Method for supporting antibody production and program for supporting antibody production

Abstract

This assistance method for assisting the production of antibodies comprises acquiring first time-series data corresponding to the amount of metabolites produced as a result of culturing a cell line for producing antibodies under predetermined culture conditions on a small scale (step S12), acquiring second time-series data corresponding to the amount of metabolites produced as a result of culturing a cell line under predetermined culture conditions on a large scale (step S12), identifying differences between the first time-series data and the second time-series data (step S14), identifying metabolites of interest due to the differences (step S16), and producing display data based on the metabolites of interest (step S17).

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 been progressing. 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 monitoring glucose concentration and feeding 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] Generally, antibody production is carried out by first attempting antibody production at the laboratory level (small scale) using small culture tanks, and then performing mass production on a large scale using large culture tanks.

[0005] In this method, in small-scale antibody production, the manufacturer specifies culture conditions considered to be optimal (for example, conditions under which the antibody can be produced appropriately). The manufacturer then produces the antibody on a large scale under the same culture conditions as those considered to be optimal. In theory, this method should be able to produce the antibody appropriately even on this large scale. However, even if the antibody is produced under culture conditions considered to be optimal on a large scale, there are cases in which the antibody cannot be produced appropriately on a large scale due to differences in the environments between the small scale and large scale. In addition, although it is preferable for the manufacturer to specify culture conditions appropriate on a large scale, it is difficult to specify the culture conditions. In this case, a problem may arise in which it becomes difficult to produce the antibody on a large scale.

[0006] The present disclosure has been made to solve such problems, and its purpose is to support large-scale antibody production. [Means for solving the problem]

[0007] 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 first time series data indicating, in a time series, the amount of metabolites generated as a result of culturing a cell line for producing an antibody under predetermined culture conditions on a small scale. The antibody production support method includes acquiring second time series data indicating, in a time series, the amount of metabolites generated as a result of culturing the cell line on a large scale under the predetermined culture conditions. The antibody production support method includes identifying a difference between the first time series data and the second time series data. The antibody production support method also includes identifying a metabolite of interest that is caused by the difference. The antibody production support method then includes generating display data based on the metabolite of interest.

[0008] 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 obtain first time series data indicating, in a time series, the amount of metabolites generated as a result of culturing a cell line for producing an antibody under a predetermined culture condition in a small scale. The antibody production support program causes a computer to obtain second time series data indicating, in a time series, the amount of metabolites generated as a result of culturing a cell line in a large scale under the predetermined culture condition. The antibody production support program causes a computer to identify a difference between the first time series data and the second time series data. The antibody production support program causes a computer to identify a metabolite of interest that is caused by the difference. The antibody production support program causes a computer to generate display data based on the metabolite of interest. Effect of the Invention

[0009] In the present disclosure, display data is generated based on a metabolite of interest resulting from a difference between a first time-series data in a small scale and a second time-series data in a large scale. Therefore, a manufacturer can recognize the metabolite of interest by visually checking an image based on the display data, and as a result, can infer the culture conditions to be adjusted in a large scale. Thus, in the present disclosure, it is possible to support the production of antibodies in a large scale. [Brief description of the drawings]

[0010] [Figure 1] 2 illustrates an example of the configuration of a support system according to the present embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Diagram 3] FIG. 1 shows an example of a graph showing the culture parameters of cell line A over time. [Figure 4] FIG. 1 shows an example of a graph showing the culture parameters of cell line B over time. [Diagram 5]FIG. 1 shows an example of a graph showing a time series of multiple metabolites produced by culturing a cell line over a period of 10 days. [Figure 6] FIG. 2 is a functional block diagram of an information processing device and the like. [Figure 7] FIG. 13 is a diagram showing an example of a principal component score plot by principal component analysis. [Figure 8] FIG. 13 is a diagram showing an example of a loading score plot by principal component analysis. [Figure 9] FIG. 2 is a diagram showing an example of a first image. [Figure 10] FIG. 1 shows an example of a metabolic map. [Figure 11] FIG. 13 is a diagram showing an example of a second image. [Figure 12] FIG. 1 is a magnified view of image X of cell line A and cell line B. [Figure 13] 3 is a flowchart showing main processing of the information processing device 100. [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. 13 is a diagram showing an example of a fifth image. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] 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.

[0012] [Example of support system configuration] In this embodiment, an antibody manufacturer (hereinafter simply referred to as "manufacturer" or "user") cultures a cell line on a small scale under specified culture conditions. The manufacturer then produces an antibody using the cultured cell line. The specified culture conditions are typically culture conditions that are considered to be optimal and known to the manufacturer. However, the culture conditions may be any culture conditions. The manufacturer may also identify culture conditions that are considered to be optimal during small-scale culture.

[0013] The manufacturer produces the antibody on a small scale under the predetermined culture conditions (for example, culture conditions considered to be optimal), and also produces the antibody on a large scale by scaling up the production to improve the efficiency of antibody production. In theory, this method should be able to produce the antibody appropriately even on this large scale. However, even if the antibody is produced under culture conditions considered to be optimal on a large scale, there are cases where the antibody cannot be produced appropriately on a large scale due to differences in the environments between the small scale and the large scale. Therefore, the antibody production support method of this embodiment supports the production of an antibody used in an antibody drug. More specifically, the antibody production support method allows the manufacturer to predict the culture conditions and the values ​​of the culture conditions to be set on a large scale.

[0014] 1 shows an example of the configuration of a support system 1 according to the present embodiment. The support system 1 includes a first culture device 10, a first sensor group 12, a second culture device 20, a second sensor group 22, 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.

[0015] The first culture device 10 and the first sensor group 12 are disposed in a first facility 16. The first facility 16 is a facility where small-scale cell culture is realized. The first culture device 10 cultures a cell line. The cell line is, for example, a CHO cell. The culture tank of the first culture device 10 is small in scale, for example, a flask.

[0016] The second culture device 20 and the second sensor group 22 are disposed in a second facility 26. The second facility 26 is a facility where large-scale cell culture is realized. In this embodiment, the second culture device 20 cultures the same cell line (i.e., CHO cells) as the first culture device 10. Furthermore, the antibodies produced by the cell line in the first culture device 10 and the second culture device 20 are discharged from the cells into the culture supernatant. The antibodies are then produced by purifying the antibodies from the culture supernatant. The cell line cultured by the first culture device 10 is also referred to as "cell line A," and the cell line cultured by the second culture device 20 is also referred to as "cell line B." Furthermore, both cell line A and cell line B are the above-mentioned CHO cells. Furthermore, cell line A and cell line B are also collectively referred to as "cell lines."

[0017] Large scale refers to the scale of antibody production that is larger than small scale, for example, the scale at which antibodies are produced in a factory. Also, the production rate of antibodies per unit time is higher in large scale than in small scale. In this way, large scale makes it possible to mass-produce antibodies.

[0018] In addition, examples of the culture method include 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 cell lines over a long period of time. In this embodiment, the first culture device 10 and the second culture device 20 employ 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 10 days.

[0019] The culture conditions set in the first culture device 10 are also referred to as "first culture conditions 14," and the culture conditions set in the second culture device 20 are also referred to as "second culture conditions 24." Hereinafter, the first culture conditions 14 and the second culture conditions 24 are also collectively referred to as "culture conditions."

[0020] The culture conditions 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.

[0021] Each of the first sensor group 12 and the second sensor group 22 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.

[0022] The manufacturer extracts a portion of the cell culture fluid of cell line A cultured in the first culture device 10 as a first sample. The manufacturer also extracts a portion of the cell culture fluid of cell line B cultured in the second culture device 20 as a second sample. The manufacturer then has the analyzer 6 analyze the first and second samples. At this time, the analyzer 6 separates the culture fluid supernatant and the cells, and analyzes them after performing pre-analysis on each. The analyzer 6 is, for example, LC-MS / MS (Liquid Chromatograph-Mass Spectrometry / Mass Spectrometry). The manufacturer also inputs the date and time of the analysis into the analyzer 6.

[0023] The analysis device 6 can measure the amount of each of a plurality of metabolites (for example, 171 metabolites) produced by culturing the cell line A and the cell line B. The manufacturer can select the number of metabolites and the type of metabolite to be measured. The analysis device 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) time series data in which the amount of each of the plurality of metabolites produced by culturing the cell line is indicated in time series. The time series data may be data in which the amount of each of the metabolites is indicated directly or indirectly. In other words, the plurality of time series data are data corresponding to the amount of each of the plurality of metabolites. In this embodiment, the manufacturer causes the analysis device 6 to analyze the sample once a day and derive the analysis result. The manufacturer may store (for example, freeze) the first sample and the second sample for the culture period (i.e., for 10 days) together. Then, the manufacturer may cause the analyzer 6 to analyze the first and second samples for the culture period (that is, for 10 days) all at once at a predetermined timing.

[0024] Furthermore, the manufacturer may or may not cause the analysis results to be displayed on a display device (not shown) of the analysis device 6 each time the analysis results are derived. In this manner, the manufacturer causes the analysis device 6 to derive analysis results for the culture period (i.e., 10 days). Hereinafter, the time series data from the first culture device 10 will be referred to as "first time series data", and the time series data from the second culture device 20 will be referred to as "second time series data".

[0025] 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.

[0026] 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 the CPU of the information processing device 100, or may be realized as hardware that performs processing separately from the CPU.

[0027] The manufacturer inputs the first time series data and the second time series data to the input device 40. The information processing device 100 acquires the first time series data and the second time series data. Hereinafter, the first time series data and the second time series data are collectively referred to as "time series data". The time series data is data corresponding to the amounts of multiple metabolites during a culture period (10 days in this embodiment).

[0028] 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.

[0029] [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 (Central Processing Unit) 160, a ROM (Read Only Memory) 162, a RAM (Random Access Memory) 164, a HDD (Hard Disk Drive) 166, a communication I / F (Interface) 168, a display I / F 170, and an input I / F 172. The components are connected to each other via a data bus.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] [Culture parameters] Next, the culture parameters will be explained. FIG. 3 is a graph showing the culture parameters of cell line A in a time series for 10 days. FIG. 4 is a graph showing the culture parameters of cell line B in a time series for 10 days. FIG. 5 is a graph showing multiple metabolites produced by culturing the cell lines in a time series for 10 days. In the example of FIG. 5, metabolite b1 and metabolite b2 are shown as multiple metabolites.

[0034] In Figures 3 to 5, the horizontal axis indicates the number of days of culture. The vertical axis of Figure 3(A) indicates the cell density and specific growth rate as culture parameters of cell line A. The vertical axis of Figure 3(B) indicates the antibody production rate as culture parameters of cell line A. The vertical axis of Figure 4(A) indicates the cell density and specific growth rate as culture parameters of cell line B. The vertical axis of Figure 4(B) indicates the antibody production rate as culture parameter of cell line B. The graphs of Figures 3 to 5 are displayed on a predetermined display device (for example, display device 30), and the manufacturer can visually confirm these graphs.

[0035] In the examples of Figures 3 and 4, 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 5, the amounts of metabolite b1 and metabolite b2 fluctuate greatly from the third to fifth days of culture. Therefore, the manufacturer can infer that a metabolite fluctuation highly related to the culture parameter fluctuation occurred 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).

[0036] In the process of producing an antibody on a large scale, the cell line being cultured is easily affected by environmental changes in the culture tank of the second culture device 20, and metabolite fluctuations occur frequently. Therefore, it is preferable for the manufacturer to set (adjust) the culture conditions on a large scale so that the fluctuation tendency of the amount of metabolites is the same between the small scale and the large scale. However, the manufacturer needs to set the culture conditions on a large scale based on limited monitoring results (for example, the monitoring results in Figures 3 to 5). Conventionally, the manufacturer has set the culture conditions on a large scale 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 on a large scale.

[0037] Generally, metabolites (such as metabolite b1 and metabolite b2 in FIG. 5) 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 cultured on a small scale and the fluctuation trend of the metabolite amount of cell line B cultured on a large scale. 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, which will be described later. In addition, the metabolite of interest is the metabolite that is most likely to cause the above difference. Therefore, the manufacturer can infer the culture conditions to be adjusted on a large scale based on the image. Therefore, in this embodiment, the purpose is to support the production of antibodies on a large scale.

[0038] [Functional block diagram of information processing device] FIG. 6 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 first time series data and second time series data are input to the input unit 102. For example, CSV (Comma Separated Value) data is input to the input unit 102 as the first time series data and the second time series data. The manufacturer may manually input the first time series data and the second time series data to the input unit 102. In addition, the analysis device 6 may input the first time series data and the second time series data to the input unit 102. The first time series data and the second time series data are 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 metabolite data, which will be described later.

[0039] The arithmetic device 200 receives a request signal, first time series data, and second time series data. The arithmetic device 200 executes a principal component analysis on the first time series data and the second time series data. The arithmetic device 200 generates metabolite data by the principal component analysis. The metabolite data 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 to the information processing device 100.

[0040] The request unit 104 of the information processing device 100 acquires the metabolite data. The request unit 104 transmits the metabolite data to the display control unit 106. The display control unit 106 generates display data based on the metabolite data. The display control unit 106 causes the display device 30 to display an image based on the display data.

[0041] [Principal component analysis] As described in FIG. 3 to FIG. 5, 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. Therefore, 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 (=2×3×3) 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.

[0042] FIG. 7 is an example of a principal component score plot by principal component analysis. The horizontal axis of FIG. 7 is the first principal component axis. The vertical axis of FIG. 7 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".

[0043] The example in Figure 7 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).

[0044] In the example of Figure 7, 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).

[0045] 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 conditions.

[0046] Fig. 8 is an example of a loading score plot by principal component analysis. The multiple plots shown in Fig. 8 are plots showing the multiple metabolites, respectively. In reality, in Fig. 8, the names of the metabolites are displayed in association with each of the multiple plots, but in the example of Fig. 8, the names of the metabolites are omitted.

[0047] In addition, in the example of FIG. 8, the horizontal axis is the first loading axis, which corresponds to the first principal component axis of FIG. 8. Therefore, the first loading axis is an axis that can explain the time axis. In addition, in the example of FIG. 8, the vertical axis is the second loading axis, which corresponds to the second principal component axis of FIG. 8. Therefore, the second loading axis is an axis that can explain the culture conditions.

[0048] In the example of FIG. 8, 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.

[0049] 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. 8 can be visually recognized. In addition, multiple metabolite plots exist in frame XA3. The information processing device 100 calculates the PC1 principal component loading amount and the PC2 principal component loading amount from the loading value (PC1 column) corresponding to the first principal component score and the loading value (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 amount 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 is specified for the classification pattern of the data group shown in the principal component score plot. The metabolite of interest is a metabolite resulting from the difference between the amount of the metabolite of cell line A cultured on a small scale and the amount of the metabolite of cell line B cultured on a large scale. Furthermore, metabolites of interest may be identified by calculating p-values ​​using the principal component loadings, and based on the p-values ​​and the significance level set by the manufacturer.

[0050] FIG. 9 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 (first image) on the display device 30. This image of the extraction result corresponds to the "image based on the metabolite of interest" of the present disclosure. In the example of FIG. 9, 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 the 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. 9, the information processing device 100 displays the cell corresponding to the metabolite of interest b7 by surrounding it with a thick line.

[0051] 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.

[0052] 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.

[0053] In the example of FIG. 10, 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.

[0054] In addition, the manufacturer can estimate the culture conditions to be adjusted on a large scale by visually checking the first 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 around the metabolite of interest b7 in 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 metabolic map around it.

[0055] Next, a description will be given of other display images displayed by the display device 30. For example, when a manufacturer designates a cell for the name of a plurality of metabolites b1 to b12 (including metabolite of interest b7) displayed in the first image of Fig. 9, an image (second image) of only the metabolic map around the metabolite may be displayed, and the metabolic map of other parts may not be displayed.

[0056] FIG. 11 is an example of an image (second image) of a metabolic map around a specified metabolite. In the example of FIG. 11, a metabolic map is displayed when a metabolite of interest b7 is specified among the multiple metabolites in FIG. 9. 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. 11, 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. 11, 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.

[0057] FIG. 12 is an enlarged view of the image X of cell line A and cell line B in FIG. 11. The manufacturer can make the information processing device 100 display the screen of FIG. 12 by performing a predetermined enlargement operation on the screen of FIG. 11. In the example of FIG. 12, the amount of metabolite b7 of cell line A and the amount of metabolite b7 of cell line B are displayed over time. 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 on a large scale based on the time series changes in 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.

[0058] [Flowchart of information processing device] FIG. 13 is a flowchart showing main processing of the information processing device 100. In the example of FIG. 13, first, in step S12, the information processing device 100 acquires the first time series data and the second time series data. Next, in step S14, the information processing device 100 outputs a request signal, the first time series data, and the second time series data to the calculation device 200. The calculation device 200 executes a principal component analysis on the first time series data and the second time series data. The calculation device 200 generates metabolite data by the principal component analysis. The calculation device 200 transmits the metabolite data to the information processing device 100, so that the information processing device 100 acquires the metabolite data. Then, the information processing device 100 identifies a difference between the first time series data and the second time series data based on the metabolite data (see FIG. 7 and FIG. 8). Next, in step S16, the information processing device 100 identifies a metabolite of interest b7 (see FIG. 9). 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 above-mentioned first image or second image) on the display device 30.

[0059] [Other display images] Next, a description will be given of other display images displayed by the display device 30. For example, in Fig. 11 and Fig. 12, a configuration has been described in which an image of only a metabolic map around the metabolite of interest b7 designated in the first image in Fig. 9 is displayed.

[0060] However, the information processing device 100 may display an image (third 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. 14 is a diagram showing an example of the third image. In the example of FIG. 14, 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. 14, and may be other manners. For example, the metabolic map displayed when one of the multiple metabolites is specified may be blinked.

[0061] The information processing device 100 may also display a fourth image including the name of the metabolic pathway of the specified metabolite. FIG. 15 is an example of the fourth image. As shown in FIG. 15, 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. 15, the name image 48 is a text image of "glutathione metabolism". With this configuration, the manufacturer can recognize the metabolic pathway of the metabolite of interest b7 (a metabolite specified by the manufacturer). 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. 14 and FIG. 16 described later.

[0062] The information processing device 100 may display a fourth image to make the designated metabolite identifiable in the metabolic map. FIG. 16 is an example of the fifth image. As shown in FIG. 16, the information processing device 100 displays a metabolic map. In addition, the information processing device 100 displays the designated metabolite in the metabolic map in a manner different from other metabolites. In the example of FIG. 16, 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. 15, 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 on a large scale based on the metabolite.

[0063] In addition, in this embodiment, a case has been described in which there are multiple metabolites (12 in the example of FIG. 9) that are caused by the above difference. However, when there is only one metabolite that is caused by the above difference, any one of the third to fifth images may be displayed for that one metabolite. Also, the display of any one of the first to fifth images may be switched by the manufacturer's operation.

[0064] [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.

[0065] (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.

[0066] (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.

[0067] [Aspects] It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0068] (Item 1) An antibody production support method according to one embodiment is a support method for supporting antibody production, comprising: acquiring first time series data showing, in a time series, the amount of metabolites produced as a result of culturing a cell line for producing an antibody on a small scale under specified culture conditions; acquiring second time series data showing, in a time series, the amount of metabolites produced as a result of culturing the cell line on a large scale under the specified culture conditions; identifying a difference between the first time series data and the second time series data; identifying a metabolite of interest resulting from the difference; and generating display data based on the metabolite of interest.

[0069] According to this configuration, display data is generated based on a metabolite of interest resulting from a difference between the first time-series data in the small scale and the second time-series data in the large scale. Therefore, the manufacturer can recognize the metabolite of interest by visually checking an image based on the display data, and as a result, can infer the culture conditions to be adjusted in the large scale, thereby supporting the production of antibodies in the large scale.

[0070] (Item 2) The antibody production supporting method according to item 1, further comprising displaying an image based on the display data on a display device.

[0071] According to this configuration, an image related to the display data based on the metabolite of interest is displayed, and therefore the manufacturer can visually check the image to infer the culture conditions that should be adjusted on a large scale.

[0072] (Clause 3) In the antibody production support method described in clause 2, displaying on the display device includes displaying the time series change in the amount of the metabolite of interest on the small scale in association with the time series change in the amount of the metabolite of interest on the large scale.

[0073] According to this configuration, the manufacturer can easily determine the small-scale culture parameters for the metabolite of interest and the Large This makes it easier to visually check the culture parameters on the scale.

[0074] (4) In the antibody production support method described in 2 or 3, displaying on a display device includes displaying a metabolic map surrounding the metabolite of interest in a manner different from the metabolic maps of other locations.

[0075] With this configuration, the manufacturer can recognize the metabolic map around the metabolite of interest, and can therefore more clearly infer the culture conditions to be adjusted on a large scale based on the metabolic map.

[0076] (Clause 5) In the antibody production support method described in clause 2 or 3, displaying on a display device includes displaying a metabolic map surrounding a metabolite of interest while not displaying metabolic maps of other areas.

[0077] With this configuration, the manufacturer can recognize the metabolic map around the metabolite of interest, and can therefore more clearly infer the culture conditions to be adjusted on a large scale based on the metabolic map.

[0078] (Item 6) In the antibody production supporting method according to any one of Items 2 to 5, displaying on the display device includes displaying the name of the metabolic pathway of the metabolite of interest.

[0079] According to this configuration, the user can recognize the metabolic pathway of the metabolite of interest, and can therefore more clearly infer the culture conditions to be adjusted on a large scale based on the metabolic pathway.

[0080] (Item 7) In the antibody production support method according to any one of items 2 to 6, displaying on the display device includes displaying the metabolite of interest in a metabolic map in an identifiable manner.

[0081] According to this configuration, the user can recognize the metabolite of interest, and therefore can more clearly infer the culture conditions to be adjusted on a large scale based on the metabolite of interest.

[0082] (Item 8) In the method for supporting antibody production according to any one of items 2 to 7, identifying the difference includes identifying the difference using principal component analysis.

[0083] According to this configuration, the difference can be identified based on the existing technique of principal component analysis.

[0084] (Item 9) An antibody production support program according to one embodiment is a program for supporting antibody production, and the antibody production support program causes a computer to acquire first time series data showing in time series the amount of metabolites produced as a result of culturing a cell line for producing an antibody on a small scale under specified culture conditions, acquire second time series data showing in time series the amount of metabolites produced as a result of culturing the cell line on a large scale under the specified culture conditions, identify a difference between the first time series data and the second time series data, identify a metabolite of interest causing the difference, and generate display data based on the metabolite of interest.

[0085] According to this configuration, display data is generated based on a metabolite of interest resulting from a difference between the first time-series data in the small scale and the second time-series data in the large scale. Therefore, the manufacturer can recognize the metabolite of interest by visually checking an image based on the display data, and as a result, can infer the culture conditions to be adjusted in the large scale, thereby supporting the production of antibodies in the large scale.

[0086] 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]

[0087] 1 Support system, 6 Analytical device, 10 First culture device, 12 First sensor group, 14 First culture condition, 16 First facility, 20 Second culture device, 22 Second sensor group, 24 Second culture condition, 26 Second facility, 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 device.

Claims

1. 1. A computer-implemented method for assisting in the generation of antibodies, comprising: acquiring first time series data corresponding to amounts of a plurality of metabolites produced as a result of culturing the cell line for producing the antibody on a small scale under predetermined culture conditions; acquiring second time series data corresponding to the amounts of the plurality of metabolites produced as a result of culturing the cell line under the predetermined culture conditions on a large scale; Identifying a metabolite of interest from among the metabolites that causes a difference between a fluctuation trend of the amount of the metabolites indicated by the first time series data and a fluctuation trend of the amount of the metabolites indicated by the second time series data; generating display data based on the metabolite of interest; Identifying the metabolite of interest comprises: calculating, for each of the metabolites, a first loading value corresponding to a first principal component score and a second loading value corresponding to a second principal component score by performing a principal component analysis on the amounts of the metabolites indicated by the first time series data and the amounts of the metabolites indicated by the second time series data; calculating a first principal component loading amount and a second principal component loading amount for each of the plurality of metabolites from the first loading amount and the second loading amount; and identifying as the metabolite of interest a metabolite having an absolute value of the first principal component loading amount greater than a first threshold and an absolute value of the second principal component loading amount greater than a second threshold.

2. The method for supporting antibody production according to claim 1 , further comprising displaying an image based on the display data on a display device.

3. The antibody production support method of claim 2, wherein displaying on the display device includes displaying the time series change in the amount of the metabolite of interest on the small scale in relation to the time series change in the amount of the metabolite of interest on the large scale.

4. The antibody production support method according to claim 2 or claim 3, wherein displaying on the display device includes displaying a metabolic map around the metabolite of interest in a manner different from metabolic maps of other locations.

5. The antibody production support method according to claim 2 or 3, wherein displaying on the display device includes displaying a metabolic map around the metabolite of interest while not displaying metabolic maps of other locations.

6. The antibody production supporting method according to any one of claims 2 to 5, wherein displaying on the display device includes displaying the name of a metabolic pathway of the metabolite of interest.

7. The antibody production support method according to any one of claims 2 to 6, wherein displaying on the display device includes displaying the metabolite of interest in an identifiable manner on a metabolic map.

8. A program for supporting antibody production, comprising: The program includes: acquiring first time series data corresponding to amounts of a plurality of metabolites produced as a result of culturing the cell line for producing the antibody on a small scale under predetermined culture conditions; acquiring second time series data corresponding to the amounts of the plurality of metabolites produced as a result of culturing the cell line under the predetermined culture conditions on a large scale; Identifying a metabolite of interest that causes a difference between a fluctuation trend of the amount of the plurality of metabolites indicated by the first time series data and a fluctuation trend of the amount of the plurality of metabolites indicated by the second time series data; generating display data based on the metabolite of interest; Identifying the metabolite of interest comprises: calculating, for each of the metabolites, a first loading value corresponding to a first principal component score and a second loading value corresponding to a second principal component score by performing a principal component analysis on the amounts of the metabolites indicated by the first time series data and the amounts of the metabolites indicated by the second time series data; calculating a first principal component loading amount and a second principal component loading amount for each of the plurality of metabolites from the first loading amount and the second loading amount; and identifying, as the metabolite of interest, a metabolite having an absolute value of the first principal component loading amount greater than a first threshold value and an absolute value of the second principal component loading amount greater than a second threshold value.

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