JP6610937B2 - Drug effect prediction method - Google Patents

Description

  The present invention relates to a drug effect prediction method and a drug effect prediction system capable of designing a sterilization system and a space design capable of effectively drawing out the effect of the sterilization system.

  Recently, countermeasures against microorganisms such as viruses, fungi, and bacteria have attracted attention regarding the occurrence of pandemics such as new influenza and SARS and bioterrorism. In addition, pharmaceutical and food production facilities are required to have a high level of in-facility environment including countermeasures against microorganisms.

  Conventionally, fumigation treatment using formalin gas has been common in aseptic preparation clean rooms of pharmaceutical factories, but due to carcinogenic problems, it has been replaced by a decontamination system using ozone gas or hydrogen peroxide.

  However, since these drugs use high-concentration solutions, the use of chlorinated drugs with higher versatility is common in facilities such as hospitals and food factories.

As an index for knowing the bactericidal effect of the above-mentioned drugs, in a sterile preparation clean room, etc., dry spore bacteria (10 ⁶ ) are used as a biological indicator (BI), and all the BIs are killed in a certain period. Although defined by ISO (see Non-Patent Document 1), there is no clear standard in a normal room space.

  Moreover, although there exists CT value (concentration time integral value) (refer nonpatent literature 2) based on the law of Chick-Watson as a suppression index of the chemical | drug | medicine with respect to a microbe, evaluation on the uniform diffusion conditions in a target room is possible. There was no general examination in detail.

Therefore, emphasis was placed on the sterilization effect, and the introduction of an overspec sterilization system was common.
ISO 11138-1, Sterilization of health care products, Biological indicators, Part 1: General requirement, 2006 Satoshi Fukuzaki: Theory and practice of cleaning and sterilization using sodium hypochlorite, Cooked food and technology, Vol. 16, No. 1, pp.1-14, 2010

  It is considered that the effect of the countermeasure against microorganisms in the indoor space varies depending not only on the drug to be used and the target microorganism, but also on the condition of the target room, air conditioning conditions, drug spraying conditions, and the like.

  Therefore, if the sterilization effect in the target space can be predicted in detail, it is not wasteful in terms of energy saving and cost, and it is effective to select an appropriate drug concentration and amount, and an appropriate drug spraying (gasification) device. In addition, the design of the sterilization system and the space design that can bring out the effect of the sterilization system become possible.

  However, a detailed method for predicting the bactericidal effect by a drug has not been proposed so far, which has been a problem.

This invention solves the said subject, The chemical | medical agent effect prediction method which concerns on this invention sprays a predetermined chemical | medical agent in the actual installation process which arrange | positions a test microbe to the predetermined position in real space, and the said real space. The verification step for verifying the drug spray effect at the predetermined position based on the test bacteria, and the actual installation step and the verification step are repeated under a plurality of spray conditions, and verification by spray condition due to the difference in spray conditions The effect, the verification effect acquisition step for each spray condition acquired by the relationship between the spray condition and the number of remaining colonies after culture, the air flow analysis step for analyzing the air flow at the predetermined position in the simulation space corresponding to the real space, Based on an air flow analysis step, a concentration time integral value calculation step for calculating the concentration time integral value of the drug, and under the plurality of spray conditions, the air flow analysis step and the concentration time integral value Out repeating the process, the spraying conditions by density time integral value due to the difference in the spray conditions, spray conditions and density time integral value and the spraying condition by density time integral value obtaining step of obtaining the relationship between the spray Conditional validation effect Corresponding the spray condition-specific verification effect acquired in the acquisition step and the spray condition-specific concentration time integrated value acquired in the spray condition acquisition step, the lowest number of colonies remaining after culturing becomes zero to obtain a density time integral value, and the minimum spraying condition acquisition step of the drug in the predetermined position to obtain the effectively acting minimum spray conditions for the test organism, Ri Tona, different real space, different predetermined Position, different test bacteria, set different drugs, the actual installation step, the verification step, the spray condition-specific verification effect acquisition step, the air flow analysis step, the concentration time integral value calculation step, Concentration time integral value acquisition step for each spray condition and the minimum spray condition acquisition step are repeated to acquire a database of minimum spray conditions for different real spaces, different predetermined positions, different test bacteria, and different drugs. It has a database acquisition process .

  The drug effect prediction method according to the present invention refers to the setting step of setting the real space, the predetermined position, the test bacteria, the drug, and the minimum spray condition database acquired in the minimum spray condition database acquisition step, A minimum spray condition calculation step of calculating a minimum spray condition corresponding to the real space, the predetermined position, the test bacteria, and the medicine set in the process.

  The drug effect prediction method and the drug effect prediction system according to the present invention include a minimum spray condition acquisition step of acquiring a minimum spray condition at which the drug effectively acts on the test bacteria at the predetermined position, According to such a drug effect prediction method and drug effect prediction system according to the present invention, it is possible to quantitatively predict the bactericidal effect of a drug, and it is effective from the viewpoint of energy saving and cost (energy saving / appropriate cost) and effective. In addition, the design of the sterilization system and the space design that can bring out the effect of the sterilization system become possible.

  Further, according to the drug effect prediction method and drug effect prediction system according to the present invention, it is possible to propose an effective sterilization system, a design of a target room, and an installation method of indoor fixtures and fixtures in advance.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, it is possible to cope with an appropriate concentration and drug amount without using a large amount or a high concentration drug as before.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, the present invention can be applied to other microbe countermeasures such as mold, if necessary.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, the present invention can be applied to other microbe countermeasures such as mold, if necessary.

It is a figure which shows an example of the computer which performs the drug effect prediction method which concerns on embodiment of this invention, and functions as a drug effect prediction system. It is a figure which shows an example of the space made into object by the chemical | medical agent effect prediction method which concerns on embodiment of this invention. It is a figure which shows an example of the flowchart (drug | medicine prediction process (1)) performed with the chemical | medical agent effect prediction method which concerns on embodiment of this invention. It is an example of the table | surface which shows the verification effect according to spray condition obtained at the verification effect acquisition process according to spray condition of the chemical | medical agent effect prediction method which concerns on embodiment of this invention. It is an example of the table | surface which shows the concentration time integrated value according to spraying conditions obtained at the concentration time integrated value acquisition process according to spraying conditions of the chemical | medical agent effect prediction method which concerns on embodiment of this invention. It is a figure which shows the data structure of the minimum spray condition database. It is a figure which shows an example of the flowchart (drug | medicine prediction process (2)) performed with the chemical | medical agent effect prediction method which concerns on embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram illustrating an example of a computer that executes a drug effect prediction method according to an embodiment of the present invention and functions as a drug effect prediction system. In FIG. 1, 10 is a system bus, 11 is a CPU (Central Processing Unit), 12 is a RAM (Random Access Memory), 13 is a ROM (Read Only Memory), and 14 is a communication control unit that controls communication with an external information device. 15 is an input control unit such as a keyboard controller, 16 is an output control unit, 17 is an external storage device control unit, 18 is an input unit composed of input devices such as a keyboard, pointing device, and mouse, 19 is an output unit such as a printing device, Reference numeral 20 denotes an external storage device such as an HDD (Hard Disk Drive), 21 denotes a graphic control unit, and 22 denotes a display device.

  In FIG. 1, the CPU 11 retrieves and acquires data by communicating with an external device in accordance with a program ROM stored in the ROM 13 or a program stored in the large-capacity external storage device 20. Processing of output data in which graphics, images, characters, tables, etc. are mixed is executed, and management of a database stored in the external storage device 20 is further executed.

  Further, the CPU 11 comprehensively controls each device connected to the system bus 10. The program ROM in the ROM 13 or the external storage device 20 stores an operating system program (hereinafter referred to as OS) that is a basic program for controlling the CPU 11. The ROM 13 or the external storage device 20 stores various data used when performing output data processing or the like. The RAM 12 as a main memory functions as a main memory, work area, and the like for the CPU 11.

  The input control unit 15 controls the input unit 18 from a keyboard or a pointing device (not shown). The output control unit 16 performs output control of the output unit 19 such as a printer.

  The external storage device control unit 17 is an external storage device 20 such as an HDD (Hard Disk Drive) or a floppy disk (FD) that stores a boot program, various applications, font data, user files, editing files, printer drivers, and the like. Control access to. A system program for realizing the drug effect prediction method of the present invention is stored in the external storage device 20 as described above. The graphic control unit 21 is configured to perform drawing processing on information to be displayed on the display device 22.

  Further, the communication control unit 14 controls communication with an external device via a network, thereby acquiring data required by the system from a database held by an external device on the Internet or an intranet, It is configured to be able to send information to an external device.

  In addition to an operating system program (hereinafter referred to as OS) that is a control program for the CPU 11, the external storage device 20 is installed with a system program for operating the drug effect prediction system of the present invention on the CPU 11, data used in the system program, and the like. Saved and remembered.

  As data used in the system program for realizing the drug effect prediction method of the present invention, it is basically assumed that the data is stored in the external storage device 20, but in some cases, these data are It is also possible to configure to acquire from an external device on the Internet or an intranet via the communication control unit 14. The data used in the system program for realizing the drug effect prediction method of the present invention can also be obtained from various media such as a USB memory, a CD, and a DVD.

  Next, a drug effect prediction method according to the present invention that can be executed by the computer having the above-described system configuration will be described below.

  FIG. 2 is a diagram illustrating an example of a space targeted by the drug effect prediction method according to the embodiment of the present invention. The outline of the drug effect prediction method according to the present invention will be described below with reference to FIG.

  (A) First, in the drug effect prediction method according to the present invention, in the actual space 50, the drug is actually sprayed from the ultrasonic spray device 70, and a predetermined position where the biological indicator (BI) 80 is installed. Understand the killing status of test bacteria in. The ultrasonic spraying device 70 used in the present embodiment is an example of a spraying device, and a spraying device or a gasification device using a principle other than ultrasonic waves may be used.

  Here, for example, a fixture such as a table 60 may be installed in the space 50. Moreover, FIG. 2 only shows an example of the installation position of the biological indicator (BI) 80, and may be arbitrary. In the example of FIG. 2, an example in which five biological indicators (BI) 80 are installed in the space 50 is shown. In general, the drug concentration in a room is not necessarily uniform (equal), and is often different for each position of each biological indicator. When a plurality of biological indicators (BI) 80 are installed in this way, data of a plurality of conditions can be acquired in one demonstration test, and drug effect prediction can be performed efficiently.

  (B) Further, the airflow at a predetermined position where the biological indicator (BI) 80 is installed in the simulation space corresponding to the actual space 50 is analyzed. In this air flow analysis, the influence of fixtures such as the table 60 is taken into consideration, and if an air conditioner (not shown) is installed, the influence thereof is also taken into consideration. Furthermore, based on the airflow analysis, the concentration time integral value (CI value) of the medicine is calculated.

  And it implements about said (A) and (B) on several spray conditions, and the lowest spray which a chemical | medical agent acts effectively with respect to a test microbe in the predetermined position where the biological indicator (BI) 80 is installed. Get the condition.

  Furthermore, by selecting different spaces 50, different predetermined positions (positions where the biological indicator (BI) 80 is installed), different test bacteria, and different drugs, data on which the drugs effectively act on the test bacteria is databased, Utilizing this, the design of the sterilization system and the space design that can bring out the effect of the sterilization system will be performed.

  Hereinafter, details of the drug effect prediction method according to the present invention will be described based on flowcharts. FIG. 3 is a diagram illustrating an example of a flowchart (drug prediction process (1)) executed by the drug effect prediction method according to the embodiment of the present invention.

  In the flowchart of FIG. 1, when the process is started in step S100, the process proceeds to step S101, where the actual space (shape, size, etc.), predetermined position (position where the biological indicator (BI) 80 is installed), test bacteria ( Type of bacteria; Staphylococcus aureus, Escherichia coli, etc.) and drug (type of drug; hypochlorous acid water, slightly acidic electrolyzed water, etc.) are set.

  In step S102, a biological indicator (BI) 80 is installed at a predetermined position in real space.

  Here, the real space includes a demonstration space that simulates the real space in addition to the actual space itself.

  In subsequent step S103, the medicine set in the real space is sprayed, and the medicine spraying effect at the predetermined position is verified based on the test bacteria. Here, verification refers to the work of quantifying the drug spray effect by, for example, culturing the taken test bacteria after spraying the drug in, for example, TSA (TRYPCASE SOY AGAR) medium and counting the number of remaining colonies. Say that.

Subsequently, in step S104, it is determined whether or not spraying under all scheduled spraying conditions has been completed. (In this example, explanation will be given in the case of 6 spray conditions of 200 ppmw, 100 ppmw, 50 ppmw, 25 ppmw, 12.5 ppmw and 5 ppmw.)
If the determination in step S104 is NO, the spraying conditions are changed in S113, and the process returns to step S102 again. On the other hand, if determination in step S104 is YES, it will progress to step S105 and will acquire the verification effect classified by spray conditions by the difference in spray conditions.

  FIG. 4 is an example of a table showing the spray condition verification effect obtained in the spray condition verification effect acquisition step (step S105) of the drug effect prediction method according to the embodiment of the present invention. In this example, in this example, when the spraying conditions are 200 ppmw, 100 ppmw, and 50 ppmw, the test bacteria are completely killed.

  Subsequently, in step S106, the airflow at the predetermined position in the simulation space corresponding to the real space is analyzed. Here, in such airflow analysis, the influence of the shape of the space, air conditioning (not shown), and the like is also taken into consideration.

  In the present embodiment, CFD (Computational Fluid Dynamics) in Step S106 is used for the analysis, FlowDesigner of fluid analysis software, and a high Reynolds number type k-ε model is used for the turbulence model, and the boundary of the wall surface is used. The condition used was a generalized logarithm rule.

  In step S107, the concentration time integral value (CT value) of the drug is calculated based on the result of step S106.

In subsequent step S108, it is determined whether or not the spray simulation is completed under all the planned spray conditions. (As in the case of real space, 6 spray conditions of 200 ppmw, 100 ppmw, 50 ppmw, 25 ppmw, 12.5 ppmw and 5 ppmw.)
If the determination in step S108 is NO, the spraying conditions are changed in S114, and the process returns to step S106 again. On the other hand, if determination in step S108 is YES, it will progress to step S109 and will acquire the density | concentration time integrated value according to spray conditions by the difference in spray conditions. FIG. 5 is an example of a table showing the concentration time integrated value for each spray condition obtained in the concentration time integrated value acquisition step for each spray condition (step S109) of the drug effect prediction method according to the embodiment of the present invention.

  In step S110, the spray condition-specific verification effect acquired in the spray condition-specific verification effect acquisition process (step S105) and the spray condition-specific concentration time integrated value acquisition process (step S109). Corresponding to the concentration time integrated value, the lowest spraying condition in which the drug effectively acts on the test bacteria at the predetermined position is acquired. Specifically, by combining the table of FIG. 4 and the table of FIG. 5, it is acquired that the minimum spray condition is 50 ppmw and the CT value at that time is 14.1. The test bacterium is Staphylococcus aureus and the drug is hypochlorous acid water.

  If a more detailed CT value is required, data of a value between 50 ppmw and 25 ppmw (see FIG. 4) can be acquired and obtained in the same step.

  In subsequent step S111, the results obtained in the steps so far are stored in the minimum spray condition database provided in the external storage device 20 or the like. FIG. 6 is a diagram showing the data structure of such a minimum spray condition database.

  In such a database, what is the lowest spraying condition (including CT value) when what test bacteria are arranged and what drug is sprayed at what predetermined position in what real space Or will be described.

  In step S112, it is determined whether or not all scheduled settings have been completed. If the determination in step S112 is no, the process proceeds to step S115 to perform the next setting, and then returns to step S101.

  If determination in step S112 is YES, it will progress to step S116 and will complete | finish a process.

  Next, a process for calculating the minimum spray condition based on the minimum spray condition database constructed as described above will be described. FIG. 7 is a diagram showing an example of a flowchart (drug prediction process (2)) executed by the drug effect prediction method according to the embodiment of the present invention.

  When the drug effect prediction process (2) is started in step S200, subsequently, in step S201, a real space, a predetermined position, a test bacteria, and a drug are set.

  Subsequently, in step S202, the lowest spray condition database is referred to, and a case that is the same as or similar to the space, the predetermined position, the test bacteria, and the medicine set in step S201 is cited. Based on this, the minimum spray condition (including CT value) is calculated.

  In step S204, the calculation result of the minimum spray condition (including the CT value) is output by the output unit 19 or the like. Then, the process proceeds to step S205, and the drug effect prediction process (2) is ended.

  If the setting of the virtual space, the predetermined position, the test bacteria, and the drug is variously changed and the drug effect prediction process (2) is performed, it is possible to perform simulations under various conditions.

  The drug effect prediction method and the drug effect prediction system according to the present invention as described above have a minimum spray condition acquisition step of acquiring the minimum spray condition at which the drug effectively acts on the test bacteria at the predetermined position. Thus, according to the drug effect prediction method and the drug effect prediction system according to the present invention, it is possible to quantitatively predict the bactericidal effect by the drug, and there is no waste in terms of energy saving and cost (energy saving / appropriate cost). ), It is possible to design the sterilization system and the space design that can effectively bring out the effect of the sterilization system.

  Further, according to the drug effect prediction method and drug effect prediction system according to the present invention, it is possible to propose an effective sterilization system, a design of a target room, and an installation method of indoor fixtures and fixtures in advance.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, it is possible to cope with an appropriate concentration and drug amount without using a large amount or a high concentration drug as before.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, the present invention can be applied to other microbe countermeasures such as mold, if necessary.

  In addition, according to the drug effect prediction method and drug effect prediction system according to the present invention, the present invention can be applied to other microbe countermeasures such as mold, if necessary.

10 ... System bus 11 ... CPU (Central Processing Unit)
12 ... RAM (Random Access Memory)
13 ... ROM (Read Only Memory)
DESCRIPTION OF SYMBOLS 14 ... Communication control part 15 ... Input control part 16 ... Output control part 17 ... External storage device control part 18 ... Input part 19 ... Output part 20 ... External storage device 21 ... Interface unit 21 ... Graphic control unit 22 ... Display device 50 ... Space 60 ... Table 70 ... Ultrasonic spray device 80 ... Biological indicator (BI)

Claims (2)

An actual installation process of arranging test bacteria at a predetermined position in real space;
A verification step of spraying a predetermined drug in the real space, and verifying a drug spray effect at the predetermined position based on the test bacteria;
By repeating the actual installation step and the verification step under a plurality of spraying conditions, the verification effect by spraying condition due to the difference in spraying conditions is acquired by the relationship between the spraying condition and the number of remaining colonies after incubation. An effect acquisition process;
An air flow analysis step of analyzing the air flow at the predetermined position in the simulation space corresponding to the real space;
Based on the air flow analysis step, a concentration time integral value calculating step for calculating a concentration time integral value of the drug,
Under the plurality of spray conditions, the air flow analysis step and the concentration time integrated value calculation step are repeated, and the concentration time integrated value for each spray condition due to the difference in spray conditions is determined by the relationship between the spray conditions and the concentration time integrated value. Concentration time integrated value acquisition step for each spray condition to be acquired;
The number of remaining colonies after culturing corresponding to the spray condition-specific verification effect acquired in the spray condition-specific verification effect acquisition step and the spray condition-specific concentration time integrated value acquired in the spray condition-specific concentration time integrated value A minimum spray condition acquisition step of acquiring a minimum spray condition at which the drug effectively acts on the test bacteria at the predetermined position by obtaining a minimum concentration time integral value that becomes zero,
Consists of
Set different real spaces, different predetermined positions, different test bacteria, different drugs,
The actual installation step, the verification step, the spray condition specific verification effect acquisition step, the air flow analysis step, the concentration time integral value calculation step, the spray condition specific concentration time integral value acquisition step, and the minimum Repeating the spray condition acquisition step,
A drug effect prediction method comprising a minimum spray condition database acquisition step of acquiring a database of minimum spray conditions using different real spaces, different predetermined positions, different test bacteria, and different drugs. A setting process for setting a real space, a predetermined position, a test bacterium, and a medicine;
The minimum spray condition for calculating the minimum spray condition corresponding to the real space, predetermined position, test bacteria, and medicine set in the setting step with reference to the minimum spray condition database acquired in the minimum spray condition database acquisition step The method for predicting drug effect according to claim 1 , further comprising a calculating step.

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