A method for assessing the concentration and joint effect of a mixture of glucocorticoids in a sample

Abstract

The application discloses a method for evaluating the concentration and combined effect of a glucocorticoid mixture in a sample, and relates to the technical field of biology. The method analyzes the relationship among concentration, time and effect according to a two-phase mechanism of receptor-ligand binding dissociation kinetics, innovatively constructs a two-phase response surface model of dexamethasone concentration, time and effect (dexamethasone model), and introduces a two-phase response surface model of glucocorticoid mixture concentration, time and effect in a sample (sample model) with the index of combined effect. The contributions of time and concentration to the effect and the two-phase complex scene are comprehensively considered, and the reasonable and scientific evaluation of the concentration and combined effect of the glucocorticoid mixture in the sample is realized without losing accuracy by combining the Levenberg-Marquardt algorithm with the Jacobian matrix method. The method provides a solution for evaluating the concentration (in terms of dexamethasone) of the glucocorticoid mixture in a sample and analyzing the combined effect based on a cell strain.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically, to a method for assessing the concentration and combined effects of a mixture of glucocorticoids in a sample. Background Technology

[0002] Glucocorticoids (GCs) are a class of steroid hormones that play a crucial role in anti-inflammatory and immunosuppressive therapies by binding to glucocorticoid receptors (GRs) and regulating gene transcription and non-genomic signaling pathways. Since the 1950s, they have been widely used in livestock farming; as human drugs for the treatment of systemic lupus erythematosus, bronchial asthma, and other endocrine and metabolic-related diseases; they are also used in the differentiation and proliferation of novel alternative proteins (cellular meat); and they may be illegally added to cosmetics and other daily chemical products. Therefore, glucocorticoid residues may exist in food, feed, environmental water bodies, cosmetics, and bodily fluids of livestock, poultry, and humans.

[0003] Known glucocorticoids mainly include dexamethasone, betamethasone, flumethasone, and prednisolone. Besides endogenous glucocorticoids, exogenous ones are mostly endocrine disrupting chemicals (EDCs). Currently, there are no effective methods to degrade these substances. The combination of these endogenous / exogenous, long-acting / intermediate-acting / short-acting glucocorticoids poses a potential risk to human health. Therefore, effectively evaluating the levels and combined effects of such glucocorticoid mixtures is crucial and urgent.

[0004] Existing technology has constructed a Hela-GFP-GR gene-modified human cervical cancer cell line. This cell line carries GR receptors labeled with the fluorescent protein GFP. When GR receptors bind to glucocorticoids, they migrate from the cytoplasm to the nucleus. The concentration of glucocorticoids can then be quantitatively detected by detecting this fluorescent signal and calculating the ratio. This method has a sensitivity of 0.1–1 nmol / L (calculated as DXMS). However, this cell bioassay encounters uncertainties when monitoring actual samples, mainly due to the temporal differences in the binding of different glucocorticoids to the GR receptor and the synergistic effects among glucocorticoids.

[0005] Specifically, the temporal differences are reflected in two aspects: First, the binding of glucocorticoids to the GR receptor and the formation of the complex exhibit temporal variations. The receptor-ligand binding process of most nuclear receptor superfamily (NRS) cells typically exhibits a biphasic characteristic, closely related to the structural dynamics of the receptor and the properties of the ligand. There is a close regulatory relationship between glucocorticoids and HSP90 (heat shock protein 90). After binding to the glucocorticoid receptor (GR), HSP90 has the ability to bind to hormone ligands, thereby playing a role in regulating gene transcription. The intracellular distribution of glucocorticoid receptors (GR) is dynamic. In the initial phase, glucocorticoids, acting as ligands, rapidly bind to GR receptors near the inactive cytoplasm. GR forms a complex with HSP90 and other receptors. Dexamethasone, acting as a ligand, causes the dissociation of heat shock proteins, exposing the nuclear localization signal of GR; this is the rapid phase. Then, in the subsequent phase, the ligand-receptor complex enters the nucleus or undergoes a conformational change, forming a dimer that enters the nucleus through the nuclear pore complex. It binds to the glucocorticoid response element (GRE) on DNA, regulating gene transcription and entering the nuclear translocation phase; this is the slow phase. Furthermore, the presence of different glucocorticoids in the glucocorticoid mixture, classified into short-, intermediate-, and long-acting types based on their plasma half-life, also exhibits similar time-dependent characteristics when acting on cells. These include: (I) Short-acting glucocorticoids, represented by hydrocortisone, which bind relatively loosely to intracellular glucocorticoid receptors and are metabolized relatively quickly within cells, resulting in a shorter duration of action; (II) Intermediate-acting glucocorticoids, such as prednisone and prednisolone, which have moderate affinity for glucocorticoid receptors and are metabolized at a moderate rate within cells; and (III) Long-acting glucocorticoids, represented by dexamethasone, which have a high affinity for glucocorticoid receptors and can occupy the receptors for a long time to exert their effects. Furthermore, there are complex combined effects between glucocorticoids, including additive, synergistic, and antagonistic effects. These effects are generally influenced by the quantity, manner, and concentration of glucocorticoid combinations in the mixture.

[0006] Therefore, existing calculation methods have limitations, and the results of glucocorticoid mixture concentrations are biased. Furthermore, the characteristics and intensity of the combined effects need to be defined and quantitatively analyzed.

[0007] In view of this, the present invention is proposed. Summary of the Invention

[0008] The purpose of this invention is to provide a method for evaluating the concentration and combined effects of a mixture of glucocorticoids in a sample.

[0009] The implementation method of this invention is as follows:

[0010] A method for assessing the concentration and combined effects of a mixture of glucocorticoids in a sample, comprising the following steps:

[0011] Using fluorescently labeled GR receptor-stable cell lines as test vectors and dexamethasone as a standard, the fluorescence effect of different concentrations of the standard at different time points was detected. E DXMS and the effects of the glucocorticoid mixture in the sample at different times. E sample ; The I 细胞核的荧光信号 and stated I 细胞质的荧光信号 The images, in order, show the fluorescence signals of the dexamethasone standard group, the sample, and the blank in the cell nucleus and cytoplasm obtained by the instrument.

[0012] Based on the effects of different concentrations of the standard at different times, a two-phase response surface model of standard concentration, time, and effect is constructed, denoted as the dexamethasone model. The parameter set is obtained through convergent optimization, and the parameter set includes: A 1. A 2. k on1 、k off1 、 k on2 、k off2 and The dexamethasone model is as follows: ;in, C DXMS Different concentrations of the standard dexamethasone; t For time; k on1 It is a rapid binding constant without HSP90 regulation. k off1 It is the rapid dissociation constant of a single phase without HSP90 regulation; k on2 The slow binding constant of the two phases is regulated by HSP90. k off2 It is a slow dissociation constant regulated by HSP90; For the binding phase weighting factor;

[0013] Constructing an index that introduces joint effects The two-phase response surface model of glucocorticoid mixture concentration, time, and effect in the sample is denoted as the sample model. The sample model is as follows: Wherein, δ is the combined effect index, which reflects the nature and intensity of the combined effect exhibited by the mixture of different glucocorticoids in the sample; δ>1 is defined as a synergistic effect, 0<δ<1 is defined as an antagonistic effect, and δ=1 is defined as an additive effect; = C equiv DXMS , The concentration of different glucocorticoids present in the sample as a mixture, expressed as the equivalent concentration of dexamethasone. C sample This represents the cumulative concentration of different glucocorticoids present in a single form in the sample.

[0014] The present invention has the following beneficial effects:

[0015] This invention constructs a two-phase response surface model (RSM) for concentration, time, and effect using dexamethasone as a standard, and introduces a combined effect index δ to define the concentration, time, and effect of a mixture of glucocorticoids in a sample. Based on this, even when the mixture is unknown, the Levenberg-Marquardt algorithm using nonlinear least squares combined with the Jacobian matrix can more scientifically estimate the concentration of the glucocorticoid mixture and the characteristics and intensity of combined effects (synergistic, antagonistic, additive, etc.) in the sample without sacrificing accuracy. This model algorithm provides a solution for assessing the concentration of a mixture of glucocorticoids (calculated as dexamethasone) in a sample and analyzing combined effects using cellular methods. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 The images show the translocation of fluorescence signals between the cytoplasm and nucleus in Hela-GFP-GR cells. The Control group received MEM containing 0.1% DMSO (phenol red-free) and 10% CD-FBS; the 17β-E2 group received 100 nM / L estradiol; and the Dexa group received 100 nM / L dexamethasone. High-content images were acquired 1 hour after drug administration, using a 20× water microscope in confocal mode. DPC represents the channel, and Merge represents the superposition effect of the GFP and Hochest 33324 channels.

[0018] Figure 2To determine the standard dexamethasone using a high-content screening instrument, where: A represents fluorescence signals collected at different concentrations and times; B is a schematic diagram of the response surface fluorescence signal, showing the concentration-time effect response surface data acquisition. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0020] Definition of noun

[0021] The term "Levenberg-Marquardt algorithm" used in this article, or LM algorithm for short, is an efficient numerical optimization method for solving nonlinear least squares problems. Its core objective is to minimize the objective function (sum of squared residuals, SSR) by iteratively adjusting the parameters.

[0022] The term "Jacobian matrix" used in this article is a core tool in vector calculus for describing the local linear approximation of multivariable functions.

[0023] The term "concentration of glucocorticoid mixture in sample as equivalent concentration of dexamethasone" in this article refers to the calculation of the mixed concentration of glucocorticoid mixture using equivalent dose conversion and uniformly using dexamethasone concentration. In essence, it uses dexamethasone as a reference to quantify the effect intensity of each hormone in the mixture.

[0024] Specific implementation methods

[0025] Embodiments of the present invention provide a method for evaluating the concentration and combined effect of a mixture of glucocorticoids in a sample, comprising the following steps:

[0026] Using fluorescently labeled GR receptor-stable cell lines as test vectors and dexamethasone as a standard, the fluorescence effect of different concentrations of the standard at different time points was detected. E DXMS and the effects of the glucocorticoid mixture in the sample at different times. E sample ; The I 细胞核的荧光信号 and stated I 细胞质的荧光信号 The images, in order, show the fluorescence signals of the dexamethasone standard group, the sample, and the blank in the cell nucleus and cytoplasm obtained by the instrument.

[0027] Based on the effects of different concentrations of the standard at different times, a two-phase response surface model of standard concentration, time, and effect is constructed, denoted as the dexamethasone model. The parameter set is obtained through convergent optimization, and the parameter set includes: A 1. A 2. k on1 、k off1 、 k on2 、k off2 and The dexamethasone model is as follows: ;in C DXMS Different concentrations of the standard dexamethasone; t For time; k on1 It is a rapid binding constant without HSP90 regulation. k off1 It is the rapid dissociation constant of a single phase without HSP90 regulation; k on2 The slow binding constant of the two phases is regulated by HSP90. k off2 It is a slow dissociation constant regulated by HSP90; For the binding phase weighting factor;

[0028] Constructing an index that introduces joint effects The two-phase response surface model of glucocorticoid mixture concentration, time, and effect in the sample is denoted as the sample model. The sample model is as follows: Wherein, δ is the combined effect index, which reflects the nature and intensity of the combined effect exhibited by the mixture of different glucocorticoids in the sample; δ>1 is defined as a synergistic effect, 0<δ<1 is defined as an antagonistic effect, and δ=1 is defined as an additive effect; = C equiv DXMS , The concentration of different glucocorticoids present in the sample as a mixture, expressed as the equivalent concentration of dexamethasone. C sample This represents the cumulative concentration of different glucocorticoids present in a single form in the sample.

[0029] In some embodiments, the detection equipment includes a high-content screening instrument or a fluorescence inverted microscope as the testing instrument.

[0030] In some embodiments, the cell line (the cell vector before fluorescent labeling modification) includes human cervical cancer cell line (HeLa), African green monkey kidney cell line (CV-1), or human embryonic kidney cell line (HEK293), etc.

[0031] In some embodiments, the fluorescent label refers to a fluorescent protein label, including GFP (green fluorescent protein), EYFP (enhanced yellow fluorescent protein), ERFP (enhanced red fluorescent protein), ECFP (cyan fluorescent protein), or iRFP (near-infrared fluorescent protein).

[0032] In some implementations, the signal acquisition time range for the standard, sample, and blank (same as blank treatment) GR receptor-fluorescently labeled cell line is selected from 0.5h to 4h. Specifically, data can be continuously acquired at 0.5h time nodes, for a total of 8 time points (m=8).

[0033] In some implementations, the concentration of the standard dexamethasone is set in the range of 0 to 1000 nmol / L, while the concentration of the glucocorticoid mixture (calculated as dexamethasone) in most samples is almost within the range of 0 to 1000 nmol / L, and may be concentrated in the relatively low dose range; the concentration gradient is also reasonably set within this range.

[0034] In some implementations, the detection time and concentration range are each controlled within 0.5 h. <t<4h, 0nmol / L< C Within the range of <1000 nmol / L, GR receptor-fluorescently labeled cell lines (cell lines that are fluorescently labeled with the GR receptor and stably express it) exhibit good compatibility.

[0035] In some implementations, before obtaining the parameter set through convergence optimization, the method further includes: processing the parameter set. Set the initial values ​​separately.

[0036] In some implementations, the A The physical meaning of 1 is the saturation value or maximum effect of dexamethasone. The initial value is set as the maximum effect of the standard dexamethasone across all concentrations and time ranges. E max .

[0037] In some implementations, the A The physical meaning of 2 is the steepness of the dose-response curve. In a drug dose-response curve, the parameter... A 2 corresponds to the Hill coefficient in the Hill equation, which physically represents the steepness of the dose-response curve, reflecting the synergistic binding of the drug and receptor. The initial value is generally set within the range of 1.5. 3.

[0038] In some implementations, a fast binding constant without HSP90 regulation is used. The initial value is set within a range of 50. 200nM -1 h -1 .

[0039] In some implementations, a rapid dissociation constant without HSP90 regulation is used in one phase. The initial value is set within a range of 15. 50h -1 .

[0040] In some implementations, the two phases have a slow binding constant regulated by HSP90. The initial value is set within the following range: 10 nM -1 h -1 .

[0041] In some implementations, the two phases have a slow dissociation constant regulated by HSP90. The initial value is generally set within the range of 0.1. 5 h -1 .

[0042] In some implementations... The initial value of the phase weighting factor is set to 0 according to the principle. α 1.

[0043] In some implementations, the final parameters in the dexamethasone model are obtained more accurately and scientifically through local convergence optimization. The effect is calculated based on the fluorescence signals of the standard dexamethasone at different concentrations and times. The possible concentration range of the glucocorticoid mixture in the test sample is estimated in advance based on relevant data (generally, concentrations are lower in food and drinking water, and relatively higher in chemical and hospital emissions). Then, data points with concentrations close to the sample and all different times and effects are selected from the standard dexamethasone for parameter convergence fitting optimization. Because the parameter set has 7 parameters, the selected data points should be ≥7. Fewer than 7 data points are unsolvable, 7 or more are unique solutions, and more than 7 are not unique solutions.

[0044] In some implementations, the convergence optimization of the dexamethasone model involves selecting data points (≥7) at different time points and within a dexamethasone concentration range close to the predicted sample concentration range for parameter convergence fitting. This method is based on the Levenberg-Marquardt algorithm using nonlinear least squares combined with the Jacobian matrix, until the sum of squared residuals is reached. USSRTo obtain the final optimized parameter set, the required accuracy (within 3%) is achieved. This yields a dexamethasone model without parameters.

[0045] In some implementations, the sample's C sample The method for obtaining the combined effect index δ is as follows: the effect is calculated by analyzing the fluorescence signals of the sample at different times, and then substituted into the sample model equation to obtain the combined effect index δ. C sample The eight equations for the joint effect exponent δ (since m=8) are obtained through pairwise combinations ( (A combination of equations) and calculate the corresponding values ​​for each equation. C sample C equiv DXMS And the joint effect index δ, and take the mean respectively ( , ),at the same time This refers to the combined effect index, the summed concentration of each glucocorticoid in its single form, and the mixed concentration (in dexamethasone equivalents) in the sample after obtaining the final weight.

[0046] In some embodiments, the glucocorticoid mixture comprises two or more glucocorticoids.

[0047] In some embodiments, the glucocorticoid is selected from endogenous and / or exogenous, long-acting, intermediate-acting, or short-acting glucocorticoids.

[0048] In some embodiments, the different substances in the glucocorticoid mixture conform to a common effect mechanism (i.e., an additive pattern) and exhibit synergistic, antagonistic, and additive combined effects, which can be assessed through a combined effect index. By definition, δ > 1 represents a synergistic effect, 0 < δ < 1 represents an antagonistic effect, and δ = 1 represents an additive effect.

[0049] In some embodiments, the matrix of the standard dexamethasone is a blank matrix or the same matrix as the test sample.

[0050] In some embodiments, the samples are derived from food, feed, environmental water, cosmetics, and bodily fluids from livestock, poultry, and humans.

[0051] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0052] Example 1

[0053] A model algorithm for assessing the concentration and combined effects of a mixture of glucocorticoids in a sample includes the following steps.

[0054] S1. Detection

[0055] The assay was performed using the Hela-GFP-GR cell line (the stable Hela-GFP-GR cell line constructed according to Example 1 of application number "CN201911221278.0" and invention term "Assay Method for Combined Effect of Glucocorticoid Endocrine Interference in Transgenic Cells"). The test groups included dexamethasone standard (or a quality control group, which consisted of a matrix similar to the sample with known concentrations of dexamethasone added), samples, and blanks. The different test groups are described below:

[0056] Sample group: 1-3 replicate samples (each test well has multiple fields of view, and the final result is the average of the fields of view) are reconstituted and diluted with an appropriate amount of phenol red-free MEM + 10% (v / v) CD-FBS medium according to different samples and their pretreatment methods;

[0057] Standard group (blank + dexamethasone) or quality control group (matrix + dexamethasone): Dexamethasone was diluted to different concentrations using phenol red-free MEM + 10% CD-FBS medium, including: 1 nmol / L, 5 nmol / L, 10 nmol / L, 50 nmol / L, 75 nmol / L, 100 nmol / L, 500 nmol / L, 750 nmol / L and 1000 nmol / L;

[0058] Blank group: Phenol red-free MEM + 10% CD-FBS medium.

[0059] Collect samples from different test wells at different test concentrations using a high-content screening system or an inverted fluorescence microscope. i concentration, of which i =n, n=10), each test concentration has different time series data (t=m, m=8); any at... t Time i The test concentration includes the nuclear GFP fluorescence signal (I 细胞核的荧光信号 ) and cytoplasmic GFP fluorescence signal (I 细胞质的荧光信号 Among them, the sample ( E Sample ) and standard groups ( E DXMS The relevant effect is defined as follows, and is a dimensionless ratio:

[0060] Formula 1

[0061] S2. Construct a two-phase response surface model of dexamethasone concentration, time, and effect (referred to as the dexamethasone model) and determine the parameter set in the model.

[0062] 2.1 Dexamethasone Model

[0063] When dexamethasone is used as a benchmark reference or standard for glucocorticoids, its effect is simultaneously affected by concentration. C and time t The influence of this is modeled as follows:

[0064] Formula 2

[0065] 2.2 Determine the initial values ​​of the parameter set in the dexamethasone model

[0066] For parameter set A 1 、A 2 、k on1 、k off1 、k on2 、k off2 and Preset initial values, the preset method is as follows:

[0067] A 1. Initial values: The maximum effect obtained from all different concentrations and time ranges determined according to step S1. E max ;

[0068] A 2. Initial value: Set the range to 1.5 3;

[0069] Initial value: Set range is 50 200nM -1 h -1 ;

[0070] Initial value: Set range is 15 50h -1 ;

[0071] Initial value: The set range is 10 nM -1 h -1 ;

[0072] Initial value: Set to a range of 0.1 5 h -1 ;

[0073] Initial value: The initial value is set to 0. α 1.

[0074] 2.3 Convergence Iteration and Determination of Parameters in the Parameter Set of the Dexamethasone Model

[0075] The Disemesis model has a parameter set of 7 parameters. Parameter optimization is based on the Levenberg-Marquardt algorithm using nonlinear least squares combined with the Jacobian matrix. In nonlinear scenarios, the optimal solution is obtained through iterative approximation. The quality of the optimal solution is evaluated by the sum of squared residuals between the measured and predicted results of the objective function. USSR (The difference is generally within the range of ≤3%), as detailed below:

[0076] Formula 3;

[0077] i For concentration ( i =n, where n>2) represents the number of concentration points. j The number of time points ( j =m, m>2), totaling i × j One data point; It is a residual; This is a parameter vector, containing a total of 7 parameters.

[0078] First, the Jacobian matrix needs to be constructed. The Jacobian matrix is ​​a matrix of the partial derivatives of the residuals with respect to the parameters; it characterizes the sensitivity of the model output to each parameter and reflects the degree of influence of the parameters on the residuals. J Each row corresponds to a data point, and each column corresponds to a parameter. Indicates the first k Parameters ( k The change of =7) affects the first i The concentration of the first j The impact of predicted values ​​at each time point. The Jacobian matrix is ​​as follows:

[0079]

[0080] The parameter set contains 7 parameters. If there are fewer than 7 data points, the solution cannot be found; a solution with 7 or more data points is unique; otherwise, the solution is not unique. This invention relates to... i × j Combinations ， There will be more than 7 solutions, so we need to use the nonlinear least squares method to find all non-unique solutions. Then, we need to consider the Jacobian matrix. J The partial derivative vector is solved by calculus, and the partial derivative of each parameter is constructed as follows:

[0081] right A The partial derivative of 1 is: .

[0082] rightA The partial derivative of 2 is: .

[0083] right k on1 The partial derivatives are: .

[0084] right k off1 The partial derivatives are: ;

[0085] right k on2 The partial derivatives are: .

[0086] right k off2 The partial derivatives are: .

[0087] right The partial derivatives are: .

[0088] Then, based on the Jacobian matrix and the Levenberg-Marquardt algorithm, the parameter set θ={ was iteratively optimized and implemented. A 1, A 2, k on1 ，k off1 , k on2 , k off2 , The iterative formula for the Levenberg-Marquardt algorithm is as follows:

[0089] Equation 4-1;

[0090] Equation 4-2;

[0091] Wherein, the parameter vector is represented as , Indicates based on x The parameter vector after round convergence. express x The parameter vector before the round converges; Representation matrix A diagonal matrix consisting of the diagonal elements; λ x This represents the damping coefficient (adjustable within the range of 0.1 to 1), and its main function is to guide the search direction of the balance algorithm. ,but ;otherwise Then, recalculate and optimize convergence. Indicates the first x The residual vector obtained after round optimization is used in the calculation process and must be consistent with Equation 3. USSR To distinguish between residual sum of squares, USSR It is a metric for verifying whether the convergence optimization is satisfactory, but it does not participate in the calculation process. Equation 4-2 is based on the current parameter vector. and model prediction residual vector R x ∆ is calculated , = + Find the new residual vector This process is repeated until the model predictions converge and approach the optimal data, until the convergence requirement is met, i.e., the sum of squared residuals (SQR). USSR The optimal solution is the predicted value that minimizes the difference between experimental observations, and the difference is considered good if it is controlled within the range of ≤3%.

[0092] To summarize the above steps, the first step is to calculate the predicted effect at a certain concentration and time based on the initial values ​​of the parameter set. E pred , and the corresponding actual detection effect ( E obv The residual values ​​between ) are obtained, and the parameter set is covered. The residual vector of the 7 parameters R x+1 The updated parameter vector is obtained through the Jacobian matrix according to Equations 4-1 and 4-2. and obtained , = + If the residual decreases, accept it; otherwise, increase it. Reconverge and optimize until the sum of squared residuals is satisfied ( USSR If the difference is ≤3%, then the convergence of this round stops, and the final optimized and updated parameter set is obtained. Substituting this parameter into the model yields the final optimized standard dexamethasone model.

[0093] S3. Constructing an interaction index that introduces joint effects A two-phase response surface model (hereinafter referred to as the sample model) was developed to study the concentration, time, and effect of a mixture of glucocorticoids in the sample.

[0094] The various glucocorticoids in the sample mixture conform to the common effect mechanism, and are added together according to the concentration addition (CA) equivalent dose ratio. That is, the total effect is equal to the sum of the equivalent concentrations of each substance in the mixture based on the standard dexamethasone.

[0095] The updated parameters obtained in step S2 ={ A 1, A 2, k on1 ，k off1 , k on2 , k off2 , Based on the dexamethasone model constructed by}, the sample model was obtained:

[0096] Formula 5;

[0097] In Equation 5, C sample The method for calculating the combined effect index δ is to calculate the effect by analyzing the fluorescence signals of the sample at different times, and then substituting this result into the sample equation to obtain the combined effect index δ. C sample The eight equations for the joint effect exponent δ (because t=m, m=8) are obtained through pairwise combinations ( (A combination of equations) and calculate the corresponding values ​​for each equation. C equiv DXMS And the joint effect index δ, and take the mean respectively ( , ),at the same time This yields the combined effect index, the summed concentrations of each glucocorticoid in its single form, and the mixed concentration (in dexamethasone equivalents) in the sample after final weighting. This solution can estimate the characteristics and intensity of the combined effect of the glucocorticoid mixture in the sample. When δ > 1, a synergistic effect is observed, meaning the concentration of the glucocorticoid mixture in the sample equivalent to dexamethasone (…). C equiv DXMS The concentration of the individual substances when they are not mixed is less than the sum of their concentrations when they are not mixed. C sample When 0 < δ < 1, an antagonistic effect is observed, meaning the concentration of the glucocorticoid mixture in the sample is equivalent to the concentration of dexamethasone. C equiv DXMS The concentration of the substance is greater than the sum of the concentrations of the individual substances when they are not mixed. C sample When δ=1, an additive effect is observed, meaning the concentration of the glucocorticoid mixture in the sample is equivalent to the concentration of dexamethasone. C equiv DXMS ) equals the summation concentration of the individual substances when they are not mixed. C sample ).

[0098] Example 2

[0099] The concentration and combined effects of glucocorticoid mixtures in dairy products were evaluated based on the evaluation method provided in Example 1.

[0100] 1. Data capture

[0101] 1.1.1 Sample Extraction

[0102] Weigh 5g of milk powder sample (accurate to 0.01g) into a 50mL stoppered plastic centrifuge tube, add 10mL of water and vortex for 30 seconds to mix. The total volume at this point is approximately 15mL. Add 20mL of acetonitrile, vortex for 10min, and centrifuge at 5000r / min for 10min. Transfer 21mL of the supernatant to a pear-shaped flask. Evaporate using a rotary evaporator in a 45℃ water bath under reduced pressure (130hpa) to concentrate the extract to a volume less than 5mL. Transfer the concentrate to a 15mL centrifuge tube, wash the pear-shaped flask with 5mL of water and mix thoroughly with the concentrate. The volume is approximately 10mL.

[0103] 1.1.2 Sample purification

[0104] Connect a C18 solid-phase extraction column or its equivalent to a solid-phase extraction apparatus. Activate the column sequentially with 3 mL of methanol and 3 mL of water until equilibrium is reached. Load 10 mL of the concentrated solution onto the column at a flow rate of 1–2 drops / second. Discard all eluent, dry under reduced pressure, and elute with 5 mL of acetonitrile at a flow rate of less than 3 mL / min. Collect the eluent in a 15 mL centrifuge tube and dry it under nitrogen in a 45°C water bath. Each sample requires two replicates.

[0105] 1.1.3 Cell preparation before drug administration

[0106] When the cells reach approximately 80% confluence, change the medium to MEM + 10% FBS, remove tet, and passage again if necessary. Once GFP fluorescence expression is observed under a fluorescence inverted microscope, proceed to the next step. Digest and count the cells, resuspend them in phenol red-free MEM + 10% CD-FBS medium, and adjust the cell density to 10⁻⁶ cells / cm². 5 Inoculate 100 μL / well into a black transparent 96-well plate and incubate overnight.

[0107] 1.1.4 Drug administration test

[0108] The culture medium was removed on the second day, and the tests were divided into the following groups, with 200 μL added to each well and two replicates for each sample;

[0109] Samples: Take two replicate samples and reconstitute them with 0.6 mL of MEM containing 0.1% DMSO without phenol red and 10% CD-FBS.

[0110] Standard groups: DXMS solutions of 1 nmol / L, 5 nmol / L, 10 nmol / L, 50 nmol / L, 75 nmol / L, 100 nmol / L, 500 nmol / L, 750 nmol / L, and 1000 nmol / L were set as blank spike groups;

[0111] Blank: Phenol red-free MEM + 10% CD-FBS medium.

[0112] After adding the above solvent groups to a 96-well plate and incubating at 37°C for approximately 20-30 minutes, it is recommended that the sample group, standard group, and solvent blank group start calculating the fluorescence effect from this time point as the t1 sequence. Then, every 0.5 hours is used as a time series interval, and high-content data is collected and recorded to obtain the effect.

[0113] 1.1.5 Extracting Data

[0114] The standard group was selected as the quality control group, with a time interval of 0.5 hours. Fluorescence data of the standard group, blank control, and samples were obtained and converted into effects based on Equation 1. The effect data of the standard and samples are shown in Tables 1-2. Figure 1~2 As shown.

[0115] Table 1. Effects of dexamethasone at different concentrations and time points in the standard group.

[0116]

[0117] Table 2. Effects of the sample at different times

[0118]

[0119] Note: According to the “1.1.1 Sample Extraction” step, after reconstituted the sample with 0.6 mL of 0.1% DMSO and phenol red-free MEM + 10% CD-FBS, add 0.2 mL to each well.

[0120] 2. Establishment of a two-phase response surface model (dexamethasone model) for the concentration, time, and effect of dexamethasone standards.

[0121] 2.1 Setting Initial Values ​​for Parameter Set

[0122] A 1. Preset initial value: The highest concentration in the data is 1000 nmol / L, and the maximum value is 3.728, so we take 3.728.

[0123] A 2. Preset initial value: 2 within the recommended range.

[0124] K on1Preset initial value: 100nM within the recommended range. -1 ∙ h -1 .

[0125] K off1 Preset initial value: 30 h within the recommended range. -1 .

[0126] K on2 Preset initial value: 5nM within the recommended range. -1 ∙ h -1 .

[0127] K off2 Preset initial value: 1 hour within the recommended range. -1 .

[0128] Predicted initial value: 0.7 is recommended within the range.

[0129] 2.2 Parameter Set Convergence and Optimization

[0130] Select data points at different times close to the predicted sample concentration, as shown in Table 3 below:

[0131] Table 3. Effects of dexamethasone in the standard group included in convergence optimization at different concentrations and time points.

[0132]

[0133] The dexamethasone model is as follows:

[0134] ;

[0135] The initial value vector is:

[0136] .

[0137] The Jacobian matrix involves residual calculations related to each data point of dexamethasone, partial derivative calculations of each parameter, determination of the residual vector, and determination of different Jacobian matrices. The regularization matrix calculations involved in the Levenberg-Marquardt convergence iteration are performed entirely according to the steps and procedures in Example 1. Because the process is too redundant, it is omitted here. The final converged and optimized parameters are as follows:

[0138] .

[0139] The dexamethasone model is as follows:

[0140] Formula 6.

[0141] 3. Establishment of a two-phase response surface model (sample model) for the concentration, time, and effect of dexamethasone standard in the sample.

[0142] The sample model is as follows:

[0143] Formula 7.

[0144] Table 4. Concentration and combined effect index δ calculated based on 28 combinations

[0145]

[0146] The final joint effect index δ and C sample The average values ​​of the above 28 numbers are 0.762 and 0.088 nM / L, respectively. = 0.762 = 0.067 nM / L, which is equivalent to 0.0263 μg / kg. This concentration is the equivalent concentration of dexamethasone. From the above results, it can be seen that: (1) the sample is indeed a mixture, because δ≠1, indicating that there is a combined effect, and as time increases, the combined effect tends to gradually shift from synergistic to antagonistic; (2) the concentration of the mixture in the sample in a single form is 0.088 nM / L, and the concentration calculated as dexamethasone equivalent is 0.067 nM / L (equivalent to 0.0263 μg / kg). There is no requirement for dexamethasone in dairy products, but since there may be endogenous metabolism of corticosterone and cortisol in dairy products, glucocorticoids are present. According to the requirements of GB 31650-2019 National Food Safety Standard Maximum Residue Limits for Veterinary Drugs in Food, the limit for dexamethasone in pork is 1 μg / kg, and the equivalent activity is still extremely low.

[0147] Example 3

[0148] Spike recovery experiment of dairy product samples.

[0149] This embodiment uses the same dairy sample as in Example 2, and performs spiked (dexamethasone DXMS) recovery testing according to two other methods and the method in Example 1 of this invention: Method 1. Tested based on the method in Ministry of Agriculture Announcement No. 1031-2-2008 "Detection of Multiple Residues of Glucocorticoid Drugs in Animal-Derived Foods by Liquid Chromatography-Tandem Mass Spectrometry"; Method 2. Tested based on the dexamethasone ELISA detection kit (product number: SC0064; Beijing Meizheng Biotechnology Co., Ltd.).

[0150] Method 1: Liquid chromatography-tandem mass spectrometry (LC-MS / MS) was used to dissolve dexamethasone in methanol for sample spikeding. The pretreatment process followed the established "Operating Procedure for Screening and Determination of Mixtures of Glucocorticoids in Dairy Products Based on Hela-GFP-GR Cell Line Bioassay". After final nitrogen drying, the sample was reconstituted using the initial mobile phase required by "Ministry of Agriculture Announcement No. 1031-2-2008 Detection of Multiple Residues of Glucocorticoids in Animal-Derived Foods by Liquid Chromatography-Tandem Mass Spectrometry", diluted, and then analyzed.

[0151] The results showed that the standard curve had good linearity. Except for spikes at 5 times the limit of quantitation, the recoveries of the pretreatment spikes were all within the range of 80%-120%, proving that there were no problems with the pretreatment method and process, and that this method could continue to be used for sample pretreatment. Sample test results showed recoveries in the range of 96.6%-110.31%.

[0152] Table 5. Results of liquid chromatography-tandem mass spectrometry (LC-MS / MS) analysis of the samples

[0153]

[0154] Method 2: The ELISA method uses dimethyl sulfoxide (DMSO) to dissolve dexamethasone and spike the samples. The pretreatment process follows the "Screening and Determination Procedure of Glucocorticoid Mixtures in Dairy Products Based on Hela-GFP-GR Cell Line Bioassay". After pretreatment, the samples are dried under nitrogen and reconstituted with raw milk. The dexamethasone ELISA kit produced by Meizheng Biotechnology is then used for detection. The results show that the standard curve has good linearity and the spiked recovery rate is in the range of 68.13-79.17%.

[0155] Table 6. ELISA kit test results for the samples

[0156]

[0157] Method 3: The method of this invention is based on all the steps of Example 2 of this invention, and is also processed and determined according to the "Screening and Determination of Glucocorticoid Mixtures in Dairy Products Based on Hela-GFP-GR Engineered Cell Line Bioassay Procedure". Based on the model described in this method, calculations and optimizations are performed. The sensitivity of this method is 0.0392~0.392 ng / mL, and the spiked recovery rate is in the range of 103.3-110.45%.

[0158] Table 6. Test results of samples based on the method of this invention

[0159]

[0160] The results show that: 1. Dexamethasone is not present in dairy products as observed by chromatographic tandem mass spectrometry, but glucocorticoids can be detected in dairy products using the method of this application and the ELISA method. This may be due to quantitative errors inherent in the bioassay method itself, as well as the possible presence of other non-dexamethasone glucocorticoids, such as corticosterone and cortisol, which are also glucocorticoids metabolized endogenously in animals; 2. The ELISA kit is for dexamethasone, while the method of this application is for all endogenous / exogenous glucocorticoids (including dexamethasone), and both showed good recovery rates in the spiked recovery experiment.

[0161] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the concentration and combined effect of a mixture of glucocorticoids in a sample, characterized in that, Includes the following steps: Using fluorescently labeled GR receptor-stable cell lines as test vectors and dexamethasone as a standard, the fluorescence effect of different concentrations of the standard at different time points was detected. E DXMS and the effects of the glucocorticoid mixture in the sample at different times. E sample ; , , The I DXMS的细胞核的荧光信号 The fluorescence signal of the dexamethasone standard group in the cell nucleus was detected by the instrument; I 空白细胞核的荧光信号 The fluorescence signal of the blank group in the cell nucleus acquired by the instrument detection; I sample的细胞核的荧光信号 The fluorescence signal of the sample in the cell nucleus acquired by the instrument; I DXMS的细胞质的荧光信号 The fluorescence signal of the dexamethasone standard group in the cytoplasm was detected by the instrument; I 空白细胞质的荧光信号 The fluorescence signal of the blank group in the cytoplasm acquired by the instrument detection; I sample的细胞质的荧光信号 The fluorescence signal of the cytoplasm sample acquired by the instrument; Based on the effects of different concentrations of the standard at different times, a two-phase response surface model of standard concentration, time, and effect is constructed, denoted as the dexamethasone model. The parameter set is obtained through convergent optimization, and the parameter set includes: A 1. A 2. k on1 、k off1 、k on2 、 k off2 and The dexamethasone model is as follows: ； in C DXMS Different concentrations of the standard dexamethasone; t For time; k on1 It is a rapid binding constant without HSP90 regulation. k off1 It is the rapid dissociation constant of a single phase without HSP90 regulation; k on2 The slow binding constant of the two phases is regulated by HSP90. k off2 It is a slow dissociation constant regulated by HSP90; For the binding phase weighting factor; A two-phase response surface model of glucocorticoid mixture concentration, time, and effect in a sample, incorporating a combined effect index δ, is constructed and denoted as the sample model. The sample model is as follows: ; Wherein, δ is the combined effect index, which reflects the nature and intensity of the combined effect exhibited by the mixture of different glucocorticoids in the sample; δ>1 is defined as a synergistic effect, 0<δ<1 is defined as an antagonistic effect, and δ=1 is defined as an additive effect; = C equiv DXMS , C equiv DXMS The concentration of different glucocorticoids present in the sample as a mixture, expressed as the equivalent concentration of dexamethasone. C sample The cumulative concentration of different glucocorticoids in the sample, present in a single form; Before obtaining the parameter set in the convergence optimization, the method further includes: processing the parameter set. Set the initial values ​​separately; The A The initial value of 1 is set as the maximum effect of the standard dexamethasone across all concentrations and time ranges. E max ; The A 2. In the drug dose-response curve, the initial value of the Hill coefficient in the Hill equation is generally set within a certain range. ; The The initial value principle is set within the following range: ; The The initial value should be set within the following range: ; The The initial value should be set within the following range: ; The The initial value should be set within the following range: ; The The initial value principle is set as follows: ; The convergence optimization steps include: performing parameter convergence on data points of dexamethasone concentrations close to the sample concentration and effects at different times, selecting ≥7 data points, implementing the Levenberg-Marquardt algorithm based on nonlinear least squares combined with the Jacobian matrix, until the required accuracy of the residual sum of squares is achieved, and obtaining the final optimized parameter set. This yields the final dexamethasone model; The accuracy required to achieve the residual sum of squares means that the residual sum of squares is ≤3%. The sample C sample The method for obtaining the combined effect index δ is as follows: the effect is calculated by analyzing the fluorescence signals of the sample at different times, and then substituted into the sample model to obtain the combined effect index δ. C sample The eight equations for the joint effect index δ correspond to eight time points, and are obtained through pairwise combinations. Each combination of equations is calculated separately. C sample And the joint effect index δ, and take the mean respectively. ,at the same time That is, to obtain the combined effect index δ in the sample after final weighting, and the superimposed concentration of each glucocorticoid existing in a single form. C sample and the mixed concentration in dexamethasone equivalents C equiv DXMS .

2. The method according to claim 1, characterized in that, The testing equipment includes a high-content screening instrument or a fluorescence inverted microscope as the testing instrument.

3. The method according to claim 1, characterized in that, The fluorescent label refers to a fluorescent protein label, and the fluorescent protein includes GFP, EYFP, ERFP, ECFP, or iRFP; The cell lines were selected from human cervical cancer cell line HeLa, African green monkey kidney cell line CV-1, or human embryonic kidney cell line HEK293. The cell line that is fluorescently labeled with the GR receptor and stably expresses it is a Hela-GFP-GR modified human cervical cancer cell line.

4. The method according to claim 1, characterized in that, The glucocorticoid mixture comprises two or more glucocorticoids.

5. The method according to claim 4, characterized in that, The glucocorticoids are selected from endogenous and / or exogenous glucocorticoids, and are long-acting, intermediate-acting, or short-acting.

6. The method according to claim 1, characterized in that, The matrix of the standard is a blank matrix or a matrix containing similar samples.

7. The method according to claim 6, characterized in that, The samples were sourced from food, feed, environmental water, or cosmetics.

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