Antibody analysis methods

Abstract

To provide a method capable of analyzing an antibody contained in a sample with high reproducibility by affinity chromatography using an insoluble carrier obtained by immobilizing Fc binding protein.SOLUTION: A separation pattern of a test antibody obtained by an affinity chromatography is corrected on the basis of a relational expression between the elution time at a plurality of characteristic points commonly existing in the separation pattern and the separation pattern of a standard antibody and an elution time difference at the plurality of characteristic points in both patterns, and then analyzed.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a method for analyzing antibodies, and in particular to a method for analyzing antibodies with high reproducibility using affinity chromatography. [Background technology]

[0002] In recent years, antibody-containing pharmaceuticals (antibody drugs) have been used to treat cancer, immune diseases, and other conditions. Antibodies used in antibody drugs are produced by culturing cells capable of expressing the antibody (e.g., Chinese hamster ovary (CHO) cells, etc.) obtained through genetic engineering techniques, followed by highly purified antibodies using techniques such as column chromatography. However, recent research has revealed that the antibodies obtained through this process are aggregates of diverse molecules due to modifications such as oxidation, reduction, isomerization, and glycosylation, raising concerns about their impact on efficacy and safety. In particular, it has been reported that the glycan structure attached to antibodies has a significant impact on the activity, kinetics, and safety of antibody drugs, making detailed analysis of the glycan structure important (Non-Patent Document 1). Furthermore, changes in the glycan structure attached to antibodies in the blood are known to occur in diseases such as rheumatism (Non-Patent Documents 2 and 3), and analyzing the glycan structure attached to antibodies may potentially enable detection of these diseases.

[0003] LC-MS analysis, which includes cleavage of glycans, is the main method used to analyze the glycan structure of antibodies (Patent Documents 1 and 2). However, this analytical method requires very complicated procedures and is time-consuming. A simpler method for analyzing the molecular structure of antibodies is affinity chromatography, which is based on the affinity between antibodies and Fc-binding proteins immobilized on an insoluble carrier, and this method allows analysis based on differences in the glycan structures bound to the Fc region of the antibody (Patent Document 3). However, the separation patterns obtained in this analysis vary greatly, making it difficult to analyze antibodies contained in a sample with high reproducibility. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-194500 A [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-099304 [Patent Document 3] WO2019 / 244901 [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 by affinity chromatography using an insoluble carrier onto which an Fc-binding protein has been immobilized. [Means for solving the problem]

[0007] As a result of extensive research to solve the above problems, the present inventors have discovered a method for analyzing antibodies contained in a sample by affinity chromatography using an insoluble carrier on which an Fc-binding protein is immobilized. When analyzing antibodies, the inventors discovered that antibodies can be analyzed with high reproducibility by comparing characteristic points obtained from the antibody separation pattern and correcting the separation pattern based on the obtained relational equation, which led to the completion of the present invention.

[0008] That is, the present invention includes the following aspects [1] to [9]. [1] A method for analyzing a test antibody contained in a sample, comprising the following steps (a) to (g): (a) adding a sample containing a standard antibody to a column packed with an insoluble carrier onto which an Fc-binding protein has been immobilized, allowing the standard antibody to be adsorbed onto the carrier, and then eluting the standard antibody adsorbed onto the carrier with an eluent to obtain a first separation pattern of the standard antibody; (b) after the step (a), adding a sample containing the test antibody to the column, allowing the test antibody to be adsorbed onto the carrier, and then eluting the test antibody adsorbed onto the carrier with the eluent to obtain a separation pattern of the test antibody; (c) performing step (a) again before or after step (b) to obtain a second separation pattern of the standard antibody; (d) selecting a plurality of feature points that are commonly present in the separation patterns of the first and second standard antibodies; (e) creating a relational expression between the elution times at the plurality of characteristic points selected in the step (d) and the differences in elution times at the plurality of characteristic points; (f) correcting the separation pattern of the test antibody obtained in step (b) based on the relational expression created in step (e); and (g) A step of analyzing the test antibody based on the separation pattern of the test antibody corrected in the step (f). [2] The analytical method according to [1], wherein the step (g) includes a step of determining a characteristic value of the separation pattern of the test antibody corrected in the step (f). [3] The analytical method according to [1] or [2], wherein step (g) comprises a step of dividing the separation pattern of the test antibody corrected in step (f) by the elution times at characteristic points that are common to the separation patterns of the standard antibody and the test antibody, and determining characteristic values ​​in the divided regions. [4] The analysis method according to any one of [1] to [3], wherein the plurality of feature points are extreme points and / or inflection points of a separation pattern. [5] The analytical method according to any one of [1] to [4], wherein the step (c) is carried out immediately before or after the step (b). [6] The analytical method according to any one of [1] to [5], wherein the sample is a body fluid. [7] The method according to any one of [1] to [6], wherein the test antibody and the standard antibody are human-derived antibodies, and the Fc-binding protein is human FcγRIIIa. [8] The method according to [7], wherein the human FcγRIIIa is any one of the following polypeptides (1) to (3): (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 substituted with phenylalanine; (2) The amino acid sequence of SEQ ID NO: 1, which contains the amino acid residues from 17th to 192nd, and in which the amino acid residues from 17th to 192nd have at least one substitution of valine at position 176 with phenylalanine, and in addition to the substitution, one or several a polypeptide further having any one or more of substitution, deletion, insertion, and addition of one or several amino acid residues at said positions, and having antibody-binding activity; (3) A polypeptide having an amino acid sequence that is 70% or more identical 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 valine at position 176 is substituted with phenylalanine, and that contains the amino acid sequence including the substitution, and that has antibody binding activity. [9] The method according to any one of [1] to [8], wherein the plurality of feature points is three or more feature points. [Effects 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.

[0010] The present invention is characterized in that, when a test antibody contained in a sample is analyzed by affinity chromatography using an insoluble carrier (FcR gel) on which an Fc-binding protein is immobilized, the separation pattern of the test antibody obtained by the affinity chromatography is corrected based on a relational expression between the elution times at multiple characteristic points that are common to the separation pattern and the separation pattern of a standard antibody, and the difference in elution times at the multiple characteristic points in both patterns, before analysis.

[0011] The separation pattern of antibodies obtained using a column packed with FcR gel may vary from measurement to measurement, even for the same sample. Blood-derived samples, in particular, may contain many components other than the antibodies that bind to the column, and the separation pattern may change due to the adsorption of unnecessary components to the column. This can result in a loss of reproducibility in the antibody separation pattern, making it difficult to compare the separation pattern with that of other samples. On the other hand, the method of the present invention can obtain a highly reproducible separation pattern by correcting for variations in the separation pattern by using a standard antibody to correct the elution time. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 shows the measurement intervals for a standard antibody and a sample (serum specimen) containing a test antibody in Example 2. [Figure 2] FIG. 1 shows the change in the separation pattern of a standard antibody before and after analysis of a sample containing a test antibody. [Figure 3] FIG. 10 is a diagram showing the relational expression used for correcting the antibody separation pattern in Example 2. [Figure 4] FIG. 1 shows (a) the results of correcting the separation pattern of a serum sample using the method described in Example 2 and (b) Comparative Example 1, and (c) the results of an uncorrected separation pattern (Comparative Example 2). [Figure 5]Figure 1 shows an example of the separation patterns of a standard sample and a serum specimen obtained by analyzing antibodies using a column packed with an Fc-binding protein-immobilized gel. DETAILED DESCRIPTION OF THE INVENTION

[0013] The present invention will be described in detail below.

[0014] In the present invention, "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 that 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 derived from a human, the Fc-binding protein may be a human Fc-binding protein. A preferred example of a human Fc-binding protein is a human Fc receptor. A human Fc receptor is a receptor that binds to a human immune system. Examples of such 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), and any of these receptors can be used as the human Fc-binding protein of the present invention. As used herein, the term "human-derived antibody" refers to an immunoglobulin having at least a human-derived Fc region. The human-derived antibody may be a human antibody, a humanized antibody, or a chimeric antibody.

[0015] Specific examples of human Fcγ receptors include polypeptides comprising at least a partial sequence of the extracellular region 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 some of the amino acid residues constituting the polypeptides have been substituted, deleted, inserted, and / or added. Among these, polypeptides comprising at least a partial sequence of the extracellular region of human FcγRIIIa and polypeptides in which some of the amino acid residues constituting the polypeptides have been substituted, deleted, inserted, and / or added are preferred as human Fcγ receptors used as human Fc-binding proteins in the present invention.

[0016] 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 such polypeptides 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 substituted with phenylalanine; (2) 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 has been substituted with phenylalanine, and further having, in addition to the substitution, any one or more of substitution, deletion, insertion, and addition 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 is 70% or more identical to the entire amino acid sequence of SEQ ID NO: 1, in which the 17th to 192nd amino acids in the amino acid sequence have a substitution of valine at position 176 with phenylalanine, and that contains the amino acid sequence including the substitution, and that has antibody binding activity.

[0017] Examples of the polypeptide described in (1) above include a polypeptide containing amino acid residues 24 to 199 of the amino acid sequence set forth 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 amino acid residue substitutions disclosed in JP 2015-086216 A, JP 2016-169167 A, JP 2017-118871 A, and WO 2019 / 083048 A.

[0018] In (3) above, "homology" means similarity or identity. Homology can be determined using an alignment program such as BLAST (Basic Local Alignment Search Tool). For example, "amino acid sequence identity" may mean identity between amino acid sequences calculated using blastp, specifically, identity between amino acid sequences calculated using blastp with default parameters. The homology may be 70% or more, 80% or more, 90% or more, or 95% or more.

[0019] 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 approximately 0 mg / L. The insoluble carrier may have a functional group (e.g., a hydroxy group) for covalently immobilizing an Fc-binding protein. Examples of insoluble carriers include carriers derived from inorganic substances such as zirconia, zeolite, silica, and coated silica; carriers derived from natural organic polymers such as cellulose, agarose, and dextran; and carriers derived from synthetic organic polymers such as polyacrylic acid, polystyrene, polyacrylamide, polymethacrylamide, polymethacrylate, and vinyl polymers.

[0020] Immobilization of an Fc-binding protein to an insoluble carrier can be achieved, for example, by utilizing functional groups present on the surface of the carrier that can be covalently immobilized to the Fc-binding protein. For example, when hydroxyl groups are present on the surface of an insoluble carrier, an activator can be used to convert the hydroxyl groups to activated groups capable of covalently binding to the Fc-binding protein, thereby covalently binding the activated groups to the Fc-binding protein. Specific examples of activators for hydroxyl groups include epichlorohydrin (which forms an epoxy group as the activated group), 1,4-butanediol diglycidyl ether (which forms an epoxy group as the activated group), tresyl chloride (which forms a tresyl group as the activated group), and vinyl bromide (which forms a vinyl group as the activated group). Alternatively, the hydroxyl groups can be converted to amino groups, carboxyl groups, or the like, and then activated by the action of an activator. 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 acids (which form a halogenated acetyl group as the activating group).

[0021] The antibody to be analyzed in the present invention may be an immunoglobulin containing at least a glycosylated Fc region, and may also contain other regions. Furthermore, 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.

[0022] The origin of the antibody is not particularly limited; it 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). Examples of antibodies include artificially structurally modified antibodies, such as bispecific antibodies, fusion antibodies of an Fc region with another protein, and Fc region-drug conjugates (ADCs). Furthermore, antibody drugs are also encompassed by the term "antibody" in the present invention. Examples of antibody drugs include infliximab, an anti-TNF-α (tumor necrosis factor α) antibody, tocilizumab, an anti-IL-6 (interleukin 6) antibody, and trastuzumab, an antibody against the oncogene HER2. In particular, the analytical method of the present invention is suitable for analyzing antibodies derived from body fluids, as described below.

[0023] 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. Examples of the sample include 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. Examples of the body fluid include, for example, a body fluid obtained from a subject, Blood samples such as blood (whole blood), diluted blood, serum, plasma, cerebrospinal fluid, umbilical cord blood, and apheresis; Samples that may contain blood-derived components, such as urine, saliva, semen, feces, sputum, amniotic fluid, and peritoneal fluid; Samples that may contain tissue fragments (tissue slices) or cells from liver, lung, spleen, kidney, skin, tumor, lymph node, etc.; samples which may contain antibodies separated therefrom or contained therein; The subject simply refers to a human individual who is the subject of measurement or the subject of risk detection. The subject is not particularly limited as long as a sample derived from the subject 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 or may not be a healthy individual.

[0024] The analytical method of the present invention is characterized by analyzing a test antibody contained in a sample by a method comprising the following steps (a) to (g). 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 (g): (a) adding a sample containing a standard antibody to a column packed with an insoluble carrier onto which an Fc-binding protein has been immobilized, allowing the standard antibody to be adsorbed onto the carrier, and then eluting the standard antibody adsorbed onto the carrier with an eluent to obtain a first separation pattern of the standard antibody; (b) after the step (a), adding a sample containing the test antibody to the column, allowing the test antibody to be adsorbed onto the carrier, and then eluting the test antibody adsorbed onto the carrier with the eluent to obtain a separation pattern of the test antibody; (c) performing step (a) again before or after step (b) to obtain a second separation pattern of the standard antibody; (d) selecting a plurality of feature points that are commonly present in the separation patterns of the first and second standard antibodies; (e) creating a relational expression between the elution times at the plurality of characteristic points selected in the step (d) and the differences in elution times at the plurality of characteristic points; (f) correcting the separation pattern of the test antibody obtained in step (b) based on the relational expression created in step (e); and (g) A step of analyzing the test antibody based on the separation pattern of the test antibody corrected in the step (f).

[0025] Each step will be described in detail below.

[0026] <Separation Pattern Obtaining Step (Steps (a) to (c) above)> The steps (a) to (c) are collectively referred to as the "separation pattern obtaining step."

[0027] The step (c) may be performed before or after the step (b). It is preferable that the step (c) is performed close to the step (b), since this can improve the accuracy of correction of the separation pattern of the test antibody. The interval between the step (b) and the step (c) (i.e., the number of analyses of any target between the step (b) and the step (c)) may be 10 times or less, 7 times or less, 5 times or less, 3 times or less, 2 times or less, or 1 time or less, or may be zero times (i.e., the step (c) is performed immediately before or immediately after the step (b)). The step (c) may particularly be performed immediately before or immediately after the step (b). An example of the case where the step (c) is performed immediately before or immediately after the step (b) is when the step (b) and the step (c) are performed alternately.

[0028] The separation pattern acquisition process includes: a step of adding an antibody-containing sample to a column (hereinafter also referred to as an "FcR column") packed with an insoluble carrier (hereinafter also referred to as an "FcR gel") onto which an Fc-binding protein has been immobilized, and adsorbing the antibody onto the carrier (hereinafter also referred to as an "adsorption step"); a step of eluting the test antibody adsorbed on the carrier using an elution solution to obtain a separation pattern of the test antibody (hereinafter also referred to as an "elution step"); Includes:

[0029] The antibody-containing sample used in the separation pattern obtaining step is a sample containing a standard antibody in the steps (a) and (c), and is a sample containing a test antibody in the step (b).

[0030] In the adsorption step, a sample containing an antibody is added to an FcR column, and the antibody is adsorbed onto the FcR gel.

[0031] The standard antibody added to the FcR column in the adsorption step 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 preferably derived from the same source as the test antibody. For example, when the test antibody is a human antibody, the standard antibody is preferably derived from human blood, human myeloma, or human cultured cells, or from a combination of two or more organisms including humans.

[0032] The sample containing the test antibody that is added to the FcR column in the adsorption step may be a mixed solution containing multiple types of antibody molecules. Specifically, the sample containing the test antibody may be a mixed solution containing multiple types of antibody molecules with different sugar chain structures. More specifically, the sample containing the test antibody may be a mixed solution containing multiple types of antibody molecules with different sugar chain structures attached to the Fc region.

[0033] When a body fluid is used as a sample containing a test antibody, the body fluid may be added to the column as is, or may be subjected to appropriate pretreatment before being added to the column. Pretreatment may be performed by methods commonly used by those skilled in the art, such as centrifugation or column purification. 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. Examples of such liquid medium include the equilibration liquid described below. In the present invention, the term "body fluid" also encompasses those that have been subjected to such pretreatment, solvent exchange, etc.

[0034] An antibody-containing sample can be added to an FcR column using a liquid delivery device such as a pump (hereinafter, adding a liquid to a column is also referred to as "delivering a liquid to the column"). The adsorption step conditions, such as the amount of antibody-containing sample added (delivered), the type of liquid phase, the liquid phase delivery rate, and the column temperature, are not particularly limited as long as the antibody is adsorbed to the FcR gel. The adsorption step conditions 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 column scale. An example of the liquid phase is the equilibration liquid described below. For example, when the column has an inner diameter of 4.6 mm, the delivery rate 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 delivery rate may be set, for example, so as to be proportional to the square of the inner diameter of the column. The temperature of the FcR column may be set appropriately within the range of, for example, 0°C or higher and 50°C or lower.

[0035] An equilibration step in which an equilibration solution is added (pumped) to the column before and / or after the adsorption step may be performed. Performing an equilibration step after the adsorption step is particularly preferable because it allows for the removal from the column of antibodies that do not bind to the Fc-binding protein or 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 herein as the "unadsorbed fraction." Specifically, the unadsorbed fraction is the fraction in the region from when the sample is added (pumped) to when the detected peak reaches its minimum value during the equilibration step in the separation pattern described below. It is preferable for the unadsorbed fraction to be separated in elution time from the peak detected after the addition of the eluent in the separation step described below, as this increases separation accuracy. In particular, a constant value detected between the unadsorbed fraction and the peak region detected after the addition of the eluent indicates that the unadsorbed fraction has been sufficiently removed from the column by the equilibration step. The term "constant value" as used herein does not necessarily mean a constant value, but also encompasses a state in which the detected value changes with a constant gradient.

[0036] An example of an equilibration solution is an aqueous buffer solution. Specific examples of aqueous buffer solutions 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 selected appropriately depending on various conditions, such as the pH of the buffer solution. Examples of buffer solution components include phosphoric acid, acetic acid, formic acid, MES (2-Morpholinoethanesulfonic acid), MOPS (3-Morpholinopropanesulfonic acid), citric acid, succinic acid, glycine, and piperazine. A salt such as sodium chloride or potassium chloride may also be added to the buffer solution. 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.

[0037] The elution step is a step in which the antibody adsorbed to the insoluble carrier in the adsorption step is eluted using an eluent to obtain a separation pattern of the antibody. Specifically, the antibody adsorbed to the FcR gel can be eluted by adding (delivering) the eluent to the FcR column. The conditions for the elution step, such as the type of eluent, the delivery format of the eluent, the liquid phase delivery rate, and the FcR column temperature, are not particularly limited as long as the antibody is separated in the desired manner, e.g., the desired separation pattern is obtained. The conditions for 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. The eluent used may be one that weakens the affinity between the antibody and the Fc-binding protein. Examples of eluents include aqueous buffer solutions with a more acidic pH than the liquid phase prior to elution (e.g., the equilibration solution used in the adsorption step, if an equilibration step is performed in that step). As a specific example, if the liquid phase before elution (e.g., the equilibration solution) is a weakly acidic to weakly alkaline buffer solution with a pH of 5.0 to 8.0, an acidic buffer solution with a pH of 2.5 to 4.5 can be used as the eluent. The components of the buffer can be selected appropriately depending on various conditions, such as the pH of the buffer. Examples of buffer components include phosphoric acid, acetic acid, formic acid, MES, MOPS, citric acid, succinic acid, glycine, and piperazine. The eluent delivery method may be linear gradient elution, in which the ratio of the eluent in the liquid phase is continuously changed, or stepwise elution, in which the ratio is gradually changed, or a combination thereof. The gradient may be set, for example, so that the ratio of the eluent in the liquid phase increases from 0% (v / v) to 100% (v / v) over 10 to 60 minutes, 15 to 50 minutes, or 20 to 40 minutes. For example, if the column has an inner diameter of 4.6 mm, the liquid flow rate 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 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, within the range of 0°C to 50°C.

[0038] The antibody eluted from the FcR 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. The antibody separation pattern can be a chromatogram obtained during antibody elution. The interval at which measurement data is acquired by the detector may be any time interval. However, since a longer time interval reduces the accuracy with which the original characteristics of the antibody separation pattern are reflected in the chromatogram, the time interval is preferably 1 minute or less, more preferably 10 seconds or less, and particularly preferably 2 seconds or less.

[0039] The elution step allows the antibody contained in the sample to be obtained in a separated form. The separated antibody may be obtained, for example, as an elution fraction containing the antibody. That is, the separated antibody is obtained by collecting the elution fraction containing the separated antibody. The elution fraction can be collected, for example, by a conventional method. Specifically, the elution fraction can be collected, 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 from the elution fraction, for example, by a conventional method. Specifically, the separated antibody can be recovered from the elution fraction by, for example, a known method used for separating and purifying proteins.

[0040] <Separation Pattern Correction Step (Steps (d) to (f) above)> The steps (d) to (f) are collectively referred to as the "separation pattern correction step."

[0041] The separation pattern correction step includes: a step of selecting a plurality of feature points that are commonly present in the separation patterns of the standard antibodies measured multiple times (hereinafter also referred to as a "feature point selection step"); a step of creating a relational expression between the elution times at the plurality of feature points selected in the feature point selection step and the differences in elution times at the plurality of feature points (hereinafter also referred to as a "relational expression creation step"); a step of correcting the separation pattern of the test antibody based on the relational expression (hereinafter also referred to as a "correction step"); Includes:

[0042] The feature point selection step is a step of selecting multiple feature points that exist in common in the separation pattern of the standard antibody that serves as a reference (the first separation pattern obtained in step (a)) and the separation pattern of the standard antibody to be corrected (the second separation pattern obtained in step (c)), both of which are obtained in the separation pattern acquisition step described above. The separation pattern of the standard antibody that serves as a reference may be the separation pattern of the standard antibody obtained in any measurement prior to step (b).

[0043] As used herein, the term "feature point" refers to a point that has a common feature between the separation pattern of a standard antibody that serves as a reference and the separation pattern of a standard antibody that is the target of correction. Feature points may be selected visually from the separation pattern, but it is preferable to extract them automatically from the separation pattern using a programming language, as this allows for automated selection of feature points and reduces differences between operators. Feature points may be selected, for example, based on an unprocessed separation pattern (raw data), or based on an approximation curve obtained from the separation pattern. Furthermore, feature points may be selected from the entire separation pattern, but it is preferable to select feature points from the region of the separation pattern that is related to antibody elution.

[0044] Preferred aspects of the feature points include extreme points obtained by differentiating the separation pattern (i.e., local maxima (corresponding to peak tops of the separation pattern) and local minima (corresponding to valleys of the separation pattern)), and inflection points obtained by second-order differentiation of the separation pattern. These points are preferred as feature points because they are easy to extract in data processing and allow the separation pattern to be corrected with high reproducibility.

[0045] The number of selected feature points is not particularly limited as long as it is two or more. The number of selected feature points may be, for example, two or more, three or more, four or more, or five or more. The number of selected feature points may particularly be three or more. The number of selected feature points may be, for example, the number of peaks to be detected or more.

[0046] The relational equation creation step is a step of creating a relational equation between the dissolution times at the plurality of feature points selected in the feature point selection step and the differences in dissolution times at the plurality of feature points (i.e., the variations in dissolution times at the plurality of feature points). The relational equation can be obtained, for example, by creating a plot diagram with the dissolution times and the differences as axes and then connecting the plots to form a line or an approximation line. Examples of methods for obtaining an approximation line include, but are not limited to, the moving average method and the least squares method. Examples of approximation lines include those based on approximations such as linear approximation, polynomial approximation, power approximation, and logarithmic approximation.

[0047] The elution time may be the value of the time elapsed from any time point to the time of elution. For example, the elution time may be the time from the addition (delivery) of a sample to the FcR column, the time from the addition (delivery) of an eluate to the FcR column, or the time from the start of analysis. Furthermore, as long as the elution time reflects the time elapsed from any time point to the time of elution, it may be a value calculated from the above-mentioned elution times, such as a value obtained by subtracting a specific time from the specified time, a value obtained by exponentiating the specified time, or a value obtained by multiplying the specified time by a coefficient.

[0048] The correction step is a step of correcting the separation pattern of the test antibody (specifically, the elution time in the separation pattern of the test antibody) based on the relational equation obtained in the aforementioned relational equation creation step. Specifically, in the correction step, the elution time in the separation pattern of the test antibody is corrected based on the relational equation obtained in the aforementioned relational equation creation step. Correction may be performed, for example, by adding or subtracting the elution time in the separation pattern to be corrected based on the correction amount (difference in elution time) obtained from the relational equation. The correction step makes it possible to correct for fluctuations in the separation pattern of the test antibody (specifically, elution time) that may occur due to, for example, long-term and / or repeated use of the column.

[0049] <Analysis step (step (g) above)> The analysis step is a step of analyzing the test antibody based on the separation pattern of the test antibody corrected in the separation pattern correction step described above. Examples of analysis of the test antibody include obtaining a value characteristic of the separation pattern of the test antibody (hereinafter also referred to as a "feature value"). Specifically, in the analysis step, for example, the separation pattern of the test antibody may be divided at specific elution times, and the feature values ​​of the divided regions may be obtained. The separation pattern of the standard antibody may also be referenced when obtaining the feature values. The elution time of the separation pattern of the standard antibody may be corrected as appropriate. The correction of the elution time in the separation pattern of the standard antibody can be performed in the same manner as the correction of the elution time in the separation pattern of the test antibody in the correction step.

[0050] The specific elution time (hereinafter also referred to as "division time") may be a specific elution time in the separation pattern of the test antibody. Furthermore, when the separation pattern of the test antibody is unclear, the division time may be the elution time at a characteristic point in the separation pattern of a standard antibody with a clear separation pattern, or a time obtained by adding or subtracting an arbitrary value from the elution time. The division time may be determined for each measurement of the test antibody, or the average or median of division times determined from the separation patterns of multiple test antibodies or standard antibodies may be used as the fixed division time.

[0051] It is preferable to use the elution time at a characteristic point that is common to the separation patterns of the standard antibody or the test antibody as the division time and obtain the characteristic value determined from the area divided by that division time, as this allows for highly reproducible analysis of the test antibody.

[0052] The feature value is not particularly limited as long as it is a value that characterizes the separation pattern, such as an extreme value (maximum or minimum value) in the region divided by the division time, the elution time at which the extreme value is reached, a value at an inflection point in the divided region or the elution time at the inflection point, the number or height of peaks present in the divided region, or the area of ​​the divided region. It is preferable to use the sum of the products of the elution time of the antibody in the divided region and the detected value at that time as the feature value, as this allows for highly reproducible analysis of the test antibody. The detected value may be, for example, a value obtained by the detector described above, as is, or may be used after appropriate baseline correction or the like.

[0053] In the preferred embodiment, the region to be summed can be the region from an arbitrary analysis start time to an arbitrary analysis end time. The analysis start time is not particularly limited and can be, for example, the time when the sample is added (flowed) to the FcR column, or the time when the eluate is added (flowed) to the FcR column. The analysis start time is preferably set between the peak of the non-adsorbed fraction that does not bind to the FcR gel packed in the column and the time when a separation peak derived from the antibody bound to the carrier is obtained. The analysis end time is also not particularly limited and can be, for example, the time when the measurement is completed or the time when the ratio of the eluate added (flowed) to the FcR column reaches 100%. When an equilibration step is performed before the adsorption step, the analysis end time is preferably set between the time when a separation peak derived from the antibody bound to the FcR gel packed in the FcR column is obtained and the time when the equilibration solution is switched to after washing the FcR column with the eluate, and the time when a change in the detection value that occurs when the equilibration solution is switched to the equilibration solution is obtained.

[0054] The sum of the products of the antibody elution times and the detection values ​​at those times may be the sum of the products of the detection values ​​for elution times at regular time intervals, the sum of the products of the detection values ​​for elution times at irregular time intervals, or the sum (integral) of the products of the detection values ​​for continuous elution times. 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 a longer time interval reduces the accuracy in reflecting the characteristics of the antibody separation pattern in numerical values, it is preferable to calculate the sum at time intervals of one 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 it may be a sum calculated from an equation expressing the relationship between elution times and detection values ​​as a function using an approximation curve, or it may be a sum obtained by integrating the approximation curve.

[0055] For example, in the separation pattern of an antibody that binds strongly to the FcR gel, high detection values ​​are observed in the region with slow elution times, and therefore the sum of the products of the elution times and the detection values ​​at those elution times (characteristic value) is high.On the other hand, in the separation pattern of an antibody that binds weakly to the carrier, high detection values ​​are observed in the region with fast elution times, and therefore the sum of the products of the elution times and the detection values ​​at those elution times (characteristic value) is low.

[0056] 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 antibodies that bind strongly to the FcR gel, and a high value is calculated for a separation pattern of antibodies that bind weakly to the carrier (inverse correlation). Therefore, when it is desired to obtain data that emphasizes the amount of antibodies that bind strongly to the FcR gel, the sum of the products of the elution time of the antibody and the detection value at that elution time may be used as the feature value.

[0057] Alternatively, the sum of the products of the elution time and the detection values ​​at that elution time divided by the sum of the detection values ​​(also referred to as the "shape normalized value" in this specification) may be used as the feature value.

[0058] Furthermore, the antibody elution time may be determined by exponentiating the elution time. That is, the sum of the products of the antibody elution time and the detection value at that elution time may be used as the characteristic value. The exponentiation increases the influence of the elution time on the sum or shape-normalized value, allowing for a more significant evaluation of differences in antibody binding ability to the carrier. On the other hand, the exponentiation increases the coefficient of variation (CV), which indicates measurement reproducibility. Therefore, the number of exponentiations (power exponent) is preferably 10 or less, and more preferably 7 or less.

[0059] On the other hand, when it is desired to obtain data that emphasizes the amount of antibody that weakly binds to the FcR gel, 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 may be used as the feature value, or the sum of the products of the value obtained by exponentiating the difference between a specific time and the elution time of the antibody and the detection value at that elution time may be used as the feature value.

[0060] The peak division values ​​may be converted from the summation value and / or the shape-normalized value. The term "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 %, particularly peak area and peak area %. To convert each peak division value, it is advisable to determine a relationship between the summation value and / or the shape-normalized value. It is highly correlative and preferable to convert each peak area from the summation value and each peak area % from the shape-normalized value. Each peak division value may be converted directly or indirectly from the summation value and / or the shape-normalized value.

[0061] The feature values ​​may also 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, if the feature values ​​are affected by the age of the subject, the obtained feature values ​​may be corrected based on the age of the subject before being used in the detection step described below. The feature corrected based on the age of the subject may be used, for example, to detect the risk of symptoms other than those caused by aging.

[0062] The peak regions identified in this way can be used to distinguish between differences in the sugar chain structures of N-linked sugar chains bound to antibodies contained in a sample (Patent Document: WO2019 / 244901). Examples of distinguishable sugar chain structures include sialic acid, galactose, mannose, N-acetylglucosamine, and fucose, which constitute N-linked sugar chains, and examples of sugar chain structures include G0, G0F, G1, G0F+GN, G1Fa, G1Fb, G1F+GN, G2, G2F, G1F+SA, G2F+SA, G2F+2SA, G2F+GN, G2+SA, G2+2SA, S1, S2, and S3.

[0063] The analytical method of the present invention may further include the following step (h): (h) A step of 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 analysis results obtained in step (g) (hereinafter also referred to as the "detection step").

[0064] Specifically, when the sample is obtained from a subject, the analytical method of the present invention may include step (h) above. When the analytical method of the present invention includes step (h) above, it is possible 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 in a subject who provided a sample containing a test antibody. 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 containing a test antibody) (hereinafter, also simply referred to as the "detection method of the present invention").

[0065] <Detection step (step (h) above)> This is a step of detecting risks 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 analysis results (e.g., characteristic values) obtained in step (g) as an index.

[0066] The following describes a case where risk in a subject is detected using a feature value as an index, but the same description can also be applied to a case where any analysis result is used as an index.

[0067] Examples of diseases include those affected by immune cell activity (e.g., damage 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.

[0068] 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, among others.

[0069] Examples of autoimmune diseases include Guillain-Barre 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, Hashimoto's disease, primary hypothyroidism, idiopathic Addison's disease, type 1 diabetes, and chronic discoid erythema. These include erythematosus, localized scleroderma, pemphigus, pustular psoriasis, plaque psoriasis, pemphigoid, herpes gestationis, linear IgA bullous dermatosis, epidermolysis bullosa acquisita, alopecia areata, vitiligo, Sutton nevus, Harada disease, autoimmune optic neuropathy, autoimmune inner ear disease, idiopathic azoospermia, recurrent abortion, rheumatoid arthritis, systemic lupus erythematosus (SLE), antiphospholipid syndrome, polymyositis, dermatomyositis, scleroderma, Sjogren's syndrome, IgG4-related disease, vasculitis syndrome, and mixed connective tissue disease. Examples of autoimmune diseases include rheumatoid arthritis and Sjogren's syndrome, among others.

[0070] Examples of infectious diseases include bacterial infections, fungal infections, parasitic protozoan infections, parasitic helminth infections, and viral infections. Examples of bacterial infections include streptococci, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Listeria, Neisseria meningitidis, Neisseria gonorrhoeae, pathogenic Escherichia coli, Klebsiella, Proteus, Bordetella pertussis, Pseudomonas aeruginosa, Serratia, Citrobacter, Acinetobacter, Enterobacter, and Mammalian. Examples of infectious diseases include infections caused by various bacteria such as Mycoplasma, Clostridium, Rickettsia, and Chlamydia; tuberculosis, nontuberculous mycobacteria, cholera, plague, diphtheria, dysentery, scarlet fever, anthrax, syphilis, tetanus, leprosy, Legionella pneumonia, leptospirosis, Lyme disease, tularemia, and Q fever; and examples of fungal infections include Aspergillus, Candida, Cryptococcus, tinea, Histoplasmosis, and Pneumocystis pneumonia (Pneumocystis carinii pneumonia). Examples of parasitic protozoan infections include amoebic dysentery, malaria, toxoplasmosis, leishmaniasis, and cryptosporidium infection. Examples of parasitic helminth infections include echinococcus, schistosomiasis Japanese, filariasis, ascariasis, and diphyllobothriasis.Examples of viral infections include influenza, viral hepatitis, viral meningitis, viral gastroenteritis, viral conjunctivitis, acquired immunodeficiency 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), and novel coronavirus disease (COVID-19). Examples of infectious diseases include 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 (swimming pool fever), Marburg hemorrhagic fever, hemorrhagic fever with renal syndrome, Lassa fever, mumps, West Nile fever, herpangina, and Chikungunya fever. The infectious disease may be, for example, an opportunistic infection.

[0071] 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, rheumatism, glomerulonephritis, hypersensitivity pneumonitis, allergic bronchopulmonary aspergillosis (ABPA), contact dermatitis, allergic encephalitis, transplant rejection, tuberculous cavities, and epithelioid cell granuloma.

[0072] 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, capillaritis, 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.), and digestive inflammation (esophagitis, gastritis, gastroenteritis). Examples of age-related diseases include inflammatory bowel disease (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, presitis, 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.

[0073] In the detection step, 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 may be detected using the feature value as an index. That is, the feature value may be considered to be data used as an index 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, 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 can be detected using a feature value determined based on a separation pattern obtained by separating an antibody obtained from the subject (test antibody) using an FcR column 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 disease progression, and / or the degree of aging in a subject using a feature value obtained by separating a test antibody using an FcR column as an index. In this specification, 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 are collectively referred to simply as "risk." Furthermore, in this specification, "risk detection" and "risk assessment" may be used synonymously.

[0074] Detection of the risk of onset in a subject includes detection of whether or not the subject is at risk of onset (qualitative detection) and detection of whether or not the subject is at high risk (quantitative detection).

[0075] The detection of the presence or absence of a disease involves determining whether or not a subject is currently at risk of developing a disease. Examples of such detection methods include detection of whether a subject is likely to currently develop a disease (qualitative detection), and detection of whether a subject is likely to currently develop a disease (quantitative detection).

[0076] Detection of the risk of developing a disease can be done by detecting whether or not a subject is likely to develop a disease in the future or if it does develop, the disease will become severe (qualitative detection), or by detecting whether or not a subject is likely to develop a disease in the future or if it does develop, the disease will become severe (quantitative detection).

[0077] Detection of the degree of progression of a disease includes detection (quantitative detection) of whether the degree of progression (for example, severity) of the current disease in a subject is large or small.

[0078] Examples of detecting the degree of progression of aging include detecting whether the current degree of progression (for example, severity) of aging in a subject is large or small (quantitative detection).

[0079] That is, "a subject is at risk of developing" may mean, for example, that the subject may currently have the disease, that the subject may develop the disease in the future, and / or that if the subject develops the disease in the future, the disease may become severe. On the other hand, "a subject is not at risk of developing" may mean, for example, that the subject is not currently at risk of developing the disease, that the subject is not likely to develop the disease in the future, and / or that if the subject develops the disease in the future, the disease may not become severe.

[0080] Furthermore, "a high risk of developing a disease in a subject" may mean, for example, that the subject is likely to currently have a disease, that the subject is likely to develop a disease in the future, that the subject is likely to develop a disease if the subject develops a disease in the future, that the disease will likely become severe if the subject develops a disease in the future, that the subject's current disease is rapidly progressing, and / or that the subject is currently aging rapidly. On the other hand, "a low risk of developing a disease in a subject" may mean, for example, that the subject is unlikely to currently have a disease, that the subject is unlikely to develop a disease in the future, that the subject is unlikely to develop a disease if the subject develops a disease in the future, that the subject's current disease is slowly progressing, and / or that the subject is currently aging slowly.

[0081] The detection step can be carried out, for example, using the magnitude of a feature value as an index. The magnitude of the feature value can be determined, for example, by comparing the feature value obtained in the analysis step with a predetermined threshold. In other words, the detection step may include, for example, a step of comparing the feature value obtained from the separation pattern with a threshold.

[0082] That is, "a high feature value" may mean, for example, that the feature value is high relative to a threshold. Furthermore, "a high feature value relative to a threshold" may mean, for example, that the feature value is equal to or greater than the threshold, that the feature value exceeds the threshold, or that the feature value is statistically significantly higher than the threshold. Specific examples of "a high feature value relative to a threshold" include a feature value that 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 of the threshold.

[0083] On the other hand, "a low feature value" may mean, for example, that the feature value is low relative to a threshold. Furthermore, "a low feature value relative to a threshold" may mean, for example, that the feature value is equal to or less than the threshold, that the feature value is less than the threshold, or that the feature value is statistically significantly lower than the threshold. Specific examples of "a low feature value relative to a threshold" include feature values ​​that are 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, and 0.9 times or less than the threshold. Examples of the ratio include 5 times or less, 0.4 times or less, and 0.3 times or less.

[0084] The feature value may be divided into a risk range based on, for example, a threshold value. The feature value may be divided into a non-risk range based on, for example, a threshold value. Specifically, the feature value may be divided into a risk range and a non-risk range based on, for example, a threshold value. The "risk range" may mean a range in which the subject is likely to be at risk for the feature value. The "non-risk range" may mean a range in which the subject is likely to be at no risk for the feature value. In other words, if the feature value 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 is in the non-risk range, it may be detected that the subject is at no risk or at low risk.

[0085] Note that "detecting whether a subject is at risk, absent, high, or low when a specific feature value meets a certain criterion (e.g., low or high, or within a specific range)" means detecting whether a subject is at risk, absent, high, or low at least within the range that meets the criterion, and does not require that risk be detected in a subject that does not meet the criterion. However, in one embodiment, "detecting whether a subject is at risk, absent, high, or low when a specific feature value meets a certain criterion (e.g., low or high, or within a specific range)" may also detect whether a subject is at risk, absent, present, low, or high, respectively, within the range that does not meet the criterion.

[0086] The threshold can be appropriately set by a person skilled in the art depending on various conditions, such as the type of feature value and the desired accuracy of determination. The threshold may be set for each symptom to be determined, such as disease or aging. The means for determining the threshold is not particularly limited. The threshold can be determined, for example, according to a known method used in data analysis for dividing a population into two groups.

[0087] The threshold can be determined, for example, based on a feature value determined from the separation pattern of a test antibody obtained from a control subject (herein, the separation pattern of a test antibody obtained from a control subject is also referred to as a "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. In other words, the detection step may involve, for example, comparing the feature value with the feature value of the control separation pattern.

[0088] The control subject may be a positive control or a negative control. A "positive control" may refer to a subject who can be detected as having or being at high risk. A "negative control" may refer to a subject who can be detected as having no or low risk. Positive controls include individuals who are suffering from or have previously suffered from the diseases exemplified above (particularly the same disease as the target disease for risk detection), individuals who have progressed in aging, and individuals with a combination thereof. Negative controls include individuals who are not suffering from or have never suffered from the diseases exemplified above (particularly the same disease as the target disease for risk detection), individuals who have not progressed in aging, and individuals with a combination thereof. The threshold may be determined solely based on the feature value determined by analyzing the positive control, solely based on the feature value determined by analyzing the negative control, or based on the feature value calculated by analyzing both the positive and negative controls. The threshold is typically determined based on the feature value determined by analyzing both the positive and negative controls. The number of positive and negative controls is not particularly limited as long as a threshold that enables risk determination with the desired accuracy is obtained. The number of positive controls and negative controls may each be one, two, or more. The number of positive controls and negative controls may each typically be multiple. The number of positive controls and negative controls may each 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 each be, for example, 10,000 or less, 1,000 or less, or 100 or less.

[0089] When determining the threshold based solely on the feature value determined by analyzing a positive control, the threshold may be set to, for example, a value selected from the range of feature values ​​determined by analyzing multiple positive control individuals, e.g., the average value. Alternatively, the threshold may be determined so that a predetermined percentage of the positive control falls within the risk range in the distribution of feature values ​​determined by analyzing multiple positive control individuals. The predetermined percentage may be, for example, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, or 100%.

[0090] When determining the threshold based solely on the feature values ​​determined by analyzing negative controls, the threshold may be set to, for example, a value selected from the range of feature values ​​determined by analyzing multiple negative control individuals, e.g., the average value. Alternatively, the threshold may be determined so that a predetermined percentage of the negative controls falls within the non-risk range in the distribution of feature values ​​determined by analyzing multiple negative control individuals. The predetermined percentage may be, for example, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, or 100%.

[0091] When determining the threshold based on both the feature values ​​obtained by analyzing a positive control and the feature values ​​obtained by analyzing a negative control, the threshold may be determined, for example, so that a predetermined percentage of the positive controls falls within the risk range and a predetermined percentage of the negative controls falls within the non-risk range. It is preferable that both the percentage of positive controls falling within the risk range and the percentage of negative controls falling within the non-risk range are high. These percentages 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 percentages, the threshold may be set so that one of the percentages is preferentially increased depending on various conditions, such as the intended use of the detection results according to the present invention. For example, to reduce the false negative rate, the threshold may be set so that the percentage of positive controls falling within the risk range is preferentially increased.

[0092] The threshold may be determined, for example, using software. For example, statistical analysis software may be used to determine a threshold that allows for the most appropriate statistical discrimination between the negative control and the positive control. Examples of such software include statistical analysis software such as "R."

[0093] The control subject may also be the target subject itself. That is, for example, a risk in a subject may be detected using a change in a feature value in the subject as an index. As used herein, a "high feature value" may also encompass an increase in the feature value. As used herein, an "increased feature value" may specifically mean that the feature value has increased compared to a previous value. As used herein, a "low feature value" may also encompass a decrease in the feature value. As used herein, a "decreased feature value" may specifically mean that the feature value has decreased compared to a previous feature value. That is, a previous feature value may also be used as the threshold. As used herein, a "past feature value" refers to a feature value of a test antibody 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.

[0094] The fluctuation of the characteristic value in the subject may be used as an index to detect an increase or decrease in the risk in the subject. As used herein, "being at or at 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.

[0095] In this specification, "obtaining a feature value and using it as an index for risk detection" is not limited to obtaining the feature value itself and using it as an index for risk detection, but also includes obtaining another value that reflects the feature value and using it as an index for detection.

[0096] The risk detection result may be used as an indicator for determining whether to implement a risk-reducing treatment (hereinafter also referred to as "risk reduction treatment") on a subject. In other words, by performing this detection process, an indicator for determining whether to implement risk reduction treatment on a subject can be obtained. That is, for example, if this detection process detects that a subject is at risk or at high risk, a decision may be made to implement risk reduction treatment on the subject. This detection process may be used, for example, alone or in combination with other means, as an indicator for determining whether to implement risk reduction treatment on a subject. For example, for a symptom detected as at risk or at high risk in a subject by this detection process, a definitive diagnosis may be made by other means, and then a decision may be made to implement risk reduction treatment on the subject. The risk reduction treatment may be a medical or 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 be, for example, a method for preventing or treating a symptom such as a disease or aging, which includes a step of implementing prevention or treatment for the subject when the subject is detected as being at risk or at high risk by this detection step. Specifically, prevention or treatment may be implemented for the symptom for which the subject is detected as being at risk or at high risk by this detection step. Prevention or treatment can be implemented, for example, by a general means for each symptom (e.g., medication or surgery). [Example]

[0097] 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.

[0098] Example 1 Preparation of an 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 described below to prepare an FcR9_F column. In FcR9_F_Cys (SEQ ID NO: 2), the portion from the 1st methionine (Met) to the 22nd alanine (Ala) is an improved PelB signal peptide (an oligopeptide consisting of the 1st to 22nd amino acid residues of UniProt No. P0C1C1, except that the 6th proline has been replaced with a serine), the portion from the 24th glycine (Gly) to the 199th glutamine (Gln) is 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 of SEQ ID NO: 1), and the portion from the 200th glycine (Gly) to the 207th glycine (Gly) is a cysteine ​​tag sequence. Furthermore, the FcR9_F is a polypeptide that contains the following amino acid substitutions among the amino acid residues constituting a polypeptide consisting of the extracellular region of human FcγRIIIa (amino acid residues from glycine at position 17 to glutamine at position 192 in the amino acid sequence set forth in SEQ ID NO: 1): Val27Glu (this notation indicates that valine at position 27 in SEQ ID NO: 1 (corresponding to position 34 in SEQ ID NO: 2) has been substituted with glutamic acid; the same applies below), Phe29Ile, Tyr35Asn, Gln48Arg, Phe75Leu, Asn92Ser, Val117Glu, Glu121Gly, Phe171Ser, and Val176Phe.

[0099] (1) After activating the hydroxyl groups on the surface of 2 mL of hydrophilic vinyl polymer for separation (Tosoh Corporation: liquid chromatography packing material) with iodoacetyl groups, An FcR9_F-immobilized gel was obtained by reacting 4 mg of FcR9_F_Cys obtained by the method of Publication No. 24.

[0100] (2) 0.8 mL of the FcR9_F-immobilized gel prepared in (1) was packed into a φ4.6 mm × 50 mm stainless steel column to prepare an FcR9_F column.

[0101] Example 2: Study of the method for correcting the dissolution time (1) A standard antibody solution was prepared by passing human myeloma plasma-derived IgG1 (mIgG1, Sigma) through a 0.2 μm filter (Merck Millipore). The protein concentration in the solution was measured using a NanoDrop ultra-microspectrophotometer (Thermo Fisher Scientific).

[0102] (2) Blood samples were collected from subjects who provided informed consent and centrifuged to obtain serum. The serum was diluted 10-fold with phosphate buffered saline (PBS) (pH 7.4) and then passed through a 0.2 μm filter (Merck Millipore) to prepare serum samples.

[0103] (3) 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 the "equilibration solution"), after which 10 μL of the standard antibody solution prepared in (1) was added at a flow rate of 1.2 mL / min. 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 reaching 100% eluate in 11 minutes) using 10 mM citrate buffer (pH 4.5) containing 500 mM sodium chloride (hereinafter also referred to as the "elution solution"). The absorbance (280 nm) of the eluate was measured using a UV detector at 1 / 5-second intervals to obtain a separation pattern.

[0104] (4) A separation pattern was obtained in the same manner as in (3), except that the serum sample prepared in (2) was used as the solution applied to the FcR9_F column.

[0105] (5) Operations (3) and (4) were alternately performed 50 times each (Figure 1).

[0106] (6) The separation patterns of the standard antibody obtained in (3) and the serum sample obtained in (4) were corrected so that the detection values ​​at the time when the pH gradient started (7 minutes after the start of elution) and the time when the pH gradient ended (i.e., the eluate reached 100%) (18 minutes after the start of elution) were both 0.

[0107] (7) For each separation pattern corrected in (6), the elution time of the separation pattern was corrected based on Equations 1 and 2 using the method shown below.

[0108]

number

[0109] (7-1) Three local maxima (peak tops) present in the separation pattern of the standard antibody (mIgG1) (Figure 2) were selected as feature points (criteria: Tcs p1 , Tcs p2 and Tcs p3 , Correction target: Tc p1 , Tc p2 and Tc p3 ).

[0110] (7-2) In the separation pattern of the standard antibody to be corrected (50th measurement), the elution time (Tc p1 , Tc p2 and Tc p3 ) on the X-axis, and the difference ((Tc p1 -TCS p1 ), (Tc p2 -TCS p2 ) and (Tc p3 -TCS p3 )) were plotted on the Y-axis, and a plot was created (Figure 3).

[0111] (7-3) From the plot diagram (Figure 3) created in (7-2), it was assumed that the fluctuation in the separation pattern due to repeated measurements varies depending on the elution time within the elution time range from the analysis start time to the analysis end time, and an approximation line (relationship equation) between the fluctuation range of the elution time at a specific elution time and the elution time itself was created using a simple regression equation based on the least squares method. In Figure 3, the plot is located approximately on the approximation line, which indicates that the elution time can be corrected using a simple regression equation based on the least squares method (Figure 3).

[0112] (7-4) Based on the relational expression created in (7-3), the elution time in the separation pattern of the serum sample to be corrected (50th measurement) was corrected.

[0113] (8) The separation pattern obtained in (7) after correcting the elution time was subjected to baseline correction so that the detection values ​​for the corrected elution times at 7 minutes and 18 minutes were 0, and a separation pattern for the serum sample was obtained.

[0114] Comparative Example 1 Separation patterns of serum samples were obtained in the same manner as described in Example 2, except that the elution time correction of the separation patterns described in Example 2(7) was performed based on Equation 3 in the manner shown below.

[0115]

number

[0116] (1) Of the three maximum values ​​(peak tops) present in the separation pattern of the standard antibody (mIgG1) (Figure 2), the maximum value with the longest elution time was selected.

[0117] (2) The difference between the elution time of the separation pattern of the standard antibody to be corrected (50th measurement) and the elution time of the separation pattern of the reference standard antibody (first measurement) at the characteristic point selected in (1) (Tc p3 -TCS p3 ) was calculated.

[0118] (3) Assuming that the fluctuations in the separation pattern due to repeated measurements are shifted by a uniform time in the elution time range from the start time of the analysis to the end time of the analysis, the elution time in the separation pattern of the serum sample to be corrected (50th measurement) was corrected using the value calculated in (2) above.

[0119] Comparative Example 2 Separation patterns of serum samples were obtained in the same manner as described in Example 2, except that the elution time correction of the separation patterns described in Example 2(7) was not performed.

[0120] The results of Example 2 and Comparative Examples 1 and 2 are shown together in Figure 4. In the separation patterns shown in Figure 4, the solid line represents the separation pattern of the serum sample from the first measurement, and the dotted line represents the separation pattern of the serum sample from the 50th measurement (subject to correction). As can be seen from Figure 4, the significant elution time shift observed without correction (Comparative Example 2) (Panel (c) of Figure 3) was eliminated, but by performing three-point correction (Example 2), the entire separation pattern was positioned on approximately the same line (Panel (a) of Figure 3). On the other hand, with one-point correction (Comparative Example 1), the elution time shift remained for peaks other than the peak to be corrected (the peak with the slowest elution time) (Panel (b) of Figure 3). From the above, it can be seen that correction based on the elution times at multiple characteristic points allows for highly accurate correction, and fluctuations in elution time due to repeated measurements are suppressed.

[0121] Example 3 (1) Rituximab (Zenyaku Kogyo) or mIgG1 (Sigma) was used as a standard antibody and diluted with PBS to a protein concentration of 1 mg / mL. The diluted solution was passed through a 0.2 μm filter (Merck Millipore) to prepare a standard antibody solution.

[0122] (2) A serum sample was prepared from blood collected from the same subject as in Example 2 (hereinafter referred to as Subject A) in the same manner as in Example 2(2).

[0123] (3) As a standard sample for determining each peak area in the antibody separation pattern, rituximab (Zenyaku Kogyo) was diluted with PBS to a protein concentration of 1 mg / mL and passed through a 0.2 μm filter (Merck Millipore) to prepare a standard sample solution.

[0124] (4) Separation patterns of the standard antibody rituximab, the standard antibody mIgG1, and the standard sample were obtained in this order in the same manner as in Example 2(3), except that the solution applied to the FcR9_F column was the standard antibody solution prepared in (1) or the standard sample solution prepared in (3).

[0125] (5) Separation patterns of the standard antibody rituximab, the standard antibody mIgG1, and the serum sample were obtained in this order in the same manner as in Example 2(3), except that the solution applied to the FcR9_F column was the standard antibody solution prepared in (1) or the serum sample prepared in (2).

[0126] (6) The procedure (5) was performed 12 times on the same day, for a total of 24 times over two days.

[0127] (7) For the serum samples obtained in each measurement run as the subject of correction in Example 2(7), the separation patterns were obtained in the same manner as in Examples 2(6) to (8), except that the elution times of the separation patterns were corrected based on the standard antibody in the same measurement run as the serum sample to be corrected.

[0128] (8) From the baseline-corrected separation pattern of the standard sample (Figure 5), the peak regions were defined as the two elution time intervals where the derivative of the valley region between the three peaks detected between 7 and 18 minutes after the start of elution (Peak 1, Peak 2, and Peak 3, in order of shorter elution time (lowest binding ability to FcR9_F)). The peak regions were defined as the intervals between 7 minutes after the start of elution and the elution time where the derivative of the valley region between Peak 1 and Peak 2 was zero. The peak region was defined as the interval from 7 minutes after the start of elution to the elution time where the derivative of the valley region between Peak 1 and Peak 2 was zero to the elution time where the derivative of the valley region between Peak 2 and Peak 3 was zero. The peak region was defined as the interval from the elution time where the derivative of the valley region between Peak 2 and Peak 3 was zero to 18 minutes after the start of elution. The peak regions defined for the standard sample were applied to the serum sample measured after the analysis of the standard sample, and the peak regions of the serum sample were defined using the standard sample.

[0129] (9) The peak area of ​​each peak region of the serum sample defined in (8) was calculated, and the peak area was divided by the total peak area between 7 and 18 minutes after the start of elution (i.e., the sum of the first, second, and third peak areas) to calculate the area percentage of each peak (hereinafter, the first peak area%, the second peak area%, and the third peak area% are also referred to as area1%, area2%, and area3%).

[0130] Comparative Example 3 The area percentage of each peak of the serum sample was calculated in the same manner as in Example 3, except that the elution time correction of the separation pattern described in Example 3(7) was not performed.

[0131] Reference Example 1 Construction of anti-interleukin 6 receptor (IL-6R) antibody-expressing cells (1) A vector capable of expressing anti-IL-6R antibody in mammalian cells was constructed using the following method.

[0132] (1-1) Dihydrofolate reductase (dihydrofolate reductase) as set forth in SEQ ID NO: 3 The genes encoding SV40 polyA and SV40 reductase (dhfr) were synthesized with restriction enzyme SacII recognition sequences (CCGCGG) added to both the 5' and 3' ends (commissioned to Integrated DNA Technologies) and cloned into a plasmid.

[0133] (1-2) The E. coli JM109 strain was transformed with the plasmid prepared in (1-1). The resulting transformant was cultured, and the plasmid was extracted and digested with the restriction enzyme SacII to prepare the gene encoding dhfr-SV40PolyA, which was named dhfr-P1.

[0134] (1-3) PCR was performed using the pIRES vector (Clontech) as a template and oligonucleotide primers consisting of the sequences set forth in SEQ ID NO: 4 (5'-TCC[CCGCGG]GCGGGACTCTGGGGTTCGAAATGACCG-3') and SEQ ID NO: 5 (5'-TCC[CCGCGG]GGTGGCTCTAGCCTTAAGTTCGAGACTG-3') (the brackets in SEQ ID NOs: 4 and 5 indicate the restriction enzyme SacII recognition sequence). Specifically, a reaction solution with the composition shown in Table 1 was prepared, and the reaction solution was heat-treated at 98°C for 30 seconds. The reaction was then repeated 25 times, with one cycle consisting of a first step at 98°C for 10 seconds, a second step at 55°C for 5 seconds, and a third step at 72°C for 5 minutes. This PCR amplified the region of the pIRES vector excluding the neomycin resistance gene.

[0135] [Table 1]

[0136] (1-4) The PCR product prepared in (1-3) was purified, digested with the restriction enzyme SacII, and ligated with the dhfr-P1 prepared in (1-2). The ligation product was transformed into Escherichia coli JM109 strain, and the plasmid was extracted from the cultured transformant to obtain the expression vector pIRES-dhfr containing the dhfr gene.

[0137] (2) Using the pIRES-dhfr prepared in (1) as a template, PCR was performed using oligonucleotide primers consisting of the sequences set forth in SEQ ID NO: 6 (5'-TTTAAATCA[GCGGCCGC]GCAGCACCATGGCCTGAAATAACCTCTG-3') and SEQ ID NO: 7 (5'-GCAAGTAAAACCTCTACAAATGTGGTAAA[CGATCG]CTCCGGTGCCCGT-3') (the brackets in SEQ ID NO: 6 indicate the restriction enzyme NotI recognition sequence, and the brackets in SEQ ID NO: 7 indicate the restriction enzyme PvuI recognition sequence). Specifically, a reaction solution with the composition shown in Table 2 was prepared, and the reaction solution was heat-treated at 98°C for 1 minute. The reaction was then repeated 30 times, with the first step being at 98°C for 10 seconds, the second step being at 55°C for 5 seconds, and the third step being at 72°C for 1 minute. The PCR product amplified by this PCR (SV40 promoter, dhfr, and the region up to SV40 PolyA) was designated dhfr-P2.

[0138] [Table 2]

[0139] (3) pFUSEss-CHIg-hG1 (InvivoGen), which contains the heavy-chain constant region of a human antibody; pFUSE2ss-CLIg-hk (InvivoGen), which contains the light-chain constant region of a human antibody; and dhfr-P2 prepared in (2) were each digested with the restriction enzymes NotI and PvuI, purified, and ligated. The ligation product was transformed into Escherichia coli JM109, and plasmids were extracted from the cultured transformants to obtain pFUSEss-CHIg-hG1 and pFUSE2ss-CLIg-hk, which contain the SV40 promoter, dhfr, and SV40 PolyA. The plasmid pFUSEss-CHIg-hG1 incorporating the SV40 promoter, dhfr, and SV40 PolyA was designated pFU-CHIg-dhfr, and the plasmid pFUSE2ss-CLIg-hk incorporating the SV40 promoter, dhfr, and SV40 PolyA was designated pFU-CLIg-dhfr.

[0140] (4) The polynucleotide set forth in SEQ ID NO: 9, which encodes the heavy chain variable region of an anti-interleukin-6 receptor (IL-6R) antibody consisting of the amino acid sequence set forth in SEQ ID NO: 8, was supplemented at its 5'-end with the restriction enzyme EcoRI recognition sequence (GAATTC) and a guanine (G) to prevent frameshifting, and at its 3'-end with the restriction enzyme NheI recognition sequence (GCTAGC). This gene was then fully synthesized and cloned into a plasmid (contracted to FASMAC). The constructed plasmid was designated pUC-VH6R. The polynucleotide set forth in SEQ ID NO: 11, which encodes the light chain variable region of an anti-IL-6R antibody consisting of the amino acid sequence set forth in SEQ ID NO: 10, was supplemented at its 5'-end with the restriction enzyme EcoRI recognition sequence (GAATTC) and a guanine (G) to prevent frameshifting, and at its 3'-end with the restriction enzyme BsiWI recognition sequence (CGTACG). This gene was then fully synthesized and cloned into a plasmid (contracted to FASMAC). The constructed plasmid was designated pUC-VL6R.

[0141] (5) pUC-VH6R prepared in (4) and pFU-CHIg-dhfr prepared in (3) were digested with the restriction enzymes EcoRI and NheI, respectively, and then purified and ligated. The ligation product was used to transform the E. coli JM109 strain, and the plasmid was extracted from the cultured transformant to obtain the plasmid pFU-6RH-dhfr, which expresses the heavy chain (H chain) of the anti-IL-6R antibody. Furthermore, pUC-VL6R prepared in (4) and pFU-CLIg-dhfr prepared in (3) were digested with the restriction enzymes EcoRI and BsiWI, respectively, and then purified and ligated. The ligation product was used to transform the E. coli JM109 strain. The transformant was transformed and cultured, and the plasmid was extracted from the transformed cells to obtain the plasmid pFU-6RL-dhfr, which expresses the light chain (L chain) of the anti-IL-6R antibody.

[0142] Reference Example 2: Construction of cells highly expressing anti-IL-6R antibody (1) pFU-6RH-dhfr and pFU-6RL-dhfr prepared in Reference Example 1 were transfected into CHO cells (DG44 strain) using the Neon Transfection System (Thermo Fisher Scientific). The transformed cells were then cultured in CD OptiCHO Medium (Thermo Fisher Scientific) containing 50 μg / mL kanamycin and 40 mL / L GlutaMAX (Thermo Fisher Scientific) to obtain anti-IL-6R antibody-expressing cells. Subsequently, 50 ng / mL methotrexate was added to the medium. Gene amplification was performed by adding MTX.

[0143] (2) The cells treated with MTX in (1) were cloned by limiting dilution, and cells capable of stably producing high levels of anti-IL-6R antibody were selected using the ELISA (Enzyme-Linked ImmunoSorbent Assay) described below.

[0144] (2-1) Anti-human Fab antibody (Bethyl) was immobilized at 1 μg / well onto the wells of a 96-well microplate (overnight at 4°C). After immobilization, the plate was blocked with 20 mM Tris-HCl buffer (pH 7.4) containing 2% (w / v) skim milk (Becton Dickinson) and 150 mM sodium chloride.

[0145] (2-2) After washing with a washing buffer (20 mM Tris-HCl buffer (pH 8.0) containing 0.05% [w / v] Tween 20 (trade name) and 150 mM NaCl), the culture supernatant containing the antibody was added, and the antibody was allowed to react with the immobilized protein (at 30°C for 1 hour).

[0146] (2-3) After the reaction was completed, the plate was washed with the washing buffer, and a peroxidase-labeled anti-human Fc antibody (Bethyl) diluted to 100 ng / mL was added at 100 μL / well.

[0147] (2-4) After incubation at 30°C for 1 hour and washing with the wash buffer, 50 μL / well of TMB Peroxidase Substrate (KPL) was added. 50 μL / well of 1 M phosphoric acid was then added to stop color development. Absorbance at 450 nm was measured using a microplate reader (Tecan), and cell lines with high anti-IL-6R antibody production values ​​were selected.

[0148] (3) Limiting dilution was performed while increasing the MTX concentration stepwise (50 nM, 500 nM, 1 μM, 2 μM, 4 μM, 8 μM, 16 μM, 32 μM, 64 μM), and clone selection was repeatedly performed by ELISA as described in (2). As a result, a cell line highly producing anti-IL-6R antibody was obtained.

[0149] Reference Example 3: Obtaining anti-IL-6R antibodies by batch culture using a jar fermenter (1) The anti-IL-6R antibody highly expressing cells prepared in Reference Example 2 were inoculated into a 250 mL Erlenmeyer flask (Corning) containing 50 mL of BalanCD CHO Growth A medium (Irvine Scientific) containing 50 μg / mL kanamycin and 30 mL / L GlutaMAX (Thermo Fisher Scientific), and cultured with shaking at 130 rpm, 37°C, and 8% CO.

[0150] (2) Three 250 mL sterilized containers containing a calibrated pH meter and a dissolved oxygen (DO) meter. A jar fermenter (BioT) was filled with 100 mL of BalanCD CHO Growth A containing 50 μg / mL kanamycin and 30 mL / L GlutaMAX. Add the medium and 0.2 × 10 cells highly expressing anti-IL-6R antibody cultured in (1) 6 After inoculation to give a total volume of 110 mL, the above-mentioned medium was added.

[0151] (3) The jar fermenter containing the medium and cells was attached to a control device (Bio Jr. 8: Biot Inc.) and batch culture was performed for 12 days at 37°C and 130 rpm while air was circulated in the gas phase at 100 mL / min. During the culture, the pH was controlled at 7.0 by adjusting the carbon dioxide concentration in the gas phase and simultaneously adding 0.5 M sodium bicarbonate solution. DO was controlled to maintain 50% of the saturated dissolved oxygen level at 37°C. During the culture, 1 to 2 mL of the culture medium was sampled, and the viable cell density was measured using a Vi-CELL XR (Beckman Coulter Inc.). Antibody productivity was measured using a Cedex Bio (Roche Diagnostics Inc.).

[0152] (4) After the culture was completed, the culture medium was centrifuged to remove cells and impurities, and the resulting supernatant was applied to a separation column (equilibrated with 20 mM Tris-HCl (pH 7.4) containing 150 mM sodium chloride) prepared by packing 1.0 mL of MabSelect SuRe LX (GE Healthcare) into an open column.

[0153] (5) After washing the separation column with 10 mL of the buffer used for equilibration, the antibody adsorbed to the separation column was eluted with 5 mL of 0.1 M glycine-HCl buffer (pH 3.0). 1 mL of 1 M Tris-HCl (pH 8.0) was added to the eluate to return the pH to the neutral range, and the eluate was concentrated using an ultrafiltration membrane while being buffer-exchanged with 50 mM citrate buffer (pH 6.5) containing 150 mM sodium chloride, yielding highly pure anti-IL-6R antibody.

[0154] Example 4 (1) The peak area percentages of each serum sample were calculated in the same manner as in Example 3, except that rituximab (Zenyaku Kogyo Co., Ltd.) or the anti-IL-6R antibody obtained in Reference Example 3 was used as the standard antibody, and serum samples were prepared from blood collected from a subject different from that used in Example 3 (hereinafter, Subject B).

[0155] Comparative Example 4 The area percentage of each peak of the serum sample was calculated in the same manner as in Example 4, except that the elution time correction of the separation pattern was not performed.

[0156] The results of Examples 3 and 4 and Comparative Examples 3 and 4 are summarized in Table 3. In Table 3, the data is shown as the variability (CV value [%]) of values ​​when the same serum sample was measured 24 times; the smaller the value, the lower the variability, and the higher the measurement reproducibility.

[0157] When the elution time correction of the separation pattern using the standard antibody was not performed (Comparative Examples 3 and 4), the CV values ​​varied widely, being 18.9% and 17.7% in area 1%, 4.2% and 3.6% in area 2%, and 7.5% and 7.3% in area 3%, respectively. On the other hand, when the elution times of the separation patterns of serum samples were corrected for elution times using rituximab or mIgG1 (Example 3) or rituximab or anti-IL-6R antibody (Example 4) as the standard antibody, the CV values ​​improved for all peak area percentages (6.7% and 6.5% in area 1%, 1.9% and 1.1% in area 2%, and 1.2% and 1.3% in area 3% for rituximab and mIgG1 (Example 3); 10.8% and 10.6% in area 1%, 2.0% and 1.4% in area 2%, and 2.3% and 2.8% in area 3% for rituximab and anti-IL-6R antibody (Example 4), respectively), demonstrating improved measurement reproducibility. In Examples 3 and 4, the difference in elution time that varies from measurement to measurement was corrected, thereby suppressing the fluctuation of each peak region defined by the elution time of the separation pattern. As a result, it is believed that the variability was suppressed compared to Comparative Examples 3 and 4.

[0158] [Table 3]

Claims

1. A method for analyzing a test antibody contained in a sample, comprising the following steps (a) to (g): (a) adding a sample containing a standard antibody to a column packed with an insoluble carrier on which an Fc-binding protein has been immobilized, allowing the standard antibody to be adsorbed onto the carrier, and then eluting the standard antibody adsorbed onto the carrier with an eluent to obtain a first separation pattern of the standard antibody; (b) after the step (a), adding a sample containing the test antibody to the column, allowing the test antibody to be adsorbed onto the carrier, and then eluting the test antibody adsorbed onto the carrier with the eluent to obtain a separation pattern of the test antibody; (c) performing step (a) again before or after step (b) to obtain a second separation pattern of the standard antibody; (d) selecting a plurality of feature points that are commonly present in the separation patterns of the first and second standard antibodies; (e) creating a relational expression between the elution times at the plurality of characteristic points selected in the step (d) and the differences in elution times at the plurality of characteristic points; (f) correcting the separation pattern of the test antibody obtained in step (b) based on the relational expression created in step (e); and (g) A step of analyzing the test antibody based on the separation pattern of the test antibody corrected in the step (f).

2. The analytical method according to claim 1 , wherein the step (g) comprises a step of determining a characteristic value of the separation pattern of the test antibody corrected in the step (f).

3. 3. The analytical method according to claim 1, wherein step (g) comprises a step of dividing the separation pattern of the test antibody corrected in step (f) by the elution times at characteristic points that are common to the separation patterns of the standard antibody and the test antibody, and determining characteristic values ​​in the divided regions.

4. The analysis method according to claim 1 , wherein the plurality of feature points are extreme points and / or inflection points of a separation pattern.

5. The analytical method according to claim 1 , wherein the step (c) is carried out immediately before or after the step (b).

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

7. The method according to any one of claims 1 to 6, wherein the test antibody and the standard antibody are antibodies of human origin, and the Fc-binding protein is human FcγRIIIa.

8. The method of claim 7, wherein the human FcγRIIIa is any one of the following polypeptides (1) to (3): (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 at least the valine at position 176 is substituted with phenylalanine in the amino acid residues 17 to 192; (2) A polypeptide comprising the amino acid residues from positions 17 to 192 of the amino acid sequence set forth in SEQ ID NO: 1, with the proviso that, in the amino acid residues from positions 17 to 192, at least valine at position 176 is substituted with phenylalanine, and further having, in addition to the substitution, any one or more of substitution, deletion, insertion, and addition of one or several amino acid residues at one or several positions, and having antibody-binding activity; (3) The entire amino acid sequence from amino acid 17 to amino acid 192 of the amino acid sequence set forth in SEQ ID NO: 1, in which valine at position 176 is substituted with phenylalanine. A polypeptide comprising an amino acid sequence having 70% or more homology to the amino acid sequence of the present invention, but including the above-mentioned substitution, and having antibody binding activity.

9. The method of claim 1 , wherein the plurality of feature points is three or more feature points.

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