Method for analyzing antibodies and method for detecting diseases using the same

Abstract

To provide a method capable of highly reproducibly analyzing an antibody contained in a sample when analyzing the antibody by affinity chromatography using an Fc-binding protein-immobilized insoluble carrier.SOLUTION: An analysis method of an antibody includes the steps of: adding a sample to a column filled with an Fc-binding protein-immobilized insoluble carrier and causing the carrier to adsorb an antibody contained in the sample; eluting the antibody adsorbed onto the carrier using an eluate to acquire a separation pattern of the antibody; and calculating a value obtained by summing up products of antibody elution times in the separation pattern and detected values in the times.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a method for analyzing antibodies and a method for detecting a disease using the same, and in particular to a method for analyzing antibodies with high reproducibility and using the results to detect the presence or absence of a disease, the risk of developing a disease, the degree of disease progression, and / or the degree of aging progression. [Background technology]

[0002] In recent years, drugs containing antibodies (antibody drugs) have been used to treat cancer, immune diseases, and the like. The antibodies used in antibody drugs are produced by culturing cells capable of expressing the antibody (e.g., Chinese hamster ovary (CHO) cells, etc.) obtained by genetic engineering techniques, and then purifying them to a high degree using column chromatography or the like. However, recent research has revealed that the antibodies obtained by the above-mentioned production are aggregates of various molecules due to modifications such as oxidation, reduction, isomerization, and glycosylation, and there are concerns about their impact on efficacy and safety. In particular, it has been reported that the sugar chain structure bound to the antibody has a significant effect on the activity, dynamics, and safety of antibody drugs, and detailed analysis of the sugar chain structure is important (Non-Patent Document 1). In addition, changes in the sugar chain structure attached to the antibody in the blood are known for diseases such as rheumatism (Non-Patent Documents 2 and 3), and it is possible to detect the disease by analyzing the sugar chain structure attached to the antibody.

[0003] As a method for analyzing the glycan structure of an antibody, LC-MS analysis including cleavage of the glycan (Patent Documents 1 and 2) is mainly performed. However, this analysis method involves very complicated operations and requires a lot of time. A simpler method for analyzing the molecular structure of an antibody is an affinity chromatography analysis method based on the affinity between an antibody and an Fc-binding protein immobilized on an insoluble carrier, and this method allows analysis based on differences in the glycan structure bound to the Fc region of the antibody (Patent Document 3). However, the separation patterns obtained in this analysis vary widely, making it difficult to analyze the antibodies contained in a sample with high reproducibility. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-194500 A [Patent Document 2] JP 2016-099304 A [Patent Document 3] WO2019 / 244901 issue [Non-patent literature]

[0005] [Non-Patent Document 1] CHROMATOGRAPHY, 34(2), 83-88(2013) [Non-Patent Document 2] Science, 320, 373-376 (2008) [Non-Patent Document 3] Nature Communication, 7, 11205(2016) Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a method for analyzing antibodies contained in a sample with high reproducibility when the antibodies are analyzed by affinity chromatography using an insoluble carrier on which an Fc-binding protein is immobilized. [Means for solving the problem]

[0007] As a result of extensive research aimed at solving the above problems, the present inventors have discovered that immobilizing Fc-binding proteins The present inventors have discovered that when analyzing antibodies contained in a sample by affinity chromatography using a polyaniline-modified insoluble carrier, the antibodies can be analyzed with high reproducibility by quantifying the separation pattern of the antibodies, and have thus completed the present invention.

[0008] That is, the present invention includes the following aspects [1] to [8]. [1] A method for analyzing an antibody contained in a sample, comprising the following steps (a) to (c): (a) adding a sample to a column packed with an insoluble carrier onto which an Fc-binding protein has been immobilized, and allowing the antibody contained in the sample to be adsorbed onto the carrier; (b) eluting the antibody adsorbed on the carrier with an eluent to obtain a separation pattern of the antibody; and (c) calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times; [2] The analytical method described in [1], wherein the elution time is corrected based on the variation in the elution time of a standard antibody. [3] The analytical method according to [1] or [2] further comprises the following steps (A) to (D): (A) before the step (a), adding a standard antibody solution to the column to adsorb the standard antibody onto the carrier, and eluting the standard antibody adsorbed onto the carrier with the elution solution to obtain a first separation pattern of the standard antibody; (B) performing step (A) again before or after steps (a) and (b) to obtain a second separation pattern of the standard antibody; (C) comparing the elution time of the standard antibody in the first separation pattern with the elution time of the standard antibody in the second separation pattern; and (D) A step of correcting the elution times of the antibodies in the separation pattern obtained in (b) based on the comparison results of (C). [4] The analytical method according to [3], wherein the step (C) is a step of comparing the elution times of two or more antibody points in the first separation pattern with the elution times of two or more antibody points in the second separation pattern. [5] The analytical method according to [3] or [4], wherein the step (B) is carried out immediately before the step (a) or immediately after the step (b). [6] The analytical method according to any one of [1] to [5], wherein the sample is a body fluid. [7] A method for detecting a disease and / or a risk of developing the disease in a subject, comprising the steps of: (a) adding a sample obtained from a subject to a column packed with an insoluble carrier onto which an Fc-binding protein has been immobilized, and allowing the antibody contained in the sample to be adsorbed onto the carrier; (b) eluting the antibody adsorbed on the carrier with an eluent to obtain a separation pattern of the antibody; (c) calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times; (d) detecting the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of progression of aging in the subject based on the sum calculated in step (c). [8] The antibody is a human antibody, and the Fc-binding protein is any one of the following (1) to (3): The method according to any one of [1] to [7], wherein the polypeptide is any one of: (1) A polypeptide comprising the amino acid residues 17 to 192 of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that, in the amino acid residues 17 to 192, at least the valine at position 176 is replaced with phenylalanine; (2) A polypeptide comprising the amino acid residues from position 17 to position 192 of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that, in the amino acid residues from position 17 to position 192, at least the valine at position 176 is substituted with phenylalanine, and further comprising, in addition to the substitution, any one or more of substitutions, deletions, insertions, and additions of one or more amino acid residues at one or more positions, and having antibody-binding activity; (3) A polypeptide having an amino acid sequence that has 70% or more homology to the entire amino acid sequence of amino acids 17 to 192 of the amino acid sequence set forth in SEQ ID NO:1, in which the 176th valine is replaced by phenylalanine, provided that the polypeptide contains an amino acid sequence including the replacement and has antibody binding activity. Effect of the Invention

[0009] According to the present invention, when an antibody contained in a sample is analyzed by affinity chromatography using an insoluble carrier on which an Fc-binding protein has been immobilized, the antibody can be analyzed with high reproducibility. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 shows an example of a separation pattern obtained by analyzing antibodies using a column packed with an Fc-binding protein-immobilized gel. [Diagram 2] FIG. 1 shows one embodiment of the difference in separation pattern obtained by analyzing a standard antibody using a column packed with an Fc-binding protein-immobilized gel before and after sample analysis. [Diagram 3] FIG. 13 shows another example of the difference in separation pattern obtained by analyzing a standard antibody using a column packed with an Fc-binding protein-immobilized gel before and after sample analysis. [Figure 4] Figure 1 shows the difference in separation patterns obtained by analyzing a standard antibody using a column packed with an Fc-binding protein-immobilized gel before and after sample analysis in Example 6. (a) shows the results when human myeloma plasma-derived IgG3 (mIgG3) was used as the standard antibody, (b) shows the results when human myeloma plasma-derived IgG1 (mIgG1) was used as the standard antibody, and (c) shows the results when human plasma-derived IgG1 (pIgG1) was used as the standard antibody. [Diagram 5] FIG. 13 shows the measurement intervals for a standard antibody and a serum sample in Example 7. [Figure 6]This figure shows the difference in separation patterns obtained when analyzing human myeloma plasma-derived IgG1 (mIgG1) as a standard antibody using an Fc-binding protein-immobilized gel-packed column before and after sample analysis. [Figure 7] FIG. 13 shows the difference in separation patterns obtained by analyzing blood samples using a column packed with Fc-binding protein-immobilized gel in the 1st and 50th measurements of blood samples for which elution times were corrected in Example 8. [Figure 8] FIG. 13 is a diagram showing an example of an approximation line obtained by plotting the peak elution time of a standard substance and the value obtained by subtracting the peak elution time of the standard substance before sample analysis, which serves as a reference value, from the peak elution time of the standard substance in Example 8, and fitting the plot to a simple regression equation by the least squares method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] The present invention will be described in detail below.

[0012] In the present invention, the term "Fc-binding protein" refers to a polypeptide that has the ability to bind to the Fc region of an antibody contained in a sample and can recognize differences in the sugar chain structure of the antibody (for example, the sugar chain structure of the Fc region). There are no particular limitations on the Fc-binding protein, so long as it is such a polypeptide. For example, when the antibody is a human-derived antibody, the Fc-binding protein can be Examples of the protein include human Fc-binding proteins. A preferred example of the human Fc-binding protein is a human Fc receptor. Examples of human Fc receptors include human Fcγ receptors, which are receptors for human immunoglobulin G (IgG), human Fcα receptors, which are receptors for human immunoglobulin A (IgA), human Fcδ receptors, which are receptors for human immunoglobulin D (IgD), and human Fcε receptors, which are receptors for human immunoglobulin E (IgE). Any of these receptors can be used as the human Fc-binding protein in the present invention. In this specification, the term "antibody derived from a human" refers to an immunoglobulin having at least a human-derived Fc region. The antibody derived from a human may be a human antibody, a humanized antibody, or a chimeric antibody.

[0013] Specific examples of human Fcγ receptors include polypeptides comprising at least a partial sequence of the extracellular domain of human FcγRI (CD64), human FcγRIIa (CD32a), human FcγRIIb (CD32b), human FcγRIIc (CD32c), human FcγRIIIa (CD16a), or human FcγRIIIb (CD16b), as well as polypeptides in which a portion of the amino acid residues constituting the polypeptide has been substituted, deleted, inserted, and / or added. Among these, polypeptides comprising at least a partial sequence of the extracellular domain of human FcγRIIIa and polypeptides in which a portion of the amino acid residues constituting the polypeptide has been substituted, deleted, inserted, and / or added are preferred as human Fcγ receptors used as human Fc-binding proteins in the present invention.

[0014] Specific examples of polypeptides comprising at least a partial sequence of the extracellular region of human FcγRIIIa, or polypeptides in which some of the amino acid residues constituting the polypeptide have been substituted, deleted, inserted, and / or added, include the polypeptides described in (1) to (3) below. (1) A polypeptide comprising the amino acid residues 17 to 192 of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that, in the amino acid residues 17 to 192, at least the valine at position 176 is replaced with phenylalanine; (2) A polypeptide comprising the amino acid residues from position 17 to position 192 of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that, in the amino acid residues from position 17 to position 192, at least the valine at position 176 is substituted with phenylalanine, and further comprising, in addition to the substitution, any one or more of substitutions, deletions, insertions, and additions of one or more amino acid residues at one or more positions, and having antibody-binding activity; (3) A polypeptide having an amino acid sequence that has 70% or more homology to the entire amino acid sequence of amino acids 17 to 192 of the amino acid sequence set forth in SEQ ID NO:1, in which the 176th valine is replaced by phenylalanine, provided that the polypeptide contains an amino acid sequence including the replacement and has antibody binding activity.

[0015] Examples of the polypeptide described in (1) above include a polypeptide containing amino acid residues from position 24 to position 199 of the amino acid sequence described in SEQ ID NO: 2, and the polypeptide (Fc binding protein) disclosed in JP 2018-197224 A. Examples of the substitution, deletion, insertion, and addition described in (2) above include the substitution of amino acid residues disclosed in JP 2015-086216 A, JP 2016-169167 A, and JP 2017-118871 A.

[0016] In the above (3), "homology" means similarity or identity. Homology can be determined using an alignment program such as BLAST (Basic Local Alignment Search Tool). For example, "identity of amino acid sequences" may mean identity between amino acid sequences calculated using blastp, specifically, when blastp is used with default parameters. The homology may refer to the identity between amino acid sequences calculated using the above data. The homology may be 70% or more, or may be 80% or more, 90% or more, or 95% or more.

[0017] In the present invention, the term "insoluble carrier" refers to a carrier that is insoluble in a liquid (e.g., a liquid used for antibody adsorption or elution, such as an equilibration liquid or an elution liquid) that is passed through a column packed with the carrier. "The carrier is insoluble in a liquid" may mean that the solubility of the carrier in a liquid at 20°C is 100 mg / L or less, 10 mg / L or less, or about 0 mg / L. The insoluble carrier may have a functional group (e.g., a hydroxyl group) for immobilizing an Fc-binding protein by a covalent bond. Examples of insoluble carriers include carriers derived from inorganic substances such as zirconia, zeolite, silica, and coated silica; carriers derived from natural organic polymeric substances such as cellulose, agarose, and dextran; and carriers derived from synthetic organic polymeric substances such as polyacrylic acid, polystyrene, polyacrylamide, polymethacrylamide, polymethacrylate, and vinyl polymers.

[0018] The immobilization of an Fc-binding protein on an insoluble carrier can be carried out, for example, by utilizing a functional group present on the surface of the carrier that can be immobilized by covalent bonding with an Fc-binding protein. For example, when a hydroxy group is present on the surface of an insoluble carrier, an activating agent can be used to form an activated group capable of covalent bonding with an Fc-binding protein from the hydroxy group, thereby covalently bonding the activated group to the Fc-binding protein. Specific examples of activating agents for hydroxy groups include epichlorohydrin (forming an epoxy group as an activated group), 1,4-butanediol diglycidyl ether (forming an epoxy group as an activated group), tresyl chloride (forming a tresyl group as an activated group), and vinyl bromide (forming a vinyl group as an activated group). In addition, the hydroxy group can be converted to an amino group, a carboxy group, or the like, and then activated by the action of an activating agent. Specific examples of activators for amino groups, carboxy groups, etc. include N-succinimidyl 3-maleimidopropionate (which forms a maleimide group as the activating group), 1,1'-carbonyldiimidazole (which forms a carbonylimidazole group as the activating group), and halogenated acetic acid (which forms a halogenated acetyl group as the activating group).

[0019] The antibody to be analyzed in the present invention may be an immunoglobulin containing at least a glycosylated Fc region, and may contain other regions. The antibody to be analyzed in the present invention may be a monoclonal antibody or a polyclonal antibody. The immunoglobulin may be any of IgG, IgM, IgA, IgD, and IgE. However, when an Fc receptor is used as the Fc-binding protein, the antibody to be analyzed in the present invention must be an immunoglobulin corresponding to the receptor. For example, when a human Fcγ receptor is used as the Fc-binding protein, the antibody to be analyzed in the present invention may be an IgG derived from a human, and in particular, a human IgG. The IgG may be any of IgG1, IgG2, IgG3, and IgG4.

[0020] The origin of the antibody is not particularly limited, and the antibody may be derived from a single organism or a combination of two or more organisms. The antibody may be, for example, a chimeric antibody, a humanized antibody, a human antibody, or a variant thereof (e.g., an amino acid substitution product). In addition, examples of the antibody include artificially structurally modified antibodies such as bispecific antibodies, fusion antibodies of an Fc region with other proteins, and Fc region-drug complexes (ADCs). Furthermore, antibody drugs are also included in the "antibody" of the present invention. Examples of antibody drugs include infliximab, which is an anti-TNF-α (tumor necrosis factor α) antibody, tocilizumab, which is an anti-IL-6 (interleukin 6) antibody, and trastuzumab, which is an antibody against the cancer gene HER2. Among them, the analysis method of the present invention is an analysis method suitable for analyzing antibodies derived from body fluids, which will be described later.

[0021] In the present invention, the term "sample" refers to a solution that contains or may contain the above-mentioned antibody. The sample may be, for example, one obtained from a subject. An example of a sample is a body fluid or a buffer solution that contains or may contain the above-mentioned antibody. Thus, the antibody may be, for example, one derived from a body fluid. An example of the body fluid is a solution obtained from a subject, Blood samples (blood samples) such as blood (whole blood), diluted blood, serum, plasma, cerebrospinal fluid, umbilical cord blood, and apheresis; Specimens (samples) that may contain blood-derived components, such as urine, saliva, semen, feces, sputum, amniotic fluid, and peritoneal fluid; specimens, which may contain tissue fragments or cells, such as from liver, lung, spleen, kidney, skin, tumors, lymph nodes, etc.; specimens which may contain antibodies isolated therefrom or contained therein; The subject simply means a human individual to be measured or a human individual to be detected for risk. The subject is not particularly limited as long as a sample derived therefrom can be used (i.e., an antibody sample can be obtained or has already been obtained). The subject may be either male or female. The subject may be of any age, such as a child, a young person, a middle-aged person, or an elderly person. Furthermore, the subject may be either a healthy individual or not.

[0022] The analytical method of the present invention is characterized in that it analyzes an antibody contained in a sample by a method comprising the following steps (a) to (c). That is, the analytical method of the present invention is a method for analyzing an antibody contained in a sample, comprising the following steps (a) to (c): (a) adding a sample to a column packed with an insoluble carrier onto which an Fc-binding protein has been immobilized, and allowing the antibody contained in the sample to be adsorbed onto the carrier (hereinafter also referred to as the "adsorption step"); (b) a step of eluting the antibody adsorbed on the carrier with an eluent to obtain a separation pattern of the antibody (hereinafter also referred to as a "separation step"); and (c) A step of calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times (hereinafter, also referred to as the "analysis step").

[0023] Each step will be described in detail below.

[0024] (a) Adsorption process The adsorption step is a step in which a sample containing an antibody is added to a column packed with an insoluble carrier to which an Fc-binding protein has been immobilized, and the antibody is adsorbed to the carrier.

[0025] The sample subjected to the adsorption step may be a mixed solution containing multiple types of antibody molecules. Specifically, the sample subjected to the adsorption step may be a mixed solution containing multiple types of antibody molecules with different glycan structures. More specifically, the sample subjected to the adsorption step may be a mixed solution containing multiple types of antibody molecules with different glycan structures added to the Fc region.

[0026] When a body fluid is used as a sample, it may be added to the column as it is, or may be subjected to appropriate pretreatment before being added to the column. When pretreatment is performed, it may be performed by a method commonly used by those skilled in the art, such as centrifugation or purification using a column. The body fluid or the body fluid after pretreatment may be added to the column after being dissolved, suspended, dispersed, or subjected to solvent exchange in an appropriate liquid medium. An example of the liquid medium is the equilibration liquid described below. In the present invention, the "body fluid" also includes those that have been subjected to such pretreatment, solvent exchange, etc.

[0027] The antibody-containing sample may be added to a column packed with an insoluble carrier on which an Fc-binding protein is immobilized, for example, by using a liquid delivery means such as a pump (hereinafter, in this specification, adding a liquid to a column is also referred to as "delivering a liquid to a column"). The conditions for carrying out the adsorption step, such as the amount of antibody-containing sample to be added (delivered), the type of liquid phase, the liquid phase delivery rate, and the column temperature, may be determined. The conditions are not particularly limited as long as the antibody is adsorbed to the carrier. The conditions for carrying out the adsorption step can be appropriately set according to various conditions such as the type of antibody, the type of Fc-binding protein, the type of insoluble carrier, and the scale of the column. The liquid phase can be exemplified by the equilibration liquid described below. For example, when the inner diameter of the column is 4.6 mm, the liquid delivery speed may be 0.1 mL / min to 2.0 mL / min, 0.2 mL / min to 1.5 mL / min, or 0.4 mL / min to 1.2 mL / min. The liquid delivery speed may be set, for example, so as to be proportional to the square of the inner diameter of the column. The column temperature may be appropriately set, for example, in the range of 0°C to 50°C.

[0028] An equilibration step of adding (delivering) an equilibration solution to the column before and / or after the adsorption step may be further performed. In particular, performing an equilibration step after the adsorption step is preferable in that it can remove from the column antibodies that do not bind to the Fc-binding protein and antibodies that bind to the protein in the sample atmosphere but not in the equilibration buffer atmosphere. The antibody-containing fraction thus removed is also referred to as "unadsorbed fraction" in this specification. Specifically, the unadsorbed fraction is a fraction in the region from when the sample is added (delivered) to the column until the peak detected in the equilibration step reaches a minimum value in the separation pattern described below. It is preferable that the unadsorbed fraction and the peak detected after the addition of the eluent in the separation step described below are separated in elution time from each other, since this results in high separation accuracy. In particular, it is preferable that the detection value between the unadsorbed fraction and the peak region detected after the addition of the eluent is a constant value, since this indicates that the unadsorbed fraction has been sufficiently removed from the column by the equilibration step. The "constant value" referred to here is not limited to the same value, but also includes a state in which the detection value changes with a constant slope.

[0029] An example of the equilibration solution is an aqueous buffer solution. Specific examples of the aqueous buffer solution include weakly acidic to weakly alkaline buffer solutions with a pH of 5.0 to 8.0. The components of the buffer solution can be appropriately selected depending on various conditions such as the pH of the buffer solution. Examples of the components of the buffer solution include phosphoric acid, acetic acid, formic acid, MES (2-Morpholinoethanesulfonic acid), MOPS (3-Morpholinopropanesulfonic acid), citric acid, succinic acid, glycine, and piperazine. The buffer solution may further contain a salt such as sodium chloride or potassium chloride. The salt is not particularly limited as long as it is a salt that can be easily imagined by a person skilled in the art.

[0030] (b) Separation process The separation step is a step of eluting the antibody adsorbed to the insoluble carrier in the step (a) with an eluent to obtain a separation pattern of the antibody. That is, the antibody adsorbed to the carrier can be eluted by adding (delivering) the eluent to the column. The conditions for carrying out the elution step, such as the type of eluent, the delivery format of the eluent, the delivery rate of the liquid phase, and the column temperature, are not particularly limited as long as the antibody is separated in a desired manner, for example, as long as a desired separation pattern is obtained. The conditions for carrying out the elution step can be appropriately set depending on various conditions such as the type of antibody, the type of Fc-binding protein, the type of insoluble carrier, and the scale of the column. As the elution solution, one that weakens the affinity between the antibody and the Fc-binding protein may be used. As the elution solution, for example, an aqueous buffer solution having a pH that is more acidic than the liquid phase before elution (for example, the equilibration solution used in the equilibration step when the equilibration step is performed) can be used. As a specific example, when the liquid phase before elution (e.g., equilibration liquid) is a weakly acidic to weakly alkaline buffer solution having a pH of 5.0 to 8.0, an acidic buffer solution having a pH of 2.5 to 4.5 may be used as the eluent. The components of the buffer solution can be appropriately selected depending on various conditions such as the pH of the buffer solution. Examples of the components of the buffer solution include phosphoric acid, acetic acid, formic acid, MES (2-Morpholinoethanesulfonic acid), MOPS (3-Morpholinopropanesulfonic acid), citric acid, succinic acid, glycine, and piperazine. The delivery method of the eluent may be a linear gradient elution in which the ratio of the eluent in the liquid phase is changed continuously to elute, or a stepwise elution in which the ratio is changed stepwise to elute, or a combination thereof. The gradient may be, for example, a linear gradient elution in which the ratio is changed stepwise to elute, or a stepwise elution in which the ratio is changed stepwise to elute, or a combination thereof. The flow rate may be set so that the ratio of the eluent in the liquid phase increases from 0% (v / v) to 100% (v / v) in 15 to 60 minutes, 15 to 50 minutes, or 20 to 40 minutes. The flow rate may be, for example, 0.1 mL / min to 2.0 mL / min, 0.2 mL / min to 1.5 mL / min, or 0.4 mL / min to 1.2 mL / min when the inner diameter of the column is 4.6 mm. The flow rate may be set, for example, so as to be proportional to the square of the inner diameter of the column. The column temperature may be set, for example, appropriately in the range of 0°C to 50°C.

[0031] The antibody eluted from the column is detected using a detector to obtain a separation pattern of the antibody. Examples of the detector include a UV detector and a mass detector. Examples of the separation pattern of the antibody include a chromatogram at the time of elution of the antibody. The measurement data obtained by the detector may be acquired at any time interval, but since the accuracy of reflecting the original characteristics of the antibody separation pattern in the chromatogram decreases as the time interval increases, the time interval is preferably 1 minute or less, more preferably 10 seconds or less, and particularly preferably 2 seconds or less.

[0032] The antibody contained in the sample is obtained in a separated form by the separation step. The separated antibody may be obtained, for example, as an elution fraction containing the antibody. That is, the separated antibody is obtained by separating the elution fraction containing the separated antibody. The elution fraction can be separated, for example, by a conventional method. Specifically, the elution fraction can be separated, for example, by an automatic fraction collector such as an autosampler. Furthermore, the separated antibody may be recovered from the elution fraction. The separated antibody can be recovered, for example, from the elution fraction by a conventional method. Specifically, the separated antibody can be recovered, for example, from the elution fraction by a known method used for separating and purifying proteins.

[0033] (c) Analysis process The analysis step is a step of calculating the sum of the products of the antibody elution time and the detection value at that time in the separation pattern obtained in the step (b). The analysis method of the present invention is characterized in that it calculates the sum of the products of the antibody elution time and the detection value at that time based on the separation pattern obtained in the step (b). Hereinafter, the sum is also referred to as the "characteristic value of the separation pattern."

[0034] The region to be summed up may be a region from an arbitrary analysis start time to an arbitrary analysis end time. The analysis start time is not particularly limited, and may be, for example, the time when the sample is added (flowed) to the column, or the time when the eluent is added (flowed) to the column. The analysis start time is preferably set between the peak of the non-adsorbed fraction that does not bind to the insoluble carrier on which the Fc-binding protein packed in the column is immobilized and the time when a separation peak derived from the antibody bound to the carrier is obtained. The analysis end time is not particularly limited, and may be, for example, the time when the measurement end time or the time when the ratio of the addition (flow) of the eluent to the column becomes 100%. When an equilibration step is performed before the step (a), the analysis end time is preferably set between the time after the separation peak derived from the antibody bound to the insoluble carrier on which the Fc-binding protein packed in the column is immobilized is obtained and the time when the column is washed with the eluent and then switched to the equilibration solution to equilibrate the column, and the time when a change in the detection value that occurs with the switching is obtained.

[0035] The elution time used in the analysis step may be the value of the time elapsed from an arbitrary time point to the elution time. The elution time used in the analysis step may be, for example, the value from the time of adding (delivering) the sample to the column, the value from the time of adding (delivering) the eluent to the column, or the value from the start of the analysis. In addition, the elution time used in the analysis step may be a value calculated from the elution time as described above, such as a value obtained by subtracting the elution time from a specific time, a value obtained by exponentiating the elution time, or a value obtained by multiplying the elution time by a coefficient, as long as it reflects the time elapsed from an arbitrary time point to the elution time.

[0036] The sum of the products of the antibody elution time and the detection value at that time is the elution time at a certain time interval. The sum may be the sum of the products of the detection values ​​for the elution times at intervals, the sum of the products of the detection values ​​for the elution times at irregular time intervals, or the sum (integral) of the products of the detection values ​​for the elution times at continuous time intervals. The time interval may be, for example, the interval at which the detection values ​​are acquired. The time interval may be, for example, in seconds or minutes. Since the accuracy of reflecting the characteristics of the antibody separation pattern in the numerical value decreases as the time interval increases, it is preferable to obtain the sum at a time interval of 1 minute or less. The sum may be, for example, the sum of the products of the elution times and the actually measured detection values, or may be a sum calculated from an equation of the relationship between the elution times and the detection values ​​as a function of an approximation curve, or may be a sum obtained by integrating based on the approximation curve.

[0037] As an example, the separation pattern of an antibody that strongly binds to an insoluble carrier on which an Fc-binding protein is immobilized shows high detection values ​​in the late elution time region, and therefore the sum of the products of the elution time and the detection values ​​at that elution time (characteristic value of the separation pattern) shows a high value.On the other hand, the separation pattern of an antibody that weakly binds to the carrier shows high detection values ​​in the early elution time region, and therefore the sum of the products of the elution time and the detection values ​​at that elution time (characteristic value of the separation pattern) shows a low value.

[0038] Furthermore, when calculating the sum of the products of the value obtained by subtracting the elution time from a specific time and the detection value at that elution time, a low value is calculated for a separation pattern of an antibody that binds strongly to an insoluble carrier on which an Fc-binding protein is immobilized, and a high value is calculated for a separation pattern of an antibody that binds weakly to the carrier (inverse correlation). Therefore, when it is desired to obtain data that emphasizes the amount of an antibody that binds strongly to an insoluble carrier on which an Fc-binding protein is immobilized, the sum of the products of the elution time of the antibody and the detection value at that elution time may be used as a feature value of the separation pattern.

[0039] In addition, the sum of the products of the elution times and the detection values ​​at the elution times divided by the sum of the detection values ​​(also referred to as the "shape normalized value" in this specification) may be used as a feature value of the separation pattern.

[0040] Furthermore, the antibody elution time may be a value obtained by exponentiating the elution time. That is, the sum of the products of the antibody elution time and the detection value at the elution time may be used as the characteristic value of the separation pattern. By exponentiating the antibody, the influence of the elution time on the sum or the shape normalized value increases, and it becomes possible to more significantly evaluate the difference in the antibody binding ability to the carrier. On the other hand, exponentiation increases the coefficient of variation CV value, which indicates the measurement reproducibility, so the number of exponentiations (power exponent) is preferably 10 or less, more preferably 7 or less.

[0041] On the other hand, when it is desired to obtain data that emphasizes the amount of an antibody that weakly binds to an insoluble carrier on which an Fc-binding protein is immobilized, the sum of the products of the difference between a specific time and the elution time of the antibody and the detection value at that elution time can be used as a feature of the separation pattern, or the sum of the products of the difference between a specific time and the elution time of the antibody raised to a power and the detection value at that elution time can also be used as a feature of the separation pattern.

[0042] The peak division value may be calculated from the sum value and / or the shape normalized value. The "peak division value" refers to the parameters of each peak appearing in the separation pattern. Examples of peak parameters include peak area, peak area%, peak width, peak width%, peak height, and peak height%, and in particular, peak area and peak area%. The "Nth peak" (N is a positive integer) in the present invention may refer to the peak that is eluted Nth after the start of elution (e.g., after the start of the gradient) unless otherwise specified. For example, the "first to third peaks" in the present invention may refer to the peaks that are eluted 1st to 3rd after the start of elution (e.g., after the start of the gradient), respectively, unless otherwise specified. The peak may particularly have a peak area% of 1% or more. In other words, the "Nth peak" (N is a positive integer) may refer to the peak that is eluted Nth after the start of elution (e.g., after the start of the gradient). The term "peak area %" (integer) may particularly mean a peak that is eluted Nth after the start of elution and has a peak area % of 1% or more. For example, the terms "first to third peaks" may particularly mean peaks that are eluted first to third after the start of elution and have a peak area % of 1% or more. The term "peak area %" means a value (percentage) obtained by dividing the area of ​​each peak (peak region) by the total area of ​​all peaks. The term "peak width %" means a value (percentage) obtained by dividing the width of each peak (peak region) by the total width of all peaks. The term "peak height %" means a value (percentage) obtained by dividing the height of each peak (peak region) by the total height of all peaks. In the present invention, the term "peak region" refers to a region having a peak and a region of detection values ​​sandwiched between specific elution times. For example, the term "Nth peak region" refers to a region having the Nth peak and a region of detection values ​​sandwiched between specific elution times. That is, for example, the "first peak region" refers to a detection value region between the elution time that is the boundary between the first and second peaks (hereinafter also referred to as the boundary time) and an arbitrary value of the elution time before the boundary time. The arbitrary value of the elution time may be a reference point when performing baseline correction, or may be the start time of elution, and is not particularly limited, but is preferably set between the peak of the non-adsorbed fraction that does not bind to the insoluble carrier on which the Fc-binding protein packed in the column is immobilized and the separation peak derived from the antibody bound to the carrier is obtained. In addition, the "second peak region" refers to a detection value region between the boundary time of the first and second peaks and the boundary time of the second and third peaks. In addition, the "third peak region" refers to a detection value region between the boundary time of the second and third peaks and an arbitrary value of the elution time after the boundary time. The arbitrary value of the elution time may be a reference point when baseline correction is performed or may be the end time of elution, and is not particularly limited. However, it is preferable to set the arbitrary value of the elution time from the time after a separation peak derived from the antibody bound to the insoluble carrier on which the Fc-binding protein packed in the column is immobilized to the column, until the time when a change in the detection value that occurs when the column is washed with the elution solution and then switched to an equilibration solution to equilibrate the column is obtained.

[0043] In order to convert each peak divided value, it is advisable to obtain a relational expression between each peak divided value and the sum value and / or the shape normalized value. It is preferable to convert each peak area from the sum value and each peak area% from the shape normalized value, respectively, since the correlation is high. Each peak divided value may be directly or indirectly converted from the sum value and / or the shape normalized value. As an example of indirect conversion, when the peak consists of the first to third peaks, the first peak area% and the third peak area% may be directly converted according to the relational expression, and then the second peak area% may be indirectly calculated by subtracting the converted value from 100, which is the total peak area%.

[0044] By converting from the sum value and / or the shape normalized value, each peak divided value can be calculated with high accuracy. By converting from the sum value and / or the shape normalized value, each peak divided value can be calculated with high accuracy, for example, even when the separation of the peaks is unclear. Each calculated peak divided value may be used for any purpose, for example.

[0045] The detection value used in the analysis step may be the value obtained by the detector used in the step (b). The value obtained by the detector used in the step (b) may be used as the detection value as is or after appropriate correction for the baseline, etc.

[0046] The elution time and detection value used in calculating the characteristics of the separation pattern may be corrected, such as by correction based on the separation pattern obtained with a standard substance or correction based on the properties of the subject. When correction based on the separation pattern obtained with a standard substance is performed, it is preferable to use a standard antibody as the standard substance, since this improves the reproducibility of the measurement. The standard antibody may be any antibody that adsorbs to the Fc-binding protein immobilized on an insoluble carrier. Specific examples of the standard antibody include IgG1, IgG2, IgG3, and IgG4 when the Fc-binding protein is an Fcγ receptor. The standard antibody is an antibody of the same origin as the antibody contained in the sample. As an example, when the antibody contained in the sample is an antibody of human origin, the standard antibody is preferably an antibody of human origin, and may be, for example, an antibody derived from human blood, a human myeloma, or a human cultured cell, without any particular limitation.

[0047] The correction of the elution time may be performed, for example, based on the difference between the elution time obtained from the separation pattern of a standard antibody (hereinafter also referred to as the "first separation pattern of the standard antibody") as a reference and the elution time obtained from the separation pattern of a standard antibody measured separately (hereinafter also referred to as the "second separation pattern of the standard antibody") (i.e., the variation in the elution time of the standard antibody). This makes it possible to correct the variation in the elution time of the antibody that may occur with long-term and / or multiple uses of the column. The first separation pattern of the standard antibody may be the separation pattern of the standard antibody obtained by any measurement before the step (a). That is, the first separation pattern of the standard antibody may be the separation pattern of the standard antibody obtained by analyzing the standard antibody before the step (a). The second separation pattern of the standard antibody may be the separation pattern of the standard antibody obtained by analyzing the standard antibody before or after the analysis of the sample (i.e., the steps (a) and (b)). That is, the second separation pattern of the standard antibody may be obtained before or after the analysis of the sample. The second separation pattern of the standard antibody is preferably obtained close to the analysis of the sample, since this can improve the accuracy of the correction of the elution time. The interval between the acquisition of the second separation pattern of the standard antibody and the analysis of the sample (i.e., the number of analyses of any object between the acquisition of the second separation pattern of the standard antibody and the analysis of the sample) may be within 10 times, within 7 times, within 5 times, within 3 times, within 2 times, or within 1 time, or may be zero times (i.e., the acquisition of the second separation pattern of the standard antibody is performed immediately before or immediately after the analysis of the sample). The acquisition of the second separation pattern of the standard antibody may be particularly performed immediately before or immediately after the analysis of the sample. An example of the case where the acquisition of the second separation pattern of the standard antibody is performed immediately before or immediately after the analysis of the sample is a case where the analysis of the standard antibody and the analysis of the sample are performed alternately. The difference between the elution time obtained from the first separation pattern of the standard antibody and the elution time of the second separation pattern of the standard antibody may be obtained from the peak time or the elution time at an arbitrary detection value, and used for the correction of the elution time.The correction may be performed based on the difference in elution time at one point, but is preferably performed based on a line connecting the elution time differences at two or more points or an approximation line including the least squares method, which is a relational expression, based on the difference in elution time at three or more points, and is even more preferably performed based on the relational expression based on the difference in elution time at three or more points. The elution time point is preferably the elution time of a peak or valley (a point with a differential value of 0) that is easily extracted from the separation pattern of the standard antibody in data processing, since this allows for highly reproducible correction. Examples of detection values ​​include the area value of a separation pattern by a correction antibody obtained with a specific antibody amount. For the analysis of the standard antibody solution, the description of the analysis of the sample (i.e., the above steps (a) and (b)) can be applied mutatis mutandis, except that the standard antibody solution is used instead of the sample.

[0048] That is, the analysis method of the present invention may further include the following steps (A) to (D): (A) before the step (a), adding a standard antibody solution to the column to adsorb the standard antibody onto the carrier, and eluting the standard antibody adsorbed onto the carrier with the elution solution to obtain a first separation pattern of the standard antibody; (B) performing step (A) again before or after steps (a) and (b) to obtain a second separation pattern of the standard antibody; (C) comparing the elution time of the standard antibody in the first separation pattern with the elution time of the standard antibody in the second separation pattern; and (D) A step of correcting the elution times of the antibodies in the separation pattern obtained in (b) based on the comparison results of (C).

[0049] As a specific example of a method for correcting the elution time based on the separation pattern obtained in the analysis of a standard antibody, a standard antibody solution is analyzed before the analysis of a sample containing an antibody (e.g., a body fluid) to obtain the first concentration of the standard antibody. An example of such a method is to obtain one separation pattern, and then reanalyze the standard antibody solution before or after (e.g., immediately before or after) the analysis of a sample containing the antibody to obtain a second separation pattern of the standard antibody, compare the first and second separation patterns (in particular, the elution time of the standard antibody), and correct the elution time of the antibody (e.g., the peak time) based on the comparison result.

[0050] The characteristics of the separation pattern may be corrected based on the characteristics of the subject. An example of such correction is correction based on the age of the subject. For example, when the characteristics of the separation pattern are affected by the age of the subject, the characteristics of the obtained separation pattern may be corrected based on the age of the subject before being used in the detection step described below. The characteristics corrected based on the age of the subject may be used, for example, to detect the risk of symptoms other than aging.

[0051] The analytical method of the present invention may further include the following step (d): (d) A process for detecting the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of progression of aging in a subject based on the sum value calculated in the process (c) (hereinafter also referred to as the "detection process").

[0052] Specifically, when the sample is obtained from a subject, the analytical method of the present invention may include the step (d) described above. When the analytical method of the present invention includes the step (d) described above, the presence or absence of a disease, the risk of developing a disease, the degree of disease progression, and / or the degree of aging in the subject who provided the sample (specimen) can be detected. That is, one embodiment of the analytical method of the present invention may be a method for detecting the presence or absence of a disease, the risk of developing a disease, the degree of disease progression, and / or the degree of aging in a subject (specifically, a subject who provided a sample) (hereinafter, also simply referred to as the "detection method of the present invention").

[0053] (d) Detection step The detection step is a step of detecting the risk in a subject (i.e., the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of progression of aging) using the sum value (characteristic of the separation pattern) calculated in step (c) as an index.

[0054] Examples of diseases include diseases affected by the activity of immune cells (e.g., damaging action and phagocytosis). Examples of the immune cells include natural killer cells, monocytes, and macrophages. Specific examples of diseases affected by the activity of immune cells include cancer, autoimmune diseases, infectious diseases, allergies, and inflammatory diseases.

[0055] Examples of cancer include brain cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, esophageal cancer, stomach cancer, appendix cancer, colon cancer, liver cancer, gallbladder cancer, bile duct cancer, pancreatic cancer, adrenal cancer, gastrointestinal stromal tumor (GIST), mesothelioma, head and neck cancer, kidney cancer, lung cancer, osteosarcoma, Ewing's sarcoma, chondrosarcoma, prostate cancer, testicular cancer, renal cell carcinoma, bladder cancer, rhabdomyosarcoma, skin cancer, and anal cancer. Examples of cancer include pancreatic cancer, stomach cancer, breast cancer, colon cancer, and kidney cancer, among others.

[0056] Examples of autoimmune diseases include Guillain-Barré syndrome, myasthenia gravis, multiple sclerosis, chronic gastritis, chronic atrophic gastritis, autoimmune hepatitis, primary biliary cholangitis, ulcerative colitis, Crohn's disease, primary biliary cholangitis, autoimmune pancreatitis, Takayasu's arteritis, Goodpasture's syndrome, rapidly progressive glomerulonephritis, megaloblastic anemia, autoimmune hemolytic anemia, autoimmune neutropenia, idiopathic thrombocytopenic purpura, Graves' disease, and pontine leukemia. This disease, primary hypothyroidism, idiopathic Addison's disease, type 1 diabetes, chronic discoid lupus erythematosus, localized scleroderma, pemphigus, pustular psoriasis, plaque psoriasis, pemphigoid, herpes gestationis, linear IgA bullous dermatosis, epidermolysis bullosa acquisita, alopecia areata, vitiligo vulgaris, Sutton's acquired centrifugal vitiligo, Sutton's nevus, Harada's disease, autoimmune optic neuropathy, autoimmune inner ear disorder, idiopathic azoospermia, habitual abortion, rheumatism, systemic lupus erythematosus Examples of autoimmune diseases include rheumatoid arthritis, antiphospholipid syndrome, polymyositis, dermatomyositis, scleroderma, Sjögren's syndrome, IgG4-related disease, vasculitis syndrome, and mixed connective tissue disease. Examples of autoimmune diseases include rheumatoid arthritis and Sjögren's syndrome, among others.

[0057] Examples of infectious diseases include bacterial infections, fungal infections, parasitic protozoan infections, parasitic helminth infections, and viral infections. Examples of bacterial infections include infections caused by various bacteria such as streptococci, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus, Listeria, Neisseria meningitidis, Neisseria gonorrhoeae, pathogenic Escherichia coli, Klebsiella, Proteus, Bordetella pertussis, Pseudomonas aeruginosa, Serratia, Citrobacter, Acinetobacter, Enterobacter, Mycoplasma, Clostridium, Rickettsia, and Chlamydia; tuberculosis, nontuberculous mycobacteria, cholera, plague, diphtheria, dysentery, scarlet fever, anthrax, syphilis, tetanus, leprosy, Legionnaires' disease, leptospirosis, Lyme disease, tularemia, and Q fever. Examples of fungal infections include aspergillosis, candidiasis, cryptococcosis, tinea, histoplasmosis, and Pneumocystis carinii pneumonia. Examples of parasitic protozoan infections include amebic dysentery, malaria, toxoplasmosis, leishmaniasis, and cryptosporidiosis. Examples of parasitic helminth infections include echinococcosis, Japanese schistosomiasis, filariasis, ascariasis, and diphyllobothrium latissimus. Examples of viral infections include influenza, viral hepatitis, viral meningitis, viral gastroenteritis, viral conjunctivitis, acquired immune deficiency syndrome (AIDS), adult T-cell leukemia, Ebola hemorrhagic fever, yellow fever, common cold syndrome, rabies, cytomegalovirus infection, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), COVID-19, progressive multifocal leukoencephalopathy, chickenpox and shingles, herpes simplex, hand, foot and mouth disease, dengue fever, Japanese encephalitis, erythema infectiosum, infectious mononucleosis, smallpox, rubella, acute poliomyelitis (polio), measles, pharyngoconjunctival fever (pool fever), Marburg hemorrhagic fever, hemorrhagic fever with renal syndrome, Lassa fever, mumps, West Nile fever, herpangina, and chikungunya fever. The infection may be, for example, an opportunistic infection.

[0058] Examples of allergies include anaphylactic shock, allergic rhinitis, conjunctivitis, bronchial asthma, urticaria, atopic dermatitis, hemolytic anemia, idiopathic thrombocytopenic purpura, drug-induced hemolytic anemia, granulocytopenia, thrombocytopenia, Goodpasture's syndrome, serum sickness, systemic lupus erythematosus (SLE), rheumatism, glomerulonephritis, hypersensitivity pneumonitis, allergic bronchopulmonary aspergillosis (ABPA), contact dermatitis, allergic encephalitis, transplant rejection, tuberculous cavities, and epithelioid cell granuloma.

[0059] Examples of inflammatory diseases include diseases induced by inflammatory cytokines such as IL-6 and TNF-α. Specific examples of inflammatory diseases include encephalitis, osteomyelitis, meningitis, neuritis, eye inflammation (dacryoadenitis, scleritis, episcleritis, keratitis, chorioretinitis, retinitis, chorioretinitis, blepharitis, conjunctivitis, uveitis, etc.), ear inflammation (otitis externa, otitis media, otitis interna, etc.), mastitis, carditis (endocarditis, myocarditis, pericarditis, etc.), vasculitis (arteritis, phlebitis, capillitis, etc.), respiratory inflammation (sinusitis, rhinitis, pharyngitis, laryngitis, tracheitis, bronchitis, bronchiolitis, pneumonia, pleuritis, mediastinitis, etc.), oral inflammation (stomatitis, gingivitis, gingivostomatitis, glossitis, tonsillitis, siladenitis, parotitis, cheilitis, pulpitis, rhinitis, etc.), digestive inflammation (esophagitis, gastritis, gastroenteritis). Examples of age-related diseases include inflammatory bowel disease, enteritis, enteritis, colitis, duodenitis, ileitis, appendicitis, proctitis, etc.), dermatitis, cellulitis, hidradenitis, arthritis, dermatomyositis, myositis, synovitis, tendonitis, panniculitis, osteitis, osteomyelitis, periostitis, nephritis, ureteritis, cystitis, ureteritis, oophoritis, salpingitis, endometritis, cervicitis, vaginitis, vulvitis, orchitis, epididymitis, prostatitis, seminal vesicle cystitis, balanitis, fossitis, chorioamnionitis, omphalitis, omphalitis, hepatitis, ascending cholangitis, cholecystitis, pancreatitis, peritonitis, hypophysitis, thyroiditis, parathyroiditis, adrenalitis, lymphangitis, lymphadenitis, cachexia, frailty, sarcopenia, and locomotive syndrome. Examples of inflammatory diseases include pancreatitis, among others.

[0060] In the detection step, the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of aging in the subject may be detected using the characteristic value of the separation pattern calculated in the step (c) as an index. That is, the characteristic value may be regarded as data used as an index for detecting the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of aging in the subject. Specifically, the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of aging in the subject can be detected using the characteristic value of the separation pattern calculated based on the separation pattern obtained by separating an antibody obtained from the subject using a column packed with an insoluble carrier having an Fc-binding protein immobilized thereon as an index. That is, the analysis method or detection method of the present invention may be provided as a method for accurately detecting the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of aging in the subject using the characteristic value of the separation pattern obtained by separating an antibody obtained from the subject using the column as an index. In this specification, the presence or absence of a disease, the risk of developing a disease, the degree of progression of a disease, and / or the degree of progression of aging are collectively referred to simply as "risk." In addition, in this specification, "detection of risk" and "assessment of risk" may be used synonymously.

[0061] Detection of the onset risk in a subject includes detection of whether or not the subject has an onset risk (qualitative detection) and detection of whether the subject has a high or low risk (quantitative detection).

[0062] Detection of the presence or absence of a disease can include detection of whether or not a subject is currently likely to have a disease (qualitative detection) and detection of whether or not a subject is currently likely to have a disease (quantitative detection).

[0063] Detection of the risk of developing a disease includes detection of whether or not a subject is likely to develop a disease in the future or to become severe if they do develop the disease (qualitative detection), and detection of whether or not a subject is likely to develop a disease in the future or to become severe if they do develop the disease (quantitative detection).

[0064] Detection of the degree of disease progression includes detection (quantitative detection) of whether the current degree of disease progression (for example, severity) in a subject is large or small.

[0065] Detection of the degree of progression of aging includes detection (quantitative detection) of whether the current degree of progression (eg, severity) of aging in a subject is large or small.

[0066] That is, "the subject is at risk of developing" may mean, for example, that the subject may currently have a disease, that the subject may develop a disease in the future, and / or that the subject may develop a severe disease if he or she develops a disease in the future. On the other hand, "the subject is not at risk of developing" may mean, for example, that the subject is not currently at risk of developing a disease, that the subject is not at risk of developing a disease in the future, and / or that the subject is not at risk of developing a severe disease if he or she develops a disease in the future.

[0067] Furthermore, "a high risk of onset in a subject" may mean, for example, that the subject is highly likely to currently have a disease, that the subject is highly likely to develop a disease in the future, that the subject is highly likely to develop a disease in the future if the subject develops a disease, that the subject's current disease is highly advanced, and / or that the subject's current aging is highly advanced. On the other hand, "a low risk of onset in a subject" may mean, for example, that the subject is low likely to currently have a disease, that the subject is low likely to develop a disease in the future, that the subject's current disease is highly advanced, and / or that the subject's current aging is highly advanced. This may mean that the size is small.

[0068] The detection step can be carried out, for example, using the magnitude of a feature value of the separated pattern as an index. The magnitude of the feature value can be determined, for example, by comparing the feature value of the separated pattern calculated in the step (c) with a predetermined threshold. In other words, the detection step can include, for example, a step of comparing the feature value obtained from the separated pattern with a threshold.

[0069] That is, "the feature value of the separated pattern is high" may mean, for example, that the feature value of the separated pattern is high based on a threshold. Furthermore, "the feature value of the separated pattern is high based on a threshold" may mean, for example, that the feature value of the separated pattern is equal to or higher than the threshold, that the feature value of the separated pattern exceeds the threshold, or that the feature value of the separated pattern is statistically significantly higher than the threshold. Specific examples of "the feature value of the separated pattern is high based on a threshold" include the case where the feature value of the separated pattern is 1.01 times or more, 1.02 times or more, 1.03 times or more, 1.05 times or more, 1.07 times or more, 1.1 times or more, 1.2 times or more, 1.3 times or more, 1.5 times or more, 1.7 times or more, 2 times or more, 2.5 times or more, or 3 times or more than the threshold.

[0070] On the other hand, "the feature value of the separation pattern is low" may mean, for example, that the feature value of the separation pattern is low based on a threshold. Also, "the feature value of the separation pattern is low based on a threshold" may mean, for example, that the feature value of the separation pattern is equal to or lower than the threshold, that the feature value of the separation pattern is less than the threshold, or that the feature value of the separation pattern is statistically significantly lower than the threshold. Specific examples of "the feature value of the separation pattern is low based on a threshold" include the case where the feature value of the separation pattern is 0.99 times or less, 0.98 times or less, 0.97 times or less, 0.95 times or less, 0.93 times or less, 0.9 times or less, 0.85 times or less, 0.8 times or less, 0.7 times or less, 0.6 times or less, 0.5 times or less, 0.4 times or less, or 0.3 times or less of the threshold.

[0071] The feature value of the separation pattern may be divided into a risk range, for example, based on a threshold value. The feature value of the separation pattern may be divided into a non-risk range, for example, based on a threshold value. Specifically, the feature value of the separation pattern may be divided into a risk range and a non-risk range, for example, based on a threshold value. The "risk range" may mean a range in which the feature value of the separation pattern is likely to be at risk for the subject. The "non-risk range" may mean a range in which the feature value of the separation pattern is likely to be at no risk for the subject. That is, if the feature value of the separation pattern is in the risk range, it may be detected that the subject is at risk or at high risk. On the other hand, if the feature value of the separation pattern is in the non-risk range, it may be detected that the subject is at no risk or at low risk.

[0072] It should be noted that "detecting whether a subject has a risk, is not, is high, or is low when the feature value of a specific separation pattern meets a certain criterion (e.g., low or high, or within a specific range)" means detecting whether a subject has a risk, is not, is high, or is low at least within the range in which the criterion is met, and does not require that risk be detected in the subject in a range in which the criterion is not met. However, in one embodiment, when "detecting whether a subject has a risk, is not, is high, or is low when the feature value of a specific separation pattern meets a certain criterion (e.g., low or high, or within a specific range)", it may be possible to detect whether a subject has no risk, is at risk, is low, or is high, respectively, in a range in which the criterion is not met.

[0073] The threshold value can be appropriately set by a person skilled in the art depending on various conditions such as the type of features of the separation pattern and the desired accuracy of judgment. The threshold value may be set for each symptom to be judged, such as disease or aging. There are no particular limitations on the means for determining the threshold value. For example, the threshold value may be set by dividing a population into two groups. The amount of the ion exchange reaction can be determined according to known techniques used in data analysis for the purpose of determining the amount of the ion exchange reaction.

[0074] The threshold can be determined, for example, based on the feature value of the separation pattern of an antibody sample obtained from a control subject (herein, the separation pattern of an antibody sample obtained from a control subject is also referred to as the "control separation pattern"). That is, the threshold may be determined based on the feature value of the control separation pattern, and the detection step may be carried out. Specifically, the feature value of the control separation pattern may be used to determine the threshold, which may then be used for comparison with the feature value of the separation pattern. In other words, in the detection step, for example, a comparison between the feature value of the separation pattern and the feature value of the control separation pattern may be carried out.

[0075] The control subject may be a positive control or a negative control. The term "positive control" may refer to a subject who may be detected as having or being at high risk. The term "negative control" may refer to a subject who may be detected as having no or low risk. The positive control may be an individual who is suffering from or has suffered from a disease such as those exemplified above (particularly, the same disease as the disease to be detected for risk), an individual who has progressed in aging, or an individual who has a combination of these characteristics. The negative control may be an individual who is not suffering from or has never suffered from a disease such as those exemplified above (particularly, the same disease as the disease to be detected for risk), an individual who has not progressed in aging, or an individual who has a combination of these characteristics. The threshold may be determined based only on the feature value of the separation pattern calculated by analyzing the positive control, may be determined only on the feature value of the separation pattern calculated by analyzing the negative control, or may be determined based on the feature value of the separation pattern calculated by analyzing both the positive control and the negative control. The threshold may usually be determined based on the feature value of the separation pattern calculated by analyzing both the positive control and the negative control. The number of positive controls and negative controls is not particularly limited as long as a threshold value that allows risk determination with a desired accuracy is obtained. The number of positive controls and negative controls may be one, two or more. The number of positive controls and negative controls may usually be more than one. The number of positive controls and negative controls may be, for example, 5 or more, 10 or more, 20 or more, or 50 or more. The number of positive controls and negative controls may be, for example, 10,000 or less, 1,000 or less, or 100 or less.

[0076] When the threshold is determined based only on the feature value of the separation pattern calculated by analyzing a positive control, the threshold may be set to a value selected from the range from the upper limit to the lower limit of the feature value of the separation pattern calculated by analyzing multiple positive control individuals, for example, an average value. In addition, the threshold may be determined so that a predetermined proportion of the positive controls falls within the danger range in the distribution of the feature values ​​of the separation pattern calculated by analyzing multiple positive control individuals. The predetermined proportion may be, for example, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, or 100%.

[0077] When the threshold is determined based only on the feature value of the separation pattern calculated by analyzing the negative control, the threshold may be set to a value selected from the range from the upper limit to the lower limit of the feature value of the separation pattern calculated by analyzing multiple negative control individuals, for example, an average value. In addition, the threshold may be determined so that a predetermined proportion of the negative control falls within a non-risk range in the distribution of the feature values ​​of the separation pattern calculated by analyzing multiple negative control individuals. The predetermined proportion may be, for example, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, or 100%.

[0078] When the threshold value is determined based on both the feature value of the separation pattern calculated by analyzing a positive control and the feature value of the separation pattern calculated by analyzing a negative control, the threshold value may be determined, for example, so that a predetermined proportion of the positive controls falls within the risk range and a predetermined proportion of the negative controls falls within the non-risk range. It is preferable that the proportion of those included in the non-risk range is high. These proportions may be, for example, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, or 100%. If it is difficult to increase both of these proportions, the threshold may be set so that one of the proportions is preferentially increased depending on various conditions such as the purpose of use of the detection results according to the present invention. For example, in order to reduce the false negative rate, the threshold may be set so that the proportion of those included in the risk range among the positive controls is preferentially increased.

[0079] The threshold value may be determined, for example, using software. For example, a threshold value that can statistically most appropriately distinguish between the negative control and the positive control may be determined using statistical analysis software. Examples of such software include statistical analysis software such as "R".

[0080] In addition, the control subject may be the target subject itself. That is, for example, the risk in a subject may be detected by using the fluctuation of the feature value of the separation pattern in the subject as an index. In this specification, "the feature value of the separation pattern is high" may also include the case where the feature value of the separation pattern is increased. In this specification, "the feature value of the separation pattern has increased" may specifically mean that the feature value of the separation pattern has increased compared to the past value. In addition, in this specification, "the feature value of the separation pattern is low" may also include the case where the feature value of the separation pattern has decreased. In this specification, "the feature value of the separation pattern has decreased" may specifically mean that the feature value of the separation pattern has decreased compared to the past feature value. In other words, the threshold value may also be a past feature value. In this specification, "past feature value" means the feature value of the separation pattern of an antibody sample obtained from a target subject at a specific time point in the past. The target subject at a specific time point in the past may be, for example, a positive control or a negative control.

[0081] The increase or decrease in risk in a subject may be detected using the fluctuation of the feature value of the separation pattern in the subject as an index. As used herein, "at risk or high risk" may also include cases where the risk has increased. As used herein, "increased risk" may specifically mean that the risk has increased compared to a specific time point in the past. On the other hand, as used herein, "no or low risk" may also include cases where the risk has decreased. As used herein, "reduced risk" may specifically mean that the risk has decreased compared to a specific time point in the past.

[0082] In this specification, "obtaining a specific separation pattern and using it as an indicator for risk detection" is not limited to the case where the feature value of the separation pattern itself is obtained and used as an indicator for risk detection, but also includes obtaining other values ​​that reflect the feature value of the separation pattern and using them as an indicator for detection.

[0083] The result of risk detection may be used as an index for determining whether to implement a treatment for reducing risk (hereinafter, also referred to as "risk reduction treatment") on the subject. In other words, by implementing the detection method of the present invention, an index for determining whether to implement a risk reduction treatment on the subject can be obtained. That is, for example, when the detection method of the present invention detects that the subject is at risk or at high risk, it may be decided to implement a risk reduction treatment on the subject. The detection method of the present invention may be used, for example, alone or in combination with other means, as an index for determining whether to implement a risk reduction treatment on the subject. For example, a definitive diagnosis may be performed by other means for a symptom detected by the detection method of the present invention as being at risk or at high risk in the subject, and then it may be decided to implement a risk reduction treatment on the subject. The risk reduction treatment may be a medical procedure or a non-medical procedure. Examples of risk reduction treatment include the prevention and treatment of diseases and aging as exemplified above. That is, the present invention may provide, for example, a method for preventing or treating symptoms such as diseases and aging. The prevention or treatment method may include, for example, a process of implementing the detection method of the present invention. The present invention may be a method for preventing or treating symptoms such as disease or aging, comprising a step of: detecting a disease or aging-related condition in a subject; and, when the detection method of the present invention detects that the subject is at risk or at high risk, implementing prevention or treatment for the subject. Specifically, prevention or treatment may be implemented for symptoms detected by the detection method of the present invention as being at risk or at high risk in the subject. Prevention or treatment can be implemented, for example, by general measures for each symptom (e.g., medication or surgery).

[0084] The present invention is a method for analyzing antibodies contained in a sample, comprising the steps of adding a sample to a column packed with an insoluble carrier to which an Fc-binding protein has been immobilized, and allowing antibodies contained in the sample to be adsorbed onto the carrier, and eluting the antibodies adsorbed onto the carrier with an elution solution to obtain a separation pattern of the antibodies, and further comprising a step of calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times.

[0085] In the separation pattern of an antibody using a column packed with an insoluble carrier on which an Fc-binding protein is immobilized, the peaks may not be clearly separated or the number of peaks may differ depending on the sample. In such a case, the reproducibility of the peak area or peak area % of the antibody separation pattern cannot be obtained, making it difficult to compare the area with other samples. The method of the present invention can quantify whether there are many antibodies with high binding ability to the column or many antibodies with low binding ability as the overall tendency of the separation pattern. Since antibodies with high binding ability to Fc-binding proteins have high ADCC (antibody-dependent cellular cytotoxicity) activity, the method of the present invention can be used to estimate the ADCC activity of the entire sample containing the antibody. By quantifying the separation pattern of the antibody to be detected, for example, a separation pattern characteristic of the risk of a specific disease or aging symptoms of the subject's antibody can be quantified with high reproducibility, and the above-mentioned risk can be detected with high accuracy by comparing it with the quantified data of a reference separation pattern. In addition, by obtaining a separation pattern before and / or after some treatment (medication, surgery, etc.) for a disease and comparing it with a reference separation pattern, the accuracy of monitoring the progress of treatment of the disease and of determining a policy for the treatment can be improved. Examples of the reference separation pattern include a separation pattern of a control subject. Specific examples of the reference separation pattern include a separation pattern of the same subject at a different time point (for example, when healthy or when the same disease is developed), a separation pattern of a healthy subject, a separation pattern of different patients who have the same disease, and a separation pattern that serves as a reference when dividing into two or more groups that have different efficacies to the treatment.

[0086] For example, the risk of having a disease or the risk of developing a disease can be evaluated by using the separation pattern of a healthy person (model) as a model separation pattern and comparing the separation data of a subject with the model separation pattern. Furthermore, by separating the peak fraction that is the cause of the difference between the separation patterns of the model and the subject, the difference in the glycan pattern of the antibody between the healthy person (model) and the subject can be extracted. In addition, the separation pattern of the patient at a certain time point can be used as a model separation pattern, and the separation pattern of the same patient at a different time point can be compared with the model separation pattern, thereby monitoring the disease of the patient. When comparing two different sample groups, a statistical probability (P value) can be used to evaluate whether the difference in the separation data obtained from the two different sample groups is significant. It is said that the evaluation result is significant when the P value is small, and when the P value is less than the significance level, it can be said that the evaluation result has a statistically significant difference. The significance level is generally 5%.

[0087] The strength of the affinity of an antibody to the Fc-binding protein of the present invention affects the activity of immune cells such as natural killer cells, monocytes, and macrophages, which have the ability to damage or phagocytose the binding substance bound by the antibody, and thus by detecting the difference in affinity, the risk of developing a disease affected by damage or phagocytosis by natural killer cells, monocytes, and macrophages can be evaluated. Such diseases include cancer, autoimmune diseases, infections, and the like. Examples of infectious diseases include opportunistic infections. Opportunistic infections are infectious diseases caused by pathogens that do not cause infections in healthy conditions. Examples of pathogens include viruses, bacteria, fungi, and protozoa. In addition, the effect of antibodies acquired by vaccination or disease on activating immune cells can also be evaluated from the strength of the affinity of the antibody to the Fc-binding protein. In addition to measuring the amount of the acquired antibody in the blood, the strength of the affinity of the antibody to the Fc-binding protein can also be measured to accurately predict the risk of developing an infectious disease. EXAMPLES

[0088] The present invention will be described in more detail below with reference to examples and comparative examples, but the present invention is not limited to these examples.

[0089] Example 1 Preparation of affinity chromatography column (FcR9_F column) The Fc-binding protein FcR9_F_Cys (SEQ ID NO: 2) obtained by the method of JP 2018-197224 A was immobilized on a gel by the method shown below to prepare an FcR9_F column. In FcR9_F_Cys (SEQ ID NO: 2), the 1st methionine (Met) to the 22nd alanine (Ala) are the improved PelB signal peptide, the 24th glycine (Gly) to the 199th glutamine (Gln) are the amino acid sequence of the Fc-binding protein FcR9_F (JP 2018-197224 A) (corresponding to the region from the 17th to the 192nd in SEQ ID NO: 1), and the 200th glycine (Gly) to the 207th glycine (Gly) are the cysteine ​​tag sequence. Furthermore, the FcR9_F is a polypeptide consisting of amino acid residues from position 17 to position 192 of native FcγRIIIa shown in SEQ ID NO: 1, with the following amino acid substitutions (I) to (X): (I) Substitution of valine (Val) at position 27 of SEQ ID NO:1 (position 34 of SEQ ID NO:4) with glutamic acid (Glu) (II) Substitution of phenylalanine (Phe) at position 29 of SEQ ID NO: 1 (position 36 of SEQ ID NO: 4) with isoleucine (Ile) (III) Substitution of tyrosine (Tyr) at position 35 of SEQ ID NO: 1 (position 42 of SEQ ID NO: 4) with asparagine (Asn) (IV) Substitution of glutamine (Gln) at position 48 of SEQ ID NO: 1 (position 55 of SEQ ID NO: 4) with arginine (Arg) (V) Substitution of phenylalanine (Phe) at position 75 of SEQ ID NO: 1 (position 82 of SEQ ID NO: 4) with leucine (Leu) (VI) Substitution of asparagine (Asn) at position 92 of SEQ ID NO: 1 (position 101 of SEQ ID NO: 4) with serine (Ser) (VII) Substitution of valine (Val) at position 117 of SEQ ID NO: 1 (position 124 of SEQ ID NO: 4) with glutamic acid (Glu) (VIII) Substitution of glutamic acid (Glu) at position 121 of SEQ ID NO: 1 (position 128 of SEQ ID NO: 4) with glycine (Gly) (IX) Substitution of phenylalanine (Phe) at position 171 of SEQ ID NO: 1 (position 178 of SEQ ID NO: 4) with serine (Ser) (X) Valine (Val) at position 176 of SEQ ID NO: 1 (position 183 of SEQ ID NO: 4) is replaced with phenylalanine (Phe).

[0090] (1) The hydroxyl groups on the surface of 2 mL of hydrophilic vinyl polymer for separation agent (manufactured by Tosoh Corporation: packing material for liquid chromatography) were activated with iodoacetyl groups, and then 4 mg of FcR9_F_Cys obtained by the method disclosed in JP 2018-197224 A was reacted to obtain an FcR9_F immobilized gel.

[0091] (2) 1.2 mL of the FcR9_F immobilized gel prepared in (1) was packed into a φ4.6 mm × 75 mm stainless steel column to prepare an FcR9_F column.

[0092] Example 2 Analysis of antibodies in serum using FcR9_F column (no elution time correction 1) (1) Blood samples were collected from subjects who had given informed consent and centrifuged to obtain serum. The serum was diluted 10-fold with phosphate buffered saline (PBS) (pH 7.4) and passed through a 0.2 μm filter (Merck Millipore) to prepare serum samples.

[0093] (2) The FcR9_F column prepared in Example 1 was connected to a high performance liquid chromatography system (manufactured by Tosoh Corporation) and equilibrated with 10 mM citrate buffer (pH 6.5) containing 100 mM sodium chloride (hereinafter also referred to as "equilibration solution"), after which 10 μL of the serum sample prepared in (1) was added at a flow rate of 1.0 mL / min. The measurement interval by the detector was set to obtain data every 1 / 5 seconds.

[0094] (3) After washing with the equilibration solution for 10 minutes at a flow rate of 1.0 mL / min, the adsorbed gamma globulin was eluted with a pH gradient (a gradient in which the elution solution reached 100% in 25 minutes) using 10 mM citrate buffer (pH 4.0) containing 500 mM sodium chloride (hereinafter also referred to as the "elution solution") to obtain a separation pattern.

[0095] (4) The procedures (2) and (3) were carried out three times using columns with different usage times.

[0096] (5) The separation pattern obtained in (3) (Figure 1) was analyzed by comparing the time when the pH gradient was started (10 min after the start of elution, T s ), and the time when the pH gradient ended (i.e., the eluate reached 100%) (35 min after the start of elution, analysis end time T f ) are both detected values ​​I T The baseline was corrected to be 0.

[0097] (6) From the baseline-corrected separation pattern, the sum of the detection values ​​in the gamma globulin separation pattern every 1 / 5 seconds, which is the same as the measurement interval by the detector, was calculated using the following formula 1, and the shape normalized value of the gamma globulin separation pattern was calculated using the following formula 2.

[0098]

number

[0099] Comparative Example 1 (1) Using the same specimen as the serum sample used in Example 2, a separation pattern of gamma globulin was obtained and baseline corrected in the same manner as in (1) to (5) of Example 2.

[0100] (2) In Figure 1 <1> The first peak area was calculated from the region, and the first peak area was <1> , <2> , and <3> The first peak area % was calculated by dividing the area by the total area (percentage).

[0101] Example 3 Analysis of antibodies in serum using FcR9_F column (no elution time correction 2) (1) Human myeloma plasma-derived IgG3 (mIgG3, Sigma) at 0.2 μm A standard antibody solution was prepared by passing the antibody through a 100% pore size filter (Merck Millipore). The protein concentration in the solution was measured using a NanoDrop ultra-microspectrophotometer (Thermo Scientific).

[0102] (2) A separation pattern was obtained in the same manner as in Example 2(2) and (3), except that the standard antibody solution prepared in (1) was used as the measurement sample.

[0103] (3) After the analysis of the standard antibody solution, the serum sample prepared in Example 2(1) was analyzed in the same manner as in Examples 2(2) and (3) to obtain a separation pattern of gamma globulin.

[0104] (4) The procedures (2) and (3) were repeated three times using columns with different usage times.

[0105] (5) Baseline correction was performed in the same manner as in Example 2(5).

[0106] (6) From the baseline-corrected separation pattern, the sum of the detection values ​​in the gamma globulin separation pattern corrected with the standard antibody sample was calculated every 1 / 5 seconds, which is the same as the measurement interval by the detector, using the following formula 3, and the shape normalized value of the gamma globulin separation pattern was calculated using the above formula 2.

[0107]

number

[0108] Example 4 Analysis of antibodies in serum using FcR9_F column (with elution time correction 1) (1) A baseline-corrected gamma globulin separation pattern was obtained in the same manner as in Example 3(2) to (5). In this example, the separation pattern obtained from the analysis of the standard antibody solution in the first set was used as the reference value.

[0109] (2) The sum of the detection values ​​in the gamma globulin separation pattern corrected using the following formulas 4 and 5 at every 1 / 5 seconds, which is the same as the measurement interval by the detector, was calculated, and the shape normalized value of the gamma globulin separation pattern was calculated using the following formulas 5 and 6. The correction formulas from formulas 4 to 6 are calculated based on the analysis start time T s There is no difference between the first set (reference value) and the second set and after (subject to correction), while the peak time is the reference value Tcs p and the correction target Tc p This formula was created assuming that differences occur in

[0110]

number

[0111] Example 5 Analysis of antibodies in serum using FcR9_F column (with elution time correction 2) (1) A separation pattern of gamma globulin was obtained in the same manner as in Example 4(1).

[0112] (2) The sum of the detection values ​​in the gamma globulin separation pattern corrected using the following formulas 7 to 9 at every 1 / 5 seconds, which is the same as the measurement interval by the detector, was calculated, and the shape normalized value of the gamma globulin separation pattern was calculated using formulas 8 to 10. Note that the correction formulas of formulas 7 to 10 are calculated using the standard values ​​(analysis start time Tcs s , peak time Tcs p ) and correction target (analysis start time Tc s , peak time Tc p ) and there is a difference between the analysis end time Tcs f and the analysis end time Tc for the correction target f The difference between Tcs p and Tcp This formula was created assuming that it is equal to the difference between

[0113]

number

[0114] The results of Examples 2 to 5 and Comparative Example 1 are summarized in Table 1. Table 1 shows the variation in values ​​(CV value [%]) when the same serum sample was measured three times. The smaller the value, the lower the variation and the higher the measurement reproducibility.

[0115] [Table 1]

[0116] When the first peak area and area % were analyzed (Comparative Example 1), the CV values ​​were 16.6% and 15.3%, respectively, and the antibody elution time (TT s ) and the detected value I T The sum of the products of these, or the sum is the detection value I T When the shape-normalized value corrected by dividing by the sum of the values ​​(Examples 2 and 3) was used for analysis, the CV value improved (6.1% for the sum and 4.5% for the shape-normalized value in both cases), and it was found that the measurement reproducibility was improved. In the analysis method of 1, the first peak ( <1> ) and other peaks (Figure 1 <2> and <3> ) is not clearly defined, and the calculation of the peak area value is likely to vary. However, in the analysis methods of Examples 2 and 3, the peaks are analyzed without being divided, and therefore it is considered that variation is less likely to occur compared to the analysis method of Comparative Example 1.

[0117] Peak time Tcs in standard antibody solution p and the peak time Tc in the antibody-containing specimen (serum sample) pWhen the sum value is corrected based on the difference from the elution time (Examples 4 and 5), the CV values ​​are 2.4% (Example 4) and 2.7% (Example 5), respectively, and it can be seen that the measurement reproducibility is improved compared to the case where no correction based on the elution time is made (Examples 2 and 3). When a serum sample is repeatedly measured, there is a risk that the elution time may change even when the same specimen is analyzed, because proteins derived from the sample may be adsorbed to the carrier in the column or the Fc-binding protein bound to the carrier may be fragmented by serum-derived proteases. Therefore, it is considered that the measurement reproducibility is improved by correcting the elution time.

[0118] Peak time Tcs for standard antibodies p and the peak time Tc in the antibody-containing specimen (serum sample) p When the shape normalized value was corrected based on the difference from the sum value (Examples 4 and 5), the CV values ​​were 1.3% (Example 4) and 0.4% (Example 5), respectively, and the measurement reproducibility was further improved compared to when the value was corrected from the sum value. In particular, the CV value was significantly reduced in Example 5. From this, it can be seen that the peak time Tcs p and the peak time Tc in the antibody-containing specimen (serum sample) p The assumption that the difference between the analysis start time T s There is no difference between the first set (reference value) and the second set and after (subject to correction), while the peak time is the reference value Tcs p and the correction target Tc p It can be seen that the measurement reproducibility is improved by correcting the elution time of a specimen (serum sample) containing an antibody using a standard antibody, rather than assuming that differences occur due to the difference in the elution time.

[0119] Example 6: Examination of standard antibodies (1) Human myeloma plasma-derived IgG3 (mIgG3, Sigma), human myeloma plasma-derived IgG1 (mIgG1, Sigma), and human plasma-derived IgG1 (pIgG1, Fitzgerald) were each passed through a 0.2 μm filter (Merck Millipore) to prepare a standard antibody solution. The protein concentration in the solution was measured using a NanoDrop ultramicrospectrophotometer (Thermo Scientific).

[0120] (2) After analyzing all of the standard antibody solutions of mIgG3, mIgG1, and pIgG1 prepared in (1), a baseline-corrected gamma globulin separation pattern was obtained in the same manner as in Example 4(1), except that a serum sample different from that in Example 2 was analyzed.

[0121] (3) Peak time Tcs for standard antibody p The summation value and shape-normalized value were determined every 1 / 5 second, which is the same as the measurement interval by the detector of the detection values ​​in the gamma globulin separation pattern, in the same manner as in Example 5(2), except that any of the mIgG3 peak shown in panel (a) of FIG. 4, the first / second / third / fourth mIgG1 peak shown in panel (b) of FIG. 4, and the pIgG1 peak shown in panel (c) of FIG. 4 were used as the peak.

[0122] The results are shown in Table 2. As in Table 1, Table 2 shows the variation in values ​​(CV value [%]) when the same serum sample was measured three times.

[0123] [Table 2]

[0124] For the summation value, the CV value was low when mIgG3 or the third peak of mIgG1 was selected as the peak in the standard antibody (mIgG3: 1.1%, mIgG1 third peak: 1.2%).For the shape-normalized value, the CV value was low when mIgG3, mIgG1 third peak, or pIgG1 was selected as the peak in the standard antibody (mIgG3: 0.6%, mIgG1 third peak: 0.4%, pIgG1: 0.3%).

[0125] The antibodies contained in the serum sample analyzed in this example are derived from pIgG1 (derived from plasma). The peak times of the IgG3 and mIgG1 third peaks are relatively close to the peak time of pIgG1 (Figure 4). From this, it can be said that, theoretically, the measurement reproducibility can be improved by correcting based on the difference in the peak time of either IgG3, mIgG1 third peak, or pIgG1, which is near the elution time of the antibody in the separation pattern of the antibody-containing sample. On the other hand, the results for the sum value of pIgG1 this time were not good (CV = 2.8%). This is presumably because the amount of unadsorbed fraction eluted before the introduction of the eluent was large for the pIgG1 used this time (panel (c) in Figure 4), which caused variation in the sum value of the standard antibody used to correct the sum value of the antibody-containing sample.

[0126] Example 7: Examination of standard antibody measurement intervals (1) The serum sample prepared in Example 2(1) was analyzed by any of the methods shown in 1) to 3) below, and analyzed a total of 30 times as a serum sample (FIG. 5). Otherwise, a baseline-corrected gamma globulin separation pattern was obtained in the same manner as in Example 4(1). 1) Six consecutive analyses of serum samples: After measuring the standard antibody solution once, the serum samples were measured six times, and this procedure was repeated for five cycles. 2) Three consecutive analyses of serum samples: After measuring the standard antibody solution once, the serum samples were measured three times, and this procedure was repeated for 10 cycles. 3) Alternate analysis of standard antibody and serum sample: The standard antibody solution was measured once, followed by one measurement of the serum sample, and this procedure was repeated 30 times.

[0127] (2) Peak time Tcs for standard antibody p The sum value and shape normalized value were calculated every 1 / 5 second, which was the same as the measurement interval by the detector of the detection value in the separation pattern of gamma globulin, in the same manner as in Example 5(2), except that the third peak of mIgG1 was used as the measurement interval.

[0128] The results are shown in Table 3. Table 3 shows the variability (C For both the sum value and the shape normalized value, 3) alternate analysis of the standard antibody and serum sample had the best measurement reproducibility (sum value: CV = 0.6%, shape normalized value: CV = 0.4%).

[0129] [Table 3]

[0130] Example 8: Study of the method for correcting dissolution time (1) 0.8 mL of the FcR9_F-immobilized gel prepared in Example 1(1) was packed into a φ4.6 mm×50 mm stainless steel column to prepare an FcR9_F column.

[0131] (2) A standard antibody solution, mIgG1, was prepared in the same manner as in Example 6(1), and a serum sample was prepared in the same manner as in Example 2(1).

[0132] (3) The FcR9_F column prepared in (1) was connected to a high-performance liquid chromatography system (manufactured by Tosoh Corporation) and equilibrated with 10 mM citrate buffer (pH 6.5) containing 100 mM sodium chloride (hereinafter also referred to as "equilibration solution"), after which 10 μL of the mIgG1 prepared in (2) was added at a flow rate of 1.2 mL / min. The measurement interval by the detector was set to obtain data every 1 / 5 seconds.

[0133] (4) After washing with the equilibration solution for 7 minutes at a flow rate of 1.2 mL / min, the adsorbed gamma globulin was eluted with a pH gradient (a gradient in which the elution solution reached 100% in 11 minutes) using 10 mM citrate buffer (pH 4.5) containing 500 mM sodium chloride (hereinafter also referred to as the "elution solution") to obtain a separation pattern.

[0134] (5) Using the measurement methods (3) and (4), the mIgG1 prepared in (2) and the serum sample were alternately measured for 50 cycles.

[0135] (6) The separation patterns of the mIgG1 and serum samples obtained in (5) were compared with the time when the pH gradient was started (7 min after the start of elution, T s ), and the time when the pH gradient ended (i.e., the eluate reached 100%) (18 min after the start of elution, analysis end time T f ) are both detected values ​​I T The baseline was corrected to be 0.

[0136] (7) The baseline-corrected separation pattern obtained in (6) was subjected to the following elution time correction methods (a) to (c) to obtain a separation pattern with corrected elution times. Note that the scope of application of the elution time correction based on the measurement results of the standard substance is the standard substance and the blood sample measured next to the standard substance. (a) No correction for elution time (b) The third peak time, Tc, of the standard substance mIgG1 in FIG. P3 Tc in Eq. 11 P As a reference value, the time to the third peak of mIgG1 obtained before the start of measurement was used. Tcs P3 Tcs in Equation 11 P This is a correction method using Equation 11, which is obtained by substituting as follows: Equation 11 is a correction equation that assumes that fluctuations in the separation pattern due to repeated measurements are shifted by a uniform amount in the elution time range from the analysis start time to the analysis end time. (c) A method of correction using the first to third peak times of mIgG1, which is the standard substance in Figure 6, under the condition that the number of peaks m used for correction in formula 13 is 3, based on formulas 12 and 13. Formulas 12 and 13 are formulas that assume that the fluctuation in the separation pattern due to repeated measurements varies in width depending on the elution time within the elution time range from the analysis start time to the analysis end time, and perform correction by subtracting from the elution time the value obtained from the approximation line (Figure 8) between the fluctuation width of the elution time at each elution time and multiple points of the elution time, based on a simple regression equation by the least squares method.

[0137] (8) For the separation pattern obtained by correcting the elution time from (7), the detection value I for the corrected elution time was 7 minutes and 18 minutes after the start of the corrected elution. Tca The separation pattern was obtained by baseline correction so that the chromatogram was 0.

[0138] (9) Using Equation 14 and Equation 16, the sum of the detection values ​​in the separation pattern obtained in (8) was calculated every 1 / 5 seconds, which is the same as the measurement interval by the detector, and using Equation 15 and Equation 17, the shape normalized value of the separation pattern obtained in (8) was calculated. Note that the power exponent n in Equation 14 to Equation 17 is all 3, which increases the influence of the elution time on the sum and shape normalized values. In addition, the values ​​of Equation 14 and Equation 15 increase when there are many antibodies detected in the region with a slow elution time of the separation pattern, and the values ​​increase in proportion to the presence of many antibodies with high binding properties to Fc-binding proteins as a tendency of the entire separation pattern. On the other hand, the values ​​of Equation 16 and Equation 17 decrease when there are many antibodies detected in the region with a slow elution time of the separation pattern, and the values ​​decrease in inverse proportion to the presence of many antibodies with high binding properties to Fc-binding proteins as a tendency of the entire separation pattern.

[0139] (10) The coefficient of variation (CV value) for 50 measurement cycles was obtained from the sum value and shape normalized value obtained from (9).

[0140]

number

[0141]

number

[0142]

number

[0143] The results of Example 8 are shown in Table 4. The value obtained from Equation 14 was the sum value power exponent 3, Equation 15 was the sum value inverse correlation power exponent 3, Equation 16 was the shape normalized value power exponent 3, and Equation 17 was the shape normalized value inverse correlation power exponent 3. As for the correction of the elution time, the CV value of the value obtained from Equation 14 to Equation 17 was 17.2% to 35.1% in the uncorrected case of (a), but the CV value was 1.9% to 5.6% in the one-point correction of (b), and the CV value was 1.0% to 3.5% in the three-point correction of (c). By correcting the elution time, the CV value was significantly reduced, and by increasing the number of peaks used to correct the elution time of the correction substance from one point to three points, the CV value was further reduced, and the fluctuation range due to repeated measurement of the measured value was reduced.

[0144] Fig. 7 is a diagram showing the separation patterns obtained in Example 7(8), in which the results of the first and 50th measurements are plotted for each elution time correction method. It can be seen from this diagram that the large elution time shift seen without correction has been corrected to the third peak position by one-point correction, and that the entire separation pattern is positioned on almost the same line after three-point correction, indicating that the correction is highly accurate and that the variation due to repeated measurements is suppressed.

[0145] The shift in elution time obtained by subtracting the peak elution time of the reference value from the peak elution time of each of the calibration substances used in the three-point calibration and the peak elution time of the standard substance was plotted, and an approximation line of the points calculated by the least squares method is shown in FIG. 8. It can be seen that the shift in elution time at each peak time differs depending on each elution time. It can also be seen that the plotted points are located almost on a straight line, and it can be seen that the elution time can be corrected by a simple regression equation calculated by the least squares method.

[0146] [Table 4]

[0147] Example 9 Correlation with each peak area (1) Standard antibody solutions and serum samples were prepared in the same manner as in Examples 8(1) and (2), except that anti-human CD20 human-mouse chimeric antibody (rituximab, Zenyaku Kogyo Co., Ltd.) was used as the standard antibody solution in Example 8(2).

[0148] (2) As the measurement procedure of Example 8(5), the rituximab prepared in (1) and the blood samples were alternately measured for 25 cycles per day, and the same blood sample was measured for a total of 5 samples at the start and end of the measurement of each day. This procedure was carried out for 8 days. The antibody separation pattern was obtained in the same manner as in Examples 8(3) to (5).

[0149] (3) As a method for correcting the elution time in Example 8(7), a corrected separation pattern was obtained in the same manner as in Examples 8(6) to (8), except that the correction method in (c) was applied.

[0150] (4) The CV values ​​of the sum value and the shape-normalized value were obtained in the same manner as in Examples 8(9) and (10), except that 1, 3, 5, and 7 were substituted into Equations 14 to 17, respectively, as the power exponent n in Example 8(9).

[0151] (5) From the separation pattern of the standard substance corrected in (3), the peak regions were determined as the regions divided by two elution times at which the derivative of the valley region between the three peaks (peaks 1, 2, and 3 from the shortest elution time) detected between 7 and 18 minutes after the start of elution was zero. The first peak region was defined as the range from 7 minutes after the start of elution to the elution time at which the derivative of the valley region between the first and second peaks was zero (boundary time between the first and second peaks), the second peak region was defined as the range from the elution time at which the derivative of the valley region between the first and second peaks was zero to the elution time at which the derivative of the valley region between the second and third peaks was zero (boundary time between the second and third peaks), and the third peak region was defined as the range from the elution time at which the derivative of the valley region between the second and third peaks was zero to 18 minutes after the start of elution. The peak regions of the samples were identified by applying the peak regions defined for the standard substance to all of the serum samples measured.

[0152] (6) Based on the peak areas obtained from (5), the first peak area, second peak area, and third peak area in the blood sample were obtained as peak division values, and the first peak area%, second peak area%, and third peak area% were obtained by dividing each peak area by the peak area from 7 minutes to 18 minutes after the start of elution. The coefficient of determination was calculated between the sum value obtained in (4) for each peak area and the shape normalized value obtained in (4) for each peak area%.

[0153] The results of Example 9 are shown in Tables 5 to 8. From the coefficient of determination between the sum value and the peak division value in Table 5, the first peak area was obtained by calculating the sum value with a power index n of 3 or more (coefficient of determination 0.97 to 1.00) of Formula 16 compared with Formula 14 (coefficient of determination 0.06 to 0.42). On the other hand, the third peak area was obtained by calculating the sum value with a power index n of 3 or more (coefficient of determination 0.91 to 0.98) of Formula 14 compared with Formula 16 (coefficient of determination 0.10 to 0.54). The first peak area was highly correlated with the value obtained by Formula 16, which emphasizes the region with early elution time, and the third peak area was highly correlated with the value obtained by Formula 14, which emphasizes the region with late elution time. In addition, by setting the power index to 3 or more compared with 1, the effect of elution time was emphasized, resulting in a high correlation with each peak area. For the second peak area, the highest coefficient of determination of 0.98 was obtained when the power exponent n of Equation 16 was 1, but because it is in the region between the first and third peaks, the correlation decreased under conditions where the elution time was strongly emphasized. From the results in Table 5, it can be seen that the coefficient of determination between each peak area and the sum was 0.98 or more for all peaks, and that each peak area can be calculated with high accuracy by finding the sum.

[0154] [Table 5]

[0155] From the coefficient of determination between the shape normalized value and the peak division value in Table 6, the first peak area% was able to obtain a high correlation value by calculating the shape normalized value using formula 17 (coefficient of determination 0.96 to 0.99) compared to formula 15 (coefficient of determination 0.74 to 0.96). On the other hand, the third peak area% was able to obtain a high correlation whether using formula 15 (coefficient of determination 0.81 to 0.98) or formula 17 (coefficient of determination 0.90 to 0.98). Since the first peak area% and the third peak area% are inversely correlated in many cases, unlike the case of the sum value, it is considered that even the third peak area% was strongly correlated with formula 17, which emphasizes the region with early elution time. In addition, the second peak area% showed a low correlation whether using formula 15 (coefficient of determination 0.71 to 0.78) or formula 17 (coefficient of determination 0.56 to 0.75). However, since the coefficient of determination between the first and third peak area percentages and the shape-normalized value is 0.98 or more, the first and third peak area percentages can be calculated with high accuracy, and the second peak area percentage can be calculated by dividing each value from the total peak area percentage of 100. It can therefore be seen that each peak area percentage can be calculated with high accuracy by determining the shape-normalized value.

[0156] [Table 6]

[0157] Tables 7 and 8 show the CV values ​​of the summation value and the shape-normalized value, respectively. It can be seen that the CV value increases slightly as the power exponent increases for the summation value in Table 7, and the CV value increases significantly as the power exponent increases for the shape-normalized value in Table 8. From this result, it can be seen that the power exponent suitable for calculating each peak division value from the summation value or the shape-normalized value must be selected so that the coefficient of determination is high and the CV value is low. As an example, when the required coefficient of determination and CV value are 0.95 or more and less than 6%, respectively, the summation value suitable for calculating the first peak area is the inverse correlation power exponent 3 obtained from Formula 16, and the suitable formulas and power exponents are the power exponent 3 or 5 for the summation value for the third peak area, the inverse correlation power exponent 1, 3, or 5 for the shape-normalized value for the first peak area%, and the power exponent 1 or 3 and the inverse correlation power exponent 1 or 3 for the shape-normalized value for the third peak area%, respectively.

[0158] [Table 7]

[0159] [Table 8]

Claims

1. A method for analyzing an antibody contained in a sample, comprising the following steps (a) to (c): (a) adding a sample to a column packed with an insoluble carrier having an Fc-binding protein immobilized thereon, and allowing an antibody contained in the sample to be adsorbed onto the carrier; (b) eluting the antibody adsorbed on the carrier with an eluent to obtain a separation pattern of the antibody; and (c) calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times;

2. The method of claim 1 , wherein the elution time is corrected based on the variation in the elution time of a standard antibody.

3. The analytical method according to claim 1 or 2, further comprising the following steps (A) to (D): (A) before the step (a), adding a standard antibody solution to the column to adsorb the standard antibody onto the carrier, and eluting the standard antibody adsorbed onto the carrier with the elution solution to obtain a first separation pattern of the standard antibody; (B) performing step (A) again before or after steps (a) and (b) to obtain a second separation pattern of the standard antibody; (C) comparing the elution time of the standard antibody in the first separation pattern with the elution time of the standard antibody in the second separation pattern; and (D) A step of correcting the elution times of the antibodies in the separation pattern obtained in (b) based on the comparison results of (C).

4. The analytical method according to claim 3 , wherein the step (C) is a step of comparing elution times of two or more antibody points in the first separation pattern with elution times of two or more antibody points in the second separation pattern.

5. The analytical method according to claim 3 or 4, wherein the step (B) is carried out immediately before the step (a) or immediately after the step (b).

6. The analytical method according to claim 1 , wherein the sample is a body fluid.

7. A method for obtaining an indicator for detecting a disease, an onset risk thereof, a degree of progression thereof, and / or a degree of progression of aging in a subject, comprising the steps of: (a) adding a sample obtained from a subject to a column packed with an insoluble carrier having an Fc-binding protein immobilized thereon, and allowing an antibody contained in the sample to be adsorbed onto the carrier; (b) eluting the antibody adsorbed on the carrier with an eluent to obtain a separation pattern of the antibody; (c) calculating the sum of the products of the elution times of the antibodies in the separation pattern and the detection values ​​at those times;

8. The method according to any one of claims 1 to 7, wherein the antibody is a human antibody and the Fc binding protein is any one of the following polypeptides (1) to (3): (1) A polypeptide comprising the amino acid residues from the 17th to the 192nd positions of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that in the amino acid residues from the 17th to the 192nd positions, at least the 176th valine is replaced with phenylalanine; (2) A polypeptide comprising the amino acid residues from the 17th to the 192nd positions of the amino acid sequence set forth in SEQ ID NO:1, with the proviso that in the amino acid residues from the 17th to the 192nd positions, at least the valine at the 176th position is substituted with phenylalanine, and further comprising, in addition to the substitution, any one or more of substitutions, deletions, insertions, and additions of one or several amino acid residues at one or several positions, and having antibody-binding activity; (3) A polypeptide having an amino acid sequence that has 70% or more homology to the entire amino acid sequence of SEQ ID NO: 1, in which the 17th to 192nd amino acid sequences contain a substitution of valine at position 176 with phenylalanine, and which contains the amino acid sequence containing the substitution and has antibody binding activity.

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