Method for supporting antibody production and program for supporting antibody production

Abstract

This antibody production assistance method for assisting the production of antibodies comprises: acquiring time-series data representing, in a time series, multiple pieces of metabolite data corresponding to each quantity of multiple metabolites produced by culturing a cell line for producing the antibodies, and culture parameters detected from the cell line in the culture (step S2); acquiring the degree of association between the culture parameters and each of the multiple metabolites on the basis of the time-series data (step S4); and producing display data based on the degree of association (step S6).

Description

[Technical field]

[0001] The present disclosure relates to a method for supporting antibody production and a program for supporting antibody production. [Background technology]

[0002] In recent years, research into antibodies produced by culturing cells has progressed. U.S. Patent No. 8911964 (Patent Document 1) discloses a technique for producing antibodies using the culture of CHO (Chinese hamster ovary) cells. Patent Document 1 also discloses that an antibody manufacturer monitors glucose concentrations and feeds back the monitoring results. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent No. 8911964 Summary of the Invention [Problem to be solved by the invention]

[0004] It is preferable for an antibody manufacturer to set appropriate culture conditions for antibody production. However, the manufacturer must set the culture conditions based on limited monitoring results. Conventionally, the manufacturer has set the culture conditions based on the manufacturer's own experience. Therefore, depending on the manufacturer, it may be difficult to set appropriate culture conditions, and in this case, a problem may arise in which it becomes difficult to produce an antibody.

[0005] The present disclosure has been made to solve such problems, and its purpose is to support antibody production. [Means for solving the problem]

[0006] An antibody production support method according to an aspect of the present disclosure is a method for supporting antibody production. The antibody production support method includes acquiring time series data showing, in a time series, a plurality of metabolite data each corresponding to the amount of a plurality of metabolites produced by culturing a cell line for producing an antibody, and a culture parameter detected from the cell line during culture. The antibody production support method also includes acquiring a degree of association between the culture parameter and each of the plurality of metabolites based on the time series data. The antibody production support method further includes generating display data based on the degree of association.

[0007] Moreover, an antibody production support program according to an aspect of the present disclosure is a program for supporting antibody production. The antibody production support program causes a computer to acquire time series data in which a plurality of metabolite data, each of which corresponds to the amount of a plurality of metabolites produced by culturing a cell line for producing an antibody, and a culture parameter detected from the cell line during culture are shown in a time series. The antibody production support program also causes the computer to acquire a degree of association between the culture parameter and each of the plurality of metabolites based on the time series data. Furthermore, the antibody production support program also causes the computer to generate display data based on the degree of association. Effect of the Invention

[0008] In the present disclosure, display data is generated based on the degree of association between the culture parameters and each of a plurality of metabolites. Therefore, the manufacturer can infer the culture conditions to be adjusted by visually checking the image based on the display data. Thus, in the present disclosure, it is possible to support the production of antibodies. [Brief description of the drawings]

[0009] [Figure 1] 1 shows an example of the configuration of a support system 1 according to the present embodiment. [Diagram 2] 1 is a diagram illustrating an example of a hardware configuration of an information processing device 100. [Diagram 3]FIG. 13 is a diagram showing an example of the transition of culture parameters. [Figure 4] FIG. 13 is a diagram showing an example of the transition of culture parameters. [Diagram 5] FIG. 13 is a diagram showing an example of the transition of culture parameters. [Figure 6] FIG. 1 is a diagram showing an example of changes in the amount of metabolites over time. [Figure 7] FIG. 13 is a diagram showing an example of a transition of oxygen partial pressure. [Figure 8] FIG. 2 is a functional block diagram of an information processing device and the like. [Figure 9] 11 is an example of a corresponding image. [Figure 10] FIG. 1 shows an example of a metabolic map. [Figure 11] 4 is a flowchart showing main processing of the information processing device. [Figure 12] FIG. 2 is a diagram showing an example of a first image. [Figure 13] FIG. 13 is a diagram showing an example of a second image. [Figure 14] FIG. 13 is a diagram showing an example of a third image. [Figure 15] FIG. 13 is a diagram showing an example of a fourth image. [Figure 16] FIG. 1 shows an example of the transition of culture parameters of cell line A. [Figure 17] FIG. 1 shows an example of the transition of culture parameters of cell line B. [Figure 18] FIG. 1 shows an example of the transition of the amounts of metabolites in cell lines A and B. [Figure 19] FIG. 13 is a diagram showing an example of a principal component score plot by principal component analysis. [Figure 20] FIG. 13 is a diagram showing an example of a loading score plot by principal component analysis. [Figure 21] FIG. 13 is a diagram showing an example of a fifth image. [Figure 22] FIG. 1 shows an example of a metabolic map. [Diagram 23] FIG. 13 is a diagram showing an example of a sixth image. [Figure 24] FIG. 1 is a magnified view of image X of cell line A and cell line B. [Diagram 25] 13 is a flowchart showing main processing of an information processing apparatus according to another embodiment; [Figure 26] FIG. 13 is a diagram showing an example of a seventh image. [Figure 27] FIG. 13 is a diagram showing an example of an eighth image. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference characters and their description will not be repeated.

[0011] [Example of support system configuration] In this embodiment, the antibody manufacturer (hereinafter simply referred to as the "manufacturer" or "user") sets appropriate culture conditions, and the cultivation device cultivates the cell line under the culture conditions and extracts antibodies from the cultured cell line. In such an antibody production method, it is preferable that appropriate culture conditions are set. The setting of these culture conditions often depends on the manufacturer's own experience, and manufacturers with little experience may find it difficult to set appropriate culture conditions. Therefore, the antibody production support method of this embodiment supports the production of antibodies used in antibody pharmaceuticals. More specifically, the antibody production support method allows the manufacturer to predict the culture conditions and culture condition values ​​to be set.

[0012] 1 shows an example of the configuration of a support system 1 according to the present embodiment. The support system 1 includes a culture device 2, a sensor group 4, an analysis device 6, an information processing device 100, a calculation device 200, a display device 30, and an input device 40. The display device 30 and the input device 40 are connected to the information processing device 100.

[0013] The culture device 2 cultures a cell line and extracts an antibody from the cultured cell line. The cell line is, for example, a CHO cell. The culture method includes, for example, ATF (Alternating Tangential Flow) culture, which is a type of perfusion culture method, and Fed-Batch culture, which is a type of fed-batch culture method. In particular, since ATF culture employs a perfusion system, manufacturers can realize continuous culture, that is, they can culture the cell line over a long culture period. In this embodiment, the culture device 2 employs the ATF culture method. The employment of the ATF culture method enables long-term cell culture. In this embodiment, the cell line is cultured over a culture period of 30 days.

[0014] The culture conditions 3 that the manufacturer can set include, for example, at least one of the following: basal medium volume, basal medium composition, bleeding flow rate, feed medium volume, feed medium composition, perfusion rate, stirring speed, air blowing volume, carbon dioxide blowing volume, alkali attachment volume, and antifoaming agent attachment volume. The manufacturer sets at least one of these culture conditions by operating a device corresponding to the culture condition. For example, the manufacturer sets the carbon dioxide blowing volume by operating a carbon dioxide blowing device.

[0015] The sensor group 4 is composed of one or more sensors. Each of the one or more sensors detects a culture parameter corresponding to the sensor. The culture parameters include cell density, cell viability, cell diameter, specific growth rate, antibody production rate, pH, temperature, dissolved oxygen (DO), oxygen partial pressure, and carbon dioxide partial pressure. The specific growth rate is the rate of cell increase per unit time calculated from the difference in cell density from the previous time (e.g., one day before) or the like.

[0016] The manufacturer extracts a portion from the culture fluid of the cell line cultured in the culture device 2. The manufacturer then has the analyzer 6 analyze the extracted portion of the cell culture fluid as a sample. At this time, the analyzer 6 separates the culture fluid supernatant and the cells, and performs pre-analysis on each of them before having the analyzer 6 analyze them. The analyzer 6 is, for example, an LC-MS / MS (Liquid Chromatograph-Mass Spectrometry / Mass Spectrometry) analyzer. The manufacturer also inputs the date and time of the analysis into the analyzer 6.

[0017] The analyzer 6 can measure the amount of each of a plurality of metabolites (for example, 171 metabolites) produced by culturing the cell line. The manufacturer can select the number of metabolites and the type of metabolite to be measured. The analyzer 6 then derives the analysis result (the amount of each of the plurality of metabolites) in association with the input date and time. The analysis result includes a plurality of (for example, 171) metabolite data in which the amount of each of the plurality of metabolites produced by culturing the cell line is indicated in time series. The metabolite data may be data in which the amount of each of the metabolites is indicated directly or indirectly. In other words, the plurality of metabolite data is data corresponding to the amount of each of the plurality of metabolites. In this embodiment, the manufacturer causes the analyzer 6 to analyze the sample once a day and derive the analysis result. In addition, the manufacturer may cause the analyzer 6 to display the analysis result on a display device (not shown) of the analyzer 6 each time the analysis result is derived, or may not cause the analysis result to be displayed. In this manner, the manufacturer causes the analyzer 6 to derive the analysis result for the culture period (i.e., for 30 days).

[0018] Furthermore, the manufacturer acquires culture parameters during the culture period based on the detection results of the sensor group 4. For example, the manufacturer acquires culture parameters when the cells cultured in the culture device 2 are harvested.

[0019] The input device 40 is a device such as a keyboard or a mouse, and receives instructions from the manufacturer. The display device 30 is configured, for example, with a liquid crystal display (LCD) panel, and displays information to the manufacturer. When a touch panel is used as the user interface, the input device 40 and the display device 30 are integrally formed.

[0020] The information processing device 100 generates display data by executing a predetermined process on the data transmitted from the input device 40. The information processing device 100 may be realized, for example, by a general-purpose computer, or may be realized by a computer dedicated to the information processing device 100. The general-purpose computer may be, for example, a PC (personal computer) or a tablet. At least a part of the calculation for generating the display data may be realized as software by a CPU (Central Processing Unit) of the information processing device 100, or may be realized as hardware that performs processing separately from the CPU.

[0021] The manufacturer inputs the analysis results during the culture period and the culture parameters to the input device 40. In this embodiment, the manufacturer inputs 30 days' worth of analysis results and 30 days' worth of culture parameters corresponding to the analysis results to the input device 40. It is not necessary to input all of the 30 days' worth of analysis results, and for example, a predetermined number of days out of the 30 days (e.g., 15 days' worth of analysis results) may be input. The information processing device 100 acquires the data input from the input device 40 as 30 days' worth of time-series data. In this embodiment, the time-series data indicates 30 days' worth of analysis results and each of the culture parameters for the 30 days.

[0022] The information processing device 100 and the arithmetic device 200 are configured to be able to communicate with each other via a network 50. When the information processing device 100 acquires time-series data from the input device 40, it outputs the data to the arithmetic device 200. The arithmetic device 200 executes a predetermined calculation, which will be described later, and outputs the calculation result to the information processing device 100. The information processing device 100 generates display data based on the calculation result.

[0023] [Hardware configuration of information processing device] Fig. 2 is a diagram showing an example of a hardware configuration of information processing device 100. With reference to Fig. 2, information processing device 100 has, as main components, a CPU 160, a read only memory (ROM) 162, a random access memory (RAM) 164, a hard disk drive (HDD) 166, a communication interface (I / F) 168, a display I / F 170, and an input I / F 172. The components are connected to each other via a data bus.

[0024] The communication I / F 168 is an interface for communicating with the arithmetic device 200. The display I / F 170 is an interface for communicating with the display device 30. The input I / F 172 is an interface for communicating with the input device 40.

[0025] ROM 162 stores programs executed by CPU 160. RAM 164 can temporarily store data generated by execution of the programs in CPU 160 and data input via communication I / F 168. RAM 164 can function as a temporary data memory used as a working area. HDD 166 is a non-volatile storage device. Instead of HDD 166, a semiconductor storage device such as a flash memory may be adopted.

[0026] The program stored in ROM 162 may be stored in a non-transitory recording medium and distributed as a program product. Alternatively, the program may be provided by an information provider as a so-called product program that can be downloaded via the Internet or the like. The information processing device 100 reads the program provided from a recording medium or via the Internet or the like. The information processing device 100 stores the read program in a predetermined storage area (for example, ROM 162). The CPU 160 can execute various processes by executing the program.

[0027] [Culture parameters] Next, the culture parameters will be explained. Figures 3 to 5 are graphs showing the culture parameters in a time series for 30 days.

[0028] In Figs. 3 to 6, the horizontal axis indicates the number of days of culture. The vertical axis of Fig. 3 indicates the cell viability as a culture parameter. The vertical axis of Fig. 4 indicates the cell density and specific growth rate as culture parameters. Fig. 5 indicates the antibody production rate as a culture parameter. Fig. 6 indicates the amounts of lactic acid and glucose as metabolites produced. The graphs of Figs. 3 to 6 are displayed on a predetermined display device (e.g., display device 30), and the manufacturer can visually confirm these graphs.

[0029] In the examples of Figures 3 to 6, as indicated by the dashed lines, the cell viability, cell density, specific growth rate, and antibody production rate fluctuate greatly between the 19th and 20th days of culture. Furthermore, in the example of Figure 6, the amounts of glucose and lactate fluctuate greatly between the 19th and 20th days of culture. Therefore, the manufacturer can infer that there may be an abnormality in the culture parameters between the 19th and 20th days of culture.

[0030] Based on this assumption, the manufacturer also checked the oxygen partial pressure as a culture parameter. Figure 7 is a graph showing the oxygen partial pressure over a 30-day period. As shown by the dashed line in Figure 7, the oxygen partial pressure also fluctuates greatly between the 19th and 20th days of culture.

[0031] In this manner, in the process of producing an antibody, the cell line being cultured is easily affected by environmental changes in the culture tank of the first culture device 10, and metabolite fluctuations occur frequently. Therefore, it is preferable for the manufacturer to set (adjust) the above-mentioned culture conditions so that the culture parameters and metabolite amount fluctuations shown in Figs. 3 to 6 are stabilized. However, the manufacturer needs to set the culture conditions based on limited monitor results (for example, the monitor results in Figs. 3 to 7). Conventionally, the manufacturer has set the culture conditions based on the manufacturer's own experience. Therefore, depending on the manufacturer, it may be difficult to set appropriate culture conditions, and in this case, a problem may arise in which it becomes difficult to produce an antibody.

[0032] Generally, metabolites (such as glucose and lactic acid in FIG. 6) are produced during the production of antibodies. Moreover, it is technically difficult to artificially control the production of metabolites. The inventors of the present disclosure focused on the degree of association between culture parameters and metabolites. Then, the information processing device 100 displays an image based on the degree of association. This image will be described later. Therefore, the manufacturer can infer the culture conditions to be adjusted based on the image. Therefore, in this embodiment, it is possible to support the production of antibodies.

[0033] [Functional block diagram of information processing device] FIG. 8 is a functional block diagram of the information processing device 100 and the like. The information processing device 100 has an input unit 102, a request unit 104, and a display control unit 106. The above-mentioned time series data is input to the input unit 102. For example, CSV (Comma Separated Value) data is input to the input unit 102 as the time series data. The manufacturer may manually input the time series data to the input unit 102. Also, the analysis device 6 may input the time series data to the input unit 102. The time series data is output to the request unit 104. The request unit 104 outputs a request signal and the time series data to the calculation device 200. The request signal is a signal for requesting the calculation device 200 to transmit relevance data described later.

[0034] The arithmetic device 200 receives the request signal and the time series data. The arithmetic device 200 executes correlation analysis on the time series data. As a method of correlation analysis, a method using Pearson's product moment correlation coefficient, Spearman's or Kendall's rank correlation coefficient is used depending on the characteristics of the analysis data, but other methods may be used to analyze the correlation between different two values. The arithmetic device 200 generates relevance data by the correlation analysis. The relevance data is data indicating the relevance between the culture parameter and each of the multiple metabolites. The arithmetic device 200 transmits the relevance data to the information processing device 100.

[0035] The request unit 104 of the information processing device 100 acquires the relevance data. The request unit 104 transmits the relevance data to the display control unit 106. The display control unit 106 generates display data based on the relevance data. The display control unit 106 causes the display device 30 to display an image based on the display data. Note that the correlation analysis may be performed by the information processing device 100 instead of the arithmetic device 200.

[0036] [Corresponding image] FIG. 9 is an example of an image (corresponding image) displayed on the display device 30. As shown in the table on the left side of FIG. 9, the corresponding image is displayed in a table format (matrix format). In the example of FIG. 9, a column for metabolic pathways, a column for metabolite names, a column for relevance, and a column for p-values ​​are displayed in correspondence with each other. Furthermore, the name of the target culture parameter is displayed in a block below the block for the name of each column (metabolic pathway, metabolite, relevance, and p-value). In the example of FIG. 9, the culture parameter is "oxygen partial pressure."

[0037] In addition, in the metabolic pathway column, identification information A to E of the metabolic pathway of the metabolite corresponding to the metabolic pathway column is shown. In addition, in the relevance column, the relevance between the metabolite corresponding to the relevance column and the culture parameter (oxygen partial pressure) is shown. In addition, in the "p-value" column, the p-value for the relevance corresponding to the p-value column is shown.

[0038] The table on the right side of Fig. 9 indicates which metabolic pathway each of the identifying information A to E indicates. For example, in the example of Fig. 9, identifying information A indicates a metabolic pathway of a TCA cycle intermediate. Furthermore, identifying information B indicates a metabolic pathway of the urea cycle. Furthermore, identifying information C indicates a metabolic pathway of glutathione metabolism. Furthermore, identifying information D indicates a metabolic pathway of a pentose phosphate pathway intermediate. Furthermore, identifying information E indicates a metabolic pathway of a glycolytic component intermediate.

[0039] Also, in the table on the left side of FIG. 9, for example, for glucose as a metabolite, it is shown that the metabolic pathway identification information E (glycolysis component intermediate) has a degree of association of 0.67514, and the p-value is 9.3778E-07. Also, for lactic acid as a metabolite, it is shown that the metabolic pathway identification information A (TCA cycle intermediate) has a degree of association of 0.63129, and the p-value is 7.3776E-06. As other metabolites, metabolites a1 to a11 are displayed, and the metabolic pathways, degrees of association, and p-values ​​of each of the metabolites a1 to a11 are shown. In this way, the information processing device 100 may display the names of metabolites in different modes (associated with different identification information) for each metabolic pathway of the metabolite. Also, as a modified example, the information processing device 100 may display the names of metabolites in different colors for each metabolic pathway of the metabolite.

[0040] As described above, the analyzer 6 measures the amount of each of the 171 types of metabolites. The display device 30 may display the relevance of each of the 171 types of metabolites. However, displaying metabolites with low relevance to the culture parameters does not provide much benefit to the manufacturer. Also, if the relevance of many metabolites is displayed, it becomes difficult for the manufacturer to see the corresponding images.

[0041] Therefore, in this embodiment, the information processing device 100 extracts the degree of association that satisfies the degree of association condition that indicates a high degree of association with the culture parameter. Here, the degree of association condition in this embodiment is a condition that the p-value is less than 0.05 and the absolute value of the degree of association is greater than 0.7.

[0042] In this way, the corresponding image in Fig. 9 is an image in which the names of a plurality of metabolites (glucose, lactic acid, a1 to a11) are associated with the degree of association between the culture parameter and each of the plurality of metabolites for the culture parameter (oxygen partial pressure). Furthermore, the corresponding image in Fig. 9 is an image in which the degree of association that satisfies the above-mentioned condition of the degree of association is associated with the name of the metabolite associated with the degree of association.

[0043] Meanwhile, the manufacturer can make the information processing device 100 create a metabolic map. The metabolic map is a map showing metabolites and the metabolic pathways of the metabolites. In addition, a metabolic map creation support application is downloaded to the information processing device 100, and the manufacturer creates a metabolic map using this creation support application.

[0044] FIG. 10 is a diagram showing an example of a metabolic map. In the example of FIG. 10, the example of the metabolic map is omitted. In the example of FIG. 10, generated metabolites, metabolic pathways, etc. are shown. In the example of FIG. 10, as the identification information of the metabolic pathways, identification information N1 of the metabolic pathway of the glycolysis system, identification information N2 of the metabolic pathway of the pentose phosphate pathway, identification information N3 of the metabolic pathway of the TCA cycle, identification information N4 of the metabolic pathway of the urea cycle, and identification information N5 of the metabolic pathway of the glutathione cycle are displayed.

[0045] In the example of FIG. 10, glucose and lactic acid are shown as metabolites, and the names of other metabolites are omitted. Under the name of the metabolite, related information related to the metabolite is displayed. The related information is, for example, a graph showing the culture parameters (e.g., specific growth rate) of the metabolite during the culture period (30 days). For example, glucose related information M1 is a graph showing the specific growth rate of glucose during the culture period. Also, lactic acid related information M2 is a graph showing the specific growth rate of lactic acid during the culture period. The manufacturer may also switch the related information (specific growth rate) to other related information (e.g., cell viability) by performing a predetermined switching operation on the input device 40.

[0046] In addition, the manufacturer can estimate the culture conditions to be adjusted by visually checking the corresponding image in FIG. 9 and the metabolic map in FIG. 10. For example, the manufacturer can estimate the culture conditions to be adjusted by visually checking each metabolite included in the metabolic map surrounding the metabolite with high correlation among the entire metabolic map. Furthermore, the manufacturer can estimate the degree to which the culture conditions to be adjusted should be adjusted by visually checking the surrounding metabolic map. Note that in the metabolic map in FIG. 10, the culture parameters of each metabolite may be displayed in association with the metabolite.

[0047] [Flowchart of information processing device] Fig. 11 is a flowchart showing main processing of the information processing device 100. In the example of Fig. 11, first, in step S2, the information processing device 100 acquires time-series data. Next, in step S4, the information processing device 100 outputs a request signal and the time-series data to the arithmetic device 200. The arithmetic device 200 executes correlation analysis on the time-series data. The arithmetic device 200 generates relevance data by the correlation analysis. The arithmetic device 200 transmits the relevance data to the information processing device 100, whereby the information processing device 100 acquires the relevance data.

[0048] Next, in step S6, the information processing device 100 generates display data based on the relevance data, and in step S8, the information processing device 100 displays on the display device 30 a corresponding image based on the display data.

[0049] [Other display images] Next, a description will be given of other display images of the corresponding image displayed by the display device 30. For example, when a manufacturer designates a block of the name of a metabolite among the names of a plurality of metabolites displayed in the corresponding image of Fig. 9, an image (first image) of only the metabolic map around the metabolite may be displayed, and the metabolic maps of other parts may not be displayed.

[0050] FIG. 12 is an example of an image (first image) of a metabolic map around a specified metabolite. In the example of FIG. 12, the metabolic map is displayed when metabolite a4 is specified among the multiple metabolites in FIG. 9. In the example of FIG. 9, metabolite a4 is a glutathione metabolic pathway, so a metabolic map corresponding to the glutathione metabolic pathway is displayed among the metabolic maps. In this way, in the example of FIG. 12, only the metabolic map around the specified metabolite among the multiple metabolites corresponding to the relevance satisfying the relevance condition is displayed, and the metabolic maps of other parts are not displayed. With this configuration, the manufacturer can more clearly identify the metabolic map around the specified metabolite. Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolic map around the specified metabolite.

[0051] The information processing device 100 may display an image (second image) of a metabolic map displayed when one of the multiple metabolites is specified, in a manner different from that of the metabolic maps of other locations. FIG. 13 is a diagram showing an example of the second image. In the example of FIG. 13, the glutathione metabolic pathway is displayed surrounded by a frame P1. That is, the metabolic map of the glutathione metabolic pathway is displayed in a manner different from that of the metabolic maps of other locations. With this configuration, the manufacturer can more clearly identify the metabolic map around the specified metabolite. Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolic map around the specified metabolite. The different manner is not limited to the example of FIG. 13, and may be other manners. For example, the information processing device 100 may blink the metabolic map displayed when one of the multiple metabolites is specified.

[0052] The information processing device 100 may display a third image including the name of the metabolic pathway of the specified metabolite. FIG. 14 is an example of the third image. As shown in FIG. 14, a name image 48 of the metabolic pathway is displayed in the display area 30A of the display device 30. In the example of FIG. 14, the name image 48 is a text image of "glutathione metabolism". With this configuration, the manufacturer can recognize the metabolic pathway of the metabolite that is highly related to the culture parameters. Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolic pathway. Note that the display area 30A is not shown in FIG. 12, FIG. 13, FIG. 15 described later, and the like.

[0053] In addition, the information processing device 100 may display the fourth image so that the designated metabolite can be identified in the metabolic map. FIG. 15 is an example of the fourth image. As shown in FIG. 15, the information processing device 100 displays the metabolic map. In addition, the information processing device 100 displays the designated metabolite in a manner different from other metabolites in the metabolic map. In the example of FIG. 15, it is assumed that the metabolite a4 is designated by the manufacturer. In this case, the information processing device 100 displays the designated metabolite a4 in a manner different from other metabolites. In the example of FIG. 15, the name of the metabolite a4 is displayed in a manner surrounded by a frame 52. According to such a configuration, the manufacturer can recognize metabolites that are highly related to the culture parameters. Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolite. Note that the different manner is not limited to the example of FIG. 15 and may be other manners. For example, the information processing device 100 may blink the designated metabolite.

[0054] In the present embodiment, a case has been described in which there are a plurality of metabolites highly associated with the culture parameters (13 in the example of FIG. 9). However, when there is only one metabolite highly associated with the culture parameters, any one of the first to fourth images may be displayed for the one metabolite.

[0055] [Time series data with multiple attributes] Next, time-series data of a plurality of attributes will be described. In cells having a complex gene sequence such as a CHO cell line that has acquired infinite proliferation ability, the produced cells may not have the intended gene sequence. Therefore, even if a plurality of cell lines are cultured under the same culture conditions, a significant difference may occur in the culture results. In the following, the two cell lines are referred to as cell line A and cell line B. The antibody production support method of the present embodiment allows a manufacturer to estimate the culture conditions to be adjusted in the culture device 2 so that an antibody produced from cell line A and an antibody produced from cell line B are appropriately produced. In this embodiment, the plurality of first metabolite data, which respectively indicate the amounts of a plurality of metabolites produced by culturing cell line A in a time series, and the plurality of second metabolite data, which respectively indicate the amounts of a plurality of metabolites produced by culturing cell line B in a time series, are used. Then, the information processing device 100 identifies a metabolite of interest caused by the difference between the first metabolite data and the second metabolite data, and displays an image based on the metabolite of interest.

[0056] FIG. 16 is a graph showing the culture parameters of cell line A in a time series for 10 days. FIG. 17 is a graph showing the culture parameters of cell line B in a time series for 10 days. FIG. 18 is a graph showing multiple metabolites produced by culturing the cell lines in a time series for 10 days. In the example of FIG. 18, metabolite b1 and metabolite b2 are shown as multiple metabolites.

[0057] In Figs. 16 to 18, the horizontal axis indicates the number of days of culture. The vertical axis of Fig. 16(A) indicates the cell density and specific growth rate as culture parameters of cell line A. The vertical axis of Fig. 16(B) indicates the antibody production rate as culture parameters of cell line A. The vertical axis of Fig. 17(A) indicates the cell density and specific growth rate as culture parameters of cell line B. The vertical axis of Fig. 17(B) indicates the antibody production rate as culture parameter of cell line B. The graphs of Figs. 16 to 18 are displayed on a predetermined display device (for example, display device 30), and the manufacturer can visually confirm these graphs.

[0058] In the examples of Figures 16 and 17, as shown by the dashed lines, the cell density, specific growth rate, and antibody production rate of cell line A and cell line B fluctuate greatly from the third to fifth days of culture. Furthermore, in the example of Figure 18, the amounts of metabolite b1 and metabolite b2 fluctuate greatly from the third to fifth days of culture. Therefore, the manufacturer can infer that there may be an abnormality in the culture parameters from the third to fifth days of culture. The period from the third to fifth days is typically the period from the cell proliferation phase to the stationary phase (the period when cell proliferation converges and antibody production is enhanced).

[0059] In the process of producing an antibody, the cultured cell line A and cell line B are easily affected by environmental changes in the culture tank of the culture device 2, and metabolite fluctuations occur frequently. Therefore, it is preferable for the manufacturer to set (adjust) the culture conditions in the culture device 2 so that the fluctuation trends of the metabolite amounts are similar for cell line A and cell line B. However, the manufacturer needs to set the culture conditions in the culture device 2 based on limited monitor results (for example, the monitor results in Figures 16 to 18). Conventionally, the manufacturer has set the culture conditions in the culture device 2 based on the manufacturer's own experience. Therefore, depending on the manufacturer, it may be difficult to set appropriate culture conditions, and in this case, a problem may occur in which it becomes difficult to produce antibodies in the culture device 2.

[0060] Generally, metabolites (such as metabolite b1 and metabolite b5 in FIG. 18) are produced during the production of antibodies. In addition, it is technically difficult to artificially control the production of metabolites. The inventor of the present disclosure focused on the difference between the fluctuation trend of the metabolite amount of cell line A and the fluctuation trend of the metabolite amount of cell line B. Then, the information processing device 100 identifies a metabolite of interest that is caused by the difference and displays an image corresponding to the metabolite of interest. This image will be described later. Therefore, based on the image, the manufacturer can infer the culture conditions that should be adjusted in the culture device 2 so that the antibody produced from cell line A and the antibody produced from cell line B are appropriately produced. Therefore, in this embodiment, the purpose is to support the production of antibodies.

[0061] In this embodiment, the above-mentioned first metabolite data and second metabolite data are input to the input unit 102 shown in FIG. 8. The manufacturer may manually input the first metabolite data and the second metabolite data to the input unit 102. Also, the analysis device 6 may input the first metabolite data and the second metabolite data to the input unit 102. The first metabolite data and the second metabolite data are output to the request unit 104. The request unit 104 outputs a request signal and metabolite data to the calculation device 200. The request signal is a signal for requesting the calculation device 200 for metabolite data of interest, which will be described later.

[0062] The arithmetic device 200 receives a request signal, the first metabolite data, and the second metabolite data. The arithmetic device 200 executes a principal component analysis on the first metabolite data and the second metabolite data. The arithmetic device 200 generates metabolite data of interest through the principal component analysis. The metabolite data of interest is data indicating a metabolite of interest resulting from a difference between the first metabolite data and the second metabolite data. The arithmetic device 200 transmits the metabolite data of interest to the information processing device 100.

[0063] The request unit 104 of the information processing device 100 acquires the metabolite data of interest. The request unit 104 transmits the metabolite data of interest to the display control unit 106. The display control unit 106 generates display data based on the metabolite data of interest. The display control unit 106 causes the display device 30 to display an image based on the display data.

[0064] As described in FIG. 16 to FIG. 18, three culture parameters (cell density, specific growth rate, and antibody production rate) fluctuate for both cell line A and cell line B from the third to fifth day of the culture period. Thus, three samples (three pieces) for each of cell line A and cell line B for three days are considered. The manufacturer inputs metabolite data (data showing the amounts of 171 metabolites) of 18 groups (=2×2×3) into the input device 40 (see FIG. 1). The metabolite data of the 18 groups is transmitted to the calculation device 200, which performs multivariate analysis (for example, principal component analysis) on the metabolite data of the 18 groups.

[0065] Fig. 19 is an example of a principal component score plot by principal component analysis. The horizontal axis of Fig. 19 is the first principal component axis. The vertical axis of Fig. 19 is the second principal component axis. The midpoint of the first principal component axis is "0", and the midpoint of the second principal component axis is "0".

[0066] The example in Figure 19 shows three plots for cell line A (day 3), three plots for cell line A (day 4), three plots for cell line A (day 5), three plots for cell line B (day 3), three plots for cell line B (day 4), and three plots for cell line B (day 5).

[0067] In the example of Fig. 19, the plots of cell line A and cell line B on day 3 are distributed in the negative region of the first principal component axis. Also, the plots of cell line A and cell line B on day 5 are distributed in the positive region of the first principal component axis. Therefore, the first principal component axis is an axis that can explain the time axis (number of days in culture).

[0068] Moreover, the plots of cell line A are distributed in the positive region of the second principal component axis. Moreover, the plots of cell line B are distributed in the negative region of the second principal component axis. Therefore, the second principal component axis is an axis that can explain the culture parameters (or culture conditions).

[0069] Fig. 20 is an example of a loading score plot by principal component analysis. The multiple plots shown in Fig. 20 are plots showing the multiple metabolites, respectively. In reality, in Fig. 20, the names of the metabolites are displayed in association with each of the multiple plots, but in the example of Fig. 20, the names of the metabolites are omitted.

[0070] Also, in the example of FIG. 20, the horizontal axis is the first loading axis, which corresponds to the first principal component axis of FIG. 20. Therefore, the first loading axis is an axis that can explain the time axis. Also, in the example of FIG. 20, the vertical axis is the second loading axis, which corresponds to the second principal component axis of FIG. 20. Therefore, the second loading axis is an axis that can explain the culture parameters.

[0071] In the example of FIG. 20, the metabolite plot showing a high value (large absolute value) in the negative region of the first loading axis corresponds to cell line A (day 3), and is therefore enclosed by frame XA3. The metabolite plot showing a high value in the positive region of the first loading axis corresponds to cell line A (day 5), and is therefore enclosed by frame XA5. The metabolite plot showing a high value (large absolute value) in the negative region of the second loading axis corresponds to cell line B (day 3), and is therefore enclosed by frame XB3. The metabolite plot showing a high value in the positive region of the second loading axis corresponds to cell line B (day 5), and is therefore enclosed by frame XB5.

[0072] For example, if a manufacturer wants to recognize a metabolite that tends to be high on the third day of cell line A, the name of the metabolite in frame XA3 in FIG. 20 can be visually recognized. In addition, a plurality of metabolite plots are present in frame XA3. For example, the information processing device 100 calculates PC1 principal component loadings and PC2 principal component loadings from the loading values ​​(PC1 column) corresponding to the first principal component score and the loading values ​​(PC2 column) corresponding to the second principal component score. Since these values ​​are correlation coefficients between the principal component score and each metabolite level, according to the general definition of correlation coefficient, if the absolute value of the principal component loading is 0.4 or more, it can be interpreted as a component showing a slightly strong correlation, and if it is 0.7 or more, it can be interpreted as a component showing a strong correlation. In this way, a metabolite of interest showing a strong correlation with respect to the classification pattern of the data group shown in the principal component score plot is identified. The metabolite of interest is a metabolite resulting from the difference between the amount of the metabolite of cell line A and the amount of the metabolite of cell line B. In addition, the identification of the metabolite of interest may be performed based on a p-value calculated using the principal component loadings.

[0073] FIG. 21 is an example of the extraction result for the cell line A on the third day. The information processing device 100 displays an image of this extraction result on the display device 30. The information processing device 100 may display an image of this extraction result (a fifth image) on the display device 30. This image of the extraction result corresponds to the "image based on a metabolite of interest" of the present disclosure. In the example of FIG. 21, a plurality of metabolites b1 to b12 of a plot surrounded by a frame XA3 are shown. The plurality of metabolites b1 to b12 are candidate metabolites of the metabolite of interest. Furthermore, the information processing device 100 specifies a metabolite of interest b7 that is most likely to be responsible for the above difference from the candidate metabolites b1 to b12. The information processing device 100 displays the metabolite of interest b7 in a manner different from other metabolites (candidate metabolites). In the example of FIG. 21, the information processing device 100 displays a block corresponding to the metabolite of interest b7 surrounded by a thick line.

[0074] FIG. 22 is a diagram showing an example of a metabolic map in this embodiment. In the example of FIG. 22, metabolite b7 of cell line A and metabolite b7 of cell line B are displayed. The names of other metabolites are omitted. Under the name of the metabolite, related information related to the metabolite is displayed. The related information may include, for example, a graph showing a culture parameter (e.g., specific growth rate) of the metabolite during the culture period (10 days). The related information may also include a graph showing a time series change in the amount of the metabolite. The manufacturer may also switch the related information (e.g., specific growth rate) to other related information (e.g., cell viability) by performing a predetermined switching operation on the input device 40.

[0075] Moreover, the manufacturer can estimate the culture conditions to be adjusted in the culture device 2 by visually checking the fifth image in Fig. 21 and the metabolic map in Fig. 22. For example, the manufacturer can estimate the culture conditions to be adjusted by visually checking each metabolite included in the metabolic map around the metabolite of interest b7 of the entire metabolic map. Furthermore, the manufacturer can estimate the extent to which the culture conditions to be adjusted should be adjusted by visually checking the surrounding metabolic maps.

[0076] Next, other display images displayed by the display device 30 will be described. For example, in the names of multiple metabolites b1 to b12 (including the metabolite of interest b7) displayed in the fifth image of FIG. 21, when a manufacturer specifies a block of the name of the metabolite, an image (sixth image) of only the metabolic map around the metabolite is displayed. Display Alternatively, metabolic maps of other locations may not be displayed.

[0077] FIG. 23 is an example of an image (sixth image) of a metabolic map around a specified metabolite. In the example of FIG. 23, a metabolic map is displayed when a metabolite of interest b7 is specified among the multiple metabolites in FIG. 21. In this example, the metabolite of interest b7 is a glutathione metabolic pathway, so a metabolic map corresponding to the glutathione metabolic pathway is displayed among the metabolic maps. In this way, in the example of FIG. 23, only a metabolic map around a specified metabolite among the multiple metabolites caused by the above difference is displayed, and metabolic maps of other parts are not displayed. Furthermore, in the example of FIG. 23, related information of the metabolite of interest b7 is displayed for both cell line A and cell line B. The related information is, for example, a graph showing a culture parameter (for example, specific growth rate) of the metabolite during a culture period (10 days). In addition, the manufacturer may switch the related information (specific growth rate) to other related information (cell viability) by performing a predetermined switching operation on the input device 40.

[0078] FIG. 24 is an enlarged view of the image X of cell line A and cell line B in FIG. 23. The manufacturer can make the information processing device 100 display the screen of FIG. 24 by performing a predetermined enlargement operation on the screen of FIG. 23. In the example of FIG. 24, the time series change of the amount of metabolite b7 of cell line A and the amount of metabolite b7 of cell line B is displayed. The manufacturer can also make the displayed information display other information (for example, culture parameters) by performing a predetermined operation. With this configuration, the manufacturer can estimate the culture conditions to be adjusted in the culture device 2 based on the time series change amount of the metabolite of interest of cell line A and the metabolite of interest of cell line B, the culture parameters, and other parts of the metabolic map.

[0079] [Flowchart of information processing device] FIG. 25 is a flowchart showing main processing of the information processing device 100 in this embodiment. In the example of FIG. 25, first, in step S12, the information processing device 100 acquires the first metabolite data and the second metabolite data. Next, in step S14, the information processing device 100 outputs a request signal, the first metabolite data, and the second metabolite data to the calculation device 200. The calculation device 200 executes a principal component analysis on the first metabolite data and the second metabolite data. The calculation device 200 generates metabolite data of interest by the principal component analysis. The calculation device 200 transmits the metabolite data of interest to the information processing device 100, so that the information processing device 100 acquires the metabolite data of interest. Then, the information processing device 100 identifies the difference between the first metabolite data and the second metabolite data based on the metabolite data of interest (see FIG. 19 and FIG. 20). Next, in step S16, the information processing device 100 identifies a metabolite b7 of interest (see FIG. 21). Next, in step S17, the information processing device 100 generates display data based on the metabolite of interest b7. Then, in step S18, the information processing device 100 displays an image based on the metabolite of interest b7 (the fifth or sixth image) on the display device 30.

[0080] [Other display images] Next, a description will be given of other display images displayed by the display device 30. For example, in Fig. 23 and Fig. 24, a configuration has been described in which an image of only a metabolic map around the metabolite of interest b7 designated in the fifth image of Fig. 21 is displayed.

[0081] However, the information processing device 100 may display an image (seventh image) of a metabolic map displayed when one of the multiple metabolites is specified, in a manner different from the metabolic maps of other locations. FIG. 26 is a diagram showing an example of the seventh image. In the example of FIG. 26, the glutathione metabolic pathway of the metabolite of interest b7 is displayed surrounded by a frame P1. That is, the metabolic map of the glutathione metabolic pathway is displayed in a manner different from the metabolic maps of other locations. With this configuration, the manufacturer can more clearly identify the metabolic map around the specified metabolite (metabolite of interest). Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolic map around the specified metabolite. Note that the different manner is not limited to the example of FIG. 26, and may be other manners. For example, the metabolic map displayed when one of the multiple metabolites is specified may be blinked.

[0082] Furthermore, the information processing device 100 may display an eighth image (see FIG. 14) including the name of the metabolic pathway of the specified metabolite. Even with this configuration, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolic pathway.

[0083] In addition, the information processing device 100 may display the eighth image so that the designated metabolite can be identified in the metabolic map. FIG. 27 is an example of the eighth image. As shown in FIG. 27, the information processing device 100 displays the metabolic map. In addition, the information processing device 100 displays the designated metabolite in a manner different from other metabolites in the metabolic map. In the example of FIG. 27, it is assumed that the noteworthy metabolite b7 is designated by the manufacturer. In this case, the information processing device 100 displays the designated noteworthy metabolite b7 in a manner different from other metabolites. In the example of FIG. 27, the name of the noteworthy metabolite b7 is displayed in a manner surrounded by a frame 52. With this configuration, the manufacturer can recognize the metabolites caused by the above-mentioned difference. Therefore, the manufacturer can more clearly infer the culture conditions to be adjusted based on the metabolite.

[0084] In addition, in this embodiment, a case has been described in which there are multiple metabolites (12 in the example of FIG. 21) that are caused by the above difference. However, when there is only one metabolite that is caused by the above difference, either the seventh image or the eighth image may be displayed for that one metabolite. Also, the display of any of the above first to eighth images may be switched by the manufacturer's operation.

[0085] In this embodiment, the cell line of the first attribute is cell line A, and the cell line of the second attribute is cell line B. Stocks and the second attribute cell line is other cell For example, the cell line of the first attribute may be a cell line cultured under a first culture condition, and the cell line of the second attribute may be a cell line cultured under a second culture condition (a culture condition different from the first culture condition). With this configuration, the manufacturer can infer, based on the displayed specific cell line, which of the first culture condition and the second culture condition is the appropriate culture condition. The number of attributes may be three or more.

[0086] [Variations] (1) In the above embodiment, the information processing device 100 generates display data and displays an image based on the display data on the display device 30. However, the information processing device 100 may be configured not to display the image on the display device 30. In such a configuration, for example, a manufacturer connects a recording medium capable of recording information to the information processing device 100. The information processing device 100 then stores the display data in the recording medium. The manufacturer may connect the recording medium in which the display data is stored to another display device and cause the other display device to display an image based on the display data. Even with such a configuration, the same effects as those of the above embodiment can be achieved.

[0087] (2) In the above embodiment, the analysis device 6 and the information processing device 100 are separate devices. However, the analysis device 6 may be configured to execute at least a part of the processing executed by the information processing device 100. For example, the analysis device 6 may be configured to display an image to be displayed on the display device 30 as described above.

[0088] (3) In the above embodiment, an example has been described in which a single information processing device 100 executes all the processes. However, one or more processes may be executed by other information processing devices. The information processing devices may be located remotely from each other. For example, a certain computer may acquire the relevance, and another computer located remotely may generate the display data. There may be a plurality of such other computers, and each computer may generate different display data.

[0089] (4) In the above embodiment, an example has been described in which multivariate analysis (principal component analysis) is used to compare metabolite data obtained from multiple cell lines and determine a metabolite of interest. However, correlation analysis may be performed on time-series metabolite data obtained from a single cell line to determine a required time range, and multivariate analysis may be performed on the metabolite data in that time range to determine the relationship between culture parameters and metabolite data.

[0090] [Aspects] It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0091] (Item 1) An antibody production support method according to one embodiment is a method for supporting antibody production, comprising: acquiring time series data showing, in a chronological order, a plurality of metabolite data each corresponding to the amount of a plurality of metabolites produced by culturing a cell line for producing an antibody, and culture parameters detected from the cell line during culture; acquiring a degree of association between the culture parameters and each of the plurality of metabolites based on the time series data; and generating display data based on the degree of association.

[0092] According to this configuration, display data is generated based on the degree of association, and therefore the manufacturer can infer the culture conditions to be adjusted by visually checking the image based on the display data, thereby supporting the production of antibodies.

[0093] (Item 2) The antibody production supporting method according to item 1 further comprises displaying an image based on the display data on a display device.

[0094] According to this configuration, an image related to the display data based on the degree of association is displayed, so that the manufacturer can infer the culture conditions to be adjusted by visually checking the image.

[0095] (Clause 3) In the antibody production support method described in paragraph 2, acquiring time series data includes acquiring a plurality of first metabolite data each corresponding to the amount of a plurality of metabolites produced by culturing a cell line of a first attribute and a plurality of second metabolite data each corresponding to the amount of a plurality of metabolites produced by culturing a cell line of a second attribute, and the antibody production support method further includes identifying a difference between the first metabolite data and the second metabolite data and identifying a metabolite of interest resulting from the difference, and displaying on the display device includes displaying an image based on the metabolite of interest.

[0096] According to such a configuration, the manufacturer can recognize metabolites of interest resulting from the difference between the first metabolite data and the second metabolite data.

[0097] (4) In the antibody production support method described in 2 or 3, displaying on the display device includes displaying a corresponding image for the culture parameter, which corresponds to the names of multiple metabolites and the degree of association between the culture parameter and each of the multiple metabolites.

[0098] According to such a configuration, the manufacturer can infer the culture conditions that should be adjusted by visually checking the corresponding image.

[0099] (Item 5) In the antibody production support method described in Item 4, the antibody production support method further includes extracting, from all calculated degrees of relevance, a degree of relevance that satisfies a degree of relevance condition indicating a high degree of relevance with the culture parameter, and the corresponding image is an image in which the degree of relevance that satisfies the degree of relevance condition is associated with the name of the metabolite associated with the degree of relevance.

[0100] According to such a configuration, metabolites that are highly related to the culture parameters are displayed, and therefore the manufacturer can infer the culture conditions that should be adjusted based on the metabolites.

[0101] (Item 6) In the antibody production support method described in Item 5, displaying on a display device includes displaying on a display device corresponding images of metabolites corresponding to a degree of association that satisfies the degree of association condition in different formats for each metabolic pathway of the metabolites.

[0102] According to such a configuration, metabolites that are highly related to the culture parameters are displayed, and therefore the manufacturer can infer the culture conditions that should be adjusted based on the metabolites.

[0103] (Clause 7) In the antibody production support method described in clause 5 or 6, displaying on a display device includes displaying a metabolic map around a metabolite corresponding to a degree of association that satisfies the degree of association condition in a manner different from the metabolic maps of other locations.

[0104] According to this configuration, the manufacturer can recognize the metabolic map surrounding the metabolites that are highly correlated with the culture parameters, and can therefore more clearly infer the culture conditions to be adjusted based on the metabolic map.

[0105] (Clause 8) In the antibody production support method described in clause 5 or 6, displaying on a display device includes displaying a metabolic map of the surrounding area of ​​a metabolite corresponding to a relevance that satisfies the relevance condition, while not displaying metabolic maps of other areas.

[0106] According to this configuration, the manufacturer can recognize the metabolic map surrounding the metabolites that are highly correlated with the culture parameters, and can therefore more clearly infer the culture conditions to be adjusted based on the metabolic map.

[0107] (Item 9) In the antibody production support method described in any one of items 5 to 8, displaying on the display device includes displaying the name of the metabolic pathway of the metabolite corresponding to the relevance that satisfies the relevance condition.

[0108] According to this configuration, the manufacturer can recognize the metabolic pathway of the metabolite that is highly related to the culture parameters, and can therefore more clearly infer the culture conditions to be adjusted based on the metabolic pathway.

[0109] (Item 10) In the antibody production support method described in any one of items 4 to 9, displaying on a display device includes displaying in an identifiable manner metabolites corresponding to a degree of association that satisfies the degree of association condition in the metabolic map.

[0110] According to this configuration, the manufacturer can recognize metabolites that are highly related to the culture parameters, and can therefore more clearly infer the culture conditions to be adjusted based on the metabolites.

[0111] (Item 11) An antibody production supporting program according to one embodiment is a program supporting antibody production, the program causing a computer to acquire time series data showing, in a chronological order, a plurality of metabolite data each corresponding to the amount of a plurality of metabolites produced by culturing a cell line for producing an antibody, and culture parameters detected from the cell line during culture; acquire, based on the time series data, a degree of association between the culture parameters and each of the plurality of metabolites; and generate display data based on the degree of association.

[0112] According to this configuration, display data is generated based on the degree of association, and therefore the manufacturer can infer the culture conditions to be adjusted by visually checking the image based on the display data, thereby supporting the production of antibodies.

[0113] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present disclosure is defined by the claims, not by the description of the embodiments described above, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0114] 1 Support system, 2 Cultivation device, 3 Cultivation conditions, 4 Sensor group, 6 Analytical device, 10 First cultivating device, 30 Display device, 30A Display area, 40 Input device, 48 Name image, 50 Network, 100 Information processing device, 102 Input unit, 104 Request unit, 106 Display control unit, 162 ROM, 164 RAM, 200 Arithmetic unit.

Claims

1. A method for supporting antibody production, comprising: acquiring time-series data showing, in a time series manner, a plurality of metabolite data each corresponding to an amount of a plurality of metabolites produced by culturing a cell line for producing the antibody, and a culture parameter detected from the cell line during the culture; generating display data showing the culture parameters, expressions of the metabolites, and degrees of association between the culture parameters and each of the metabolites in association with each other based on the time-series data; and displaying a corresponding image based on the display data on a display device.

2. acquiring the time-series data includes acquiring a plurality of first metabolite data each corresponding to an amount of a plurality of metabolites produced by culturing a cell line having a first attribute, and a plurality of second metabolite data each corresponding to an amount of a plurality of metabolites produced by culturing a cell line having a second attribute, The method for assisting antibody production comprises: identifying a difference between the first metabolite data and the second metabolite data; and identifying metabolites of interest that are responsible for the difference; The antibody production support method according to claim 1 , wherein the display on the display device includes displaying an image based on the metabolite of interest.

3. The antibody production support method further includes extracting, from all the calculated degrees of association, a degree of association that satisfies a degree of association condition indicating that the degree of association with the culture parameter is high; The antibody generation support method according to claim 1 , wherein the corresponding image includes an image in which a degree of association that satisfies the degree of association condition is associated with a representation of a metabolite that is associated with the degree of association.

4. The antibody generation support method of claim 3, wherein displaying on the display device includes displaying on the display device the corresponding image of the metabolite corresponding to the relevance that satisfies the relevance condition in a different manner for each metabolic pathway of the metabolite.

5. The antibody generation support method of claim 3, wherein displaying on the display device includes displaying a metabolic map surrounding a metabolite corresponding to a degree of association that satisfies the degree of association condition in a manner different from metabolic maps of other locations.

6. The antibody production support method of claim 3, wherein displaying on the display device includes displaying a metabolic map surrounding a metabolite corresponding to the relevance that satisfies the relevance condition, while not displaying metabolic maps of other locations.

7. The antibody generation support method according to claim 3 , wherein the displaying on the display device includes displaying a name of a metabolic pathway of a metabolite corresponding to the degree of association that satisfies the degree of association condition.

8. The antibody production support method according to claim 3 , wherein the displaying on the display device includes displaying in an identifiable manner metabolites corresponding to the degree of association that satisfies the degree of association condition in the metabolic map.

9. A program for supporting antibody production, comprising: The program includes: acquiring time-series data showing, in a time series manner, a plurality of metabolite data each corresponding to an amount of a plurality of metabolites produced by culturing a cell line for producing the antibody, and a culture parameter detected from the cell line during the culture; generating display data showing the culture parameters, expressions of the metabolites, and degrees of association between the culture parameters and each of the metabolites in association with each other based on the time-series data; and displaying, on a display device, a corresponding image based on the display data.

Images