Estimation Device, Learning Device, Optimization Device, Estimation Method, Learning Method, and Optimization Method

An estimation device (200) generates quality prediction data (540) indicating a quality of a drug substance of a biopharmaceutical manufactured by culturing cells, by inputting, into a prediction model (420), measurement data (510) including a measurement result obtained by measuring a substance in a culturing vessel at least one timing after a predetermined time period has passed since the cells have been seeded in a medium. The prediction model (420) is generated by executing a learning process using training data (530) that includes measurement data including a measurement result obtained by measuring a substance in a culturing vessel at a plurality of timings after seeding of the cells in the medium, and quality data obtained by analyzing a drug substance of the biopharmaceutical manufactured from the cells.

TECHNICAL FIELD

The present disclosure relates to an estimation device, a learning device, an optimization device, an estimation method, a learning method, and an optimization method.

BACKGROUND ART

In recent years, research on biopharmaceuticals manufactured by culturing cells has been advanced. Japanese Patent No. 4496086 (PTL 1) discloses a cell culture method for generating a target protein by culturing host cells.

Biopharmaceuticals are produced using metabolism of cells that are difficult to be artificially controlled, and drugs to be used may contain components derived from living cells. Accordingly, even if they are produced according to a defined protocol, the qualities of produced biopharmaceuticals cannot be uniformed. Typically, characteristic analysis is performed for a drug substance of a biopharmaceutical obtained by cell culture, and the quality of the drug substance is verified.

CITATION LIST

Patent Literature

SUMMARY OF INVENTION

Technical Problem

In consideration also of a culture step, a purification step, and a processing step, production of biopharmaceuticals requires significantly long time period, such as several weeks to several months. A drug substance to be subjected to characteristic analysis is obtained in the purification step after the culture step is completed. That is, since no result of characteristic analysis can be obtained in the culture step, determination as to whether an objective drug substance has been obtained by culture or not will be made only after completion of the purification step.

An object of the present disclosure is to predict the quality of the drug substance obtained by progress of the culture in a stage during the culture. Furthermore, another object of the present disclosure is to optimize a condition for improving the quality of the drug substance obtained by progress of the culture in the stage during the culture.

Solution to Problem

An estimation device of the present disclosure includes: a receiving unit that receives input of measurement data; a prediction unit that generates quality prediction data indicating a quality of a drug substance, by inputting, into a prediction model, the measurement data received by the receiving unit; and an output unit that outputs the quality prediction data generated by the prediction unit. The measurement data includes a measurement result obtained by measuring at least one substance in a culturing vessel containing cells and a medium, at least one timing after a predetermined time period has passed since the cells have been seeded in the medium. The prediction model is a model for predicting the quality of the drug substance of a biopharmaceutical manufactured by culturing the cells.

A learning device of the present disclosure includes: a receiving unit that receives training data; and a model generation unit that generates a prediction model by executing a learning process using the training data received by the receiving unit. The training data includes measurement data including a measurement result obtained by measuring at least one substance in a culturing vessel containing cells and a medium at a plurality of timings after seeding of the cells in the medium, and quality data obtained by analyzing a drug substance of a biopharmaceutical manufactured from the cells. The prediction model is a model for generating quality prediction data indicating the quality of a drug substance of a biopharmaceutical manufactured from cells contained in a culturing vessel during culture of the cells, based on a measurement result obtained by measuring at least one substance in the culturing vessel during culture of the cells.

An estimation method of the present disclosure includes: a step of putting cells and a medium in a culturing vessel, and culturing the cells; a step of measuring at least one substance in the culturing vessel at least one timing after a predetermined time period has passed since the cells have been seeded in the medium; and a step of generating quality prediction data indicating a quality of a drug substance, by inputting measurement data including a measurement result obtained in the measuring step, into a prediction model; and a step of outputting the quality prediction data. The prediction model is a model for predicting the quality of the drug substance of a biopharmaceutical manufactured by culturing the cells.

A learning method of the present disclosure includes: a step of putting cells and a medium in a culturing vessel, and culturing the cells; a step of measuring at least one substance in the culturing vessel at a plurality of timings after the cells are seeded in the medium; a step of analyzing a quality of a drug substance of a biopharmaceutical manufactured from the cells; and a step of generating a prediction model by executing a learning process using training data. The training data includes measurement data including a measurement result obtained by the step of measuring, and quality data obtained by the step of analyzing. The prediction model is a model for generating quality prediction data indicating the quality of a drug substance of a biopharmaceutical manufactured from cells contained in a culturing vessel during culture of the cells, based on a measurement result obtained by measuring at least one substance in the culturing vessel during culture of the cells.

An optimization device of the present disclosure includes: a receiving unit that receives a plurality of parameter values that define a culture condition for seeding cells in a medium, and quality data obtained by analyzing a drug substance of a biopharmaceutical manufactured by culturing the cells; an estimation unit that receives the plurality of parameter values and the quality data received by the receiving unit, and estimates an optimal combination of the plurality of parameter values; and an output unit that outputs the combination of the plurality of parameter values estimated by the estimation unit.

An optimization method of the present disclosure includes: a step of putting cells and a medium in a culturing vessel, and culturing the cells under a culture condition defined by a plurality of parameter values; a step of analyzing a quality of a drug substance of a biopharmaceutical manufactured by culturing the cells; and a step of receiving the plurality of parameter values, and quality data obtained by the step of analyzing the plurality of parameter values, and estimating an optimal combination of the plurality of parameter values. The step of culturing the cells adopts the optimal combination of the plurality of parameter values estimated by the step of estimating, as a new culture condition, and cultures the cells under the new culture condition.

Advantageous Effects of Invention

According to the present disclosure, the quality of the drug substance obtained by progress of the culture in a stage during the culture can be predicted. Alternatively, according to the present disclosure, a condition for improving the quality of the drug substance obtained by progress of the culture in the stage during the culture can be optimized. Accordingly, based on the predicted quality of the drug substance, a user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, or stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of the present disclosure are described in detail with reference to the drawings. In the drawings, the same or corresponding portions are assigned the same symbols, and the description of them is not repeated.

[Entire Configuration of Prediction System]

FIG.1is a diagram schematically showing an entire configuration of a prediction system according to this Embodiment 1. A prediction system SYS1is a system for predicting the quality of a drug substance of a biopharmaceutical manufactured by cell culture. In this Embodiment 1, “biopharmaceutical” is any of pharmaceuticals manufactured using cells, e.g., antibody pharmaceuticals, and vaccines. In this Embodiment 1, biopharmaceuticals can include cells themselves used for regenerative medicine. In this Embodiment 1, “drug substance” is an objective substance obtained by cell culture, and for example, obtained by a culture step for culturing cells, and subsequently a purification step for taking out an objective component. Prediction system SYS1includes a measurement device100, an estimation device200, a learning device300, and a server400.

Measurement device100measures a measurement target that is a substance in a culturing vessel10that contains cells1, and a medium2. Preferably, the substance as the measurement target is a substance that changes in the course of culturing cells1, and is, for example, any of cells1themselves, a metabolite of cells1, a nutrient of cells1and the like. The user can select any measurement device100in accordance with the measurement target. For example, in a case where the measurement target is cells1, the user can select a microscope, and software for analyzing images obtained by the microscope, as measurement device100, in order to obtain measurement results that include the number, the living ratio, and the shape (the roundness, the length of major axis, the length of minor axis, etc.) of cells in a culture solution made up of cells1and medium2in culturing vessel10. In a case where the measurement target is a metabolite or a nutrient of cells1, the user can select a liquid chromatography mass spectrometer (LC-MS), ICP (inductively coupled plasma) mass spectrometer or the like, as measurement device100, in order to obtain measurement results, such as the concentration of a metabolite and the concentration of a nutrient in the culture solution.

Estimation device200predicts the quality of the drug substance, using measurement data510that includes measurement results about the measurement target which is obtained after a predetermined time period has passed since cells1have been seeded in medium2. For example, measurement data510is obtained by collecting the culture solution in culturing vessel10at least one timing after the predetermined time period has passed since cells1have been seeded in medium2, and measuring a substance in the collected culture solution using measurement device100.

Estimation device200includes a receiving unit210, a prediction unit220, and an output unit230, as an example of a software configuration implemented by a processor21(seeFIG.2) executing an estimation program560(seeFIG.2). Receiving unit210receives input of measurement data510.

Prediction unit220generates quality prediction data540indicating the quality of the drug substance, by inputting measurement data510received by receiving unit210into a prediction model420. Prediction model420is a model for predicting the quality of the drug substance of the biopharmaceutical manufactured by culturing cells1, and outputs a prediction result indicating the quality of the drug substance upon input of measurement data510. Prediction model420is generated by a model generation unit320of learning device300executing a supervised learning process.

Quality prediction data540may be the prediction result itself output from prediction model420, or may be what is generated based on the prediction result output from prediction model420. The prediction result output from prediction model420is a result that corresponds to quality data520used as correct data when model generation unit320is generated. For example, in a case where quality data520is data itself indicating an analysis result obtained by applying any analysis to a drug substance3, prediction model420outputs an analysis result predicted as the prediction result. In a case where quality data520is data indicating the similarity to the objective drug substance obtained based on the analysis result, prediction model420outputs the similarity predicted as the prediction result.

Output unit230outputs quality prediction data540generated by prediction unit220to any output destination, such as a display, a printer, and a server.

Learning device300generates prediction model420using training data530. Learning device300includes a receiving unit310, and model generation unit320, as an example of a software configuration implemented by a processor31(seeFIG.3) executing a learning program580(seeFIG.3).

Receiving unit310receives input of training data530. Training data530includes time series data512, and quality data520.

Time series data512is a type of measurement data510, and is obtained by measuring at least one substance in culturing vessel10at a plurality of timings (timing t1, . . . , timing tx) after seeding of cells1in medium2. Time series data512is data indicating the temporal change of the measurement target, which is a substance in culturing vessel10containing cells1and medium2. Time series data512is obtained by collecting the culture solution in culturing vessel10at a plurality of timings (timing t1, . . . , timing tx) after seeding of cells1in medium2, and measuring each of portions of collected culture solution using measurement device100. For example, time series data512indicates the temporal change in cell number, temporal change in living cell ratio, temporal change in shape of cells, temporal change in concentration of a nutrient, temporal change in concentration of a metabolite and the like.

Quality data520is obtained by analyzing drug substance3of a biopharmaceutical manufactured from cells1. Quality data520is data obtained after culturing cells1, and subsequently applying the purification step and an analysis step. Quality data520indicates the quality of drug substance3that is obtained by applying any analysis in the analysis step to drug substance3obtained through the purification step. The analysis performed in the analysis step is freely selected in accordance with the type of drug substance3. The quality of drug substance3is evaluated from one or more perspectives, and for example, is evaluated from perspectives of physical property, chemical property, bioactivity, immunochemical property and the like. Quality data520may be data indicating an analysis result obtained by applying any analysis to drug substance3, or data indicating the similarity to the objective drug substance obtained, based on the analysis result.

For example, in a case where drug substance3is utilized as an antibody pharmaceutical, amino acid composition analysis, amino acid sequence analysis, peptide mapping analysis, analysis of the cross-linked position of disulfide bonding, glycosylation structure analysis and the like are performed for drug substance3in the analysis step. In this case, for example, as a result of amino acid composition analysis, data indicating how much degree of similarity between the amino acid composition and the objective drug substance may be included in quality data520.

Model generation unit320generates prediction model420by executing the learning process, with quality data520included in training data530being adopted as correct data. For example, model generation unit320inputs time series data512included in training data530into prediction model420, obtains an error between the output prediction result of the quality and quality data520that is correct data, and optimizes prediction model420so as to reduce the error.

Server400is an example of a storage device for storing prediction model420. Note that prediction model420may be stored in a storage23of estimation device200(seeFIG.2), or stored in a storage33of learning device300(seeFIG.3). Estimation device200and learning device300may be implemented by a single device. In the case where estimation device200and learning device300are implemented by a single device, receiving unit210and receiving unit310may be implemented by a single receiving unit.

Note that in this embodiment, the culture condition is assumed to be the same between a case of generating prediction model420, and a case of generating quality prediction data540using generated prediction model420. In this embodiment, the fact that “culture condition” is the same means that culture is performed according to a common protocol.

Even if cell culture is performed according to the common protocol, the drug substance obtained by the cell culture may exhibit characteristics different from those of the objective drug substance because the cells themselves are different, and a component derived from living cells is contained in a drug to be used in some cases. Accordingly, characteristic analysis is typically performed for a drug substance obtained by cell culture in order to secure the quality of biopharmaceuticals.

Drug substance3is obtained by activity of cells1. Accordingly, it is expected that there is a relationship between the characteristics of drug substance3and the activity situations of cells1. It is expected that the substance in culturing vessel10containing cells1exhibits different temporal changes between cases where the activities of cells1, such as growth and metabolism, are active and inactive. Accordingly, it is expected that there is a relationship between the state of the substance in culturing vessel10and the activity situations of cells1. That is, it is expected that there is indirect relationship between the state of the substance in culturing vessel10and the characteristics of drug substance3.

Learning device300generates prediction model420by executing the learning process using training data530that includes time series data512and quality data520indicating temporal change in substance in culturing vessel10and the quality of drug substance3respectively. In other words, learning device300generates prediction model420by learning of the relationship between the temporal change in substance in culturing vessel10and the quality of drug substance3.

The user repeatedly executes a culture examination that includes the culture step and the purification step, while changing the culture condition. In each culture examination, quality data520is obtained. Measurement device100outputs time series data512with respect to each culture examination. By repeatedly executing the culture examination, a large number of training data530in accordance with the number of culture examinations are collected. Learning device300repeatedly optimizes prediction model420, based on the large number of training data.

In this Embodiment 1, measurement data510is obtained by collecting the culture solution in culturing vessel10at least one timing after the predetermined time period has passed since cells1have been seeded in medium2, and measuring a substance in the collected culture solution using measurement device100. That is, measurement data510indicates the state of the substance in the culture solution at least one timing after the predetermined time period has passed since cells1have been seeded in medium2.

Measurement data510indicates the state of the substance after the predetermined time period has passed since cells1have been seeded in medium2. Accordingly, by inputting measurement data510into prediction model420generated by learning of the relationship between the temporal change in substance and the quality of drug substance3, a prediction result is obtained that indicates the quality of drug substance3obtained after progress of culture.

As described above, estimation device200according to this Embodiment 1 generates quality prediction data540indicating the quality of the drug substance obtained by progress of culture of cells1in culturing vessel10, based on measurement data510including the measurement result obtained by measuring the measurement target in culturing vessel10. Accordingly, the quality of the drug substance obtained by progress of the culture in a stage during the culture can be predicted. In the stage during the culture, learning device300can generate a prediction model for predicting the quality of the drug substance obtained by progress of the culture. Accordingly, based on quality prediction data540, the user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, and stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

Learning device300according to this Embodiment 1 can generate prediction model420where the relationship between the temporal change in substance in culturing vessel10and the quality of drug substance3has been learned, by executing the learning process using training data530that includes time series data512and quality data520indicating the temporal change in substance in culturing vessel10and the quality of drug substance3, respectively.

[Hardware Configuration of Estimation Device]

FIG.2is a block diagram showing a hardware configuration of estimation device200. Estimation device200includes processor21, a main memory22, storage23, a communication interface (I/F)24, a USB (universal serial bus) interface (I/F)25, an input unit26, and a display unit27. These components are connected to each other via a processor bus28.

Processor21is made up of a CPU (central processing unit), a GPU (graphics processing unit) and the like, reads a program (for example estimation program560) stored in storage23, loads the program in main memory22and executes the program. Processor21performs a series of processes for predicting the quality of the drug substance by executing the program.

Main memory22is made up of, for example, a volatile storage device, such as a RAM (random access memory), or a DRAM (dynamic RAM). Storage23is made up of, for example, a nonvolatile storage device, such as an HDD (hard disk drive) or an SSD (solid state drive).

Storage23stores measurement data510transmitted via USB I/F25, quality prediction data540generated based on measurement data510, estimation program560for performing the series of processes for predicting the quality of the drug substance, and the like.

Communication I/F24exchanges a signal with server400that stores prediction model420, using wired communication or wireless communication.

A USB memory (not shown) is detachably attached to USB I/F25, through which measurement data510stored in the USB memory is read. Note that measurement data510may be transmitted from measurement device100to estimation device200by communicably connecting measurement device100and estimation device200to each other.

Input unit26accepts user operation, and is typically made up of a touch panel, a keyboard, a mouse and the like. Display unit27is an example of an output destination of quality prediction data540, and is made up of a liquid crystal panel or the like that can display an image.

[Hardware Configuration of Learning Device]

FIG.3is a block diagram showing a hardware configuration of learning device300. Learning device300includes processor31, a main memory32, storage33, a communication I/F34, a USB I/F35, an input unit36, and a display unit37. These components are connected to each other via a processor bus38.

Processor31includes a CPU and a GPU, reads a program (e.g., learning program580) stored in storage33, loads the program in main memory32, and executes the program. Processor31performs a series of processes for generating prediction model420by executing the program.

Main memory32is made up of, for example, a volatile storage device, such as a RAM or a DRAM. Storage33is made up of, for example, a nonvolatile storage device, such as an HDD or an SSD.

Storage33stores training data530transmitted via USB I/F35, and learning program580for performing the series of processes for generating prediction model420.

Communication I/F34exchanges a signal with server400that stores prediction model420, using wired communication or wireless communication.

A USB memory (not shown) is detachably attached to USB I/F35, through which training data530stored in the USB memory is read. Note that training data530may be transmitted from an information processing device to estimation device200, by accepting input of measurement data510and quality data520, associating measurement data510and quality data520with each other, and communicably connecting the information processing device that generates training data530and learning device300to each other.

Input unit36accepts user operation, and is typically made up of a touch panel, a keyboard, a mouse and the like. Display unit37is made up of a liquid crystal panel or the like that can display an image.

[Example of Measurement Data510Utilized for Quality Prediction]

Measurement data510that estimation device200accepts to predict the quality of drug substance3includes a measurement result obtained by measuring the measurement target at least one timing after the predetermined time period has passed since cells1have been seeded in medium2. Note that measurement data510used to predict the quality of drug substance3may be time series data512indicating the temporal change in measurement target, as a measurement result obtained by measuring the measurement target at a plurality of timings after seeding of cells1in medium2.

FIG.4is a diagram for illustrating the measurement timings. InFIG.4, a typical growth curve is depicted. As shown inFIG.4, typically, immediately after seeding of cells1in medium2, immediate growth is not exhibited, the phase transitions to an induction phase (or also called a lag phase) and then to a growing logarithmic growth phase. Subsequently, due to reduction in nutrient or accumulation of waste (metabolite), the logarithmic growth phase is finished, and the phase transitions to a stationary phase (or also called a quiescent phase). In the stationary phase, the balance between the number of growing cells1, and the number of extinct cells1is maintained. The stationary phase continues for a while, subsequently the extinct cells1increase, and then the phase transitions to the death phase (or also called a decline phase).

Measurement data510may include a measurement result obtained by measuring the measurement target at a timing t0of starting to culture cells1. Note that the timing of starting to culture cells1can include a timing before seeding cells1in medium2, and a timing immediately after seeding cells1in medium2. A measurement result at timing t0indicates an initial state in culturing vessel10. If measurement data510includes the measurement result indicating the initial state in culturing vessel10, measurement data510indicates change from the initial state.

Measurement data510may include a measurement result obtained by measuring the measurement target between a timing t1and a timing t2which indicate a logarithmic growth phase. That is, at least one timing after a predetermined time period has passed since cells1have been seeded in medium2is a timing between timing t1and timing t2, which indicate the logarithmic growth phase. In the logarithmic growth phase, the number of cells1largely varies. Accordingly, the state of the substance in culturing vessel10largely varies. By including, in time series data512, the result obtained by measuring the measurement target at the timing when a large change in state occurs as described above, estimation device200can predict the quality of drug substance3using data more correctly indicating the change in state of cells1.

Measurement data510may include a measurement result obtained by measuring the measurement target between timing t2and a timing t3which indicate the stationary phase. That is, at least one timing after a predetermined time period has passed since cells1have been seeded in medium2is a timing between timing t2and timing t3, which indicate the stationary phase. In the stationary phase, the balance between the number of growing cells1, and the number of extinct cells1is maintained, and the number of cells1becomes the maximum. By including, in time series data512, the result obtained by measuring the measurement target at the timing when the number of cells1becomes the maximum as described above, estimation device200can predict the quality of drug substance3using data indicating the state of the substance in culturing vessel10when the number of cells1becomes the maximum.

Measurement data510(time series data512) includes a measurement result measured in the death phase after timing t3. Since the cells become extinct, enzymes and the like in the cells are exposed into the medium, which possibly affects the quality of the drug substance. In this case, the measurement result measured in the death phase of the cells can be possibly utilized for machine learning as an example of data of an inferior quality.

FIG.5is a diagram showing an example of measurement data510. Note that in an example shown inFIG.5, time series data512ato512feach obtained by measurement at a plurality of timings after seeding of cells1in medium2are exemplified. However, each of time series data512ato512fmay be a data item including a measurement result obtained by measuring the measurement target at least one timing after a predetermined time period has passed since cells1have been seeded in medium2.

Measurement data510includes, for example, time series data item512aon the cell number, time series data item512bon the living cell ratio, time series data item512con the cell shape, time series data item512don an microscopic image or the like, as the measurement results obtained by measuring cells1.

Measurement data510includes, for example, time series data item512eon the metabolite, as a measurement result obtained by measuring the metabolite generated by metabolism of cells1. Note that the metabolite includes at least one of a metabolite secreted by cells1to the outside, and a metabolite contained in cells1themselves.

Furthermore, measurement data510includes, for example, time series data item512fon the medium component, as a measurement result obtained by measuring a nutrient to be ingested by cells1. Note that in a case where cells1generate a component common to a component contained in the medium by metabolism, time series data item512fon the medium component can serve as the time series data item on the metabolism.

That is, a substance as a measurement target is at least one of a nutrient to be ingested by cells1, a metabolite generated by metabolism of cells1, and cells1. Note that measurement data510may include information on the pH, temperature and the like of the culture solution in culturing vessel10.

Referring toFIGS.4and5, measurement data510accepted by estimation device200has been described. Similar to measurement data510accepted by estimation device200, time series data512included in training data530accepted by learning device300may also include multiple types of time series data.

For example, time series data512included in training data530may indicate the temporal change in the measurement target, with at least one substance among the nutrient ingested by cells1, the metabolite generated by metabolism of cells1, and cells1being adopted as the measurement target.

More specifically, time series data512included in training data530includes, for example, a time series data item on the cell number, a time series data item on the living cell ratio, a time series data item on the cell shape, a time series data item on an microscopic image and the like, which are obtained by measuring the cells. Time series data512included in training data530includes a time series data item on the metabolite obtained by measuring the metabolite. Time series data512included in training data530includes a time series data item on the medium component obtained by measuring the nutrient. Note that time series data512included in training data530can include information on the pH, temperature and the like of the culture solution in culturing vessel10.

Time series data512used when prediction model420is generated, and measurement data510input into prediction model420when quality prediction data540is generated using prediction model420include at least a measurement result obtained by adopting a mutually common substance as the measurement target. That is, if time series data indicating the temporal change in cell number is used when prediction model420is generated, measurement data510input into prediction model420includes at least a measurement result indicating the cell number.

FIG.6is a block diagram showing an example of a configuration of prediction unit220. Prediction unit220includes an estimation process unit222, and a determination unit224. Estimation process unit222inputs measurement data510received by receiving unit210, into prediction model420, and obtains a prediction result. The prediction result may be, for example, a predicted value542.

Typically, the quality of drug substance3is evaluated from one or more perspectives. Accordingly, prediction model420may be configured to output, as the prediction result, predicted value542evaluated from a specific perspective. Predicted value542is, for example, an estimate of a result obtained when drug substance3obtained from cells in culture are analyzed in a specific perspective, or a similarity indicating how much degree the obtained estimate is similar to the result of the objective drug substance.

Perspectives for evaluating the quality of drug substance3include, for example, the physical property, chemical property, bioactivity, immunochemical property and the like. Prediction model420may be configured to output a plurality of predicted values obtained by evaluating drug substance3from the respective perspectives. For example, prediction model420may output a first predicted value obtained in the case of evaluation from a first perspective (e.g., the physical property), and a second predicted value obtained in the case of evaluation from a second perspective (e.g., bioactivity).

Note that prediction model420may be configured to include a first prediction model for outputting the first predicted value in the case of evaluation of the quality of the drug substance from the first perspective, and a second prediction model for outputting the second predicted value in the case of evaluation of the quality of the drug substance from the second perspective.

In a case where prediction model420is configured to output a plurality of predicted values obtained when drug substance3is evaluated from the respective perspectives, determination unit224determines the quality of drug substance3from the plurality of predicted values, and generates determination result544. Note that in this case, determination unit224may weight the plurality of predicted values in accordance with the degrees of importance of the respective perspectives.

Quality prediction data540includes predicted value542generated by estimation process unit222, and determination result544generated by determination unit224. For example, in the case where prediction model420is configured to output a plurality of predicted values obtained when drug substance3is evaluated from the respective perspectives, quality prediction data540includes the plurality of predicted values (the first predicted value, second predicted value, etc.).

By including, in quality prediction data540, the plurality of predicted values obtained when drug substance3is evaluated at the respective perspectives, the quality of the drug substance can be evaluated from various perspectives.

Since quality prediction data540includes determination result544, the prediction result for the quality of the drug substance can be presented in a more easily understandable manner.

Output unit230outputs quality prediction data540.FIG.7is a diagram showing an example of an output result output by output unit230.

As shown inFIG.7, output unit230may output determination result544in combination with predicted value542obtained with respect to each perspective.

Note that prediction unit220is not necessarily include determination unit224that comprehensively determines the quality of the drug substance.

FIG.8is a flowchart showing a flow of an estimation method according to this Embodiment 1. The estimation method includes: a culture step (S100) of culturing cells; a measurement step (S200) of measuring the substance in the culturing vessel; a prediction step (S300) of predicting the quality of drug substance3, based on measurement data510that includes a measurement result; and an output step (S400) of outputting quality prediction data540obtained in the prediction step.

Culture step (S100) includes a step (S110) of seeding cells1in medium2. Culture step (S100) can include a step (S112) of replacing medium2according to the culture condition (predefined protocol), and a step (S114) of adding a reagent. Note that medium2may be a liquid or a solid.

Measurement step (S200) can be performed once or multiple times in culture step (S100). Measurement step (S200) is performed at any timing, for example, the timing when step (S110) of seeding cells1in medium2is performed, the timing when step (S112) of replacing medium2is performed, the timing when step (S114) of adding a reagent is performed, etc.

That is, the measurement timing may be any of the timing when step (S110) of seeding cells1in medium2is performed, the timing when step (S112) of replacing medium2is performed, the timing when step (S114) of adding a reagent is performed, etc. By matching the operation timing occurring in culture step (S100) with the measurement timing as described above, contamination can be prevented. Note that as described with reference toFIG.4, the measurement timing may be in the logarithmic growth phase, in the stationary phase, or in the death phase.

By performing measurement step (S200) once or multiple times in culture step (S100), the substance in culturing vessel10is measured at least one timing after the predetermined time period has passed since cells1have been seeded in medium2, and a measurement result is obtained.

Note that culture step (S100) and measurement step (S200) may be performed manually, or automated or semi-automated by a machine.

Prediction step (S300) includes a step (S310) of accepting input of measurement data510; a step (S312) of inputting measurement data510into prediction model420; a step (S314) of obtaining a prediction result from prediction model420; and a step (S316) of generating quality prediction data540. That is, in prediction step (S300), by inputting, into prediction model420, measurement data510that includes the measurement result obtained in measurement step (S200), quality prediction data540can be generated. Specifically, S310to S316and S400are performed by estimation device200.

FIG.9is a flowchart showing the flow of the learning method according to this Embodiment 1. The learning method includes: a culture step (S10) of culturing cells; a measurement step (S20) of measuring a substance in the culturing vessel; a purification step (S30) of obtaining a drug substance; an analysis step (S40) of analyzing the quality of the drug substance; and a learning step (S50) of executing the learning process using training data530, and generating prediction model420.

Culture step (S10) is in common with culture step (S100) included in the estimation method. Specifically, culture step (S10) includes a step (S11) of seeding cells1in medium2. Culture step (S10) can include a step (S12) of replacing medium2according to the culture condition (predefined protocol), and a step (S13) of adding a reagent. Note that medium2may be a liquid or a solid.

Measurement step (S20) can be performed multiple times in culture step (S10). Accordingly, time series data512indicating the temporal change in measurement target is obtained. Measurement step (S20) is performed at any timing, for example, the timing when step (S11) of seeding cells1in medium2is performed, the timing when step (S12) of replacing medium2is performed, the timing when step (S13) of adding a reagent is performed, etc.

That is, the measurement timing may be any of the timing when step (S11) of seeding cells1in medium2is performed, the timing when step (S112) of replacing medium2is performed, the timing when step (S13) of adding a reagent is performed, etc. By matching the operation timing occurring in culture step (S100) with the measurement timing as described above, contamination can be prevented. Note that as described with reference toFIG.4, the measurement timing may be in the logarithmic growth phase, in the stationary phase, or in the death phase.

By performing measurement step (S20) multiple times in culture step (S10), the substance in culturing vessel10is measured at a plurality of timings after seeding of cells1in medium2, and a measurement result is obtained.

Note that culture step (S10) and measurement step (S20) may be performed manually, or automated or semi-automated by a machine.

Purification step (S30) is operation of extracting only an objective component from the culture solution in culturing vessel10. Through purification step (S30), the drug substance that contains the objective component of high purity can be obtained.

In analysis step (S40), the drug substance is evaluated from one or more perspectives. Thus, one or more types of analyses are performed, and quality data520is obtained. In the case where the multiple types of analyses are performed, quality data520can include multiple types of analysis results (analytical results).

Learning step (S50) includes: a step (S51) of accepting input of training data530; a step (S52) of performing the learning process using training data530; and a step (S53) of outputting prediction model420generated by the learning process. Note that in this Embodiment 1, the output destination of prediction model420is server400. Alternatively, prediction model420may be stored in storage33of learning device300without being output. Specifically, S51to S53are performed by learning device300.

Modification Example

[Modification Example of Learning Device]

In Embodiment 1 described above, learning device300is assumed to obtain training data530from the outside. Note that the learning device may have a function of generating training data530. In Embodiment 1 described above, referring toFIG.4, the measurement timing for obtaining measurement data510accepted by estimation device200is described. The growth curve of cells1varies depending on the type of cells1. Accordingly, the learning device may have a function for identifying the measurement timing in prediction for generating quality prediction data540.

FIG.10is a block diagram showing the configuration of a learning device according to a modification example.FIG.10shows a configuration that learning device300according to Embodiment 1 described above does not include. A learning device300ais different from learning device300in that learning device300afurther includes a unit312of generating training data, and a timing identification unit330.

Unit312of generating training data generates training data530, based on time series data512and quality data520. Although not shown, unit312of generating training data transmits generated training data530to receiving unit310. Receiving unit310receives input of transmitted training data530.

Timing identification unit330identifies the measurement timing in prediction for generating quality prediction data540, based on time series data item512aon the cell number. More specifically, timing identification unit330identifies the induction phase, logarithmic growth phase, stationary phase, and death phase (seeFIG.4), based on time series data item512a, and generates information that can identify timing t1to timing t3. The information that can identify timing t1to timing t3includes, for example, a time period after seeding of cells1in medium2. The user can determine the measurement timing in prediction, based on information that can identify timing t1to timing t3.

Note that in Embodiment 1 described above, the culture condition is assumed to be the same between a case of generating prediction model420, and a case of generating quality prediction data540using generated prediction model420. Note that the culture condition is not necessarily common between the case of generating prediction model420, and the case of generating quality prediction data540using generated prediction model420. The learning method and the estimation method in this case are described with reference toFIGS.11to13.

FIG.11is a diagram showing a modification example of a model generation unit. A model generation unit320aaccording to the modification example generates a prediction model420a, based on multiple types of training data530aand530b.

Each of training data530aand530bincludes condition data550. Condition data550is information indicating the culture condition. For example, by repeating the culture step, measurement step, purification step, and analysis step, while changing the culture condition (seeFIG.9), training data530aand530bgenerated under culture conditions different from each other are obtained.

The difference of the culture condition can include the difference in type of drug to be used, such as the difference in component of medium2, and the difference in type of reagent to be added, and can further include the difference in temperature condition, the difference in timing of adding the reagent, etc.

Condition data550can include information on the drug to be used, the temperature condition, and information on the timing of adding the reagent. Note that condition data550is not necessarily information on a specific culture technique, and may be information that only indicates that training data530aand530bhave been obtained in culture conditions (e.g., a condition A, a condition B, etc.) different from each other.

Model generation unit320agenerates prediction model420athrough learning of the relationship between the temporal change of the substance in culturing vessel10and the quality of drug substance3, and further through the relationship between the difference in culture condition and the temporal change of the substance in culturing vessel10, or the relationship between the difference in culture condition and the quality of drug substance3.

FIG.12is a block diagram showing the configuration of an estimation device according to the modification example. InFIG.12, illustration of the configuration of an output unit according to Embodiment 1 described above is omitted. An estimation device200ais different from estimation device200according to Embodiment 1 described above in that estimation device200aincludes a receiving unit210ainstead of receiving unit210, and includes prediction unit220ainstead of prediction unit220.

Receiving unit210areceives measurement data510, and additionally receives input of condition data550indicating the culture condition. Prediction unit220ainputs condition data550in addition to measurement data510into prediction model420agenerated by model generation unit320ashown inFIG.13, and obtains a prediction result indicating the quality of the drug substance.

Prediction model420ais a model in consideration of the relationship between the difference in culture condition and the temporal change of the substance in culturing vessel10, or the relationship between the difference in culture condition and the quality of drug substance3. Accordingly prediction model420acan more correctly predict the quality of drug substance3than prediction model420can. Even in a case where the culture condition is different between generation of prediction model420a, and generation of quality prediction data540using generated prediction model420a, prediction unit220acan obtain the prediction result indicating the quality of the drug substance by inputting condition data550in addition to measurement data510into prediction model420abecause prediction model420ais a model in consideration of the difference in culture condition. That is, prediction unit220acan obtain the prediction result in consideration of the relationship between the difference in culture condition and the quality.

FIG.13is a diagram showing modification examples of prediction models. For example, as shown inFIG.13, the model generation unit may generate prediction models for the respective culture conditions, for example, a prediction model420-1dedicated for a culture condition A, and a prediction model420-2dedicated for a culture condition B.

In this case, the estimation device may use prediction models in conformity respectively with the culture conditions, based on condition data550.

[Entire Configuration of Prediction System]

FIG.14is a diagram schematically showing an entire configuration of a prediction system according to this Embodiment 2. Similar to prediction system SYS1, a prediction system SYS2 has a function of predicting the quality of a drug substance of a biopharmaceutical manufactured by cell culture. Prediction system SYS2 has a function of optimizing the culture condition in addition to the functions that prediction system SYS1 has. Prediction system SYS2 includes an optimization device600that optimizes the culture condition.

Optimization device600estimates an optimal culture condition to obtain drug substance3of high quality, based on time series data512and on quality data520, which are included in training data530. Optimization device600outputs the estimated optimal culture condition. The user executes a new culture examination, based on the culture condition output from optimization device600. Culture examination includes a culture step (step S10), a purification step (step S30), and an analysis step (step S40). These steps are similar to the respective steps described in Embodiment 1. The details of each step are shown inFIG.9. Accordingly, the description is not repeated here.

When the culture examination is newly executed, new training data530is input into optimization device600. Optimization device600re-estimates the optimal culture condition using training data530having already been input, and newly input training data530. Optimization device600outputs the newly estimated culture condition. The user executes the next culture examination, based on the culture condition output from optimization device600.

Consequently, as the number of culture examinations increases, the culture condition is further optimized. By advancing the optimization of the culture condition, the property of training data530is changed in a direction toward generation of drug substance3of higher quality. In other words, by advancing the optimization of the culture condition, the quality of training data530is improved. By improving the quality of training data530, the accuracy of optimizing prediction model420is improved.

Optimization device600outputs the optimized culture condition in response to an instruction by the user. When the user predicts the quality of the drug substance using estimation device200, the user can culture cells, based on the culture condition output from optimization device600. Consequently, the user can predict the quality of the drug substance in an environment allowing a drug substance of high quality to be generated.

Consequently, optimization device600functions significantly effectively in both the processes that are the process of generating prediction model420, and the process of predicting the quality of the drug substance by utilizing prediction model420. According to prediction system SYS2 of Embodiment 2, the possibility of effectively obtaining the drug substance of higher quality is improved.

[Hardware Configuration of Optimization Device]

FIG.15is a block diagram showing the hardware configuration of optimization device600. Optimization device600includes a processor61, a main memory62, a storage63, a communication I/F64, a USB I/F65, an input unit66, and a display unit67. These components are connected to each other via a processor bus68.

Processor61includes a CPU and a GPU, reads a program (e.g., optimization program630) stored in storage63, loads the program in main memory62, and executes the program. Processor61performs a series of processes for retrieving the optimal parameter values of the culture condition by executing the program.

Main memory62is made up of, for example, a volatile storage device, such as a RAM or a DRAM. Storage63is made up of, for example, a nonvolatile storage device, such as an HDD or an SSD.

Storage63stores training data (estimation)530transmitted via USB I/F65, an optimization program630for retrieving optimal parameter values620of the culture condition, and optimal parameter values620of the culture condition newly determined by processor61.

Communication I/F64exchanges a signal with another communication device using wired communication or wireless communication.

A USB memory (not shown) is detachably attached to USB I/F65, through which training data530stored in the USB memory is read.

Input unit66accepts user operation, and is typically made up of a touch panel, a keyboard, a mouse and the like. Display unit67is made up of a liquid crystal panel or the like that can display an image.

FIG.16is a block diagram showing an example of the configuration of optimization device600. Optimization device600includes a receiving unit601, an estimation unit602, an output unit603, and a storage unit604, as an example of a software configuration implemented by processor61(seeFIG.15) executing optimization program630(seeFIG.15).

Optimization device600retrieves the culture condition improving the level of the quality of drug substance3(hereinafter also called an optimal solution) according to the Bayesian optimization. Here, the culture condition is a combination of plurality of parameter values. The Bayesian optimization is a method of estimating the optimal event from among observed events by a statistical approach, based on a Bayesian probability. According to the Bayesian optimization, a trial based on a set optimal solution is executed, and another optimal solution is retrieved based on the result of the trial. The retrieved optimal solution is set as an optimal solution used for the next trial.

Receiving unit601receives data for estimation. The data for estimation is included in training data530shown inFIG.14. The data for estimation includes at least part of time series data512, and quality data520. Estimation unit602retrieves an optimal solution of the culture condition according to the Bayesian optimization, by executing a program for estimation. Storage unit604includes a plurality of memories that store the program for estimation, the input data for estimation, and the retrieved optimal solution. Output unit603is, for example, display unit67(seeFIG.15) that displays the optimal solution of the culture condition. Output unit603may be an interface for outputting the optimal solution of the culture condition to a display or a printer.

As shown in a window W10, one data item for estimation includes data on the culture condition and quality data used in one culture examination. The data on the culture condition includes a plurality of parameters p1, p2, . . . , pn that define the culture condition. Various parameters are, for example, any of the nutrient concentration, PH, temperature, humidity, medium component, type of a reagent to be added, reagent adding timing and the like.

Various parameters are, for example, the medium condition collected at timing of seeding cells in the medium. The timing corresponds to measurement timing t0shown inFIG.4. That is, the values of various parameters are the parameter values measured at measurement timing t0in time series data512shown inFIG.14. Note that various parameter values measured at various measurement timings shown inFIG.4may be further input into receiving unit601. For example, values that represent the temporal change in nutrient concentration, the temporal change in metabolite concentration, the change in pH of the culture solution in the culturing vessel, the change in temperature, the change in humidity and the like may be input as the data for estimation into receiving unit601. Estimation unit602may retrieve the optimal culture condition, based on these data for estimation.

FIG.17is a flowchart showing the flow of a process of optimization device600. Hereinafter, referring toFIGS.16and17, the flow of process of estimating the optimal culture condition by optimization device600is described.

First, estimation unit602determines whether the data for estimation is input into receiving unit601or not (step S600). If the data for estimation is input into receiving unit601, estimation unit602stores the input data for estimation in storage unit604(step S601). By repetitively executing step S601, the data for estimation input into receiving unit601is accumulated in storage unit604. Next, estimation unit602determines the values of parameters p1, p2, . . . , pn of the optimal culture condition, using the entire data for estimation stored in storage unit604(step S602). In step S602, estimation unit602retrieves the culture condition providing the optimal value for the level of the quality of drug substance3, according to Bayesian optimization.

It is desired that the retrieval of the culture condition be executed at the timing of input of a plurality of data for estimation corresponding to the respective culture examinations, in comparison with the case of execution every time input of one data item for estimation is executed. For example, it can be conceivable that the process in step S602is executed every time of input of four to ten data for estimation that respectively correspond to four to ten culture examinations.

If estimation unit602determines NO in step S600, estimation unit602determines whether a request for outputting the parameter values has been made or not (step S605). For example, if the user intends to predict, through estimation device200, the quality of the drug substance obtained by a certain culture examination, the user is required to determine the culture condition of the culture examination. It is useful to refer to the parameter values stored in optimization device600when the culture condition of the culture examination is determined. If it is determined in step S600that a request for outputting the parameter values has been made, estimation unit602outputs the parameter values stored in storage unit604from output unit603(step S604), and finishes the process based on this flowchart.

[Modification Example of Prediction System]

FIG.18is a diagram showing a modification example of a prediction system according to this Embodiment 2. In the modification example, learning device300includes an optimization unit6000that has the function of optimization device600shown inFIG.16. Similar to optimization device600, optimization unit6000includes receiving unit601, estimation unit602, output unit603, and storage unit604. Learning device300optimizes prediction model420, based on training data530, and retrieves the optimal parameter values of the culture condition. Consequently, according to the modification example, the added value of learning device300can be improved. Furthermore, according to the modification example, no optimization device600is required to be provided besides learning device300. Accordingly, reduction in cost can be facilitated.

The fact that the aforementioned embodiments are specific examples of the following aspects is understood by those skilled in the art.

(First item) An estimation device according to an aspect includes: a receiving unit that receives input of measurement data; a prediction unit that generates quality prediction data indicating a quality of a drug substance, by inputting, into a prediction model, measurement data received by the receiving unit; and an output unit that outputs the quality prediction data generated by the prediction unit. The measurement data includes a measurement result obtained by measuring at least one substance in a culturing vessel containing cells and a medium, at least one timing after a predetermined time period has passed since the cells have been seeded in the medium. The prediction model is a model for predicting the quality of the drug substance of a biopharmaceutical manufactured by culturing the cells.

In the estimation device according to the first item, the quality of the drug substance obtained by progress of the culture in a stage during the culture can be predicted. Accordingly, based on the predicted quality of the drug substance, a user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, or stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

(Second item) In the estimation device according to the first item, the measurement data includes a measurement result obtained by measuring the at least one substance at a plurality of timings after seeding of the cells in the medium.

(Third item) The estimation device according to item1or2, wherein the measurement data includes a measurement result obtained by measuring the at least one substance at a start of culturing the cells.

(Fourth item) The estimation device according to any one of items1to3, wherein the measurement data includes a measurement result obtained by measuring the at least one substance in a logarithmic growth phase of the cells.

In the logarithmic growth phase, the number of cells largely varies. Accordingly, the state of the substance in the culturing vessel is expected to largely vary. The result obtained by measuring the measurement target at the timing when a large change in state occurs is included in the measurement data. Accordingly, the estimation device according to the fourth item can predict the quality of drug substance using data more correctly indicating the change in state of cells.

(Fifth item) The estimation device according to any one of items1to4, wherein the measurement data includes a measurement result obtained by measuring the at least one substance in a stationary phase of the cells.

In the stationary phase, the balance between the number of growing cells, and the number of extinct cells is maintained, and the number of cells becomes the maximum. The result obtained by measuring the measurement target at the timing when the number of cells becomes the maximum is included in the measurement data. Accordingly, the estimation device according to the fifth item can predict the quality of drug substance using data indicating the state of the substance in the culturing vessel when the number of cells becomes the maximum.

(Sixth item) The estimation device according to any one of items1to4, wherein the measurement data includes a measurement result obtained by measuring the at least one substance in a death phase of the cells (FIG.5).

In the death phase, the cells become extinct, and enzymes and the like in the cells are exposed into the medium accordingly, which possibly affects the quality of the drug substance. In this case, the measurement result measured in the death phase of the cells can be possibly utilized for machine learning as an example of data of an inferior quality.

(Seventh item) The estimation device according to any one of items1to6, wherein the at least one substance is at least one of a nutrient ingested by the cell, a metabolite generated by metabolism of the cells, and the cells.

(Eighth item) The estimation device according to any one of items1to7, wherein a first predicted value in a case where the quality of the drug substance is evaluated from a first perspective, and a second predicted value in a case where the quality of the drug substance is evaluated from a second perspective are output from the prediction model, by input of the measurement data received by the receiving unit. The quality prediction data includes the first predicted value, and the second predicted value.

In the estimation device according to the eighth item, the quality of the drug substance can be evaluated from various perspectives.

(Ninth item) The estimation device according to any one of items1to7, wherein a predicted value in a case where the quality of the drug substance is evaluated from a predetermined perspective is output from the prediction model, by input of the measurement data received by the receiving unit. The prediction unit includes a determination unit that determines the quality of the drug substance, based on the predicted value. The quality prediction data includes a determination result obtained by determining the quality of the drug substance.

The estimation device according to the ninth item can present the prediction result for the quality of the drug substance in a more easily understandable manner.

(Tenth item) The estimation device according to any one of items1to8, wherein the receiving unit further receives input of condition data indicating a culture condition of the cells. The prediction unit generates quality prediction data, by inputting measurement data and condition data received by the receiving unit, into a prediction model.

In the estimation device according to the tenth item, the prediction result in consideration of the relationship between the difference in culture condition and the quality is obtained, and a more correct prediction result is obtained.

(Eleventh item) A learning device according to an aspect includes: a receiving unit that receives training data; and a model generation unit that generates a prediction model by executing a learning process using the training data received by the receiving unit. The training data includes measurement data including a measurement result obtained by measuring at least one substance in a culturing vessel containing cells and a medium at a plurality of timings after seeding of the cells in the medium, and quality data obtained by analyzing a drug substance of a biopharmaceutical manufactured from the cells. The prediction model is a model for generating quality prediction data indicating the quality of a drug substance of a biopharmaceutical manufactured from cells contained in a culturing vessel during culture of the cells, based on a measurement result obtained by measuring at least one substance in the culturing vessel during culture of the cells.

In the stage during the culture, the learning device according to the eleventh item can generate the prediction model for predicting the quality of the drug substance obtained by progress of the culture. Accordingly, based on the predicted quality of the drug substance, a user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, or stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

(Twelfth item) In the learning device according to an aspect, training data further includes condition data indicating the culture condition of the cells, the condition data includes a plurality of parameter values that define the culture condition, the learning device further includes an optimization unit that optimizes a combination of a plurality of parameter values, and the optimization unit estimates an optimal combination of the plurality of parameter values, using, as an input, the plurality of parameter values and the quality data obtained during culture of cells, based on the plurality of parameter values.

The learning device according to the twelfth item can estimate the combination of a plurality of parameter values that define the culture condition of cells in a stage during culture. Accordingly, the user can optimize the condition for improving the quality of the drug substance obtained by progress of the culture in the stage during the culture.

(Thirteenth item) In the learning device according to the eleventh item, the training data further includes condition data that indicates a condition for culturing cells contained in the culturing vessel. The training data received by the receiving unit includes first training data obtained when the cells are cultured under a first condition, and second training data obtained when the cells are cultured under a second condition that is different from the first condition. The model generation unit generates the prediction model by executing the learning process using the first training data and the second training data.

The learning device according to the thirteenth item can generate the prediction model in consideration of the relationship between the difference in culture condition and the temporal change in the substance in the culturing vessel, or the relationship between the difference in culture condition and the quality of the drug substance. Accordingly, the learning device can generate the prediction model for obtaining a more correct prediction result.

(Fourteenth item) An estimation method according to an aspect includes: putting cells and a medium in a culturing vessel, and culturing the cells; measuring at least one substance in the culturing vessel at least one timing after a predetermined time period has passed since the cells have been seeded in the medium; generating quality prediction data indicating a quality of a drug substance, by inputting the measurement data including a measurement result obtained in the measuring, into a prediction model; and outputting the quality prediction data. The prediction model is a model for predicting the quality of the drug substance of a biopharmaceutical manufactured by culturing the cells.

In the estimation method according to the fourteenth item, the quality of the drug substance obtained by progress of the culture in a stage during the culture can be predicted. Accordingly, based on the predicted quality of the drug substance, a user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, or stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

(Fifteenth item) A learning method according to an aspect includes: putting cells and a medium in a culturing vessel, and culturing the cells; measuring at least one substance in the culturing vessel at a plurality of timings after the cells are seeded in the medium; analyzing a quality of a drug substance of a biopharmaceutical manufactured from the cells; and generating a prediction model by executing a learning process using training data. The training data includes measurement data including a measurement result obtained by the step of measuring, and quality data obtained by the step of analyzing. The prediction model is a model for generating quality prediction data indicating the quality of a drug substance of a biopharmaceutical manufactured from cells contained in a culturing vessel during culture of the cells, based on a measurement result obtained by measuring at least one substance in the culturing vessel during culture of the cells.

In the stage during the culture, the learning method according to the fifteenth item can generate a prediction model for predicting the quality of the drug substance obtained by progress of the culture. Accordingly, based on the predicted quality of the drug substance, a user can take various measures, such as reviewing the protocol in the stage of the culture step before the purification step for obtaining the drug substance, extending the culture time period, or stopping the culture. As a result, the biopharmaceutical manufacturing cost can be reduced.

(Sixteenth item) An optimization device according to an aspect includes: a receiving unit that receives a plurality of parameter values that define a culture condition for seeding cells in a medium, and quality data obtained by analyzing a drug substance of a biopharmaceutical manufactured by culturing the cells; an estimation unit that receives the plurality of parameter values and the quality data received by the receiving unit, and estimates an optimal combination of the plurality of parameter values; and an output unit that outputs the combination of the plurality of parameter values estimated by the estimation unit.

The optimization device according to the sixteenth item can estimate the combination of a plurality of parameter values that define the culture condition of cells in a stage during culture. Accordingly, the user can optimize the condition for improving the quality of the drug substance obtained by progress of the culture in the stage during the culture. Note that the estimation unit may estimate the optimal combination of the plurality of parameter values by adopting, as input, a plurality of parameter values, and the quality data obtained by the step of analyzing, and executing the Bayesian optimization.

(Seventeenth item) An optimization method according to an aspect includes: putting cells and a medium in a culturing vessel, and culturing the cells under a culture condition defined by a plurality of parameter values; analyzing a quality of a drug substance of a biopharmaceutical manufactured by culturing the cells; and estimating an optimal combination of the plurality of parameter values using, as an input, the plurality of parameter values and the quality data obtained by the analyzing. The culturing the cells adopts the optimal combination of the plurality of parameter values estimated by the estimating, as a new culture condition, and cultures the cells under the new culture condition.

In the estimation method according to the seventeenth item, the condition for improving the quality of the drug substance obtained by progress of the culture in the stage during the culture can be optimized.

(Eighteenth item) A prediction system according to an aspect includes: the estimation device according to any one of the first to tenth items; the learning device according to any one of the eleventh to thirteenth items; and a measurement device that measures at least one substance in the culturing vessel.

Note that in the prediction system according to the eighteenth item, the estimation device and the measurement device may be implemented by a single information processing device. In this case, the receiving unit of the estimation device, and the receiving unit of the measurement device may be implemented as a single receiving unit. That is, the receiving unit receives the measurement data obtained by the measurement device measuring the substance.

(Nineteenth item) In the prediction system according to the eighteenth item, the measurement device includes a first measurement device that measures a first substance, and a second measurement device that measures a second substance.

Each of the embodiments disclosed this time are assumed to be implemented in a manner of combination in a range without technical contradiction. The embodiments disclosed this time are only examples in every respect, and should not be construed to be limitative. The scope of the present invention is not indicated by the description of the aforementioned embodiments but is indicated by the claims, and is intended to encompass all the changes within the meaning and scope equivalent to those of claims.

REFERENCE SIGNS LIST