JP2008086588A - Apparatus and system for evaluating risk of arrhythmia - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem that the risk of arrhythmia can not be evaluated in living things such as human beings with the use of data to be obtained in an early stage of development of new drugs. <P>SOLUTION: The arrhythmia risk evaluating apparatus comprises: an animal medicinal effect information acquiring part for acquiring animal medicinal effect information being information about a medicinal effect relative to animals by using a parameter set after administering drugs; a medicinal effect converting part for converting the animal medicinal effect information into object living body medicinal effect information through the use of conversion source information; a cardiomyocyte simulation part for acquiring cardiomyocyte action information being the information about the action of the cardiomyocyte of a living body through the use of the object living body medicinal effect information; an arrhythmia information acquiring part for acquiring arrhythmia information being the information about the side-effect of drugs on the arrhythmia with respect to the heart through the use of the cardiomyocyte action information; an output information constituting part for constituting output information being the information to be output by using the arrhythmia information; and an output part for outputting output information. Then, the risk of the arrhythmia in the living things such as the human beings is evaluated through the use of the data to be obtained in the early stage of the development of ne drugs. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an arrhythmia risk evaluation apparatus that estimates the degree of arrhythmia risk of a target drug to the heart of a target organism such as a human.

  In the drug development process, it is essential to confirm the efficacy and side effects of new candidate compounds on the heart. Currently, pharmaceutical companies predict drug efficacy and side effects when administered to humans based on evaluation test data such as cultured cell tests and animal experiments. However, due to the effects of species differences and the like, it cannot be deduced until it is actually administered to humans in clinical trials. In addition, enormous costs, human power, and time are required to perform clinical trials. Therefore, if the risk of arrhythmia for the human heart of the target drug can be estimated by computer simulation from animal evaluation test data obtained at an earlier stage of drug development, the drug discovery process can be shortened and cost cuts can be expected. As a result, it is expected to contribute to improving the safety of pharmaceuticals. Further, in order to reduce arrhythmogenicity, it is expected that what kind of medicinal effect should be added to or deleted from the compound and provide guidelines for the synthesis of drug candidate compounds.

  On the other hand, there are the following simulation methods as conventional related techniques. This simulation method is a simulation method for diffusion of a drug in a living tissue that can accurately analyze the diffusion phenomenon of the drug in the living tissue (see Patent Document 1). Such a simulation method is a method of simulating the diffusion of a specific substance in a living body using a finite element method, and using a part of tissue separated from a living body, without using the finite element method. A reference diffusion characteristic constant is determined, a reference diffusion characteristic is set based on the reference diffusion characteristic constant, a biological structure to be analyzed is determined based on a finite element method, and the living body determined using the reference diffusion characteristic constant The diffusion in the structure is calculated based on the finite element method, and the calculation diffusion characteristic based on the calculation result based on the finite element method of diffusion in the living body is compared with the reference diffusion characteristic determined without using the finite element method. The optimal diffusion characteristic coefficient based on the finite element method is calculated by correcting the reference diffusion characteristic constant so that the deviation between the diffusion characteristic and the reference diffusion characteristic is minimized. It is a simulation method.

  In addition, there is a technique regarding a method for determining the site of the electrical activity of the heart that can determine the site extremely accurately and at high speed (see Patent Document 2). In such a method for determining the site of electrical activity of the heart, a multi-channel measurement device is used to measure the body surface potential generated by the electrical activity of the heart at a plurality of measurement points, and the body surface potential at each measurement point is measured. A characteristic value is stored, and a body surface potential characteristic value is compared to a comparison value stored in the data bank, where the comparison value is an electrical value of a comparative heart whose location at the heart is known. Sites of comparative heart electrical activity, which show comparative surface potentials due to physical activity, and where the comparative values of cardiac electrical activity have very similarities with values that characterize body surface potentials In the method of sending out as a result of determining the site of the electrical activity of the heart, the comparison value is obtained by using a heart model provided in the thorax model. Is the method.

Further, as a related technique, there is a simulation apparatus that receives a biological parameter set as an input, simulates a cell, and acquires action potential waveform information (see Non-Patent Document 1).
JP 08-016551 (first page, FIG. 1 etc.) JP 08-280644 (first page, FIG. 1 etc.) Nobuaki Sarai, Akinori Noma “SimBio: Biological Dynamic Model Development Platform”, MME Japan Journal BME, vol. 18, no. 2, p. 3-11, 2004 (issued in February 2004)

  However, in the prior art, the risk of arrhythmia for the human heart of the target drug could not be obtained from the animal evaluation test data. In addition, in the prior art, it has not been possible to obtain the risk of arrhythmia for the heart of an organism other than humans from animal evaluation test data.

  In addition, in the conventional technique, the calcium ion concentration or cell membrane potential in the sarcoplasmic reticulum uptake site is calculated using ion flow rate information that is information on the electric flow rate of one or more ion channels, and the sarcoplasmic reticulum uptake site is calculated. The risk of arrhythmia could not be obtained from the internal calcium ion concentration or cell membrane potential.

  Accordingly, one object of the present invention is to evaluate the risk of arrhythmia for organisms such as humans using data obtained at an early stage of drug discovery. Another object of the present invention is to show what properties should be added to or deleted from a compound in order to reduce the risk of arrhythmia, for example, in order not to extend the QT interval.

  Another object of the present invention is to evaluate the risk of arrhythmia from the calcium ion concentration and cell membrane potential in the sarcoplasmic reticulum uptake site.

  The arrhythmia risk evaluation apparatus according to the first aspect of the invention stores a parameter set before and after drug administration that stores a parameter set before and after drug administration, which is a set of a parameter set of an animal before drug administration and a parameter set of an animal after drug administration. Using the parameter set before and after the drug administration, an animal medicinal information acquisition unit that obtains animal medicinal information that is information on medicinal effects on animals, and the animal medicinal information as target biomedical information that is information on medicinal properties of organisms. A conversion source information storage unit that stores conversion source information that is information for conversion, a medicinal effect conversion unit that converts the animal drug effect information into target biomedical effect information using the conversion source information, and the target A cardiomyocyte simulation unit that obtains cardiomyocyte movement information, which is information about the behavior of living cardiomyocytes using biomedical efficacy information; Using operation information, arrhythmia information acquisition unit that acquires arrhythmia information that is information on side effects of arrhythmia on the heart of the drug, and output information that is information output using the arrhythmia information acquired by the arrhythmia information acquisition unit An arrhythmia risk evaluation apparatus comprising: an output information configuration unit that outputs the output information; and an output unit that outputs the output information.

  With this configuration, the risk of arrhythmia in a living organism such as a human can be evaluated using data obtained at an early stage of drug discovery.

  The arrhythmia risk evaluation apparatus according to the second aspect of the present invention is a fixed parameter information storage unit for storing fixed parameter information, which is parameter information for fixing a value, and arrhythmia information acquisition. When the arrhythmia information acquired by the unit is information indicating side effects of arrhythmia that is greater than or equal to a predetermined value, the parameter value of the fixed parameter information included in the target biomedical effect information is fixed, and the values of other parameters are changed. A next target biomedical efficacy information acquiring unit for acquiring new target biomedical efficacy information, and the new target biomedical efficacy information acquiring unit acquired by the next target biomedical efficacy information acquiring unit to the cardiomyocyte simulation unit, A controller for acquiring the arrhythmia information acquired by the arrhythmia information acquiring unit, and the next target biomedical efficacy information acquiring unit. It is arrhythmia risk evaluation device which constitutes the output information is information for outputting with the target biological efficacy information.

  With such a configuration, it is possible to present what properties should be added to or deleted from a compound in order to reduce the risk of arrhythmia.

  The arrhythmia risk evaluation apparatus according to the third aspect of the present invention provides a threshold value storing means for storing a threshold value for cardiomyocyte operation information, and the cardiomyocyte simulation unit for the first and second aspects of the invention. The information indicating that the arrhythmia information indicates a side effect of arrhythmia that is greater than or equal to a predetermined value using the comparison means that compares the acquired cardiomyocyte operation information with the threshold value of the threshold value storage means, and the comparison result of the comparison means. An arrhythmia determination unit that includes an arrhythmia determination unit that determines whether or not, and the output information configuration unit is an arrhythmia risk evaluation device that configures output information also using a determination result of the arrhythmia determination unit .

  With this configuration, it is possible to automatically determine whether there is a risk of arrhythmia.

  Further, the arrhythmia risk evaluation apparatus according to the fourth aspect of the present invention provides an action potential which is information indicating a calculation formula for acquiring an action potential in the cardiomyocyte simulation unit according to any one of the first to third aspects of the invention. Action card model information storage means for storing model information; and action potential information acquisition means for reading the action potential model information and acquiring action potential information using the action potential model information. The operation information is an arrhythmia risk evaluation apparatus including the action potential information.

  With this configuration, the risk of arrhythmia can be evaluated from the action potential.

  Further, in the arrhythmia risk evaluation apparatus according to the fifth aspect of the present invention, with respect to any one of the first to fourth aspects, the cardiomyocyte simulation unit is information indicating a calculation formula for acquiring a QT interval. QT interval model information storing means for storing model information, QT interval model information for reading out the QT interval model information and acquiring QT interval information using the QT interval model information, and The information is an arrhythmia risk evaluation apparatus including the QT interval information.

  With this configuration, the risk of arrhythmia can be evaluated from the QT interval.

  Further, in the arrhythmia risk evaluation apparatus according to the sixth aspect of the present invention, in contrast to any one of the first to fifth aspects, the cardiomyocyte simulation unit uses spiral wave model information that is information for simulating spiral waves. A spiral wave model information storage means for storing, and a spiral wave simulation means for reading the spiral wave model information and performing a spiral wave simulation using the spiral wave model information, the cardiomyocyte operation information is The arrhythmia risk evaluation apparatus includes a simulation result in the spiral wave.

  With this configuration, the risk of arrhythmia can be evaluated from the spiral wave.

  The arrhythmia risk evaluation apparatus according to the seventh aspect of the present invention is a receiving unit that receives target biomedical efficacy information including one or more ion flow rate information that is information about the electric flow rate of one or more ion channels indicating the influence of drug administration. And using the target biomedical effect information, a cardiomyocyte simulation unit that acquires cardiomyocyte movement information that is information about the behavior of a living cardiomyocyte, and output information that is information output using the cardiomyocyte movement information An arrhythmia risk evaluation apparatus comprising: an output information configuration unit that configures the output information; and an output unit that outputs the output information.

  With this configuration, it is possible to evaluate the risk of arrhythmia using one or more pieces of ion flow rate information as input.

  Further, the arrhythmia risk evaluation apparatus according to the eighth aspect of the invention relates to the seventh aspect of the invention, a fixed parameter information storage unit that stores fixed parameter information that is parameter information for fixing values, and the cardiomyocytes When the motion information is information indicating a side effect of arrhythmia of a predetermined value or more, the parameter value of the fixed parameter information included in the target biomedical effect information is fixed, the value of other parameters is changed, and a new target A next target biomedical efficacy information acquisition unit that acquires biomedical efficacy information, and a control unit that provides the new target biomedical efficacy information acquired by the next target biomedical efficacy information acquisition unit to the cardiomyocyte simulation unit and acquires cardiomyocyte operation information The output information configuration unit outputs the cardiomyocyte operation information and the target biomedical efficacy information acquired by the next target biomedical efficacy information acquisition unit. It is arrhythmia risk evaluation device which constitutes the output information is information.

  With such a configuration, it is possible to present what properties should be added to or deleted from a compound in order to reduce the risk of arrhythmia.

  The arrhythmia risk evaluation apparatus according to the ninth aspect of the present invention provides the cardiomyocyte simulation unit in the sarcoplasmic reticulum uptake site using one or more pieces of ion flow rate information as a parameter with respect to any of the seventh and eighth aspects of the invention. Ion concentration calculation model information storage means for storing ion concentration calculation model information which is information of an arithmetic expression for calculating the calcium ion concentration, and one or more ions received by the receiving unit by reading the ion concentration calculating model information Ion concentration calculation means for substituting flow rate information into the ion concentration calculation model information, executing an arithmetic expression indicated by the ion concentration calculation model information, and calculating a calcium ion concentration in the sarcoplasmic reticulum uptake site which is cardiomyocyte operation information This is an arrhythmia risk evaluation apparatus.

  With this configuration, it is possible to evaluate the risk of arrhythmia using the calcium ion concentration in the sarcoplasmic reticulum uptake site.

  Further, in the arrhythmia risk evaluation apparatus according to the tenth aspect of the present invention, in the seventh to ninth aspects of the invention, the cardiomyocyte simulation unit calculates a cell membrane potential using one or more ion flow rate information as a parameter. Cell membrane potential calculation model information storage means for storing cell membrane potential calculation model information that is information of the equation, and reading the cell membrane potential calculation model information, and calculating one or more ion flow rate information received by the receiving unit as the cell membrane potential This is an arrhythmia risk evaluation apparatus comprising cell membrane potential calculation means for calculating a cell membrane potential, which is cardiomyocyte operation information, by substituting into model information and executing an arithmetic expression indicated by the cell membrane potential calculation model information.

  With this configuration, the risk of arrhythmia using cell membrane potential can be evaluated.

  The arrhythmia risk evaluation apparatus according to the eleventh aspect of the invention relates to the seventh or eighth aspect of the invention, wherein the myocardial cell simulation unit uses one or more ion flow rate information as a parameter as a sarcoplasmic reticulum uptake site. An ion concentration calculation model information storage means storing ion concentration calculation model information which is information of an arithmetic expression for calculating the internal calcium ion concentration, and one or more of the ion concentration calculation model information read out and received by the receiving unit Ion concentration calculation means for substituting ion flow rate information into the ion concentration calculation model information, executing an arithmetic expression indicated by the ion concentration calculation model information, and calculating a calcium ion concentration in the sarcoplasmic reticulum uptake site which is cardiomyocyte operation information And cell membrane potential calculation model information which is information of an arithmetic expression for calculating cell membrane potential using one or more ion flow rate information as a parameter Reading the cell membrane potential calculation model information storage means, reading the cell membrane potential calculation model information, substituting one or more ion flow rate information received by the receiving unit into the cell membrane potential calculation model information, and the cell membrane potential calculation model information The cell membrane potential calculating means for calculating the cell membrane potential that is the cardiomyocyte operation information, and the output information component is a calcium ion in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculating means. A value is plotted on a two-dimensional plane with the value of the first axis as the value of the first axis and the cell membrane potential calculated by the cell membrane potential calculation means as the value of the second axis, and the calcium in the one or more sarcoplasmic reticulum uptake sites is plotted. An arrhythmia risk evaluation apparatus that constitutes output information that clearly indicates a threshold value for an ion concentration and a threshold value for the one or more cell membrane potentials on the two-dimensional plane. is there.

  With such a configuration, it is possible to evaluate the arrhythmia risk from the calcium ion concentration in the sarcoplasmic reticulum uptake site and the cell membrane potential, and to obtain an output that can easily grasp the arrhythmia risk.

  The arrhythmia risk evaluation system according to the twelfth aspect of the present invention is an arrhythmia risk evaluation system including an arrhythmia risk evaluation device and a biological parameter determination device, as compared with any of the first to eleventh inventions. The biological parameter determination device includes a biological parameter set storage unit that stores two or more biological parameter sets having one or more biological parameters that are biological parameters, and one of the two or more biological parameter sets. The action potential information which is information indicating the action potential of the heart by simulating the activity of the heart by inputting the parameter set selection section for selecting the living body parameter set and the one living body parameter set selected by the parameter set selection section A simulation execution unit that obtains the action potential information acquisition unit that acquires the action potential information, and an animal experiment Calculate the degree of difference between the experimental action potential information storage unit storing experimental action potential information, which is the resulting action potential information, the action potential information acquired by the action potential information acquisition unit, and the experimental action potential information A dissimilarity calculating unit; an allowable parameter set determining unit that determines whether the one biological parameter set is a biological parameter set within an allowable range using the dissimilarity calculated by the dissimilarity calculating unit; When the allowable parameter set determination unit determines that the one biological parameter set is not within the allowable range, the biological parameter set is selected from the unselected biological parameter sets by the parameter set selection unit. A living body in which the one living body parameter set is in an allowable range A parameter set output unit for outputting the one biological parameter set when the parameter set is determined to be a parameter set, and the biological parameter set output by the allowable parameter set output unit is a drug of the arrhythmia risk evaluation apparatus It is an arrhythmia risk evaluation system which is a parameter set of at least one of an animal parameter set before drug administration and an animal parameter set after drug administration stored in a parameter set storage unit before and after administration.

  With this configuration, the risk of arrhythmia can be evaluated using the parameter set determined by the biological parameter determination device.

  According to the arrhythmia risk evaluation apparatus according to the present invention, it is possible to evaluate the arrhythmia risk of a drug to a living organism such as a human.

Hereinafter, embodiments of an arrhythmia risk evaluation apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, re-explanation may be abbreviate | omitted.
(Embodiment 1)

  In this embodiment, an arrhythmia risk evaluation apparatus that evaluates arrhythmia risk using cardiomyocyte simulation, QT interval simulation, and spiral simulation will be described.

  FIG. 1 is a block diagram showing the configuration of the arrhythmia risk evaluation apparatus. The arrhythmia risk evaluation apparatus includes a reception unit 100, a parameter set storage unit 101 before and after drug administration, an animal drug effect information acquisition unit 102, a conversion source information storage unit 103, a drug effect conversion unit 104, a cardiomyocyte simulation unit 105, and an arrhythmia information acquisition unit 106. Arrhythmia determination unit 107, output information configuration unit 108, and output unit 109.

  The cardiomyocyte simulation unit 105 includes action potential model information storage means 1051, action potential information acquisition means 1052, QT interval model information storage means 1053, QT interval simulation means 1054, spiral wave model information storage means 1055, and spiral wave simulation means 1056. It has.

  The arrhythmia determination unit 107 includes a threshold storage unit 1071, a comparison unit 1072, and an arrhythmia determination unit 1073.

  The receiving unit 100 receives various instructions such as an operation start instruction and an end instruction of the arrhythmia risk evaluation apparatus from the user. The input means for various instructions may be anything such as a keyboard, mouse or menu screen. The accepting unit 100 can be realized by a device driver for input means such as a keyboard, control software for a menu screen, or the like.

The parameter set storage unit 101 before and after drug administration stores a parameter set before and after drug administration which is a set of a parameter set of an animal before drug administration and a parameter set of an animal after drug administration. A parameter set is one or more biological parameters. Biological parameters include current flowing through various channels of cells (for example, Na channel, Ca channel, _KATP channel, Kr channel, K1 channel, Ks channel, etc.), opening / closing speed of each channel, ion affinity, intracellular and extracellular There are ion concentrations. In addition, there are hundreds of types of biological parameters. It is preferable that the parameter set of the animal before drug administration and the parameter set of the animal after drug administration are, for example, biological parameter sets acquired by the biological parameter determination device described in the fourth embodiment. In addition, when a later-described biological parameter determination device acquires a parameter set before and after drug administration and accumulates it in the parameter set storage unit 101 before and after drug administration, the experimental action potential information used by the biological parameter determination device is one animal (for example, The action potential information measured before the drug administration to the mouse) and the action potential information measured after the drug administration to the same animal. The parameter set before and after drug administration has two biological parameter sets. An example of the parameter set before and after drug administration is shown in FIG. In FIG. 2, the value of the biological parameter set before drug administration and the value of the biological parameter set after drug administration are provided for each biological parameter. In FIG. 2, the biological parameter “IKr” does not change between “2.0” before drug administration and “2.0” after drug administration, but the biological parameter “IK1” is “1.10” before drug administration, It changes at “2.20” after drug administration. The parameter set storage unit 101 before and after drug administration is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The animal drug effect information acquisition unit 102 acquires animal drug effect information, which is information about drug effects on animals, using the parameter set before and after drug administration. Animal drug efficacy information is usually information based on the difference between a biological parameter set before drug administration and a biological parameter set after drug administration. For example, when the value of the parameter P1 constituting the biological parameter set before drug administration is “4” and the value of the parameter P1 constituting the biological parameter set after drug administration is “2”, the animal drug efficacy information is constituted. The numerical value to be set is “0.5”, which is the rate of change of the biological parameter affected by drug administration. That is, the animal drug effect information acquisition unit 102 calculates animal drug effect information based on, for example, changes in the biological parameter of interest before and after drug administration. The animal drug efficacy information acquisition unit 102 reads, for example, a predetermined arithmetic expression “value of biological parameter after drug administration / value of biological parameter before drug administration”, and sets a biological parameter set before drug administration in the arithmetic expression. Substituting the value “4” of the parameter P1 constituting and the value “2” of the parameter P1 constituting the biological parameter set after drug administration, the animal drug efficacy information “P1, 0.5” is obtained. Here, the animal drug efficacy information includes information for identifying a parameter and information on the rate of change of the parameter value before and after the animal drug efficacy information. The animal drug efficacy information may be, for example, a difference between values of a biological parameter before drug administration and a biological parameter value after drug administration. That is, the “information based on the difference” may be information indicating the degree of difference between the biological parameter set before drug administration and the biological parameter set after drug administration, and the acquisition method is not limited. The animal medicinal effect information acquisition unit 102 can be usually realized by an MPU, a memory, or the like. The processing procedure of the animal medicinal effect information acquisition unit 102 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The conversion source information storage unit 103 is a conversion source that is information for converting animal medicinal effect information into target biomedical effect information that is medicinal effect information of other living organisms (organisms other than living organisms that have acquired experimental data such as humans). Stores information. The conversion source information is “0.5”, for example. The conversion source information is, for example, the ratio of the medicinal effect given to an animal such as a mouse and the medicinal effect received by another organism such as a human (excluding the animal to be experimented). Also, the conversion source information includes, for example, information on mathematical expressions of animals such as mice that define [0] ion current. The conversion source information includes, for example, [0] information of a human mathematical expression that defines the same ion current, and a parameter “0.5”. Here, as shown in FIG. 3, there is a large species difference between animals (here, “guinea pigs”) and humans in the waveforms of myocardial action potentials, temporal changes in contractile force, the current of each ion channel, and the nature of each ion channel. To do. In view of the experimental result shown in FIG. 3, in this specific example, the conversion source information is set to “0.5”, for example. In FIG. 3, (1) is human data, and (2) is animal data. 3, (a) is a graph of action potential, (b) to (d) are transient outward potassium ion current, L-type calcium ion current, early activation delayed rectifier potassium ion current, (e ) Is the flow rate of calcium ions, (f) is the contractile force of cardiomyocytes, and (g) is the potential dependence of the transient outward potassium current. The conversion source information storage unit 103 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium. The conversion source information may be information input by the user, information transmitted from an external device, or information read from a recording medium.

  The medicinal effect conversion unit 104 converts the animal medicinal effect information into the target biomedical effect information using the conversion source information. The medicinal effect conversion unit 104 obtains the target biomedical effect information “P1, 0.25” using the animal medicinal effect information “P1, 0.5” and the conversion source information “0.5”, for example. For example, the medicinal effect conversion unit 104 multiplies the numerical value “0.5” constituting the animal medicinal effect information by the conversion source information “0.5”, and sets the numerical value “0.25” constituting the target biomedical effect information. obtain. The target biomedical efficacy information here is information on the rate of change of the biological parameter of the organism to be simulated, such as a human, due to drug administration, but the value of the biological parameter after drug administration or the biological parameter after drug administration A set or the like may be used. “P1” is an identifier of a biological parameter. In addition, the medicinal effect conversion unit 104 reads, for example, information of a human mathematical expression that defines the ionic current from the conversion source information storage unit 103, and uses the information of the two mathematical formulas and the animal medicinal effect information “P1, 0.5”. The target biomedical efficacy information is calculated. Specifically, the conversion source information storage unit 103 stores a model for calculating IKr in the animal shown in FIG. Further, the medicinal effect conversion unit 104 stores a model for calculating IKr in the human shown in FIG. 50 and 51, “Vm” is the membrane potential “Ek” is the reverse potential of potassium ion, “[K] o” is the extracellular potassium concentration, and “Cm” is the membrane capacity. Then, the medicinal effect conversion unit 104 reads, for example, the model of FIG. 51 and the numerical value “0.5” constituting the animal medicinal effect information, multiplies GKr by the numerical value “0.5” constituting the animal medicinal effect information, A value such as medicinal effect information “IKr” is calculated. The medicinal effect conversion unit 104 can be usually realized by an MPU, a memory, or the like. The processing procedure of the medicinal effect conversion unit 104 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The cardiomyocyte simulation unit 105 receives target biomedical efficacy information, and uses the target biomedical efficacy information to obtain cardiomyocyte operation information that is information about the behavior of living cardiomyocytes. The cardiomyocyte operation information is, for example, action potential information that is information related to action potential waveforms, information about the contractile force of the heart, information about the flow rate of ions such as calcium ions, and the like. The action potential information is, for example, information about action potential duration (APD), information about action potential amplitude (APA), and the like. APD refers to the time difference between the rising phase of the action potential and the repolarization phase, which indicates the potential at an arbitrary ratio of APA. The information about APD includes, for example, the value of APD30, the value of APD60, and the value of APD90. APA is the sum of the absolute value of the resting membrane potential and the peak value of the action potential waveform (excluding artifacts and noise). The potential (Vm (x)) at an arbitrary ratio of APA is a value (potential) obtained by calculating an arbitrary ratio value of APA and subtracting the value from the peak value. The rising phase of the action potential is a period from the resting membrane potential to the peak. The repolarization phase of the action potential is between the peak and the resting membrane potential. If neither mathematical model nor experimental data has potential and time data that match Vm (x), values interpolated by some method (eg, linear interpolation) from values before and after Vm (x) Use. When the height from the highest potential (point a) to the lowest potential (point b) of the waveform is 100, the width of the waveform at the height 30 points below the point a is APD30. Similarly, assuming that the height from the highest potential (point a) to the lowest potential (point b) of the waveform is 100, the width of the waveform at heights 60 and 90 below the point a is APD60 and APD90. . The cardiomyocyte simulation unit 105 has a function of receiving a biological parameter set as an input and outputting cardiomyocyte operation information, for example. Further, the cardiomyocyte simulation unit 105 may perform a process of acquiring a QT interval described later, or may perform a spiral wave simulation. Since the process of obtaining the QT interval and the process of performing the simulation of spiral waves are known techniques, detailed description thereof will be omitted. The cardiomyocyte simulation unit 105 can usually be realized by an MPU, a memory, or the like. The processing procedure of the cardiomyocyte simulation unit 105 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The action potential model information storage unit 1051 stores action potential model information that is information indicating a calculation formula for acquiring action potential information that is information of action potential. The action potential model information is, for example, Equation 1 below.

  In Equation 1, “x” is an arbitrary ratio of APA, and a numerical value such as 30, 60, or 90 is substituted. Vm (x) is a potential calculated as described above.

“F (Vm (x)) _{repolarization phase} ” indicates the time during which the action potential shows a potential corresponding to an arbitrary ratio of APA in the repolarization phase.

“F (Vm (x)) _{rising phase} ” indicates a time during which the action potential shows a potential corresponding to an arbitrary ratio of APA in the rising phase.

  In addition, since the technique which acquires action potential model information and APD is a well-known technique, detailed description is abbreviate | omitted. The action potential model information is preferably information on a human action potential model, but may be information on an action potential model of a mouse or the like. This is because even when a simulation is performed using information on an action potential model of a mouse or the like, a side effect or the like can be detected.

  The action potential model information storage unit 1051 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

The action potential information acquisition unit 1052 reads the action potential model information in the action potential model information storage unit 1051 and acquires the action potential information using the action potential model information. More specifically, the action potential information acquisition unit 1052 reads the action potential model information of Formula 1, acquires Vm (x), and sets Vm (x) to “f (Vm (x)) _{repolarization phase} ”. Substituting and calculating “f (Vm (x)) _{repolarization phase} ”, the result is stored in the memory. Further, the action potential information acquisition unit 1052 substitutes the acquired Vm (x) for the read “f (Vm (x)) _{rising phase} ” and obtains the result of “f (Vm (x)) _{rising phase} ”. Then, the difference between the calculation results of “f (Vm (x)) _{repolarization phase} ” on the memory and the calculation result of “f (Vm (x)) _{rising phase} ” is calculated and stored on the memory. An example of action potential information is the graph of (1) in FIG. The action potential information acquisition unit 1052 acquires information such as the amount of calcium ions (FIG. 4 (2)) and Force (FIG. 4 (3)), which is information other than the action potential information, and temporarily stores it. Also good. Note that “Force” is a contractile force generated by cardiomyocytes. Further, the information acquired by the action potential information acquisition unit 1052 may be information on intracellular ion flow rate and time-varying information on myocardial contraction force shown in FIG.

  The action potential information acquisition unit 1052 can usually be realized by an MPU, a memory, or the like. The processing procedure of the action potential information acquisition unit 1052 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The QT interval model information storage unit 1053 stores QT interval model information that is information indicating a calculation formula for acquiring QT interval information that is information indicating the QT interval. The QT interval model information is, for example, Expressions 2 to 4.

In Equation 2, “I _x ” is an arbitrary biological parameter, “coefficient” is a coefficient specific to a cell type, and “I _{x — original} ” is a basic cell model. The basic cell model is, for example, an endocardium-side ventricular myocyte (Endo cell). The basic cell model may be another cell model.

  Further, it is assumed that the QT interval model information storage unit 1053 holds the coefficient “coefficient” specific to the cell type shown in FIG. 6 as the QT interval model information. In FIG. 6, it is desirable to use three types of cell models as epicardial cells, epi cells (epicardial cells), M cells (cardiac central membrane cells), and endo cells (endocardial cells). Epi cells, M cells, and Endo cells are configured by changing biological parameters of cell models Ito, IKr, and IKs as shown in FIG. 6 as an example.

Further, the QT interval model information storage unit 1053 holds information of the following Expression 3 as QT interval model information.

  Formula 3 shows the following concept. That is, the above three types of cells are arranged in the order of Endo cell-M cell-Epi cell, the cell models are connected by a gap junction, and a stimulation current is applied to the leftmost cell of the Endo cell. FIG. 7 shows such a concept.

  In FIG. 7, “stim” is a stimulation current applied to the leftmost cell of the Endo cell. The cell model is indicated by “Cell”, and the gap junction is indicated by “Gap Junction”. In FIG. 7, the cell has a size of 100 μm.

  The QT interval model information is preferably information on a human QT interval model, but may be information on a QT interval model such as a mouse. This is because even when a simulation is performed using information of a QT interval model such as a mouse, a side effect or the like can be detected.

Then, the QT interval simulation means 1054 reads the information of Formula 3 from the QT interval model information storage means 1053 and calculates in order to calculate the potential of each cell. When calculating the potential of the n th cell, the QT interval simulation means 1054 calculates the sum of the potential difference between the (n−1) th cell and the n th cell and the potential difference between the n + 1 th cell and the n th cell as a one-dimensional cable. The value (gap junction current [((V _i−1 −V _i ) + (V _{i + 1} −V _i ) / R)]) divided by the resistance (R) is calculated. The QT interval simulation means 1054 subtracts the ionic current (I _ion ) and the stimulation current (I _stim ) of each cell calculated by the normal cell model from the gap junction current to calculate the i-th voltage.

In Equation 3, C _m is the cell membrane capacity.

Next, the QT interval simulation means 1054 acquires an electrocardiogram waveform (ECG) using the calculation formula shown in the following formula 4 for the potential of each connected cell after stimulation. Information of the calculation formula shown in Formula 4 is also stored in the QT interval model information storage unit 1053.

In Equation 4, “Φ _o ” is the electrocardiogram potential, “a” is the radius of the one-dimensional cable, “ρ _i ” is the conductance inside the cell, “ρ _o ” is the conductance outside the cell, and “r” is the distance to the observation point. The distance, “x”, is the distance from the origin to the cell. “V _m ” is a cell membrane potential. Note that Equation 4 is a known equation and will not be described in detail.

  The QT interval model information storage unit 1053 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium.

The QT interval simulation means 1054 reads QT interval model information, and acquires QT interval information using the QT interval model information. Specifically, for example, the QT interval simulation unit 1054 reads the information of Equations 2 to 4 from the QT interval model information storage unit 1053. Then, the above-described calculation is performed using the information of Formula 2 to Formula 4, and an electrocardiogram waveform as shown in FIG. 8 is obtained. Then, the QT interval simulation unit 1054 acquires the QT interval time from the calculated electrocardiogram waveform (see FIG. 8). Note that “a”, “ρ _i ”, “ρ _o ”, “r”, and “x” in Equation 4 are constants and are held in advance by the QT interval simulation unit 1054, and the QT interval simulation unit 1054 The constant value is read out and substituted into Equation 4. Moreover, since the process which acquires QT interval time from the information of an electrocardiogram waveform is a well-known technique, detailed description is abbreviate | omitted. The QT interval simulation means 1054 can usually be realized by an MPU, a memory, or the like. The processing procedure of the QT interval simulation means 1054 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The spiral wave model information storage unit 1055 stores spiral wave model information which is information for simulating spiral waves. The spiral wave can be obtained by a simulation model (see FIG. 9) in which cardiomyocyte models are arranged in two dimensions. In FIG. 9, G ₀ , G _f, etc. are gap junctions, and “cell” is a cardiomyocyte model. In FIG. 9, a cardiomyocyte model (cell) is arranged on a square lattice, and each cell is connected by a gap junction (G _f , G ₀ ). The gap junction model between cells can perform more detailed simulation by changing the conductance (ease of current passage) in the fiber direction and the orthogonal direction. As the conductance, for example, G _f = 0.2 (s) in the fiber direction and G ₀ = G _{f /} Ka (Ka = 5.8) in the orthogonal direction.

The spiral wave model information is, for example, information of the following formula 5, formula 6, and formula 7.

  Using Equation 5 to Equation 7, the spiral wave simulation means 1056 described later sets the addition of the intercellular current and the stimulation current as a stimulation current for each step of each cell model, and calculates the change in membrane potential of each cell model. To do. Further, the spiral wave simulation means 1056 calculates the intercellular current from the potential of each cell.

In Equation 5, I _{ext (i, j)} is the external current of the i-th cell and the j-th cell in the two-dimensional cell array. I _{inter (i, j)} is the intercellular current of the i-th cell in the vertical direction and the j-th cell in the horizontal direction. I _{stim (i, j)} is the stimulation current of the i-th cell in the vertical direction and the j-th cell in the horizontal direction.

In Expression 6, Vm ^t1 _{(i, j)} is a membrane potential at time t1 of the i-th cell in the vertical direction and the j-th cell in the horizontal direction. P ^t0 _{(i, j)} is the membrane potential held by the i-th cell and the j-th cell itself at time t0 (time immediately before t1). f is the i-th vertical and j-th horizontal cell from the stimulation current (Iext) applied from the outside at time t0 and the membrane potential (P) held by itself to the i-th vertical and j-th horizontal at the next time t1. It is a function for calculating the membrane potential of a cell.

Equation 7 is an equation for calculating an intercellular current value ( _Iinter ^t1 _{(i, j)} ) from the potential of each cell. In Formula 7, G _f is the conductance in the fiber direction, and is 0.2 (s), for example. In Equation 7, G ₀ is the conductance in the orthogonal direction, and is calculated by, for example, “G _{f /} Ka (Ka ₌ 5.8)”. “Ka” is a numerical value indicating the degree of deflection. Furthermore, cardiomyocytes have gap junctions developed in the fiber direction, have high conductance, and easily propagate current. On the contrary, in the cardiomyocytes, the gap junction is not so developed in the direction perpendicular to the fiber direction, and the conductance is low. It is the deflection rate (Ka) that represents these physiological states.

  The spiral wave model information is preferably information on a human spiral wave model, but may be information on a spiral wave model such as a mouse. This is because even when simulation is performed using information of a spiral wave model such as a mouse, side effects and the like can be detected.

  The spiral wave model information storage means 1055 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The spiral wave simulation means 1056 reads the spiral wave model information, and performs a spiral wave simulation using the spiral wave model information.

  The spiral wave simulation means 1056 can usually be realized by an MPU, a memory, or the like. The processing procedure of the spiral wave simulation means 1056 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The arrhythmia information acquisition unit 106 acquires arrhythmia information, which is information on side effects of arrhythmia on the heart of the drug, using cardiomyocyte operation information. The cardiomyocyte operation information is, for example, action potential information acquired by the action potential information acquisition unit 1052, a simulation result in the QT interval simulation unit 1054, a simulation result in the spiral wave simulation unit 1056, and the like. The arrhythmia information includes, for example, no side effect (arrhythmia), degree of side effect (arrhythmia), ADP 30, 60, 90, QT interval, and the like. The arrhythmia information acquisition unit 106 acquires, for example, cardiomyocyte movement information as a numerical value. The cardiomyocyte operation information and the arrhythmia information may match. In such a case, the arrhythmia information acquisition unit 106 performs a process of passing cardiomyocyte movement information, which is arrhythmia information, to the arrhythmia determination unit 107 (this process is also referred to as “process for acquiring arrhythmia information”). The arrhythmia information acquisition unit 106 can be usually realized by an MPU, a memory, or the like. The processing procedure of the arrhythmia information acquisition unit 106 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The arrhythmia determination unit 107 determines arrhythmia using cardiomyocyte operation information. More specifically, the arrhythmia determination unit 107 determines whether or not the arrhythmia information is information indicating a side effect of arrhythmia that is equal to or greater than a predetermined value. In the arrhythmia risk evaluation apparatus, the arrhythmia determination unit 107 is not essential. That is, if the user can determine the presence or absence of arrhythmia without determining whether the arrhythmia determination unit 107 determines whether or not the arrhythmia depends on the output mode of output information in the output unit 109 described later, the determination performed by the arrhythmia determination unit 107 No processing is necessary. The arrhythmia determination unit 107 can usually be realized by an MPU, a memory, or the like. The processing procedure of the arrhythmia determination unit 107 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The threshold storage unit 1071 stores a threshold for the cardiomyocyte operation information. The threshold value for the cardiomyocyte operation information is, for example, a threshold value for the QT interval, a threshold value for the spiral wave, or the like. The threshold storage means 1071 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The comparison unit 1072 compares the cardiomyocyte movement information acquired by the cardiomyocyte simulation unit 105 with the threshold value stored in the threshold value storage unit 1071. The comparison unit 1072 compares the action potential information acquired by the action potential information acquisition unit 1052 with the threshold value. The comparison unit 1072 also compares the QT interval information with a threshold value for the QT interval. The comparison unit 1072 compares the simulation result of the spiral wave with the threshold value for the spiral wave. The comparison means can be realized by 1072, usually MPU, memory or the like. The processing procedure of the comparison means 1072 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The arrhythmia determination means 1073 uses the comparison result of the comparison means 1072 to determine whether or not the arrhythmia information is information indicating a side effect of arrhythmia that is equal to or greater than a predetermined value. The arrhythmia determination means 1073 can usually be realized by an MPU, a memory, or the like. The processing procedure of the arrhythmia determination means 1073 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output information configuration unit 108 configures output information that is information to be output using the arrhythmia information acquired by the arrhythmia information acquisition unit 106. The output information configuration unit 108 may configure output information that is information to be output using the determination result of the arrhythmia determination unit 107. The output information configuration unit 108 can be usually realized by an MPU, a memory, or the like. The processing procedure of the output information configuration unit 108 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 109 outputs the output information configured by the output information configuration unit 108. Here, the output is a concept including display on a display, printing on a printer, sound output, transmission to an external device, accumulation in a recording medium, and the like. The output unit 109 may or may not include an output device such as a display or a speaker. The output unit 109 can be realized by output device driver software, or output device driver software and an output device.

  Next, the operation of the arrhythmia risk evaluation apparatus will be described with reference to the flowcharts of FIGS.

  (Step S1001) The animal medicinal effect information acquisition unit 102 reads the parameter set before and after drug administration from the parameter set storage unit 101 before and after drug administration.

  (Step S1002) The animal drug effect information acquisition unit 102 acquires animal drug effect information that is information about drug effects on the animal from the parameter set before and after drug administration read in step S1001.

  (Step S1003) The medicinal effect conversion unit 104 reads conversion source information from the conversion source information storage unit 103.

  (Step S1004) The medicinal effect conversion unit 104 converts the animal medicinal effect information acquired in step S1002 into target biomedical effect information using the conversion source information read in step S1003.

  (Step S1005) The action potential information acquisition unit 1052 of the cardiomyocyte simulation unit 105 reads the action potential model information from the action potential model information storage unit 1051.

  (Step S1006) The action potential information acquisition unit 1052 receives the target biomedical efficacy information obtained in step S1004, and acquires action potential information using the target biomedical efficacy information and the action potential model information read in step S1005. .

  (Step S1007) The QT interval simulation means 1054 reads the QT interval model information in the QT interval model information storage means 1053.

  (Step S1008) The QT interval simulation means 1054 acquires QT interval information using the QT interval model information read in step S1006.

  (Step S1009) The spiral wave simulation means 1056 reads the spiral wave information in the spiral wave model information storage means 1055.

  (Step S1010) The spiral wave simulation means 1056 performs spiral wave simulation using the spiral wave model information read out in step S1008.

  (Step S1011) The arrhythmia information acquisition unit 106 uses the action potential information obtained in step S1006, the simulation result in step S1008, or the simulation result in step S1010, to provide information on arrhythmia information that is information regarding the side effects of arrhythmia on the heart of the drug. To get.

  (Step S1012) The arrhythmia determination unit 107 determines whether or not the arrhythmia information acquired in step S1011 is information indicating a side effect of arrhythmia of a predetermined value or more. The arrhythmia determination process will be described with reference to the flowchart of FIG.

  (Step S1013) The output information configuration unit 108 configures output information that is information to be output using the determination result of the arrhythmia determination unit 107. The output information may be only information indicating “there is a risk of arrhythmia” or the like.

  (Step S1014) The output unit 109 outputs the output information configured in step S1013. The process ends.

  In the flowchart of FIG. 10, action potential information, QT interval information, and simulation results in spiral wave simulation means 1056 are preferable examples of cardiomyocyte operation information.

  Further, in the flowchart of FIG. 10, when it is determined from the acquired action potential information that there is an arrhythmia, output information indicating that there is an arrhythmia without acquiring the QT interval information or the simulation result in the spiral wave simulation means 1056. You may get it.

  In the flowchart of FIG. 10, it is determined whether or not the arrhythmia is in the order of action potential information, QT interval information, and simulation results in the spiral wave simulation means 1056, but it goes without saying that the order of the determination is not limited.

  In the flowchart of FIG. 10, the arrhythmia is determined after acquiring the three types of cardiomyocyte motion information. However, after acquiring each cardiomyocyte motion information, the arrhythmia may be determined individually. In such a case, when the determination result that there is a risk of arrhythmia is obtained, the arrhythmia determination may not be performed based on other cardiomyocyte operation information.

  Furthermore, in the flowchart of FIG. 10, the arrhythmia is determined using the three types of cardiomyocyte motion information, but the arrhythmia may be determined using one or two cardiomyocyte motion information.

  Next, the arrhythmia determination process of step S1012 will be described using the flowchart of FIG.

  (Step S1101) The comparison means 18072 of the arrhythmia determination unit 107 reads the action potential information acquired by the arrhythmia information acquisition unit 106.

  (Step S1102) The comparison unit 18072 reads the threshold value for the action potential information stored in the threshold value storage unit 18071, and compares the threshold value with the action potential information acquired in step S1101. Then, the arrhythmia determination means 18073 determines whether or not the arrhythmia information is information indicating a side effect of the arrhythmia that is equal to or greater than a predetermined value. If the information indicates an arrhythmic side effect, the process proceeds to step S1103. If the information does not indicate an arrhythmic side effect, the process proceeds to step S1104.

  (Step S1103) The arrhythmia determination means 18073 substitutes information indicating that “arrhythmia is present” into the output variable. Return to upper function.

  (Step S1104) The comparison unit 18072 reads the simulation result in the QT interval simulation unit 1054 acquired by the arrhythmia information acquisition unit 106.

  (Step S1105) The comparison unit 18072 reads the threshold value for the QT interval of the threshold value storage unit 18071, and compares the simulation result acquired in step S1103 with the threshold value. Then, the arrhythmia determination means 18073 determines whether or not the arrhythmia information is information indicating a side effect of the arrhythmia that is equal to or greater than a predetermined value. If the information indicates an arrhythmic side effect, the process proceeds to step S1103. If the information does not indicate an arrhythmic side effect, the process proceeds to step S1106.

  (Step S1106) The comparison means 18072 reads the spiral wave simulation result acquired by the arrhythmia information acquisition unit 106.

  (Step S1107) The comparison means 18072 reads the threshold value for the spiral wave stored in the threshold value storage means 18071, and compares the simulation result acquired in step S1103 with the threshold value. Then, the arrhythmia determination means 18073 determines whether or not the arrhythmia information is information indicating a side effect of the arrhythmia that is equal to or greater than a predetermined value. If the information indicates an arrhythmic side effect, the process proceeds to step S1103. If the information does not indicate an arrhythmic side effect, the process proceeds to step S1108.

  (Step S1108) The arrhythmia determination means 18073 substitutes information indicating “no arrhythmia” into the output variable. Return to upper function.

  In the flowchart of FIG. 11, the order of determination of arrhythmia with respect to three types of cardiomyocyte operation information (action potential information, QT interval, spiral wave) is not limited.

The specific operation of the arrhythmia risk evaluation apparatus in the present embodiment will be described below.
(Specific example 1)

  First, a specific example of arrhythmia determination using action potential information will be described.

  FIG. 12 is a parameter set before and after drug administration stored in the parameter set storage unit 101 before and after drug administration. In FIG. 12, the value of the biological parameter set before drug administration and the value of the biological parameter set after drug administration are provided for each biological parameter. In FIG. 12, the effect (%) of drug administration is retained. The parameter set before and after drug administration in FIG. 12 shows changes in biological parameters when a drug (dl-Sotal) is administered.

  Further, it is assumed that the conversion source information storage unit 103 holds the conversion source information “0.98”.

  In such a case, the arrhythmia risk evaluation apparatus operates as follows.

  That is, the animal drug efficacy information acquisition unit 102 reads the parameter set before and after drug administration in FIG.

  Next, the animal drug effect information acquisition unit 102 acquires animal drug effect information “IKr, 0” which is information about drug effects on animals. Here, “0” is the rate of change of the parameter identified by the biological parameter identifier “IKr” that changes before and after drug administration. That is, “IKr” is inhibited by drug administration.

  Next, the medicinal effect conversion unit 104 reads the conversion source information “0.98” from the conversion source information storage unit 103. As described above, the conversion source information is information for converting the animal drug efficacy information into the target biological drug drug information that is the drug drug drug information.

  Next, the medicinal effect conversion unit 104 obtains target biomedical effect information “IKr, 0” using the acquired animal medicinal effect information “IKr, 0” and the read conversion source information “0.98”. The value “0” constituting the target biomedical efficacy information is obtained by “0 × 0.98”, for example.

  Next, the action potential information acquisition unit 1052 of the cardiomyocyte simulation unit 105 receives the obtained target biomedical effect information “IKr, 0”, and uses the target biomedical effect information as information on the behavior of the living cardiomyocytes. Certain cardiomyocyte operation information (here, action potential information) is acquired. The action potential information acquisition unit 1052 receives, for example, information that the biological parameter “IKr” is zero from a normal state, performs cardiomyocyte simulation, and acquires action potential information.

  Note that the simulation performed by the action potential information acquisition unit 1052 is expressed by Equation 1 above. The action potential information acquisition unit 1052 reads information on the arithmetic expression of Formula 1 stored in advance, performs simulation of cardiomyocytes, acquires action potential information, and temporarily stores it. An example of action potential information after drug administration is the graph (2) in FIG. In addition, the graph of (1) of FIG. 13 is an example of the action potential information before drug administration which is separately calculated and output.

  Next, the arrhythmia information acquisition unit 106 calculates the rate of change of the APD 90 here from the action potential information graph of FIG. The rate of change of APD90 is the rate of change of APD90 of action potential information before drug administration and APD90 of action potential information after drug administration. First, the arrhythmia information acquisition unit 106 calculates the APD 90 of the action potential information before drug administration as 266.3 (ms) from the graph of FIG. 13 (1), and stores it in the temporary memory. Then, the arrhythmia information acquisition unit 106 calculates the APD 90 of the action potential information after drug administration as 311.4 (ms) from the graph of FIG. 13 (2), and stores it in the temporary memory. Next, the arrhythmia information acquisition unit 106 calculates the rate of change of the APD 90 as “311.4 / 266.3 * 100−100 = 16.9 (%)”. Then, the comparison unit 1072 of the arrhythmia determination unit 107 reads the upper threshold “10 (%)” of the normal range of the change rate of the APD 90 stored in the threshold storage unit 1071, and the change rate “16” of the APD 90 as the experimental result. .9 (%) ”.

  Next, the arrhythmia determination means 1073 determines that the arrhythmia occurs because the change rate “16.9 (%)” of the APD 90 as the experimental result is larger than the upper limit threshold “10 (%)” of the normal range.

Next, the output information configuration unit 108 configures output information including, for example, a character string “arrhythmia occurs” and the graph of FIG. 13, and the output unit 109 includes, for example, “arrhythmia” together with the graph of FIG. 13. "Will occur" is output.
(Specific example 2)

  Next, a specific example of arrhythmia determination using QT interval information will be described. First, the pre- and post-drug parameter set storage unit 101 stores the pre- and post-drug parameter set shown in FIG. Further, the conversion source information storage unit 103 holds conversion source information “0.98”.

  Next, the medicinal effect conversion unit 104 reads the conversion source information “0.98” from the conversion source information storage unit 103. As described above, the conversion source information is information for converting the animal drug efficacy information into the target biological drug drug information that is the drug drug drug information.

  Next, the medicinal effect conversion unit 104 obtains target biomedical effect information “IKr, 0” using the acquired animal medicinal effect information “IKr, 0” and the read conversion source information “0.98”. The value “0” constituting the target biomedical efficacy information is obtained by “0 × 0.98”, for example.

  Next, the QT interval simulation means 1054 of the cardiomyocyte simulation unit 105 receives the obtained target biomedical efficacy information “IKr, 0”, reads the QT interval model information, and stores the target biomedical efficacy information “IKr, “0” is substituted to obtain QT interval information (here, QT interval). FIG. 14 shows an electrocardiogram waveform and the QT interval when calculating the QT interval. In FIG. 14, the upper graph is a graph before drug administration, and the lower graph is a graph after drug administration. In FIG. 14, the QT interval before drug administration is “144.73 (ms)”, and the QT interval after drug administration is “179.01 (ms)”.

  Next, the arrhythmia information acquisition unit 106 calculates the rate of change “179.01 / 144.14” from the QT interval “144.73 (ms)” before drug administration and the QT interval “179.01 (ms)” after drug administration. 73 * 100 = 13.7 (%) ”is calculated and placed on the memory. The “123.7 (%)” is arrhythmia information here.

  Next, the comparison unit 1072 reads the threshold value “+ 10%” of the threshold value storage unit 1071. Then, the increment “+23.7 (%)” in “123.7 (%)” is compared with the threshold “+ 10%”.

  Next, the arrhythmia determination means 1073 determines that the increment “+23.7 (%)” in “123.7 (%)” does not fall within the range of the threshold value “+ 10%”. It is determined that it occurs.

  Next, the output information configuration unit 108 configures output information including, for example, a character string “arrhythmia occurs” and the graph of FIG. 14, and the output unit 109 displays “arrhythmia occurs” together with the graph of FIG. 14. The character string “occurs” is output.

The QT interval model information in this specific example uses animal QT interval model information. However, there is no problem when detecting side effects when administered to humans.
(Specific example 3)

  Next, a specific example of arrhythmia determination using spiral waves will be described. First, the pre- and post-drug parameter set storage unit 101 stores the pre- and post-drug parameter set shown in FIG. Further, the conversion source information storage unit 103 holds conversion source information “0.98”.

  Next, the medicinal effect conversion unit 104 reads the conversion source information “0.98” from the conversion source information storage unit 103. As described above, the conversion source information is information for converting animal medicinal information into target biomedical information that is medicinal information of other organisms such as humans.

  Next, the medicinal effect conversion unit 104 obtains target biomedical effect information “IKr, 0” using the acquired animal medicinal effect information “IKr, 0” and the read conversion source information “0.98”. The value “0” constituting the target biomedical efficacy information is obtained by “0 × 0.98”, for example.

  Next, the spiral wave simulation means 1056 reads the spiral wave model information, and performs a spiral wave simulation using the spiral wave model information.

  First, the spiral wave simulation means 1056 applies a stimulus to the upper four rows of the spiral wave model (see FIG. 15) arranged on a square lattice (steady stimulus in FIG. 15A), and after 208 msec of the stimulus, the spiral wave model 1056 An extraordinary stimulus deviating from the normal rhythm is applied to the right half of the model (extrastimulus stimulus in FIG. 15B). The spiral wave simulation means 1056 outputs such information on the screen. Such a situation is shown in FIGS. 15 (a) and 15 (b).

  Next, the spiral wave simulation means 1056 gives the target biomedical effect information “IKr, 0” to the spiral wave model information (information of Formulas 5 to 7) in the spiral wave model information storage means 1055, and the spiral wave model after drug administration. Is obtained and output. Then, a spiral wave was generated as shown in FIG. As for the spiral wave, the user may confirm the spiral wave on the screen and input information indicating that the spiral wave has occurred. In such a case, the arrhythmia information acquisition unit 106 receives the user's “information indicating that a spiral wave has occurred”. In such a case, the arrhythmia determination unit 107 may not operate. On the other hand, the arrhythmia determination unit 107 may automatically detect the occurrence of a spiral wave and determine that an arrhythmia has occurred. Automatic detection of spiral wave generation is possible using an image processing technique that detects the presence of a waveform pattern on the display.

  FIG. 15C shows a spiral wave model produced using a normal cell model. According to FIG. 15 (c), spiral waves indicating the occurrence of arrhythmia (reentry) did not occur even when extra-stimulation was applied to the normal cell model before drug administration, and the cells returned to a resting state.

  In addition, the spiral wave model information in this specific example uses animal spiral wave model information. However, there is no problem when detecting side effects when administered to humans.

  As described above, according to the present embodiment, the risk of arrhythmia for a living organism such as a human can be evaluated using data obtained at an early stage of drug discovery. More specifically, according to the present embodiment, the risk of arrhythmia can be evaluated from action potential information, QT interval, and spiral wave.

  In the arrhythmia risk evaluation apparatus of this embodiment, the pre- and post-drug parameter set storage unit 101 and the animal drug efficacy information acquisition unit 102 do not exist, and animal drug efficacy information may be given first. That is, in place of the pre- and post-drug parameter set storage unit 101 and the animal drug efficacy information acquisition unit 102, the arrhythmia risk evaluation apparatus includes an animal drug efficacy information storage unit that stores animal drug efficacy information that is information on drug efficacy for animals. You may have.

  Further, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. In addition, the software which implement | achieves the arrhythmia risk evaluation apparatus in this Embodiment is the following programs. In other words, this program uses an animal drug efficacy information acquisition step for acquiring animal drug efficacy information, which is information about drug efficacy on an animal, using the parameter set before and after drug administration stored in a recording medium, and the animal drug efficacy information. Is converted into target biomedical efficacy information using the stored conversion source information, and the target biomedical efficacy information is used to convert cardiomyocyte operation information, which is information about the behavior of living cardiomyocytes. A cardiomyocyte simulation step to be acquired, an arrhythmia information acquisition step to acquire arrhythmia information, which is information relating to side effects of arrhythmia on the heart of the drug, using the cardiomyocyte movement information, and the arrhythmia information acquired in the arrhythmia information acquisition step. Output information configuration step for configuring output information that is information to be output using, Program for executing an output step of outputting a serial output information is.

  The program compares the cardiomyocyte operation information acquired in the cardiomyocyte simulation step with a stored threshold value on a computer, and uses the comparison result to indicate side effects of the arrhythmia when the arrhythmia information exceeds a predetermined value. It is preferable to further execute an arrhythmia determination step for determining whether or not the output information is, and to configure output information using the determination result of the arrhythmia determination step in the output information configuration step.

  Further, the cardiomyocyte simulation step in the program reads action potential model information, which is information indicating a calculation formula for acquiring action potentials, and acquires action potential information using the action potential model information. It is preferable that the cardiomyocyte operation information includes the action potential information.

  Further, the cardiomyocyte simulation step in the program reads QT interval model information which is information indicating a calculation formula for acquiring a QT interval, and acquires QT interval information using the QT interval model information. It is preferable that the cardiomyocyte operation information includes the QT interval information.

The cardiomyocyte simulation step in the program further includes a spiral wave simulation step of reading spiral wave model information that is information for simulating spiral waves, and performing spiral wave simulation using the spiral wave model information. It is preferable that the cardiomyocyte operation information includes a simulation result in the spiral wave.
(Embodiment 2)

  In the present embodiment, an arrhythmia risk evaluation apparatus that evaluates the risk of arrhythmia at the single cell level, in particular, an apparatus that evaluates abnormal automatic ability will be described. Here, the function of automatically generating an electrical signal is called “automatic function”, and this function in the myocardial tissue is performed by a pacemaker cell called a sinus node. Under various pathological conditions, automatic ability may occur in parts other than sinus nodules, such as ventricular muscle and atrial muscle, which is called abnormal automatic ability and is considered to be the cause of arrhythmia. Further, “DAD”, “EAD”, “Low voltage oscillation” and the like are one type of abnormal automatic ability.

Although this arrhythmia risk assessment device does not cause abnormal automation even when given to normal cells isolated from the ventricular muscle of experimental animals, it does not cause abnormal automation when administered under pathological conditions. It is intended for drugs that may cause That is, a drug that has already caused an abnormal automatic ability in action potential recording under physiological conditions has a clear risk, and is not an object of analysis by the arrhythmia risk evaluation apparatus. In addition, two types of pathological conditions are stored in the apparatus, that is, intracellular Ca ²⁺ overload and decrease in cell membrane K ⁺ conductance. Furthermore, it is a device that mainly considers the inhibitory effect of IKr from the fact that most of so-called QT prolonging drugs are “HERG channel blockers”.

  FIG. 16 is a block diagram of the arrhythmia risk evaluation apparatus in the present embodiment.

  The arrhythmia risk evaluation apparatus includes a receiving unit 1601, a cardiomyocyte simulation unit 1602, an arrhythmia determination unit 1603, an output information configuration unit 1604, and an output unit 1605.

  The cardiomyocyte simulation unit 1602 includes an ion concentration calculation model information storage unit 16021, an ion concentration calculation unit 16022, a cell membrane potential calculation model information storage unit 16023, and a cell membrane potential calculation unit 16024.

  The arrhythmia determination unit 1603 includes an ion concentration threshold storage unit 16031, an ion concentration arrhythmia determination unit 16032, a cell membrane potential threshold storage unit 16033, and a cell membrane potential arrhythmia determination unit 16034.

  The receiving unit 1601 receives target biomedical efficacy information including one or more ion flow rate information, which is information about the electric flow rate of one or more ion channels indicating the influence of drug administration. The ion flow rate information is, for example, information indicating the influence of all ion channels and carrier currents in the apparatus on the electric flow rate. The ion flow rate information is, for example, ICaL suppression rate (for example, 50%). Moreover, it is preferable that the ion flow rate information received by the reception unit 1601 is obtained by converting the parameter set before and after drug administration obtained from the biological parameter set acquired by the biological parameter determination apparatus described in the fourth embodiment. . The receiving unit 1601 may read target biomedical efficacy information from a recording medium, may receive it from an external device, or may receive an input from a user. The receiving unit 1601 can be realized by a device driver of an input unit such as a keyboard, a receiving unit, or the like.

  The cardiomyocyte simulation unit 1602 acquires cardiomyocyte operation information, which is information about the behavior of living cardiomyocytes, using the target biomedical efficacy information received by the receiving unit 1601. The cardiomyocyte operation information here is information on the calcium ion concentration in the sarcoplasmic reticulum uptake site (intracellular) and information on the cell membrane potential. Information on the calcium ion concentration in the sarcoplasmic reticulum uptake site is a set of the average value of calcium ion concentration in the sarcoplasmic reticulum uptake site for each cycle (400 ms) and the calcium ion concentration in the sarcoplasmic reticulum uptake site itself. Etc. The information on the cell membrane potential includes an average membrane potential for each cycle (400 ms) of the cell membrane potential, a set of values of the cell membrane potential itself, and the like. The cardiomyocyte simulation unit 1602 can usually be realized by an MPU, a memory, or the like. The processing procedure of the cardiomyocyte simulation unit 1602 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The ion concentration calculation model information storage means 16021 stores ion concentration calculation model information which is information of an arithmetic expression for calculating the calcium ion concentration in the sarcoplasmic reticulum uptake site using one or more ion flow rate information as a parameter. An example of the ion concentration calculation model information is Expression 8 below.

In Equation 8, [Ca] _up is the calcium ion concentration in the sarcoplasmic reticulum uptake site. In addition, I _{SR U} is a calcium flux into the sarcoplasmic reticulum uptake site. In addition, I _{SR T} is a flux to the release site from the sarcoplasmic reticulum uptake site. Also, I _{SR L} is a calcium leak flux from sarcoplasmic reticulum uptake site. Z _Ca is the ionic valence (= 2) of calcium. F is a Faraday constant. V _up is the volume of the sarcoplasmic reticulum uptake site.

  The ion concentration calculation model information is preferably information on a human ion concentration calculation model, but may be information on an ion concentration calculation model of a mouse or the like. This is because even when a simulation is performed using information of an ion concentration calculation model of a mouse or the like, a side effect or the like can be detected.

  The ion concentration calculation model information storage unit 16021 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The ion concentration calculation unit 16022 reads the ion concentration calculation model information in the ion concentration calculation model information storage unit 16021, substitutes one or more ion flow rate information received by the receiving unit 1601 into the ion concentration calculation model information, and calculates the ion concentration. An arithmetic expression indicated by the model information is executed, a calcium ion concentration in the sarcoplasmic reticulum uptake site, which is cardiomyocyte operation information, is calculated, and the calculation result is stored in the memory. It is preferable that the ion concentration calculation means 16022 calculates the differential equation of Formula 8 using a fourth-order Runge-Kutta. Since the fourth-order Runge-Kutta is a known technique, a detailed description thereof will be omitted. The ion concentration calculating means 16022 can be usually realized by an MPU, a memory, or the like. The processing procedure of the ion concentration calculation means 16022 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The cell membrane potential calculation model information storage means 16023 stores cell membrane potential calculation model information which is information of an arithmetic expression for calculating the cell membrane potential using one or more ion flow rate information as a parameter. An example of cell membrane potential calculation model information is Equation 9 below.

In Equation 9, “V _m ” is a cell membrane potential. “I _tot ” is a total current of ionic currents of the whole cell. “I _ext ” is a stimulation current applied from outside the cell. “C _m ” is a cell membrane capacity.

  The cell membrane potential calculation model information is preferably information on a human cell membrane potential calculation model, but may be information on a cell membrane potential calculation model of a mouse or the like. This is because even when a simulation is performed using information of a cell membrane potential calculation model of a mouse or the like, a side effect or the like can be detected.

  The cell membrane potential calculation model information storage means 16023 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The cell membrane potential calculation unit 16024 reads the cell membrane potential calculation model information from the cell membrane potential calculation model information storage unit 16023, substitutes one or more ion flow rate information received by the receiving unit 1601 into the cell membrane potential calculation model information, and calculates the cell membrane potential calculation. An arithmetic expression indicated by the model information is executed to calculate a cell membrane potential which is cardiomyocyte operation information. Then, the cell membrane potential calculation unit 16024 stores the calculated cell membrane potential on the memory. The cell membrane potential calculation means 16024 preferably calculates the differential equation of Equation 9 using a fourth-order Runge-Kutta. The cell membrane potential calculation means 16024 can usually be realized by an MPU, a memory, or the like. The processing procedure of the cell membrane potential calculation means 16024 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The arrhythmia determination unit 1603 determines arrhythmia using the cardiomyocyte movement information acquired by the cardiomyocyte simulation unit 1602. The cardiomyocyte operation information here is information about the calcium ion concentration in the sarcoplasmic reticulum uptake site and information about the cell membrane potential as described above. The arrhythmia determination unit 1603 can usually be realized by an MPU, a memory, or the like. The processing procedure of the arrhythmia determination unit 1603 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The ion concentration threshold storage unit 16031 stores a threshold for calcium ion concentration in one or more sarcoplasmic reticulum uptake sites. The threshold value may be one or more, for example, “3.58 (mM)” and “3.84 (mM)”. The ion concentration threshold storage unit 16031 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium.

  The ion concentration arrhythmia determination means 16032 makes a determination on arrhythmia using the calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculation means 16022 and the threshold value of the ion concentration threshold storage means 16031. Specifically, for example, the ion concentration arrhythmia determination means 16032 is normal if the calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculation means 16022 is equal to or less than the threshold value “3.58 (mM)”. Judge that. Further, for example, the ion concentration arrhythmia determination means 16032 has a calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculation means 16022 that is greater than the threshold value “3.58 (mM)”, and “3.84 (mM)”. If it is below, it is determined to be DAD. Further, for example, the ion concentration arrhythmia determining means 16032 is both DAD and EAD if the calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculating means 16022 is larger than the threshold value “3.84 (mM)”. Judge that. Here, DAD and EAD are a kind of abnormal automatic ability, and are well known, and thus description thereof is omitted. The ion concentration arrhythmia determination means 16032 can be normally realized by an MPU, a memory, or the like. The processing procedure of the ion concentration arrhythmia determining means 16032 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The cell membrane potential threshold storage unit 16033 stores thresholds for one or more cell membrane potentials. One threshold value for the cell membrane potential is suitable, for example, “−19.6 (mV)”. The cell membrane potential threshold storage means 16033 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The cell membrane potential arrhythmia determination unit 16034 makes a determination on arrhythmia using the cell membrane potential calculated by the cell membrane potential calculation unit 16024 and the threshold value of the cell membrane potential threshold storage unit 16033. Specifically, for example, when the cell membrane potential calculated by the cell membrane potential calculation unit 16024 is equal to or lower than the threshold “−19.6 (mV)”, it is determined that the cell membrane potential is normal (arrhythmia does not occur), and the threshold “ If it is larger than “−19.6 (mV)”, it is determined that the voltage oscillation is low. Low voltage oscillation is a kind of abnormal automatic ability that can cause arrhythmia. The cell membrane potential arrhythmia determining means 16034 can usually be realized by an MPU, a memory, or the like. The processing procedure of the cell membrane potential arrhythmia determining means 16034 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output information configuration unit 1604 configures output information that is information to be output using the cardiomyocyte motion information acquired by the cardiomyocyte simulation unit 1602. The output information configuration unit 1604 may configure output information that is information to be output using the determination result of the arrhythmia determination unit 1603. The output information configuration unit 1604 uses the calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculation unit 16022 as the value of the first axis, and the cell membrane potential calculated by the cell membrane potential calculation unit 16024 as the value of the second axis. The value is plotted on the two-dimensional plane, and output information is specified on the two-dimensional plane that clearly indicates the threshold value for the calcium ion concentration in one or more sarcoplasmic reticulum uptake sites and the threshold value for one or more cell membrane potentials. That is preferred. An example of such output information will be described later. The output information may be a simple character string such as “arrhythmia may occur” or the type of arrhythmia “DAD, EAD, Low voltage oscillation”. The data structure of output information does not matter. The output information configuration unit 1604 can usually be realized by an MPU, a memory, or the like. The processing procedure of the output information configuration unit 1604 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 1605 outputs the output information configured by the output information configuration unit 1604. Here, the output is a concept including display on a display, printing on a printer, sound output, transmission to an external device, accumulation in a recording medium, and the like. The output unit 1605 may be considered as including or not including an output device such as a display or a speaker. The output unit 1605 can be implemented by output device driver software, or output device driver software and an output device.

  Next, the operation of the arrhythmia risk evaluation apparatus will be described with reference to the flowcharts of FIGS.

  (Step S1701) The receiving unit 1601 determines whether or not target biomedical efficacy information including one or more ion flow rate information has been received. If the target biomedical effect information is accepted, the process goes to step S1702, and if the target biomedical effect information is not accepted, the process returns to step S1701.

  (Step S1702) The cardiomyocyte simulation unit 1602 places the target biomedical efficacy information received by the receiving unit 1601 on the memory.

  (Step S1703) The cardiomyocyte simulation unit 1602 acquires cardiomyocyte operation information that is information about the behavior of the living cardiomyocytes using the target biomedical efficacy information acquired in step S1702. Such cardiomyocyte simulation will be described with reference to the flowchart of FIG.

  (Step S1704) The arrhythmia determination unit 1603 determines arrhythmia using the cardiomyocyte operation information acquired in step S1703. Such arrhythmia determination processing will be described with reference to the flowchart of FIG.

  (Step S1705) The output information configuration unit 1604 configures output information which is information to be output using the determination result in step S1704. For example, from the simulation result (value), the output information configuration unit 1604 configures the output information in a visually different manner depending on whether the result (value) is normal or abnormal.

  (Step S1706) The output unit 1605 outputs the output information configured in step S1705. The process ends.

  In the flowchart of FIG. 17, the determination process in step S1704 may not be performed. In such a case, the output information configuration unit 1604 reads the output of the cardiomyocyte simulation unit 1602 and configures output information.

  Next, the cardiomyocyte simulation in step S1703 will be described using the flowchart of FIG.

  (Step S1801) The ion concentration calculation unit 16022 reads the ion concentration calculation model information from the ion concentration calculation model information storage unit 16021.

  (Step S1802) The ion concentration calculation means 16022 reads the stored steady state parameters. The steady state parameters are stored in advance in the ion concentration calculation means 16022. The parameters in the steady state may be information input by the user or information transmitted from an external device. The steady-state parameter is calculated in advance and stored in the ion concentration calculation unit 16022, for example.

  (Step S1803) The ion concentration calculation means 16022 substitutes 1 for the counter t.

  (Step S1804) The ion concentration calculation means 16022 determines whether or not to end the ion concentration calculation process. If the process ends, the process goes to step S1808, and if not, the process goes to step S1805.

(Step S1805) The ion concentration calculation means 16022 adds the steady-state parameter acquired in step S1802, the one or more ion flow rate information received by the reception unit 1601, and t to the ion concentration calculation model information read in step S1801. Substituting and [Ca] _up is calculated.

(Step S1806) The ion concentration calculation means 16022 adds [Ca] _up calculated in step S1805 to the memory.

  (Step S1807) The ion concentration calculation means 16022 increments t by 1. The process returns to step S1804.

  (Step S1808) The cell membrane potential calculation means 16024 reads the cell membrane potential calculation model information from the cell membrane potential calculation model information storage means 16023.

  (Step S1809) The cell membrane potential calculation means 16024 reads the stored steady-state parameters. The cell membrane potential calculation means 16024 stores the steady state parameters in advance. The parameters in the steady state may be information input by the user or information transmitted from an external device. The steady-state parameters are calculated in advance and stored in the cell membrane potential calculation means 16024, for example.

  (Step S1810) Cell membrane potential calculation means 16024 substitutes 1 for counter t.

  (Step S1811) The cell membrane potential calculation means 16024 determines whether or not to end the cell membrane potential calculation process. If the process ends, the process returns to the upper function. If the process does not end, the process goes to step S1812.

(Step S1812) The cell membrane potential calculation means 16024 adds, to the cell membrane potential calculation model information read in Step S1808, the steady-state parameters acquired in Step S1809, one or more ion flow rate information received by the reception unit 1601, and t. assignment, and calculates the cell membrane potential calculated model information V _m.

(Step S1813) cell membrane potential calculation means 16024 additionally writes the _{V m} calculated in step S1812 in the memory.

  (Step S1814) The cell membrane potential calculation means 16024 increments t by 1. The process returns to step S1811.

  Next, the arrhythmia determination process of step S1704 will be described using the flowchart of FIG.

  (Step S1901) The ion concentration arrhythmia determination means 16032 reads the threshold values (v1, v2) for the calcium ion concentration in the sarcoplasmic reticulum uptake site from the ion concentration threshold storage means 16031.

  (Step S1902) The ion concentration arrhythmia determination means 16032 substitutes 1 for the counter i.

  (Step S1903) The ion concentration arrhythmia determination means 16032 reads the value of the i-th simulation result from the memory, and determines whether or not the value of the i-th simulation result exists. If the i-th value exists, the process goes to step S1904, and if the i-th value does not exist, the process goes to step S1911.

  (Step S1904) The ion concentration arrhythmia determination means 16032 determines whether or not “i-th value <= v1”. If “i-th value <= v1”, the process goes to step S1905. If “i-th value <= v1”, the process goes to step S1906.

  (Step S1905) The ion concentration arrhythmia determination means 16032 substitutes information indicating “normal” into the variable “determination”. Go to step S1909.

  (Step S1906) The ion concentration arrhythmia determination means 16032 determines whether or not “v1 <i-th value <= v2”. If the condition is satisfied, the process goes to step S1907. If the condition is not satisfied, the process goes to step S1908.

  (Step S1907) The ion concentration arrhythmia determination means 16032 substitutes information indicating “DAD” into the variable “determination”. Go to step S1909.

  (Step S1908) The ion concentration arrhythmia determination means 16032 substitutes information indicating “DAD + EAD” into the variable “determination”.

  (Step S1909) The ion concentration arrhythmia determination means 16032 stores the i-th value and the variable “determination” information as a pair.

  (Step S1910) The ion concentration arrhythmia determination means 16032 increments the counter i by one. The process returns to step S1903.

  (Step S1911) The cell membrane potential arrhythmia determination means 16034 reads the threshold (v2) for the cell membrane potential from the cell membrane potential threshold storage means 16033.

  (Step S1912) The cell membrane potential arrhythmia determination means 16034 substitutes 1 for the counter i.

  (Step S1913) Cell membrane potential arrhythmia determination means 16034 reads the value of the i-th simulation result and determines whether or not the value of the i-th simulation result exists. If the i-th value exists, the process goes to step S1914. If the i-th value does not exist, the process returns to the upper function.

  (Step S1914) Cell membrane potential arrhythmia determination means 16034 determines whether or not “i-th value <= v3”. If “i-th value <= v3”, the process goes to step S1915. If “i-th value <= v1”, the process goes to step S1916.

  (Step S1915) Cell membrane potential arrhythmia determination means 16034 substitutes information indicating “normal” into variable “determination”. Go to step S1917.

  (Step S1916) Cell membrane potential arrhythmia determining means 16034 substitutes information indicating “Low voltage oscillation” into variable “determination”. Go to step S1917.

  (Step S1917) The cell membrane potential arrhythmia determining means 16034 stores the i-th value and the variable “determination” information in a pair on the memory.

  (Step S1918) Cell membrane potential arrhythmia determination means 16034 increments counter i by one. The process returns to step S1913.

In the flowcharts of FIGS. 17 to 19, first, simulation and evaluation of Ca ²⁺ overload were performed, and second, simulation and evaluation of Low K ⁺ conductance were performed. However, the simulation and evaluation of Low K ⁺ conductance may be performed prior to the simulation and evaluation of Ca ²⁺ overload, or may be performed independently.

  The specific operation of the arrhythmia risk evaluation apparatus in the present embodiment will be described below.

  First, the accepting unit 1601 of the arrhythmia risk evaluation apparatus changes the risk of arrhythmia by changing the suppression of IKr alone from 0 to 100% in 5% increments, which is target biomedical efficacy information including ion flow rate information. It is assumed that an instruction to be evaluated (target biomedical efficacy information including ion flow rate information) is received. That is, the reception unit 1601 sequentially selects “IKr, −0”, “IKr, −5”, “IKr, −10”,..., “IKr, −90”, “IKr, −95”, “IKr, Target biomedical efficacy information “−100” is received.

  Next, the ion concentration calculation unit 16022 of the cardiomyocyte simulation unit 1602 reads the ion concentration calculation model information of Formula 8 from the ion concentration calculation model information storage unit 16021 as described above. The calcium concentration of the sarcoplasmic reticulum uptake site calculated here is the maximum average concentration in the course of the simulation.

Then, the ion concentration calculation means 16022 then stores the stored steady state parameters (for example, Vm = −91.26 mV, [Na ⁺ ] = 6.499 mM, [K ⁺ ] = 141.5 mM, [Ca ^{2 +} ] = 0.00003118 mM).

  Then, the ion concentration calculation means 16022 substitutes the target biomedical efficacy information including the steady state parameters and the ion flow rate information such as “IKr, −0” into the arithmetic expression indicated by the read ion concentration calculation model information, To do.

  Then, the ion concentration calculation means 16022 obtains the numerical value of “calcium concentration” in FIG. 20 for each target biomedical efficacy information. FIG. 20 is a cardiomyocyte simulation result management table, which stores a calcium ion concentration “calcium concentration” and a cell membrane potential “membrane potential” with respect to target biomedical efficacy information that is an IKr inhibition rate.

  The ion concentration calculation means 16022 stores the calculated calcium ion concentration in the cardiomyocyte simulation result management table in combination with the target biomedical effect information that is the IKr inhibition rate.

  Next, the cell membrane potential calculation unit 16024 reads the cell membrane potential calculation model information of Formula 9 from the cell membrane potential calculation model information storage unit 16023. The cell membrane potential calculated here is the average membrane potential of the cells in the steady state.

Next, the cell membrane potential calculation means 16024 stores stored steady-state parameters (for example, Vm = −89.00 mV, [Na ⁺ ] = 3.823 mM, [K ⁺ ] = 144.3 mM, [Ca ²⁺ ]. = 0.00001544 mM).

  Then, the cell membrane potential calculation means 16024 substitutes the target biological medicinal information including the steady state parameters and the ion flow rate information such as “IKr, −0” into the arithmetic expression indicated by the read cell membrane potential calculation model information, To do.

  Then, the cell membrane potential calculation means 16024 obtains the numerical value of “membrane potential” in FIG. 20 for each target biomedical efficacy information.

  Next, the ion concentration arrhythmia determination means 16032 reads the threshold values “3.58” and “3.84” for the calcium ion concentration in the sarcoplasmic reticulum uptake site from the ion concentration threshold storage means 16031.

  Then, the ion concentration arrhythmia determining means 16032 sequentially reads out the values of “calcium concentration” from the first to the 21st in the table of FIG. 20, and determines that it is normal if “value <= 3.58”. If “3.58 <value <= 3.84”, it is determined as “DAD”, and if “3.84 <value”, it is determined as “DAD + EAD”. Then, the ion concentration arrhythmia determination means 16032 stores the determination results in association with the respective values.

  Next, the cell membrane potential arrhythmia determination means 16034 reads the threshold value “−19.6” for the cell membrane potential from the cell membrane potential threshold storage means 16033.

  Then, the cell membrane potential arrhythmia determination means 16034 sequentially reads the values of “calcium concentration” from the first to the 21st in the table of FIG. 20, and determines that the value is normal if “value <= − 19.6”. If “−19.6 <value”, it is determined that “Low voltage oscillation”. Then, the cell membrane potential arrhythmia determining means 16034 accumulates the determination results in association with the respective values.

  Next, the output information configuration unit 1604 configures output information that is information to be output using the determination result of the ion concentration arrhythmia determination unit 16032.

  Then, the output unit 1605 outputs the configured output information. An example of such output is shown in FIG.

As mentioned above, according to this Embodiment, the arrhythmogenicity of a chemical | medical agent can be determined. Specifically, according to the present embodiment, it is possible to evaluate the risk of arrhythmia at the single cell level, particularly the abnormal automatic ability. More specifically, according to the present embodiment, it is possible to automatically detect the occurrence of abnormal automatic ability due to a decrease in intracellular Ca ²⁺ overload or cell membrane K ⁺ conductance.

  Note that according to the present embodiment, the arrhythmia determination unit 1603 determines the abnormal automatic ability such as “DAD”, “EAD”, and “Low voltage oscillation”. However, the determination process of the arrhythmia determination unit 1603 may be omitted. The output information may be configured so that the abnormal automatic ability can be determined from the output of the cardiomyocyte simulation unit 1602 (see FIG. 21).

  In addition, according to the specific example of the present embodiment, the arrhythmia risk evaluation apparatus centered on the effect of suppressing IKr has been described. However, it goes without saying that the arrhythmia risk evaluation apparatus of the present embodiment can evaluate the influence on any channel / carrier current existing in the model cell.

  Furthermore, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. In addition, the software which implement | achieves the arrhythmia risk evaluation apparatus in this Embodiment is the following programs. That is, the program includes a receiving step of receiving target biomedical efficacy information including one or more ion flow rate information, which is information about the electric flow rate of one or more ion channels indicating the influence of drug administration, and the receiving step. A cardiomyocyte simulation step of reading the received target biomedicine information, using the target biomedicine information, obtaining cardiomyocyte operation information, which is information about the behavior of living cardiomyocytes, and storing the information in a memory; It is a program for reading out cell movement information and executing an output information configuration step that constitutes output information that is information that is output using the cardiomyocyte movement information, and an output step that outputs the output information.

  Further, in the above program, in the cardiomyocyte simulation step, the stored ion concentration calculation model information is read, and one or more ion flow rate information received in the reception step is substituted into the ion concentration calculation model information, and the ion It is preferable to calculate the calcium ion concentration in the sarcoplasmic reticulum uptake site, which is cardiomyocyte operation information, by executing an arithmetic expression indicated by the concentration calculation model information.

Furthermore, in the above program, in the cardiomyocyte simulation step, cell membrane potential calculation model information, which is information of an arithmetic expression for calculating a cell membrane potential using one or more ion flow rate information as a parameter, is read, and the one or more received in the receiving step It is preferable to substitute the ion flow rate information into the cell membrane potential calculation model information, execute an arithmetic expression indicated by the cell membrane potential calculation model information, and calculate a cell membrane potential which is cardiomyocyte operation information.
(Embodiment 3)

  In the present embodiment, the risk of arrhythmia is “Yes” based on one or more arrhythmia risk evaluation results (cardiomyocyte simulation, QT interval simulation, spiral simulation, etc.) in the arrhythmia risk evaluation apparatus described above. An arrhythmia risk evaluation apparatus that searches for a biological parameter set that restores normal heart function (reduces the risk of arrhythmia) when it is determined, outputs the search result, and presents a compound development guideline will be described.

  FIG. 22 is a block diagram of the arrhythmia risk evaluation apparatus in the present embodiment.

  The arrhythmia risk evaluation apparatus includes a reception unit 100, a parameter set storage unit 101 before and after drug administration, an animal drug effect information acquisition unit 102, a conversion source information storage unit 103, a drug effect conversion unit 104, a cardiomyocyte simulation unit 105, and an arrhythmia information acquisition unit 106. Arrhythmia determination unit 107, fixed parameter information storage unit 2201, next target biomedical efficacy information acquisition unit 2202, control unit 2203, output information configuration unit 2208, and output unit 109.

  The fixed parameter information storage unit 2201 stores fixed parameter information that is information on parameters for fixing values. The fixed parameter information is a biological parameter that is considered to be the main drug effect, and may be one biological parameter or a plurality of biological parameters. For example, the fixed parameter information may be input by the user using an input unit such as a keyboard, or a fixed parameter determination unit (not shown) reads the pre- and post-drug parameter set storage unit 101 in the pre- and post-drug administration parameter set storage unit 101, A parameter having a change (large change) greater than or equal to a predetermined threshold value before administration and after drug administration may be determined as a fixed parameter, and information indicating the parameter may be fixed parameter information. Further, the fixed parameter information is a single parameter indicating a maximum rate of change before and after drug administration by a fixed parameter determination unit (not shown) reading the parameter set before and after drug administration in the parameter set storage unit 101 before and after drug administration. May be determined as fixed parameters, and information indicating the parameters may be used as fixed parameter information. The fixed parameter information storage unit 2201 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium.

  When the arrhythmia information acquired by the arrhythmia information acquisition unit 106 is information indicating a side effect of arrhythmia that is greater than or equal to a predetermined value, the next target biomedical effect information acquisition unit 2202 Is fixed, the values of other parameters are varied, and new target biomedical efficacy information is acquired. The next target biomedical efficacy information acquisition unit 2202 does not matter how the parameter value is changed after the parameter to be changed is determined. The next target biomedical efficacy information acquisition unit 2202 holds, for example, information on a range to be changed in advance, and the value may be sequentially increased or decreased within the range, or by generating a random number. The value may be varied. The next target biomedical efficacy information acquisition unit 2202 can be normally realized by an MPU, a memory, or the like. The processing procedure of the next target biomedical efficacy information acquisition unit 2202 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The control unit 2203 gives the new target biomedical effect information acquired by the next target biomedical effect information acquisition unit 2202 to the cardiomyocyte simulation unit 105 (or the cardiomyocyte simulation unit 1602), and acquires cardiomyocyte operation information. Cardiomyocyte movement information includes, for example, action potential information described in the first embodiment, QT interval information, spiral wave simulation results, calcium ion concentration in the sarcoplasmic reticulum uptake site described in the second embodiment, Information on cell membrane potential. The control unit 2203 can be usually realized by an MPU, a memory, or the like. The processing procedure of the control unit 2203 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output information configuration unit 2208 configures output information that is information to be output using the arrhythmia information acquired by the arrhythmia information acquisition unit 106 and the target biomedical effect information acquired by the next target biomedical effect information acquisition unit 2202. The output information configuration unit 2208 may also configure output information using the determination result of the arrhythmia determination unit 107. The output information configuration unit 2208 can be usually realized by an MPU, a memory, or the like. The processing procedure of the output information configuration unit 2208 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  Next, the operation of the arrhythmia risk evaluation apparatus will be described using the flowchart of FIG. In the flowchart of FIG. 23, only the steps different from the flowchart of FIG. 10 will be described.

  (Step S2301) The next target biomedical efficacy information acquisition unit 2202 determines whether or not the result of the arrhythmia determination in step S1012 is “arrhythmia present”. If “arrhythmia is present”, the process proceeds to step S2302, and if “arrhythmia is not present”, the process proceeds to step S2304.

  (Step S2302) The next target biomedical efficacy information acquisition unit 2202 determines whether or not to end the process. The termination of the process here means that the process of changing the biological parameter other than the parameter indicated by the fixed parameter information is terminated. If it is determined that the process is to be terminated, the process proceeds to step S2304. If it is determined that the process is not to be terminated, the process proceeds to step S2303.

  (Step S2303) The next target biomedical effect information acquisition unit 2202 reads the fixed parameter information from the fixed parameter information storage unit 2201, determines the medicinal effect information of the biological parameter other than the parameter indicated by the fixed parameter information, and then determines the next target biomedical effect. Configure information. Any algorithm that changes the values of other parameters (determines drug efficacy information) may be used. A specific example of this algorithm will be described later. The process returns to step S1005.

  (Step S2304) The output information configuration unit 2208 configures output information that is information to be output using the determination result of the arrhythmia determination unit 107 and the target biomedical efficacy information acquired by the next target biomedical efficacy information acquisition unit 2202. An example of the algorithm constituting the output information will be described with reference to the flowchart of FIG.

  (Step S2305) The output unit 109 outputs the output information configured in step S2304. The process ends.

  Next, the output information configuration processing in step S2304 will be described using the flowchart in FIG.

  (Step S2401) The output information configuration unit 2208 determines whether or not the “arrhythmia is present” even once in the simulation process using the biological parameter set received before and after the first drug administration. If it has never become “arrhythmic,” go to step S2402, and if it has once “arrhythmic,” go to step S2403. Note that once “arrhythmia is present”, for example, a predetermined variable (flag) is turned ON “1”, and in other cases, OFF “0” is set.

  (Step S2402) The output information configuration unit 2208 substitutes “target biomedical efficacy information” and “judgment result of arrhythmia risk” into the output information. Return to upper function. A specific example of the method for obtaining the “arrhythmia risk determination result” will be described later.

  (Step S2403) The output information structure part 2208 acquires the information of the biometric parameter changed in the next target biomedical efficacy information compared with the beginning.

  (Step S2404) The output information configuration unit 2208 substitutes the “changed biometric parameter information” acquired in step S2403 for the output information. Return to upper function.

  The specific operation of the arrhythmia risk evaluation apparatus in the present embodiment will be described below.

  Now, it is assumed that the fixed parameter information storage unit 2201 stores fixed parameter information indicating “IKr”, which is a biological parameter considered to be the main drug effect. In addition, the cardiomyocyte simulation unit 105 receives the target biomedical efficacy information (ICaL = 100%, IK1 = 100%, IKr = 0%, IKs = 100%) by the arrhythmia risk evaluation apparatus according to the first embodiment. The action potential information acquisition unit 1052 reads the action potential model information stored in advance using the target biomedical efficacy information, performs cardiomyocyte simulation using the action potential model information (calculation formula), and the action potential Suppose that information is acquired. Here, the action potential model information stored in advance is an animal model. Then, the action potential information acquisition unit 1052 obtains action potential waveform information (here, information of an animal model) shown in FIG. In FIG. 25, (1) simulates cardiomyocytes using target biomedical efficacy information (ICaL = 100%, IK1 = 100%, IKr = 0%, IKs = 100%), and the action potential obtained It is a graph (action potential wave). Further, (2) is a normal action potential waveform. Information on a normal action potential waveform is stored in advance, for example.

  Next, the arrhythmia information acquisition unit 106 determines, based on the information in the graphs (1) and (2) of FIG. 25, that “APD30 change rate = 12.7%”, “APD60 change rate = 17.7%”, “ "Change rate of APD90 = 18.7%" is acquired. The rate of change of APD30 is the rate of change of APD30 before and after drug administration.

  Next, the arrhythmia determination unit 1073 reads out the “range of change of APD 30”, “rate of change of APD 60”, and “rate of change of APD 90” from the threshold storage unit 1071 in the normal range. It is assumed that the threshold storage means 1071 stores in advance the “change rate of APD 30”, “change rate of APD 60”, and “change rate of APD 90” in the normal range.

  Next, the arrhythmia determination means 1073 displays the “change rate of APD 30” acquired by the arrhythmia information acquisition unit 106 within the normal ranges of “change rate of APD 30”, “change rate of APD 60”, and “change rate of APD 90” of the read experimental results. = 12.7% ”,“ Change rate of APD60 = 17.7% ”, and“ Change rate of APD90 = 18.7% ”are not included, so it is determined that an arrhythmia occurs. . In this specific example, acquisition of QT interval information and spiral wave simulation processing are omitted, but it goes without saying that such processing may be performed.

  Next, the next target biomedical efficacy information acquisition unit 2202 reads the fixed parameter information “IKr” in the fixed parameter information storage unit 2201 and automatically sets the biological parameters (ICaL, IK1, IKs) other than “IKr” (here, For example, it is changed to “ICaL” = 84.375%, “IK1” = 151.25%, “IKs” = 107.5%), and information on such biological parameters (“ICaL” = 84.375%, “IK1”) “= 151.25%,“ IKs ”= 107.5%). The next target biomedical efficacy information acquisition unit 2202 acquires, for example, a biological parameter other than “IKr” at random and so as not to overlap with previously acquired biological parameter information.

  Then, the action potential information acquisition unit 1052 of the cardiomyocyte simulation unit 105 receives target biomedical efficacy information (ICaL = 84.375%, IK1 = 151.25%, IKr = 0%, IKs = 107.5%), It is assumed that information on the action potential waveform shown in FIG. 26, which is information on the behavior of the living body's cardiomyocytes, is acquired using the target biomedical efficacy information. In FIG. 26, (1) is obtained by performing cardiomyocyte simulation using target biomedical efficacy information (ICaL = 84.375%, IK1 = 151.25%, IKr = 0%, IKs = 107.5%) It is a graph (action potential wave) of action potential. Further, (2) is a normal action potential waveform. (1) and (2) generally overlap, and according to the target biomedical efficacy information (ICaL = 84.375%, IK1 = 151.25%, IKr = 0%, IKs = 107.5%), normal heart It can be determined that the biological parameter set restores the function.

  Next, it is assumed that the arrhythmia information acquisition unit 106 acquires the values of the change rate of the APD 30, the change rate of the APD 60, and the change rate of the APD 90 from the action potential waveform information of (1) in FIG. Further, the arrhythmia information acquisition unit 106 acquires the value of the change rate of the APD 30 of “7.2%”, the value of the change rate of the APD 60 of “9.4%”, and the value of the change rate of the APD 90 of “7”. .9% ".

  Next, the arrhythmia determination unit 1073 reads out the “range of change of APD 30”, “rate of change of APD 60”, and “rate of change of APD 90” from the threshold storage unit 1071 in the normal range.

  Next, the arrhythmia determination means 1073 displays the “change rate of APD 30” acquired by the arrhythmia information acquisition unit 106 within the normal ranges of “change rate of APD 30”, “change rate of APD 60”, and “change rate of APD 90” of the read experimental results. = 7.2% "," Change rate of APD60 = 9.4% ", and" Change rate of APD90 = 7.9% "are all within the normal range, so no arrhythmia occurs. Judge. In this specific example, acquisition of QT interval information and spiral wave simulation processing are omitted, but it goes without saying that such processing may be performed.

  Next, the output information configuration unit 108 configures output information that is information to be output using the determination result of the arrhythmia determination unit 107. Specifically, the output information configuration unit 108 uses the determination result of the arrhythmia determination unit 107 and the cardiomyocyte operation information (here, information of action potential waveform) acquired by the cardiomyocyte simulation unit 105 to output the output information. The configuration is as shown in FIG. The output information configuration unit 108 uses the format information (information indicating the arrangement of the character string and cardiomyocyte operation information) for configuring the output information of FIG. 27 or the character string “<information on biological parameter set>” , <The arrhythmia determination result> "is held, and the output information configuration unit 108 substitutes the target biomedical efficacy information into <biological parameter set information>, and the <arrhythmia determination result> has an arrhythmia determination unit. The determination result of 107 is substituted.

  Next, the output unit 109 displays the output information configured by the output information configuration unit 108 on the display.

  As described above, according to the present embodiment, when the risk of arrhythmia is determined to be “Yes” based on one or more results of the arrhythmia risk evaluation (cardiomyocyte simulation, QT interval simulation, spiral simulation, etc.) In addition, it is possible to search a biological parameter set that restores normal heart function (reduces the risk of arrhythmia), outputs the search result, and presents a compound development guideline.

  In the experiment in the specific example of the present embodiment, the target biomedical efficacy information in which arrhythmia does not occur is (Pattern A) “ICaL = 84.375%, IK1 = 151.25%, IKr = 0%, IKs = 107 (Pattern B) “ICaL = 78.75%, IK1 = 143.125%, IKr = 0%, IKs = 110%”, (pattern C) “ICaL = 60.625%, The biological parameter set of “IK1 = 150%, IKr = 0%, IKs = 47.5%” has been acquired as a biological parameter set that restores normal heart function in the animal experiment stage.

  When the biometric parameter set of pattern B is given to the cardiomyocyte simulation unit 105, the action potential information acquisition unit 1052 acquires the information on the action potential waveform of (c) of FIG. 28, and the arrhythmia information acquisition unit 106 selects “APD30 Change rate = 5.9% ”,“ change rate of APD 60 = 8.7% ”, and“ change rate of APD 90 = 7.5% ”.

  When the biological parameter set of pattern C is given to the cardiomyocyte simulation unit 105, the action potential information acquisition unit 1052 acquires the information of the action potential waveform in (d) of FIG. 28, and the arrhythmia information acquisition unit 106 “APD30 change rate = 24.6%”, “APD60 change rate = 28.0%”, and “APD90 change rate = 25.1%” are acquired. In pattern C, it is determined that there is no improvement in the arrhythmia risk, and another biological parameter pattern that restores normal cardiac function is similarly input to the arrhythmia risk evaluation apparatus. Finally, the arrhythmia risk evaluation apparatus acquires an arbitrary number of biological parameter sets at which there is no risk of arrhythmia (the number to be acquired may be one, or the user specifies an arbitrary number) Alternatively, the end condition may be to automatically try all patterns obtained from the biological parameter determination device). The finally obtained biological parameter set has the drug efficacy necessary for having the drug efficacy with a low risk of arrhythmia while maintaining the main drug efficacy (IKr inhibition) of the drug.

  In FIG. 28, (a) receives target biomedical efficacy information (ICaL = 100%, IK1 = 100%, IKr = 0%, IKs = 100%), and uses the target biomedical efficacy information to use action potential. The information acquisition unit 1052 performs the simulation of cardiomyocytes and acquires the action potential information acquired.

  Further, according to the specific example of the present embodiment, the arrhythmia risk evaluation uses action potential information that is a result of cardiomyocyte simulation, but it goes without saying that other cardiomyocyte operation information may be used. Yes. That is, for example, in FIG. 21 of the second embodiment, in the case of IKr 60% single suppression, the point is in the zone of “DAD” occurrence, but in addition to this IKr suppression, ICaL is suppressed to 50%. As a result, the point moves to the “normal” zone. That is, the next target biomedical efficacy information acquisition unit 2202 fluctuates the parameter “ICaL” that is not the fixed parameter information “IKr” by 50%, and the control unit 2203 performs “IKr 60% suppression, ICaL 50% suppression” cardiomyocyte simulation. When given to the unit 1602, the arrhythmia determination unit 1603 determines that no arrhythmia occurs. This is indicated by the arrows in FIG. That is, by adding ICaL suppression, it can be determined that arrhythmia is improved.

  Furthermore, the software that realizes the arrhythmia risk evaluation apparatus in the present embodiment is the following program. That is, this program stores an animal drug efficacy information acquisition step for obtaining animal drug efficacy information, which is information on drug efficacy for animals, using the stored parameter set before and after drug administration, and the animal drug efficacy information stored in the computer. A medicinal effect conversion step for converting into the target biomedical efficacy information using the converted source information, and the myocardial cell operation information that is information about the behavior of the living cardiomyocytes using the target biomedical efficacy information Using the cell simulation step, using the cardiomyocyte movement information, the arrhythmia information acquisition step for acquiring arrhythmia information that is information on the side effects of arrhythmia on the drug heart, and the arrhythmia information acquired in the arrhythmia information acquisition step are output. Output information constituting step that constitutes output information that is information to be output, and the output information A program, for executing an output step of outputting.

When the arrhythmia information acquired in the arrhythmia information acquisition step is information indicating side effects of arrhythmia that is greater than or equal to a predetermined value, the program fixes the parameter value of the fixed parameter information included in the target biomedical effect information. Then, the values of other parameters are changed, new target biomedical efficacy information is acquired, and the next target biomedical efficacy information acquisition step arranged on the memory and the new target acquired in the next target biomedical efficacy information acquisition step Read out biomedicine information, give the target biomedicine information to the cardiomyocyte simulation step, and further execute a control step of acquiring cardiomyocyte operation information, and in the output information configuration step, the arrhythmia acquired in the arrhythmia information acquisition step Information and the target living body acquired in the next target biomedical efficacy information acquiring step It reads the efficiency information, constitute the output information, it is preferable.
(Embodiment 4)

  In the present embodiment, a biological parameter determination apparatus and the like that outputs biological parameters by inputting information on action potentials as a result of animal experiments will be described. Then, using this biological parameter determination device, if the biological parameter set is acquired from the action potential information before drug injection, and the biological parameter set is acquired from the action potential information after drug injection, the effect of drug injection Can be obtained quantitatively. Note that the effect of drug injection is obtained from the difference in biological parameter set before and after drug injection.

  FIG. 29 is a block diagram of the biological parameter determination device in the present embodiment.

  The biological parameter determination apparatus includes a biological parameter set storage unit 2901, a parameter set selection unit 2902, a simulation execution unit 2903, a steady state determination unit 2904, an action potential information acquisition unit 2905, an experimental action potential information storage unit 2906, a preprocessing unit 2907, A dissimilarity calculation unit 2908, an allowable parameter set determination unit 2909, a control unit 2910, and an allowable parameter set output unit 2911 are provided.

  The parameter set selection unit 2902 includes search range determination means 29021 and parameter set selection means 29022.

  The preprocessing unit 2907 includes a scaling unit 29071 and a smoothing unit 29072.

  The difference calculation unit 2908 includes an absolute value difference information acquisition unit 29081, a change difference information acquisition unit 29082, and a difference calculation unit 29083.

The biological parameter set storage unit 2901 stores two or more biological parameter sets having one or more biological parameters that are biological parameters. Biological parameters include current flowing through various channels of cells (for example, Na channel, Ca channel, _KATP channel, Kr channel, K1 channel, Ks channel, etc.), opening / closing speed of each channel, ion affinity, intracellular and extracellular There are hundreds of parameters, such as the ion concentration. The biological parameter usually has a biological parameter identifier for identifying the biological parameter and a parameter value. However, if the enumeration order of the biometric parameters is determined in the biometric parameter set, the biometric parameters may be values alone. The biological parameter set may be stored in advance or may be calculated by means not shown. Holds information on the range of values that each biological parameter can take, and a means (not shown) determines one value of each biological parameter from the range of values that each biological parameter can take and builds a biological parameter set You may do it. The biological parameter set storage unit 2901 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The parameter set selection unit 2902 selects one biological parameter set from two or more biological parameter sets. There is no limitation on the algorithm for selecting one biological parameter set. One biological parameter set may be arbitrarily selected, or one biological parameter set may be selected by a predetermined algorithm. It is preferable that the parameter set selection unit 2902 selects one biological parameter set by the processing of the search range determination unit 29021 and the parameter set selection unit 29022. An example of an algorithm for selecting one biological parameter set will be described later. The parameter set selection unit 2902 can usually be realized by an MPU, a memory, or the like. The processing procedure of the parameter set selection unit 2902 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The search range determining means 29021 limits the range of the one or more parameters using the range of the values of the one or more parameters constituting two or more biological parameter sets, and has information on the limited range. Determine the search space. Note that the range of parameter values is usually determined in advance, and information on the range is stored in the recording medium. Further, the search range determining means 29021 divides the range of n parameter values (n is an integer of 1 or more) constituting the previous search range into two, and the combination of the n parameters divided into two 2 ⁿ search ranges having a value range are acquired, and a search range having a parameter set having the smallest difference among the representative parameter sets of the 2 ⁿ search ranges is set as the previous search range. The search range may be narrowed. The search range determination unit 29021 holds, for example, information on the range of values of each parameter constituting the previous two or more parameter sets, and the parameter set having the smallest difference in the previous time among the two or more parameter sets. The search range may be narrowed by setting a half of the range of each parameter value as the next search range. Search range determining means 29021 can be normally realized by an MPU, a memory, or the like. The processing procedure of the search range determining means 29021 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The parameter set selection unit 29022 selects one parameter set from one or more parameter sets existing in the search space determined by the search range determination unit 29021. The parameter set selection unit 29022 can usually be realized by an MPU, a memory, or the like. The processing procedure of the parameter set selection means is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The simulation execution unit 2903 receives one biological parameter set selected by the parameter set selection unit 2902, simulates the activity of the heart, and obtains action potential information, which is information indicating the action potential of the heart, at predetermined time intervals. . Since the simulation technique for inputting the biological parameter set and obtaining the action potential information at a predetermined time interval is a known technique, detailed description thereof is omitted. Note that the action potential information is, for example, a set of pairs of information indicating the potential of heart activity and information indicating time or time. Further, for example, the action potential information may include information on various ion concentrations (potassium ions, sodium ions, calcium ions, etc.) in the heart cells. For example, the action potential information may be information such as action potential duration (for example, APD 90, APD 60, APD 30). The data structure of action potential information is not limited. The “predetermined time interval” described above is not necessarily regular, and does not need to be determined in advance, and may be a random interval (an interval acquired by generating a random number). The simulation execution unit 2903 can be usually realized by an MPU, a memory, or the like. The processing procedure of the simulation execution unit 2903 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The steady state determination unit 2904 changes the action potential information output from the simulation execution unit 2903 (for example, changes in ion concentration of potassium ions, sodium ions, calcium ions, etc.) to change the activity state of the simulation target heart to a steady state. Judge whether or not. The steady state determination unit 2904, for example, the potassium ion concentration or / and sodium ion concentration or / and calcium ion concentration, which is the output of the simulation execution unit 2903 at time t, and the output of the simulation execution unit 2903 at time (t + 1). The potassium ion concentration or / and the sodium ion concentration or / and the calcium ion concentration are compared, and if the difference is less than a predetermined difference, it is determined that the activity state of the heart has become a steady state. Moreover, steady-state decision unit 2904, for example, the action potential information which is the output of the simulation executing unit 2903 at time t and _{(V t),} the time (t + 1) is the output of the simulation executing unit 2903 is action potential information _{(V t + 1} ) And if the difference is less than a predetermined difference, it is determined that the heart activity has become steady. The steady state determination unit 2904 can usually be realized by an MPU, a memory, or the like. The processing procedure of the steady state determination unit 2904 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The action potential information acquisition unit 2905 acquires action potential information that is an output of the simulation execution unit 2903 when the steady state determination unit 2904 determines that the activity state of the simulation target heart has become a steady state. The action potential information acquisition unit 2905 usually obtains action potential information when it is determined that a steady state has been reached, and action potential information that is the final output result of the simulation execution unit 2903. The action potential information acquisition unit 2905 can be usually realized by an MPU, a memory, or the like. The processing procedure of the action potential information acquisition unit 2905 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The experimental action potential information storage unit 2906 stores experimental action potential information which is action potential information as a result of animal experiments. The experimental action potential information may be information that accepts manual input or information received from an external device (not shown). The experimental action potential information storage unit 2906 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium.

  The preprocessing unit 2907 reads out the experimental action potential information stored in the experimental action potential information storage unit 2906, performs preprocessing that is a process for removing noise on the experimental action potential information, and creates a new experimental action potential. get information. There may be several types of preprocessing, and one or more processes may be combined. An example of the preprocessing is scaling or smoothing processing described later. The preprocessing unit 2907 is usually realized by an MPU, a memory, or the like. The processing procedure of the preprocessing unit 2907 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). Note that the preprocessing performed by the preprocessing unit 2907 is necessary for improving the accuracy of calculating the degree of difference described later, but is not essential.

  Scaling means 299071 is the value of the potential indicated by the experimental action potential information so that the maximum value of the potential indicated by the action potential information acquired by the action potential information acquisition unit 2905 matches the maximum value of the potential indicated by the experimental action potential information. To change. Such processing is called scaling. The scaling unit 29071 can be usually realized by an MPU, a memory, or the like. The processing procedure of the scaling unit 29071 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The smoothing means 29072 calculates a moving average with respect to the potential value indicated by the experimental action potential information, and sets the calculated moving average value as the potential value indicated by the new experimental action potential information. Such processing is called smoothing. Since the process of calculating the moving average is a known technique, detailed description thereof is omitted. The smoothing means 29072 can usually be realized by an MPU, a memory, or the like. The processing procedure of the smoothing means 29072 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The difference calculation unit 2908 calculates the difference between the action potential information acquired by the action potential information acquisition unit 2905 and the experimental action potential information. Here, the “difference” means a difference (difference) between the action potential information acquired by the action potential information acquisition unit 2905 and the experimental action potential information, and may be a similarity. It goes without saying that “the degree of difference is small (low)” is equivalent to “the degree of similarity is high (high)”. The difference calculation unit 2908 preferably calculates the difference between the action potential information acquired by the action potential information acquisition unit 2905 and the experimental action potential information acquired by the preprocessing unit 2907. The dissimilarity calculation unit 2908 normally holds information on a calculation formula for calculating the dissimilarity on a recording medium (not shown), reads the information on the calculation formula, and uses the calculation formula as an action potential information acquisition unit 2905. Substituting the action potential information acquired by, and the information obtained from the experimental action potential information as parameters, and performing calculations, the degree of difference is obtained. The difference calculation unit 2908 uses the action potential information acquired by the action potential information acquisition unit 2905 and the preprocessing unit 2907 using only the absolute value difference described later, only the change difference information, or other information. The degree of difference from the experimental action potential information may be calculated. The dissimilarity calculation unit 2908 can usually be realized by an MPU, a memory, or the like. The processing procedure of the difference calculation unit 2908 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The absolute value difference information acquisition means 29081 is absolute value difference information that is information on the difference between the action potential value indicated by the action potential information acquired by the action potential information acquisition unit 2905 and the action potential value indicated by the experimental action potential information. To get. It does not matter whether the absolute value difference information acquisition means 29081 acquires the absolute value of the difference between the ranges of the values indicated by the two action potential information. The absolute value difference information acquisition means 29081 may obtain, for example, the accumulation of absolute values of the difference between Vm (cell membrane potential) and RMP (resting membrane potential) of two action potentials, or the difference between the two action potentials Vm. It is also possible to obtain only the accumulation of absolute values of. The absolute value difference information acquisition unit 29081 can usually be realized by an MPU, a memory, or the like. The processing procedure of the absolute value difference information acquisition unit 29081 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The change difference information acquisition means 29082 is a change that is information relating to the difference between the action potential change value indicated by the action potential information acquired by the action potential information acquisition unit 2905 and the action potential change value indicated by the experimental action potential information. Get difference information. This change is a change over time. A temporal change is a change that occurs over time. For example, the change difference information acquisition unit 29082 accumulates the difference between the value obtained by time differentiation of the action potential information acquired by the action potential information acquisition unit 2905 and the value obtained by time differentiation of the action potential information indicated by the experimental action potential information. Calculate and get. The change difference information acquisition unit 29082 can usually be realized by an MPU, a memory, or the like. The processing procedure of the change difference information acquisition unit 29082 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The difference calculation means 29083 calculates the difference between the action potential information acquired by the action potential information acquisition unit 2905 and the experimental action potential information using the absolute value difference information and the change difference information. The difference degree calculation means 29083, for example, weights the absolute value difference information and the change difference information, and obtains the sum as the difference degree. The dissimilarity calculation means 29083 can usually be realized by an MPU, a memory, or the like. The processing procedure of the dissimilarity calculation means 29083 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The permissible parameter set determination unit 2909 uses the dissimilarity calculated by the dissimilarity calculation unit 2908 to determine whether one biometric parameter set is a biometric parameter set within an allowable range. For example, the allowable parameter set determination unit 2909 determines whether or not one biological parameter set is a biological parameter set within an allowable range by using the response surface method with respect to the difference calculated by the difference calculation unit 2908. To do. Since the response surface method is a known technique, a description thereof will be omitted. In addition, the allowable parameter set determination unit 2909 outputs, for example, a biometric parameter set having the same degree of difference to the user, receives an input of an instruction indicating whether the allowable range is received from the user, and receives information on the instruction (indicating the allowable range). Information or information indicating information outside the allowable range) may be performed. When accepting a user instruction, the user makes a substantial decision, and the allowable parameter set determination unit 2909 performs a process of accepting and holding the user instruction. The permissible parameter set determination unit 2909 can be usually realized by an MPU, a memory, or the like. The processing procedure of the allowable parameter set determination unit 2909 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  When the allowable parameter set determining unit 2909 determines that one biological parameter set is not within the allowable range, the control unit 2910 determines that one biological parameter set from among the biological parameter sets not selected by the parameter set selecting unit 2902 Instructs the user to select a parameter set. Note that even when the allowable parameter set determination unit 2909 determines that one biological parameter set is a biological parameter set within the allowable range, the control unit 2910 obtains a biological parameter set within another allowable range, The parameter set selection unit 2902 may be instructed to select one biological parameter set from among unselected biological parameter sets. In such a case, for example, it is determined whether or not all parameter sets in the biological parameter set storage unit 2901 are within an allowable range. In such a case, for example, until a predetermined number (for example, “5”) of allowable parameter sets are found, the control unit 2910 causes the parameter set selection unit 2902 to select one biological parameter from unselected biological parameter sets. You will be prompted to select a set. The control unit 2910 can usually be realized by an MPU, a memory, or the like. The processing procedure of the control unit 2910 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The allowable parameter set output unit 2911 outputs the one biological parameter set when the allowable parameter set determination unit 2909 determines that the one biological parameter set is in the allowable range. Here, the output is a concept including display on a display, printing on a printer, sound output, transmission to an external device, accumulation in a recording medium, and the like. The permissible parameter set output unit 2911 may be considered as including or not including an output device such as a display or a speaker. The permissible parameter set output unit 2911 may be realized by output device driver software, or output device driver software and an output device.

  Next, the operation of the biological parameter determination device will be described with reference to the flowcharts of FIGS.

  (Step S3001) The parameter set selection unit 2902 selects one biological parameter set from two or more biological parameter sets. For example, the parameter set selection unit 2902 reads one biological parameter set from the biological parameter sets stored in the biological parameter set storage unit 2901 in the order in which they are stored.

  (Step S3002) The simulation execution unit 2903 receives the one biological parameter set selected by the parameter set selection unit 2902 as an input, simulates the activity of the heart, and obtains action potential information that is information indicating the action potential of the heart as a predetermined value. Obtain and output at time intervals. The predetermined time interval is not necessarily equal.

  (Step S3003) The steady state determination unit 2904 determines whether or not the activity state of the simulation target heart has become a steady state from the change in the two or more action potential information of the simulation result in step S3002. Normally, when there is no change between the immediately preceding action potential information and the current action potential information, or when there is a small difference within a predetermined difference, the steady state determination unit 2904 determines that a steady state has been reached. If it is in the steady state, the process goes to step S3004. If it is not in the steady state, the process returns to step S3002, and the simulation by the simulation execution unit 2903 is continued. Normally, when returning to step S3002, the process of inputting one biological parameter set selected by the parameter set selection unit 2902 is not performed again, and the simulation by the simulation execution unit 2903 is merely continued.

  (Step S3004) The action potential information acquisition unit 2905 acquires action potential information that is an output of the simulation execution unit 2903 when the steady state determination unit 2904 determines that the activity state of the simulation target heart has reached a steady state.

  (Step S3005) The preprocessing unit 2907 reads the experimental action potential information stored in the experimental action potential information storage unit 2906 on the memory.

  (Step S3006) The preprocessing unit 2907 performs preprocessing on the experimental action potential information read in step S3005. Details of the preprocessing will be described with reference to the flowchart of FIG.

  (Step S3007) The dissimilarity calculation unit 2908 calculates the dissimilarity between the action potential information acquired in step S3004 and the experimental action potential information that is the result of the preprocessing in step S3006. Details of the algorithm for calculating the difference between the two action potential information will be described with reference to the flowchart of FIG.

  (Step S3008) The difference calculation unit 2908 stores the set of the selected biological parameter set and the difference calculated in step S3007 in a recording medium (not shown) (may be a main memory).

  (Step S3009) The allowable parameter set determination unit 2909 determines whether or not the selected one biological parameter set is a biological parameter set within an allowable range, using the degree of difference calculated in step S3007. Whether or not it is within the allowable range may be determined by the user and input by an input means (not shown) or may be automatically determined using the response surface method. If it is within the allowable range, the process goes to step S3010, and if it is outside the allowable range, the process goes to step S3012.

  (Step S3010) The control unit 2910 determines whether or not to end the process. Whether or not to end the process may be determined to end the process if a biological parameter set within one allowable range is found, or determined to end if a biological parameter set within a predetermined number of allowable ranges is found. Alternatively, it may be determined that the process is ended by inputting a user's end instruction. In addition, the algorithm which judges that a process is complete | finished does not matter. If it is determined that the process is to be terminated, the process proceeds to step S3011. If it is determined that the process is not to be terminated, the process proceeds to step S3013.

  (Step S3011) The permissible parameter set output unit 2911 reads out and outputs the biometric parameter set that the permissible parameter set determination unit 2909 determines to be a biometric parameter set within the permissible range. The process ends.

  (Step S3012) The control unit 2910 deletes the biological parameter set that is not within the allowable range.

  (Step S3013) The control unit 2910 instructs the parameter set selection unit 2902 to select the next biological parameter set. The process returns to step S3001.

  In the flowchart of FIG. 30, another algorithm may be used as the biometric parameter set selection algorithm.

  Next, the preprocessing (step S3006) of the biological parameter determination device will be described using the flowchart of FIG.

(Step S3101) The scaling means 29071 obtains the average value (V _SRMP ) and the maximum value (V _SH ) of the resting membrane potential of the potential indicated by the action potential information of the simulation result acquired by the action potential information acquisition unit 2905 as the action potential. Obtained from the values that make up the information. Note that the information constituting the action potential information includes, for example, voltage (mV) and time information in pairs.

(Step S3102) The scaling means 29071 acquires the average value (V _ERMP ) and the maximum value (V _EH ) of the resting membrane potential among the information on the experimental action potential.

(Step S3103) The scaling means ₂₉₀₀₇ changes the maximum value (V _EH ) of the experimental action potential information to the maximum value (V _SH ) and the minimum value (V _ERMP ) to the minimum value (V _SRMP ).

  (Step S3104) The scaling means 29071 substitutes 1 for the counter i.

  (Step S3105) The scaling means 29007 determines whether or not there is an i-th value in the experimental action potential information. If there is an i-th value, the process goes to step S3106, and if there is no i-th value, the process goes to step S3109.

(Step S3106) The scaling means 29071 changes the i-th value (V _i ) using V _SL , V _SH , and V _ERMP V _SRMP . Specifically, the i-th value is determined by (V _i −V _ERMP ) × (V _SH −V _SRMP ) / (V _EH −V _ERMP ) + V _SRMP . Note that the scaling unit 29071 holds information of an arithmetic expression (V _i −V _ERMP ) × (V _SH −V _SRMP ) / (V _EH −V _ERMP ) + V _SRMP , reads information on the arithmetic expression, The acquired parameter is substituted into the arithmetic expression to obtain the scaled i-th value.

(Step S3107) The scaling means 29071 stores the new i-th value (V _i ) obtained in step S3106.

  (Step S3108) The scaling means 29071 increments the counter i by 1. The process returns to step S3105.

  (Step S3109) The smoothing means 29072 substitutes 1 for the counter i.

  (Step S3110) The smoothing means 29072 determines whether or not there is an i-th point (value) in the information of the experimental action potential. If there is an i-th point, the process goes to step S3111, and if there is no i-th value, the process returns to the upper function.

  (Step S3111) The smoothing means 29072 calculates the moving average of the i-th point.

  (Step S3112) The smoothing means 29072 stores the moving average of the i-th point calculated in step S3111 in a recording medium (including a memory).

  (Step S3113) The smoothing means 29072 increments the counter i by 1. The process returns to step S3110.

  In the flowchart of FIG. 31, the processing from step S3101 to step S3108 is scaling processing. Further, the process from step S3109 to step S3113 is a smoothing process.

  In the flowchart of FIG. 31, the arithmetic expression for the scaling process is not limited to the above expression.

  Next, the dissimilarity calculation process (step S3007) of the biological parameter determination device will be described with reference to the flowchart of FIG.

  (Step S3201) The dissimilarity calculation unit 2908 reads out information on the arithmetic expression for the dissimilarity stored in a recording medium (not shown).

  (Step S3202) The absolute value difference information acquisition unit 29081 performs initialization processing. The initialization process is a process for substituting 1 into the counter i. Further, this is a process of setting the value of the variable that is the basis for calculating the degree of difference to “0”. Here, there is an absolute value difference information variable that is a variable for storing absolute value difference information.

  (Step S3203) The absolute value difference information acquisition unit 29081 determines whether or not the i-th value exists in the action potential information and / or the experimental action potential information acquired by the action potential information acquisition unit 2905. The action potential information or / and experimental action potential information acquired by the action potential information acquisition unit 2905 is a set of pairs of action potential values and time or time information.

  (Step S3204) Absolute value difference information acquisition means 29081 acquires the i-th value (action potential) of the two action potential information. The two pieces of action potential information are action potential information acquired by the action potential information acquisition unit 2905 and experimental action potential information.

  (Step S3205) The absolute value difference information acquisition unit 29081 determines whether or not the point corresponding to the i-th value acquired in step S3204 is a point acquired as RMP. If it is a point acquired as RMP, go to step S3206, and if it is not a point acquired as RMP, go to step S3209.

  (Step S3206) The absolute value difference information acquisition unit 29081 determines whether or not the point corresponding to the i-th value acquired in step S3204 is the point acquired as the first RMP. If it is a point acquired as the first RMP, go to step S3207, and if it is not a point acquired as the first RMP, go to step S3209.

  Here, the absolute value difference information acquisition unit 29081 may calculate the difference between the two action potential information RMPs.

  (Step S3207) The absolute value difference information acquisition unit 29081 calculates the absolute value of the difference between the i-th values of the two action potential information. Then, the absolute value difference information acquisition unit 29081 temporarily stores the calculated value.

  (Step S3208) The absolute value difference information acquisition unit 29081 increments the counter i by one. The process returns to step S3203.

  (Step S3209) The absolute value difference information acquisition unit 29081 acquires the absolute value of the difference between the i-th values of the two action potential information acquired in Step S3204.

  (Step S3210) The absolute value difference information acquisition unit 29081 adds the value calculated in step S3209. Go to step S3208.

  (Step S3211) The change difference information acquisition unit 29082 calculates time differentiation of two pieces of action potential information.

  (Step S3212) The change difference information acquisition unit 29082 performs initialization processing. The initialization process is a process for substituting 1 into the counter i. Further, this is a process of setting the value of the variable that is the basis for calculating the degree of difference to “0”. Here, there is a change difference information variable, which is a variable for storing change difference information, as a variable for calculating the degree of difference.

  (Step S3213) The change difference information acquisition unit 29082 determines whether or not the i-th value exists in the action potential information and / or the experimental action potential information acquired by the action potential information acquisition unit 2905. If the i-th value exists, the process goes to step S3214. If the i-th value does not exist, the process goes to step S3217.

  (Step S3214) The change difference information acquisition unit 29082 acquires time differential values of the two action potential information corresponding to the i-th value, and calculates information regarding the difference between the two time differential values. Information on the difference between the values of these two time derivatives is also called the rate of change.

  (Step S3215) The change difference information acquisition unit 29082 adds the change rate (value) acquired in step S3214.

  (Step S3216) The change difference information acquisition unit 29082 increments the counter i by one. The process returns to step S3212.

  (Step S3217) The difference calculation unit 2908 calculates the difference by substituting the parameter into the arithmetic expression read in step S3201. Here, the parameter is a value calculated in step S3207, step S3210, or step S3215.

  (Step S3218) The difference calculation unit 2908 stores the difference calculated in step S3217 in the recording medium.

  In the flowchart of FIG. 32, the processes in steps S3202 to S3210 are processes for calculating absolute value difference information. Among the processes for calculating the absolute value difference information, the processes in steps S3206 and S3207 are processes for obtaining RMP difference information (hereinafter, referred to as “stationary membrane distance information” as appropriate), and in step S3209. The process of step S3210 is a process of acquiring information on the difference between the repolarization phases (hereinafter referred to as “repolarization phase distance information” as appropriate). Further, the processes in steps from step S3211 to step S3216 are processes for calculating change difference information. In the flowchart of FIG. 32, when calculating the difference, absolute value difference information and change difference information are calculated, and the difference is calculated based on these two pieces of information (values). However, the difference degree may be calculated using only one of the absolute value difference information and the change difference information. Further, the absolute value difference information is calculated using the stationary membrane distance information and the repolarization phase distance information, but only the repolarization phase distance information may be used. Further, absolute value difference information may be calculated for all counters i without being separated into stationary membrane distance information and repolarization distance information.

32, in step S3201, the parameter set selection unit 2902 arbitrarily selects one biological parameter set from one or more biological parameter sets. However, the parameter set selection unit 2902 may select one biological parameter set by the following algorithm. This algorithm is, for example, a simple bisection method described below. A conceptual diagram of the simple bisection method is shown in FIG. The simple two-division method divides each of n parameter value ranges constituting the previous search range into two parts, and 2 ⁿ search ranges each having a value range of a combination of the n parameter values divided into two. This is a method of acquiring and narrowing the search range with the search range having the smallest parameter set as the previous search range among the representative parameter sets of the 2 ⁿ search ranges. More specifically, the algorithm of the simple bisection method will be described with reference to the flowcharts of FIGS.

  (Step S3401) Search range determining means 29021 reads information on the search range of each parameter. It is assumed that the initial value of the search range for each parameter is determined in advance and stored in a recording medium.

(Step S3402) Search range determination means 29021 acquires a value obtained by dividing the search range of each parameter into two from the search range information of each parameter read in step S3401. For example, if the range of one parameter is “1 to 10”, the search range determination unit 29021 divides the range into “1 to 5” and “6 to 10”, for example. Search range determination means 29021 divides the range into two for all parameters (n). Then, the search range determination unit 29021 divides all n parameters into two, and obtains “2 ⁿ ” search ranges that are combinations thereof.

  (Step S3403) The parameter set selection means 29002 substitutes 1 for the counter i.

  (Step S3404) The parameter set selection unit 29022 determines whether there is an i-th search range. If there is an i-th search range, go to step S3405, and if there is no i-th search range, go to step S3407.

  (Step S 3405) The parameter set selection unit 29022 obtains an optimal solution for the i-th search range. The optimal solution is a biological parameter set with the smallest difference. An algorithm for obtaining an optimal solution will be described with reference to the flowchart of FIG.

  (Step S 3406) The parameter set selection unit 29022 increments the counter i by 1. The process returns to step S3404.

  (Step S3407) The parameter set selection unit 29022 acquires the solution (biological parameter set) having the smallest difference among the differences of the optimal solutions obtained in the search range of (i-1).

  (Step S3408) The parameter set selection unit 29022 acquires information on the search range of each parameter constituting the biological parameter set selected in step S3407.

(Step S3409) The parameter set selection unit 29022 determines whether or not to end the process. If the process ends, the process goes to step S3410, and if not, the process goes to step S3411. Note that whether or not to end the process is a determination whether or not all the parameter sets in the 2 ⁿ search ranges have been acquired. If all the parameter sets have been acquired, the process ends. You may judge. Also, the user may determine whether to end the process. That is, the user may specify and select a parameter set for only the necessary search range, and the process may be completed when the parameter set for the selected search range is acquired. In such a case, there are provided means for prompting the user to select (means for displaying a menu or input screen for selection on the screen) and means for accepting the user's input.

  (Step S3410) The parameter set selection unit 29022 outputs the biological parameter set (optimal solution) acquired in step S3407. The process ends.

  (Step S3411) The parameter set selection unit 29022 performs parameter selection processing recursively.

  Note that the flowchart of FIG. 34 is a recursive process.

  Next, an algorithm for obtaining an optimal solution will be described using the flowchart of FIG. In the flowchart of FIG. 35, the description of the same steps as those of the flowchart of FIG. 30 is omitted.

  (Step S3501) The parameter set selection unit 29022 acquires n types of parameter sets for searching for the optimum solution within the search range. The n types of parameter sets may be input by the user or automatically determined within the search range. As an algorithm to be automatically determined, for example, when the search range of a certain parameter is “4 to 6”, an algorithm that acquires “4”, “5”, and “6” at equal intervals of “1” may be used.

  (Step S3502) The parameter set selection unit 29002 substitutes 1 for the counter i.

  (Step S3503) The parameter set selection unit 29022 determines whether or not “i <= n”. If “i <= n”, go to step S3504, and if “i <= n”, go to step S3506.

  (Step S3504) The parameter set selection unit 29002 reads the i-th parameter set out of n types. Go to step S3002.

  (Step S 3505) The parameter set selection unit 29022 increments the counter i by 1.

  (Step S3506) The parameter set selection unit 29002 acquires a parameter set having the smallest difference among the n types of parameter sets. Return to upper function.

  Next, an algorithm for selecting another biological parameter set performed by the parameter set selection unit 2902 will be described. This algorithm is called a range reduction method. A conceptual diagram of the range reduction method is shown in FIG. In FIG. 36, the search range (1), (2), (3) is narrowed to half. The range reduction method holds information on the range of values of each parameter constituting the previous two or more parameter sets, and among the two or more parameter sets, the parameter set with the smallest difference in the previous time is the center. This is a method of narrowing the search range by setting a half of the value range of each parameter as the next search range.

  A specific range reduction algorithm will be described with reference to the flowchart of FIG.

  (Step S3701) Search range determination means 29021 reads the previous search range of each parameter. When there is no previous time (first time), the search range of each parameter stored in advance is read.

  (Step S3702) Search range determination means 29021 reads the optimal solution. The optimum solution is information stored in advance in a recording medium or input by the user. The optimal solution is a biological parameter set.

  (Step S 3703) The search range determining means 29021 calculates half of the width of the search range read out in step S 3701 for each parameter, and uses the optimum solution acquired in step S 3702 as the center point and the width obtained by halving each parameter. Is calculated for each parameter. However, the range is calculated so as not to exceed the initially stored range or the search range specified by the user. If it exceeds the initially stored range or the search range specified by the user, the range including the center point of the optimum solution is calculated while maintaining the range of the width half of each parameter. Go to step S3405.

  (Step S 3704) The parameter set selection unit 29022 determines whether or not to end the process. If the process is to end, go to step S3705, and if the process is not to end, go to step S3706. Whether or not to end the process may be determined in advance by the number of loops of this process, or by an instruction from the user.

  (Step S3705) The parameter set selection unit 29022 outputs the optimal solution calculated in step S3405. The process ends.

  (Step S3706) The parameter set selection unit 29022 performs parameter set selection processing recursively.

  Hereinafter, a specific operation of the biological parameter determination apparatus in the present embodiment will be described.

  First, the biological parameter determination apparatus holds information on ranges and step sizes that can be taken by each biological parameter in FIG. 38 for each biological parameter by means not shown. Here, the biological parameters are exemplified by parameters identified by “IKr”, “IK1”, and “IKs”. Also, in FIG. 38, the biological parameter “IKr” indicates that “0.0, 0.1, 0.2,... 4.8, 4.9, 5.0” can be taken. “IKr” is a current flowing through a rapid activation delayed rectifier potassium channel (Kr channel), “IK1” is a current flowing through an inward rectifying potassium channel (K1 channel), and “IKs” is a slow activation delay. This is a current flowing through a rectifying potassium channel (Ks channel).

  Then, the biological parameter determination device generates a biological parameter set based on the range of each biological parameter shown in FIG. 38 and the step size information by means not shown, and accumulates it in the biological parameter set storage unit 2901. A biological parameter set management table for managing such biological parameter sets is shown in FIG. In FIG. 39, one or more biometric parameter sets (table records) including biometric parameter values such as “ID” and “IKr”, “IK1”, and “IKs” are managed. “ID” is information for identifying a record and exists for record management in the table.

  That is, this biological parameter determination device is a device that acquires a biological parameter set within an allowable range from the biological parameter set of FIG.

  FIG. 40 is data representing the experimental action potential information stored in the experimental action potential information storage unit 2906 as a graph. The experimental action potential information is a set of information having a pair of time (ms) and potential (mV), and is a set of action potential information as a result of animal experiments.

  First, the parameter set selection unit 2902 includes the biological parameter set “IKr: 0.0, IK1: 0.20, IKs: 1.00,. ... ”Is read out.

  Next, the simulation execution unit 2903 receives the one biological parameter set selected by the parameter set selection unit 2902, simulates the heart activity, and sets action potential information, which is information indicating the heart action potential, for a predetermined time. Obtain and output at intervals.

  Next, the steady state determination unit 2904 determines whether or not the activity state of the heart to be simulated has become a steady state from the change in the two or more action potential information of the simulation result in the simulation execution unit 2903, and enters the steady state. Action potential information in the case of becoming. The graph in which the action potential information in the steady state is displayed in a graph has a shape similar to that in FIG.

  Next, the action potential information acquisition unit 2905 extracts action potential information that is an output of the simulation execution unit 2903 when it is determined that the activity state of the heart to be simulated has become a steady state.

  FIG. 41 is a diagram in which a graph of experimental action potential information (Experiment data) and a graph of action potential information (Simulation) that is an output of the simulation execution unit 2903 are overlapped. In FIG. 41, the vertical axis represents potential (mV) and the horizontal axis represents time (msec).

Next, the preprocessing unit 2907 reads the experimental action potential information stored in the experimental action potential information storage unit 2906 on the memory and performs preprocessing. That is, the scaling means 29907 of the preprocessing unit 2907 calculates the average value (V _SRMP ) and the maximum value (V _SH ) of the resting membrane potential indicated by the action potential information of the simulation result acquired by the action potential information acquisition unit 2905. The action potential information is acquired from the value. Then, the scaling means 29071 obtains the average value (V _ERMP ) and the maximum value (V _EH ) of the resting membrane potential in the experimental action potential information. Next, the scaling means 29071 sets the maximum value (V _EH ) of the experimental action potential information to the maximum value (V _SH ) and the average value of the resting membrane potential (V _ERMP ) to the average value of the resting membrane potential (V _SRMP ). Change to The scaling means 29071 also scales the data at other points so that the maximum value, the resting membrane potential, matches the value of the experimental action potential. As a result, as shown in FIG. 42, the potential width of the experimental action potential information becomes the same as the potential width of the action potential information of the simulation result. By the scaling process, the experimental data (Experiment data) can be scaled in the vertical direction so that the maximum amplitude of the action potential matches the simulation, and an appropriate difference can be calculated. In FIG. 42, the vertical axis represents potential (mV) and the horizontal axis represents time (msec).

  Next, the smoothing unit 29072 performs the following smoothing process on the experimental action potential information subjected to the scaling process and the action potential information obtained as a result of the simulation. That is, the smoothing means 29072 calculates a moving average value for all points (information consisting of potential (mV) and time (ms)) of the experimental action potential information subjected to the scaling process, and calculates the value. The point value of the experimental action potential information. Similarly, the smoothing means 29072 calculates a moving average value for all points (information consisting of potential (mV) and time (ms)) of the action potential information of the simulation result, and calculates the value as a simulation. The resulting action potential information points. FIG. 43 shows action potential information after the smoothing process. The noise of the experimental data can be removed by the smoothing process. FIG. 43 shows the result of removing noise from experimental data by taking a moving average stepwise. The upper line (a) is a data group obtained by taking a moving average at intervals of 10.0 msec. The band (b) on the outer side of the line is a data group obtained by taking a moving average at intervals of 1.0 msec. Further, the outer band (c) is a data group obtained by taking a moving average at intervals of 0.1 msec. Further, (d) is raw data before taking a moving average.

  FIG. 44 shows the waveforms of the experimental action potential waveform before the drug administration (Before) and after the drug administration (After) when the above scaling process and smoothing process are completed. In FIG. 44, the vertical axis represents potential (mV) and the horizontal axis represents time (msec).

  Next, the dissimilarity calculation unit 2908 is a simulation result, and calculates the dissimilarity between the acquired action potential information and the experimental action potential information that is the result of preprocessing as follows.

First, the dissimilarity calculation unit 2908 reads out information on the arithmetic expression for the dissimilarity stored in a recording medium (not shown). Here, it is assumed that the formula for calculating the degree of difference is, for example, Formula 10. FIG. 45 is a diagram showing the correspondence between the calculation formula for calculating the degree of difference and the original information, and is a diagram showing the concept of the degree of difference.

  In Equation 10, parameters A, B, and C are weighting coefficients, and are usually positive values. Also, the first and second terms to which the parameters A and B are applied are terms for calculating absolute value difference information, which is the distance between the action potential information of the simulation result and the experimental action potential information. The first term is a term for calculating resting membrane distance information which is a distance of resting membrane potential (RMP). The second term is a term for calculating repolarization phase distance information that is the distance of the repolarization phase. The third term on which C is applied is a term for calculating change difference information calculated by normalizing the distance between the action potential information of the simulation result and the experimental action potential information.

In Equation 10, RMP _model is an average value of resting membrane potential (RMP) in action potential information of simulation results, and RMP _exp is an average of resting membrane potential (RMP) in experimental action potential information. A value is preferred.

In Equation 10, Vm _model is the action potential of the simulation result, and Vm _exp is the action potential of the experimental action potential information. Phase 2 indicates the point (time and membrane potential) at which the membrane potential of the action potential reaches the maximum value. Phase 3 indicates a point (phase 3 end point) where repolarization (approaching the resting membrane potential) can be completed from the point where the membrane potential of the action potential reaches the maximum value (starting point of Phase 2) to the resting membrane potential. Therefore, calculating the sum of the absolute value difference information of Vm from Phase 2 to Phase 3 in the second term of Equation 1 means that the action potential information (time, membrane potential) included from the start point of Phase 2 to the end point of Phase 3 It means calculating absolute value difference information.

Furthermore, in Equation 10, dVm / dt _model is a value obtained by differentiating Vm _model with respect to time, and dVm / dt _exp is a value obtained by differentiating Vm _exp with respect to time.

  Then, the absolute value difference information acquisition means 29081 inputs the action potential information of the simulation result and the experimental action potential information into the first term and the second term of Equation 1, calculates the absolute value difference information, and the recording medium Temporarily store in. Details of this processing are as described in the flowchart of FIG. Next, the change difference information acquisition unit 29082 inputs the action potential information of the simulation result and the experimental action potential information into the third term of Equation 10, calculates the change difference information, and temporarily stores it in the recording medium.

  Then, the dissimilarity calculation unit 2908 reads the calculation results of the first term, the second term, and the third term of Equation 10 from the recording medium, calculates their sum, and obtains the dissimilarity. The smaller the difference is, the more similar the action potential information of the simulation result and the experimental action potential information are.

  Next, the allowable parameter set determination unit 2909 is a biological parameter set in which the selected one biological parameter set (the biological parameter set with “ID = 1” in FIG. 39) is within the allowable range using the calculated degree of difference. Determine whether or not. Preferably, the allowable parameter set determination unit 2909 calculates a determination coefficient using the response surface method, and determines whether the biological parameter set is within the allowable range based on whether the determination coefficient is within the allowable range. It is. When the response surface method is used, there are two or more pairs of the biological parameter set stored in the biological parameter set storage unit 2901 and the degree of difference (difference degree calculated by the difference degree calculation unit 2908). Necessary to calculate. Further, if the applied curved surface method is used, it is preferable that a biological parameter set predicted as an optimal solution can be calculated as a calculation result of the response curved surface method.

  Then, the allowable parameter set output unit 2911 outputs the biological parameter set within the allowable range. Here, the output includes concepts such as display on a display, storage in a recording medium, and transmission to an external device.

  Next, the biological parameter determination apparatus performs the same process as described above on the biological parameter set after “ID = 2” in FIG. 39 to obtain a biological parameter set in an allowable range.

  In the above specific example, when selecting a biological parameter set within the allowable range, all the biological parameter sets that are candidates are processed, but the parameter selection method (simple bisection method or range reduction method shown in FIGS. 34 and 37) is used. ) To obtain an optimal biological parameter set efficiently.

  As described above, according to this embodiment, a biological parameter set can be obtained with high accuracy from experimental action potential information that is action potential information as a result of animal experiments.

  Further, according to the present embodiment, since it is determined whether or not one biological parameter set is an acceptable biological parameter set by using the response surface method, it is possible to easily and quickly perform the operation without bothering humans. In addition, a biological parameter set can be obtained with high accuracy.

  In the present embodiment, the time differentiation (dVm / dt) information of the action potential information is used when calculating the degree of difference. This is because the information on the time derivative (dVm / dt) of action potential information shows a characteristic change due to changes in each current system (FIG. 46). This is because it is possible to evaluate the direct influence on each current system. FIG. 46 shows changes in the dVm / dt waveform when the parameters of each channel are changed from 0 to 200%. Specifically, FIG. 46A shows a change in the dVm / dt waveform of the biological parameter “ICaL”, FIG. 45B shows a change in the dVm / dt waveform of the biological parameter “IKr”, and FIG. The dVm / dt waveform change of the biological parameter “IK1”, FIG. 46D shows the dVm / dt waveform change of the biological parameter “IKs”.

  Further, using the biological parameter determination device in the present embodiment, if the biological parameter set is obtained from the action potential information before drug injection, and the biological parameter set is obtained from the action potential information after drug injection, The effects and side effects of drug input can be obtained quantitatively. Note that the effect of drug injection is obtained from the difference in biological parameter set before and after drug injection.

  In addition, another device or software may accept the biological parameter set that is the output of the biological parameter determination device according to the present embodiment. For example, the biological parameter sets acquired by the biological parameter determination apparatus according to the present embodiment are the parameter set of the animal before drug administration and the parameter set of the animal after drug administration stored in the parameter set storage unit 101 before and after drug administration. At least one of the parameter sets. In such a case, the arrhythmia risk evaluation system shown in FIG. 47 is configured. The arrhythmia risk evaluation system includes a biological parameter determination device and an arrhythmia risk evaluation device. The arrhythmia risk evaluation apparatus is any one of the arrhythmia risk evaluation apparatuses described in the first to third embodiments.

  Furthermore, the software that implements the biological parameter determination apparatus in the present embodiment is a program as described below. That is, the program stores a parameter set selection step for selecting one biological parameter set from two or more biological parameter sets stored in the computer, and one biological parameter set selected in the parameter set selection step. From the simulation execution step of obtaining the action potential information, which is information indicating the action potential of the heart, and the change in the action potential information that is the output in the simulation execution step. Output in the simulation execution step when it is determined in the steady state determination step that the activity state of the heart to be simulated is in the steady state. Life Action potential information acquisition step for acquiring potential information, action potential information acquired in the action potential information acquisition step, difference calculation step for calculating a difference between stored experimental action potential information, and the difference Using the degree of difference calculated in the calculating step, an allowable parameter set determining step for determining whether or not the one biological parameter set is a biological parameter set in an allowable range; and A control step for instructing the parameter set selection unit to select one biological parameter set from unselected biological parameter sets when it is determined that the biological parameter set is not within an allowable range; and In the allowable parameter set determining step, the one biological parameter set is permitted. If it is determined that the biological parameter set in the range, a program, for executing the allowable parameter set output step of outputting the biological parameter set of the one.

  The difference calculation step in the program is information relating to a difference between an action potential value indicated by the action potential information acquired in the action potential information acquisition step and an action potential value indicated by the experimental action potential information. Absolute value difference information acquisition step for acquiring absolute value difference information, action potential change value indicated by action potential information acquired in action potential information acquisition step, and action potential change value indicated by experimental action potential information A change difference information acquisition step for acquiring change difference information that is information relating to a difference between the action potential information acquired by the action potential information acquisition unit using the absolute value difference information and the change difference information, and the experiment. It is preferable to include a difference degree calculating step for calculating a difference degree from the action potential information.

  The program further causes a computer to perform a preprocessing that is a process of removing noise from the experimental action potential information to obtain new experimental action potential information, and the difference degree calculating step includes: It is preferable to calculate the difference between the action potential information acquired in the action potential information acquisition step and the experimental action potential information acquired in the preprocessing step.

  In the preprocessing step in the program, the experimental action potential is set such that the maximum value of the potential indicated by the action potential information acquired in the action potential information acquisition step matches the maximum value of the potential indicated by the experimental action potential information. It is preferable to provide a scaling step for scaling the potential value indicated by the information.

  In the preprocessing step in the program, a moving average is calculated for the potential value indicated by the experimental action potential information, and the calculated moving average value is used as a potential value indicated by the new experimental action potential information. It is preferable to include a smoothing step.

  In addition, the parameter set selection step in the program limits the range of the one or more parameters using a range of values of the one or more parameters that constitute the two or more sets of biological parameter sets. A search range determining step for determining a search space having the information of the selected range, and a parameter set selecting step for selecting one parameter set from one or more parameter sets existing in the search space determined in the search range determination step It is suitable to have.

Further, the search range determining step in the above program divides the value range of n parameters constituting the previous search range into two, and 2 ⁿ having a value range of the combination of the n parameters divided into two. The search range is acquired and the search range having the parameter set with the smallest difference among the representative parameter sets of the 2 ⁿ search ranges is set as the previous search range, and the search range is narrowed. That is preferred.

  Further, the search range determination step in the program holds information on the value ranges of the parameters constituting the previous two or more parameter sets, and the most different degree of difference among the previous two or more parameter sets. Centering on a small parameter set, it is preferable to narrow the search range by setting a half of the range of each parameter value as the next search range.

  Further, in the program, whether the allowable parameter set determination step is a biological parameter set in which the one biological parameter set is within an allowable range using a response surface method with respect to the difference calculated in the difference calculation step. It is preferred to determine whether or not. Furthermore, an optimal solution calculated from the response surface method and a predicted biological parameter set may be selected.

  In each of the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or by distributed processing by a plurality of devices. May be.

  FIG. 48 shows the external appearance of a computer that executes the program described in this specification to realize the arrhythmia risk evaluation apparatus or the biological parameter determination apparatus according to various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 48 is an overview of the computer system 340, and FIG. 49 is a block diagram of the computer system 340.

  48, a computer system 340 includes a computer 341 including a FD (Flexible Disk) drive and a CD-ROM (Compact Disk Read Only Memory) drive, a keyboard 342, a mouse 343, and a monitor 344.

  49, in addition to the FD drive 3411 and the CD-ROM drive 3412, the computer 341 includes a CPU (Central Processing Unit) 3413, a bus 3414 connected to the CPU 3413, the CD-ROM drive 3412, and the FD drive 3411, and a boot. A ROM (Read-Only Memory) 3415 for storing a program such as an up program, and a RAM (Random Access Memory) connected to the CPU 3413 for temporarily storing instructions of an application program and providing a temporary storage space 3416 and a hard disk 3417 for storing application programs, system programs, and data. Although not shown here, the computer 341 may further include a network card that provides connection to the LAN.

  A program for causing the computer system 340 to execute the functions of the arrhythmia risk evaluation apparatus and the biological parameter determination apparatus according to the above-described embodiment is stored in the CD-ROM 3501 or the FD 3502 and stored in the CD-ROM drive 3412 or the FD drive 3411. It may be inserted and further transferred to the hard disk 3417. Alternatively, the program may be transmitted to the computer 341 via a network (not shown) and stored in the hard disk 3417. The program is loaded into the RAM 3416 at the time of execution. The program may be loaded directly from the CD-ROM 3501, the FD 3502, or the network.

  The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 341 to execute the functions of the arrhythmia risk evaluation apparatus according to the above-described embodiment. The program only needs to include an instruction portion that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 340 operates is well known and will not be described in detail.

  In the above program, the step of transmitting information does not include processing performed by hardware, for example, processing performed by a modem or an interface card in the transmission step (processing performed only by hardware).

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the arrhythmia risk evaluation apparatus according to the present invention has an effect of being able to evaluate the arrhythmia risk of a drug to a living organism such as a human, and is useful as an arrhythmia risk evaluation system and the like.

FIG. 3 is a block diagram showing the configuration of the arrhythmia risk evaluation apparatus in the first embodiment. The figure which shows the example of the parameter set before and after the drug administration The figure which shows the experimental result such as the time change of the waveform of the myocardial action potential and contraction force The figure which shows the example of the same action potential information The figure which shows the example of the information which the action potential information acquisition means acquires Diagram showing coefficients specific to the same cell type The figure which shows the concept of the QT interval model information Concentric ECG The figure which shows the simulation model which arranged the cardiomyocyte model in 2 dimensions Flowchart explaining operation of the arrhythmia risk evaluation device Flow chart explaining the arrhythmia determination process The figure which shows the example of the parameter set before and after the drug administration The figure which shows the example of the same action potential information Diagram showing an example of a concentric electrocardiogram The figure which shows the example of the result of the same spiral wave simulation Block diagram of arrhythmia risk evaluation apparatus in Embodiment 2 Flowchart explaining operation of the arrhythmia risk evaluation device Flow chart explaining the operation of the cardiomyocyte simulation Flow chart explaining the arrhythmia determination process A figure showing an example of the acquired ion concentration Figure showing the same output example Block diagram of arrhythmia risk evaluation apparatus in Embodiment 3 Flowchart explaining operation of the arrhythmia risk evaluation device Flowchart explaining the output information configuration process The figure which shows the information of the action potential waveform The figure which shows the information of the action potential waveform The figure which shows the example of the output information The figure which shows the example of the information of the action potential waveform Block diagram of biological parameter determination apparatus in Embodiment 4 A flowchart for explaining the operation of the biological parameter determination device Flow chart for explaining the operation of the pre-process Flow chart explaining the difference calculation processing Conceptual diagram of the simple bisection method Flow chart explaining the simple bisection method Flow chart explaining the simple bisection method Conceptual diagram of the same range reduction method Flowchart explaining the same range reduction method The figure which shows the information of the same body parameter The figure which shows the same body parameter set management table Graphical representation of the experimental action potential information Overlapping graph of action potential information (simulation) and experiment action potential information Diagram showing the corrected graph Diagram showing action potential information after the smoothing process The figure which shows action potential information after the same scaling processing and smoothing processing are completed The figure which shows the correspondence of the calculation formula which calculates the degree of difference, and its original information The figure which shows the dVm / dt waveform change at the time of changing the parameter of each same channel Block diagram of the arrhythmia risk assessment system Overview of the computer system Block diagram of the computer system The figure which shows the model which calculates IKr in an animal The figure which shows the model which calculates IKr in a human

Explanation of symbols

100, 1601 Reception unit 101 Pre- and post-drug parameter set storage unit 102 Animal drug effect information acquisition unit 103 Conversion source information storage unit 104 Drug effect conversion unit 105, 1602 Cardiomyocyte simulation unit 106 Arrhythmia information acquisition unit 107, 1603 Arrhythmia determination unit 108, 1604 2208 Output information component 109, 1605 Output unit 1051 Action potential model information storage means 1052 Action potential information acquisition means 1053 QT interval model information storage means 1054 QT interval simulation means 1055 Spiral wave model information storage means 1056 Spiral wave simulation means 1071 Threshold value Storage unit 1072 Comparison unit 1073 Arrhythmia determination unit 2201 Fixed parameter information storage unit 2202 Next target biomedical efficacy information acquisition unit 2203, 291 Control unit 2901 Biological parameter set storage unit 2902 Parameter set selection unit 2903 Simulation execution unit 2904 Steady state determination unit 2905 Action potential information acquisition unit 2906 Experimental action potential information storage unit 2907 Preprocessing unit 2908 Difference calculation unit 2909 Allowable parameter set determination unit 2911 Allowable parameter set output unit 16021 Ion concentration calculation model information storage unit 16022 Ion concentration calculation unit 16023 Cell membrane potential calculation model information storage unit 16024 Cell membrane potential calculation unit 16031 Ion concentration threshold storage unit 16032 Ion concentration arrhythmia determination unit 16033 Cell membrane potential threshold storage unit 16034 Cell membrane potential arrhythmia determination means 18071 Threshold storage means 18072 Comparison means 18073 Arrhythmia determination means 29021 Search range Determining means 29022 parameter set selection means 29071 scaling means 29072 smoothing means 29,081 absolute value difference information acquiring means 29082 change difference information acquiring means 29083 dissimilarity calculating means

Claims (18)

An animal medicinal effect information storage unit storing animal medicinal effect information that is information on medicinal effects on animals,
A conversion source information storage unit that stores conversion source information that is information for converting the animal medicinal information into target biomedical effect information that is medicinal effect information on a target living body to be simulated;
A medicinal effect conversion unit that converts the animal medicinal effect information into target biomedical effect information using the conversion source information;
Using the target biomedical efficacy information, a cardiomyocyte simulation unit that acquires cardiomyocyte operation information that is information about the behavior of the cardiomyocytes of the organism;
Using the cardiomyocyte operation information, an arrhythmia information acquisition unit that acquires arrhythmia information that is information on side effects of arrhythmia on the heart of the drug,
An output information component that constitutes output information that is information that is output using the arrhythmia information acquired by the arrhythmia information acquisition unit;
An arrhythmia risk evaluation apparatus comprising an output unit for outputting the output information. A parameter set storage unit before and after drug administration storing a parameter set before and after drug administration, which is a set of a parameter set of an animal before drug administration and a parameter set of an animal after drug administration;
Using the parameter set before and after the drug administration, further comprising an animal drug effect information acquisition unit for acquiring animal drug effect information that is information about drug effects on animals,
The arrhythmia risk evaluation apparatus according to claim 1, wherein the animal drug efficacy information in the animal drug efficacy information storage unit is animal drug efficacy information acquired by the animal drug efficacy information acquisition unit. A fixed parameter information storage unit that stores fixed parameter information that is parameter information for fixing values;
When the arrhythmia information acquired by the arrhythmia information acquisition unit is information indicating a side effect of arrhythmia greater than or equal to a predetermined value, the parameter value of the fixed parameter information included in the target biomedical efficacy information is fixed, and other parameters A next target biomedical efficacy information acquisition unit that varies the value and acquires new target biomedical efficacy information;
A new target biomedical efficacy information acquired by the next target biomedical efficacy information acquisition unit is provided to the cardiomyocyte simulation unit, and further includes a control unit for acquiring cardiomyocyte operation information,
The output information component is
The arrhythmia risk evaluation apparatus according to claim 1, comprising output information which is information output using the arrhythmia information acquired by the arrhythmia information acquisition unit and the target biomedical effect information acquired by the next target biomedical effect information acquisition unit. . Threshold storage means for storing a threshold for cardiomyocyte movement information;
Comparison means for comparing the cardiomyocyte operation information acquired by the cardiomyocyte simulation unit with a threshold value of the threshold value storage means;
Using the comparison result of the comparison means, further comprising an arrhythmia determination unit including an arrhythmia determination means for determining whether the arrhythmia information is information indicating a side effect of arrhythmia of a predetermined value or more,
The output information component is
The arrhythmia risk evaluation apparatus according to claim 1, wherein output information is configured also using a determination result of the arrhythmia determination unit. The cardiomyocyte simulation unit
Action potential model information storage means for storing action potential model information which is information indicating a calculation formula for acquiring action potential;
Read the action potential model information, using the action potential model information, comprising action potential information acquisition means for acquiring action potential information,
The arrhythmia risk evaluation apparatus according to claim 1, wherein the cardiomyocyte operation information includes the action potential information. The cardiomyocyte simulation unit
QT interval model information storage means for storing QT interval model information which is information indicating a calculation formula for acquiring the QT interval;
Comprising QT interval simulation means for reading the QT interval model information and using the QT interval model information to obtain QT interval information;
The arrhythmia risk evaluation apparatus according to claim 1, wherein the cardiomyocyte operation information includes the QT interval information. The cardiomyocyte simulation unit
Spiral wave model information storage means for storing spiral wave model information which is information for simulating spiral waves;
It further comprises a spiral wave simulation means for reading the spiral wave model information and performing a spiral wave simulation using the spiral wave model information,
The arrhythmia risk evaluation apparatus according to claim 1, wherein the cardiomyocyte operation information includes a simulation result in the spiral wave. A receiving unit that receives target biomedical efficacy information including one or more ion flow rate information that is information about the electric flow rate of one or more ion channels indicating the influence of drug administration;
Using the target biomedical efficacy information, a cardiomyocyte simulation unit that acquires cardiomyocyte operation information that is information about the behavior of the cardiomyocytes of the organism;
An output information constituting unit constituting output information which is information to be output using the cardiomyocyte operation information;
An arrhythmia risk evaluation apparatus comprising an output unit for outputting the output information. A fixed parameter information storage unit that stores fixed parameter information that is parameter information for fixing values;
When the cardiomyocyte operation information is information indicating side effects of arrhythmia of a predetermined value or more, fix the parameter value of the fixed parameter information that the target biomedical effect information has, and change the value of other parameters, Next target biomedical efficacy information acquisition unit for acquiring new target biomedical efficacy information;
A new target biomedical efficacy information acquired by the next target biomedical efficacy information acquisition unit is provided to the cardiomyocyte simulation unit, and further includes a control unit for acquiring cardiomyocyte operation information,
The output information component is
The arrhythmia risk evaluation apparatus according to claim 8, comprising output information which is information output using the cardiomyocyte operation information and the target biomedical effect information acquired by the next target biomedical effect information acquisition unit. The cardiomyocyte simulation unit
Ion concentration calculation model information storage means for storing ion concentration calculation model information which is information of an arithmetic expression for calculating the calcium ion concentration in the sarcoplasmic reticulum uptake site using one or more ion flow rate information as a parameter;
Reading the ion concentration calculation model information, substituting one or more ion flow rate information received by the reception unit into the ion concentration calculation model information, executing an arithmetic expression indicated by the ion concentration calculation model information, and cardiomyocyte operation information The arrhythmia risk evaluation apparatus according to claim 8, further comprising ion concentration calculation means for calculating a calcium ion concentration in the sarcoplasmic reticulum uptake site. The cardiomyocyte simulation unit
Cell membrane potential calculation model information storage means for storing cell membrane potential calculation model information which is information of an arithmetic expression for calculating a cell membrane potential using one or more ion flow rate information as a parameter;
Reading the cell membrane potential calculation model information, substituting one or more ion flow rate information received by the receiving unit into the cell membrane potential calculation model information, executing an arithmetic expression indicated by the cell membrane potential calculation model information, and cardiomyocyte operation information 9. The arrhythmia risk evaluation apparatus according to claim 8, further comprising cell membrane potential calculation means for calculating a cell membrane potential. The cardiomyocyte simulation unit
Ion concentration calculation model information storage means for storing ion concentration calculation model information which is information of an arithmetic expression for calculating the calcium ion concentration in the sarcoplasmic reticulum uptake site using one or more ion flow rate information as a parameter;
Reading the ion concentration calculation model information, substituting one or more ion flow rate information received by the reception unit into the ion concentration calculation model information, executing an arithmetic expression indicated by the ion concentration calculation model information, and cardiomyocyte operation information An ion concentration calculating means for calculating the calcium ion concentration in the sarcoplasmic reticulum uptake site,
A cell membrane potential calculation model information storage means for storing cell membrane potential calculation model information which is information of an arithmetic expression for calculating a cell membrane potential using one or more ion flow rate information as a parameter;
Reading the cell membrane potential calculation model information, substituting one or more ion flow rate information received by the receiving unit into the cell membrane potential calculation model information, executing an arithmetic expression indicated by the cell membrane potential calculation model information, and cardiomyocyte operation information Comprising a cell membrane potential calculating means for calculating a cell membrane potential which is
The output information component is
The calcium ion concentration in the sarcoplasmic reticulum uptake site calculated by the ion concentration calculating means is a value on the first axis, and the cell membrane potential calculated by the cell membrane potential calculating means is on a two-dimensional plane having the second axis value. The value is plotted, and output information is configured to clearly indicate a threshold value for the calcium ion concentration in the one or more sarcoplasmic reticulum uptake sites and a threshold value for the one or more cell membrane potentials on the two-dimensional plane. Arrhythmia risk assessment device. An arrhythmia risk evaluation system comprising the arrhythmia risk evaluation apparatus according to any one of claims 1 to 12 and a biological parameter determination apparatus,
The biological parameter determination device includes:
A biological parameter set storage unit that stores two or more biological parameter sets having one or more biological parameters that are biological parameters;
A parameter set selection unit for selecting one biological parameter set from the two or more biological parameter sets;
A simulation execution unit that inputs the one biological parameter set selected by the parameter set selection unit, simulates the activity of the heart, and obtains action potential information that is information indicating the action potential of the heart;
An action potential information acquisition unit for acquiring the action potential information;
An experimental action potential information storage unit storing experimental action potential information which is action potential information of the result of animal experiments;
The difference calculation unit that calculates the difference between the action potential information acquired by the action potential information acquisition unit and the experimental action potential information;
Using the dissimilarity calculated by the dissimilarity calculating unit, an allowable parameter set determining unit that determines whether the one biological parameter set is a biological parameter set within an allowable range;
When the allowable parameter set determination unit determines that the one biological parameter set is not a biological parameter set within an allowable range, the biological parameter set is selected from the unselected biological parameter sets by the parameter set selection unit. A control unit instructing to
When the allowable parameter set determining unit determines that the one biological parameter set is a biological parameter set within an allowable range, the allowable parameter set determining unit includes an allowable parameter set output unit that outputs the one biological parameter set,
The biological parameter set output by the allowable parameter set output unit includes an animal parameter set before drug administration stored in a parameter set storage unit before and after drug administration of the arrhythmia risk evaluation apparatus, and an animal parameter after drug administration. An arrhythmia risk assessment system that is a parameter set of at least one of the sets. On the computer,
A medicinal effect conversion step of reading out stored medicinal efficacy information, reading out stored conversion source information, using the animal medicinal efficacy information and the conversion source information, obtaining target biomedical efficacy information, and placing it on a memory; ,
A cardiomyocyte simulation step of reading out the target biomedicine information, using the target biomedicine information, obtaining cardiomyocyte operation information that is information on the behavior of a living cardiomyocyte, and placing the information on a memory;
Reading the cardiomyocyte operation information, using the cardiomyocyte operation information, acquiring arrhythmia information that is information on the side effects of arrhythmia on the heart of the drug, and arranging the arrhythmia information on the memory; and
Read out the arrhythmia information acquired in the arrhythmia information acquisition step, and configure output information that is output information using the arrhythmia information, output information configuration step,
A program for executing an output step for outputting the output information. On the computer,
When the arrhythmia information acquired in the arrhythmia information acquisition step is information indicating side effects of arrhythmia that is greater than or equal to a predetermined value, the parameter value of the fixed parameter information included in the target biomedical efficacy information is fixed, and other parameters Fluctuating the value, acquiring new target biomedical efficacy information, and placing the next target biomedical efficacy information on the memory; and
Read out the new target biomedical efficacy information acquired in the next target biomedical efficacy information acquisition step, give the target biomedical efficacy information to the cardiomyocyte simulation step, and further execute a control step of acquiring cardiomyocyte operation information,
In the output information configuration step,
The program according to claim 14, wherein the arrhythmia information acquired in the arrhythmia information acquisition step and the target biomedical effect information acquired in the next target biomedical effect information acquisition step are read to constitute output information. On the computer,
The cardiomyocyte operation information acquired in the cardiomyocyte simulation step and the stored threshold value are read, the cardiomyocyte operation information and the threshold value are compared, and the comparison result is used to determine whether the arrhythmia information is greater than or equal to a predetermined value. Further executing an arrhythmia determination step of determining whether the information indicates a side effect,
In the output information configuration step,
14. The program according to claim 13, wherein the output information is configured also using the determination result of the arrhythmia determination step. On the computer,
An accepting step of receiving target biomedical efficacy information including one or more ion flow rate information that is information about the electric flow rate of one or more ion channels indicating the influence of drug administration;
A cardiomyocyte simulation step of reading the target biomedical effect information received in the receiving step, using the target biomedical effect information, acquiring cardiomyocyte operation information that is information about the behavior of a living cardiomyocyte and storing it in a memory When,
An output information configuration step for configuring the output information that is information to be read out and output using the cardiomyocyte operation information.
A program for executing an output step for outputting the output information. On the computer,
When the cardiomyocyte operation information is information indicating side effects of arrhythmia of a predetermined value or more, fix the parameter value of the fixed parameter information that the target biomedical effect information has, and change the value of other parameters, The next target biomedical effect information acquisition step for acquiring new target biomedical effect information and placing it on the memory;
Read out the new target biomedical efficacy information acquired in the next target biomedical efficacy information acquisition step, give the target biomedical efficacy information to the cardiomyocyte simulation step, and further execute a control step of acquiring cardiomyocyte operation information,
In the output information configuration step,
Claims comprising: outputting the cardiomyocyte operation information and the target biomedical effect information acquired in the next target biomedical effect information acquiring step, and forming output information that is information output using the cardiomyocyte operation information and the target biomedical effect information. Item 18. The program according to Item 17.