Management system and management method

Abstract

To provide a management system that can stabilize the quality of cell-processed products.SOLUTION: A management system 101 includes: a calculation unit 106 that performs a simulation of the culture process based on parameter information regarding parameters for processing the cells to be cultured through culture and cell characteristic information, which is information regarding the probability distribution of the cell characteristics of the cells; and an output unit 108 that outputs information regarding first manufacturing parameter criteria, which is information regarding the manufacturing conditions that must be met when culturing the cells, based on experimental data from the cell culture experiment and information regarding product quality standards for the cells. The calculation unit 106 calculates evaluation index information, which is an evaluation index of the quality index when the cells are cultured for each value included in the parameter information, relative to the quality standard, based on the results of the simulation and the information regarding the quality standard, and outputs information regarding a proposed revision of the first manufacturing parameter criteria based on the evaluation index information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a management system and a management method. [Background technology]

[0002] Regenerative medicine is a medical treatment that uses regenerated tissues or cells to restore dysfunctional or damaged tissues that are difficult to treat using conventional methods. The process leading up to treatment involves collecting a biological sample from the patient or another person, for example, at a medical institution. After collection, the biological sample is transported to a CPF (Cell Processing Facility). At the CPF, the biological sample is subjected to separation, purification, gene transfer, etc., and the cells are grown and organized by culturing, etc. The processed cell products that meet quality evaluation standards are transported to a medical institution, etc., and used to treat the patient.

[0003] Traditionally, the manufacturing of cell-based products in regenerative medicine has been based on the concept of QbT (Quality by Test), similar to pharmaceuticals, with quality assured by the results of final quality testing after manufacturing. Recently, pharmaceuticals have increasingly adopted QbD (Quality by Design), which builds quality into the product development process. QbD ensures quality by understanding the product, its development process, and its manufacturing process, and then managing and developing the product's production process. QbD clarifies the QTPP (Quality Target Product Profile) of the product being developed, extracts the QAs (Quality Attributes) required for the product based on the QTPP, and identifies CQAs (Critical Quality Attributes), which are quality attributes that are particularly important for ensuring quality. QbD also identifies CMAs (Critical Material Attributes) and CPPs (Critical Process Parameters) from MAs (Material Attributes) and PPs (Process Parameters) that affect CQAs, and develops a control strategy. Furthermore, QbD also establishes a design space that allows for production at any scale or lot size. Design space is a region formed by the multidimensional combination and interaction of input variables (e.g., CPPs) such as manufacturing process parameters. If a manufactured cell-based product is located within this region, it is proven that its quality is assured. It is also effective to determine the acceptable range of parameters that affect quality and set manufacturing parameter acceptance criteria, which ensure that the product meets the release criteria if manufactured within those acceptable ranges. The control strategy is continuously validated and improved. While design space specifies ranges primarily based on critical factors, manufacturing parameter acceptance criteria specify the acceptable range of any parameter that affects quality (whether or not it is a critical factor). Therefore, manufacturing parameter acceptance criteria can be considered a concept that expands on design space, or in other words, encompasses design space.Therefore, the concept of manufacturing parameter acceptance criteria includes design space based on QbD, as well as CQA, CMA, CPP, etc., used to identify it. In the following explanation, when there is no need to distinguish between manufacturing parameter acceptance criteria and design space, they may be collectively referred to simply as "manufacturing parameter acceptance criteria."

[0004] While the introduction of QbD is also progressing in regenerative medicine, there are differences between regenerative medicine and pharmaceuticals, such as the difficulty of standardizing quality because cells and biological samples are used as raw materials, and the fact that many processes are currently performed manually, which can lead to variations in the work content. Therefore, the introduction of QbD to regenerative medicine requires the accumulation and management of various information throughout the life cycle of cell-processed products, such as each process (collection, purification, gene transfer, culture, concentration, transportation, transplantation, etc.), material management of raw materials, and clinical information (such as adverse events and / or safety information after transplantation), as well as an understanding of the relationship between quality characteristics, variability characteristics, and treatment outcomes, identifying indicators that affect quality, and developing a control strategy.

[0005] However, as mentioned above, regenerative medicine uses cells and biological samples, whose quality is difficult to standardize, as raw materials, and currently many processes are performed manually. Cells themselves fluctuate during processes such as culturing, which means that cell characteristics can become distorted. Furthermore, manual processes can result in variability between operators, even for the same parameters. Even automated processes can result in variability due to control errors within the equipment itself. When building a control strategy based on QbD, parameters must be determined through experiments, just as with pharmaceuticals, to ensure that the quality of cell-processed products falls within the manufacturing parameter acceptance criteria. However, experiments using cells are costly and time-consuming, and the reproducibility of experiments is difficult due to variations in cell characteristics.

[0006] Patent Document 1 discloses a method for cells produced in tank culture, the method including obtaining current values ​​of one or more cell culture attributes associated with the cell culture for each of one or more time intervals during the cell culture process, predicting one or more future values ​​of specific cell culture attributes associated with the cell culture, and controlling one or more physical inputs to the cell culture process, in order to improve post-production yield, etc.

[0007] Furthermore, Patent Document 2 discloses a method for predicting cell culture results, which involves inputting a culture environment consisting of a medium composition and culture conditions for culturing cells, predicting a biological behavior amount based on the culture environment, modifying the cellular environment based on the predicted biological behavior amount, repeating the prediction of the biological behavior amount and the modification of the cellular environment based on the modified cellular environment, and outputting the modified cellular environment. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Special Publication No. 2023-544029 [Patent Document 2] International Publication No. 2023-276450 Summary of the Invention [Problem to be solved by the invention]

[0009] In the methods of Patent Documents 1 and 2, manufacturing parameter acceptance criteria (e.g., design space) are fixed, and production using each parameter is evaluated by simulation, making it possible to identify parameters that will result in better manufacturing results. However, neither Patent Document 1 nor 2 evaluates manufacturing parameter acceptance criteria for the purpose of quality assurance. As mentioned above, regenerative medicine uses cells or biological samples as raw materials, and currently involves many manual processes. Experiments to determine manufacturing parameter acceptance criteria are costly and time-consuming, and ensuring experimental reproducibility due to variations in cell characteristics makes it difficult to ensure experimental reproducibility. Therefore, it is not easy to increase the number of experiments and obtain reliable data with high accuracy. Therefore, it is possible to evaluate manufacturing parameter acceptance criteria using methods other than experiments, but Patent Documents 1 and 2 do not mention such methods. Furthermore, manufacturing parameter acceptance criteria, particularly the design space, are determined experimentally based on QbD. To achieve this, it is desirable to experiment with a large number of conditions and conduct a large number of experiments to obtain reliable data with high accuracy. However, this is practically difficult due to the reasons mentioned above. For the reasons explained above, it has been difficult to stabilize the quality of cell processed products using conventional techniques.

[0010] The present invention has been made to solve these problems, and an object of the present invention is to provide a management system and a management method that can stabilize the quality of cell processed products. [Means for solving the problem]

[0011] The management system according to the present invention, which has solved the above-mentioned problem, comprises: a memory unit that stores cell characteristic information, which is information about a probability distribution of cell characteristics of cells to be cultured, experimental data of an experiment in which the cells to be cultured are cultured, and information about quality standards of a product related to the cells to be cultured; an input unit to which parameter information related to parameters for processing the cells to be cultured at least by culturing is input; a calculation unit that performs a simulation of a culture process of the cells to be cultured based on the parameter information and the cell characteristic information input to the input unit; and an output unit that outputs information about first manufacturing parameter judgment criteria, which is information about manufacturing conditions that must be met when culturing the cells to be cultured, based on the experimental data and the quality standard information. The calculation unit calculates, for each value included in the parameter information, based on the results of the simulation and the quality standard information, evaluation index information, which is an evaluation index for the quality standard of a quality index when the cells to be cultured are cultured at that value, and outputs information about a suggested revision of the first manufacturing parameter judgment criteria based on the evaluation index information. [Effects of the Invention]

[0012] According to the present invention, it is possible to provide a management system and a management method that can stabilize the quality of cell processed products. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a configuration diagram showing the configuration of a management system 101 according to a first embodiment. [Figure 2A] 1 is a block diagram showing the generation of all information in the life cycle of a cell-processed product, etc., which is accumulated and managed by the management system 101. FIG. [Figure 2B] FIG. 4 is an explanatory diagram showing an example of quality information and manufacturing information. [Figure 3] FIG. 2 is an explanatory diagram showing a screen example SE of the management system 101 according to the first embodiment. [Figure 4A]This is an example of a graph showing the experimental values ​​of each parameter and the quality index, and is a graph showing an example of the results when one parameter is varied and one quality index is plotted. [Figure 4B] This shows an example of a graph of the experimental values ​​of each parameter and the quality index, and is a graph showing an example of the results when two parameters are varied and one quality index is plotted. [Figure 4C] An explanatory diagram showing a table (left) of the input variables that make up the manufacturing parameter judgment criteria and the values ​​of each quality index obtained when the parameters are assigned, and an example screen SE of a graph (right) based on this table. [Figure 5A] FIG. 10 is an explanatory diagram showing an example of two-dimensional manufacturing parameter evaluation criteria. [Figure 5B] FIG. 10 is an explanatory diagram showing an example of a three-dimensional manufacturing parameter judgment standard. [Figure 5C] FIG. 10 is an explanatory diagram showing an example of one-dimensional manufacturing parameter judgment criteria. [Figure 5D] An explanatory diagram showing an example screen SE of a table (left) of each input variable that constitutes the manufacturing parameter evaluation criteria and the values ​​of each quality index obtained when the parameters are assigned, and a graph (right) plotting lots (No. 1 to 17) when each parameter in the manufacturing parameter evaluation criteria is assigned. [Figure 6] FIG. 10 is an explanatory diagram showing an example of calculation results from a cell culture simulation that takes cell fluctuations into consideration. [Figure 7A] An explanatory diagram showing a table (upper left) showing the calculation results of a cell culture simulation that takes cell fluctuations into account against manufacturing parameter evaluation criteria obtained from experiments, and example screens SE of graphs (upper right, lower left, lower right) plotting the calculation results. [Figure 7B] A graph showing an example of the results when one parameter is varied and one quality indicator is plotted. [Figure 7C] This is a graph showing another example of the results of varying one parameter and plotting one quality indicator. [Figure 8A] FIG. 10 is an explanatory diagram illustrating an index for evaluating the stability of calculation results produced using each parameter. [Figure 8B]FIG. 10 is an explanatory diagram illustrating another index for evaluating the stability of the calculation results produced using each parameter. [Figure 8C] FIG. 10 is an explanatory diagram illustrating another index for evaluating the stability of the calculation results produced using each parameter. [Figure 9A] FIG. 10 is an explanatory diagram showing an example screen SE that uses scores and ranks to present a proposed revision of manufacturing parameter evaluation criteria created based on experimental data. [Figure 9B] FIG. 10 is an explanatory diagram showing another example screen SE that uses scores and ranks to present a proposed revision of manufacturing parameter evaluation criteria created based on experimental data. [Figure 10A] FIG. 2 is an explanatory diagram showing an example screen SE of the management system 101 used by an administrative user. [Figure 10B] FIG. 2 is an explanatory diagram showing an example screen SE of the management system 101 used by an administrative user. [Figure 10C] FIG. 2 is an explanatory diagram showing an example screen SE of the management system 101 used by an administrative user. [Figure 11] 4 is a flowchart illustrating the contents of a management method according to the first embodiment. [Figure 12] 4 is a flowchart illustrating a more specific series of steps of the management method according to the first embodiment. [Figure 13] 10 is a flowchart illustrating a specific series of steps of a management method according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] In order to achieve the above-mentioned objects, the present invention has the following configuration. The objects, features, advantages, and ideas of the present invention will be apparent to those skilled in the art from the description in this specification, and those skilled in the art will be able to easily reproduce the present invention from the description in this specification. The specific embodiments of the invention described below show preferred embodiments of the present invention and are shown for illustrative or explanatory purposes, and are not intended to limit the present invention thereto. It will be apparent to those skilled in the art that various changes and modifications can be made based on the description in this specification within the spirit and scope of the present invention disclosed in this specification.

[0015] An embodiment of the present invention will be described below with reference to the drawings. The embodiment described below is an example for explaining the present invention, and appropriate omissions and simplifications have been made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0016] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. Also, when there is no need to distinguish between these multiple components, the subscripts may be omitted. Duplicate descriptions of components with the same or similar functions may be omitted.

[0017] In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., CPU, GPU), and performs processing defined by the program using storage resources (e.g., memory), interface devices (e.g., communication ports), etc. Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0018] A program may be installed on a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0019] The present invention relates to a management system and a management method for processing parameters related to cell-processed products. For example, in the case of cell-processed products, the present invention evaluates the impact of each parameter on quality using a cell culture simulation that takes into account cellular fluctuations on the manufacturing parameter acceptance criteria (including design space) created based on experimental data, and modifies the manufacturing parameter acceptance criteria (first manufacturing parameter acceptance criteria) created based on the experimental data. The broadness of the concepts of manufacturing parameter acceptance criteria and design space has been described above. In the following explanation, the terms "manufacturing parameter acceptance criteria" and "design space" are interchangeable. Furthermore, the term "design space" will be used when specifically referring to design space.

[0020] <Embodiment 1> <Management system> 1 is a configuration diagram showing the configuration of a management system 101 according to embodiment 1. The management system 101 includes input units 102, 103, and 104 (input devices), an output unit 108 (output device), a calculation unit 106 (processor), a main memory unit 105 (storage device), and an auxiliary memory unit 107 (storage device).

[0021] The management system 101 handles information related to cell-processed products. In this specification, cell-processed products broadly encompass products manufactured using cells or cell tissues, and particularly include regenerative medicine products as defined in the Act on Ensuring the Quality, Efficacy, and Safety of Medical Devices, etc. (PMD Act) and specific cell-processed products as defined in the Act on Ensuring the Safety of Regenerative Medicine, etc. (Regenerative Medicine Safety Assurance Act). The unit of a cell-processed product can be defined arbitrarily; for example, one manufacturing lot can be considered as one unit of cell-processed product.

[0022] The input units 102, 103, and 104 have a mechanism for importing various data by linking with other systems and equipment, and by supporting manual data input. These import various parameters related to manufacturing, etc. as input, but there may be three types of input units depending on the import method. In other words, the input units 102, 103, and 104 input parameter information related to at least the parameters for processing the cells to be cultured by culturing.

[0023] The data and parameters acquired from the input units 102, 103, and 104 are temporarily stored in the main storage unit 105 within the memory. The acquired data is then processed and / or linked by the calculation unit 106 within the CPU (Central Processing Unit), and the data is valorized. For example, the calculation unit 106 determines the manufacturing parameter judgment criteria from the experimental values ​​that are input, and further performs a simulation of the culture process from each parameter. The calculation unit 106 then calculates quality information corresponding to each parameter and compares the manufacturing parameter judgment criteria determined from the experimental values ​​with the quality information corresponding to each parameter determined by the calculation. The calculation unit 106 then outputs information regarding a proposed modification to the manufacturing parameter judgment criteria determined from the experimental values. That is, based on the results of the simulation and the information on the quality standards, the calculation unit 106 calculates, for each value included in the parameter information, evaluation index information (i.e., the quality information) that is an evaluation index for the quality index relative to the quality standard when the cells to be cultured are cultured using that value, and causes the output unit 108 to output information regarding a proposed revision of the first manufacturing parameter judgment criterion based on the evaluation index information. Note that parameters (manufacturing parameters) include, for example, various elements during manufacturing and culturing, such as serum lot, flow rate, discharge position, operation time, and temperature during operation, and are not limited to those listed above. The parameter information may be any information related to a parameter, and specific examples include serum lot number, flow rate value, coordinates of the discharge position, time required for operation, and room temperature during operation. Examples of quality indexes include cell count, cell viability, and purity, but any information related to the tissue or cells obtained by culture is also applicable, and are not limited to those listed above. In many cases, there are multiple quality indexes. The evaluation index information is set appropriately depending on the quality index. Furthermore, there may be multiple manufacturing parameter judgment criteria. One manufacturing parameter criterion selected from a plurality of manufacturing parameter criterions, or the only manufacturing parameter criterion, may be referred to as a "first manufacturing parameter criterion."

[0024] The results and the original data that was imported are stored in auxiliary memory unit 107 within the storage. They are output to the outside of management system 101 via output unit 108, and the data is displayed on display unit 109. In one variation, display unit 109 may be configured as an output device of management system 101. Examples of display unit 109 include a display, a printer, and a speaker. Examples of output unit 108 include an HDMI (registered trademark) terminal, DisplayPort, Mini DisplayPort, DVI terminal, USB Type-C, and VGA terminal.

[0025] The main memory unit 105 and / or the auxiliary memory unit 107 correspond to so-called storage units. The main memory unit 105 and / or the auxiliary memory unit 107 may store a program. The calculation unit 106 may execute this program, causing the management system 101, which is a computer, to perform the functions described in this embodiment. In other words, this program causes the computer to function as the management system 101 according to this embodiment. The main memory unit 105 and / or the auxiliary memory unit 107 may be, for example, a memory, a ROM (Read Only Memory), a RAM (Random Access Memory), or an HDD (Hard Disk Drive). Furthermore, the main memory unit 105 and / or the auxiliary memory unit 107 may store cell characteristic information, which is information about the probability distribution of cell characteristics, experimental data obtained through experiments, information on product quality standards, etc. Note that some or all of this information and experimental data may be stored in a storage device or storage system outside the configuration of the management system 101.

[0026] The input units 102, 103, and 104 each have different functions corresponding to different data import methods. The input unit 102 imports data by linking with other systems. Examples of other systems include a manufacturing execution system (MES), a materials management system, an electronic medical record, a patient registry, and a laboratory information management system (LIMS). The input unit 102 accesses a database 110 (DB) of each system and imports the referenced data.

[0027] The input unit 103 inputs data by cooperating with manufacturing equipment, monitoring devices, etc. Examples of manufacturing equipment and monitoring devices include an automatic culture device that automatically cultures cells, a cell observation system, etc. Other examples of manufacturing equipment and monitoring devices include a cleanliness monitoring device that monitors the number of airborne bacteria and particles in a manufacturing environment. Other examples of manufacturing equipment and monitoring devices include a monitoring system that monitors the details of manual work and the movements of workers in manufacturing equipment, and a transportation monitoring device that measures the temperature and / or pressure during transportation, etc.

[0028] The input unit 103 accesses these manufacturing equipment / monitoring devices 111 (at least one of the manufacturing equipment and the monitoring devices) and takes in data. Note that the input unit 102 is used when the data obtained by the manufacturing equipment / monitoring devices 111 is temporarily stored in some system and the database 110 is referenced. The input unit 103 is intended to take in data directly from the manufacturing equipment / monitoring devices 111 without going through the database 110.

[0029] The input unit 104 takes in manually input data. For example, in regenerative medicine, there may be a process that is manually performed by an operator in accordance with a work instruction 112. In particular, in such a case, the results of the manually performed work and monitoring results measured during the work may be manually entered into the work instruction 112. The input unit 104 is a data input method that assumes that an operator or the like manually inputs the contents described in the work instruction 112 via an input terminal 113 after the work, etc. Furthermore, data may be directly input into the work instruction 112 via the input unit 103. The input unit 104 may be, for example, a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a microphone, or a sensor.

[0030] In addition, work results and monitoring results measured during work may be recorded on an electronic terminal. In this case, data may be directly imported into the input unit 103, or may be temporarily stored in the database 110 and then imported from the input unit 102. The data to be imported may be generated not only during commercial production after obtaining marketing approval, but also throughout the entire life cycle of cell-processed products, such as clinical trials, clinical research, and basic research. It is believed that the greater the amount of information, the higher the accuracy of the analysis. However, in this case, it is fully expected that the type, quantity, and / or quality of information will vary depending on the development stage, and it is preferable to handle the data taking this into consideration.

[0031] Fig. 2A is a block diagram showing the generation of all information in the life cycle of cell processed products, etc., stored and managed by the management system 101. Fig. 2A shows examples of the generation of various types of information (quality information) in the life cycle of cell processed products, etc., such as data on the manufacture of cell processed products, etc., stored and / or managed by the management system 101, material management of raw materials, etc., medical information management, treatment information management of adverse events and / or safety information after transplantation, and basic experiment data.

[0032] As shown in Figure 2A, each step in manufacturing 201 may vary depending on the type of cell-processed product being manufactured and the type of disease being treated. Here, the steps of collection, purification, gene transfer, culture, concentration, formulation, and transplantation are described. A transportation step may also be included. Furthermore, although it is stated that data related to materials management 202 is stored in a materials management system, medical information management 203 in an electronic medical record, treatment information management 204 in a patient registry, and basic experiment 205 in a laboratory information management system, other methods may also be used.

[0033] Although three types of input units 102-104 were mentioned in Figure 1, as shown in Figure 2A, in the manufacture of cell processed products and the like, the location of each process may differ. For example, the collection process may be performed at a medical institution, etc. Purification, gene transfer, culture, concentration, and formulation are generally performed at a CPF. The types of input units 102-104 used may vary depending on the location of the process. Furthermore, even for processes performed within the same CPF, the types of input units 102-104 used may vary for each process or for each more detailed task.

[0034] For example, when an automated culture device is used in the culture process, data may be directly input into the management system 101 via the input unit 103 shown in FIG. 1, or the data may first be entered into the database 110 of the manufacturing execution system and then input into the management system 101 via the input unit 102. Alternatively, when an operator manually performs the culture process in a safety cabinet and manually records the work results, etc., in the work instruction sheet 112, the data may be manually input using the input unit 104. Furthermore, when manufacturing, transplantation, etc. are performed at multiple facilities, the work content and data generation method may vary from facility to facility, and therefore the input method used may also vary. Note that FIG. 2A shows, as an example, a case where the input unit 102 is used in the gene transfer and culture process, the input unit 103 is used in the concentration process, and the input unit 104 is used in the formulation and transport process.

[0035] This section describes various types of information in the life cycle of a cell processed product, etc., that is stored and / or managed by the management system 101. Figure 2B is an explanatory diagram that explains an example of various types of information in the life cycle of a cell processed product, etc., that is stored and / or managed by the management system 101. As shown in Figure 2B, this information includes, for example, quality information and manufacturing information.

[0036] The quality information includes a plurality of parameters related to a plurality of cell-processed products, and in particular includes parameters related to at least one of manufacturing information, treatment information, treatment result information, and transportation information related to the cell-processed products. The quality information may also include the proficiency of the worker performing the manual work and / or the training history of the worker performing the manual work, which allows for quality evaluation that takes into account the worker's skills.

[0037] 2B, the manufacturing information may include at least one of the following: the equipment for manufacturing the cell processed product, the facility for manufacturing the cell processed product, the layout of the equipment in the facility for manufacturing the cell processed product, information on the maintenance of the equipment in the facility for manufacturing the cell processed product, environmental information (e.g., cleanliness) of the facility for manufacturing the cell processed product, and the environmental maintenance method (e.g., cleaning method) of the facility for manufacturing the cell processed product. In this way, various information about the cell processed product can be handled.

[0038] Of course, the manufacturing information may include other information as well, other examples of which are described below. The manufacturing information described above is divided into manual and automated manufacturing processes, either for each process or for each more detailed task. The types and / or quantities of data generated may differ depending on the process. Generally, manual processes are not specified numerically as compared to automated manufacturing processes, and the results are not recorded in detail. However, whether manual or automated, all data is accumulated and can be used for subsequent analysis.

[0039] For example, in the cell seeding work of the culture process, examples of manufacturing information include the number of cells to be seeded when they are collected, cell survival rate, expression level of a specific protein, culture vessel used for culture, etc. For the culture vessel used for culture, in addition to the type (shape, type of substrate, culture method), additional information such as the manufacturer's name, lot number, manufacturing date, expiration date, etc. may be stored as data in a materials management system as material management.

[0040] Furthermore, in the cell seeding work of the culture process, examples of manufacturing information include the type of solvent in the cell suspension at the time of seeding, the amount of solvent, the solvent composition, the cell seeding density, the amount of medium at the time of culture, the medium composition, the liquid delivery speed at the time of seeding, the location of liquid delivery, the cell distribution in the culture vessel after seeding, the shear stress generated on the cells, and the total work time.

[0041] Examples of the transportation information include, when the culture vessel is transported after seeding from a work area such as a safety cabinet to an incubator where the culture is performed, the transportation speed, vibration during transportation, total transportation time, etc. The transportation information may also include the temperature and / or pressure during transportation.

[0042] These items can affect the quality of cell-processed products and intermediates after production to a greater or lesser extent, and the management system 101 evaluates the magnitude and manner of their impact. For example, if the cell viability is low when harvested, it can be assumed that the cells will have low activity in subsequent culture. If the liquid supply speed during seeding is too high, the shear stress experienced by the cells will also increase, which may affect subsequent cell proliferation.

[0043] When seeding cells, the temperature and gas phase in a safety cabinet are generally not as controlled as in an incubator where the cells are cultured. As a result, a drop in temperature and / or a change in the pH of the culture medium may occur depending on the duration of the seeding process, which may affect the cells. When cells are transported from a work area such as a safety cabinet to an incubator where the cells are cultured, vibrations during transport may cause vibrations and / or shocks to the cells. Forces may be applied to the cells due to acceleration or route changes caused by changes in transport speed, which may change the cell distribution in the culture vessel and affect the subsequent culture results. This information may be included in the transport information.

[0044] The total transport time, like the sowing time, can affect temperature changes and changes in the pH of the medium. Note that the range of control and the items monitored often differ between automated production equipment and manual operations due to factors such as cost and / or labor. It is entirely conceivable that in the future, the range of control will be expanded and the number of monitored items will be increased, thereby increasing the types and / or number of data to enable more accurate analysis by the management system 101. Such information may be included in the transport information.

[0045] In particular, assuming that manufacturing and / or transplantation are performed at multiple facilities, such information is also important so that differences between facilities can be analyzed by the management system 101 when they may affect quality. For example, for the equipment being used, there is the manufacturer name, model number, maintenance information (date of implementation, frequency, maintenance content), and initialization method each time it is used. This information may be included in the manufacturing information.

[0046] Regarding manual processes performed by workers, the proficiency level and training history of each worker generally differ between facilities. Furthermore, even for the same worker, the work content and / or results may differ each time the work is performed. Therefore, information such as the worker number linked to the worker's name, proficiency level, and training history is also important. Regarding the layout of the cell preparation room, information such as the number of devices, the distance between devices, and routes between devices is also important. This information may be included in the quality information.

[0047] For example, when a worker transports a culture vessel from a safety cabinet to an incubator, the temperature and / or gas phase are not controlled, so temperature drops and pH changes can affect the cells. Therefore, for example, the transport time may affect quality. Regarding the manufacturing environment, information on the temperature and cleanliness of the cell preparation room in the CPF, the worker's usage methods regarding aseptic operation, the cleaning method and frequency of the cell preparation room, and the worker's entry and exit procedures, such as gowning, are also important. This information may be included in the quality information.

[0048] An example of information linked to the collection step shown in FIG. 2A is donor information that has been provided to the recipient of the tissue to be collected. In the management system 101, information about the donor is accumulated as donor information. Specific donor information includes the donor's registration date, informed consent acquisition date, registration ID, age, biological sample donation history, the number of cells collected at the most recent donation, medical history such as infectious diseases, height, weight, travel history, and results of blood tests and serological tests. This information may also be included in the quality information.

[0049] Data that can be stored in the material management system as material management 202 includes the name of the manufacturer, lot number, manufacturing date, expiration date, type (shape and / or type of base material, etc.) of the materials used. In this embodiment, information managed when purchasing materials (material management information) is assumed. This information may be included in the quality information.

[0050] Regarding lot numbers, the lot number of the serum used in culture, in particular, is said to have a significant impact on the culture results. Specific material management information includes, for example, order number, orderer, order date, product name, manufacturer, item, delivery date, purchase price, delivery destination information, expiration date, etc. In the case of biological samples, the animal species, use site, and use process are also added. Since it is expected that some materials will be divided and used by aliquoting, etc., information such as the opening date, dispensing container, number of aliquots, dispensing volume, and branch number will also be generated, but this information may be treated as manufacturing information. This is because it is linked to information on the work date, worker, and work results when work such as dispensing is performed. This information may also be included in quality information.

[0051] Data that can be stored in the electronic medical record as medical information management 203 includes basic information and medical information about the patient undergoing transplantation. Note that it is expected that this data may overlap with data that can be stored in the patient registry as treatment information management, which will be described later, so in such cases it is preferable to adjust the overlapping data. Specific medical information includes, for example, the name of the medical institution, patient ID for the target disease, consent acquisition date, date of birth, sex, height, weight, primary disease, medical history, complications, allergies, transplant date or transplant start date, transplant end date, etc. This information may also be included in the quality information.

[0052] Of the quality information, data that can be accumulated in the patient registry as treatment information management 204 includes treatment information and treatment outcome information. Specific treatment information includes transplanted cell information, raw material information, manufacturing process flow information, transplant date and time, dosage, person in charge of administration, efficacy information (whether complete response was achieved as of a specific date after administration, survival status), etc. Examples of treatment outcome information include post-transplant efficacy, adverse events, safety information, adverse event information (whether or not an adverse event occurred, total of all adverse events, name of adverse event, date of onset, severity, treatment for adverse events, outcome date, causality assessment), etc. The adverse event information may include more detailed data, such as infectious / parasitic diseases, benign / malignant / unspecified neoplasms, blood / lymphatic system disorders, immune system disorders, endocrine system disorders, metabolic / nutritional disorders, mental disorders, nervous system disorders, eye disorders, ear / labyrinth disorders, cardiac disorders, vascular disorders, respiratory / thoracic / mediastinal disorders, gastrointestinal disorders, hepatobiliary system disorders, skin / subcutaneous tissue disorders, musculoskeletal / connective tissue disorders, kidney / urinary tract disorders, reproductive system / breast disorders, congenital / familial / genetic disorders, etc.

[0053] Regarding basic experiments 205, an example of data that can be accumulated in the laboratory information management system is data from basic research in the early stages of development. Another example of such data is data that supports the therapeutic effect as development progresses and the therapeutic mechanism becomes clear, leading to the addition of more detailed experiments. Another example of such data may be data from basic research when an opinion different from the hypothesis emerges. Specific information about basic experiments 205 includes, for example, the date of the experiment, the name of the experimenter, the experimenter ID, the experiment name, the start time of the experiment, the end time of the experiment, the ID of the cells used, the name of the cell type used, and the number of cells. This information may be included in the quality information.

[0054] The type and amount of data acquired may vary depending on the content of the experiment. For example, in the case of an experiment evaluating cell morphology and cell proliferation, the data to be acquired may include, for example, cell images, image IDs, image capture times, culture vessel numbers, image capture locations within the culture vessels, morphology evaluation results, details of morphological abnormalities, cell counts / cell occupancy rates, etc. In the case of an experiment evaluating differentiation potential, the data to be acquired may include, for example, culture vessel numbers, differentiation induction destinations, cell images, image IDs, cell morphology evaluation results, flow cytometry measurement results, marker expression evaluation results, etc. This information may be included in the quality information.

[0055] The data stored in the management system 101 is first linked. The data for each process is linked by date and / or worker ID, etc., and consistent data is created for each lot for various information related to the life cycle from collection to transplantation. Linking is particularly important for information stored in a system different from the management system 101.

[0056] The data format is changed as necessary. When accumulating data that was stored in a system different from the management system 101, the data is aligned to the specifications of the management system 101. For example, if the amount of fluid delivered is shown in different units, such as "10 ml" and "10 cc," the units are unified. Basic statistical information is obtained for all data. For numerical data, maximum / minimum values, average, variance, standard deviation (SD), distribution charts, etc. are obtained. Categorical data is scored and examined in the same way. The validity of the scoring method is evaluated as appropriate.

[0057] Fig. 3 is an explanatory diagram showing a screen example SE of the management system 101 according to embodiment 1. The screen example SE of Fig. 3 illustrates a list of statistical information on accumulated data. The screen example SE of Fig. 3 may display various information on the life cycle of a cell processed product, etc., such as data on the manufacture of the cell processed product, etc., materials management 202 such as raw materials, medical information management 203, treatment information management 204 such as adverse events and / or safety information after transplantation, and basic experiment 205, which are accumulated and / or managed by the management system 101.

[0058] The example screen SE in Figure 3 displays both input data for possible changes to manufacturing parameters and other conditions, as well as output data analyzed as a result of changing input conditions, such as post-manufacturing quality and post-transplant prognosis information. When the amount of information is too large to display all of it on a single screen, the information is divided and displayed according to the type of information, the time of occurrence, etc. Examples of input data include material management 202 for the manufacture of cell-processed products and raw materials. Examples of data include serum lot, flow rate when delivering fluid to the culture vessel, discharge position when delivering fluid to the culture vessel, and operation time. Examples of output data include the aforementioned medical information management 203 and treatment information management 204 for adverse events and / or safety information after transplantation.

[0059] In the case of numerical data, the example screen SE of FIG. 3 displays the average, standard deviation, maximum value, minimum value, distribution diagram, etc. In the case of categorical data such as manufacturing location, serum lot, name of device used, model number of device used, and operator name, the example screen SE of FIG. 3 displays the frequency of occurrence of each category in a table. In this embodiment, scoring is performed by assigning a number to each category as necessary. In the case of numerical data, the example screen SE of FIG. 3 displays the average, standard deviation, maximum value, minimum value, distribution diagram, etc. The appropriateness of the scoring method is evaluated as appropriate. In the case of date data, the example screen SE of FIG. 3 sorts the data from oldest to newest, just like categorical data, and displays the frequency of occurrence of each date in a table.

[0060] In the case of image data of cells or the like photographed during microscopic observation, the example screen SE in Figure 3 displays a screen in which the images are displayed small so that they can be viewed at a glance, a screen in which the desired image is enlarged as needed, a screen in which, for example, images of the same lot taken on different dates are lined up, and a screen in which images of multiple different lots with the same number of days of culture are lined up for comparison.

[0061] In the case of graph data, the example screen SE in Figure 3 displays, as with image data, a screen that displays the graphs in a small size so that they can be viewed at a glance, a screen that displays the desired graph in an enlarged size as needed, a screen that displays graphs of the same lot but taken on different dates, and a screen that displays graphs of multiple different lots with the same number of culture days for comparison.

[0062] In the case of coordinate data such as the coordinates where the tip of a pipette or the like was positioned inside the culture vessel when liquid was delivered to the culture vessel, the coordinates for each lot are displayed in a schematic diagram of the culture vessel shown in XY coordinates on the screen example SE of Figure 3. The screen example SE of Figure 3 also displays the average, standard deviation, variance, etc. for each of the X and Y coordinates.

[0063] In the case of character string data, all data is displayed in a list on the screen example SE in Fig. 3. Note that character string data that is used frequently, such as "no abnormality," "abnormal appearance," and "cloudy culture medium," is entered in advance as default data in the management system 101. Then, each time such information is entered into the management system 101, any data that can be replaced with default data is replaced, making analysis easier.

[0064] Using experimental data obtained through experiments, manufacturing parameter acceptance criteria are established based on QbD as described above (note that, as mentioned above, manufacturing parameter acceptance criteria are a concept that encompasses the design space). This establishment involves first clarifying the product's QTPP, extracting the QAs required for the product based on the QTPP, and identifying CQAs, which are quality attributes that are particularly important for ensuring quality. Next, this establishment involves identifying CMAs and CPPs from MAs and PPs that affect the CQAs. In this process, Figure 4A shows an example graph of the experimental values ​​of each parameter versus quality index, showing an example of the results when one parameter is assigned and one quality index is plotted. Figure 4B shows an example graph of the experimental values ​​of each parameter versus quality index, showing an example of the results when two parameters are assigned and one quality index is plotted.

[0065] There are multiple parameters and quality indicators. Input variables, such as CQAs, that constitute the manufacturing parameter judgment criteria are determined as a result of QbD-based studies. FIGS. 4A and 4B illustrate examples in which the parameters that constitute the manufacturing parameter judgment criteria are parameter M, and parameters M and N, respectively. Note that parameters M and N are, for example, flow rate and operating temperature. Because these are parameters that constitute the manufacturing parameter judgment criteria, FIGS. 4A and 4B also show the manufacturing parameter judgment criteria 401 and 402 ultimately obtained through experiments. Also, FIGS. 4A and 4B show the quality indicator values ​​403 and 404 obtained when the parameters are varied. The quality indicator thresholds that determine whether or not to release a product are determined in advance through experiments, theory, performance, and so on. The release judgment thresholds can be treated as reference values ​​for the quality standards. If the quality indicator values ​​403 and 404 exceed the release judgment thresholds, the product can be shipped; if they are below the release judgment thresholds, the product cannot be shipped as a rejected product.

[0066] When manufacturing is performed with parameters M and N located within the manufacturing parameter acceptance criteria 401 and 402, the quality index values ​​403 and 404 will exceed the release acceptance criteria threshold. In this embodiment, the manufacturing parameter acceptance criteria may be interpreted as the design space, and therefore, when manufacturing is performed within the design space, the quality index values ​​403 and 404 will exceed the release acceptance criteria threshold. In this embodiment, after going through this examination process, manufacturing parameter acceptance criteria are set as regions formed by the multidimensional combinations and interactions of input variables (CPP, etc.) such as manufacturing process parameters, and the like, within which quality is ensured.

[0067] FIG. 4C is an explanatory diagram showing a screen example SE of a table (left) of each input variable constituting the manufacturing parameter judgment criteria and the values ​​of each quality index obtained when the parameters are assigned, and a graph based on this table (right). As in the screen example SE of FIG. 4C, each input variable constituting the manufacturing parameter judgment criteria and the values ​​of each quality index obtained when the parameters are assigned can be displayed on the screen in the form of a table and a graph. The types of input variables constituting the manufacturing parameter judgment criteria and the types of quality indexes can be selected by the user to display any combination on the screen. For example, by selecting a parameter and a quality index in the table displayed on the screen, a graph related to the selected parameter and quality index is displayed on the screen.

[0068] Here, Fig. 5A is an explanatory diagram showing an example of a two-dimensional manufacturing parameter judgment criterion, Fig. 5B is an explanatory diagram showing an example of a three-dimensional manufacturing parameter judgment criterion, and Fig. 5C is an explanatory diagram showing an example of a one-dimensional manufacturing parameter judgment criterion. FIG. 5A shows a manufacturing parameter judgment standard 501 and a product position 502 representing a cell-processed product plotted as the quality when manufactured using certain parameters M and N. The manufacturing parameter judgment standard 501 is set by taking into consideration the multidimensional combination and interaction of input variables (e.g., CPPs) of manufacturing processes and the like that have been proven to ensure quality. If the plotted point for a certain lot during manufacturing falls within the manufacturing parameter judgment standard 501, the lot can be considered to have ensured quality. If the plotted point falls outside the manufacturing parameter judgment standard 501, the lot can be considered to have failed to ensure quality.

[0069] In FIG. 5A, the area enclosed by the manufacturing parameter judgment criterion 501 (the mesh portion in FIG. 5A) is the area where quality is ensured. Also, while FIG. 5A shows the parameters M and N in two dimensions, it is generally multidimensional. In some cases, quality is considered to be ensured if the parameter is within the area enclosed by the manufacturing parameter judgment criterion, and in other cases, a probability of ensuring quality is set at each point of the manufacturing parameter judgment criterion. In the latter case, the manufacturing parameter judgment criterion is a probabilistic one.

[0070] As mentioned above, the manufacturing parameter judgment criterion 501 is often a multidimensional space. While FIG. 5A shows a two-dimensional example, it can be expressed as a three-dimensional manufacturing parameter judgment criterion 503, consisting of parameters L, M, and N, as shown in FIG. 5B. In this case, too, whether the quality of the lot is ensured can be determined based on whether product position 504 is inside or outside the manufacturing parameter judgment criterion 503. On the other hand, FIG. 5C shows a one-dimensional manufacturing parameter judgment criterion 505 consisting of parameter M. In this case, too, whether the quality of the lot is ensured can be determined based on whether product position 506 is inside or outside the manufacturing parameter judgment criterion 505. Instead of using a multidimensional manufacturing parameter judgment criterion, it is also possible to reduce the dimension for analysis by performing principal component analysis, a statistical method. Furthermore, it is quite conceivable that the number of parameters, CPPs, etc., that constitute the manufacturing parameter judgment criterion will be greater than three, in which case the criterion will be multidimensional.

[0071] Figure 5D is an explanatory diagram showing an example screen SE of a table (left side) of each input variable that constitutes the manufacturing parameter evaluation criteria and the values ​​of each quality index obtained when the parameters are assigned, and a graph (right side) plotting lots (No. 1 to 17) when each parameter in the manufacturing parameter evaluation criteria is assigned.

[0072] As shown in screen example SE of FIG. 5D, the manufacturing parameter judgment standard and the plotted points of lots when each parameter in the manufacturing parameter judgment standard is varied can be displayed on the screen as a graph using symbols such as ●, ▲, and × (Lot Nos. 1 to 17 in the right diagram of screen example SE of FIG. 5D). Note that ● is a point plotted as the quality when manufactured using certain parameters at a certain time, and indicates that it is within manufacturing parameter judgment standard 507. ▲ is a point plotted as the quality when manufactured using certain parameters at a certain time, and indicates that it is on the boundary separating the inside and outside of manufacturing parameter judgment standard 507. × is a point plotted as the quality when manufactured using certain parameters at a certain time, and indicates that it is outside manufacturing parameter judgment standard 507.

[0073] In the first embodiment, as shown in the screen example SE of FIG. 5D, a table showing the parameters and quality indexes for each lot can also be displayed on the screen (the left diagram of the screen example SE of FIG. 5D). For example, by selecting any lot (plot 508 (●), plot 509 (▲), or plot 510 (×) shown for lot Nos. 1 to 17) in the graph displayed on the screen, the user can display information about that lot in the table. Furthermore, by selecting any lot in the table displayed on the screen, the user can display information about that lot (plot 508, plot 509, or plot 510 shown for lot Nos. 1 to 17) in the graph displayed on the screen.

[0074] In this way, manufacturing parameter evaluation criteria can be obtained through experiments based on QbD. However, as mentioned above, regenerative medicine uses cells and biological samples as raw materials, and currently involves many manual processes. Therefore, it is difficult to obtain reliable data with high accuracy by varying numerous conditions and conducting numerous experiments. If the data is unreliable, the manufacturing parameter evaluation criteria obtained from it will also be unreliable. Therefore, the management system 101 according to the first embodiment adds a process for evaluating the impact on quality of each parameter using a cell culture simulation that takes cell fluctuations into account, and for revising the manufacturing parameter evaluation criteria created based on the experimental data.

[0075] Cell populations are inherently heterogeneous and not uniform. Furthermore, the culture environment is heterogeneous. For example, while the temperature inside an incubator where culture is performed is generally maintained at 37°C, the temperature is not uniform across space or time. The internal temperature distribution is uneven due to factors such as the opening and closing of the incubator door and the incubator's performance itself. Furthermore, cells and biological samples are used as raw materials, the quality of which is difficult to standardize, and at present, many processes are performed manually. Taking this background into consideration, cell culture simulation techniques that take cell fluctuations into account have already been published in References 1 and 2 (Reference 1: Thi Nhu Trang Nguyen, Kei Sasaki, and Masahiro Kino-Oka. Development of a kinetic model expressing anomalous phenomena in human induced pluripotent stem cell culture. J of Bioscience and Bioengineering. 2021. 131(3). 305-313.; Reference 2: Eri Shuzui, Mee-Hae Kim, Keisuke Azuma, Yukako Fujinaga, and Masahiro Kino-Oka. Maintenance of an undifferentiated state of human-induced pluripotent stem cells through botulinum hemagglutinin-mediated regulation of cell behavior. J of Bioscience and Bioengineering. 2019. 127(6). 744-751.).

[0076] It is possible to calculate single-cell-level cell behaviors such as migration, division, differentiation, and cell death; interactions with surrounding cells such as contact inhibition and cell-cell interactions; and the behavior and characteristics of entire tissues, such as collective migration and spatial heterogeneity of structure. It is also possible to reflect the effects of culture operations performed in the manufacture of cell-processed products. Because cell populations are heterogeneous and the culture environment is heterogeneous, probabilistic calculations are performed as cell fluctuations. In other words, cell culture simulations that take cell fluctuations into account will output different calculation results even if the same manufacturing conditions are input. Cell culture simulation technology that takes cell fluctuations into account differs from deterministic simulation technology, which outputs the same calculation results when the same manufacturing conditions are input.

[0077] FIG. 6 is an explanatory diagram showing an example of calculation results from a cell culture simulation that takes cell variability into account. In FIG. 6, multiple calculations are performed using a certain value as an input value for one of multiple parameters (in the example shown in FIG. 6, multiple calculations are performed using 5.00 (ml) as an input value), and one of multiple quality indicators is calculated. Note that the manufacturing conditions are a combination of the values ​​of multiple parameters. Furthermore, the quality information is a combination of the values ​​of multiple quality indicators. Because the cell culture simulation that takes cell variability into account performs probabilistic calculations, the calculation results will differ each time. For example, the approximately V-shaped graph (distribution) obtained for n = 1, 2, 3, etc. will be different. As shown in FIG. 6, the mean and standard deviation (mean ± SD) are calculated for all calculation results, but other statistical features may also be calculated. Note that if cell variability is not taken into account, when a certain value (5.00 (ml) in the example shown in FIG. 6) is input, the calculation results will be calculated, for example, as a normal distribution each time.

[0078] Even if a parameter value, which is an input value, is specified as a specific value in a standard procedure manual or the like, it may actually fluctuate. For example, in a manual process, variability may occur depending on the proficiency of the operator. Furthermore, in an automated process, variability may occur due to control errors in the device itself. For example, when delivering 5 ml of liquid, the actual delivered volume may vary, such as 5.00 ± 0.05 ml. Therefore, variability in input values ​​may also be taken into account in calculations using cell culture simulations. The magnitude of variability in input values ​​may be determined through experiments or set by the user. Therefore, in this embodiment, the calculation unit 106 acquires parameter probability distribution information (e.g., the value of the probability distribution specific to each parameter, such as the liquid delivery volume and the flow rate) for parameter information (e.g., the value of the liquid delivery volume and the flow rate) input to the input units 102, 103, and 104, and then simulates the culture process of the target cells based on the parameter probability distribution information. In this way, a more appropriate simulation can be performed that takes into account the variability (probability distribution of the parameters input to the input section).

[0079] FIG. 7A is an explanatory diagram showing a table (upper left) showing the calculation results of a cell culture simulation that takes cell fluctuations into account for experimentally determined manufacturing parameter criteria, and example screens SE showing graphs (upper right, lower left, and lower right) plotting the calculation results. Each graph in FIG. 7A, like FIG. 4A, shows an example of the results obtained by varying one parameter and plotting one quality index. There are multiple parameters and quality indexes, such as flow rate (P3), cell count (Q1), cell viability (Q2), purity (Q5), etc. While input variables such as CPPs that constitute the manufacturing parameter criteria are determined through QbD-based considerations, FIG. 7A shows an example of a parameter (flow rate (P3)) that constitutes the manufacturing parameter criteria. Because this parameter constitutes the manufacturing parameter criteria, FIG. 7A also shows the manufacturing parameter criteria 701 obtained from experiments. In contrast, FIG. 7A shows the quality index values ​​obtained when parameters are varied using a cell culture simulation that takes cell fluctuations into account. Figure 7A displays graphs for three quality indicators: cell number (Q1), cell viability (Q2), and purity (Q5). However, graphs for all quality indicators can be displayed by scrolling or switching the screen.

[0080] In Figure 7A, P1 on the horizontal axis indicates one end of parameter P3 that is located within the manufacturing parameter criterion 701, and P2 indicates the other end. As described in Figure 6, the quality index values ​​for each parameter are plotted as mean ± standard deviation (mean ± SD). Note that in this specification and drawings, mean ± standard deviation may also be expressed as mean ± variance. Figure 7A shows an example of a case where the quality index for parameter P3, which is within the manufacturing parameter criterion 701, is manufactured within the range from P1 to P2 of parameter P3, and the results calculated using a cell culture simulation that takes cellular variability into account are above the release criterion threshold, just as in the experimental results. In Figure 7A, the quality index values ​​were calculated using a cell culture simulation that takes cellular variability into account for the parameters of the manufacturing parameter criterion obtained from the experiment. As a result, Figure 7A shows a case where the calculated results, like the experimental results, are above the release criterion threshold, and therefore no modification of the manufacturing parameter criterion 701 is necessary.

[0081] Note that the results calculated using parameters included in the manufacturing parameter judgment criteria 701 exceed all of the shipping judgment criteria thresholds, but the results calculated using parameters not included in the manufacturing parameter judgment criteria 701 do not necessarily fall below all of the shipping judgment criteria thresholds, and there may be cases where the results are both above and below the shipping judgment criteria thresholds. These will be described with reference to Figures 7B and 7C.

[0082] FIG. 7B, like FIG. 7A, is a graph showing an example of the results of varying one parameter and plotting one quality index. FIG. 7B illustrates an example where parameter M is a component of the manufacturing parameter criterion. Therefore, the manufacturing parameter criterion 702 obtained through experimentation is also shown. In contrast, FIG. 7B illustrates an example where the quality index values ​​obtained when parameter M is varied using a cell culture simulation that takes cellular fluctuations into account are plotted. The graph shows an example where parameters (in the range from M2 to M3) outside the manufacturing parameter criterion 702, which is within the range from M1 to M2 of parameter M obtained through experimentation, exceed the release criterion threshold. Note that M1 represents one end of the manufacturing parameter criterion 702 obtained through experimentation, and M2 represents the other end. M3 represents the end where the quality index value exceeds the manufacturing parameter criterion 702, as calculated using a cell culture simulation that takes cellular fluctuations into account. As shown in FIG. 7B, unlike the experimental results, the calculated results exceed the manufacturing parameter criterion 702 within the range from M2 to M3 of parameter M.

[0083] As mentioned above, when developing a control strategy based on QbD, parameters must be determined experimentally so that the quality of the cell-processed product falls within the manufacturing parameter criteria. However, experiments using cells present the risk of obtaining unreliable data due to the difficulty in ensuring experimental reproducibility due to variations in cell characteristics. If manufacturing parameter criteria are determined using such experimental results, calculation results using a cell culture simulation that takes cell fluctuations into account may result in the parameters falling within the manufacturing parameter criteria, unlike the experimental results. Therefore, the calculation unit 106 performs a cell culture simulation that takes cell fluctuations into account and presents a proposed revision 703 (the range of M1 to M3 for parameter M) for the manufacturing parameter criteria 702 based on the results. Here, this proposed revision 703 for the manufacturing parameter criteria 702 is within a range that also satisfies the release criterion thresholds for other quality indicators. Information regarding this is output to the display unit 109, such as a screen, as quantitative information, such as scores and ranks, showing the manufacturing stability of all quality indicators relative to the release criterion thresholds, as shown in the example table in the screen example SE of Figure 9B (described later). That is, the calculation unit 106 calculates information on the average value and standard deviation or variance of the quality index for each value included in the parameter information, and outputs the evaluation index information to the display unit 109, such as a screen, based on the calculated information and information on the reference value (shipping criterion threshold) of the quality standard. The information is also displayed on the display unit 109, such as a screen, as visual information, plotting the calculation results of each quality index against the shipping criterion threshold, as a graph in the example screen SE of FIG. 9B (described later). This makes it easier for the operator to decide whether to adopt the proposed revision 703. Returning to FIG. 7B , the explanation continues. In the first embodiment, the user conducts a new experiment using the proposed revision 703 of the manufacturing parameter judgment criterion 702 shown in FIG. 7B and considers the advisability of the proposed revision 703 based on QbD. In this case, the experimental results are used to extend the manufacturing parameter judgment criterion 702. The evaluation index information may include qualitative information in addition to the quantitative information described above. Examples of qualitative information include information on cell morphology, information on the presence or absence of turbidity in the medium, and information indicating the results of a determination based on these whether or not the shipping criteria are met.

[0084] FIG. 7C, like FIG. 7A, is a graph showing another example of the results of plotting one quality index by varying one parameter. FIG. 7C also shows an example where parameter M, which constitutes the manufacturing parameter criterion, is used. Therefore, the manufacturing parameter criterion 704 obtained through experimentation, is also shown. In contrast, FIG. 7C shows an example where the quality index values ​​obtained when parameter M is varied using a cell culture simulation that takes cellular fluctuations into account, are plotted. The plot shows a case where parameters (in the range from M4 to M2) located within the manufacturing parameter criterion 704 within the range from M1 to M2 of parameter M obtained through experimentation are below the shipping criterion threshold. Note that M1 and M2 are the same as described above. M4 indicates the end of the quality index value calculated using the cell culture simulation that takes cellular fluctuations into account, where the value falls below the manufacturing parameter criterion 704. As shown in FIG. 7C, unlike the experimental results, the calculated results show that the value falls below the manufacturing parameter criterion 702 within the range from M4 to M2 of parameter M.

[0085] In the embodiment shown in FIG. 7C , the computing unit 106 of the management system 101 performs a cell culture simulation that takes cell fluctuations into account and presents a proposed revision 705 (a range of M1 to M4 for parameter M) for the manufacturing parameter criterion 704 based on the results. As in the case of FIG. 7B , the proposed revision 705 for the manufacturing parameter criterion 704 is within a range that also satisfies the release criterion thresholds for other quality indicators. As in the case of FIG. 7B , the corresponding information is displayed on the display unit 109, such as a screen, as quantitative information (evaluation index information), such as scores and ranks, indicating the manufacturing stability relative to the release criterion thresholds for all quality indicators, as shown in the example table in the screen example SE of FIG. 9B (described later). Furthermore, the above information is displayed on the display unit 109, such as a screen, as visual information, plotting the calculation results relative to the release criterion thresholds for each quality indicator, as a graph in the screen example SE of FIG. 9B (described later). Returning to FIG. 7C , we will continue the explanation. In the first embodiment, the user conducts a new experiment using proposed revision 705 of the manufacturing parameter evaluation criteria 704 shown in Fig. 7C and considers the advisability of the proposed revision 705 based on QbD. In this case, the experiment results are used to narrow down the manufacturing parameter evaluation criteria 704.

[0086] When presenting proposed revisions 703 and 705 for the manufacturing parameter criteria 702 and 704, the calculation unit 106 evaluates the stability of the calculation results obtained using each parameter by using a cell culture simulation that takes into account cell fluctuations, relative to the manufacturing parameter criteria 702 and 704 and the shipping criteria threshold determined through experiments. Figures 8A to 8C are explanatory diagrams illustrating indices for evaluating the stability of the calculation results obtained using each parameter. Note that the mean ± SD in Figures 8A to 8C all show the results of simulations for parameter M at certain points M1 to M6.

[0087] If the calculation results for each parameter outside the manufacturing parameter criteria satisfy the shipping criterion threshold, based on the manufacturing parameter criteria and the shipping criterion threshold determined through experiments, then the manufacturing parameter criteria can be expanded and modified. The calculation results for each parameter are the result of multiple calculations, and in this embodiment, they are expressed as an average value and standard deviation (or variance). If all of the multiple calculation results for a certain parameter satisfy the shipping criterion threshold, then it is considered appropriate to include that parameter as part of the manufacturing parameter criteria. On the other hand, it is possible that some of the multiple calculation results for a certain parameter satisfy the shipping criterion threshold, while the rest do not. The percentage of the multiple calculation results that satisfy the shipping criterion threshold for a parameter to be considered part of the manufacturing parameter criteria is expected to differ depending on the institution that performs regenerative medicine using cell-processed products. Therefore, the degree to which the multiple calculation results for a certain parameter satisfy the shipping criterion threshold is determined as the stability of the calculation results for each parameter. Specifically, the relationship between the calculation results obtained by multiple runs using certain parameters and the shipping determination threshold value can be quantified using the three indices shown in Figures 8A to 8C, and the scores indicating whether the calculation results obtained by multiple runs using those parameters satisfy the shipping determination threshold value can be calculated from these three indices. Furthermore, all parameters can be ranked in descending order of the scores that satisfy the shipping determination threshold value.

[0088] The index shown in FIG. 8A indicates how high the average value (●) of the results of multiple calculations is relative to the shipping determination criterion threshold. For example, the calculation unit 106 calculates the ratio between the average value of the results of multiple calculations and the shipping determination criterion threshold. As another example, the calculation unit 106 calculates the difference between the average value of the results of multiple calculations and the shipping determination criterion threshold. The larger this value, the greater the average of the results of multiple calculations manufactured using those parameters will be than the shipping determination criterion threshold. In other words, there is a higher possibility that better quality will be obtained. In the example shown in FIG. 8A, the ratio and difference values ​​are larger for M2 than for M1, so it can be determined that there is a higher possibility that better quality will be obtained.

[0089] The index shown in FIG. 8B indicates how small the standard deviation (error bars) and variance of the results of multiple calculations are. For example, the calculation unit 106 calculates the reciprocal of the standard deviation (reciprocal of variance) of the results of multiple calculations. The larger this value, the smaller the standard deviation and variance of the results of multiple calculations manufactured using those parameters. The smaller the standard deviation and variance of the results of multiple calculations, the smaller the variation in the manufactured results in that quality index, and the more stable the quality can be said to be. In the example shown in FIG. 8B, M4 has a smaller standard deviation (variance) than M3, so it can be determined that the quality is stable.

[0090] The index shown in FIG. 8C indicates the proportion of multiple calculation results that satisfy the shipping determination criterion threshold. For multiple calculation results manufactured using certain parameters, the calculation unit 106 sets the upper limit of the quality index value to "mean + standard deviation" (or "mean + variance") and the lower limit to "mean - standard deviation" (or "mean - variance"). The calculation unit 106 then calculates the proportion of that region that is included in the region that satisfies the shipping determination criterion threshold. The higher the proportion that is included in the region that satisfies the shipping determination criterion threshold, the more frequently multiple calculation results manufactured using certain parameters will satisfy the shipping determination criterion threshold. In other words, there is a higher possibility of obtaining better quality. In the example shown in FIG. 8C, M6 has a higher proportion that is included in the region that satisfies the shipping determination criterion threshold than M5, so it can be determined that there is a higher possibility of obtaining better quality.

[0091] From the three indices thus obtained, the results of multiple calculations performed using each parameter are calculated as a score that satisfies the shipping criteria threshold. The score may be calculated, for example, as the product or sum of the three indices. Furthermore, each index may be weighted at the user's discretion. These depend on the concept of quality, and it is expected that this concept will differ depending on the institution that performs regenerative medicine using cell-processed products. Since the method of calculating the score and the weighting of each index depend on the concept of the institution that performs regenerative medicine, it should be possible for the administrative user, rather than a general user, to set the score according to the concept of each institution. Details of this will be described later with reference to Figure 10B.

[0092] The scores obtained so far are the result of assigning parameter values ​​for a combination of a certain parameter and a certain quality index. Different combinations of parameters and quality indexes result in different calculation results and evaluations of quality stability. Basically, only one parameter is assigned and the value of one quality index is compared. While it is possible to assign the same parameter and compare all quality index values, it is preferable to assign only one parameter. This is because comparing the quality index values ​​of multiple parameters makes it difficult to interpret the results. In some cases, multiple parameters may be linked or dependent. In such cases, multiple parameters with such relationships may be assigned and the quality index values ​​may be compared. Analysis may be performed by reducing the dimension using principal component analysis, a statistical method. An overall score may be calculated using the scores obtained from various combinations of parameters and quality indexes. For example, it may be calculated as the product or sum of all scores. Weighting may also be added. The calculation method and whether or not weighting is used can be set by the administrative user according to the institution's preferences. Furthermore, the calculation unit 106 may rank the parameters in descending order of overall score to determine an overall ranking. Note that performing calculations for all combinations of parameters and all quality indices can result in a huge amount of calculations. Therefore, it is conceivable to calculate an overall score and an overall rank for the parameters that are input variables constituting the manufacturing parameter evaluation criteria, based on the scores and ranks obtained for particularly important quality indices.

[0093] 9A and 9B are explanatory diagrams showing example screens SE that use scores and ranks to present suggested revisions to the manufacturing parameter criterion (experimental MPC) created based on experimental data. The tables in both Fig. 9A and 9B display the experimental results for each lot, along with multiple calculation results calculated using a cell culture simulation that takes cell fluctuations into account.

[0094] In the example screen SE of Figure 9A, a portion of the lot is selected from the table, and experimental values ​​901 for the parameters to be evaluated are displayed in a graph (in this figure, three measurements at time P6 for parameter P3). Also, in the same graph, the results of multiple calculations for the parameters to be evaluated for the lot selected in the table, calculated using a cell culture simulation that takes cellular fluctuations into account, are displayed as mean values ​​902 and standard deviations 903 (in this figure, mean ± SD at time P7 for parameter P3). In the example screen SE of Figure 9A, the values ​​of three stability indicators obtained by comparing the multiple calculation results with the aforementioned release criteria thresholds are displayed in the table. The values ​​of the three stability indicators are: (1) the mean position of the calculated values, (2) the SD range of the calculated values, and (3) the proportion of calculated values ​​within the experimental MPC. When calculating scores and ranks from the values ​​of these three stability indicators, the indicators may be weighted. The weighting depends on the concept of quality, and since it is expected that concepts will differ depending on the institution that performs regenerative medicine using cell-processed products, it should be possible for the administrative user, not the general user, to set it according to the concept of each institution. This will be described in more detail later with reference to Figure 10B. Also, in the example screen SE of Figure 9A, as described above, it is possible to calculate an overall score and an overall rank for scores and ranks calculated for particularly important quality indicators.

[0095] The example screen SE in Figure 9B displays experimentally determined manufacturing parameter criteria (experimental MPC) and multiple calculation results calculated using a cell culture simulation that takes cell fluctuations into account, in graphs and tables. The table in the example screen SE in Figure 9B displays the three indices (1) through (3), scores, and ranks for the selected quality indicators for the selected parameters. For example, if a parameter outside the manufacturing parameter criteria achieves better values ​​than the three indices, scores, and ranks of a parameter within the experimentally determined manufacturing parameter criteria, the range that includes that parameter is added to the experimentally determined manufacturing parameter criteria. In other words, the manufacturing parameter criteria can be expanded (experimental MPC revision). This expansion corresponds to adding a flow rate of 5.5 to 6.5 ml / sec to the table in the example screen SE in Figure 9B. Conversely, if the three indices, scores, and ranks of a parameter within the experimentally determined manufacturing parameter criteria are clearly inferior to those of other parameters within the manufacturing parameter criteria, the range that includes that parameter is excluded from the experimentally determined manufacturing parameter criteria. In other words, narrowing down the manufacturing parameter evaluation criteria is possible (not shown in the example screen SE of Figure 9B). The thresholds for the three indicators, score, and rank in the proposal to expand and narrow down the manufacturing parameter evaluation criteria depend on the concept of quality, and it is expected that the concept will differ depending on the institution that performs regenerative medicine using cell-processed products. Therefore, it should be possible for the administrative user, not the general user, to set the criteria according to the concept of each institution. Details of this will be described later with reference to Figure 10C.

[0096] The functions of the management system 101 used by the administrative user and their screen example SE will be described with reference to Figures 10A to 10C. Figures 10A to 10C are explanatory diagrams each illustrating a screen example SE of the management system 101 used by the administrative user. It is assumed that approaches to quality vary among institutions that perform regenerative medicine using cell-processed products. The related functions and screen example SE are intended for use by an administrative user. Figure 10A shows a screen example SE of the management system 101's functions used when experimentally determining manufacturing parameter acceptance criteria. A method for determining manufacturing parameter acceptance criteria based on QbD involves conducting experiments using Design of Experiments (DoE) or other methods, and creating a response surface that represents the relationship between multiple CQAs and CMAs or CPPs using response surface methodology. Quality indicators may be included in CQAs. Parameters may be included in CMAs and CPPs. The calculation unit 106 sets search conditions for CQAs, i.e., target ranges for quality indicators, and finds simultaneous optimal solutions. Based on the obtained simultaneous optimal solutions, the calculation unit 106 determines the range of CMAs or CPPs as manufacturing parameter acceptance criteria from the range that overlaps the allowable ranges of the identified CMAs or CPPs. As shown in FIG. 10A , during this process, the calculation unit 106 visualizes, in a graph, how the experimental data obtained when one parameter in a CMA or CPP is varied in setting the CQA search conditions and determining the range of a CMA or CPP as the manufacturing parameter judgment criterion is plotted against each quality index. The table in the example screen SE of FIG. 10A also displays quantitative information (evaluation index information) such as a score or rank as quality stability. The X-axis of the graph in the example screen SE of FIG. 10A changes depending on the selection of a CMA or CPP. The Y-axis also changes depending on the type of quality index. The quality stability information in the table also changes accordingly. When multiple ranges are set in determining the range of a CMA or CPP as the manufacturing parameter judgment criterion, the above information can be displayed comparatively on a single screen to show how each data changes. Using these functions, the management system 101 supports the administrative user's judgment in setting the CQA search conditions and determining the range of a CMA or CPP as the manufacturing parameter judgment criterion. For example, as shown in the graph of the screen example SE in FIG. 10A, the screen displays Manufacturing Parameter Criteria (MPC) allowable range (Proposal 1), MPC allowable range (Proposal 2), etc., to assist the administrative user in making a decision.

[0097] Next, referring to Figure 10B, we will explain a screen example SE in which the scores and ranks are calculated by weighting the indicators when calculating the scores and ranks from the values ​​of the three stability indicators mentioned in Figure 9A. Figure 10B illustrates how the administrative user inputs weighting values ​​for each of the three stability indicators based on their approach to quality, and the scores and ranks are displayed using the values ​​of the three indicators that have changed according to the weighting values. As shown in Figure 10B, the weighting values ​​can be, for example, 0.5 for (1) the average position of the calculated values, 2 for (2) the SD range of the calculated values, and 1 for (3) the ratio of the calculated values ​​within the experimental MPC. The values ​​after changing the weighting can be displayed on the screen as shown in the table at the bottom of the screen example SE in Figure 10B. By referring to the values ​​before and after the change, appropriate weighting values ​​can be set.

[0098] Referring to FIG. 10C , a screen example SE will be described in which an administrative user displays and compares examples of acceptable ranges of manufacturing parameter criteria in the process of expanding and narrowing the manufacturing parameter criteria described in FIG. 9B . Regarding a selected parameter, the three indices (score, rank) shown in the table of the screen example SE in FIG. 10C are used for the selected quality index. For example, if a parameter outside the manufacturing parameter criteria has better values ​​than the three indices (score, rank) of a parameter within the manufacturing parameter criteria determined through experimentation, the range including that parameter is added to the manufacturing parameter criteria determined through experimentation. In other words, the manufacturing parameter criteria can be expanded. Here, the values ​​of the three indices (score, rank) that are considered appropriate for inclusion in the manufacturing parameter criteria depend on the quality perspective. Conversely, if the three indices (score, rank) of a parameter within the manufacturing parameter criteria determined through experimentation are clearly inferior to those of other parameters within the manufacturing parameter criteria, the range including that parameter is excluded from the manufacturing parameter criteria determined through experimentation. In other words, the manufacturing parameter criteria can be narrowed.

[0099] It is assumed that different approaches are taken by different institutions that perform regenerative medicine using cell-processed products, and the related functions and screen examples are intended for use by an administrative user. Therefore, based on the administrative user's judgment, multiple examples of acceptable manufacturing parameter acceptance criteria ranges are displayed and compared. In this example, below the graph in the screen example SE of Figure 10C, an experimental MPC with parameter (P3: flow rate) of 3.5 to 5.0 ml / sec, an experimental MPC revision plan <1> with parameter (P3: flow rate) of 3.5 to 6.5 ml / sec, and an experimental MPC revision plan <2> with parameter (P3: flow rate) of 3.0 to 5.0 ml / sec are displayed. In this example, in the screen example SE of Figure 10C, the calculated average positions of the index <1> for parameter (P3: flow rate) of 3.0 ml / sec and 7.0 ml / sec are -0.3 and -0.5, respectively, which are close to but slightly below the release acceptance criteria threshold. Therefore, in this example, the administrative user selects experimental MPC revision proposal <1> that does not include these parameters in the manufacturing parameter judgment criteria, and causes it to be displayed on the output unit 108. Note that although the screen example SE in FIG. 10C does not show tables, graphs, or revision proposals related to narrowing down, it is conceivable that the calculation unit 106 selects a range of 4.0 to 5.0 ml / sec for the parameter (P3: flow rate) as a narrowing down revision proposal and causes it to be displayed on the output unit 108. In this case, it is possible to place even more emphasis on stability than the experimental MPC.

[0100] <Management method> A management method for correcting manufacturing parameter judgment criteria created based on experimental data using the management system 101 having the above functions will be described. Fig. 11 is a flowchart illustrating the content of the management method according to the first embodiment. As shown in FIG. 11, the management method includes an input step S111, a calculation step S112, and an output step S113.

[0101] In input step S111, parameter information relating to at least parameters for processing the cells to be cultured by culturing is input. In the calculation step S112, a simulation of the culture process of the cells to be cultured is performed based on the parameter information input in the input step S111 and cell characteristic information, which is information on the probability distribution of the cell characteristics of the cells to be cultured. In the output step S113, information on the first manufacturing parameter judgment criteria, which is information on the manufacturing conditions that must be met when culturing the cells to be cultured, is output based on experimental data from an experiment in which the cells to be cultured are cultured and information on product quality standards related to the cells to be cultured.

[0102] Here, in the management method according to the first embodiment, the calculation step S112 calculates, based on the results of the simulation and information on the quality standard, evaluation index information, which is an evaluation index for the quality index relative to the quality standard when the cells to be cultured are cultured at that value, for each value included in the parameter information, and outputs information on a proposed revision of the first manufacturing parameter judgment criterion based on the evaluation index information in the output step S113.

[0103] Fig. 12 is a flowchart illustrating a more specific series of steps of the management method according to the first embodiment described above. As shown in Fig. 12, the management method has steps S1 to S15. The input step S111 described above corresponds to step S2. Steps S3 to S14 are performed in the calculation step S112 described above. The output step S113 described above is performed when steps S6 to S8, S10, etc. are displayed. Steps S1 to S15 in the management method will be described below.

[0104] <Step S1: Start> The management system 101 is started.

[0105] <Step S2: Experimental data input> All information on the life cycle of a cell processed product, such as each process (collection, purification, gene transfer, culture, concentration, transportation, transplantation, etc.), material management (raw materials, etc.), and clinical information (adverse events after transplantation and / or safety information, etc.) is input to the management system 101 using input units 102, 103, and 104. The input method is selected according to the format of the data to be input, as shown in Figures 1 and 2A.

[0106] After input, the feature quantities of various information are displayed on the display unit 109, as shown in the example screen SE in Figure 3. For example, in the case of numerical data, the average, standard deviation, maximum value, minimum value, distribution chart, etc. are displayed. In the case of categorical data, the occurrence frequency of each category is displayed in a table. In the case of character string data, depending on the content, processing such as replacing it with data entered in advance as default data in the management system 101 makes analysis easier. In addition, various output data such as post-manufacturing quality and post-transplant prognosis information, which will be analyzed as a result of changing the input conditions, are displayed.

[0107] <Step S3: Creating manufacturing parameter criteria from experimental data> Based on QbD, manufacturing parameter criteria are set, which are areas formed by the multidimensional combination and interaction of input variables (CPP, etc.) such as manufacturing process parameters.

[0108] <Step S4: Selection of parameters to be considered> The user selects the parameters to be considered, primarily input variables (CPP, etc.) such as manufacturing process parameters that constitute the manufacturing parameter criterion.

[0109] <Step S5: Calculation of parameters to be examined> The user calculates the quality index values ​​using a cell culture simulation that takes into account cell fluctuations for the parameters to be examined selected in step S4. Since there are multiple quality indexes, calculations are performed for all quality indexes.

[0110] <Step S6: Displaying the distribution of calculated values ​​of the parameters to be considered within the manufacturing parameter judgment criteria obtained through experiments> As shown in the example screen SE of FIG. 7A, the calculation results are output to the display unit 109 of the management system 101, for example, on a screen. The values ​​of multiple quality indexes obtained by calculation are expressed, for example, as "average value ± standard deviation" (mean ± SD). In particular, the calculation results indicate whether or not the shipping criterion threshold is satisfied in the classification of whether the parameter is inside or outside the manufacturing parameter criterion.

[0111] <Step S7: Display of quality stability of calculated values ​​of each parameter under consideration> As shown in Figures 8A to 8C, the quality stability is calculated by using three indicators: the average value of multiple calculation results, the standard deviation of multiple calculation results, and the proportion of the quality index value included in the quality pass range (the range above the shipping determination standard threshold) defined by the shipping determination standard threshold when the upper limit of the quality index value is set to "average + standard deviation" and the lower limit is set to "average - standard deviation", as well as a score expressing quality stability calculated from the three indicators, and a rank assigned in descending order of score, and this is output to the display unit 109 of the management system 101.

[0112] <Step S8: Display of proposed modifications to the manufacturing parameter criteria obtained through experiments> As shown in Figure 9B, in the classification of when the parameters are inside the manufacturing parameter judgment criteria and when they are outside the manufacturing parameter judgment criteria, whether the calculation results meet the shipping judgment criteria threshold value or not, taking into account the quality stability quantitatively indicated in step S7, a proposed modification to the manufacturing parameter judgment criteria obtained through experiments is output to the display unit 109 of the management system 101.

[0113] <Step S9: Have all the target parameters to be compared and considered been determined?> After step S8, if all parameters to be considered (parameters to be compared) that are considered necessary for reexamining the manufacturing parameter judgment criteria obtained through experiments have been determined (Yes in step S9), the process proceeds to step S10. If all parameters to be considered have not been determined (No in step S9), the process returns to step S4 and a new examination is performed. Whether or not all parameters to be considered have been determined can basically be determined by determining all input variables (CPP, etc.) such as parameters of the manufacturing process that constitute the manufacturing parameter judgment criteria, but parameters that are considered to affect quality may also be added as appropriate.

[0114] <Step S10: Displaying target parameters to be reexamined through experiments> The parameters to be examined, which are output by the management system 101 and are considered to be necessary for reexamining the manufacturing parameter judgment criteria determined by the experiment, are displayed on the display unit 109.

[0115] <Step S11: Experimental verification and resetting of manufacturing parameter criteria> The user conducts the experiment again using the displayed parameters under consideration and resets the manufacturing parameter judgment criteria based on QbD.

[0116] <Step S12: Calculation of parameters to be examined> As in step S5, the user calculates the quality index values ​​for the parameters under consideration using a cell culture simulation that takes into account cell fluctuations. Since there are multiple quality indexes, calculations are performed for all quality indexes.

[0117] <Step S13: Is the modification of the manufacturing parameter criteria appropriate?> After step S12, the results of the reexamination of the manufacturing parameter evaluation criteria obtained through the experiment and the calculation results of the cell culture simulation that takes cell fluctuations into account are evaluated for consistency. If there is no consistency (No in step S13), return to step S10. If there is consistency (Yes in step S13), proceed to step S14.

[0118] <Step S14: Confirm the modification of the manufacturing parameter judgment criteria> If the results of the reexamination of the manufacturing parameter evaluation criteria obtained through experiments are consistent with the calculation results of the cell culture simulation that takes cell fluctuations into account (Yes in step S13), the revised manufacturing parameter evaluation criteria are confirmed.

[0119] <Step S15: End> When the review is completed, the review results are electronically stored in the auxiliary storage unit 107. The user terminates the operation of the management system 101 by performing an appropriate operation.

[0120] The management system 101 and management method according to the first embodiment described above can evaluate the impact of each parameter on quality using a cell culture simulation that takes cell fluctuations into account and revise the manufacturing parameter criteria (first manufacturing parameter criteria) created based on experimental data. The management system 101 and management method perform multiple calculations under all manufacturing conditions in the cell culture simulation and output a quality index that reflects the variations in cell characteristics. The management system 101 and management method quantify the proportion of manufacturing conditions that fall within the manufacturing parameter criteria (design space) created based on experimental data. The management system 101 and management method evaluate the distribution of cell culture simulation results relative to the manufacturing parameter criteria created based on experimental data and determine whether the manufacturing parameter criteria need to be revised, such as by expanding or narrowing them. In this way, the management system 101 and management method combine experimental data with calculation data for evaluation, thereby improving the accuracy of the manufacturing parameter criteria and, as a result, stabilizing the quality of cell-processed products.

[0121] <Embodiment 2> A management system 101 according to the second embodiment will be described. In the second embodiment, first, various data relating to the life cycle of a cell processed product or the like, obtained up to a certain point in time, are input as quality information into the management system 101. Examples of such data include each process related to manufacturing 201, such as collection, purification, gene transfer, culture, concentration, formulation, transportation, and transplantation, material management 202 of raw materials, etc., and medical information management 203, such as adverse events and / or safety information after transplantation. The quality information is input using input units 102, 103, and 104.

[0122] In manufacturing after a certain point, data up to an intermediate stage of manufacturing (at least one of manufacturing information, treatment information, and transportation information for the cell-processed product in the middle of manufacturing) is input into management system 101. This data input is also performed using input units 102, 103, and 104. Calculation unit 106 calculates where the data up to that point falls on the manufacturing parameter judgment criteria, and displays the result on display unit 109. At the same time, calculation unit 106 uses a cell culture simulation that takes cell fluctuations into account to obtain calculation results regarding the quality that would be obtained if manufactured using parameters up to the intermediate stage of manufacturing (calculates the quality state at that time). In the calculation results, for example, three indices, scores, and ranks are evaluated for selected parameters and selected quality indicators as product stability.

[0123] The calculation unit 106 uses these to predict the quality at the end of production from data up to the intermediate stages of production. As in embodiment 1, the quality standard at the time of completion of the cell processed product is used as the quality standard. The calculation unit 106 predicts whether each cell processed product will pass or fail at completion based on the production parameter judgment criteria and the quality information of each cell processed product at the intermediate stages of production (i.e., predicts whether it will pass or fail against the quality standard at the time of completion).

[0124] A specific method for predicting data at the time of completion (which may be quality information or product position in the manufacturing parameter judgment standard) based on quality information at an intermediate manufacturing stage can be appropriately designed by a person skilled in the art. For example, a function that receives quality information at an intermediate manufacturing stage as input and outputs product position may be stored in advance. The specific content of the function can be appropriately defined by a person skilled in the art based on publicly known techniques, etc. Machine learning can also be used.

[0125] When it is found that the data obtained up to an intermediate stage of production is outside the manufacturing parameter judgment criteria, an option to stop production of the product can be adopted, either by a user's instruction or automatically by the management system 101. Rather than evaluating the quality at the end of production and finding that the shipping judgment criteria are not met, it is more cost-effective to predict that the shipping judgment criteria will not be met during the intermediate stage of production and stop production.

[0126] In addition, if it is predicted that the product will not meet the release criteria during the manufacturing process, it is possible to change the manufacturing method so that the product will meet the release criteria at the end of manufacturing. In this case, however, it is preferable that changes to the manufacturing method during manufacturing be permitted at the time of manufacturing approval.

[0127] As another example, a threshold value for determining whether an intermediate product in the middle of production should proceed to the next process, i.e., a manufacturing parameter judgment standard for the intermediate product, may be set and evaluated against that threshold. Alternatively, the manufacturing parameter judgment standard for the intermediate product, previously determined through experiments, may be evaluated based on the calculation results of a cell culture simulation that takes cell fluctuations into account, as in the first embodiment, and a proposed revision of the manufacturing parameter judgment standard for the intermediate product may be output. That is, the calculation unit 106 may calculate at least one of the quality state of the cell processed product in the middle of production relative to the quality standard of the intermediate product and the quality state at the end of each process relative to the quality standard, and output a proposed revision of the manufacturing parameter judgment standard for the cell processed product on the display unit 109. This may then be examined experimentally. This allows the proposed revision of the manufacturing parameter judgment standard to be confirmed and examined during the middle of production, thereby more appropriately stabilizing the quality of the cell processed product. Furthermore, by confirming and examining the proposed revision during the middle of production, if it is determined that the release judgment standard is not met, the product may not proceed to the next process, thereby reducing costs.

[0128] <Embodiment 3> A management system 101 according to a third embodiment will be described. In the third embodiment, data obtained through an experiment and calculation results from a cell culture simulation that takes cell fluctuations into consideration are input to the management system 101, and these are treated equally. That is, the calculation unit 106 adds the calculation results from the simulation of the culture process of the cells to be cultured to the experimental data, and performs further simulation. Then, the management system 101 sets manufacturing parameter evaluation criteria, etc., based on QbD. Experiments in regenerative medicine are costly and time-consuming, and it is difficult to ensure experimental reproducibility due to variations in cell characteristics, so it is not easy to increase the number of experiments and obtain highly reliable data. Therefore, the management system 101 improves the quantity and quality of data by adding calculation results from cell culture simulations that take cell fluctuations into account to the data obtained from experiments.

[0129] It is also possible to change the weighting of experimental data and calculated data by setting a weighting value. If experimental data is the result of actually using cells and is to be given more weight than calculated data, the weighting value of the calculated data can be lowered below 1. On the other hand, if experimental data is subject to variability depending on the operator and it is important to respect the fact that calculated data can be obtained by increasing the number of trials, the weighting value of the calculated data can be higher than 1.

[0130] <Embodiment 4> A management method according to the fourth embodiment will now be described. Fig. 13 is a flowchart illustrating a specific series of steps of the management method according to the fourth embodiment. As can be seen by comparing with the flowchart in Fig. 12, the management method according to the fourth embodiment differs from the management method according to the first embodiment in that it includes steps S21 and S22. The following description will focus on the differences.

[0131] The management method according to the fourth embodiment uses machine learning to present a proposal for correcting the manufacturing parameter criteria obtained through experiments. As shown in FIG. 13, the management method according to the fourth embodiment accumulates the results of the user's calculations in step S112, specifically, in steps S6, S7, S8, and S12, and uses the results to generate a machine learning model that selects parameters, etc., and presents suggestions for modifying the manufacturing parameter evaluation criteria (first manufacturing parameter evaluation criteria) in steps S4, S8, S12, etc. Specifically, as shown in FIG. 13, data is accumulated as appropriate in step S21, machine learning is performed in step S22, and the results are reflected in steps S4, S8, S12, etc.

[0132] As the machine learning model, well-known or publicly known techniques such as neural networks and logistic regression may be adopted, and therefore will not be described in detail in this embodiment. When the user makes selections in steps S4, S8, S12, etc., the machine learning selections are also displayed.

[0133] Machine learning can input the parameter range before the change and the manufacturing parameter judgment criterion, and output the parameter range after the change and the manufacturing parameter judgment criterion with the highest change priority. Also, for example, machine learning can create training data in steps S8 and / or S12 that input the parameter range before the change and the manufacturing parameter judgment criterion, and output the parameter range and the manufacturing parameter judgment criterion with the highest change priority.

[0134] By using the trained model created in this way, it is possible to calculate an appropriate changed parameter range based on the parameter range before the change in steps S4, S8, S12, etc. The calculated range may be reflected as the range with the highest change priority in step S12. In this way, change recommendation information, i.e., a revision proposal, is output. As a result, the management method according to the fourth embodiment improves the accuracy of the examination content, and as a result, it is possible to stabilize the quality of cell processed products, etc. Furthermore, it is possible to realize product manufacturing that takes into account a huge number of parameters related to cell processed products. [Explanation of symbols]

[0135] 101 Management System 102 Input section 103 Input section 104 Input section 105 Main memory 106 Arithmetic section 107 Auxiliary storage 108 Output section 109 Display section 110 databases 111 Manufacturing equipment and monitoring devices 112 Work Instructions 113 Input terminal 201 Manufacturing 202 Materials Management 203 Medical information management 204 Treatment information management 205 Basic Experiments 401 Manufacturing Parameter Criteria 403 Quality Index Values 404 Quality Index Values 501 Manufacturing Parameter Criteria 502 Product position 503 Manufacturing Parameter Criteria 504 Product position 505 Manufacturing Parameter Criteria 506 Product position 507 Manufacturing Parameter Criteria 508 plots 509 plots 510 plots 701 Manufacturing Parameter Judgment Criteria 702 Manufacturing Parameter Criteria 703 Amendment 704 Manufacturing Parameter Criteria 705 Amendment 901 experimental value 902 average 903 standard deviations

Claims

1. a memory unit that stores cell characteristic information, which is information about a probability distribution of cell characteristics of cells to be cultured, experimental data of an experiment in which the cells to be cultured are cultured, and information about product quality standards related to the cells to be cultured; an input unit into which parameter information relating to at least parameters for processing the cells to be cultured by culture is input; a calculation unit that performs a simulation of a culture process of the cells to be cultured based on the parameter information and the cell characteristic information inputted to the input unit; an output unit that outputs information on a first manufacturing parameter judgment standard, which is information on a manufacturing condition that should be satisfied when culturing the target cells, based on the experimental data and the information on the quality standard; The calculation unit calculates, for each value included in the parameter information, based on the results of the simulation and information on the quality standard, evaluation index information that is an evaluation index for the quality standard of a quality index when the cells to be cultured are cultured using that value, and outputs information regarding a proposed revision of the first manufacturing parameter judgment criterion based on the evaluation index information.

2. 2. The management system according to claim 1, The calculation unit calculates the average value and standard deviation or variance of the quality index for each value included in the parameter information, and outputs the evaluation index information based on the calculated information and information on the reference value in the quality standard.

3. 2. The management system according to claim 1, The calculation unit acquires information on the probability distribution of the parameters for the parameter information input to the input unit, and performs a simulation of the culture process of the cells to be cultured based on the information on the probability distribution of the parameters.

4. 2. The management system according to claim 1, The calculation unit receives as input at least one of manufacturing information, treatment information, and transportation information for a cell-processed product in the process of being manufactured, uses this information to calculate the quality status at that time, and predicts whether the product will pass or fail the quality standards upon completion.

5. 2. The management system according to claim 1, The calculation unit calculates at least one of the quality status of an intermediate product in the middle of production relative to a quality standard and the quality status of the cell processed product relative to a quality standard at the end of each process, and outputs a suggested revision of the manufacturing parameter judgment standard relative to the quality status of the cell processed product via the output unit.

6. 2. The management system according to claim 1, The management system is characterized in that the calculation unit adds calculation results from a simulation of a culture process of the cells to be cultured to the experimental data and performs further simulations.

7. an input step in which parameter information relating to at least parameters for processing the cells to be cultured by culture is input; a calculation step of simulating a culture process of the cells to be cultured based on the parameter information input in the input step and cell characteristic information which is information on a probability distribution of cell characteristics of the cells to be cultured; an output step of outputting information on a first manufacturing parameter judgment standard, which is information on manufacturing conditions that should be satisfied when culturing the cells to be cultured, based on experimental data of an experiment in which the cells to be cultured are cultured and information on product quality standards related to the cells to be cultured; The calculation step calculates, based on the results of the simulation and information on the quality standard, evaluation index information that is an evaluation index for the quality index of the quality index when the cells to be cultured are cultured at that value, for each value included in the parameter information, and outputs information related to a proposed revision of the first manufacturing parameter judgment criterion based on the evaluation index information in the output step.

8. The management method according to claim 7, A management method characterized in that the calculation step uses the results obtained from the simulation to generate a machine learning model that proposes modifications to the first manufacturing parameter judgment criterion.

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