Information processing device

Abstract

The present invention provides a processing device comprising an input device, an output device, a processor, and a storage device. The input device receives, as input, quality information including a plurality of parameters related to a plurality of processed cell products, the quality information including a parameter related to at least one of manufacturing information, therapeutic information, therapeutic result information, and transportation information related to the processed cell products. The storage device stores a predetermined quality criterion and the quality information. The processor calculates a first region on the basis of the quality criterion, calculates a quality state of the processed cell products on the basis of the first region and the quality information, and calculates a corresponding range of one or more types of another parameter on the basis of a range specified by one or more types of the parameter. The output device outputs the corresponding range.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, particularly to a device for processing parameters related to cell-processed products, for example, a device for managing quality by visualizing the relationship between parameters such as manufacturing data and quality of cell-processed products. [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. Cell-processed products that meet quality evaluation standards are transported to a medical institution, etc., and used to treat the patient.

[0003] Traditionally, the manufacture 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. The QTPP (Quality Target Product Profile) for the product being developed is clarified, the QAs (Quality Attributes) required for the product are extracted based on the QTPP, and critical quality attributes (CQAs) that are particularly important for ensuring quality are identified. CMAs (Critical Material Attributes) and CPPs (Critical Process Parameters) are identified from the MAs (Material Attributes) and PPs (Process Parameters) that affect the CQAs, and a control strategy is developed. A design space is also established to enable production at any scale or lot size. Design space refers to the multidimensional combination and interaction of input variables (CQAs, etc.) of manufacturing processes, etc. that have been proven to ensure quality. Control strategies are continually validated and improved. The introduction of QbD is also being considered for regenerative medicine, but progress has been slow due to the difficulty of standardizing quality in regenerative medicine compared to pharmaceuticals, as cells and biological samples are used as raw materials, and the fact that many processes are currently performed manually, which can lead to variability in the work content.

[0004] To introduce QbD into regenerative medicine, it is necessary to accumulate and manage various information throughout the life cycle of cell-processed products, such as collection, purification, gene transfer, culture, concentration, transportation, and transplantation, as well as materials management for raw materials and other materials, and clinical information such as adverse events and / or safety information after transplantation. It is also necessary to understand the relationship between quality characteristics, variability, and treatment outcomes, identify indicators that affect quality, and develop a control strategy. However, as mentioned above, regenerative medicine uses cells and biological samples, whose quality is difficult to standardize, as raw materials, and many processes are currently performed manually. Therefore, it is difficult to understand the causal relationships and mechanisms throughout the life cycle of cell-processed products and to identify factors from various life cycle information that have a significant impact on quality characteristics, variability, treatment outcomes, etc. It is also difficult to understand the relationship between parameters such as manufacturing data and quality.

[0005] Patent Document 1 discloses a method for accumulating information on manufacturing processes, etc. based on QbD and determining whether quality is met for pharmaceuticals. Patent Document 2 discloses a method for improving manufacturing conditions, etc., based on accumulated manufacturing information and raw material information, etc., for regenerative medicine. However, both documents primarily focus on the manufacturing process. They do not anticipate accumulating information on the lifecycle of cell-processed products, such as all manufacturing processes, as well as information on material management of raw materials and post-transplant clinical information. Furthermore, they do not mention methods for identifying factors that strongly influence quality characteristics, variability characteristics, treatment outcomes, etc., from various pieces of information in the lifecycle in order to understand causal relationships and mechanisms in the lifecycle. In regenerative medicine, the diverse and complex manufacturing processes make it difficult to recognize the relationships between each piece of information in the lifecycle. For example, they do not mention methods for managing quality by visualizing the relationship between parameters such as manufacturing data and quality. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication No. 2020-158581 [Patent Document 2] International Publication No. 2013-008733 Summary of the Invention [Problem to be solved by the invention]

[0007] As shown in Patent Documents 1 and 2, it is possible to accumulate information on manufacturing processes, etc. based on QbD, determine whether quality is met, improve manufacturing conditions, etc. However, it does not anticipate accumulating various information on the life cycle of cell-processed products, such as raw materials other than the manufacturing process and post-transplant medical information, as parameters.

[0008] Furthermore, it is not expected to identify parameters that have a strong influence on quality characteristics, variability characteristics, treatment outcomes, etc. from various information in the life cycle in order to understand the causal relationships and mechanisms in the life cycle, nor to recognize the relationships between each parameter in the life cycle.

[0009] Furthermore, it is not intended to visualize the relationship between each parameter.

[0010] The present invention has been made to solve the above problems, and has an object to provide an information processing device that can generate more useful information regarding the relationship between parameters of a cell-processed product. [Means for solving the problem]

[0011] An example of an information processing device according to the present invention is An information processing device comprising an input device, an output device, a processor, and a storage device, the input device receives as input quality information including a plurality of parameters related to a plurality of cell processed products, the quality information including parameters related to at least one of manufacturing information, treatment information, treatment result information, and transportation information related to the cell processed products; the storage device stores a predetermined quality standard and the quality information; The processor: - calculating a first region based on said quality criterion, - calculating the quality status of the cell-processed product based on the first region and the quality information; - calculating corresponding ranges of one or more other parameters based on the ranges specified for one or more of said parameters; The output device outputs the corresponding range.

[0012] An example of a program according to the present invention causes a computer to function as the information processing device described above.

[0013] An example of an information processing method according to the present invention is a step of receiving, by an input device, quality information including a plurality of parameters relating to a plurality of cell processed products as input, the quality information including parameters relating to at least one of manufacturing information, treatment information, treatment result information, and transportation information relating to the cell processed products; a storage device storing a predetermined quality criterion and the quality information; a processor calculating a first region based on the quality metric; the processor calculating a quality state of the cell-processed product based on the first region and the quality information; the processor calculating corresponding ranges of one or more other parameters based on ranges specified for one or more of the parameters; an output device outputting the corresponding range; Equipped with. [Effects of the Invention]

[0014] The information processing device according to the present invention can generate more useful information regarding the relationships between parameters of cell-processed products.

[0015] For example, the information processing device according to the present invention can visualize the relationship between inputs related to manufacturing, etc., and output quality. By changing the range of each input parameter in various patterns, it is possible to visualize how other input parameters and output quality move within the design space.

[0016] The distance of a plotted point representing the quality of a parameter from the center of gravity and / or boundaries of the design space can be quantified as product stability, and the corresponding range of product stability for each parameter can be quantified.

[0017] From these values, it is possible to rank the priorities of items that should be improved in manufacturing, etc.

[0018] Users of information processing devices can prioritize improvements to parameters that have the greatest impact on quality. Examples of improvements include manufacturing parameters, the selection of equipment used in manufacturing, training for workers, and changes to the layout of equipment in the cell manufacturing room, as well as the environment, such as cleanliness. As a result, the quality of cell-processed products can be stabilized. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a configuration diagram of a quality control system according to a first embodiment of the present invention. [Figure 2A] FIG. 1 shows examples of factors that generate information in the life cycle of a cell-processed product. [Figure 2B] FIG. 10 is a diagram showing an example of quality information. [Figure 3] A diagram illustrating the distance to the center of gravity and boundaries of the design space. [Figure 4] Examples of a three-dimensional design space and a one-dimensional design space. [Figure 5A] An example of displaying statistical information on quality information. [Figure 5B] Example of product positioning relative to the design space. [Figure 5C]An example of quality information for a specified cell-based product. [Figure 5D] Example of product positioning of a specified cell-based product against the design space. [Figure 5E] An example of displaying quality information for multiple cell-based processed products specified from a graph. [Figure 5F] An example of displaying quality information for multiple cell-based processed products selected from a table. [Figure 5G] Example display showing product positions for multiple specified cell-based products against the design space. [Figure 5H] An example of the distribution of quality information when changing the parameter range of a cell-based processed product. [Figure 5I] An example display showing the distribution of quality information when multiple parameter ranges of a cell-based processed product are changed. [Figure 6] 4 is a flowchart showing the operation of the quality control system according to the first embodiment. [Figure 7] 10 is a flowchart showing the operation of a quality control system according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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 examples of the invention described below show preferred embodiments of the present invention and are presented for illustrative or explanatory purposes, and are not intended to limit the present invention. 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.

[0021] [Example 1] 1 shows the configuration of a quality control system 101 (information processing device) according to Example 1. The quality control 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).

[0022] The quality control system 101 handles information related to cell-processed products. In this specification, the term "cell-processed product" broadly encompasses products manufactured using cells or cell tissues, and particularly includes regenerative medicine products as defined by the Pharmaceutical and Medical Device Act. The unit of a cell-processed product can be defined arbitrarily, but for example, one manufacturing lot can be considered as one unit of a cell-processed product.

[0023] The input units 102, 103, and 104 have a mechanism for importing various data by linking with other systems and equipment, and by handling 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. The data imported from these units is temporarily stored in the main storage unit 105 in the memory.

[0024] The acquired data is then processed and / or linked in a calculation unit 106 within a CPU (Central Processing Unit) to give value to the data.

[0025] For example, the impact of changing each input parameter on other parameters (how they change passively) is calculated, and in particular the impact of changes within the design space on the output quality is calculated. The distance between the point plotted as the quality of a certain parameter and the center of gravity and / or boundary of the design space is calculated as product stability. The corresponding range of product stability for changes in each parameter is calculated. From these values, priorities are ranked for items that need to be improved in manufacturing, etc.

[0026] These 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 quality control 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 for quality control system 101. The relationship between each parameter related to manufacturing, etc. that is imported as input and the quality obtained as output is displayed using various calculation results. Examples of output unit 108 include a display, printer, and speaker.

[0027] 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 quality control 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 information processing device 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).

[0028] The input units 102, 103, and 104 each have different functions corresponding to a different data import method. 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 the database 110 (DB) of each system and imports the referenced data.

[0029] 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, a cleanliness monitoring device that monitors the number of airborne bacteria and particles in a manufacturing environment, a monitoring system that monitors the details of manual work and the movements of workers in manufacturing equipment, a transportation monitoring device that measures the temperature and / or pressure during transportation, etc.

[0030] 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 kind of system and that database is referenced. The input unit 103 is designed to take in data directly from the manufacturing equipment / monitoring devices 111 without going through a database.

[0031] 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.

[0032] 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 a database and then imported from the input unit 102. The data to be imported may not only be generated during commercial production after obtaining marketing approval, but may also include information collected throughout the entire life cycle of cell-processed products, such as clinical trials, clinical research, and basic research. 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 with this in mind.

[0033] Figure 2A shows examples of the generation of various types of information (quality information) during the life cycle of cell-processed products, etc., which are accumulated and / or managed by the quality control system, such as data on the manufacture of cell-processed products, materials management of raw materials, etc., medical information management, treatment information management of adverse events and / or safety information after transplantation, and basic experiment data.

[0034] The manufacturing process 201 may vary depending on the type of cell-processed product being manufactured and the type of disease being treated. The processes described here include collection, purification, gene transfer, culture, concentration, formulation, and transplantation. A transportation process may also be included.

[0035] Furthermore, although it is stated that the data relating to materials management 202 is stored in a materials management system, the data relating to medical information management 203 is stored in an electronic medical record, the data relating to treatment information management 204 is stored in a patient registry, and the data relating to basic experiments 205 is stored in a laboratory information management system, other methods may also be used.

[0036] Although three types of input units 102 to 104 were mentioned in Figure 1, as shown in Figure 2A, in the manufacture of cell-processed products and the like, the location of execution may vary depending on the process. For example, the collection process may be performed at a medical institution, etc. Purification, gene transfer, culture, concentration, and formulation are generally performed in a CPF. The type of input unit used may vary depending on the execution location. Furthermore, even for processes performed within the same CPF, the type of input unit used may vary for each process or for each more detailed task.

[0037] For example, when an automated culture device is used in the culture process, data may be directly input into the quality control system via input unit 103 shown in FIG. 1, or the data may be first entered into the database of the manufacturing execution system and then input into the quality control system via input unit 102. Alternatively, when an operator manually performs the culture process in a safety cabinet and manually records the work results, etc., in a work instruction sheet, data may be manually entered using 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 input unit 102 is used in the gene transfer and culture processes, input unit 103 is used in the concentration process, and input unit 104 is used in the formulation and transportation processes.

[0038] Various types of information stored and / or managed by the quality control system 101 in the life cycle of cell processed products and the like will be described.

[0039] An example of the quality information is shown in Figure 2B. 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.

[0040] The quality information may 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.

[0041] 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. Naturally, the quality information may include information other than these. Other examples are described below.

[0042] Manufacturing information is divided into manual and automated manufacturing processes, either by process or by more specific 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.

[0043] Examples of quality information that is generated, for example, in the cell seeding work of the culture process, include the number of cells when the cells to be seeded are collected, the cell survival rate, the expression level of a specific protein, etc. Regarding the culture vessel used for culture, in addition to the type (shape, type of substrate, culture method), additional information such as the manufacturer name, lot number, date of manufacture, and expiration date may be stored in a materials management system as material management data.

[0044] Examples of manufacturing information include information for seeding into a culture vessel, such as 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 during 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 operation time.

[0045] Examples of transportation information include the transportation speed, vibration during transportation, total transportation time, etc. when transporting a culture vessel from a work area such as a safety cabinet to an incubator where the culture is performed after seeding. The information may also include the temperature and / or pressure during transportation.

[0046] These items can have a greater or lesser impact on the quality of cell-processed products and intermediates after production, and the quality control system evaluates the magnitude and manner of their impact. For example, if cell viability is low at the time of collection, it can be assumed that the cells will have low activity in subsequent culture. If the liquid delivery speed during seeding is too high, the cells will be subjected to greater shear stress, which may affect their subsequent proliferation.

[0047] When seeding cells, the temperature and gas phase in a safety cabinet or other similar environment are generally not as controlled as in an incubator where the cells are cultured. This can result in a drop in temperature and / or a change in the pH of the culture medium over the course of the seeding process, which can affect the cells. When cells are transported from a work area such as a safety cabinet to an incubator where they are cultured, vibrations during transport can cause vibrations and / or shocks to the cells. Forces can be applied to the cells due to acceleration or route changes caused by changes in transport speed, which can change the cell distribution within the culture vessel and affect the subsequent culture results. This information can also be included in the transport information.

[0048] The total transport time, like the sowing time, can affect temperature changes and changes in the pH of the medium. Furthermore, the range of control and the items monitored often differ between automated manufacturing 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, the number of monitored items will be increased, and the types and / or number of data will be increased to enable more accurate analysis of the quality control system. This information may be included in the transport information.

[0049] In particular, assuming that manufacturing and / or implantation are carried out at multiple facilities, such information is also important so that differences between facilities can be analyzed in the quality control system if they may affect quality. For example, for the equipment used, the manufacturer's name, model number, maintenance information (date, frequency, and maintenance content), and initialization method for each use may be included. This information may also be included in the manufacturing information.

[0050] 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.

[0051] 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, important information includes 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. This information may be included in the quality information.

[0052] The donor information that receives the tissue to be collected includes information about the donor. In Figure 2A, this information is linked to the collection process. Specific donor information is expected to include 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.

[0053] Data that can be stored in a materials management system for material management 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. This is assumed to be information managed when purchasing materials. This information may also be included in quality information.

[0054] Lot numbers, especially those for serum used in culture, are said to have a significant impact on culture results. Specific material management information is expected to include order number, orderer, order date, product name, manufacturer, item, delivery date, purchase price, delivery destination information, and expiration date. In the case of biological samples, additional information is required, such as animal species, site of use, and use process. Since some materials may be divided and used by aliquoting, information such as the date of opening, aliquot container, number of aliquots, aliquot volume, and subnumber may also be generated, but this information may be treated as manufacturing information. This is because it is also linked to information on the work date, worker, and work results when aliquoting or other work is performed. This information may also be included in quality information.

[0055] Data that can be stored in electronic medical records for medical information management includes basic information and medical information on patients undergoing transplants. Note that it is expected that this data may overlap with data that can be stored in patient registries for treatment information management, which will be described later, and it is preferable to adjust this information accordingly. Specific medical information is expected to include 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 start date of transplant, and end date of transplant. This information may also be included in the quality information.

[0056] Among the quality information, data that can be stored in the patient registry as treatment information management includes treatment information and treatment result information.

[0057] Specific treatment information includes information on the transplanted cells, 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.

[0058] Examples of treatment outcome information are expected to include post-transplant efficacy, adverse events, safety information, and 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. Further detailed adverse event information is expected to include 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, and congenital / familial / genetic disorders.

[0059] Data regarding basic experiments that can be accumulated in the laboratory information management system may include data acquired as basic research in the early stages of development, data acquired as more detailed experiments are added as development progresses and the treatment mechanism becomes clearer to support the therapeutic effect, and data acquired as basic research when an opinion that differs from the hypothesis emerges. Specific basic experiment information is expected to include the date of the experiment, the name of the experimenter, the experimenter ID, the experiment name, the start time of the experiment, the time the experiment was completed, the ID of the cells used, the name of the cell type used, the number of cells, etc. This information may also be included in the quality information.

[0060] The type and amount of data to be acquired may vary depending on the content of the experiment. For example, in the case of an experiment to evaluate cell morphology and cell proliferation, expected data include cell images, image ID, time of image capture, culture vessel number, location of image capture within the culture vessel, morphology evaluation results, details of morphological abnormalities, cell number / cell occupancy rate, etc. In the case of an experiment to evaluate differentiation potential, expected data include culture vessel number, differentiation induction destination, cell images, image ID, cell morphology evaluation results, flow cytometry measurement results, marker expression evaluation results, etc. This information may also be included in the quality information.

[0061] The data stored in the quality control system is first linked. Data from each process is linked using date and / or worker ID, etc., and consistent data is created for each lot for various information related to the lifecycle from collection to transplant. Linking is particularly important for information stored in systems different from the quality control system.

[0062] Change the data format as necessary. When accumulating data that was stored in a system different from the quality control system, align it to the specifications of the quality control system. For example, if the amount of fluid delivered is shown in different units, such as "10ml" and "10cc," unify them. Obtain basic statistical information for all data. For numerical data, obtain maximum / minimum values, average, variance, distribution charts, etc. Categorical data will be scored and examined in the same way. The validity of the scoring method will be evaluated as appropriate.

[0063] Using Figure 3, we explain the calculation method for quantifying product stability as the distance between the center of gravity and / or boundary of the design space and a point plotted as the quality when manufactured under certain parameters at a certain time.

[0064] As mentioned above, the design space is the multidimensional combination and interaction of input variables (CQAs, etc.) of manufacturing processes, etc. that have been proven to ensure quality.

[0065] The design space represents, for example, a specific region (first region) in a parameter space formed by parameters of quality information, and is calculated based on a predetermined quality criterion that is separately input. In particular, the design space corresponds to the first region of the completed cell-based processed product. The quality criterion represents a preferred value or range for at least one parameter included in the quality information.

[0066] The quality standard is stored in the main memory 105 and / or the auxiliary memory 107 together with the quality information.

[0067] If the plotted point for a certain lot during manufacturing is within the design space, it can be considered that quality has been ensured. Furthermore, even if input variables such as the manufacturing process are changed, if the plotted point for that lot continues to be within the design space, it can be considered that quality has been ensured for that manufacturing method as well. This allows production at any scale and / or lot size as long as the plotted point for the lot of the selected manufacturing method continues to be within the design space.

[0068] Here, as long as the plotted points for the batches using the selected manufacturing process remain within the design space, it can be considered that quality is assured, but when new analysis is conducted, including data obtained from subsequent manufacturing and research and development, the range and / or shape of the design space that ensures quality may change. Considering this possibility, it was considered that quality was stable when the plotted points for the batches using the selected manufacturing process were located further inside the design space, or further away from the boundary within the design space, at the time of evaluation.

[0069] The point plotted as the quality of a product manufactured under certain parameters at a certain time point was evaluated based on the relationship between the center of gravity and / or boundaries of the design space to avoid it being located outside the design space if the range and / or shape of the design space changes in subsequent analysis.

[0070] For the former, the closer the point plotted as the quality when manufactured using certain parameters at a certain time point is to the center of gravity of the design space, the more stable the quality is. For the latter, the farther the point plotted as the quality when manufactured using certain parameters at a certain time point is to the boundary of the design space, the more stable the quality is.

[0071] Based on the above, both the distance from the center of gravity of the design space and the distance from the boundary of the design space are used as indicators of product stability (quality state). Alternatively, only one of these may be used as an indicator of quality state.

[0072] This section explains how to calculate the distance from the center of gravity and / or boundary of the design space as the quality stability of a point plotted within the design space as the quality when manufactured under certain parameters at a certain time point.

[0073] Figure 3(A) shows a design space 301 and a product position 302 that represents the quality of a cell-processed product plotted as it is manufactured using certain parameters at a certain point in time. The design space 301 is a multidimensional combination and interaction of input variables (CQAs, etc.) of the manufacturing process that have been proven to ensure quality. If the plotted point for a certain lot during manufacturing is within the design space, it can be considered that quality has been ensured.

[0074] In Figure 3(A), the area enclosed by design space 301 (the gray area in Figure 3(A)) is the area where quality is ensured. Also, while Figure 3(A) shows two dimensions with parameters N and M, it is generally multidimensional. In some cases, quality is considered to be ensured as long as it is within the area enclosed by the design space, and in other cases, the probability of ensuring quality is set at each point in the design space. In the latter case, it becomes a probabilistic design space. To satisfy both methods, the coordinates of design space 301 and the probability of ensuring quality at that point are expressed as follows: Coordinates: (x m1 , x m2 , x m3 , …, x mi , …, x mn ) Probability of ensuring quality at that point: DS% m where m is the product index and i is the parameter index, i=1, 2, 3, ..., n.

[0075] n-dimensional coordinate (x m1 , x m2 , x m3 , …, x mi , …, x mn ) Probability of quality being maintained at each coordinate DS% m If quality is considered to be assured as long as it is within the area enclosed by design space 301, the possible values ​​for DS% are 0 or 100. If the product position plotted for a certain lot at the time of manufacturing is in the area where DS% = 100 (the gray area in Figure 3(A)), quality is considered to be assured.

[0076] On the other hand, when setting the probability of ensuring quality at each point in design space 301, the DS% can take values ​​between 0 and 100. The magnitude of the DS% value at each point represents the probability that the quality of the plotted point for a certain lot at the time of manufacturing is ensured. The larger the DS% value, the higher the probability that quality is ensured.

[0077] Information about the design space is entered in advance from outside the quality control system. In addition, in Figure 3(A), the points that make up the inside and outside of the design space 301 are expressed as grid points where each axis intersects. Increasing the increments of each axis increases the number of grid points that make up the inside and outside of the design space, and therefore increases accuracy, but this increases the calculation load and processing time. The accuracy of the points that make up the inside and outside of the design space 301 is determined depending on the required calculation accuracy.

[0078] In the space where the design space 301 is set, a product position 302 (here, X represents the Ath product) is plotted as the quality when manufactured with certain parameters at a certain time point to be evaluated. Ai X Ai The coordinates of are as follows: X Ai =(x A1 , x A2 , x A3 , …, x Ai , …, x An )

[0079] The calculation method for determining the center of gravity 303 of the design space will be explained using Figure 3(B). The coordinates of the center of gravity 303 of the design space, X Gi X Gi =(x G1 , x G2 , x G3 , …, x Gi , …, x Gn ) is calculated using the following formula: X Gi =[Σ(x mi *DS% m ) / Σ(DS% m )] It may also be found by integration. X Gi =∫(x mi *DS% m )dx / ∫(DS% m )dx

[0080] In this way, the calculation unit 106 calculates the design space based on the quality criteria.

[0081] Design space center of gravity X Gi and the product position 302 (X Ai ) squared distance d(X Gi , X Ai ) is the center of gravity distance 304. d(X Gi , X Ai )=(x A1 -x G1 ) 2 +(x A2 -x G2 ) 2 +(x A3 -x G3 ) 2 + …+(x Ai -x Gi ) 2 +…+(x An -x Gn ) 2

[0082] Next, the calculation method for determining the boundary of the design space will be explained using Figure 3(C). In the design space 301, the coordinates of the point where DS% = 100 or the coordinates of the point where DS% is close to 100 are determined. A grid point 305 in the vicinity that is tangent to the inside of the boundary of the design space 301 is determined. A "grid point tangent to the inside of the boundary" refers to, for example, a grid point inside the design space 301, where one of the grid points adjacent to that grid point is outside the design space 301.

[0083] mth grid point X that touches the inside of the boundary Bm The coordinates of are expressed as follows: X Bm =(x Bm1 , x Bm2 , x Bm3 , …, x Bmi , …, x Bmn ) Next, the mth grid point X Bm and the product position X, which is plotted as the quality of the product manufactured under certain parameters at a certain time, to be evaluated. Ai The squared distance d(X Bmi , X Ai ) at the m-th grid point X Bm and product position X Ai The distance is d(X Bmi , X Ai )=(x A1 -x Bm1 ) 2 +(x A2 -x Bm2 ) 2 +(x A3 -x Bm3 ) 2 + …+(x Ai -x Bmi ) 2 +…+(x An -x Bmn ) 2

[0084] mth grid point X Bm Among them, product position X Ai The squared distance d(X Bmi , XAi ) is the minimum distance boundary grid point 306, and the minimum distance boundary grid point 306 and the product position X Ai The squared distance d min (X Bmi , X Ai ) is the boundary distance 307 (minimum distance). Ai If is outside the design space, the squared distance d min (X Bmi , X Ai ) is multiplied by -1 and expressed as a negative value. min (X Bmi , X Ai ) is 0, it is on the boundary of the design space.

[0085] The centroid distance 304 and boundary distance 307 in the design space thus obtained are used in the evaluation as the quality stability indicating the quality state. It can be said that the smaller the centroid distance 304 and / or the larger the boundary distance 307, the better the quality.

[0086] In this way, the calculation unit 106 calculates the quality state of the cell processed product based on the design space 301 and the quality information. For example, the calculation unit 106 calculates the center of gravity 303 of the design space 301, and calculates the product position 302 representing the cell processed product relative to the design space 301 based on the quality information. The calculation unit 106 then calculates the quality state based on the center of gravity distance 304 between the center of gravity 303 and the product position 302 (the center of gravity distance 304 may be used as the quality state directly). In this way, the center of gravity 303, which represents the desired quality, can be used as the calculation standard for the quality state, allowing the quality of the cell processed product to be appropriately evaluated.

[0087] Furthermore, for example, the calculation unit 106 calculates the boundary of the design space 301 (in this embodiment, represented by the grid points that touch the inside of the boundary), and calculates the product position 302 for the design space 301 based on the quality information. The calculation unit 106 then calculates the quality state based on the boundary distance 307 between the boundary and the product position 302 (the boundary distance 307 may be used as the quality state directly). In this way, the distance from the boundary that represents undesirable quality can be used as the calculation criterion for the quality state, allowing the quality of the cell processed product to be appropriately evaluated.

[0088] Although squared distance was used as an example of distance here, Euclidean distance or Mahalanobis distance may be used, especially when determining the distance from the center of gravity of the design space. When Mahalanobis distance is used, it becomes an evaluation index as a distance that takes into account the variability of each coordinate that makes up the design space.

[0089] The design space 301 can be a multidimensional space. While Figure 3 shows a two-dimensional design space as an example, Figure 4(A) shows a three-dimensional design space 401, and Figure 4(B) shows a one-dimensional design space 402. Instead of using a multidimensional design space, it is also possible to perform analysis by reducing the dimension by performing principal component analysis, a statistical method. It is entirely possible that the number of parameters (CQAs) used to construct the design space will be more than three, in which case the design will be multidimensional.

[0090] The flow of analysis using data accumulated in the quality control system will be explained using example screens.

[0091] Figure 5A is a screen that displays various information about the life cycle of cell-processed products, etc., such as the manufacturing of cell-processed products, etc., material management of raw materials, etc., medical information management, treatment information management such as adverse events and / or safety information after transplantation, and data related to basic experiments, which are accumulated and / or managed by the quality control system.

[0092] It displays both input data for possible changes to conditions such as manufacturing parameters, and output data to be analyzed as a result of changing input conditions such as quality after manufacturing and prognosis information after transplantation. When the amount of information is too large to display all of it on one screen, it is divided and displayed according to the type of information, the time of occurrence, etc.

[0093] For numerical data, display the average, standard deviation, maximum value, minimum value, distribution chart, etc. For categorical data such as manufacturing location, serum lot, name of equipment used, model number of equipment used, and worker name, show the frequency of occurrence of each category in a table. If necessary, assign a number to each category to create a score, and for numerical data, display the average, standard deviation, maximum value, minimum value, distribution chart, etc. Evaluate the appropriateness of the scoring method as appropriate. For date data, arrange the data from oldest to newest as with categorical data, and show the frequency of occurrence for each date in a table.

[0094] In the case of image data of cells etc. photographed during microscopic observation, there are screens that display the images small so that they can be viewed at a glance, screens that display desired images enlarged as needed, screens that display images of the same lot taken on different dates, and screens that display images of multiple different lots with the same number of days of culture for comparison.

[0095] In the case of graph data, as with image data, there is a screen that displays the graphs small so that they can be viewed at a glance, a screen that displays the desired graph enlarged 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, etc.

[0096] 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 delivering liquid to the culture vessel, the coordinates for each lot are shown in a schematic diagram of the culture vessel shown in XY coordinates. The average and variance for each X and Y coordinate are also displayed.

[0097] In the case of character string data, all data is displayed in a list. Note that frequently used character string data such as "No abnormalities," "Abnormal appearance," and "Culture medium is cloudy" are entered as default data in advance in the quality control system, and whenever such information is entered into the quality control system, any data that can be replaced with the default data is replaced, making analysis easier.

[0098] Figure 5B shows various output data, such as post-manufacturing quality and post-transplant prognosis information, that will be analyzed as a result of changing the input conditions. It shows a diagram showing the distribution within design space 501, and a table showing the centroid distance and boundary distance of the design space for each manufacturing lot, as well as its position relative to the design space (inside / outside / on the boundary). The average and standard deviation of the centroid distance and boundary distance of the design space for each manufacturing lot are also shown.

[0099] Figure 5B also shows a table displaying various data that will be output on a separate screen. In the diagram showing the distribution within the design space 501, points corresponding to each manufacturing lot are plotted, with product locations within the design space 502 plotted as circles, product locations at the boundary of the design space 503 plotted as triangles, and product locations outside the design space 504 plotted as crosses.

[0100] In addition, a manufacturing lot number or serial number is assigned near each point. By selecting each product position on the screen, quality information (such as the manufacturing lot, manufacturing date, manufacturing location, etc.) related to the product corresponding to the selected product position is displayed.

[0101] Figures 5C and 5D show a diagram of the distribution of points for a particular production lot within the design space, as shown in Figure 5C, when a particular production lot is selected in the table. Figure 5D shows the distribution of points for that production lot within the design space, as well as a table showing the centroid distance and boundary distance of the design space for that production lot, and its position relative to the design space (inside / outside / on the boundary).

[0102] Figure 5D also shows the product position 505 within the design space for that manufacturing lot point, the center of gravity 506 of the design space, and the grid points inside the boundary of the design space 507. The center of gravity distance 508 and boundary distance 509 are also shown. The center of gravity 506 of the design space and the grid points inside the boundary of the design space 507 may also be displayed in Figure 5B.

[0103] Figures 5E, 5F, and 5G show diagrams in which multiple production lots are selected in Figures 5E and 5F, diagrams showing the distribution of product positions within the design space for multiple production lots selected in Figure 5G, and tables showing the centroid distance and boundary distance of the design space for those production lots, and their positions relative to the design space (inside / outside / on the boundary).

[0104] 5E, when selecting a portion of any input item, the user determines a selection range 510 specified in the graph. In response, the range of production lots included in the selection range 510 changes from all lots (full range) to production lots 511 (corresponding range) that correspond to the selection range specified in the graph, and the production lots 511 that correspond to the selection range are highlighted in the table.

[0105] In addition, each production lot included in the selection range 510 is displayed in other items as a production lot 512 (corresponding range) in the graph corresponding to the selection range specified in the graph, and a production lot 513 (corresponding range) in the category corresponding to the selection range specified in the graph.

[0106] In this way, the calculation unit 106 (FIG. 1) calculates the corresponding range of one or more other parameters based on the range specified in one or more parameters included in the quality information, and the output unit 108 (FIG. 1) outputs the calculated corresponding range.

[0107] 5F, when selecting a portion of any input item, the user determines a selection range 514 (one or more ranges including one or more production lots) specified in the table. The production lots included in the selection range 514 are displayed in other items as production lots 515 in the graph corresponding to the selection range specified in the table, and as production lots 516 in the category corresponding to the selection range specified in the table.

[0108] For multiple manufacturing lots selected in Figures 5E and 5F, Figure 5G displays a diagram showing the distribution within the design space, as well as a table showing the centroid distance and boundary distance of the design space for that manufacturing lot, and its position relative to the design space (inside / outside / on the boundary).

[0109] As shown in Figures 5E, 5F, and 5G, by setting the range of the parameter you want to study for input information, you can understand the distribution of input parameters other than the parameter you want to study, as well as the distribution of the output results. You can also see where the input parameters are located in the graph or category table. Furthermore, you can see where the output results, such as the quality status of a manufacturing lot, are located in the design space.

[0110] As shown in Figure 5G, the results showing the distribution of the quality status of manufacturing lots in the design space make it possible to quantitatively grasp the trends in the quality status of manufacturing lots based on the distance to the center of gravity and boundary distance of the design space, and their position relative to the design space (inside / outside / on the boundary).

[0111] As shown in Figure 5E, if there is a parameter to be studied, the user determines a selected range 510 specified from the parameter, and plots the quality state based on the specified range in the design space. In this case, the closer the set of plotted product positions is to the center of gravity of the design space, the lower the risk that a subsequently manufactured lot will be outside the design space by manufacturing within the range set for the parameter to be studied.

[0112] Furthermore, the further the product location is from the boundary inside the design space, the lower the risk that subsequent lots will be outside the design space, for example, if they can be manufactured within the range set for the parameter being studied.

[0113] Furthermore, by examining parameters sequentially, the set of plotted points within the design space may be distributed widely or locally. The mean, median, standard deviation, confidence interval, etc. of the set of plotted points within the design space are calculated, and statistical comparisons are made with the set of plotted points within the design space obtained when each parameter was examined to quantitatively determine the differences, and these are also used as material for understanding the relationship between input and output.

[0114] While Figures 5E, 5F, and 5G illustrate screen examples in which input data is selected and the behavior of output data is displayed, the reverse is also possible. In other words, output data is selected and the behavior of input data is displayed. For example, on the screen shown in Figure 5B, the data to be examined is selected from the design space diagram on the left side of the screen or the table on the right side. Then, the data features, such as parameters corresponding to the data in the selected design space, are displayed as shown in Figures 5E and 5F. These also help to understand the relationships between the data.

[0115] Figure 5H is a display screen showing the values ​​of the centroid distance and boundary distance of the design space of the production lot that will be output as a result of making multiple changes to one parameter of interest.

[0116] For example, suppose that the distribution of "flow velocity," one of the parameters, is in the range of 2.0 to 5.0 for all products. The user of the quality control system 101 specifies a new range for this parameter. In the example of FIG. 5H, four different ranges, Change No. (1) to No. (4), are specified.

[0117] The calculation unit 106 (Figure 1) calculates each quality state based on multiple ranges specified for the parameter to be changed. In the example of Figure 5H, the average centroid distance, the standard deviation of centroid distance, the average boundary distance, the standard deviation of boundary distance, and the DS frequency (the frequency of products within the range whose product position is within the design space) are calculated and output. In addition, the ratio of the changed value to the value before the change (shown in parentheses in Figure 5H) is also displayed.

[0118] The range of product positions for each manufacturing lot is also shown in the design space, both before and after the change. The example in Figure 5H visualizes the positional relationship between the set of manufacturing lots before the change 517, the set of manufacturing lots corresponding to change No. (2) 518, and the set of manufacturing lots corresponding to change No. (4) 519.

[0119] In this way, the calculation unit 106 calculates each quality state based on a plurality of ranges specified for one or more types of parameters. The output unit 108 outputs the calculated quality states.

[0120] Among the collection of manufacturing lots before and after the change, those that are closer to the center of the design space are considered stable using the centroid distance and boundary distance as indicators, and are displayed with a priority for changing the process, etc. The user considers changing the manufacturing method for those with higher priority. If changing the parameters does not change the position of the collection of manufacturing lots relative to the design space, it is determined that there is little point in considering a change.

[0121] Figure 5I shows a display screen in which one or more changes are made to multiple parameters to be considered, and the resulting output is the display of the values ​​of the centroid distance and boundary distance of the design space of the production lot before and after the change.

[0122] For example, suppose that the distribution of one parameter, "flow velocity," across all products is in the range of 2.0 to 5.0. The user of the quality control system 101 specifies a new range for this parameter. In the example of FIG. 5I, two ranges, Change No. (1) and Change No. (2), are specified for "flow velocity." Although not specifically shown in FIG. 5I, information indicating which parameter has been changed (in this case, "flow velocity") may also be displayed.

[0123] In the example of Figure 5I, two ranges, change No. (3) and No. (4), are specified for "Working Time." Furthermore, in the example of Figure 5I, one range, change No. (5), is specified for "Working Temperature."

[0124] In addition, the example of Figure 5I visualizes the relative positions of a set of production lots before the change 517, a set of production lots corresponding to change No. (2) 520, a set of production lots corresponding to change No. (4) 521, and a set of production lots corresponding to change No. (5).

[0125] For the multiple parameters to be considered, methods for changing the process for each are identified, and consideration is given to which change should be prioritized to move the position of the set of manufacturing lots relative to the design space to a more stable location, starting with the change that will most likely contribute to improving quality.

[0126] In this study, Figure 5H also applies, but the need for a change can also be considered by including indicators such as the cost of the process change, the time required, and the risks associated with the change.The method of evaluating the changes in multiple parameters to be considered using indicators such as the center of gravity distance and boundary distance of the design space of the production lot that will be the output, and prioritizing improvements, is carried out in the same manner as Figure 5I.

[0127] The calculation unit 106 (FIG. 1) may generate change priorities (change recommendation information) representing recommended ranges for one or more types of parameters based on multiple ranges specified for the one or more types of parameters. The output unit 108 may also output the generated change priorities. For example, in the example of FIG. 5H, change priorities are determined for all post-change sets based on the frequency within the DS. The higher the frequency within the DS, the higher the change priority. Similarly, in the example of FIG. 5I, change priorities are determined for all post-change sets based on the frequency within the DS. Note that in the example of FIG. 5I, change priorities are determined using not only the frequency within the DS but also other information (not specifically described, but which can be set as appropriate). By generating change priorities in this way, it is possible to more easily grasp a preferred parameter range.

[0128] In addition, in the screens shown in Figures 5H and 5I, it is possible to specifically extract parameters related to biological samples such as cells and serum, which are raw materials and are difficult to control in regenerative medicine, and determine the control range by comparing them with parameters that can be controlled.

[0129] This section explains an example of an improvement method in which the results obtained from the quality control system are used to prioritize improvements starting with the parameters that have the greatest impact on quality. When changing a manufacturing parameter, if the parameter is related to the equipment being used, the setting value is changed. If it is difficult to change the parameter using the equipment being used, it may be possible to use another piece of equipment with the same functions. For example, it may be possible to use another piece of equipment with the same functions that is used in another manufacturing facility.

[0130] If the parameters relate to manual work performed by workers, the instructions written in the work instructions are changed. If simply changing the instructions is difficult and there is a possibility that variations in the work content and / or work results may occur due to the worker's level of skill, education and training are provided to ensure that the work content and / or work results are uniform. Workers and / or education and training often differ, especially between facilities, but by entering this information into the quality control system and using it for analysis, if it is suggested that differences in workers and / or education and training may be affecting the work content and / or work results, this can be investigated.

[0131] Even for the same worker, there is a possibility that the work content and / or work results may vary when it comes to parameters related to the worker's manual work. This perspective is also taken into consideration. If there is a need to control the work content and / or work results of the parameters related to the worker's manual work with higher precision, it is possible to, for example, take a video of the work scene, quantitatively analyze the work content and / or work results through image analysis, and provide feedback to the worker in the form of education and training, etc., in order to ensure uniformity in the work content and / or work results. Furthermore, if it is concluded that the work content and / or work results obtained through manual work are not sufficient to ensure quality, improvements such as automating the work may be considered.

[0132] Regarding the manufacturing environment, the temperature and cleanliness of the cell preparation room and other areas in the CPF are constantly managed, but they are also affected by factors such as the operator's use of the room in relation to aseptic procedures, cleaning methods, cleaning frequency, and the operator's entry and exit procedures, including gowning. By inputting this information into the quality control system as quality information and using it for analysis, if it is suggested that differences in the manufacturing environment may be affecting quality, this can be investigated. Possible measures include changing the setpoints for the temperature and cleanliness of the cell preparation room and other areas, and amending the work instructions for operator aseptic procedures, as well as providing education and training, which are similar to measures for parameters manually performed by operators in manufacturing. If a safety cabinet is used, the following should be considered: wiping and disinfecting materials before placing them in the cabinet; operating the pass boxes used when transferring materials between rooms; and gowning procedures for operators when entering and exiting the cabinet.

[0133] When manufacturing is carried out at multiple facilities, differences may arise in the CPF operation method, the work content of workers, the layout of the cell preparation room, the use of the equipment used in manufacturing, and maintenance management. By inputting this information into the quality control system as quality information and using it for analysis, if it is suggested that differences between facilities may be affecting quality, etc., these can be investigated. The investigation and improvement of the CPF operation method and the work content of workers is basically the same as that described above.

[0134] Regarding the layout of the cell preparation room, the type, number, model number, and maintenance information of the equipment used will be used for analysis. For example, even if the same equipment is used, differences in maintenance and periodic initialization methods may affect quality. This information may be included in the quality information. Furthermore, even for equipment with the same functions, slight differences in specifications may have an impact.

[0135] For example, incubators for culturing cells are generally used at an incubation temperature of 37°C, but the upper and lower temperature limits when set at 37°C may vary depending on the manufacturer. Furthermore, the frequency with which incubators are opened and closed may differ from facility to facility, and the temperature and gas phase (e.g., carbon dioxide concentration) change when the door is opened. The vibrations and shocks experienced when opening and closing the door vary depending on the door's specifications (such as vibration and shock buffering, door weight, and the height of the door handle) and the operator's method of use. It is advisable to input this information into a quality control system as quality information and analyze it to determine whether these differences are negligible or not in terms of quality.

[0136] Regarding the layout of the cell preparation room, it is desirable to input the distances and routes between devices as quality information into the quality control system for analysis. For example, when a worker manually works on a culture vessel in a safety cabinet and then transports the vessel to an incubator, the transport time is determined by the worker's walking speed and the distance. During the work time and transport time in a safety cabinet, the temperature and gas phase of the culture vessel are generally not controlled, and temperature drops and pH changes can affect cells. This is a different environment from that inside an incubator, where the temperature and gas phase are controlled. It is desirable to analyze whether these differences in quality are negligible or not.

[0137] Figure 6 shows the series of steps for evaluating quality and determining priorities for process improvement using a quality control system with the above functions.

[0138] <Step S1: Start> Activate the quality control system.

[0139] <Step S2: Data input> Quality information throughout the life cycle of cell-processed products, such as collection, purification, gene transfer, culture, concentration, transportation, transplantation, etc., material management of raw materials, etc., and clinical information such as adverse events and / or safety information after transplantation, is entered into the quality control system. The input method is selected according to the format of the data to be input, as shown in Figure 1.

[0140] After input, the feature quantities of various information are displayed. 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 frequency of occurrence of each category is shown in a table. In the case of character string data, depending on the content, processing is performed, such as replacing it with data that has been entered in advance as default data in the quality control system, to make analysis easier. In addition, various output data, such as post-manufacturing quality and post-transplant prognosis information, that will be analyzed as a result of changing the input conditions, are displayed.

[0141] A diagram showing the distribution within the design space, the centroid distance and boundary distance of the design space for each production lot, and its position relative to the design space (inside / outside / on the boundary) are shown. The mean, standard deviation, etc. of the centroid distance and boundary distance of the design space for each production lot are also displayed. A table showing all the output data is also provided.

[0142] <Step S3: Selection of parameters to be examined> The user selects and inputs the parameters to be considered as input conditions. The feature quantities of each parameter identified in step S2 and the feature quantities of output information such as post-manufacturing quality and post-transplant prognosis are used as materials for the study.

[0143] <Step S4: Selecting the range of parameters to be considered> The user selects and specifies the range to be considered for the parameter selected in step S3. As shown in Figures 5E and 5F, the selection range may be determined from a graph showing the distribution of input items, or from a table listing various data.

[0144] <Step S5: Displaying the distribution of other parameters according to the selected range of the parameter under study and the distribution within the design space> The system displays the distribution of other parameters corresponding to the selected range of the parameter to be studied selected in step S3. That is, based on the range specified for one or more parameters, the corresponding range of one or more other parameters is calculated and displayed. The system also displays the distribution within the design space. For each, the feature values ​​corresponding to the selected range are displayed. For example, in the case of numerical data, the mean, standard deviation, maximum value, minimum value, etc. are displayed. In the case of categorical data, the frequency of occurrence of each category is displayed in a table. This step S5 results in a display like that shown in Figure 5E or 5F.

[0145] <Step S6: Display of quality stability regarding distribution within the design space> As quality stability, the centroid distance and boundary distance of the design space for each production lot, corresponding to the range of the parameters to be considered selected in step S3, and its position relative to the design space (inside / outside / on the boundary) are displayed. The average, standard deviation, etc. of the centroid distance and boundary distance of the design space for each production lot are also displayed. Various output data are also displayed. Step S6 results in a display like that shown in Figure 5G.

[0146] After step S6, if all the parameters to be considered have been determined, the process proceeds to step S7. If all the parameters to be considered have not been determined, the process returns to step S3 and the consideration is carried out again. Whether all the parameters to be considered have been determined may be determined based on an input from the user or may be determined automatically.

[0147] In the case of automatic judgment, if all calculated product positions are within the design space in the display in step S6, it is judged that all the parameters to be studied have been determined, and if not, it is judged that all the parameters to be studied have not been determined. In addition, in the case of automatic judgment, more complex judgment criteria using the centroid distance and / or boundary distance may be used.

[0148] <Step S7: Display a list of various information on the parameters to be examined> In this step, information on various parameters (e.g., all parameters) that are the subject of comparison and consideration is displayed in a list. As shown in FIG. 5H, the subject of consideration may be the results of evaluation of multiple selection ranges for one parameter, or the results of evaluation of selection ranges for multiple parameters. This step S7 again displays the display shown in FIG. 5E or 5F.

[0149] <Step S8: Display a list of quality stability of parameters under consideration> As quality stability, the centroid distance and boundary distance of the design space for each production lot, corresponding to the range of the parameter to be considered selected in step S7, and its position relative to the design space (inside / outside / on the boundary) are displayed. The average, standard deviation, etc. of the centroid distance and boundary distance of the design space for each production lot are also displayed. All output data are also displayed. This step S8 results in displays like those shown in Figures 5H and 5I.

[0150] <Step S9: Display improvement priorities based on the impact on quality> The change priority is calculated using the centroid distance and boundary distance of the design space of the output production lot, using the ratio of the values ​​before and after the change to the values ​​before the change. Using the centroid distance and boundary distance of the design space as indicators, those closer to the center of the design space are considered more stable. In addition to the centroid distance and boundary distance of the design space, the cost of the process change, the time required, and the risk associated with the change may also be used as indicators to calculate the change priority. These calculation results are used to display the priority for changing the process, etc. In this way, the calculation unit 106 (Figure 1) generates and displays change recommendation information indicating the recommended ranges for one or more parameters based on multiple ranges specified for the one or more parameters. This step S9 displays the change priority in Figures 5H and 5I.

[0151] <Step S10: Consider how to improve the parameters that have been decided to be improved> Consider changing the manufacturing method for those with high priority. For example, store the importance (priority) of each parameter in the quality information in advance, select a set from the set of parameters after parameter changes that will result in smaller changes to the parameters with high importance, and propose improvements based on the selected set.

[0152] More specifically, first, all sets of parameter changes for which the product positions of all cell-processed products are within the design space are selected. Next, for each such set, the ratio of the range width of the parameter after the change to the range width before the change is calculated for the parameter with the highest importance (e.g., the parameter with the highest importance). Then, the set with the highest ratio is selected, and the parameter range of the selected set is output as an improvement proposal.

[0153] If changing a parameter results in little change to the position of the set of manufacturing lots relative to the design space, it is judged that there is little point in considering the change. For example, information identifying the set of lots after the change with the highest change priority is displayed.

[0154] <Step S11: End> Once the review is complete, the review results are electronically stored in the auxiliary memory in the storage unit, and the operation of the quality control system is terminated by appropriate operations.

[0155] A quality control system configured as described above can generate more useful information about the relationships between parameters of cell-processed products. For example, it can visualize the relationship between input parameters related to manufacturing and output quality and prognosis information. Using the centroid distance and boundary distance of the design space, which are quantified as product stability, it becomes possible to prioritize items that need to be improved in manufacturing, etc. As a result, it is possible to stabilize the quality of cell-processed products, etc.

[0156] [Example 2] Regarding the quality control system described in the first embodiment, an embodiment different from the first embodiment will be described.

[0157] Various data on the life cycle of cell-processed products, etc., obtained up to a certain point in time, such as each process (collection, purification, gene transfer, culture, concentration, formulation, transportation, transplantation, etc.), material management of raw materials, etc., and clinical information (adverse events and / or safety information after transplantation), are entered into the quality control system as quality information.

[0158] For manufacturing after a certain point, data up to an intermediate stage of manufacturing is entered. Calculate and display where the data up to that point is located in the design space. Quantitatively evaluate the positional relationship not only by determining the positional relationship (inside, outside, or boundary) relative to the design space, but also by calculating the distance to the center of gravity and boundary distance of the design space as a measure of product stability.

[0159] Using these, the quality at the end of production is predicted from data up to the intermediate stages of production. As in Example 1, the quality standard used is the quality standard at the time of completion of the cell processed product. That is, in Example 2 as well, the design space corresponds to the first region at the time of completion of the cell processed product. The calculation unit 106 (Figure 1) judges the pass / fail of each cell processed product at the time of completion based on the design space and the quality information of each cell processed product at the intermediate stages of production.

[0160] A specific method for predicting data at the time of completion (which may be quality information or product position in the design space) based on quality information at an intermediate manufacturing stage can be designed as appropriate 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 defined as appropriate by a person skilled in the art based on publicly known techniques, etc. Machine learning can also be used.

[0161] If the product position representing the quality information of the cell processed product is inside the design space, the cell processed product is determined to be pass. If the product position is outside the design space, the cell processed product is determined to be fail. The output unit 108 may output the pass / fail judgment result. The judgment result can be output, for example, as a screen like that shown in Figure 5B.

[0162] If the data obtained up to the intermediate stage of manufacturing indicates that the product is outside the design space, the option to discontinue manufacturing of the product, either by user instruction or automatically by the system, can be considered. This is because discontinuing manufacturing when a product is predicted not to meet the release criteria during manufacturing can reduce costs, rather than evaluating the quality at the end of manufacturing and finding that the release criteria are not met.

[0163] 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 approved at the time of manufacturing approval.

[0164] [Example 3] Regarding the quality control system described in the first embodiment, an embodiment different from the first embodiment will be described.

[0165] Data on various information related to the life cycle of cell-processed products, etc., such as each process (collection, purification, gene transfer, culture, concentration, formulation, transportation, transplantation, etc.), material management of raw materials, etc., and medical information (adverse events and / or safety information after transplantation) will be input into the quality control system.

[0166] In visualizing each input parameter and the output result, the output is one of the following: quality at the end of the collection process, quality at the end of the transportation process immediately after the collection process, quality at the end of the manufacturing process, quality of the intermediate product during the manufacturing process, quality at the end of the transportation process immediately after the manufacturing process, quality just before or just after the end of the transplant process, etc.

[0167] In Example 3, the calculation unit 106 (FIG. 1) receives quality information up to a specific process (first process) as input, and calculates the quality state at the time when the process after the first process (second process) is completed based on this information. In Examples 1 and 2, the first region related to the quality standard was the design space at the time when the cell-processed product was completed, but in Example 3, the first region is calculated based on the quality standard at the time when the second process is completed during production.

[0168] The first and second steps can be selected arbitrarily in the manufacturing process of a cell-processed product. The quality standards at the end of each step can be appropriately defined by those skilled in the art.

[0169] In Example 3, the centroid distance and boundary distance are calculated for the parameter space during production, which is the output, rather than for the design space at the time of completion of the product. In addition, the centroid distance and boundary distance may be calculated for the design space at the time of completion.

[0170] A specific method for calculating the quality state at the end of the second process based on the quality information up to the first process can be designed as appropriate by a person skilled in the art. For example, a function that receives the quality information up to the first process as input and outputs the product position in the second process may be stored in advance. The specific content of the function can be defined as appropriate by a person skilled in the art based on publicly known techniques, etc. Machine learning can also be used.

[0171] For a selected output, input is basically data prior to the time when the selected output information is generated. Data after the time when the selected output information is generated is not included in the input. The reason is that events that occur in the future do not affect the past. However, for example, if the quality at the end of the transportation process immediately after the collection process is used as the output, evaluating cell viability, etc. as quality at the end of the transportation process does not have much of an impact, but if an impact appears on cell proliferation, etc. when purified cells are seeded in culture vessels and cultured in the subsequent manufacturing process, then cell proliferation, etc. would be included in the output, and data generated up to the time when the cell proliferation data was obtained would be included in the input.

[0172] Quality standards are set for each of the outputs, such as the quality at the end of the harvesting process, the quality at the end of the transportation process immediately after the harvesting process, the quality at the end of the manufacturing process, the quality of intermediate products during the manufacturing process, the quality at the end of the transportation process immediately after the manufacturing process, and the quality immediately before or immediately after the end of the transplantation process.

[0173] If the quality criteria are met, the process proceeds to the next step; if the quality criteria are not met, the process does not proceed to the next step. Each quality criterion is used to determine the range in which the quality criteria are met. If the quality criteria include one parameter, the range in which all quality criteria are met is one-dimensional. If the quality criteria include two parameters, the range in which all quality criteria are met is two-dimensional. If the quality criteria include three parameters, the range in which all quality criteria are met is three-dimensional. If the quality criteria include more than three parameters, the range in which all quality criteria are met is more dimensional. The range in which all quality criteria are met (first region) set in this way can be treated like the design space shown in Example 1. The calculation results can be output as a screen such as that shown in Figure 5B.

[0174] In the quality control system, the outputs are set as quality at the end of the collection process, quality at the end of the transport process immediately after the collection process, quality at the end of the manufacturing process, quality of intermediate products during the manufacturing process, quality at the end of the transport process immediately after the manufacturing process, quality just before or just after the end of the transplant process, etc. As input, for the selected output, data prior to the time when the information for the selected output was generated is entered.

[0175] Depending on the type of output selected, data that will occur in the future from the selected output is also input, as described above. By changing each input parameter using the same method as described in Example 1, we visualize how other input parameters and the information of the selected output, which is the output, move within the range that satisfies all quality standards.

[0176] The distance between the point plotted as the quality of a certain parameter and the center of gravity distance and boundary distance of the range that satisfies all quality standards is quantified as product stability, and the fluctuation range of product stability relative to the fluctuation range of each parameter is also quantified. From these values, the priorities of items to be improved in the process up to the selected output are ranked. Then, priority is given to improving parameters that have the greatest impact on the quality standards. The improvement content is the same as in Example 1. As a result, the quality stabilization of cell-processed products, etc. can be achieved.

[0177] [Example 4] Regarding the quality control system described in the first embodiment, an embodiment different from the first embodiment will be described.

[0178] In the flow described in Figure 6, the results obtained by the operator in steps S6 and S9 are accumulated, and a machine learning model is generated as a prognosis information prediction model using these results to select parameters, etc. in steps S3, S6, S9, etc.

[0179] 7, data is accumulated in step S20, machine learning is performed in step S21, and the results are reflected in steps S3, S6, S9, etc. As the machine learning model, well-known or publicly known methods such as neural networks and logistic regression may be adopted, and therefore will not be described in detail in this embodiment. When the operator makes selections in steps S3, S6, S9, etc., the selections made by the machine learning are also displayed.

[0180] Machine learning can be performed by inputting the parameter range before the change and outputting the parameter range after the change with the highest change priority. For example, in steps S6 and / or S9, training data can be created in which the parameter range before the change is input and the parameter range after the change with the highest change priority is output.

[0181] By using such a trained model, it is possible to calculate an appropriate parameter range after the change based on the parameter range before the change in steps S3, S6, S9, etc. The calculated range may be reflected as the range with the highest change priority in step S9. In this way, recommended change information is output.

[0182] This improves the accuracy of the examination content, resulting in the realization of stable quality of cell-processed products, etc. Furthermore, it enables the realization of product manufacturing that takes into account the vast number of parameters related to cell-processed products. [Explanation of symbols]

[0183] 101...Quality control system (information processing system) 102 to 104...input section (input device) 105...Main memory (storage device) 106...Processor 107…Auxiliary storage unit (storage device) 108...output unit (output device) 109...Display section (output device) 110...Database 111...Monitoring device 112...Work Instructions 113...input terminal 201...Process 202...Materials Management 203…Medical information management 204…Treatment information management 205...Basic Experiments 301…Design Space 302…Product position 303...Center of gravity 304…Center of gravity distance 305...Lattice point 306…Minimum distance boundary grid point 307...Boundary distance (minimum distance between the boundary and the product position) 401, 402...Design Space 501…Design Space 502~505…Product position 506...Center of gravity 507...Lattice point 508…Center of gravity distance 509...Boundary distance 510...Selection range 511~513...Production lot 514...Selection range 515, 516...Production lot 517~522...Collection of production lots

Claims

1. An information processing device comprising an input device, an output device, a processor, and a storage device, the input device receives as input quality information including a plurality of parameters related to a plurality of cell processed products, the quality information including parameters related to at least one of manufacturing information, treatment information, treatment result information, and transportation information related to the cell processed products; the storage device stores a predetermined quality standard and the quality information; The processor: - calculating a first region based on said quality criterion, - calculating the quality status of the cell-processed product based on the first region and the quality information; - calculating corresponding ranges of one or more other parameters based on the ranges specified for one or more of said parameters; the output device outputs the corresponding range. Information processing device.

2. The processor: - calculating the center of gravity of said first region, - calculating a product position representing the cell-processed product relative to the first region based on the quality information; - calculating the quality state based on the distance between the center of gravity and the product position; The information processing device according to claim 1 .

3. The processor: - calculating the boundary of said first region, - calculating a product position representing the cell-processed product relative to the first region based on the quality information; - calculating the quality state based on the minimum distance between the boundary and the product position; The information processing device according to claim 1 .

4. The quality information is - the skill level of the workers performing the manual tasks, and - the worker's training history, The information processing device according to claim 1 , comprising at least one of:

5. the quality information includes the manufacturing information; The manufacturing information - an apparatus for producing said cell-processed product, - a facility that manufactures said cell-engineered products; - the layout of the equipment in the facility where the cell-processed product is manufactured; - information regarding the maintenance of equipment at facilities that manufacture said cell-processed products; - environmental information of the facility where the cell-based product is manufactured; and - a method for maintaining the environment of a facility for producing said cell-processed product; The information processing device according to claim 1 , comprising at least one of:

6. The predetermined quality standard is A preferred value or range of at least one type of parameter included in the quality information. The information processing device according to claim 1 .

7. The processor: generating change recommendation information representing recommended ranges for the one or more types of parameters based on a plurality of ranges specified for the one or more types of parameters; The output device further outputs the change recommendation information. The information processing device according to claim 1 .

8. The quality standards are the quality standards at the time of completion of the cell processed product, The processor determines whether the cell processed product is acceptable or not upon completion based on the first area and the quality information during the manufacturing process; The output device further outputs a pass / fail judgment result. The information processing device according to claim 1 .

9. the processor calculates the quality state at the time when a second process subsequent to the first process is completed based on the quality information up to the first process; The first region is calculated based on a quality standard at the time when the second process is completed. The information processing device according to claim 1 .

10. A program that causes a computer to function as the information processing device according to claim 1.

11. a step of receiving, by an input device, quality information including a plurality of parameters relating to a plurality of cell processed products as input, the quality information including parameters relating to at least one of manufacturing information, treatment information, treatment result information, and transportation information relating to the cell processed products; a storage device storing a predetermined quality criterion and the quality information; a processor calculating a first region based on the quality metric; The processor calculates a quality state of the cell-processed product based on the first region and the quality information; the processor calculating corresponding ranges of one or more other parameters based on ranges specified for one or more of the parameters; an output device outputting the corresponding range; An information processing method comprising:

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