct but there is still a gap with the target, and there is still an over-suppression phenomenon, which may increase the risk of infection during the suppression process. Therefore, it is necessary to reduce the dosage of immunosuppressants proportionally according to the deviation amount. That is, the part of the deviation that exceeds the upper limit of the deviation threshold interval is regarded as a positive deviation, and the concentration of immunosuppressants is reduced accordingly according to this deviation ratio. If the immunosuppressant concentration is equal to the upper limit of the deviation threshold interval in the previous round of experiments and is evaluated as a normal item, then the concentration in this experiment is adjusted to be below the concentration value corresponding to the normal item and located in the center position above the next one digit of the normal item, so as to gradually approach the optimal concentration while ensuring safety. When the deviation is negative, it indicates that the current immunosuppression is insufficient. In this case, the dosage will be increased proportionally. The specific process can be referred to the adjustment logic of the positive deviation above, but in the opposite direction. After each dose adjustment, peripheral blood samples from the subject are collected again, and the immunosuppressive effect is repeatedly evaluated based on the newly collected peripheral blood samples. This process continues until the difference between the immunosuppressive effect evaluation result and the target evaluation value falls within the deviation threshold range. If the number of repeated evaluations of the immunosuppressive effect reaches the iteration limit, the iteration stops and an early warning signal indicating that the adjustment scheme has failed is issued. Subsequently, the concentration with the immunosuppressive effect evaluation value closest to the target evaluation value is selected as the optimal recommended dosage concentration of the immunosuppressive for the current subject. This completes a single full immunosuppressive effect evaluation process. The iterative adjustment design can gradually narrow down the candidate concentration range, taking into account both the accuracy of the evaluation and the feasibility of the experimental operation, and avoiding the waste of resources caused by a large-scale concentration test at once.

[0028] Based on the content described in steps S1 to S5, a set of in vitro evaluation experimental data based on tacrolimus, a common clinical immunosuppressant, is provided as follows: Immunosuppressants: Tacrolimus (FK506), the clinical therapeutic window is usually 5–15 ng / mL; Concentration gradient settings: 0 ng / mL (blank control), 0.5 ng / mL, 2 ng / mL, 10 ng / mL, 50 ng / mL, covering and exceeding the clinical therapeutic window; Test sample: Venous blood was collected from a kidney transplant patient, and peripheral blood mononuclear cells (PBMCs) were isolated and adjusted to a density of 1×10⁻⁶. 6 cells / mL; Stimulant: Anti-CD3 / CD28 monoclonal antibody (T cell-specific stimulant); Detection of cytokines: IL-2, IFN-γ, TNF-α; The 24-hour culture period was divided into four sub-time windows (0–6h, 6–12h, 12–18h, and 18–24h). The supernatant was collected at equal intervals and the concentration was measured. Table 1 shows the typical dynamic concentration (pg / mL) of IL-2. Table 1: Dynamic secretion concentration of IL-2 under different concentrations of FK506 treatment

[0029] Taking the 10 ng / mL group as an example, the three-dimensional characteristics of IL-2 were calculated: Peak secretion: 30 pg / mL (6-12 h window); Secretion rate: (30-8) / (12-6)≈3.67pg / mL / h; Cumulative secretion: (15+30)×6 / 2+(30+15)×6 / 2+(15+8)×6 / 2=135+135+69=339pg·h / mL; Z-score normalization was performed on the peak values, rates, and cumulative amounts of the three cytokines to construct a data matrix. The covariance matrix was calculated and eigenvalue decomposition was performed. A cumulative contribution rate threshold of 85% was set, and the main feature dimensions (assumed to be the principal components corresponding to the peak values ​​and cumulative amounts) were extracted to obtain the functional state index (FSI) value for this group. 10 =0.65, and similarly, the FSI for other concentration groups was obtained; For the initial assessment of this patient, historical data was insufficient, so the initial default threshold was used: The first threshold (median surrogate) was set to 0.3, the second threshold (upper quartile surrogate) was set to 0.6 (response annotation 3), and the target assessment value was set to 0.8 (representing the clinically expected strong inhibitory effect). Three sets of measured data (0.5, 10, and 50 ng / mL) were selected. The Pearson correlation coefficient between the change in functional status indicators and the concentration gradient was calculated to be -0.92, and the evaluation factor was 0.12. The deviation threshold range is set to (0.75, 0.85). 0.92 is not within the range and is a positive deviation. Mark 10 ng / mL as a deviation item. A positive deviation indicates that the current immunosuppressive effect exceeds the target (over-suppression). Reduce the dosage proportionally and adjust to 7 ng / mL. Then, re-evaluate the blood count until the evaluation value falls within the 0.75-0.85 range.

[0030] Please see Figure 2 An immunosuppressant efficacy evaluation system, using the aforementioned immunosuppressant efficacy evaluation method, includes: An initialization module is used to obtain peripheral blood samples from subjects receiving immunosuppressant therapy and isolate peripheral blood mononuclear cells from them; The cell culture module is used for the dispersion culture of peripheral blood mononuclear cells and the activation treatment using T cell-specific stimulants in an environment containing immunosuppressants. The indicator extraction module is used to collect cytokine secretion data of T cells in real time during the activation process, and to determine the functional status indicators of T cells based on the cytokine secretion data. The immune assessment module is used to generate immune efficacy assessment results of immunosuppressants based on functional status indicators, and to generate deviation items between the immune efficacy assessment results and the target results. The iterative optimization module is used to administer the dosage based on the deviation and to re-collect peripheral blood samples from the subjects for iterative evaluation until the immune effect evaluation results reach the target results.

[0031] The execution process of the above evaluation system corresponds to the process of the aforementioned method, so it will not be repeated here.

[0032] Please see Figure 3 An electronic device, comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the above-described method for evaluating the immune efficacy of immunosuppressants.

[0033] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for evaluating the immunogenicity of an immunosuppressant, characterized in that: include: Peripheral blood samples were obtained from subjects receiving immunosuppressant therapy, and peripheral blood mononuclear cells were isolated from them; Peripheral blood mononuclear cells were dispersed and cultured, and then activated using T-cell-specific stimulants in an environment containing immunosuppressants. Real-time acquisition of cytokine secretion data from T cells during the activation process, and determination of T cell functional status indicators based on cytokine secretion data; The immune efficacy assessment results of immunosuppressants are generated based on functional status indicators, and a deviation term between the immune efficacy assessment results and the target results is generated based on the immune efficacy assessment results. Based on the deviation, the dosage was administered, and peripheral blood samples were collected from the subjects again for iterative evaluation until the immune effect evaluation results reached the target results.

2. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of obtaining an ex vivo peripheral blood sample from a subject receiving immunosuppressant therapy and isolating peripheral blood mononuclear cells therefrom includes: Venous blood was collected from the subjects to form whole blood samples, which were then placed in blood collection tubes containing anticoagulant and mixed by inverting. Add a whole blood sample to the top of an equal volume of lymphocyte separation medium and centrifuge at room temperature; Peripheral blood mononuclear cells were obtained by aspirating the intermediate white membrane layer, and then washed with phosphate buffer and resuspended for later use.

3. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of dispersing and culturing peripheral blood mononuclear cells and activating them using T-cell-specific stimulants in an environment containing immunosuppressants includes: The resuspended peripheral blood mononuclear cells were adjusted to the preset cell density with complete culture medium, seeded in culture plates, and immunosuppressants were added to the target concentration. T cell-specific stimulants were added to the culture plate and incubated in an incubator to activate T cells and induce them to secrete cytokines. The culture plates have a consistent cell density in each well, and the immunosuppressant concentration gradient covers the clinical therapeutic window.

4. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of acquiring real-time cytokine secretion data of T cells during the activation process and determining functional status indicators of T cells based on the cytokine secretion data includes: The culture period was divided into multiple sub-time windows, and cell culture supernatant was collected at equal intervals during the activation process within each sub-time window. The concentrations of various cytokines were then determined based on the cell culture supernatant. The secretion peak, secretion rate, and cumulative secretion amount of each cytokine within each sub-time window were statistically analyzed. The secretion peak of all cytokines was organized into a peak feature vector, the secretion rate of all cytokines was organized into a rate feature vector, and the cumulative secretion amount of all cytokines was organized into a cumulative feature vector. The peak eigenvector, rate eigenvector, and cumulative eigenvector are standardized respectively to form a standardized data matrix; Calculate the covariance matrix of the corresponding features under each dimension, and perform eigenvalue decomposition on the covariance matrix to extract the eigenvalues ​​and corresponding eigenvectors; Calculate and output the cumulative contribution rate of each covariance matrix, and select the main feature dimensions from the peak eigenvector, rate eigenvector and cumulative eigenvector respectively based on the cumulative contribution rate; The main feature dimensions of the peak feature vector, the main feature dimension of the rate feature vector, and the main feature dimension of the cumulative feature vector are used together as indicators of the functional state of T cells.

5. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of generating the immunosuppressant efficacy evaluation results based on functional status indicators includes: The functional status indicators and the functional status indicators obtained from historical assessments are arranged in chronological order of assessment time to form a sequence of functional status changes across rounds. Based on the functional state change sequence, the change amplitude of functional state indicators between two adjacent assessments was extracted, and the correlation coefficient between the change amplitude and the immunosuppressant concentration gradient was calculated. The correlation coefficient was used to quantify the strength of the association between the concentration of immunosuppressants and the immune effect, and an evaluation value of the immunosuppressive effect was obtained.

6. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 5, characterized in that: The step of quantifying the correlation between the concentration of immunosuppressants and the immunization effect based on correlation coefficients to obtain an immunosuppressive effect assessment value includes: Collect the statistical distribution of correlation coefficients in historical assessments, and use the median of the historical correlation coefficient distribution as the first threshold and the upper quartile of the distribution as the second threshold, wherein the first threshold is less than the second threshold. The absolute value of the correlation coefficient is compared with the first threshold and the second threshold; When the absolute value of the correlation coefficient is less than the first threshold, it indicates that the current dose adjustment has not caused a discernible change in the immune effect, and it is determined that there is no significant correlation between the change in the concentration of the immunosuppressant and the change in the functional status index. When the absolute value of the correlation coefficient is between the first threshold and the second threshold, it is determined that there is a weak correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. When the absolute value of the correlation coefficient is greater than the second threshold, it is directly determined that there is a strong correlation between the concentration change of the corresponding immunosuppressant and the change of functional status indicators. The sign of the correlation coefficient indicates the consistency between the direction of concentration changes and changes in functional status indicators. If the correlation coefficient is negative, it means that the functional status index decreases when the concentration of immunosuppressant increases, which is a normal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the negative correlation strength between the immunosuppressant concentration and the functional status index. If the correlation coefficient is positive, it means that the functional status index increases synchronously when the concentration of immunosuppressant increases, which is an abnormal immunosuppressive response. The absolute value of the corresponding correlation coefficient is taken and output as the positive correlation strength value between the immunosuppressant concentration and the functional status index. Different weighting coefficients were assigned to the correlation coefficient ranges corresponding to weak and strong associations, and the positive and negative association strength values ​​were weighted and corrected to obtain the final immunosuppressive effect evaluation value.

7. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of generating a deviation term between the immunization efficacy evaluation results and the target results includes: The immunosuppression effect assessment value is compared with the target assessment value, and the difference between the immunosuppression effect assessment value and the target assessment value is calculated and output as the assessment factor. Obtain the difference between all immunosuppressive effect assessment values ​​and target assessment values ​​in historical data, and calculate their mean and standard deviation. Construct a deviation threshold interval with the mean as the center and the standard deviation as the scale. The evaluation factor is compared with a preset deviation threshold range. If the evaluation factor is not within the deviation threshold range, the concentration of the corresponding immunosuppressant is marked as a deviation item; otherwise, it is marked as a normal item.

8. The method for evaluating the immunosuppressive effect of an immunosuppressant according to claim 1, characterized in that: The step of administering the dosage based on the deviation and re-collecting peripheral blood samples from the subject for iterative evaluation until the immune effect evaluation results reach the target results includes: Based on the sign and magnitude of the deviation, determine the direction and magnitude of adjustment for the dosing regimen: When the deviation is positive, it means that the current direction of immunosuppression is correct but there is still a gap with the target. The dosage of immunosuppressant will be reduced proportionally according to the amount of deviation. When the deviation is negative, it indicates that the current immunosuppression is insufficient, and the dosage should be increased proportionally. After each dose adjustment, peripheral blood samples were collected from the subjects again, and the immunosuppressive effect was repeatedly evaluated based on the newly collected peripheral blood samples until the difference between the immunosuppressive effect evaluation result and the target evaluation value fell within the deviation threshold range. If the number of repeated evaluations of the immunosuppressive effect reaches the iteration limit, the iteration will stop and a warning signal indicating that the adjustment scheme has failed will be issued.

9. A system for evaluating the immunogenicity of immunosuppressants, characterized in that: The method for evaluating the immunogenicity of an immunosuppressant according to any one of claims 1 to 8 includes: An initialization module is used to obtain peripheral blood samples from subjects receiving immunosuppressant therapy and isolate peripheral blood mononuclear cells from them; The cell culture module is used for the dispersion culture of peripheral blood mononuclear cells and the activation treatment using T cell-specific stimulants in an environment containing immunosuppressants. The indicator extraction module is used to collect cytokine secretion data of T cells in real time during the activation process, and to determine the functional status indicators of T cells based on the cytokine secretion data. The immune assessment module is used to generate immune efficacy assessment results of immunosuppressants based on functional status indicators, and to generate deviation items between the immune efficacy assessment results and the target results. The iterative optimization module is used to administer the dosage based on the deviation and to re-collect peripheral blood samples from the subjects for iterative evaluation until the immune effect evaluation results reach the target results.

10. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the immunosuppressant efficacy evaluation method according to any one of claims 1 to 8.

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