Information processing device, method for operating information processing device, program for operating information processing device, and evaluation method

Abstract

This information processing device comprises a processor that calculates the amount of change in nerve action data between before and after the administration of a test substance, the data being between a first period preceding burst synchronization of nerve cells and a second period following the burst synchronization, and presents evaluation assistance information, corresponding to the amount of change, for evaluating the effect that the test substance has on the nerve cells.

Description

Information processing device, operating method of information processing device, operating program of information processing device, and evaluation method

[0001] The technology of the present disclosure relates to an information processing device, an operating method for an information processing device, an operating program for an information processing device, and an evaluation method.

[0002] Conventionally, for example, in the drug discovery process, tests have been conducted to evaluate the effects of pharmaceutical test substances on neurons, such as convulsant induction. Specifically, before and after administering the test substance to neurons derived from human pluripotent stem cells, such as human iPS (induced pluripotent stem) cells, the extracellular potential of the neurons is measured using a microelectrode array (MEA). In this way, measurement data of the extracellular potential before and after administration of the test substance are obtained. Then, based on the results of comparing the measurement data before and after administration, the effect of the test substance on the neurons is evaluated.

[0003] Japanese Patent Publication No. 2020-052865 describes a technology in which measurement data itself or a raster plot obtained by analyzing the measurement data (data showing the presence or absence of neuronal spikes at each electrode of an MEA in a time series) is input into a trained model, and the trained model predicts unknown characteristics of a test substance. Japanese Patent Publication No. 2020-051968 also describes a technology in which, among various parameters such as the total number of spikes, the total number of synchronous bursts, the time from the start to the end of a synchronous burst, and the number of spikes in a synchronous burst, parameters that contribute to predicting the unknown characteristics of the test substance are identified through multivariate analysis, and the effects of the test substance on neurons are evaluated based on the identified parameters. Synchronized bursts are a phenomenon in which continuous spikes occur simultaneously and frequently throughout a population of spontaneously active neurons for a certain period of time. Synchronized bursts are deeply involved in brain activity, and their frequency of occurrence changes in response to drugs with convulsive properties, making them attractive for assessing seizure risk and elucidating pathology.

[0004] In the techniques described in JP 2020-052865 A and JP 2020-051968 A, it was necessary to prepare measurement data or analytical data such as raster plots related to reference substances whose degree of effect on neurons is known for model learning or multivariate analysis. Furthermore, in the techniques described in JP 2020-052865 A and JP 2020-051968 A, there was a risk that the effect on neurons of test substances with a new mechanism of action different from the reference substance used in model learning or multivariate analysis could not be correctly evaluated.

[0005] One embodiment of the technology disclosed herein provides a new index capable of evaluating the effect of a test substance on neurons. This enables evaluation of the mechanism of action of the test substance on neurons, which was not possible with conventional techniques. Furthermore, an information processing device, an operating method for the information processing device, an operating program for the information processing device, and an evaluation method are provided that enable accurate evaluation of the effect of a test substance with a new mechanism of action different from that of the reference substance, even when using only data related to the test substance without using data related to the reference substance.

[0006] The information processing device of the present disclosure includes a processor that calculates the amount of change in neural activity data before and after administration of a test substance, which is neural activity data for a first period before and a second period after a synchronous burst of neurons, and presents evaluation support information for evaluating the effect of the test substance on neurons based on the amount of change.

[0007] It is preferable that the processor detects a synchronous burst from measurement data of the extracellular potential of a neuron, sets a first period and a second period according to the detected synchronous burst, and generates neural activity data from the measurement data during the set first period and second period.

[0008] The measurement data is preferably measured by a microelectrode array.

[0009] The neural activity data is preferably data relating to spikes of neural cells.

[0010] The neural activity data is preferably the number of spikes per unit time.

[0011] The processor preferably performs emphasis processing on the amount of change.

[0012] The enhancement process is preferably a process of raising the amount of change to an odd power.

[0013] It is preferable that the processor generates neural activity data from measurement data of the extracellular potential of neurons measured by the microelectrode array, calculates the amount of change for each electrode of the microelectrode array, and selects the amount of change to be used for deriving evaluation support information from the amount of change for each electrode based on predetermined selection conditions.

[0014] The first period and the second period are preferably set in advance.

[0015] The first and second periods are preferably set based on the period of the synchronization burst.

[0016] The period of the synchronous bursts is preferably derived from a correlogram of the neuronal spike histogram.

[0017] Preferably, the pair of first and second periods is set for one synchronization burst.

[0018] The pair of first and second periods is preferably set for two different synchronization bursts.

[0019] The processor preferably presents the amount of change itself as evaluation support information.

[0020] Preferably, the neural cells are derived from human pluripotent stem cells.

[0021] Preferably, the neuronal cells include glutamatergic neuronal cells.

[0022] Preferably, the neurons are co-cultured with astrocytes.

[0023] Preferably, the astrocytes are derived from human pluripotent stem cells.

[0024] The method of operating the information processing device disclosed herein includes calculating the amount of change in neural activity data before and after administration of a test substance, which is neural activity data for a first period before and a second period after a synchronous burst of neurons, and presenting evaluation support information for evaluating the effect of the test substance on neurons based on the amount of change.

[0025] The operating program of the information processing device disclosed herein causes a computer to execute processing including calculating the amount of change in neural activity data before and after administration of a test substance, which is neural activity data for a first period before and a second period after a synchronous burst of neurons, and presenting evaluation support information for evaluating the effect of the test substance on neurons based on the amount of change.

[0026] The evaluation method of the present disclosure includes calculating the amount of change in neural activity data before and after administration of a test substance, which is neural activity data for a first period before and a second period after a synchronous burst of neurons, and presenting evaluation support information for evaluating the effect of the test substance on neurons based on the amount of change.

[0027] It is preferable to evaluate the toxicity of the test substance to the central nervous system.

[0028] According to the technology disclosed herein, by providing a new index capable of evaluating the effect of a test substance on neurons, it becomes possible to evaluate the mechanism of action of the test substance on neurons, which could not be grasped by conventional technologies. Furthermore, it is possible to provide an information processing device, an operating method for the information processing device, an operating program for the information processing device, and an evaluation method that can correctly evaluate the effect on neurons of a test substance that has a new mechanism of action different from that of the reference substance, even when using only data related to the test substance without using data related to the reference substance.

[0029] 1 is a diagram illustrating an information processing server, an operator terminal, and a well plate. FIG. 2 is a block diagram illustrating computers constituting the information processing server and the operator terminal. FIG. 3 is a block diagram illustrating a processing unit of a CPU of the information processing server. FIG. 4 is a diagram illustrating first detection conditions and processing of a first detection unit. FIG. 5 is a diagram illustrating first detection results. FIG. 6 is a diagram illustrating second detection conditions and processing of a second detection unit. FIG. 7 is a diagram illustrating second detection results. FIG. 8 is a diagram illustrating processing of a period setting unit. FIG. 9 is a diagram illustrating processing of a generation unit. FIG. 10 is a diagram illustrating a spike count data group. FIG. 11 is a diagram illustrating a detailed configuration of a derivation unit. FIG. 12 is a diagram illustrating processing of a change amount calculation unit. FIG. 13 is a diagram illustrating a change amount data group. FIG. 14 is a diagram illustrating processing of an emphasis processing unit. FIG. 15 is a diagram illustrating processing of a representative value calculation unit. FIG. 16 is a diagram illustrating processing of a representative value calculation unit. FIG. 17 is a diagram illustrating evaluation support information. FIG. 18 is a block diagram illustrating a processing unit of a CPU of an operator terminal. FIG. 19 is a diagram illustrating a measurement data input screen. FIG. 20 is a diagram illustrating an evaluation support information display screen. FIG. 21 is a flowchart illustrating the processing procedure of the information processing server. FIG. 22 is a diagram illustrating a rate of change in synchronous burst occurrence of DMSO and evaluation support information. FIG. 23 is a diagram illustrating a rate of change in synchronous burst occurrence of picrotoxin and evaluation support information. FIG. 24 is a diagram illustrating a rate of change in synchronous burst occurrence of PTZ and evaluation support information. 1 is a diagram showing the rate of change in synchronous burst occurrence and evaluation support information for amoxapine. 2 is a diagram showing the rate of change in synchronous burst occurrence and evaluation support information for 4-AP. 3 is a diagram showing the rate of change in synchronous burst occurrence and evaluation support information for pilocarpine. 4 is a diagram showing the detailed configuration of a derivation unit of a second embodiment. 5 is a diagram showing selection conditions and processing of a selection unit. 6 is a diagram showing a third embodiment in which a first period and a second period are set based on the period of a synchronous burst. 7 is a diagram showing how the period of a synchronous burst is derived from a correlogram of a spike histogram. 8 is a diagram showing a fourth embodiment in which a pair of first and second periods is set for two different synchronous bursts.

[0030] First Embodiment As shown in FIG. 1 , an information processing server 10 is connected to an operator terminal 11 via a network 12. The information processing server 10 is an example of an “information processing device” according to the technology of the present disclosure. The operator terminal 11 is installed, for example, at a pharmaceutical company developing pharmaceuticals or at an organization contracted by a pharmaceutical company to develop pharmaceuticals, i.e., a contract research organization (CRO). The operator terminal 11 is operated by an operator OP who is involved in pharmaceutical development at the pharmaceutical company or CRO. The network 12 is, for example, a wide area network (WAN) such as the Internet or a public communication network. While FIG. 1 illustrates only one operator terminal 11 connected to the information processing server 10, in reality, multiple operator terminals 11 from multiple pharmaceutical companies or CROs are connected to the information processing server 10.

[0031] A measuring device (not shown) for measuring the extracellular potential of neurons 15 is connected to the operator terminal 11, and a well plate 13 is installed in the measuring device. The well plate 13 has a plurality of wells 14. The wells 14 are arranged at equal intervals in the vertical and horizontal directions. FIG. 1 shows an example of a well plate 13 having 8 x 6 = 48 wells 14. The wells 14 are numbered. The number of wells 14 is not limited to the illustrated 48, but may be 24, 96, or the like.

[0032] The well 14 is a cylindrical depression with an open top. The well 14 is filled with a culture medium, in which neurons 15 and astrocytes 16 are co-cultured. The neurons 15 are differentiated from human iPS cells 17A, and the astrocytes 16 are differentiated from human iPS cells 17B. In other words, both the neurons 15 and the astrocytes 16 are derived from human iPS cells 17. The human iPS cells 17 are an example of a "human pluripotent stem cell" according to the technology of the present disclosure. The neurons 15 are glutamatergic neurons. The neurons 15 grow until they become spontaneously active. More specifically, the neurons 15 undergo synaptic pruning and grow until they form a mature neural circuit. Furthermore, a drug candidate substance CS is administered to the well 14. The dosage (also referred to as the amount added) of the candidate substance CS is preset, and is, for example, one of four dosage levels: D1, D2, D3, and D4 (D1<D2<D3<D4). The candidate substance CS is an example of a "test substance" according to the technology of the present disclosure. Except when measuring the extracellular potential, the well plate 13 is placed in a thermostatic chamber (not shown) to co-culture the neurons 15 and astrocytes 16 under a constant environment.

[0033] The 48 wells 14 are divided into multiple groups, for example, Group 1 (Nos. 1 to 4, 13 to 16, 25 to 28, and 37 to 40), Group 2 (Nos. 5 to 8, 17 to 20, 29 to 32, and 41 to 44), and Group 3 (Nos. 9 to 12, 21 to 24, 33 to 36, and 45 to 48), with 16 wells in each group. Different types of candidate substances CS are administered to the wells 14 in each group at doses D1, D2, D3, and D4. This allows multiple types of candidate substances CS (here, three types) to be tested simultaneously in one well plate 13.

[0034] A microelectrode array 18 is provided on the bottom surface of the well 14. The microelectrode array 18 is composed of a plurality of electrodes 19 arranged at equal intervals. FIG. 1 illustrates a microelectrode array 18 having 4 x 4 = 16 electrodes 19. Like the wells 14, the electrodes 19 are also numbered. The number of electrodes 19 is not limited to the illustrated 16, but may be any number equal to or greater than two, such as 8, 64, or 100. The arrangement of the electrodes 19 is also not limited to the illustrated square arrangement, but may be a staggered arrangement.

[0035] The electrodes 19 are exposed on the bottom surface of the well 14 and come into contact with the neurons 15. The electrodes 19 are connected to a potential measurement circuit (not shown) of the measurement device. The potential measurement circuit measures the electrical signal from each of the multiple electrodes 19, i.e., the extracellular potential of the neurons 15. Therefore, the extracellular potential is measured for each of the multiple electrodes 19, 16 electrodes 19 in this example. This method of measuring extracellular potential using a microelectrode array 18 is much simpler and less expensive than a method known as manual patch clamp, in which electrodes are inserted directly into neurons 15 to measure intracellular potential. The measurement frequency of the extracellular potential using the potential measurement circuit is, for example, 12.5 kHz. In this case, the extracellular potential is measured 12,500 times per second.

[0036] The measurement results of the extracellular potential from the potential measurement circuit are input into the operator terminal 11. The operator terminal 11 generates measurement data 20, which is a fluctuating waveform that indicates the temporal change in the measurement results of the extracellular potential. Here, the measurement of the extracellular potential is performed for a preset period, for example, 10 minutes. Therefore, the measurement data 20 represents the temporal change in the measurement results of the extracellular potential for the preset period. In other words, the horizontal axis of the measurement data 20 represents time, and the vertical axis represents the strength (magnitude) of the extracellular potential. The measurement data 20 is stored in association with the number of the well 14 and the number of the electrode 19 where the extracellular potential was measured. The frequency band of the measurement data 20 is, for example, 200 Hz to 3 kHz.

[0037] The operator terminal 11 transmits an evaluation support request 21 to the information processing server 10. The evaluation support request 21 is a request to have the information processing server 10 evaluate the convulsant potential of the candidate substance CS, which is one of the effects of the candidate substance CS on neurons 15. Convulsant potential is an example of "toxicity to the central nervous system" according to the technology of the present disclosure. The evaluation support request 21 includes a measurement data group 22 composed of multiple measurement data 20. The number of measurement data 20 constituting the measurement data group 22 depends on the number of wells 14 and electrodes 19. In this example, there are 48 wells 14 and 16 electrodes 19, and there are a total of five patterns, one before administration of the candidate substance CS and one after administration of the candidate substance CS at four dose levels. Therefore, the measurement data group 22 is composed of 48 x 16 x 5 = 3,840 measurement data 20. Although not shown in the figure, the evaluation support request 21 also includes a terminal ID (Identification Data) for uniquely identifying the operator terminal 11 that is the sender of the evaluation support request 21 .

[0038] When the evaluation support request 21 is received, the information processing server 10 generates evaluation support information 23 for evaluating the convulsant-inducing potential of the candidate substance CS. The information processing server 10 distributes the evaluation support information 23 to the operator terminal 11 that sent the evaluation support request 21. When the evaluation support information 23 is received, the operator terminal 11 makes the evaluation support information 23 available for viewing by the operator OP.

[0039] The measurement data 20 contains spikes, which are pulses resulting from spontaneous activity of neurons 15. Spikes are also called firings and are responsible for transmitting information between adjacent neurons 15. The phenomenon in which these spikes appear multiple times within a set period is called a burst (continuous firing), and the phenomenon in which bursts are simultaneously observed in the measurement data 20 from electrodes 19 of a set size in one well 14 is called a synchronous burst NB (see FIG. 8 ). The information processing server 10 evaluates the convulsion-inducing potential of the candidate substance CS based on this synchronous burst NB.

[0040] 2, the computers that make up the information processing server 10 and the operator terminal 11 basically have the same configuration, and include a storage 30, a memory 31, a CPU (Central Processing Unit) 32, a communication unit 33, a display 34, and an input device 35. These are interconnected via a bus line 36.

[0041] The storage 30 is a hard disk drive built into the computer that constitutes the information processing server 10 and the operator terminal 11, or connected via a cable or network. Alternatively, the storage 30 is a disk array with multiple hard disk drives connected in series. The storage 30 stores control programs such as an operating system, various application programs (hereinafter referred to as APs (Application Programs)), and various data associated with these programs. Note that a solid state drive may be used instead of a hard disk drive.

[0042] The memory 31 is a work memory for the CPU 32 to execute processing. The CPU 32 loads programs stored in the storage 30 into the memory 31 and executes processing in accordance with the programs. In this way, the CPU 32 comprehensively controls each part of the computer. The CPU 32 is an example of a "processor" according to the technology of the present disclosure. The memory 31 may be built into the CPU 32.

[0043] The communication unit 33 is a network interface that controls the transmission of various information via the network 12, etc. The display 34 displays various screens. The various screens are provided with an operation function using a GUI (Graphical User Interface). The computers that make up the information processing server 10 and the operator terminal 11 accept input of operation instructions from an input device 35 via the various screens. The input device 35 is a keyboard, a mouse, a touch panel, a microphone for voice input, etc.

[0044] In the following explanation, the parts of the computer that make up the information processing server 10 (storage 30 and CPU 32) are distinguished by adding the suffix "A" to their symbols, and the parts of the computer that make up the operator terminal 11 (storage 30, CPU 32, display 34, and input device 35) are distinguished by adding the suffix "B" to their symbols.

[0045] 3, an operating program 40 is stored in the storage 30A of the information processing server 10. The operating program 40 is an AP for causing a computer to function as the information processing server 10. In other words, the operating program 40 is an example of an "operating program for an information processing device" according to the technology of the present disclosure. The storage 30A also stores a first detection condition 41, a second detection condition 42, and the like.

[0046] When the operating program 40 is started, the CPU 32A of the computer constituting the information processing server 10 works in cooperation with the memory 31 and the like to function as a request receiving unit 45, a read / write (hereinafter abbreviated as RW (Read Write)) control unit 46, a first detection unit 47, a second detection unit 48, a period setting unit 49, a generation unit 50, a derivation unit 51, and a screen distribution control unit 52.

[0047] The request receiving unit 45 receives various requests from the operator terminal 11, including the evaluation support request 21. When the evaluation support request 21 is received, the request receiving unit 45 outputs the measurement data group 22 included in the evaluation support request 21 to the RW control unit 46. Although not shown in the figure, the request receiving unit 45 also outputs the terminal ID of the operator terminal 11 included in the evaluation support request 21 to the screen distribution control unit 52.

[0048] The RW control unit 46 controls the storage of various data in the storage 30A and the reading of various data from the storage 30A. In particular, the RW control unit 46 controls the storage of the measurement data group 22 in the storage 30A and the reading of the measurement data group 22 from the storage 30A. The RW control unit 46 outputs the read measurement data group 22 to the first detection unit 47.

[0049] The RW control unit 46 reads the first detection condition 41 from the storage 30A and outputs the read first detection condition 41 to the first detection unit 47. The RW control unit 46 also reads the second detection condition 42 from the storage 30A and outputs the read second detection condition 42 to the second detection unit 48.

[0050] The first detection unit 47 detects spikes from the measurement data 20 based on the first detection conditions 41. The first detection unit 47 outputs a first detection result 55, which is the spike detection result, to the second detection unit .

[0051] The second detector 48 detects the synchronization burst NB based on the second detection condition 42. The second detector 48 outputs a second detection result 56, which is the detection result of the synchronization burst NB, to the period setting unit 49.

[0052] The period setting unit 49 sets a first period P1 (see FIG. 8) before the synchronization burst NB and a second period P2 (see FIG. 8) after the synchronization burst NB. The period setting unit 49 outputs period setting results 57 of the first period P1 and the second period P2 to the generation unit 50.

[0053] The generating unit 50 generates spike count data 65 (see FIG. 9 ) from the measurement data 20 in the first period P1 and the second period P2. The generating unit 50 generates spike count data 65 for each well 14. The generating unit 50 outputs a spike count data group 58, which is a collection of spike count data 65 for each well 14, to the derivation unit 51. The spike count data 65 is an example of "neural activity data" and "spike-related data" according to the technology of the present disclosure.

[0054] The derivation unit 51 derives the evaluation support information 23 according to the spike count data 65. The derivation unit 51 outputs the evaluation support information 23 to the screen distribution control unit 52.

[0055] The screen distribution control unit 52 controls the distribution of various screens to the operator terminal 11. Specifically, the screen distribution control unit 52 distributes and outputs various screens to the operator terminal 11 that has sent the various requests in the form of screen data for web distribution created using a markup language such as XML (Extensible Markup Language). At this time, the screen distribution control unit 52 identifies the operator terminal 11 that has sent the various requests based on the terminal ID from the request receiving unit 45. Note that instead of XML, other data description languages ​​such as JSON (Javascript (registered trademark) Object Notation) may be used.

[0056] The various screens include a measurement data input screen 95 (see FIG. 19) for inputting measurement data 20, and an evaluation support information display screen 100 (see FIG. 20) for presenting evaluation support information 23 to the operator OP. In addition to these processing units 45 to 52, the CPU 32A also includes an instruction receiving unit that receives various operation instructions from the input device 35.

[0057] 4, the first detection condition 41 is that the absolute value of the intensity of the extracellular potential is equal to or greater than the threshold intensity THI. Therefore, the first detection unit 47 detects, as spikes, waveforms in the measurement data 20 whose absolute value of the intensity is equal to or greater than the threshold intensity THI.

[0058] The threshold strength THI is based on, for example, the baseline strength of the measurement data 20. More specifically, when the average value of the baseline strength of the measurement data 20 is set to 0, the threshold strength THI is a value that is an integer multiple (e.g., six times) of the representative value of the measurement data 20. The representative value is the standard deviation, quartile, etc. of the baseline strength. The threshold strength THI is set using the measurement data 20 for a preset period (e.g., 10 minutes).

[0059] 5, the first detection result 55 is information in which the spike detection times for each electrode 19 in each well 14 before and after administration of the candidate substance CS (at four dose levels D1 to D4 after administration) are registered. The detection times are values ​​measured with the measurement start time of the measurement data 20 set to 0. The spike detection times may also be referred to as the spike occurrence times.

[0060] As shown in FIG. 6 as an example, the second detection unit 48 generates a spike histogram 60 and a raster plot 61 from the first detection results 55. The spike histogram 60 is a graph showing the number of spikes detected per unit time (e.g., one second) in one well 14, with the horizontal axis representing time and the vertical axis representing the number of spikes detected. To generate the spike histogram 60, the second detection unit 48 calculates the number of spikes detected per unit time by weighted averaging using an appropriate weighting function, such as a Gaussian function with a time frame of 1000 ms. The raster plot 61 is a graph in which strip-shaped markers are added to the locations where spikes are detected for each electrode 19 in one well 14, with the horizontal axis representing time. The second detection unit 48 outputs the raster plot 61 to the generation unit 50.

[0061] The second detection condition 42 includes the following three conditions: 1. The number of spike detections is equal to or greater than the threshold detection number THND. 2. A burst occurs at an electrode 19 that is equal to or greater than the threshold rate THP. 3. If the interval between two adjacent waveforms that satisfy conditions 1 and 2 is less than the threshold interval THIV, one of the waveforms is adopted.

[0062] Therefore, the second detection unit 48 detects waveforms with spike detection counts equal to or greater than the threshold detection count THND from the spike histogram 60. The second detection unit 48 also derives the burst occurrence rate during the occurrence time of waveforms with spike detection counts equal to or greater than the threshold detection count THND from the raster plot 61. Then, it detects waveforms with the derived occurrence rate equal to or greater than the threshold rate THP. Finally, if the interval between two adjacent waveforms with spike detection counts equal to or greater than the threshold detection count THND and burst occurrence rates equal to or greater than the threshold rate THP is less than the threshold interval THIV, the second detection unit 48 adopts one of the waveforms. For example, the second detection unit 48 adopts the waveform with the later detection time of the two waveforms. The second detection unit 48 may also adopt the waveform with the greater spike detection count or the greater spike occurrence rate of the two waveforms. In this way, the second detection unit 48 detects a waveform that satisfies the second detection condition 42 as a synchronous burst NB.

[0063] The threshold number of detected spikes THND is based on the number of detected spikes at the baseline of the spike histogram 60. More specifically, the threshold number of detected spikes THND is an integer multiple (e.g., six times) of the representative value of the number of detected spikes at the baseline of the spike histogram 60. The representative value may be the average value, median, or mode of the number of detected spikes at the baseline itself, or the average value, median, or mode of the standard deviation of the number of detected spikes at the baseline per unit time (e.g., per second). The threshold number of detected spikes THND may be set using the spike histogram 60 for a preset period (e.g., 10 minutes) or may be updated at set intervals (e.g., every 10 seconds). The threshold number of detected spikes THND may also be set to the average value of the number of detected spikes at the baseline of the spike histogram 60 plus an integer multiple (e.g., three times) of the standard deviation of the number of detected spikes at the baseline of the spike histogram 60.

[0064] The threshold percentage THP is, for example, 75%. In this example, since the number of electrodes 19 is 16, the threshold percentage THP is 16×0.75=12.

[0065] The threshold interval THIV is a representative value of the interval between two adjacent waveforms that satisfy conditions 1 and 2. The representative value is the minimum value of the interval between two adjacent waveforms (waveforms that are determined to be separate synchronization bursts NB) that satisfy conditions 1 and 2. The threshold interval THIV may be a fixed value such as 4000 ms.

[0066] 7, the second detection result 56 is information in which the detection times of synchronous bursts NB are registered for each well 14 before and after administration of the candidate substance CS (four levels of doses D1 to D4 after administration). Like the detection times of spikes, the detection times of synchronous bursts NB are values ​​measured with the measurement start time of the measurement data 20 set to 0. The detection times of synchronous bursts NB may also be referred to as the occurrence times of synchronous bursts NB.

[0067] The period of the synchronous burst NB is set using a period setting threshold. Specifically, the time when the number of detected spikes in the spike histogram 60 exceeds the period setting threshold is set as the start time of the synchronous burst NB, and the time when the number of detected spikes falls below the period setting threshold is set as the end time of the synchronous burst NB. The period between the start time and the end time is set as the period of the synchronous burst NB. The period setting threshold is calculated based on the first and third quartiles of the number of detected spikes.

[0068] As an example, as shown in FIG. 8 , the period setting unit 49 sets a pair of a first period P1 and a second period P2 for each detected synchronization burst NB. Specifically, the period setting unit 49 sets the period −T from the detection time of the synchronization burst NB as the first period P1. The period setting unit 49 also sets the period +T from the detection time of the synchronization burst NB as the second period P2. The pair of the first period P1 and the second period P2 is a set that together generates spike count data 65 and calculates the amount of change. Note that T is a time longer than the period of the synchronization burst NB. Therefore, the first period P1 and the second period P2 include periods other than the period of the synchronization burst NB. T is a preset value, which is 42.5 seconds in this example. It should be noted that T is not limited to the example of 42.5 seconds, but may be 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, 50 seconds, 55 seconds, 60 seconds, 90 seconds, 120 seconds, 180 seconds, etc.

[0069] The period setting result 57 is data in which the start and end times of the first period P1 and the second period P2 are registered before and after administration of the candidate substance CS (four levels of doses D1 to D4 after administration) for each well 14. The end time of the first period P1 and the start time of the second period P2 are the same, i.e., the detection time of the synchronous burst NB.

[0070] As an example, as shown in FIG. 9 , the generation unit 50 generates spike count data 65 by counting the number of spikes per unit time at each electrode 19 with reference to a raster plot 61 during a first period P1 and a second period P2. Here, the unit time is 5 seconds. Therefore, for example, the number of spikes at 40 seconds, the end of the second period P2, is the number of spikes occurring between 37.5 and 42.5 seconds (37.5 < TX ≦ 42.5 = T). Similarly, the number of spikes at −5 seconds of the first period P1 is the number of spikes occurring between −7.5 and −2.5 seconds (−7.5 ≦ TX < −2.5). Furthermore, the number of spikes at 0 seconds, which is the end of the first period P1, the start of the second period P2, and the detection time of the synchronization burst NB, is the number of spikes occurring between −2.5 and 2.5 seconds (−2.5 < TX ≦ 2.5). Thus, in the spike count data 65, the center time of the 5-second unit time is represented as 40 seconds, which is the center of the 37.5-42.5 second range; -5 seconds, which is the center of the -7.5-2.5 second range; and 0 seconds, which is the center of the -2.5-2.5 second range. The same applies to the subsequent change amount data 75 and processed change amount data 75AP (see FIG. 14 ), first average value data 78 (see FIG. 15 ), first average value data group 80 and second average value data 82 (see FIG. 16 ), and post-sorting change amount data 75AS (see FIG. 29 ). The unit time for counting the number of spikes is not limited to the illustrative 5 seconds, but may be, for example, 1 second, 2 seconds, 3 seconds, 4 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds, etc. A heat map 66 of the spike count data 65 is shown below the dashed arrow. The horizontal axis of the heat map 66 is time, the vertical axis is the electrode 19, and the shade of color indicates the number of spikes.

[0071] As an example, as shown in FIG. 10, spike count data group 58 is a collection of spike count data 65 for each well 14 before and after administration of candidate substance CS (four levels of doses D1 to D4 after administration).

[0072] As an example, as shown in FIG. 11 , the derivation unit 51 has a change amount calculation unit 70, an emphasis processing unit 71, and a representative value calculation unit 72. The spike count data group 58 is input to the change amount calculation unit 70. The change amount calculation unit 70 calculates the change in the number of spikes before and after administration of the candidate substance CS based on the spike count data group 58. The change amount calculation unit 70 outputs the calculation result of the change amount as change amount data 75 (see FIG. 12 ). The change amount calculation unit 70 outputs the change amount data 75 for each well 14. The change amount calculation unit 70 outputs a change amount data group 73, which is a collection of multiple change amount data 75, to the emphasis processing unit 71.

[0073] The emphasis processing unit 71 applies emphasis processing to the amount of change in the change amount data 75. The emphasis processing unit 71 outputs a post-processing change amount data group 73AP, which is a collection of change amount data 75 after emphasis processing (hereinafter referred to as post-processing change amount data 75AP, see FIG. 14 ), to the representative value calculation unit 72.

[0074] The representative value calculation unit 72 calculates a representative value of the amount of change in the post-processing change amount data 75AP and outputs the calculated representative value to the screen distribution control unit 52 as the evaluation support information 23.

[0075] As an example, as shown in FIG. 12 , the change amount calculation unit 70 calculates the change amount data 75 by subtracting the number of spikes in the spike count data 65 before administration of the candidate substance CS from the number of spikes in the spike count data 65 after administration of the candidate substance CS. FIG. 12 illustrates an example in which the change amount data 75 for the No. 1 well 14 (denoted as "well 01") at dose D1 of the candidate substance CS is calculated by subtracting the number of spikes in the spike count data 65 for the No. 1 well 14 before administration of the candidate substance CS from the number of spikes in the spike count data 65 for the No. 1 well 14 at dose D1. The change amount calculation unit 70 similarly calculates the change amount data 75 for the other wells 14 at dose D1. The change amount calculation unit 70 also similarly calculates the change amount data 75 for each well 14 at doses D2 to D4.

[0076] If the number of spikes in the spike count data 65 before administration of the candidate substance CS is less than the number of spikes in the spike count data 65 after administration of the candidate substance CS (if the number of spikes after administration of the candidate substance CS is greater than before administration), the amount of change will be a positive value. Conversely, if the number of spikes in the spike count data 65 before administration of the candidate substance CS is greater than the number of spikes in the spike count data 65 after administration of the candidate substance CS (if the number of spikes after administration of the candidate substance CS is less than before administration), the amount of change will be a negative value. If the number of spikes in the spike count data 65 after administration of the candidate substance CS is the same as the number of spikes in the spike count data 65 before administration of the candidate substance CS (if the number of spikes after administration of the candidate substance CS is the same as before administration), the amount of change will naturally be zero.

[0077] As an example, as shown in FIG. 13, the change amount data group 73 is a collection of change amount data 75 for each well 14 for doses D1 to D4 of the candidate substance CS.

[0078] 14, as an example, the emphasis processing unit 71 performs emphasis processing by cubed each change amount in the change amount data 75. For example, the change amount of "-5" at 30 seconds for the No. 01 electrode 19 (denoted as "electrode 01") is cubed to become "-125" in the processed change amount data 75AP. Similarly, the change amount of "19" at 0 seconds for the No. 16 electrode 19 (denoted as "electrode 16") is cubed to become "6859" in the processed change amount data 75AP. Hereinafter, the change amount in the processed change amount data 75AP will be referred to as the "post-processing change amount."

[0079] As an example, as shown in FIG. 15 , the representative value calculation unit 72 calculates a first average value as a representative value of the post-treatment change amount of the post-treatment change amount data 75AP, generating first average value data 78. The first average value is calculated by adding the post-treatment change amounts of each electrode 19 over a certain time period and dividing the sum by the number of electrodes 19. For example, the first average value for 35 seconds is calculated as "74" by calculating (-64 + 8 + ... + 1000) / 16. The representative value calculation unit 72 generates first average value data 78 for the post-treatment change amount data 75AP of wells 14 in the same group administered with the same candidate substance CS. More specifically, the representative value calculation unit 72 generates first average value data 78 for the post-treatment change amount data 75AP of each well 14 in a group associated with a certain candidate substance CS at doses D1 to D4. In this way, a first average value data group 80 (see FIG. 16 ) is obtained, which is a collection of the first average value data 78 for each well 14 for each of doses D1 to D4.

[0080] As an example, as shown in FIG. 16 , the representative value calculation unit 72 calculates a second average value as a representative value of the first average values ​​of the first average value data group 80, generating second average value data 82. The second average value is calculated by adding the first average values ​​of each well 14 with the same dose of the same candidate substance CS for a certain time and dividing by the number of wells 14 with the same dose. For example, the second average value at −40 seconds is calculated as “162” by calculating (182 + 155 + 162 + 149) / 4. The representative value calculation unit 72 generates second average value data 82 for each first average value data group 80 for doses D1 to D4. This results in a second average value data group 85 (see FIG. 17 ), which is a collection of second average value data 82, for each of doses D1 to D4. The representative value calculation unit 72 outputs this second average value data group 85 as the evaluation support information 23. Note that a bar graph 83 of the second average value data 82 is shown below the dashed arrow. The horizontal axis of the bar graph 83 represents time, and the vertical axis represents the second average value.

[0081] 17 , the evaluation support information 23 as a second average value data group 85 is a collection of second average value data 82 for each of the doses D1 to D4. Here, since the second average value is the amount of change when corrected, by using the second average value data group 85 as the evaluation support information 23, the amount of change itself is used as the evaluation support information 23.

[0082] 18 , an evaluation AP 90 is stored in the storage 30B of the operator terminal 11. The evaluation AP 90 is installed in the operator terminal 11 by the operator OP. The evaluation AP 90 is an AP for causing the information processing server 10 to evaluate the convulsant-inducing potential of the candidate substance CS. When the evaluation AP 90 is started, the CPU 32B of the operator terminal 11 functions as a browser control unit 92 in cooperation with the memory 31 and the like. The browser control unit 92 controls the operation of a web browser dedicated to the evaluation AP 90.

[0083] The browser control unit 92 reproduces various screens based on various screen data from the information processing server 10 and displays the reproduced various screens on the display 34B. The browser control unit 92 also accepts various operation instructions input by the operator OP from the input device 35B via the various screens. The browser control unit 92 transmits various requests, including the evaluation support request 21, to the information processing server 10 in response to the operation instructions.

[0084] When the evaluation AP 90 is launched, a measurement data input screen 95 shown in FIG. 19 is displayed on the display 34B under the control of the browser control unit 92. The measurement data input screen 95 has an input box 96 for the measurement data group 22. A file of the measurement data group 22 can be dropped into the input box 96. After dropping the file of the desired measurement data group 22 into the input box 96, the operator OP selects the evaluation button 97. When the evaluation button 97 is selected, the browser control unit 92 generates an evaluation support request 21 including the measurement data group 22 input into the input box 96, and transmits the generated evaluation support request 21 to the information processing server 10.

[0085] Furthermore, when the convulsant-inducing potential of a candidate substance CS is evaluated in the information processing server 10, an evaluation support information display screen 100, as shown in FIG. 20 as an example, is displayed on the display 34B under the control of the browser control unit 92. The evaluation support information display screen 100 displays a bar graph 83 of second average value data 82 of doses D1 to D4, which is the evaluation support information 23. In this manner, the evaluation support information 23 is presented to the operator OP in the form of distributed screen data. Note that the evaluation support information display screen 100 may highlight bar graphs 83 whose second average values ​​are equal to or greater than a threshold value, for example by displaying them in a different color.

[0086] A save button 101 and an OK button 102 are provided at the bottom of the evaluation support information display screen 100. When the save button 101 is selected, the evaluation support information 23 is associated with the measurement data group 22 and stored in the storage 30B of the operator terminal 11. When the OK button 102 is selected, the display of the evaluation support information display screen 100 is cleared.

[0087] Next, the operation of the above configuration will be described with reference to the flowchart shown in Fig. 21 as an example. When the operating program 40 is started in the information processing server 10, the CPU 32A of the information processing server 10 functions as a request receiving unit 45, an RW control unit 46, a first detection unit 47, a second detection unit 48, a period setting unit 49, a generation unit 50, a derivation unit 51, and a screen distribution control unit 52, as shown in Fig. 3. When the evaluation AP 90 is started in the operator terminal 11, the CPU 32B of the operator terminal 11 functions as a browser control unit 92, as shown in Fig. 18.

[0088] 19 is displayed on the display 34B of the operator terminal 11 under the control of the browser control unit 92. When the operator OP inputs a file of the desired measurement data group 22 into the input box 96 on the measurement data input screen 95 and selects the evaluation button 97, an evaluation support request 21 is sent from the browser control unit 92 to the information processing server 10.

[0089] In the information processing server 10, the request receiving unit 45 receives the evaluation support request 21 (YES in step ST100). The measurement data group 22 included in the evaluation support request 21 is output from the request receiving unit 45 to the RW control unit 46 and stored in the storage 30A under the control of the RW control unit 46 (step ST110). In addition, the terminal ID of the operator terminal 11 included in the evaluation support request 21 is output from the request receiving unit 45 to the screen distribution control unit 52.

[0090] The measurement data group 22 is read from the storage 30A by the RW control unit 46 (step ST120). The measurement data group 22 is output from the RW control unit 46 to the first detection unit 47.

[0091] 4, the first detector 47 detects spikes from the measurement data 20 based on the first detection condition 41, and generates the first detection result 55 shown in FIG. 5 (step ST130). The first detector 47 outputs the first detection result 55 to the second detector 48.

[0092] As shown in Fig. 6, the second detection unit 48 detects the synchronization burst NB based on the second detection condition 42, and generates the second detection result 56 shown in Fig. 7 (step ST140). The second detection result 56 is output from the second detection unit 48 to the period setting unit 49. In addition, the raster plot 61 generated in the process of detecting the synchronization burst NB is output from the second detection unit 48 to the generation unit 50.

[0093] 8, the period setting unit 49 sets a first period P1 before the synchronization burst NB and a second period P2 after the synchronization burst NB (step ST150). The period setting results 57 of the first period P1 and the second period P2 are output from the period setting unit 49 to the generation unit 50.

[0094] As shown in Fig. 9, spike count data 65 is generated in the generation unit 50 from the raster plot 61 for the first period P1 and the second period P2 (step ST160). The spike count data 65 is generated for each well 14, and this constitutes the spike count data group 58 shown in Fig. 10. The spike count data group 58 is output from the generation unit 50 to the derivation unit 51.

[0095] As shown in FIG. 11 , the derivation unit 51 includes a change amount calculation unit 70, an emphasis processing unit 71, and a representative value calculation unit 72. As shown in FIG. 12 , the change amount calculation unit 70 subtracts the number of spikes in the spike count data 65 before administration of the candidate substance CS from the number of spikes in the spike count data 65 after administration of the candidate substance CS to calculate change amount data 75 (step ST170). The change amount data 75 is calculated for each well 14 of doses D1 to D4, thereby generating the change amount data group 73 shown in FIG. 13 . The change amount data group 73 is output from the change amount calculation unit 70 to the emphasis processing unit 71.

[0096] 14 , the emphasis processing unit 71 performs emphasis processing by cube-plying the change in the change amount data 75 (step ST180). This converts the change amount data 75 into processed change amount data 75AP. A processed change amount data group 73AP, which is a collection of the processed change amount data 75AP, is output from the emphasis processing unit 71 to the representative value calculation unit 72.

[0097] As shown in FIG. 15 , the representative value calculation unit 72 calculates a first average value of the post-processing change amounts of the post-processing change amount data 75AP, generating first average value data 78 (step ST190). The first average value data 78 is generated for each well 14, thereby generating the first average value data group 80 shown in FIG. 16 . Next, as shown in FIG. 16 , the representative value calculation unit 72 calculates a second average value of the first average values ​​of the first average value data group 80, generating second average value data 82 (step ST200). The second average value data 82 is generated for each of the first average value data groups 80 for the doses D1 to D4. This generates the second average value data group 85 shown in FIG. 17 . This second average value data group 85 is output from the representative value calculation unit 72 to the screen distribution control unit 52 as evaluation support information 23.

[0098] The screen distribution control unit 52 generates screen data for the evaluation support information display screen 100 shown in Fig. 20 based on the evaluation support information 23. Under the control of the screen distribution control unit 52, the screen data for the evaluation support information display screen 100 is distributed to the operator terminal 11 that sent the evaluation support request 21 (step ST210).

[0099] In the operator terminal 11, under the control of the browser control unit 92, the screen data of the evaluation support information display screen 100 is reproduced, and the reproduced evaluation support information display screen 100 is displayed on the display 34B. In this way, the evaluation support information 23 is presented to the operator OP.

[0100] As described above, the change amount calculation unit 70 of the derivation unit 51 of the information processing server 10 calculates the change amount of the spike count data 65 before and after administration of the candidate substance CS, using the spike count data 65 for the first period P1 before the synchronous burst NB of the neuron 15 and the second period P2 after the synchronous burst NB. The screen distribution control unit 52 distributes the evaluation support information display screen 100 to the operator terminal 11, thereby presenting evaluation support information 23 for evaluating the effect of the candidate substance CS on the neuron 15 according to the change amount. Unlike the conventional technology, there is no need to prepare measurement data related to a reference substance whose degree of effect on the neuron 15 is known. Furthermore, because data related to the reference substance is not used, the effect on the neuron 15 of a candidate substance CS with a new mechanism of action different from the reference substance can also be accurately evaluated. Therefore, it is possible to more accurately evaluate the effect of the candidate substance CS on the neuron 15 with less effort than conventional methods.

[0101] If a test substance has convulsive properties, some change should occur in the neural activity of the neuron 15 administered with the test substance. The change in neural activity is thought to be reflected in the number of spikes. However, the spikes represented by the raster plot 61 contain a lot of noise. Therefore, if the entire period of the raster plot 61 is evaluated and the number of spikes represented by the raster plot 61 is averaged over time, the spikes related to the changes in neural activity caused by the test substance will be buried in the noise. Therefore, in the technology disclosed herein, the evaluation period is narrowed to the first period P1 and the second period P2 before and after the synchronous burst NB. This reduces the risk that spikes related to the changes in neural activity caused by the test substance will be buried in the noise, and successfully captures the changes in neural activity caused by the test substance. The evaluation support information 23 (bar graph 83) enables accurate evaluation of the effects on the neuron 15 of candidate substances CS that do not show consistent characteristics in the rate of change in the occurrence of synchronous burst NB.

[0102] The effects of the technology of the present disclosure will be described in detail below with reference to Figures 22 to 27. Figures 22 to 27 show bar graphs 110 (top row) showing the rate of change in the occurrence of synchronous burst NB for each of doses D1 to D4 for multiple test substances whose effects on neurons 15 are known, and bar graphs 83 (bottom row) showing the second average values ​​for each of doses D1 to D4 in the technology of the present disclosure. Bar graph 110 lists doses D1, D2, and D3 from left to right, with dose D4 at the far right. Bar graph 83 lists doses D1, D2, and D3 from top to bottom, with dose D4 at the bottom. Only in the case of Figure 25 was synchronous burst NB undetectable, so bar graphs 110 and 83 for dose D4 are not shown. Incidentally, the rate of change in the occurrence of synchronous burst NBs is calculated by subtracting the number of synchronous burst NBs detected before administration of the test substance from the number of synchronous burst NBs detected after administration of the test substance, dividing the result by the number of synchronous burst NBs detected before administration of the test substance, and multiplying by 100.

[0103] The test substances are DMSO (Dimethyl Sulfoxide) in Figure 22, Picrotoxin in Figure 23, and PTZ (Pentylenetrazol) in Figure 24. The test substances are Amoxapine in Figure 25, 4-AP (4-Aminopyridine) in Figure 26, and Pilocarpine in Figure 27. DMSO is often used as a negative control substance in tests to evaluate convulsant-inducing properties. All test substances except DMSO are known to have convulsant-inducing properties. Picrotoxin and PTZ are known to have convulsant-inducing properties, such as GABAergic activity. Ａ (Gamma-Amino Butyric Acid A) receptor inhibition has convulsant-inducing properties. Amoxapine has convulsant-inducing properties due to dopamine D2 receptor inhibition. 4-AP has convulsant-inducing properties due to potassium channel inhibition. Pilocarpine has convulsant-inducing properties due to muscarinic Ach (Acetylcholine) receptor inhibition.

[0104] In FIG. 22, for the negative control substance DMSO, no notable features were observed in either the rate of change in the occurrence of synchronous burst NB or the second average value (the amount of change in the number of spikes before and after the synchronous burst NB).

[0105] In Figures 23 to 25, the rate of change in the occurrence of synchronous burst NBs increases with increasing dose for picrotoxin, PTZ, and amoxapine. For picrotoxin in Figure 23, a decrease is observed in the change in the number of spikes preceding the synchronous burst NB, particularly at dose D4. Similarly, for PTZ in Figure 24, a decrease is observed in the change in the number of spikes preceding the synchronous burst NB, particularly at doses D3 and D4. For PTZ in Figure 24, a decrease is also observed in the change in the number of spikes following the synchronous burst NB, particularly at doses D3 and D4. For amoxapine in Figure 25, a decrease is observed in the change in the number of spikes preceding the synchronous burst NB at doses D2 and D3. For amoxapine in Figure 25, a decrease and a slight increase are observed in the change in the number of spikes following the synchronous burst NB at doses D2 and D3. 23 to 25, the evaluation support information 23 (bar graph 83) of the technology of the present disclosure shows changes that are clearly distinguishable from those of DMSO in Fig. 22. Therefore, it has been confirmed that the effect of the candidate substance CS on nerve cells 15 can be evaluated more accurately than in the past.

[0106] In FIG. 26 , for 4-AP, the rate of change in the occurrence of synchronous burst NB increased at doses D1 and D2, but then decreased at doses D3 and D4. Also, in FIG. 27 , for Pilocarpine, the rate of change in the occurrence of synchronous burst NB decreased at doses D1, D2, and D3, but then increased at dose D4. For 4-AP in FIG. 26 , an overall increase in the change in the number of spikes following synchronous burst NB was observed. Also, for Pilocarpine in FIG. 27 , an increase in the change in the number of spikes following synchronous burst NB was observed at doses D1, D2, and D3. Even for test substances such as 4-AP and Pilocarpine, whose effects on neurons 15 are difficult to accurately evaluate based solely on the rate of change in the occurrence of synchronous burst NB, the evaluation support information 23 (bar graph 83) of the technology of the present disclosure shows changes that are clearly distinguishable from those of DMSO in FIG. 22 . Therefore, it was confirmed that it is possible to evaluate the effect of the candidate substance CS on the nerve cells 15 more accurately than before.

[0107] Bar graph 83 represents GABA Ａ The patterns differ depending on the mechanism of action, such as receptor inhibition or dopamine D2 receptor inhibition. Therefore, the mechanism of action of the candidate substance CS can be considered by comparing the evaluation support information 23 (bar graph 83) for the candidate substance CS with the evaluation support information 23 (bar graph 83) for substances whose effects on neurons 15 are known. Specifically, a bar graph 83 for a known substance that shows a similar increase or decrease trend to the bar graph 83 for the candidate substance CS is searched for, and the mechanism of action of the known substance is inferred to be the mechanism of action of the candidate substance CS. To enable such inference of the mechanism of action, the bar graph 83 for the known substance may be displayed as evaluation support information 23 on the evaluation support information display screen 100 or on a screen separate from the evaluation support information display screen 100 so that it can be compared with the bar graph 83 for the candidate substance CS. Alternatively, the similarity between the bar graph 83 for the known substance and the bar graph 83 for the candidate substance CS may be derived, and the derived similarity may be displayed on the evaluation support information display screen 100 as evaluation support information 23.

[0108] The evaluation support information 23 according to the amount of change is a new index that can evaluate the effect of the candidate substance CS on the nerve cells 15. As described above, this evaluation support information 23 makes it possible to evaluate the mechanism of action of the candidate substance CS on the nerve cells 15, which could not be captured by conventional techniques.

[0109] As shown in FIG. 6 , the second detection unit 48 detects synchronous bursts NB from the measurement data 20 of the extracellular potential of the neuron 15, more precisely, from the spike histogram 60 and raster plot 61. As shown in FIG. 8 , the period setting unit 49 sets a first period P1 and a second period P2 according to the detected synchronous bursts NB. As shown in FIG. 9 , the generation unit 50 generates spike count data 65 from the measurement data 20 for the set first period P1 and second period P2, more precisely, from the raster plot 61. This ensures reliable generation of the spike count data 65. Note that the spike count data 65 may be generated by a device separate from the information processing server 10 and transmitted to the information processing server 10 from a device separate from the information processing server 10.

[0110] 1, the measurement data 20 is measured by the microelectrode array 18. Therefore, spikes and synchronous bursts NB can be detected from the measurement data 20, and the convulsant-inducing ability of the candidate substance CS can be evaluated based on the measurement data 20.

[0111] 23 to 27, substances with convulsive properties exhibit some change in the number of spikes before and after the synchronous burst NB. Therefore, as shown in Fig. 9, by using data related to the spikes of the neuron 15, more specifically, spike count data 65 in which the number of spikes per unit time is registered, as neural activity data, it is possible to accurately capture changes in neural activity caused by the candidate substance CS when the candidate substance CS is a substance with convulsive properties.

[0112] 14, the emphasis processing unit 71 applies emphasis processing to the amount of change. This makes it possible to highlight the change in spikes caused by the administration of the candidate substance CS. As a result, it becomes possible to evaluate the effect of the candidate substance CS on the nerve cells 15 more accurately than in the past.

[0113] As shown in Figure 14, the enhancement process is a process of cubed the amount of change. This makes it possible to emphasize the spike change caused by the administration of the candidate substance CS while maintaining the positive / negative information of the original amount of change. Note that the enhancement process is not limited to the example process of cubed the amount of change, but may be a process of quintupling the amount of change. Furthermore, the enhancement process may be a process in which the amount of change and the amount of change after the process have a relationship according to a tangent function.

[0114] 8, the first period P1 and the second period P2 are set in advance, so that the first period P1 and the second period P2 can be set easily.

[0115] 8, a pair of the first period P1 and the second period P2 is set for one synchronization burst NB, which makes it possible to easily set the first period P1 and the second period P2.

[0116] The screen distribution control unit 52 presents the amount of change itself as the evaluation support information 23. Therefore, the operator OP can evaluate the influence of the candidate substance CS on the nerve cell 15 based on the amount of change.

[0117] As shown in Figure 1, the neuron 15 is derived from the human iPS cell 17A, which allows accurate evaluation of the convulsant-inducing potential of the candidate substance CS in humans.

[0118] As shown in Figure 1, the neuron 15 is a glutamatergic neuron. Glutamatergic neurons are abundant in the cerebral cortex, which is deeply involved in the occurrence of seizures, and are responsible for excitatory transmission. Therefore, they are suitable for evaluating the convulsion-inducing properties of the candidate substance CS.

[0119] As shown in Figure 1, neurons 15 are co-cultured with astrocytes 16. By co-culturing neurons 15 and astrocytes 16, which cooperate in the human body, it is possible to reproduce the interaction between neurons 15 and astrocytes 16 in the human body. Note that instead of or in addition to astrocytes 16, microglia and neurons 15 may be co-cultured.

[0120] As shown in Figure 1, the astrocytes 16 were derived from human iPS cells 17B, which allows accurate evaluation of the convulsant-inducing potential of the candidate substance CS in humans.

[0121] [Example] In this example, frozen human iPS cell-derived neurons (iCell glutamatergic neurons, manufactured by FUJIFILM Cellular Dynamics, Inc.) were used as neurons. Frozen human iPS cell-derived astrocytes (iCell astrocytes, manufactured by FUJIFILM Cellular Dynamics, Inc.) were used as astrocytes. These neurons and astrocytes were thawed, and each of the thawed neurons and astrocytes was suspended in medium (BrainPhys Neuronal Medium, manufactured by STEMCELL Technologies). The medium contains supplements (N-2 Supplement (100X) from Thermo Fisher Scientific, iCell Neuronal Supplement B from FUJIFILM Cellular Dynamics, Inc., and iCell Nervous System Supplement from FUJIFILM Cellular Dynamics, Inc.), antibiotics (Penicillin-Streptomycin from Thermo Fisher Scientific), and laminin (Laminin from EHS Sarcoma Basement Membrane from Sigma-Aldrich). Then, the neurons and astrocytes were mixed and seeded into the wells of a well plate (CytoView MEA 48, manufactured by Axion BioSystems, Inc.). The cell density of the neurons was 120,000 cells / well, and the cell density of the astrocytes was 20,000 cells / well. Half of the medium was replaced every 2 to 3 days, and the cells were incubated in a CO ２ The cells were co-cultured in an incubator for about 4 weeks.

[0122] After approximately four weeks of co-culture, the well plate was placed in a measuring device (Maestro Pro, manufactured by Axion BioSystems, Inc.), and the extracellular potential was measured before the administration of test substances A to F. Then, test substance A (DMSO (dimethyl sulfoxide)), test substance B (picrotoxin), test substance C (PTZ (pentylenetrazol)), test substance D (amoxapine), test substance E (4-AP (4-aminopyridine)), and test substance F (pilocarpin) (all test substances B to F contained 0.1% DMSO) were administered to each well of the well plate, and the extracellular potential was measured. For each of test substances A to F, the extracellular potential was measured at four or three concentrations by cumulative addition. The final concentrations of test substance A were 0.1%, 0.18%, 0.25%, and 0.31%, the final concentrations of test substance B were 0.10 μM, 1.0 μM, 10 μM, and 100 μM, the final concentrations of test substance C were 100 μM, 300 μM, 1000 μM, and 3000 μM, the final concentrations of test substance D were 0.30 μM, 1.0 μM, 3.0 μM, and 10 μM, the final concentrations of test substance E were 3.0 μM, 30 μM, 300 μM, and 3000 μM, and the final concentrations of test substance F were 3.0 μM, 30 μM, 300 μM, and 3000 μM. Furthermore, when the doses of test substances A to F are assigned as D1 to D4 in order of the final concentration of each substance, the DMSO concentrations of test substances B to F are 0.1% for D1, 0.18% for D2, 0.25% for D3, and 0.31% for D4.

[0123] The extracellular potential measurement data were analyzed using analysis software (Neural Metric Tool, manufactured by Axion BioSystems, Inc.). A spike was detected as a waveform with an intensity six times higher than the intensity when no change in intensity related to a synchronized burst occurred. A burst was detected as a spike occurring 10 times consecutively. A synchronized burst was detected when bursts occurred simultaneously at 75% or more of the electrodes in a well.

[0124] The effects of the technology of the present disclosure will be described in detail below with reference to Figures 22 to 27. Figures 22 to 27 show bar graphs 110 (top row) showing the rate of change in the occurrence of synchronous burst NB for each of doses D1 to D4 of multiple test substances whose effects on neurons 15 are known, and bar graphs 83 (bottom row) showing the second average values ​​for each of doses D1 to D4 in the technology of the present disclosure. Bar graph 110 lists doses D1, D2, and D3 from left to right, with dose D4 at the far right. Bar graph 83 lists doses D1, D2, and D3 from top to bottom, with dose D4 at the bottom. Only in the case of Figure 25, the number of spikes was so small that synchronous burst NB could not be detected, and therefore bar graphs 110 and 83 for dose D4 are not shown. The rate of change in the occurrence of synchronous burst NBs was calculated by subtracting the number of synchronous burst NBs detected before administration of the test substance from the number of synchronous burst NBs detected after administration of the test substance, dividing the result by the number of synchronous burst NBs detected before administration of the test substance, and multiplying the result by 100. To calculate the second average value (the change in the number of spikes before and after a synchronous burst NB), T was set to 42.5 seconds, and a period of −42.5 seconds to +42.5 seconds from the detection period of a synchronous burst NB was used. The unit time for counting the number of spikes was 5 seconds. Furthermore, an emphasis process was performed in which the difference between each spike count data after administration of the test substance and each spike count data before administration (i.e., change data) was cubed.

[0125] The test substances are test substance A (DMSO) in Figure 22, test substance B (picrotoxin) in Figure 23, and test substance C (PTZ) in Figure 24. Furthermore, test substances are test substance D (amoxapine) in Figure 25, test substance E (4-AP) in Figure 26, and test substance F (pilocarpine) in Figure 27. DMSO is often used as a negative control substance in tests evaluating convulsant-inducing properties. All test substances except DMSO are known to have convulsant-inducing properties. Picrotoxin and PTZ have convulsant-inducing properties due to GABAA (Gamma-Amino Butyric Acid A) receptor inhibition. Amoxapine has convulsant-inducing properties due to dopamine D2 receptor inhibition. 4-AP has convulsant-inducing properties due to potassium channel inhibition. Pilocarpine has proconvulsant properties due to activation of muscarinic Ach (Acetylcholine) receptors.

[0126] In FIG. 22, DMSO, the negative control substance, did not show any particular changes in either the rate of change in the occurrence of synchronous burst NB or the second average value (the amount of change in the number of spikes before and after the synchronous burst NB).

[0127] In Figures 23 to 25, picrotoxin, PTZ, and amoxapine showed an increased rate of change in the occurrence of synchronous burst NB with increasing dose. Furthermore, picrotoxin (Figure 23) and PTZ (Figure 24) showed a concentration-dependent decrease in the change in the number of spikes before the synchronous burst NB. PTZ (Figure 24) showed a concentration-dependent decrease in the change in the number of spikes before and after the synchronous burst NB. Amoxapine (Figure 25) showed a concentration-dependent decrease in the change in the number of spikes before the synchronous burst NB. Furthermore, amoxapine (Figure 25) showed a concentration-dependent decrease and slight increase in the change in the number of spikes after the synchronous burst NB. For all test substances in Figures 23 to 25, the evaluation support information 23 (bar graph 83) of the technology disclosed herein clearly shows changes that are distinct from those of DMSO (Figure 22). Therefore, it was confirmed that it is possible to evaluate the effect of the candidate substance CS on the nerve cells 15 more accurately than before.

[0128] In Figure 26, for 4-AP, the rate of change in the occurrence of synchronous burst NB increased at doses D1 and D2, then decreased at doses D3 and D4, showing no consistent characteristics. Similarly, in Figure 27, for Pilocarpine, the rate of change in the occurrence of synchronous burst NB decreased at doses D1, D2, and D3, then increased at dose D4, showing no consistent characteristics. On the other hand, for 4-AP in Figure 26, an overall increase in the change in the number of spikes after synchronous burst NB was observed. Also, for Pilocarpine in Figure 27, an increase in the change in the number of spikes after synchronous burst NB was observed at doses D1, D2, and D3. Even with test substances such as 4-AP and pilocarpine, whose effects on neurons 15 are difficult to accurately evaluate based solely on the rate of change in the occurrence of synchronous burst NBs, the technology of the present disclosure was able to confirm consistent changes that were clearly distinguishable from those of DMSO in Figure 22. Therefore, it was confirmed that the effect of the candidate substance CS on neurons 15 can be evaluated more accurately than in the past.

[0129] 28 , a derivation unit 115 of the second embodiment has a selection unit 116 instead of the emphasis processing unit 71 of the first embodiment. The selection unit 116 receives an input of a change amount data group 73 from the change amount calculation unit 70. The selection unit 116 also receives input of selection conditions 117. The selection conditions 117 are stored in the storage 30A. The selection conditions 117 are read from the storage 30A by the RW control unit 46 and output from the RW control unit 46 to the selection unit 116.

[0130] The selection unit 116 selects, based on selection conditions 117, the change amounts to be used for deriving the evaluation support information 23 from the change amounts for each electrode 19. The selection unit 116 outputs a post-selection change amount data group 73AS, which is a collection of post-selection change amount data 75 (hereinafter referred to as post-selection change amount data 75AS, see FIG. 29 ), to the representative value calculation unit 72.

[0131] As an example, as shown in FIG. 29 , the selection condition 117 specifies that the absolute value of the change amount must be equal to or greater than a threshold amount. The threshold amount is, for example, 15. The selection unit 116 selects all change amounts of electrodes 19 having at least one change amount whose absolute value is equal to or greater than the threshold amount as change amounts to be used in deriving the evaluation support information 23. In contrast, the selection unit 116 does not select all change amounts of electrodes 19 having no change amounts whose absolute value is equal to or greater than the threshold amount as change amounts to be used in deriving the evaluation support information 23. FIG. 29 shows an example in which all change amounts of electrodes 19, such as No. 1 and No. 2, which do not have any change amounts whose absolute value is equal to or greater than the threshold amount are not selected, but all change amounts of electrodes 19, such as No. 16, whose absolute value is equal to or greater than the threshold amount are selected. Note that the threshold amount may be changeable.

[0132] As described above, in the second embodiment, the selection unit 116 selects the change amount to be used for deriving the evaluation support information 23 from the change amount for each electrode 19 based on the preset selection condition 117. This makes it possible to highlight spike changes caused by the administration of the candidate substance CS. As a result, it becomes possible to evaluate the effect of the candidate substance CS on the nerve cells 15 more accurately than in the past. Note that the selection in the second embodiment may be performed after the emphasis processing in the first embodiment.

[0133] [Third Embodiment] In the first embodiment, the first period P1 and the second period P2 are preset, but this is not limiting. For example, as shown in Fig. 30, the first period P1 and the second period P2 may be set to CT / 2, which is half the period CT of the synchronization burst NB.

[0134] The period CT of the synchronization burst NB is derived, for example, using the method shown in FIG. 31 . Specifically, first, multiple shifted spike histograms 60S are generated by shifting the spike histogram 60 by unit time. Here, the unit time is one second. Next, a correlation calculation is performed between the original spike histogram 60 and each of the multiple shifted spike histograms 60S to calculate autocorrelation coefficients. The calculated autocorrelation coefficients are plotted for each shift time to generate a correlogram 120 of the spike histogram 60. The shift time corresponding to the first peak of the autocorrelation coefficient in the correlogram 120 is then derived as the period CT of the synchronization burst NB.

[0135] As described above, in the third embodiment, the first period P1 and the second period P2 are set based on the period CT of the synchronous burst NB. Therefore, the first period P1 and the second period P2 can be set to be more suitable for the actual neural activity of the neuron 15. Note that the first period P1 and the second period P2 are not limited to CT / 2, which is half the period CT of the synchronous burst NB shown in the example, but may be the period CT of the synchronous burst NB multiplied by a specific coefficient. For example, the coefficient by which the period CT of the synchronous burst NB is multiplied may be 0.3, 0.4, 0.6, 0.7, or the like.

[0136] The period CT of the synchronous burst NB is derived from the correlogram 120 of the spike histogram 60 of the neuron 15. Therefore, the period CT of the synchronous burst NB can be derived more accurately.

[0137] If the first period P1 and the second period P2 are set based on the period CT of the synchronization burst NB, the first period P1 and the second period P2 change, and the scale of the horizontal axis of the bar graph 83 also changes accordingly. Therefore, it is preferable to normalize the horizontal axis of the bar graph 83 so that the scale of the horizontal axis of the bar graph 83 does not change even when the first period P1 and the second period P2 change. For example, if 40 seconds is used as the reference and half of the period CT of the synchronization burst NB is 32 seconds, the scale of the horizontal axis of the bar graph 83 is multiplied by 40 / 32 (=1.25).

[0138] [Fourth Embodiment] In the first embodiment, a pair of first and second periods P1 and P2 is set for one synchronization burst NB, but this is not limited to this. As an example, as shown in FIG. 32, a pair of first and second periods P1 and P2 may be set for two different synchronization bursts NB. FIG. 32 shows an example in which a pair of first and second periods P1 and P2 is set for two adjacent synchronization bursts NB. Note that the pair of first and second periods P1 and P2 may be set not only for two adjacent synchronization bursts NB, but also for two synchronization bursts NB sandwiching one or more synchronization bursts NB therebetween. However, it is preferable to stick to the method of setting one first period P1 and one second period P2 for one candidate substance CS.

[0139] The neural activity of the nerve cell 15 has high periodicity. Therefore, it is considered that the result will not change much whether the pair of first period P1 and second period P2 is set for one synchronous burst NB or for two different synchronous bursts NB.

[0140] A method for detecting spikes from the measurement data 20 may involve extracting data from the measurement data 20 in a frequency band with a relatively good signal-to-noise (SN) ratio, for example, the 1.3 kHz band, using wavelet transform or the like, and then detecting spikes from the extracted data using a threshold method. This reduces the risk of falsely detecting noise as a spike. Alternatively, a waveform related to a typical spike may be prepared as a template, and spikes may be detected by template matching. Furthermore, spikes may be detected using a trained model that has trained on waveforms related to typical spikes. The waveform related to a spike prepared as training data for the template or trained model may be an actual waveform or a waveform created by a simulator.

[0141] The dosage of the candidate substance CS is not limited to the four stages of D1 to D4 shown in the example, but may be at least one stage of D1, or may be two stages, three stages, or five stages or more.

[0142] The dose of the candidate substance CS may be prepared by cumulative addition, thereby obtaining data for multiple concentrations from the same well 14, or a separate well 14 may be used for each concentration without using cumulative addition. From the viewpoint of improving throughput by being able to reduce the number of wells 14 used, it is preferable to prepare the dose of the test substance CS by cumulative addition.

[0143] The neurons 15 and astrocytes 16 do not have to be derived from the illustrated human iPS cells 17, but may be cultured cells of a laboratory animal such as a rat. Furthermore, the human pluripotent stem cells are not limited to the illustrated human iPS cells 17, but may also be human embryonic stem (ES) cells.

[0144] The information processing server 10 may be installed in a pharmaceutical company or a pharmaceutical contract research organization, or may be installed in a data center independent from the pharmaceutical company or the pharmaceutical contract research organization.

[0145] Instead of delivering the screen data of the evaluation support information display screen 100 to the operator terminal 11, the evaluation support information 23 (bar graph 83) itself may be delivered to the operator terminal 11. In this case, the operator terminal 11 generates the evaluation support information display screen 100 based on the evaluation support information 23 under the control of the browser control unit 92.

[0146] The method of presenting the evaluation support information 23 to the operator OP is not limited to the example of delivering screen data. The evaluation support information 23 (bar graph 83) may be presented to the operator OP by printing it on a paper medium, or by attaching the evaluation support information 23 to an e-mail and sending it to the operator terminal 11.

[0147] The test substance is not limited to the exemplary drug candidate substance CS, but may also be a candidate pesticide or a candidate supplement.

[0148] The hardware configuration of the computer constituting the information processing server 10 according to the technology of the present disclosure can be modified in various ways. For example, the information processing server 10 can be configured with multiple computers separated as hardware to improve processing power and reliability. For example, the functions of the request reception unit 45, RW control unit 46, first detection unit 47, and second detection unit 48, and the functions of the period setting unit 49, generation unit 50, derivation unit 51, and screen distribution control unit 52 can be distributed and performed by two computers. In this case, the information processing server 10 is configured with two computers. Some or all of the functions of the information processing server 10 may be performed by the operator terminal 11.

[0149] In this way, the hardware configuration of the computer of the information processing server 10 can be changed as appropriate according to the required performance such as processing power, safety, reliability, etc. Furthermore, not only the hardware but also APs such as the operating program 40 can be duplicated or stored in a distributed manner in multiple storage devices in order to ensure safety and reliability.

[0150] In each of the above embodiments, the hardware structure of the processing units that execute various processes, such as the request receiving unit 45, the RW control unit 46, the first detection unit 47, the second detection unit 48, the period setting unit 49, the generation unit 50, the derivation units 51 and 115, the screen distribution control unit 52, the change amount calculation unit 70, the emphasis processing unit 71, the representative value calculation unit 72, the browser control unit 92, and the selection unit 116, can be various processors as shown below. As described above, the various processors include the CPUs 32A and 32B, which are general-purpose processors that execute software (the operating program 40 and the evaluation AP 90) and function as various processing units, as well as programmable logic devices (PLDs) that are processors whose circuit configuration can be changed after manufacture, such as a field programmable gate array (FPGA), and dedicated electrical circuits that are processors having a circuit configuration designed specifically for executing specific processing, such as an application specific integrated circuit (ASIC).

[0151] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs and / or a combination of a CPU and an FPGA).Furthermore, multiple processing units may be configured with a single processor.

[0152] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, as typified by computers such as client and server, and this processor functions as multiple processing units. Second, a form in which a processor is used to realize the functions of the entire system including multiple processing units with a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0153] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit (circuitry) that combines circuit elements such as semiconductor elements.

[0154] From the above description, the technology described in the following supplementary paragraphs can be understood.

[0155] [Supplementary Item 1] An information processing device comprising a processor, the processor calculating a change in neural activity data before and after administration of a test substance, the change being neural activity data for a first period before a synchronous burst of a neuron and a second period after the synchronous burst of the neuron, and presenting evaluation support information for evaluating the effect of the test substance on the neuron according to the change. [Supplementary Item 2] The information processing device of Supplementary Item 1, wherein the processor detects the synchronous burst from measurement data of the extracellular potential of the neuron, sets the first period and the second period according to the detected synchronous burst, and generates the neural activity data from the measurement data for the set first period and second period. [Supplementary Item 3] The information processing device of Supplementary Item 2, wherein the measurement data is measured using a microelectrode array. [Supplementary Item 4] The information processing device of any one of Supplementary Items 1 to 3, wherein the neural activity data is data related to spikes of the neuron. [Supplementary Item 5] The information processing device of Supplementary Item 4, wherein the neural activity data is the number of spikes per unit time. [Supplementary Item 6] The information processing device of any one of Supplementary Items 1 to 5, wherein the processor applies emphasis processing to the amount of change. [Supplementary Item 7] The information processing device of Supplementary Item 6, wherein the emphasis processing is processing of raising the amount of change to an odd power. [Supplementary Item 8] The information processing device of any one of Supplementary Items 1 to 7, wherein the processor generates the neural activity data from measurement data of the extracellular potential of the nerve cell measured by a microelectrode array, calculates the amount of change for each electrode of the microelectrode array, and selects an amount of change to be used for deriving the evaluation support information from the amount of change for each electrode based on a predetermined selection condition. [Supplementary Item 9] The information processing device of any one of Supplementary Items 1 to 8, wherein the first period and the second period are preset. [Supplementary Item 10] The information processing device of any one of Supplementary Items 1 to 8, wherein the first period and the second period are set based on a period of the synchronization burst. [Supplementary Item 11] The information processing device according to Supplementary Item 10, wherein the period of the synchronous burst is derived from a correlogram of a histogram of spikes of the nerve cell.[Supplementary Item 12] The information processing device according to any one of Supplementary Items 1 to 11, wherein the pair of the first period and the second period is set for one of the synchronized bursts. [Supplementary Item 13] The information processing device according to any one of Supplementary Items 1 to 11, wherein the pair of the first period and the second period is set for two different synchronized bursts. [Supplementary Item 14] The information processing device according to any one of Supplementary Items 1 to 13, wherein the processor presents the amount of change itself as the evaluation support information. [Supplementary Item 15] The information processing device according to any one of Supplementary Items 1 to 14, wherein the neurons are derived from human pluripotent stem cells. [Supplementary Item 16] The information processing device according to any one of Supplementary Items 1 to 15, wherein the neurons include glutamatergic neurons. [Supplementary Item 17] The information processing device according to any one of Supplementary Items 1 to 16, wherein the neurons are co-cultured with astrocytes. [Supplementary Item 18] The information processing device according to Supplementary Item 17, wherein the astrocytes are derived from human pluripotent stem cells.

[0156] The technology of the present disclosure can be appropriately combined with the various embodiments and / or various modified examples described above. Furthermore, it is not limited to the above-described embodiments, and various configurations can be adopted without departing from the spirit of the present disclosure. Furthermore, the technology of the present disclosure extends not only to programs, but also to storage media that non-temporarily store programs, and computer program products that include programs.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] In this specification, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

Claims

a processor; The processor: Calculating the change in neural activity data before and after administration of the test substance, the change being neural activity data for a first period before and a second period after the synchronous burst of the neuron; presenting evaluation support information for evaluating the effect of the test substance on the nerve cells according to the amount of change; Information processing device. 　 The processor: detecting the synchronized burst from measurement data of the extracellular potential of the nerve cell; setting the first period and the second period according to the detected synchronization burst; The information processing device according to claim 1 , wherein the nerve activity data is generated from the measurement data during the first period and the second period. 　 The information processing apparatus according to claim 2 , wherein the measurement data is measured by a microelectrode array. 　 The information processing device according to claim 1 , wherein the neural activity data is data relating to spikes of the nerve cells. 　 The information processing device according to claim 4 , wherein the nerve activity data is the number of spikes per unit time. 　 The processor: The information processing apparatus according to claim 1 , wherein emphasis processing is performed on the amount of change. 　 The information processing apparatus according to claim 6 , wherein the enhancement processing is a processing of raising the amount of change to an odd power. 　 The processor: generating the neural activity data from measurement data of the extracellular potential of the nerve cell measured by a microelectrode array; Calculating the amount of change for each electrode of the microelectrode array; The information processing apparatus according to claim 1 , wherein the amount of change used to derive the evaluation support information is selected from the amount of change for each electrode based on a preset selection condition. 　 The information processing apparatus according to claim 1 , wherein the first period and the second period are preset. 　 The information processing device according to claim 1 , wherein the first period and the second period are set based on a cycle of the synchronization burst. 　 The information processing device according to claim 10 , wherein the period of the synchronous burst is derived from a correlogram of a histogram of spikes of the nerve cells. 　 The information processing device according to claim 1 , wherein a pair of the first period and the second period is set for one of the synchronization bursts. 　 The information processing device according to claim 1 , wherein the pair of the first period and the second period is set for two different synchronization bursts. 　 The processor: The information processing apparatus according to claim 1 , wherein the amount of change itself is presented as the evaluation support information. 　 The information processing device according to claim 1 , wherein the nerve cells are derived from human pluripotent stem cells. 　 The information processing device according to claim 1 , wherein the neurons include glutamatergic neurons. 　 The information processing device according to claim 1 , wherein the nerve cells are co-cultured with astrocytes. 　 The information processing device according to claim 17 , wherein the astrocytes are derived from human pluripotent stem cells. 　 Calculating the amount of change in neural activity data before and after administration of the test substance, the neural activity data being for a first period before and a second period after the synchronous burst of the neuron; and presenting evaluation support information for evaluating the effect of the test substance on the nerve cells according to the amount of change; A method for operating an information processing device, comprising: 　 Calculating the amount of change in neural activity data before and after administration of the test substance, the neural activity data being for a first period before and a second period after the synchronous burst of the neuron; and presenting evaluation support information for evaluating the effect of the test substance on the nerve cells according to the amount of change; An operating program for an information processing device that causes a computer to execute processing including the above. 　 Calculating the amount of change in neural activity data before and after administration of the test substance, the neural activity data being for a first period before and a second period after the synchronous burst of the neuron; and presenting evaluation support information for evaluating the effect of the test substance on the nerve cells according to the amount of change; A method for evaluating the effect of a test substance on nerve cells, comprising: 　 The evaluation method according to claim 21, wherein the toxicity of the test substance to the central nervous system is evaluated.

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