WO2022059509A1 - Drug distribution state analysis method and drug distribution state analysis system - Google Patents

Drug distribution state analysis method and drug distribution state analysis system Download PDF

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WO2022059509A1
WO2022059509A1 PCT/JP2021/032354 JP2021032354W WO2022059509A1 WO 2022059509 A1 WO2022059509 A1 WO 2022059509A1 JP 2021032354 W JP2021032354 W JP 2021032354W WO 2022059509 A1 WO2022059509 A1 WO 2022059509A1
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drug
distribution
analysis method
state analysis
distribution state
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PCT/JP2021/032354
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French (fr)
Japanese (ja)
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北斗 田中
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コニカミノルタ株式会社
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/15—Medicinal preparations ; Physical properties thereof, e.g. dissolubility
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing

Definitions

  • the present invention relates to a drug distribution state analysis method and a drug distribution state analysis system. More specifically, the present invention relates to a drug distribution state analysis method or the like in which a digital image is acquired from a biological sample and the correlation with the spatial distribution of the drug or the microenvironment is automatically analyzed according to a certain model rule.
  • Patent Document 1 discloses a method for investigating the distribution of a drug in a biological sample and quantifying a drug in a biological sample by using a matrix-assisted laser desorption / ionization method (MALDI). ..
  • Patent Document 1 can grasp the distribution of the drug in the biological sample
  • the drug and other structures in the biological sample for example, biomolecules, intracellular structures such as cell nuclei, cells, cell groups, etc.
  • the positional relationship between the target or drug and the microenvironment in the biological sample the spatial distribution tendency of the target or drug distribution, and the factors that influence it. It is important to detect, quantify, and evaluate the drug distribution in consideration of the above.
  • the present invention has been made in view of the above problems / situations, and the solution thereof is to acquire a digital image from a biological sample and automatically correlate the drug with the spatiotemporal distribution or the microenvironment according to a certain model rule. It is to provide a drug distribution state analysis method and a drug distribution state analysis system for physical analysis.
  • the present inventor is a drug distribution state analysis method for automatically analyzing the correlation between the spatiotemporal distribution of a drug or the microenvironment according to a certain model rule. And the present invention has been found to be able to provide a drug distribution state analysis system. That is, the above-mentioned problem according to the present invention is solved by the following means.
  • Step 1 It is a drug distribution state analysis method based on a biological sample image containing a drug.
  • Step 2 of detecting and quantifying a drug signal from the biological sample image and obtaining drug state information,
  • a drug distribution state characterized by having at least a step 3 of analyzing a spatial distribution or a statistical distribution including a concentration distribution of the drug associated with a region including an analysis target in the biological sample image based on the drug state information. Analysis method.
  • the step 4 further includes a step 4 of extracting information on at least a target object of the drug to be analyzed or a non-target object having a relationship with the target from the biological sample image.
  • No. 3 is characterized in that the concentration distribution of the drug associated with the object or the non-target object is analyzed as the concentration distribution of the drug associated with the region including the analysis target in the biological sample image.
  • the step 3 for analyzing the drug concentration distribution associated with the object which is performed via the step 4, is the analysis of the drug concentration distribution associated with the object inner region and the object outer region, respectively, and is the analysis.
  • the step 3 for analyzing the drug concentration distribution associated with the object is a step for comparing and analyzing the drug concentration distribution associated with each area distinguished by at least one index of distance or orientation from each object.
  • the drug distribution state analysis method according to any one of the items 1 to 4.
  • the first item is characterized by further comprising a step 6 for analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4, in addition to the steps.
  • the drug distribution state analysis method according to any one of the items from to 6.
  • Item 2 The drug according to Item 11, wherein there are a plurality of types of the objects to be comparatively analyzed, and the drug concentration distribution associated with the first object is comparatively analyzed according to the spatial distribution from the second object. Distribution state analysis method.
  • the step 1 of acquiring the biological sample image is a step of individually acquiring drug state information and object information to be analyzed from a plurality of consecutive sections, and in addition to the above steps, the continuous plurality of steps.
  • the drug distribution state analysis method according to any one of items 1 to 15, further comprising a step 9 of image alignment by alignment of sections of the above.
  • the step 10 of performing comparative analysis of the drug distribution among the biological sample images obtained in the plurality of biological sample images obtained in the step 1 is further provided.
  • the described drug distribution state analysis method is not limited to:
  • a drug distribution state analysis system based on images of biological samples containing drugs.
  • a drug distribution state analysis system comprising a process means for carrying out the drug distribution state analysis method according to any one of paragraphs 1 to 19.
  • a drug distribution state analysis method and a drug distribution state analysis system that acquire a digital image from a biological sample by the above means of the present invention and automatically analyze the correlation between the spatiotemporal distribution of a drug or the microenvironment according to a certain model rule. Can be provided.
  • the drug distribution state analysis method of the present invention at least a step of acquiring a biological sample image, a step of detecting and quantifying a drug signal from the biological sample image to obtain drug state information, and the drug based on the drug state information.
  • the steps set according to certain model rules including the step of analyzing the spatial distribution or the statistical distribution including the concentration distribution of the above are sequentially carried out. Therefore, quantitative analysis of drug distribution in vivo and relationship analysis with drug administration target tissue structure, cell distribution, etc. can be automatically performed, which is effective in drug discovery research and development.
  • Example of process constituting the drug analysis method of the present invention Schematic diagram showing the structure and procedure of the process in the second embodiment Schematic diagram A showing an example of the analysis procedure and the result in the second embodiment.
  • Schematic diagram B showing an example of the analysis procedure and results in the second embodiment
  • Schematic diagram showing the configuration and procedure of the process in the third embodiment Schematic diagram showing an example of the analysis procedure and results in the third embodiment
  • Schematic diagram showing an example of the analysis procedure and results in the fourth embodiment Schematic diagram showing an example of the analysis procedure and results in the fourth embodiment
  • Schematic diagram showing an example of the analysis procedure and results in the fourth embodiment Schematic diagram showing the configuration and procedure of the process in the fifth embodiment
  • Schematic diagram A showing an example of the analysis result in the sixth embodiment
  • Schematic diagram B showing an example of the analysis result in the sixth embodiment
  • Schematic diagram C showing an example of the analysis result in the sixth embodiment
  • the drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, and is a step 1 of acquiring the biological sample image and detecting and quantifying a drug signal from the biological sample image. , Step 2 to obtain drug state information, and step 3 to analyze the spatial distribution or statistical distribution including the concentration distribution of the drug associated with at least the region including the analysis target in the biological sample image based on the drug state information. It is characterized by having. This feature is a technical feature common to or corresponding to the following embodiments.
  • the object to be the target of the drug to be analyzed from the biological sample image or the non-relevant to the target has a step 4 of extracting information on a target object, and in the step 3, the concentration distribution of the drug associated with the object or a non-target object is set in a region including an analysis target in the biological sample image. It is preferable to analyze as the concentration distribution of the associated drug.
  • the drug concentration distribution associated with the object inner region and the object outer region in step 3 for analyzing the drug concentration distribution associated with the object which is performed via the step 4, respectively.
  • the analysis method is representative value calculation or correlation analysis.
  • the drug distribution state analysis method of the present invention has a biomarker-positive cell region in the inner region of the object and a biomarker in the outer region of the object for the purpose of pharmacokinetic analysis (particularly, target selection system evaluation of a drug). Has a negative cell region.
  • the drug distribution state analysis method of the present invention has a biomarker-positive cell region in the inner region of the object and a biomarker-negative cell region in the outer region of the object. It is preferable in that it can be evaluated.
  • step 3 of analyzing the drug concentration distribution associated with the object in step 3 of analyzing the drug concentration distribution associated with the object, the drug concentration distribution associated with each area distinguished by at least one index of distance or orientation from each object. It is preferable that the step is a step of comparative analysis because it enables pharmacokinetic analysis (particularly, evaluation of drug accumulation / diffusion).
  • the drug distribution state analysis method of the present invention further includes, in addition to each of the above steps, a step 5 of analyzing the spatial distribution of the object via the step 4, from the viewpoint of analysis of related factors.
  • step 6 for analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4 is performed. Further possession is preferable from the viewpoint of correlation analysis between pharmacokinetics and related factors.
  • the drug and the object are a plurality of types from the viewpoint of comparative analysis of effects.
  • the drug distribution state analysis method of the present invention has a therapeutic effect by comparing and analyzing the concentration distributions of different types of drugs related to a specific object because a plurality of drugs such as concomitant drugs are co-localized. It is preferable because the medicinal effect of the case can be evaluated.
  • the plurality of drugs to be comparatively analyzed are an antibody drug conjugate and a payload from the viewpoint that the efficacy of the antibody drug conjugate can be evaluated.
  • the drug distribution state analysis method of the present invention has a plurality of types of the objects to be comparatively analyzed, and it is preferable to compare and analyze the drug concentration distribution between different object types because the side effects and toxicity of the drug can be evaluated.
  • the drug distribution state analysis method of the present invention there are a plurality of types of the objects to be compared and analyzed, and the drug concentration distribution associated with the first object according to the spatial distribution from the second object can be compared and analyzed. It is preferable because it can analyze the heterogeneity of drug accumulation and the degree of drug diffusion.
  • the drug distribution state analysis method of the present invention there are a plurality of types of the objects to be comparatively analyzed, and the case where a plurality of drugs having different targets are to be compared and analyzed for the concentration distribution of different drug types among the different types of objects. It is preferable from the viewpoint that the medicinal effect of
  • the drug distribution state analysis method of the present invention further includes, in addition to each of the above steps, a step 7 of selecting a detailed analysis site based on the analysis result obtained in the above step 3 or the above step 6, which is a more accurate concentration. It is preferable because the area where the calculation can be performed can be selected.
  • the drug distribution state analysis method of the present invention further includes a step 8 for analyzing the selected detailed analysis points in addition to the above steps, because more accurate drug concentration calculation can be performed.
  • the step 1 of acquiring the biological sample image is a step of individually acquiring the drug state information and the object information to be analyzed from a plurality of consecutive sections, and the above-mentioned method.
  • comparative analysis is performed as a continuous image of the plurality of sections. It is preferable in that it enables.
  • a step 10 of comparatively analyzing the drug distribution among the biological sample images obtained in the plurality of biological sample images obtained in the step 1 is further performed. It is preferable to have it because it is possible to evaluate changes over time after drug administration.
  • the drug distribution state analysis method of the present invention further includes a step 11 of selecting a region of interest based on the analysis result of the drug concentration distribution or the object distribution, which is a factor related to the analysis result. For identification, it is preferable from the viewpoint of comparative analysis of in vivo spatial heterogeneity in places where the characteristics of drug distribution are different.
  • the drug distribution state analysis method of the present invention further comprises, in addition to the above-mentioned steps, a step 12 of identifying a related biomarker from the gene expression difference between the plurality of said regions of interest. It is preferable because it can comprehensively analyze changes in the living body that are not expressed as biomarker expression.
  • the drug distribution state analysis system of the present invention is a drug distribution state analysis system based on a biological sample image containing a drug, and carries out the drug distribution state analysis method according to any one of paragraphs 1 to 19. It is preferable that the system has an embodiment of a process means for the above-mentioned analysis method in that various merits of the analysis method can be exhibited.
  • a “drug” is a substance derived from a substance or chemical substance in the natural world, which is artificially administered from outside the body or ingested, inhaled, or absorbed depending on a specific external environment, and has some medicinal effect and toxicity to the living body. It refers to substances derived from biologically active substances and bioactive chemical substances that exhibit bioactivity. For example, small molecule drugs, biopharmacy (antibody drugs, RNA, viruses, etc.) and the like can be mentioned.
  • Biological sample refers to a sample in which a biological tissue is in a state where an image can be acquired.
  • a mouse or the like made observable by permeation treatment, a tissue sample collected from a living body, a cultured cell, a sample (tissue section) in which a living tissue is immobilized, and the like can be mentioned.
  • an image acquisition means such as computed tomography (abbreviation: CT) or magnetic resonance imaging (abbreviation: MRI
  • CT computed tomography
  • MRI magnetic resonance imaging
  • the "object” means an analysis target other than a drug such as a biomolecule, a cell, or a structure, or a region containing the same. Not only the analysis target that is the target of the drug, but also the analysis target related to the target and the region containing the analysis target are included. Specifically, specific cell types (including classification by differentiation of stem cells, glia cells, T cells, etc., pathological conditions such as necrotic cells and inflammation, classification under specific conditions such as cell cycle), or tissue internal structure (including classification). Examples include blood vessels, necrotic areas, spatial arrangement / shape characteristics classification such as invasion and protrusions), or intracellular structures (organellas such as nuclei and vesicles).
  • Payload means a molecule or material delivered to a target cell or tissue.
  • the payload is not particularly limited and may be any pharmaceutical compound intended for use in the diagnosis, treatment or prevention of a disease of interest.
  • the payload may include small molecule compounds, nucleotides (eg, DNA, plasmid DNA, RNA, siRNA, antisense oligonucleotides, aptamers, etc.), peptides, proteins (eg, enzymes), fluorescent dyes, etc. It is a quantum dot or a nanoparticle.
  • “Spatial distribution” is a distribution state (coordinate position, distribution area) when the state of a drug, object, related object, etc. to be analyzed or observed in a two-dimensional or three-dimensional image is expressed in a coordinate system. * Including changes over time such as area, concentration, density, increase / decrease, accumulation / diffusion azimuth velocity, etc.). In addition, paying particular attention to the analysis / observation target, it is also simply referred to as “drug distribution” or “object distribution”.
  • the “statistical distribution” refers to distribution information (frequency distribution, aggregated value such as average value, etc.) aggregated from a statistical viewpoint, for example, the concentration, density, etc. of a drug, etc., among the above spatial distributions.
  • the “concentration” refers to the amount of a drug that can be detected by binding a drug, a marker, or the like per predetermined section, object area, or unit area in a biological image. However, if the amount of the drug correlates with a general drug concentration ([L / kg], [ ⁇ g / L], [ppm], [mol / L], etc.), unit conversion is indirectly performed. It is possible.
  • the "biological sample image” needs to be able to identify the position of the drug in the biological sample. Further, it is preferable that the position of the object can be specified.
  • a fluorescent image can be used, but depending on the embodiment, it is preferable to use a bright field image together with the fluorescent image.
  • the drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, in which step 1 of acquiring the biological sample image and the biological sample. Spatial distribution including step 2 of detecting and quantifying a drug signal from an image to obtain drug state information, and at least the concentration distribution of the drug associated with a region including an analysis target in the biological sample image based on the drug state information. Alternatively, it is characterized by having a step 3 for analyzing a statistical distribution.
  • steps 4 to 12 in addition to the steps 1 to 3.
  • the numbers of steps 1 to 12 do not necessarily indicate the order of the steps, but by following a model rule in which the procedure including these steps is determined in advance, the numbers are automatically in vivo without trial and error.
  • the drug distribution state can be analyzed.
  • Steps for constructing a drug distribution state analysis method In the present invention, the following steps 1 to 3 are at least necessary, but the analysis method may be configured to include various steps depending on other purposes. Is preferable. For example, it is preferable to use an analysis method having a configuration including any of the steps 1 to 12 shown below (see FIG. 1).
  • Step 1 Obtaining a biological sample image
  • Step 2 Detecting and quantifying a drug signal from the biological sample image to obtain drug state information
  • Step 3 Includes at least an analysis target in the biological sample image based on the drug state information The step of analyzing the spatial distribution or statistical distribution including the concentration distribution of the drug associated with the region.
  • Step 4 Extract information from the biological sample image at least the target object of the drug to be analyzed or a non-target object related to the target
  • Step 5 Analyze the spatial distribution of the object.
  • 6 A step of analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4.
  • Step 7 Step of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6
  • Step 8 Step of analyzing the selected detailed analysis point
  • Step 9 Image by alignment of a plurality of consecutive sections Alignment process
  • Step 10 A step of comparatively analyzing a plurality of biological sample images acquired in Step 1 between the biological sample images.
  • Step 11 A step of selecting a region of interest based on the analysis result of a drug concentration distribution or an object distribution.
  • Step 12 Identifying the relevant biomarker from the gene expression differences between the plurality of regions of interest.
  • Step 1 is a step of acquiring a biological sample image.
  • the method is not limited as long as a biological sample image can be obtained, but spectroscopic imaging (imaging using absorption / emission spectroscopy, infrared spectroscopy, Raman spectroscopy, etc.) and mass imaging (imaging using absorption / emission spectroscopy, Raman spectroscopy, etc.) generally known as bioimaging techniques are used.
  • MALDI-MS matrix-assisted laser desorption / ionization mass spectrometry
  • TOF-SIMS time-of-flight secondary ion mass spectrometry
  • Step 2 is a step of detecting and quantifying a drug-derived signal (hereinafter, also referred to as "drug signal") from a biological sample image and obtaining drug state information. From the viewpoint of comparative analysis, it is preferable that there are a plurality of types of drugs for which drug status information is obtained.
  • drug signal a drug-derived signal
  • the "drug-derived signal” is a signal of the drug itself or a signal of a labeling substance such as a color-developing substrate or a fluorescent dye that is labeled on the drug.
  • drug status information is, for example, the type and concentration of a drug.
  • the drug concentration can be quantified from the quantified drug signal by a method such as threshold processing, fluorescence bright spot integration, or fluorescence particle calculation.
  • noise reduction processing is, for example, a process of suppressing the autofluorescent luminance of the read fluorescent image.
  • Step 3 is a spatial distribution or statistics including at least the concentration distribution of the drug associated with the region including the analysis target in the biological sample image based on the drug state information. This is the process of analyzing the distribution. Further, in step 3, the concentration distribution of the drug associated with the object or non-target object extracted in step 4 described later is used as the concentration distribution of the drug associated with the region including the analysis target in the biological sample image. , It is preferable to analyze. It is preferable that there are a plurality of types of drugs for which the drug distribution is analyzed from the viewpoint of comparative analysis.
  • drug distribution analysis refers to spatial distribution analysis, statistical distribution analysis, comparative analysis, and the like of drugs.
  • “Spatial distribution analysis” refers to mapping drug types / concentrations and defining drug distribution areas by threshold processing.
  • the analyzed spatial distribution can be used to correlate the drug distribution with the object extracted in step 4.
  • the defined drug distribution area can be treated like an object.
  • Statistical distribution analysis is a frequency distribution (histogram) of drug concentration, total drug amount of drugs associated with an object, mean concentration value, representative concentration value, mode of concentration, drug distribution area area, drug for object area. It is to analyze statistical distributions such as distribution area density, medicinal space area ratio, and toxicity space area ratio. Homogeneity evaluation can be performed from these statistical distribution analysis results.
  • “Comparative analysis” is to compare the analysis results of spatial distribution and statistical distribution.
  • the objects to be compared are, for example, different types of drugs and drugs having different methods of associating with objects (described later).
  • the "different drug” is, for example, an antibody drug conjugate and a payload, a different anticancer drug in a combination therapy of cancer, a different antiviral drug in a cocktail therapy of a viral infection, and the like.
  • Items to be compared are the results of the above-mentioned spatial distribution analysis (drug type / drug concentration map, drug distribution area, etc.) or the results of statistical distribution analysis (total drug amount of drugs associated with the object, mean concentration, representative concentration). , Concentration mode, drug distribution area area, drug distribution area density with respect to object area, drug effect space area ratio, toxicity space area ratio, etc.). Further, as a comparative analysis, it is possible to compare the spatial distribution and the statistical distribution analyzed for different biological sample images, but this is performed in step 10.
  • ⁇ Statistical distribution analysis example (1)> As an example of statistical distribution analysis, analysis of the medicinal space or toxic space area ratio by concentration threshold processing will be described. First, threshold processing is performed at a concentration that serves as a reference for pharmacological determination. At this time, a region having a certain area or less may be excluded as noise, and spatial smoothing may be performed. After the threshold treatment, the medicinal effect space area ratio and the toxic space area ratio can be analyzed to evaluate the medicinal effect or toxicity. It is also possible to evaluate the medicinal effect from the variation in the medicinal effect space.
  • ⁇ Statistical distribution analysis example (2)> As an example of statistical distribution analysis, a histogram-based evaluation of drug concentration uniformity will be described. First, a drug concentration range that satisfies a predetermined frequency ratio (95 [%], etc.) based on the most frequent concentration value is defined as the drug concentration distribution variation performance, and a basic evaluation (whether it is within the optimum concentration range, etc.) is performed. Based on this evaluation, it can be determined that a drug having a plurality of modes has a non-uniform distribution characteristic and has low drug efficacy stability.
  • a predetermined frequency ratio 95 [%], etc.
  • the drug distribution analyzed in step 3 can be a drug distribution associated with the object from which the information was extracted in step 4.
  • the method of associating with an object is the spatial distribution from the second object when associating with the inner area and the outer area of the object, respectively, or when associating with the area distinguished by at least one index of the distance or the direction from each object. Depending on the case, it may be associated with the first object.
  • the biomarker-positive cell region can be a HER2-positive cell region
  • the biomarker-negative cell region can be a negative cell region
  • analyzing the drug distribution in association with the object it is possible to perform drug distribution analysis that combines the detected drug signal amount / coordinates and the object arrangement. For example, spatial distribution analysis and statistical distribution analysis focused only on the object area, statistical distribution analysis per object in which a drug is assigned to an object, or significant difference comparison analysis of drug concentrations inside and outside the object.
  • Example of drug distribution analysis focused only on the object As an example of drug distribution analysis focused only on the object region, drug concentration distribution analysis within the region of the object that is the target of the drug will be described. First, only the area of the object that is the target of the drug is set as the analysis target area. Next, only the drug signal in the target area is detected and quantified, and the drug concentration is quantified. By assigning a drug to each object from the object coordinates and the drug detection position, the average drug concentration in the object can be calculated.
  • Example of comparative analysis of drug distribution associated with an object An example of comparative analysis of drug distribution associated with the inner and outer regions of an object will be described.
  • the spatial distribution of the drug analyzed in step 3 is associated with the object extracted in step 4 to the inner region and the outer region, respectively.
  • the object region may be extracted from the bright-field DAB (diaminobenzidine) stained image by color separation using the DAB color vector and threshold processing.
  • DAB diaminobenzidine
  • the step 3 for analyzing the drug concentration distribution associated with the object which is performed via step 4 described later, is the analysis of the drug concentration distribution associated with the object inner region and the object outer region, respectively. It is preferable that the analysis method is representative value calculation or correlation analysis from the viewpoint of pharmacokinetic analysis, pharmacological analysis and the like.
  • the "representative value calculation" of the drug concentration distribution associated with the inner region of the object and the outer region of the object is a representative of the average value, mode value, median value, maximum value, etc. of the drug concentration distribution associated with each region. To calculate the value.
  • the "correlation analysis" of the drug concentration distribution associated with the inner region of the object and the outer region of the object is to analyze the correlation of the above representative values.
  • the tumor region as the second object and the immune cells as the first object, it is possible to compare and analyze the drug distribution (tumor region infiltrating immune cell drug distribution) focusing on immune cells having different distances from the tumor region. ..
  • Step 4 is a step of extracting information on at least the target object of the drug to be analyzed or a non-target object related to the target from the biological sample image. .. It is preferable that there are a plurality of types of objects for which object information is obtained from the viewpoint of comparative analysis.
  • the method for extracting the object information is not particularly limited as long as the object information can be extracted.
  • the object information can be extracted from the bright field DAB (diaminobenzidine) stained image by color separation / threshold processing using the DAB color vector. can.
  • object information can also be extracted by extracting cell contours in a bright field and then selecting those having an expression level of a biomarker specifically expressed in an object of a threshold value or more.
  • preprocessing such as noise reduction processing and area setting processing may be performed as in step 2.
  • the first object is divided into two or more according to the spatial distribution from the second object. It is also a preferable embodiment to divide the groups (areas) and extract information for each group.
  • the second object can be, for example, a tumor area or an infiltrating area of an invasive cancer.
  • the first object can be, for example, an immune cell, a cell classifier, a cancer cell extracted using a discrimination marker stain, or the like.
  • immune cells can be divided into three groups: internal to tumor, marginal area, and distal to tumor, depending on the distance threshold from the tumor area (second object). It is also possible to divide the groups based on the nearest neighbor distance of the tumor cells centered on each first object.
  • Step 5 is a step of analyzing the spatial distribution and the statistical distribution of the object, and is performed via the step 4. Having step 5 is preferable from the viewpoint of analysis of related factors. It is preferable that there are a plurality of types of objects for which the object distribution is analyzed from the viewpoint of comparative analysis.
  • objects of object distribution analysis include, for example, biomarker expression level, spatial distribution of objects, and statistical distribution information thereof.
  • the spatial distribution of an object includes coordinate positions, distribution area area, density, increase / decrease, localization pattern, etc.
  • statistical distribution information includes aggregated values such as frequency distribution, mean value, and the like.
  • the object distribution can be analyzed by calculating the feature amount for each section based on the object information extracted in step 4.
  • Step 6 is a step of analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4. .. Having step 6 is preferable from the viewpoint of correlation analysis between pharmacokinetics and related factors. From the viewpoint of comparative analysis, it is preferable that there are a plurality of types of drugs and objects for which the relationship is analyzed.
  • a method of analyzing the relationship between the drug concentration distribution and the object distribution for example, there is a method of analyzing the spatial distribution of the object in the drug concentration threshold unit after assigning the detected drug signal to the object. There is also a method for analyzing the correlation between the spatial distribution tendency of drugs and the object distribution tendency.
  • a normalized drug concentration distribution can be obtained by dividing the drug concentration feature by the object density feature (drug concentration feature ⁇ object density feature). If this distribution is uniform, it can be evaluated that the density of the object and the drug concentration are correlated.
  • the object density may be the number / unit section as well as the area ratio.
  • Step 7 is a step of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6. It is preferable to have the step 7 from the viewpoint that a region where the concentration can be calculated with higher accuracy can be selected.
  • the detailed analysis location can be selected by visual selection, selection based on the object area area and the specified number of locations, and so on.
  • Step 8 is a step of analyzing the selected detailed analysis point. It is preferable to have the step 8 from the viewpoint that the drug concentration can be calculated with higher accuracy.
  • acquiring an image of the detailed analysis location selected in step 7 by, for example, high-magnification imaging or 3D imaging, it is possible to perform more detailed analysis than the analysis performed in step 3, step 5 or step 7.
  • Step 9 is a step of aligning images by aligning sections of a plurality of continuous tissues. Having step 9 is preferable in that even in a case where multiple staining cannot be performed with one section, comparative analysis can be performed as a continuous image of a plurality of sections.
  • Step 10 is a step of comparatively analyzing a plurality of biological sample images acquired in step 1 between the biological sample images. It is preferable to have the step 10 from the viewpoint of being able to evaluate changes over time and the like. For example, by performing comparative analysis between multiple biological sample images with different elapsed times after drug administration, it is possible to capture changes in drug distribution over time, and to evaluate drug increase / decrease, accumulation / diffusion azimuth velocity, and the like. can.
  • Step 11 is a step of selecting the region of interest based on the analysis result of the drug concentration distribution or the object distribution. Having step 11 is preferable from the viewpoint of comparative analysis of in vivo spatial heterogeneity at locations having different characteristics of drug distribution in order to identify factors related to the analysis results.
  • the “region of interest” is a region that is considered to be useful for identifying the relevant biomarker in step 12, such as a highly drug-accumulated cell population.
  • Step 12 is a step of identifying the related biomarker from the gene expression difference between the plurality of regions of interest. It is preferable to have the step 12 from the viewpoint that it is possible to comprehensively analyze changes in the living body that are not expressed as drug accumulation or specific biomarker expression as a measurement target.
  • the drug highly accumulated cell population selected in step 11 is subjected to gene mutation analysis by laser dissection / NGS (next generation sequencing) to analyze the difference in gene expression state and identify related biomarkers.
  • Embodiment 1 Example of a fluorescent image acquisition method
  • a tissue section collected from a living body after administration of a drug to be distributed is subjected to immunostaining (fluorescent dye, etc.).
  • immunostaining fluorescent dye, etc.
  • An example in which the position can be specified and an image is acquired using an imaging device such as a fluorescence microscope will be described.
  • an example of acquiring a biological sample image by performing, for example, a drug staining step, an object staining step, a focusing step, and an image acquisition step will be described.
  • ⁇ Drug staining process> This is a step of staining a drug so that the position of the drug in a biological sample can be identified.
  • Immunostaining agent antibody-fluorescent nanoparticles conjugate
  • the primary antibody and fluorescent nanoparticles indirectly, that is, using an antigen-antibody reaction or the like, other than covalent bonds. It is preferable to use a complex linked by the binding of.
  • a complex in which fluorescent nanoparticles are directly linked to the primary antibody or the secondary antibody can also be used as the immunostaining agent.
  • immunostaining agent examples include [primary antibody against the target substance] ... [antibody against the primary antibody (secondary antibody)] to [fluorescent nanoparticles].
  • “...” Indicates that the bond is bound by an antigen-antibody reaction, and the mode of binding indicated by “ ⁇ ” is not particularly limited.
  • covalent bond, ion bond, hydrogen bond, coordination bond, antigen-antibody bond examples thereof include biotin avidin reaction, physical adsorption, chemical adsorption, etc., and may be mediated by a linker molecule if necessary.
  • an antibody (IgG) that specifically recognizes and binds to the target substance as an antigen can be used.
  • an anti-HER2 antibody can be used
  • HER3 is the target substance
  • an anti-HER3 antibody can be used.
  • an antibody (IgG) that specifically recognizes and binds to the primary antibody as an antigen can be used.
  • Both the primary antibody and the secondary antibody may be polyclonal antibodies, but monoclonal antibodies are preferable from the viewpoint of quantitative stability.
  • the type of animal (immune animal) that produces an antibody is not particularly limited, and may be selected from mice, rats, guinea pigs, rabbits, goats, sheep, and the like as in the past.
  • Fluorescent nanoparticles are nano-sized particles that fluoresce and emit light when irradiated with excitation light, and emit fluorescence with sufficient intensity to represent the target substance as bright spots one by one. Fluorescent particles.
  • fluorescent nanoparticles phosphor integrated nanoparticles (PID: Phosphor Integrated Dot nanoparticles) can be used.
  • Fluorescent substance-accumulated nanoparticles are based on particles made of organic or inorganic substances, and a plurality of fluorescent substances (for example, the above-mentioned quantum dots, organic fluorescent dyes, etc.) are contained therein. Nano-sized particles having a structure that is and / or is adsorbed on the surface thereof. As the fluorescent substance-accumulated nanoparticles, quantum dot-accumulated nanoparticles, fluorescent dye-accumulated nanoparticles and the like are used.
  • Fluorescent material used for integrated nanoparticles is visible to near-infrared light with a wavelength in the range of 400 to 900 nm when excited by ultraviolet to near-infrared light with a wavelength in the range of 200 to 700 nm. It is preferable that the mother body and the fluorescent substance have substituents or sites having opposite charges and that an electrostatic interaction acts.
  • the average particle size of the nanoparticles accumulating fluorescent substances is not particularly limited, but those having a diameter of about 30 to 800 nm can be used. When the average particle size is less than 30 nm, the amount of fluorescent substance contained in the accumulated particles is small, which makes quantitative evaluation of the target substance difficult, and when it exceeds 800 nm, it is difficult to bind to the target substance in the pathological tissue. This is to become.
  • the average particle size is more preferably in the range of 40 to 500 nm.
  • the reason why the average particle size is set to 40 to 500 nm is that if it is less than 40 nm, an expensive detection system is required, and if it exceeds 500 nm, the quantification range is narrowed due to the physical size. ..
  • For the average particle size take an electron micrograph using a scanning electron microscope (SEM), measure the cross-sectional area of a sufficient number of particles, and use the diameter of the circle as the area of the circle as the area of each measured value. Can be obtained as.
  • SEM scanning electron microscope
  • the organic substances are generally classified as thermosetting resins such as melamine resin, urea resin, aniline resin, guanamine resin, phenol resin, xylene resin and furan resin.
  • Resins generally classified as thermoplastic resins such as styrene resin, acrylic resin, acrylonitrile resin, AS resin (acrylonitrile-styrene copolymer), ASA resin (acrylonitrile-styrene-methyl acrylate copolymer); poly Other resins such as lactic acid; polysaccharides can be exemplified.
  • the inorganic substance in the mother body include silica and glass.
  • Quantum dot integrated nanoparticles have a structure in which the quantum dots are contained in the mother body and / or are adsorbed on the surface thereof.
  • the quantum dots may or may not be chemically bonded to the mother body itself as long as they are dispersed inside the mother body.
  • quantum dots semiconductor nanoparticles containing a II-VI group compound, a III-V group compound, or an IV group element are used.
  • CdSe, CdS, CdTe, ZnSe, ZnS, ZnTe, InP, InN, InAs, InGaP, GaP, GaAs, Si, Ge and the like can be mentioned.
  • a quantum dot having the above quantum dot as a core and a shell provided on the core.
  • the core is CdSe and the shell is ZnS
  • CdSe / ZnS when the core is CdSe and the shell is ZnS, it is expressed as CdSe / ZnS.
  • CdSe / ZnS, CdS / ZnS, InP / ZnS, InGaP / ZnS, Si / SiO 2 , Si / ZnS, Ge / GeO 2 , Ge / ZnS and the like can be used, but the present invention is not limited thereto.
  • the quantum dots may be surface-treated with an organic polymer or the like.
  • an organic polymer or the like for example, CdSe / ZnS having a surface carboxy group (manufactured by Invitrogen), CdSe / ZnS having a surface amino group (manufactured by Invitrogen), and the like can be mentioned.
  • Quantum dot integrated nanoparticles can be produced by a known method.
  • silica nanoparticles encapsulating quantum dots can be synthesized with reference to the synthesis of CdTe-encapsulating silica nanoparticles described in New Journal of Chemistry Vol. 33, p. 561 (2009).
  • silica nanoparticles enclosing quantum dots refer to the synthesis of silica nanoparticles in which particles capped with 5-amino-1-pentanol and APS of CdSe / ZnS described on page 2670 (2009) of Chemical Communication are integrated on the surface. Can be synthesized into.
  • Polymer nanoparticles containing quantum dots can be produced by using the method of impregnating polystyrene nanoparticles with quantum dots described in Nature Biotechnology Vol. 19, page 631 (2001).
  • Fluorescent dye-accumulated nanoparticles have a structure in which a fluorescent dye is contained in the mother body and / or is adsorbed on the surface thereof.
  • fluorescent dye examples include organic fluorescent dyes such as rhodamine-based dye molecules, squarylium-based dye molecules, cyanine-based dye molecules, aromatic ring-based dye molecules, oxazine-based dye molecules, carbopyronine-based dye molecules, and pyrromesen-based dye molecules.
  • organic fluorescent dyes such as rhodamine-based dye molecules, squarylium-based dye molecules, cyanine-based dye molecules, aromatic ring-based dye molecules, oxazine-based dye molecules, carbopyronine-based dye molecules, and pyrromesen-based dye molecules.
  • Alexa Fluor registered trademark, manufactured by Invigen
  • BODIPY registered trademark, manufactured by Invigen
  • Cy registered trademark, manufactured by GE Healthcare
  • the fluorescent dye When the fluorescent dye is contained in the mother body, the fluorescent dye may or may not be chemically bonded to the mother body itself as long as it is dispersed inside the mother body.
  • Fluorescent dye-accumulated nanoparticles can be produced by a known method.
  • silica nanoparticles encapsulating a fluorescent dye can be synthesized with reference to the synthesis of FITC-encapsulating silica particles described in Langmuir Vol. 8, p. 2921 (1992).
  • FITC-encapsulating silica particles described in Langmuir Vol. 8, p. 2921 (1992).
  • FITC a desired fluorescent dye instead of FITC
  • various fluorescent dye-accumulated nanoparticles can be synthesized.
  • polystyrene nanoparticles containing a fluorescent dye For polystyrene nanoparticles containing a fluorescent dye, a copolymerization method using an organic dye having a polymerizable functional group described in US Pat. No. 4,326,008 (1982) or US Pat. No. 5,326,692 (1992) can be used. ), The polystyrene nanoparticles can be prepared by using the method of impregnating the polystyrene nanoparticles with a fluorescent dye.
  • Example of Staining Method for Tissue Section An example of the staining method will be described.
  • the method for preparing a tissue section to which this staining method can be applied (also referred to simply as a “section” and including a section such as a pathological section) is not particularly limited, and a tissue section prepared by a known procedure can be used.
  • Specimen preparation process [4.1.1] Deparaffin treatment The section is immersed in a container containing xylene to remove paraffin.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, xylene may be replaced during immersion.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, ethanol may be replaced during immersion.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, the water may be replaced during the immersion.
  • Activation treatment The activation treatment of the target substance is performed according to a known method.
  • the activation conditions are not particularly specified, but the activating solution includes 0.01 M citric acid buffer (pH 6.0), 1 mM EDTA solution (pH 8.0), 5% urea, and 0.1 M Tris-hydrochloric acid buffer. A liquid or the like can be used.
  • the pH condition is such that a signal is output from the range of pH 2.0 to 13.0 depending on the tissue section to be used and the tissue roughness is such that the signal can be evaluated.
  • the pH is 6.0 to 8.0, but for special tissue sections, for example, pH 3.0 is also used.
  • the heating equipment can be an autoclave, microwave, pressure cooker, water bath, etc.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the temperature can be 50 to 130 ° C. and the time can be 5 to 30 minutes.
  • the section after activation treatment is immersed in a container containing PBS and washed.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, the PBS may be replaced during the immersion.
  • Immunostaining Step in order to stain the target substance, a solution of an immunostaining agent containing fluorescent nanoparticles having a site that can directly or indirectly bind to the target substance is applied to a section. Place and react with the target substance.
  • the solution of the immunostaining agent used in the immunostaining step may be prepared in advance before this step.
  • immunostaining is performed with multiple immunostaining agents corresponding to the target substances.
  • the plurality of immunostaining agents used in this case may be those containing at least one immunostaining agent (PID stain) using fluorescent substance-accumulated nanoparticles, and the antibody and the fluorescent substance (fluorescent wavelength) are different from each other.
  • PID stain immunostaining agent
  • multiple target substances are detected by multiple staining using a plurality of PID stains or a combination of a PID stain and an immunostaining agent using a fluorescent label such as an organic fluorescent substance or a quantum dot.
  • a solution of each immunostaining agent is prepared, placed on a section, and reacted with the target substance.
  • the solution of each immunostaining agent may be mixed in advance when the solution is placed on the section, or separately. It may be placed sequentially in.
  • the excitation / emission wavelengths of the fluorescent substance-accumulated nanoparticles and the excitation / emission wavelengths of the fluorescent labels of other immunostainers are so far apart that crosstalk can be ignored. ..
  • the conditions for performing the immunostaining step should be appropriately adjusted so as to obtain an appropriate signal according to the conventional immunostaining method. Can be done.
  • the temperature is not particularly limited, but it can be carried out at room temperature.
  • the reaction time is preferably 30 minutes or more and 24 hours or less.
  • a known blocking agent such as PBS containing BSA or a surfactant such as Tween 20 before performing the treatment as described above.
  • tissue specimen after the immunostaining step is subjected to treatments such as immobilization / dehydration, permeation, and encapsulation so as to be suitable for observation.
  • the tissue section may be immersed in a fixing treatment solution (crosslinking agent such as formalin, paraformaldehyde, glutaraldehyde, acetone, ethanol, methanol, etc.).
  • a fixing treatment solution crosslinking agent such as formalin, paraformaldehyde, glutaraldehyde, acetone, ethanol, methanol, etc.
  • the tissue sections that have been immobilized and dehydrated may be immersed in a permeation solution (xylene or the like).
  • the encapsulation treatment may be performed by immersing the tissue section that has been subjected to the permeation treatment in the encapsulation liquid.
  • the conditions for performing these treatments for example, the temperature and the soaking time when the tissue section is immersed in a predetermined treatment solution, may be appropriately adjusted so as to obtain an appropriate signal according to the conventional immunostaining method. can.
  • ⁇ Object dyeing process> This is a step of staining an object so that the position of the object in the biological sample can be specified.
  • Staining of objects can be performed by immunostaining as in the drug staining step described above, but can also be performed by other standard methods.
  • staining with eodin which stains cytoplasm, interstitial, various fibers, erythrocytes, and keratinized cells in red to deep red, and indigo to pale blue in cell nuclei, lime, cartilage tissue, bacteria, and mucus. It may be stained with hematoxylin for staining.
  • the method of simultaneously performing these two stainings is known as hematoxylin / eosin staining (HE staining).
  • the object dyeing step when the object dyeing step is included, it may be performed after the drug dyeing step or before the drug dyeing step.
  • Fluorescent image acquisition step A bright-field image of a biological sample is acquired by a bright-field image acquisition unit of a microscope, a region to be analyzed is set based on this, and focusing is performed based on the bright-field image. Further, the fluorescent image acquisition unit irradiates the fluorescent substance-accumulated nanoparticles fluorescently labeled on the biological sample with excitation light, and acquires a fluorescent image based on the fluorescence emission from the detected fluorescent substance-accumulated nanoparticles.
  • the drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, and is a step of acquiring the biological sample image and a drug signal from the biological sample image. Based on the step of detecting / quantifying and obtaining drug state information, and analyzing at least the spatial distribution or statistical distribution including the concentration distribution of the drug associated with the region including the analysis target in the biological sample image. It is characterized by having a process.
  • the uniformity and appropriate concentration range of the drug concentration distribution by area ratio will be evaluated based on the histogram created based on the analysis results obtained by the analysis method according to the process order of image acquisition, drug signal detection, and drug distribution analysis.
  • An example of the embodiment will be described (see FIGS. 2 and 3).
  • the position of the drug can be specified by immunostaining (fluorescent dye, etc.) on the tissue section collected from the living body after administration of the drug, and the image is acquired using an imaging device such as a fluorescence microscope. For example, acquisition of a fluorescent image of a section stained with a drug using nanoparticles accumulating fluorescent substances.
  • ⁇ Drug signal detection> When detecting a drug signal, pretreatment may be performed in order to improve the extraction accuracy of drug information.
  • the ratio of signal intensity to noise (S / N) can be improved by removing self-fluorescent noise by signal processing such as a high-pass filter (HPF: high-pass filter). It is preferable from the viewpoint of.
  • a marker stain for discriminating the region may be extracted, or discrimination may be performed using machine learning. Manual work is also acceptable.
  • ⁇ Drug distribution analysis> Calculation of the integrated brightness of fluorescent staining that extracts spatial distribution information of drug concentration from drug staining intensity, detection of bright spots of fluorescent staining, quantification of drug concentration by calculation of fluorescent particles, etc. are performed. For statistical calculation of drug spatial distribution and concentration distribution, uniformity evaluation of drug concentration by histogram, drug effect space or toxicity spatial area ratio evaluation by concentration threshold processing, etc. are performed.
  • the drug concentration range that satisfies a predetermined frequency ratio (for example, 95 [%], etc.) based on the most frequent concentration value is defined as the drug concentration distribution variation performance, and the basic evaluation (optimal concentration) is performed. Whether it is within the range, etc.). It should be noted that a drug having a plurality of modes has a non-uniform distribution characteristic, and it is judged that the drug efficacy stability is low (see FIG. 3A).
  • the medicinal effect or toxicity is evaluated using the area ratio as a judgment criterion. It is also possible to evaluate the drug efficacy based on the spatial variation in the effective range (see FIG. 3B).
  • Embodiment 3 An embodiment example in which the drug concentration distribution analysis limited to the object region is performed and the accumulation state in the object which is the target of the drug is evaluated by performing the analysis according to the following process order will be described (see FIG. 4).
  • object information can be extracted from a bright-field DAB (diaminobenzidine) stained image by color separation / threshold processing using a DAB color vector.
  • object information can also be extracted by extracting cell contours in a bright field and then selecting those having an expression level of a biomarker specifically expressed in an object of a threshold value or more.
  • Quantitative analysis is performed on the drug concentration distribution narrowed down to the object region targeted by the drug (see FIG. 5). For example, a method in which only the object area is set as the area to be analyzed, a method in which only the fluorescence in the object area in the area is used for concentration determination, a drug is assigned to each object from the object coordinates and the drug detection position, and the unit is set. There is a method of calculating the drug concentration distribution as a concentration / object.
  • Embodiment 4 An embodiment of comparing drug concentration differences inside and outside the object region and determining a significant difference in drug accumulation in a drug target will be described (see FIGS. 6, 7, and 8).
  • ⁇ Distribution-related analysis of drugs and objects For example, quantification of drug concentration in each section in the inner region and outer region of the object, total concentration in the region, average drug concentration per unit area, etc. are calculated, and comparative analysis of drug distribution and drug concentration associated with each other inside and outside the object. (See FIGS. 7 and 8).
  • Embodiment 5 An embodiment example in which the correlation between the drug space distribution and the object space distribution is analyzed, for example, the drug concentration distribution in consideration of the object density distribution is analyzed, and the related factors affecting the drug distribution are evaluated will be described (Fig.). 9).
  • ⁇ Drug and object distribution association analysis> analysis is performed by creating a drug concentration map normalized by an object density feature, creating an object density map normalized by a drug feature, and the like.
  • the object density may be the number / unit section. For example, if the distribution after density normalization is uniform, it is evaluated that the density of the object and the drug concentration are correlated.
  • Embodiment 6 An embodiment of confirming the accumulation state of the drug in the tumor infiltrating immune cells by comparing the drug concentration in the object region according to the position of the second object will be described (see FIGS. 10 and 11).
  • ⁇ Image acquisition> For example, an image of a biological sample administered with an anti-PD-1 antibody as an immune checkpoint inhibitor is acquired. At this time, the anti-PD-1 antibody is fluorescently stained, the immune cells are stained with DAB or the like, and the fluorescent image and the bright field image are obtained.
  • the drug to be administered varies depending on the first object described later.
  • the first object when it is a cancer cell, it may be an anti-HER2 antibody, an anti-PD-L1 antibody, or the like. Multiple stains (fluorescent multicolor) may be performed to extract multiple objects. Objects may be extracted from the morphological information without staining or only with hematoxylin / eosin staining (HE staining) or hematoxylin staining (H staining).
  • ⁇ Object information extraction> For example, information on the tumor region is extracted as a second object, and the area is distinguished from the inside of the tumor region, the vicinity of the tumor region, and the remote portion of the tumor region according to the distance from the tumor region. Information is extracted with immune cells belonging to each area as the first object. Furthermore, information on target cells, such as tumor region infiltrating CD8-positive cells, is obtained.
  • the second object is the tumor region, but it may also be the infiltrating region of the invasive cancer.
  • the first object is an immune cell, but it may also be a cancer cell or the like.
  • ⁇ Drug signal detection, drug distribution analysis> For example, an object-based drug concentration distribution histogram of FIG. 11C created based on the image of FIG. 11A and an object-based drug concentration distribution histogram of FIG. 11D created based on the image of FIG. 11B are obtained and the drug distribution associated with the first object is obtained. By analyzing Since the rate is low, it is evaluated as having low immune checkpoint inhibition.
  • Embodiment 7 An experimental example for investigating changes over time such as drug distribution and object distribution using a mouse will be described (see FIGS. 12 to 15). After administering the anti-HER2 antibody drug conjugate (bonded antibody and drug (loading)) to the mouse, a sample is taken from the mouse at predetermined elapsed time, stained with fluorescent substance-accumulated nanoparticles (PID), and a fluorescent image is obtained. Acquire (see FIGS. 12A and 12B). After that, object detection using continuous sections and drug quantitative analysis are performed for each sample.
  • PID fluorescent substance-accumulated nanoparticles
  • HER2 positive / negative regions are identified by staining, two types of drug distribution (concentration) are measured in each region, and the concentrations are compared between time-series samples (see FIGS. 13A and 13B).
  • the HER2 positive region is extracted (DAB stained region detection).
  • DAB stained region detection After aligning the HER2-positive region, the field of view of the HER2-positive / negative region is specified, and a microscope image is obtained at a magnification of 40 times. A HER2-positive region corresponding to the field of view to be photographed is generated, and a PID score is calculated based on the HER2-positive region image. The PID scores of the positive and negative regions in the tissue sections by time after drug administration are compared (see FIG. 14).
  • FIG. 15 shows a method of analyzing from a histogram a state change in which a drug (payload) reaches an object and is further accumulated (localized) and diffused with the passage of time after administration of the anti-HER2 antibody drug conjugate.
  • the horizontal axis shows the fluorescence intensity of the fluorescent dye labeled on the drug (papment)
  • the vertical axis shows the frequency
  • the frequency of the high-intensity band decreases with the passage of time, and the peak of the mode value. Can be seen to shift to the high brightness side.
  • the decrease in the frequency of the high-intensity fluorescent region indicates the decrease in the integrated payload
  • the increase in the brightness of the mode indicates the diffusion of the free payload (the increase in the brightness of the entire region due to the decrease in the region without the payload).
  • Drug distribution state analysis system The drug distribution state analysis system of the present invention is characterized by having a process means for carrying out the drug distribution state analysis method of the present invention.
  • each process means for carrying out the steps 1 to 3 is at least necessary, but it is preferable to use an analysis system having a configuration including various process means depending on other purposes.
  • the analysis system has a configuration including any of the process means or the process unit corresponding to each of the processes 1 to 12.
  • the "process means” refers to chemical substances required as means in each process for carrying out the drug distribution state analysis method of the present invention, devices for measurement / analysis, imaging devices, information processing devices, and the like. say.
  • the present invention is used in a drug distribution state analysis method and a drug distribution state analysis system that acquires digital images from biological samples and automatically analyzes the correlation between the spatiotemporal distribution of drugs or the microenvironment according to certain model rules. be able to.

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Abstract

The present invention addresses the problem of providing a drug distribution state analysis method and a drug distribution state analysis system by which a digital image is acquired from a biological sample, and the correlation of the drug with a space-time distribution or a micro environment is automatically analyzed in accordance with a fixed model rule.　This drug distribution state analysis method is based on an image of a biological sample containing a drug, the method being characterized by including: a step 1 for acquiring a biological sample image; a step 2 for detecting/quantifying drug signals from the biological sample image to acquire drug state information; and a step 3 for analyzing, on the basis of the drug state information, the space-time distribution or the statistical distribution that include the concentration distribution of the drug associated with at least a region including an object to be analyzed in the biological sample image.

Description

薬物分布状態解析法及び薬物分布状態解析システムDrug distribution state analysis method and drug distribution state analysis system
　本発明は、薬物分布状態解析法及び薬物分布状態解析システムに関する。
　より詳しくは、生体試料からデジタル画像を取得し、薬物の空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する薬物分布状態解析法等に関する。 The present invention relates to a drug distribution state analysis method and a drug distribution state analysis system.
More specifically, the present invention relates to a drug distribution state analysis method or the like in which a digital image is acquired from a biological sample and the correlation with the spatial distribution of the drug or the microenvironment is automatically analyzed according to a certain model rule.
　薬物開発において生体内での薬物分布の定量解析及び薬物投与ターゲット組織構造・細胞分布等との関連性解析は、創薬研究開発（薬物動態解析・薬効薬理解析・毒性検査・ドラッグデザイン）において重要である。 In drug development, quantitative analysis of drug distribution in vivo and analysis of relevance to drug administration target tissue structure, cell distribution, etc. are important in drug discovery research and development (pharmacokinetic analysis, pharmacological analysis, toxicity test, drug design). Is.
　そこで、薬物投与後の生体試料に対して薬物を標的（ターゲット）としたイメージングにより薬物分布を解析する必要がある。
　例えば、特許文献１には、マトリックス支援レーザー脱離イオン化法（MALDI）を用いて、生体試料中の薬物の分布を調べたり、生体試料中の薬物を定量するための方法等が開示されている。 Therefore, it is necessary to analyze the drug distribution by imaging the biological sample after drug administration with the drug as a target.
For example, Patent Document 1 discloses a method for investigating the distribution of a drug in a biological sample and quantifying a drug in a biological sample by using a matrix-assisted laser desorption / ionization method (MALDI). ..
　しかし、特許文献１に開示の技術では、生体試料における薬物の分布を把握できるものの、薬物と、生体試料における他の構造（例えば、生体分子、細胞核などの細胞内構造、細胞、細胞群などの組織内構造など、以下に微小環境ともいう。）との位置関係を把握することはできない。また、標的の存在量・分布が一定ではない生体内での薬物分布の解析には、標的や薬物と生体試料における微小環境との位置関係、標的や薬物分布の空間分布傾向及び影響を及ぼす因子を考慮した上での薬物分布の検出・定量・評価が重要である。 However, although the technique disclosed in Patent Document 1 can grasp the distribution of the drug in the biological sample, the drug and other structures in the biological sample (for example, biomolecules, intracellular structures such as cell nuclei, cells, cell groups, etc.) can be grasped. It is not possible to grasp the positional relationship with the microenvironment, such as the structure inside the tissue. In addition, in the analysis of drug distribution in the living body where the abundance and distribution of the target are not constant, the positional relationship between the target or drug and the microenvironment in the biological sample, the spatial distribution tendency of the target or drug distribution, and the factors that influence it. It is important to detect, quantify, and evaluate the drug distribution in consideration of the above.
　また、ホール・スライド・イメージ（ｗｈｏｌｅ　ｓｌｉｄｅ　ｉｍａｇｅ：ＷＳＩ）等広域の生体試料全域に対して薬物分布解析を行う場合、人手による作業時間や主観による結果のバラつきを抑えることが重要となる。 In addition, when performing drug distribution analysis over a wide range of biological samples such as whole slide images (WSI), it is important to suppress variations in the results due to manual work time and subjectivity.
　したがって、生体試料からデジタル画像を取得し、薬物の空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する手法の開発が要望されている。 Therefore, there is a demand for the development of a method for acquiring digital images from biological samples and automatically analyzing the correlation with the spatial distribution of drugs or the microenvironment according to certain model rules.
特開２０１４－２０６３８９号公報Japanese Unexamined Patent Publication No. 2014-206389
　本発明は、上記問題・状況に鑑みてなされたものであり、その解決課題は、生体試料からデジタル画像を取得し、薬物の時空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する薬物分布状態解析法及び薬物分布状態解析システムを提供することである。 The present invention has been made in view of the above problems / situations, and the solution thereof is to acquire a digital image from a biological sample and automatically correlate the drug with the spatiotemporal distribution or the microenvironment according to a certain model rule. It is to provide a drug distribution state analysis method and a drug distribution state analysis system for physical analysis.
　本発明者は、上記課題を解決すべく、上記課題の原因等について検討した結果、薬物の時空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する薬物分布状態解析法及び薬物分布状態解析システムを提供できることを見出し本発明に至った。
　すなわち、本発明に係る上記課題は、以下の手段により解決される。 As a result of examining the cause of the above problem in order to solve the above problem, the present inventor is a drug distribution state analysis method for automatically analyzing the correlation between the spatiotemporal distribution of a drug or the microenvironment according to a certain model rule. And the present invention has been found to be able to provide a drug distribution state analysis system.
That is, the above-mentioned problem according to the present invention is solved by the following means.
　１．薬物を含む生体試料画像に基づく薬物分布状態解析法であって、
　前記生体試料画像を取得する工程１と、
　前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程２と、
　前記薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程３とを有することを特徴とする薬物分布状態解析法。 1. 1. It is a drug distribution state analysis method based on a biological sample image containing a drug.
Step 1 of acquiring the biological sample image and
Step 2 of detecting and quantifying a drug signal from the biological sample image and obtaining drug state information,
A drug distribution state characterized by having at least a step 3 of analyzing a spatial distribution or a statistical distribution including a concentration distribution of the drug associated with a region including an analysis target in the biological sample image based on the drug state information. Analysis method.
　２．前記各工程に加えて、前記生体試料画像から少なくとも解析対象である前記薬物のターゲットとなるオブジェクト又は当該ターゲットと関連性を有する非ターゲット・オブジェクトの情報を抽出する工程４を更に有し、前記工程３においては、前記オブジェクト又は非ターゲット・オブジェクトに関連付けられた前記薬物の濃度分布を、前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布として解析することを特徴とする第１項に記載の薬物分布状態解析法。 2. In addition to the above steps, the step 4 further includes a step 4 of extracting information on at least a target object of the drug to be analyzed or a non-target object having a relationship with the target from the biological sample image. No. 3 is characterized in that the concentration distribution of the drug associated with the object or the non-target object is analyzed as the concentration distribution of the drug associated with the region including the analysis target in the biological sample image. The drug distribution state analysis method according to item 1.
　３．前記工程４を経由して行う、前記オブジェクトに関連づけた薬物濃度分布を解析する工程３が、当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の解析であり、かつ、前記解析の方法が代表値算出又は相関解析であることを特徴とする第２項に記載の薬物分布状態解析法。 3. The step 3 for analyzing the drug concentration distribution associated with the object, which is performed via the step 4, is the analysis of the drug concentration distribution associated with the object inner region and the object outer region, respectively, and is the analysis. The drug distribution state analysis method according to item 2, wherein the method is representative value calculation or correlation analysis.
　４．前記オブジェクト内側領域にバイオマーカー陽性細胞領域を有し、前記オブジェクト外側領域にバイオマーカー陰性細胞領域を有することを特徴とする第３項に記載の薬物分布状態解析法。 4. The drug distribution state analysis method according to Item 3, wherein the biomarker-positive cell region is contained in the inner region of the object, and the biomarker-negative cell region is contained in the outer region of the object.
　５．前記オブジェクトに関連付けた薬物濃度分布を解析する工程３が、各オブジェクトからの距離又は方位のいずれか少なくとも一つの指標で区別したエリアにそれぞれ関連付けた薬物濃度分布を比較解析する工程であることを特徴とする第１項から第４項までのいずれか一項に記載の薬物分布状態解析法。 5. The step 3 for analyzing the drug concentration distribution associated with the object is a step for comparing and analyzing the drug concentration distribution associated with each area distinguished by at least one index of distance or orientation from each object. The drug distribution state analysis method according to any one of the items 1 to 4.
　６．前記各工程に加えて、前記工程４を経由して前記オブジェクトに関する空間分布を解析する工程５を更に有することを特徴とする第１項から第５項までのいずれか一項に記載の薬物分布状態解析法。 6. The drug distribution according to any one of the items 1 to 5, further comprising a step 5 of analyzing the spatial distribution of the object via the step 4 in addition to the steps. State analysis method.
　７．前記各工程に加えて、前記工程３において得た前記薬物濃度分布と前記工程４において得た前記オブジェクトの前記空間分布との関連性を解析する工程６を更に有することを特徴とする第１項から第６項までのいずれか一項に記載の薬物分布状態解析法。 7. The first item is characterized by further comprising a step 6 for analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4, in addition to the steps. The drug distribution state analysis method according to any one of the items from to 6.
　８．前記薬物又は前記オブジェクトのいずれか若しくは両方が複数種であることを特徴とする第１項から第７項までのいずれか一項に記載の薬物分布状態解析法。 8. The drug distribution state analysis method according to any one of items 1 to 7, wherein any or both of the drug and the object are a plurality of types.
　９．特定の前記オブジェクトに関連する異なる種類の前記薬物の濃度分布を比較解析することを特徴とする第８項に記載の薬物分布状態解析法。 9. The drug distribution state analysis method according to Item 8, wherein the concentration distribution of different types of the drug related to the specific object is comparatively analyzed.
　１０．前記比較解析する複数の薬物が、抗体薬物複合体とペイロードであることを特徴とする第９項に記載の薬物分布状態解析法。 10. The drug distribution state analysis method according to Item 9, wherein the plurality of drugs to be comparatively analyzed are an antibody drug conjugate and a payload.
　１１．前記比較解析する前記オブジェクトが複数種あり、異なるオブジェクト種間の前記薬物濃度分布を比較解析することを特徴とする第８項に記載の薬物分布状態解析法。 11. The drug distribution state analysis method according to Item 8, wherein there are a plurality of types of the objects to be comparatively analyzed, and the drug concentration distribution between different object types is comparatively analyzed.
　１２．前記比較解析する前記オブジェクトが複数種あり、第２のオブジェクトからの空間分布に応じた、第１のオブジェクトに関連付けた前記薬物濃度分布を比較解析することを特徴とする第１１項に記載の薬物分布状態解析法。 12. Item 2. The drug according to Item 11, wherein there are a plurality of types of the objects to be comparatively analyzed, and the drug concentration distribution associated with the first object is comparatively analyzed according to the spatial distribution from the second object. Distribution state analysis method.
　１３．前記比較解析する前記オブジェクトが複数種あり、種類の異なる前記オブジェクト間での、それぞれ異なる薬物種の濃度分布を比較解析することを特徴とする第８項に記載の薬物分布状態解析法。 13. The drug distribution state analysis method according to Item 8, wherein there are a plurality of types of the objects to be comparatively analyzed, and the concentration distributions of different drug types are compared and analyzed among the different types of objects.
　１４．前記各工程に加えて、前記工程３又は前記工程６において得た解析結果に基づいて詳細解析箇所を選定する工程７を更に有することを特徴とする第１項から第１３項までのいずれか一項に記載の薬物分布状態解析法。 14. Any one of the items 1 to 13, further comprising a step 7 of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6 in addition to the steps. The drug distribution state analysis method described in the section.
　１５．前記各工程に加えて、前記選定した詳細解析箇所を解析する工程８を更に有することを特徴とする第１４項に記載の薬物分布状態解析法。 15. The drug distribution state analysis method according to claim 14, further comprising a step 8 for analyzing the selected detailed analysis site in addition to each of the above steps.
　１６．前記生体試料画像を取得する工程１が、連続する複数の切片からそれぞれ解析対象となる薬物状態情報及びオブジェクト情報を個別に取得する工程であり、かつ、前記各工程に加えて、前記連続する複数の切片の位置合わせによる画像位置合わせをする工程９を更に有することを特徴とする第１項から第１５項までのいずれか一項に記載の薬物分布状態解析法。 16. The step 1 of acquiring the biological sample image is a step of individually acquiring drug state information and object information to be analyzed from a plurality of consecutive sections, and in addition to the above steps, the continuous plurality of steps. The drug distribution state analysis method according to any one of items 1 to 15, further comprising a step 9 of image alignment by alignment of sections of the above.
　１７．前記各工程に加えて、前記工程１において取得した複数の生体試料画像に対して、当該生体試料画像間での薬物分布について比較解析をする工程１０を更に有することを特徴とする第１項から第１６項までのいずれか一項に記載の薬物分布状態解析法。 17. From the first item, in addition to each of the above steps, the step 10 of performing comparative analysis of the drug distribution among the biological sample images obtained in the plurality of biological sample images obtained in the step 1 is further provided. The drug distribution state analysis method according to any one of items up to item 16.
　１８．前記各工程に加えて、前記薬物濃度分布又はオブジェクト分布の解析結果に基づいて、注目領域を選定する工程１１を更に有することを特徴とする第１項から第１７項までのいずれか一項に記載の薬物分布状態解析法。 18. In any one of the items 1 to 17, further comprising a step 11 for selecting a region of interest based on the analysis result of the drug concentration distribution or the object distribution in addition to each of the above steps. The described drug distribution state analysis method.
　１９．前記各工程に加えて、複数の前記注目領域の間の遺伝子発現差分から関連バイオマーカーを同定する工程１２を更に有することを特徴とする第１８項に記載の薬物分布状態解析法。 19. The drug distribution state analysis method according to Item 18, wherein, in addition to each of the above steps, a step 12 of identifying a related biomarker from the difference in gene expression between the plurality of regions of interest is further included.
　２０．薬物を含む生体試料画像に基づく薬物分布状態解析システムであって、
　第１項から第１９項までのいずれか一項に記載の薬物分布状態解析法を実施するための工程手段を有することを特徴とする薬物分布状態解析システム。 20. A drug distribution state analysis system based on images of biological samples containing drugs.
A drug distribution state analysis system comprising a process means for carrying out the drug distribution state analysis method according to any one of paragraphs 1 to 19.
　本発明の上記手段により、生体試料からデジタル画像を取得し、薬物の時空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する薬物分布状態解析法及び薬物分布状態解析システムを提供することができる。 A drug distribution state analysis method and a drug distribution state analysis system that acquire a digital image from a biological sample by the above means of the present invention and automatically analyze the correlation between the spatiotemporal distribution of a drug or the microenvironment according to a certain model rule. Can be provided.
　本発明の薬物分布状態解析法では、少なくとも生体試料画像を取得する工程と、前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程と、前記薬物状態情報に基づき、前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程とを含む一定のモデル・ルールに従って設定された工程を順次実施する。
　したがって、生体内での薬物分布の定量解析及び薬物投与ターゲット組織構造・細胞分布等との関連性解析が自動的にすることができ、創薬研究開発において有効である。 In the drug distribution state analysis method of the present invention, at least a step of acquiring a biological sample image, a step of detecting and quantifying a drug signal from the biological sample image to obtain drug state information, and the drug based on the drug state information. The steps set according to certain model rules including the step of analyzing the spatial distribution or the statistical distribution including the concentration distribution of the above are sequentially carried out.
Therefore, quantitative analysis of drug distribution in vivo and relationship analysis with drug administration target tissue structure, cell distribution, etc. can be automatically performed, which is effective in drug discovery research and development.
本発明の薬物解析法を構成する工程例Example of process constituting the drug analysis method of the present invention 実施形態２における工程の構成及び手順を示す模式図Schematic diagram showing the structure and procedure of the process in the second embodiment 実施形態２における解析手順及び結果の例を示す模式図ＡSchematic diagram A showing an example of the analysis procedure and the result in the second embodiment. 実施形態２における解析手順及び結果の例を示す模式図ＢSchematic diagram B showing an example of the analysis procedure and results in the second embodiment 実施形態３における工程の構成及び手順を示す模式図Schematic diagram showing the configuration and procedure of the process in the third embodiment 実施形態３における解析手順及び結果の例を示す模式図Schematic diagram showing an example of the analysis procedure and results in the third embodiment 実施形態４における工程の構成及び手順を示す模式図Schematic diagram showing the configuration and procedure of the process in the fourth embodiment 実施形態４における解析手順及び結果の例を示す模式図Schematic diagram showing an example of the analysis procedure and results in the fourth embodiment 実施形態４における解析手順及び結果の例を示す模式図Schematic diagram showing an example of the analysis procedure and results in the fourth embodiment 実施形態５における工程の構成及び手順を示す模式図Schematic diagram showing the configuration and procedure of the process in the fifth embodiment 実施形態６における工程の構成及び手順を示す模式図Schematic diagram showing the configuration and procedure of the process in the sixth embodiment 実施形態６における解析結果の例を示す模式図ＡSchematic diagram A showing an example of the analysis result in the sixth embodiment 実施形態６における解析結果の例を示す模式図ＢSchematic diagram B showing an example of the analysis result in the sixth embodiment 実施形態６における解析結果の例を示す模式図ＣSchematic diagram C showing an example of the analysis result in the sixth embodiment 実施形態６における解析結果の例を示す模式図ＤSchematic diagram D showing an example of the analysis result in the sixth embodiment 実施形態７における解析対象の例を示す模式図ＡSchematic diagram A showing an example of the analysis target in the seventh embodiment 実施形態７における解析対象の例を示す模式図ＢSchematic diagram B showing an example of the analysis target in the seventh embodiment 実施形態７における解析結果の例を示す模式図ＡSchematic diagram A showing an example of the analysis result in the seventh embodiment 実施形態７における解析結果の例を示す模式図ＢSchematic diagram B showing an example of the analysis result in the seventh embodiment 実施形態７における染色切片の解析工程の構成及び手順を示す模式図Schematic diagram showing the configuration and procedure of the analysis step of the stained section in the seventh embodiment. 実施形態７における解析結果の別の例を示す模式図Schematic diagram showing another example of the analysis result in the seventh embodiment
　本発明の薬物分布状態解析法は、薬物を含む生体試料画像に基づく薬物分布状態解析法であって、前記生体試料画像を取得する工程１と、前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程２と、前記薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程３とを有することを特徴とする。
　この特徴は、下記実施態様に共通する又は対応する技術的特徴である。 The drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, and is a step 1 of acquiring the biological sample image and detecting and quantifying a drug signal from the biological sample image. , Step 2 to obtain drug state information, and step 3 to analyze the spatial distribution or statistical distribution including the concentration distribution of the drug associated with at least the region including the analysis target in the biological sample image based on the drug state information. It is characterized by having.
This feature is a technical feature common to or corresponding to the following embodiments.
　本発明の実施態様としては、本発明の効果発現の観点から、前記各工程に加えて、前記生体試料画像から少なくとも解析対象である前記薬物のターゲットとなるオブジェクト又は当該ターゲットと関連性を有する非ターゲット・オブジェクトの情報を抽出する工程４を更に有し、前記工程３においては、前記オブジェクト又は非ターゲット・オブジェクトに関連付けられた前記薬物の濃度分布を、前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布として解析することが好ましい。 As an embodiment of the present invention, from the viewpoint of exhibiting the effect of the present invention, in addition to the above steps, at least the object to be the target of the drug to be analyzed from the biological sample image or the non-relevant to the target. Further, it has a step 4 of extracting information on a target object, and in the step 3, the concentration distribution of the drug associated with the object or a non-target object is set in a region including an analysis target in the biological sample image. It is preferable to analyze as the concentration distribution of the associated drug.
　本発明の薬物分布状態解析法は、前記工程４を経由して行う、前記オブジェクトに関連づけた薬物濃度分布を解析する工程３が、当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の解析であり、かつ、前記解析の方法が代表値算出又は相関解析であることが、薬物動態解析・薬効薬理解析等の観点から好ましい。 In the drug distribution state analysis method of the present invention, the drug concentration distribution associated with the object inner region and the object outer region in step 3 for analyzing the drug concentration distribution associated with the object, which is performed via the step 4, respectively. From the viewpoint of pharmacokinetic analysis, pharmacological analysis, etc., it is preferable that the analysis method is representative value calculation or correlation analysis.
　本発明の薬物分布状態解析法は、薬物動態解析（特に、薬物のターゲット選択制評価）を目的とした場合、前記オブジェクト内側領域にバイオマーカー陽性細胞領域を有し、前記オブジェクト外側領域にバイオマーカー陰性細胞領域を有する。
　本発明の薬物分布状態解析法は、前記オブジェクト内側領域にバイオマーカー陽性細胞領域を有し、前記オブジェクト外側領域にバイオマーカー陰性細胞領域を有することが、薬物動態解析（特に、薬物のターゲット選択性を評価）ができる点で好ましい。 The drug distribution state analysis method of the present invention has a biomarker-positive cell region in the inner region of the object and a biomarker in the outer region of the object for the purpose of pharmacokinetic analysis (particularly, target selection system evaluation of a drug). Has a negative cell region.
The drug distribution state analysis method of the present invention has a biomarker-positive cell region in the inner region of the object and a biomarker-negative cell region in the outer region of the object. It is preferable in that it can be evaluated.
　本発明の薬物分布状態解析法は、前記オブジェクトに関連付けた薬物濃度分布を解析する工程３が、各オブジェクトからの距離又は方位のいずれか少なくとも一つの指標で区別したエリアにそれぞれ関連付けた薬物濃度分布を比較解析する工程であることが、薬物動態解析（特に、薬物の集積・拡散について評価）ができる点で好ましい。 In the drug distribution state analysis method of the present invention, in step 3 of analyzing the drug concentration distribution associated with the object, the drug concentration distribution associated with each area distinguished by at least one index of distance or orientation from each object. It is preferable that the step is a step of comparative analysis because it enables pharmacokinetic analysis (particularly, evaluation of drug accumulation / diffusion).
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記工程４を経由して前記オブジェクトに関する空間分布を解析する工程５を更に有することが関連因子の解析の観点から好ましい。 It is preferable that the drug distribution state analysis method of the present invention further includes, in addition to each of the above steps, a step 5 of analyzing the spatial distribution of the object via the step 4, from the viewpoint of analysis of related factors.
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記工程３において得た前記薬物濃度分布と前記工程４において得た前記オブジェクトの前記空間分布との関連性を解析する工程６を更に有することが薬物動態と関連因子との相関性解析の観点から好ましい。 In the drug distribution state analysis method of the present invention, in addition to the above steps, step 6 for analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4 is performed. Further possession is preferable from the viewpoint of correlation analysis between pharmacokinetics and related factors.
　本発明の薬物分布状態解析法は、前記薬物又は前記オブジェクトのいずれか若しくは両方が複数種であることが効果の比較解析の観点から好ましい。 In the drug distribution state analysis method of the present invention, it is preferable that either or both of the drug and the object are a plurality of types from the viewpoint of comparative analysis of effects.
　本発明の薬物分布状態解析法は、特定の前記オブジェクトに関連する異なる種類の前記薬物の濃度分布を比較解析することが併用剤など複数の薬物が共局在していることで治療効果を持つケースの薬効を評価できる点から好ましい。 The drug distribution state analysis method of the present invention has a therapeutic effect by comparing and analyzing the concentration distributions of different types of drugs related to a specific object because a plurality of drugs such as concomitant drugs are co-localized. It is preferable because the medicinal effect of the case can be evaluated.
　本発明の薬物分布状態解析法は、前記比較解析する複数の薬物が、抗体薬物複合体とペイロードであることが、抗体薬物複合体の薬効を評価できる点から好ましい。 In the drug distribution state analysis method of the present invention, it is preferable that the plurality of drugs to be comparatively analyzed are an antibody drug conjugate and a payload from the viewpoint that the efficacy of the antibody drug conjugate can be evaluated.
　本発明の薬物分布状態解析法は、前記比較解析する前記オブジェクトが複数種あり、異なるオブジェクト種間の前記薬物濃度分布を比較解析することが薬物の副作用・毒性を評価できる点から好ましい。 The drug distribution state analysis method of the present invention has a plurality of types of the objects to be comparatively analyzed, and it is preferable to compare and analyze the drug concentration distribution between different object types because the side effects and toxicity of the drug can be evaluated.
　本発明の薬物分布状態解析法は、前記比較解析する前記オブジェクトが複数種あり、第２のオブジェクトからの空間分布に応じた、第１のオブジェクトに関連付けた前記薬物濃度分布を比較解析することが薬物集積のヘテロ性及び薬物拡散の度合いを解析できる点で好ましい。 In the drug distribution state analysis method of the present invention, there are a plurality of types of the objects to be compared and analyzed, and the drug concentration distribution associated with the first object according to the spatial distribution from the second object can be compared and analyzed. It is preferable because it can analyze the heterogeneity of drug accumulation and the degree of drug diffusion.
　本発明の薬物分布状態解析法は、前記比較解析する前記オブジェクトが複数種あり、種類の異なる前記オブジェクト間での、それぞれ異なる薬物種の濃度分布を比較解析することがターゲットの異なる複数薬物併用ケースの薬効を評価できる観点から好ましい。 In the drug distribution state analysis method of the present invention, there are a plurality of types of the objects to be comparatively analyzed, and the case where a plurality of drugs having different targets are to be compared and analyzed for the concentration distribution of different drug types among the different types of objects. It is preferable from the viewpoint that the medicinal effect of
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記工程３又は前記工程６において得た解析結果に基づいて詳細解析箇所を選定する工程７を更に有することがより精度の高い濃度算出が行える領域を選定できる点から好ましい。 The drug distribution state analysis method of the present invention further includes, in addition to each of the above steps, a step 7 of selecting a detailed analysis site based on the analysis result obtained in the above step 3 or the above step 6, which is a more accurate concentration. It is preferable because the area where the calculation can be performed can be selected.
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記選定した詳細解析箇所を解析する工程８を更に有することがより精度の高い薬物濃度算出が行える点から好ましい。 It is preferable that the drug distribution state analysis method of the present invention further includes a step 8 for analyzing the selected detailed analysis points in addition to the above steps, because more accurate drug concentration calculation can be performed.
　本発明の薬物分布状態解析法は、前記生体試料画像を取得する工程１が、連続する複数の切片からそれぞれ解析対象となる薬物状態情報及びオブジェクト情報を個別に取得する工程であり、かつ、前記各工程に加えて、前記連続する複数の切片の位置合わせによる画像位置合わせをする工程９を更に有することが１枚の切片では多重染色ができないケースでも、複数の切片が連続した画像として比較解析が可能になるという点から好ましい。 In the drug distribution state analysis method of the present invention, the step 1 of acquiring the biological sample image is a step of individually acquiring the drug state information and the object information to be analyzed from a plurality of consecutive sections, and the above-mentioned method. In addition to each step, even in the case where multiple staining cannot be performed with one section by further having the step 9 of image alignment by the alignment of the plurality of consecutive sections, comparative analysis is performed as a continuous image of the plurality of sections. It is preferable in that it enables.
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記工程１において取得した複数の生体試料画像に対して、当該生体試料画像間での薬物分布について比較解析をする工程１０を更に有することが、薬物投与後の経時変化等を評価できる点で好ましい。 In the drug distribution state analysis method of the present invention, in addition to each of the above steps, a step 10 of comparatively analyzing the drug distribution among the biological sample images obtained in the plurality of biological sample images obtained in the step 1 is further performed. It is preferable to have it because it is possible to evaluate changes over time after drug administration.
　本発明の薬物分布状態解析法は、前記各工程に加えて、前記薬物濃度分布又はオブジェクト分布の解析結果に基づいて、注目領域を選定する工程１１を更に有することが解析結果と関連する因子を特定するため、薬物分布の特徴が異なる箇所の生体内空間ヘテロ性を比較解析する観点から好ましい。 In addition to each of the above steps, the drug distribution state analysis method of the present invention further includes a step 11 of selecting a region of interest based on the analysis result of the drug concentration distribution or the object distribution, which is a factor related to the analysis result. For identification, it is preferable from the viewpoint of comparative analysis of in vivo spatial heterogeneity in places where the characteristics of drug distribution are different.
　本発明の薬物分布状態解析法は、前記各工程に加えて、複数の前記注目領域の間の遺伝子発現差分から関連バイオマーカーを同定する工程１２を更に有することが計測対象とした薬物集積や特定バイオマーカー発現として表出していない生体内の変化を網羅的に解析できる点で好ましい。 The drug distribution state analysis method of the present invention further comprises, in addition to the above-mentioned steps, a step 12 of identifying a related biomarker from the gene expression difference between the plurality of said regions of interest. It is preferable because it can comprehensively analyze changes in the living body that are not expressed as biomarker expression.
　本発明の薬物分布状態解析システムは、薬物を含む生体試料画像に基づく薬物分布状態解析システムであって、第１項から第１９項までのいずれか一項に記載の薬物分布状態解析法を実施するための工程手段を有する態様のシステムであることが、上記解析方法における各種メリットを発現できる点で好ましい。 The drug distribution state analysis system of the present invention is a drug distribution state analysis system based on a biological sample image containing a drug, and carries out the drug distribution state analysis method according to any one of paragraphs 1 to 19. It is preferable that the system has an embodiment of a process means for the above-mentioned analysis method in that various merits of the analysis method can be exhibited.
　以下、本発明とその構成要素、及び本発明を実施するための形態・態様について詳細な説明をする。なお、本願において、「～」は、その前後に記載される数値を下限値及び上限値として含む意味で使用する。その他、本発明に係る技術用語の意義については、適宜説明する。 Hereinafter, the present invention, its constituent elements, and the forms and modes for carrying out the present invention will be described in detail. In addition, in this application, "-" is used in the sense that the numerical values described before and after it are included as the lower limit value and the upper limit value. In addition, the meaning of the technical terms according to the present invention will be described as appropriate.
（主要用語の定義）
　あらかじめ、以下において、本発明に係る主要な技術用語の意義について説明する。
　「薬物」とは、自然界の物質及び化学物質に由来する物質であって、生体外から、人為的に投与又は特定の外部環境依存的に摂取・吸引・吸収され、生体に対し何らかの薬効及び毒性を発揮する生物活性物質及び生理活性を持つ生体内化学物質由来の物質のことをいう。例えば低分子医薬品、バイオ医薬品（抗体医薬品、ＲＮＡ、ウイルスなど）などが挙げられる。 (Definition of main terms)
In advance, the meanings of the main technical terms according to the present invention will be described below.
A "drug" is a substance derived from a substance or chemical substance in the natural world, which is artificially administered from outside the body or ingested, inhaled, or absorbed depending on a specific external environment, and has some medicinal effect and toxicity to the living body. It refers to substances derived from biologically active substances and bioactive chemical substances that exhibit bioactivity. For example, small molecule drugs, biopharmacy (antibody drugs, RNA, viruses, etc.) and the like can be mentioned.
　「生体試料」とは、生体組織を画像取得ができる状態にした試料のことをいう。例えばマウスなどを透過処理により観察可能にしたもの、生体から採取した組織検体、培養細胞、生体組織を固定化した標本（組織切片）などが挙げられる。また、コンピュータ断層撮影（ｃｏｍｐｕｔｅｄ　ｔｏｍｏｇｒａｐｈｙ、略称：ＣＴ）や核磁気共鳴画像法（ｍａｇｎｅｔｉｃ　ｒｅｓｏｎａｎｃｅ　ｉｍａｇｉｎｇ、略称：ＭＲＩ）などの画像取得手段使用時は生体そのものを生体試料とすることもできる。 "Biological sample" refers to a sample in which a biological tissue is in a state where an image can be acquired. For example, a mouse or the like made observable by permeation treatment, a tissue sample collected from a living body, a cultured cell, a sample (tissue section) in which a living tissue is immobilized, and the like can be mentioned. Further, when using an image acquisition means such as computed tomography (abbreviation: CT) or magnetic resonance imaging (abbreviation: MRI), the living body itself can be used as a biological sample.
　「オブジェクト」とは、生体分子・細胞・構造などの薬物以外の解析対象又はそれを含む領域のことをいう。薬物のターゲットとなる解析対象だけでなく、ターゲットと関連性のある解析対象及びそれを含む領域なども含まれる。
　具体的には、特定細胞種（幹細胞、グリア細胞、Ｔ細胞などの分化による分類の他、壊死細胞や炎症などの病態、細胞周期などの特定条件下での分類含む）、又は組織内構造（血管、壊死領域、浸潤や突起などの空間配置・形状特徴分類）、又は細胞内構造（核、小胞体などのオルガネラ）が挙げられる。 The "object" means an analysis target other than a drug such as a biomolecule, a cell, or a structure, or a region containing the same. Not only the analysis target that is the target of the drug, but also the analysis target related to the target and the region containing the analysis target are included.
Specifically, specific cell types (including classification by differentiation of stem cells, glia cells, T cells, etc., pathological conditions such as necrotic cells and inflammation, classification under specific conditions such as cell cycle), or tissue internal structure (including classification). Examples include blood vessels, necrotic areas, spatial arrangement / shape characteristics classification such as invasion and protrusions), or intracellular structures (organellas such as nuclei and vesicles).
　「ペイロード」とは、標的細胞又は組織に送達される分子又は材料をいう。ペイロードは特に限定されず、対象の病気の診断、治療、又は予防に用いることを意図される任意の医薬品化合物であってもよい。
　例えば、目的・態様によって、当該ペイロードは、小分子化合物、ヌクレオチド（例えば、ＤＮＡ、プラスミドＤＮＡ、ＲＮＡ、ｓｉＲＮＡ、アンチセンスオリゴヌクレオチド、アプタマーなど）、ペプチド、タンパク質（例えば、酵素）、蛍光性色素、量子ドット又はナノ粒子である。 "Payload" means a molecule or material delivered to a target cell or tissue. The payload is not particularly limited and may be any pharmaceutical compound intended for use in the diagnosis, treatment or prevention of a disease of interest.
For example, depending on the purpose / embodiment, the payload may include small molecule compounds, nucleotides (eg, DNA, plasmid DNA, RNA, siRNA, antisense oligonucleotides, aptamers, etc.), peptides, proteins (eg, enzymes), fluorescent dyes, etc. It is a quantum dot or a nanoparticle.
　「空間分布」とは、二次元又は三次元の画像における、解析又は観察対象である薬物、オブジェクト及び関連オブジェクト等の状態を座標系で表現したときの、それぞれの分布状態（座標位置、分布領域*面積、濃度、密度、増減、集積／拡散方位速度などの経時変化も含む。）をいう。なお、特に解析・観察対象に着目して、単に、「薬物分布」又は「オブジェクト分布」とも称する。
　「統計分布」とは、上記の空間分布のうち、例えば薬物等の濃度、密度等について統計学的観点から集約した分布情報（頻度分布、平均値等の集約値等）をいう。 "Spatial distribution" is a distribution state (coordinate position, distribution area) when the state of a drug, object, related object, etc. to be analyzed or observed in a two-dimensional or three-dimensional image is expressed in a coordinate system. * Including changes over time such as area, concentration, density, increase / decrease, accumulation / diffusion azimuth velocity, etc.). In addition, paying particular attention to the analysis / observation target, it is also simply referred to as “drug distribution” or “object distribution”.
The "statistical distribution" refers to distribution information (frequency distribution, aggregated value such as average value, etc.) aggregated from a statistical viewpoint, for example, the concentration, density, etc. of a drug, etc., among the above spatial distributions.
　なお、「濃度」とは、生体画像における、所定の区画当たり若しくはオブジェクト領域当たり又は単位面積当たりの薬物又はマーカー等を結合させて検出可能とした薬物の量をいう。ただし、当該薬物の量が、一般的な薬物濃度（［Ｌ／ｋｇ］、［μｇ／Ｌ］、［ｐｐｍ］、［ｍｏｌ／Ｌ］など）と相関する場合には、間接的に単位変換が可能である。 The "concentration" refers to the amount of a drug that can be detected by binding a drug, a marker, or the like per predetermined section, object area, or unit area in a biological image. However, if the amount of the drug correlates with a general drug concentration ([L / kg], [μg / L], [ppm], [mol / L], etc.), unit conversion is indirectly performed. It is possible.
　なお、「生体試料画像」は、生体試料内の薬物の位置が特定可能となっている必要がある。また、オブジェクトの位置が特定可能となっていることが好ましい。
　本発明においては、例えば蛍光画像を用いることができるが、実施形態によっては蛍光画像と併せて明視野画像を用いることが好ましい。 The "biological sample image" needs to be able to identify the position of the drug in the biological sample. Further, it is preferable that the position of the object can be specified.
In the present invention, for example, a fluorescent image can be used, but depending on the embodiment, it is preferable to use a bright field image together with the fluorescent image.
１　本発明の薬物分布状態解析法の概要
　本発明の薬物分布状態解析法薬物を含む生体試料画像に基づく薬物分布状態解析法であって、前記生体試料画像を取得する工程１と、前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程２と、前記薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程３とを有することを特徴とする。 1 Outline of the drug distribution state analysis method of the present invention The drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, in which step 1 of acquiring the biological sample image and the biological sample. Spatial distribution including step 2 of detecting and quantifying a drug signal from an image to obtain drug state information, and at least the concentration distribution of the drug associated with a region including an analysis target in the biological sample image based on the drug state information. Alternatively, it is characterized by having a step 3 for analyzing a statistical distribution.
　また、工程１～工程３に加えて、下記工程４～工程１２を有していることが好ましい。なお、工程１～工程１２の番号は、必ずしも工程の順番を表すものではないが、これらの工程を含む手順をあらかじめ決めたモデル・ルールに従うことで、試行錯誤することなく、自動的に生体内薬物分布状態を解析することができる。 Further, it is preferable to have the following steps 4 to 12 in addition to the steps 1 to 3. The numbers of steps 1 to 12 do not necessarily indicate the order of the steps, but by following a model rule in which the procedure including these steps is determined in advance, the numbers are automatically in vivo without trial and error. The drug distribution state can be analyzed.
（１．１）薬物分布状態解析法を構成する工程
　本発明においては、下記工程１～３は最低限必要であるが、その他目的に応じて種々の工程を含めた構成の解析法とすることが好ましい。
　例えば、以下に示す、工程１～１２の各工程のいずれかを含む構成の解析法であることが好ましい（図１参照）。 (1.1) Steps for constructing a drug distribution state analysis method In the present invention, the following steps 1 to 3 are at least necessary, but the analysis method may be configured to include various steps depending on other purposes. Is preferable.
For example, it is preferable to use an analysis method having a configuration including any of the steps 1 to 12 shown below (see FIG. 1).
　工程１：生体試料画像を取得する工程
　工程２：生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程
　工程３：薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程 Step 1: Obtaining a biological sample image Step 2: Detecting and quantifying a drug signal from the biological sample image to obtain drug state information Step 3: Includes at least an analysis target in the biological sample image based on the drug state information The step of analyzing the spatial distribution or statistical distribution including the concentration distribution of the drug associated with the region.
　工程４：前記生体試料画像から少なくとも解析対象である前記薬物のターゲットとなるオブジェクト又は当該ターゲットと関連性を有する非ターゲット・オブジェクトの情報を抽出する工程
　工程５：オブジェクトに関する空間分布を解析する工程
　工程６：前記工程３において得た薬物濃度分布と前記工程４において得たオブジェクトの前記空間分布との関連性を解析する工程 Step 4: Extract information from the biological sample image at least the target object of the drug to be analyzed or a non-target object related to the target Step 5: Analyze the spatial distribution of the object. 6: A step of analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4.
　工程７：前記工程３又は前記工程６において得た解析結果に基づいて詳細解析箇所を選定する工程
　工程８：選定した詳細解析箇所を解析する工程
　工程９：連続する複数の切片の位置合わせによる画像位置合わせをする工程 Step 7: Step of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6 Step 8: Step of analyzing the selected detailed analysis point Step 9: Image by alignment of a plurality of consecutive sections Alignment process
　工程１０：工程１において取得した複数の生体試料画像に対して、当該生体試料画像間について比較解析をする工程
　工程１１：薬物濃度分布又はオブジェクト分布の解析結果に基づいて、注目領域を選定する工程
　工程１２：複数の前記注目領域の間の遺伝子発現差分から関連バイオマーカーを同定する工程 Step 10: A step of comparatively analyzing a plurality of biological sample images acquired in Step 1 between the biological sample images. Step 11: A step of selecting a region of interest based on the analysis result of a drug concentration distribution or an object distribution. Step 12: Identifying the relevant biomarker from the gene expression differences between the plurality of regions of interest.
（１．２）各工程における目的・操作等
≪工程１≫：生体試料画像を取得する工程
　工程１は、生体試料画像を取得する工程である。生体試料画像を取得できればその方法は限定されないが、一般にバイオイメージング手法として知られている分光イメージング（吸収・発光分光法、赤外分光法、又はラマン分光法等を利用するイメージング）、質量イメージング（マトリックス支援レーザー脱離イオン化質量分析法（ＭＡＬＤＩ－ＭＳ）又は飛行時間型２次イオン質量分析法（ＴＯＦ－ＳＩＭＳ）等を利用するイメージング）、又は蛍光顕微鏡や電子顕微鏡によるイメージング等を利用する方法を採ることができる。 (1.2) Purpose, operation, etc. in each step << Step 1 >>: Step of acquiring a biological sample image Step 1 is a step of acquiring a biological sample image. The method is not limited as long as a biological sample image can be obtained, but spectroscopic imaging (imaging using absorption / emission spectroscopy, infrared spectroscopy, Raman spectroscopy, etc.) and mass imaging (imaging using absorption / emission spectroscopy, Raman spectroscopy, etc.) generally known as bioimaging techniques are used. Imaging using matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MS) or time-of-flight secondary ion mass spectrometry (TOF-SIMS)), or imaging using a fluorescent microscope or an electron microscope, etc. Can be taken.
　本発明では、特に、吸収・発光分光法/蛍光顕微鏡の利用により蛍光画像を取得する方法を用いることが好ましい。 In the present invention, it is particularly preferable to use a method of acquiring a fluorescence image by using absorption / emission spectroscopy / fluorescence microscopy.
≪工程２≫：薬物状態情報を得る工程
　工程２は、生体試料画像から薬物由来のシグナル（以下において「薬物シグナル」ともいう。）を検出・定量し、薬物状態情報を得る工程である。薬物状態情報を得る薬物は複数種であることが比較解析の点から好ましい。 << Step 2 >>: Step of obtaining drug state information Step 2 is a step of detecting and quantifying a drug-derived signal (hereinafter, also referred to as "drug signal") from a biological sample image and obtaining drug state information. From the viewpoint of comparative analysis, it is preferable that there are a plurality of types of drugs for which drug status information is obtained.
　ここで、「薬物由来のシグナル」とは、薬物自体のシグナル又は薬物に標識している発色基質や蛍光色素等の標識物質のシグナルのことである。 Here, the "drug-derived signal" is a signal of the drug itself or a signal of a labeling substance such as a color-developing substrate or a fluorescent dye that is labeled on the drug.
　ここで、「薬物状態情報」とは、例えば薬物の種類や濃度のことである。薬物濃度は、定量した薬物シグナルから閾値処理、蛍光輝点積分又は蛍光粒子算出などの方法により定量することができる。 Here, "drug status information" is, for example, the type and concentration of a drug. The drug concentration can be quantified from the quantified drug signal by a method such as threshold processing, fluorescence bright spot integration, or fluorescence particle calculation.
　薬物シグナルの検出・定量の精度を上げるために、ノイズ除去処理や、領域設定処理などの前処理を行ってもよい。ノイズ除去処理は、例えば、読み込んだ蛍光画像について、自家蛍光輝度を抑圧する処理である。 In order to improve the accuracy of drug signal detection and quantification, preprocessing such as noise reduction processing and area setting processing may be performed. The noise reduction process is, for example, a process of suppressing the autofluorescent luminance of the read fluorescent image.
≪工程３≫：空間分布又は統計分布を解析する工程
　工程３は、薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程である。
　また、工程３においては、後述する工程４で抽出したオブジェクト又は非ターゲット・オブジェクトに関連付けられた薬物の濃度分布を、前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布として、解析することが好ましい。
　なお、薬物分布を解析する薬物は複数種であることが比較解析の点から好ましい。 << Step 3 >>: Step of analyzing the spatial distribution or statistical distribution Step 3 is a spatial distribution or statistics including at least the concentration distribution of the drug associated with the region including the analysis target in the biological sample image based on the drug state information. This is the process of analyzing the distribution.
Further, in step 3, the concentration distribution of the drug associated with the object or non-target object extracted in step 4 described later is used as the concentration distribution of the drug associated with the region including the analysis target in the biological sample image. , It is preferable to analyze.
It is preferable that there are a plurality of types of drugs for which the drug distribution is analyzed from the viewpoint of comparative analysis.
＜薬物分布解析＞
　本発明において、「薬物分布解析」とは、薬物の空間分布解析、統計分布解析、及び比較解析などのことをいう。 <Drug distribution analysis>
In the present invention, "drug distribution analysis" refers to spatial distribution analysis, statistical distribution analysis, comparative analysis, and the like of drugs.
　「空間分布解析」とは、薬物種・薬物濃度のマップ化や、閾値処理による薬物分布領域の規定などのことである。解析した空間分布を用いて、薬物分布を工程４で抽出したオブジェクトと関連付けることができる。また、規定した薬物分布領域はオブジェクトのように扱うこともできる。 "Spatial distribution analysis" refers to mapping drug types / concentrations and defining drug distribution areas by threshold processing. The analyzed spatial distribution can be used to correlate the drug distribution with the object extracted in step 4. Also, the defined drug distribution area can be treated like an object.
　「統計分布解析」とは、薬物濃度の頻度分布（ヒストグラム）や、オブジェクトに関連付けた薬物の薬物量総和、濃度平均値、濃度代表値、濃度最頻値、薬物分布領域面積、オブジェクト領域に対する薬物分布領域密度、薬効空間面積率、及び毒性空間面積率などの統計分布を解析することである。これらの統計分布解析結果から均一性評価などを行うことができる。 "Statistical distribution analysis" is a frequency distribution (histogram) of drug concentration, total drug amount of drugs associated with an object, mean concentration value, representative concentration value, mode of concentration, drug distribution area area, drug for object area. It is to analyze statistical distributions such as distribution area density, medicinal space area ratio, and toxicity space area ratio. Homogeneity evaluation can be performed from these statistical distribution analysis results.
　「比較解析」とは、空間分布や統計分布の解析結果を比較することである。比較する対象は、例えば種類の異なる薬物や、オブジェクトに関連付ける方法（後述）が異なる薬物などである。ここで、「異なる薬物」とは、例えば抗体薬物複合体とペイロード、がんの併用療法における異なる抗がん剤、ウイルス感染症のカクテル療法における異なる抗ウイルス薬などである。 "Comparative analysis" is to compare the analysis results of spatial distribution and statistical distribution. The objects to be compared are, for example, different types of drugs and drugs having different methods of associating with objects (described later). Here, the "different drug" is, for example, an antibody drug conjugate and a payload, a different anticancer drug in a combination therapy of cancer, a different antiviral drug in a cocktail therapy of a viral infection, and the like.
　比較する項目は、上述した空間分布解析の結果（薬物種・薬物濃度のマップ、薬物分布領域など）又は統計分布解析の結果（オブジェクトに関連付けた薬物の薬物量総和、濃度平均値、濃度代表値、濃度最頻値、薬物分布領域面積、オブジェクト領域に対する薬物分布領域密度、薬効空間面積率及び毒性空間面積率など）などである。
　また、比較解析として、異なる生体試料画像について解析した空間分布や統計分布を比較することもできるが、これについては工程１０にて行う。 Items to be compared are the results of the above-mentioned spatial distribution analysis (drug type / drug concentration map, drug distribution area, etc.) or the results of statistical distribution analysis (total drug amount of drugs associated with the object, mean concentration, representative concentration). , Concentration mode, drug distribution area area, drug distribution area density with respect to object area, drug effect space area ratio, toxicity space area ratio, etc.).
Further, as a comparative analysis, it is possible to compare the spatial distribution and the statistical distribution analyzed for different biological sample images, but this is performed in step 10.
＜統計分布解析例（１）＞
　統計分布解析の例として、濃度閾値処理による薬効空間又は毒性空間面積率の解析について記載する。まず、薬理判定の基準となる濃度で閾値処理を行う。この際、一定面積以下の領域をノイズとして除外し、空間スムーシングを行ってもよい。閾値処理後、薬効空間面積率及び毒性空間面積率を解析し、薬効又は毒性を評価することができる。また、薬効空間のバラつきから、薬効を評価することもできる。 <Statistical distribution analysis example (1)>
As an example of statistical distribution analysis, analysis of the medicinal space or toxic space area ratio by concentration threshold processing will be described. First, threshold processing is performed at a concentration that serves as a reference for pharmacological determination. At this time, a region having a certain area or less may be excluded as noise, and spatial smoothing may be performed. After the threshold treatment, the medicinal effect space area ratio and the toxic space area ratio can be analyzed to evaluate the medicinal effect or toxicity. It is also possible to evaluate the medicinal effect from the variation in the medicinal effect space.
＜統計分布解析例（２）＞
　統計分布解析の例として、ヒストグラムによる薬物濃度の均一性評価について記載する。まず、濃度最頻値を基準として既定の頻度割合（９５［％］など）を満たす薬物濃度幅を薬物濃度分布バラつき性能とし、基礎評価（至適濃度範囲内であるかなど）を行う。この評価により、最頻値が複数となるような薬物は不均一な分布特性を持ち、薬効安定性が低いと判断することができる。 <Statistical distribution analysis example (2)>
As an example of statistical distribution analysis, a histogram-based evaluation of drug concentration uniformity will be described. First, a drug concentration range that satisfies a predetermined frequency ratio (95 [%], etc.) based on the most frequent concentration value is defined as the drug concentration distribution variation performance, and a basic evaluation (whether it is within the optimum concentration range, etc.) is performed. Based on this evaluation, it can be determined that a drug having a plurality of modes has a non-uniform distribution characteristic and has low drug efficacy stability.
＜オブジェクトに関連付けた薬物の分布解析＞
　工程３で解析する薬物分布は、工程４で情報を抽出したオブジェクトに関連付けた薬物分布とすることができる。
　オブジェクトに関連付ける方法は、オブジェクトの内側領域及び外側領域にそれぞれ関連付ける場合や、各オブジェクトからの距離又は方位のいずれか少なくとも一つの指標で区別したエリアに関連付ける場合、第２のオブジェクトからの空間分布に応じて第１のオブジェクトに関連付ける場合などが考えられる。 <Distribution analysis of drugs associated with objects>
The drug distribution analyzed in step 3 can be a drug distribution associated with the object from which the information was extracted in step 4.
The method of associating with an object is the spatial distribution from the second object when associating with the inner area and the outer area of the object, respectively, or when associating with the area distinguished by at least one index of the distance or the direction from each object. Depending on the case, it may be associated with the first object.
　オブジェクトの内側領域及び外側領域にそれぞれ関連付ける場合、オブジェクトの内側領域にバイオマーカー陽性細胞領域を有し、オブジェクトの外側領域にバイオマーカー陰性細胞領域を有することが薬物のターゲット選択性を評価できる点から好ましい。例えば、バイオマーカー陽性細胞領域がＨＥＲ２陽性細胞領域で、バイオマーカー陰性細胞領域が陰性細胞領域とすることができる。 When associating with the inner and outer regions of the object, having a biomarker-positive cell region in the inner region of the object and a biomarker-negative cell region in the outer region of the object can evaluate the target selectivity of the drug. preferable. For example, the biomarker-positive cell region can be a HER2-positive cell region, and the biomarker-negative cell region can be a negative cell region.
　オブジェクトに関連付けて薬物分布を解析することで、検出薬物シグナル量・座標とオブジェクト配置を組み合わせた薬物分布解析を行うことができる。
　例えばオブジェクト領域内だけに絞った空間分布解析及び統計分布解析や、オブジェクトに薬物を帰属させたオブジェクト当たりでの統計分布解析、又はオブジェクト内外薬物濃度の有意差比較解析などである。 By analyzing the drug distribution in association with the object, it is possible to perform drug distribution analysis that combines the detected drug signal amount / coordinates and the object arrangement.
For example, spatial distribution analysis and statistical distribution analysis focused only on the object area, statistical distribution analysis per object in which a drug is assigned to an object, or significant difference comparison analysis of drug concentrations inside and outside the object.
＜オブジェクト内だけに絞った薬物分布解析例＞
　オブジェクト領域内だけに絞った薬物分布解析の例として、薬物のターゲットとなるオブジェクトの領域内の薬物濃度分布解析について説明する。
　まず、薬物のターゲットとなるオブジェクトの領域内のみを解析の対象領域として設定する。その次に、対象領域内の薬物シグナルのみを検出・定量し、薬物濃度を定量する。オブジェクト座標と薬物検出位置から、オブジェクト単位で薬物を帰属させることで、オブジェクト内の薬物濃度平均値を算出することができる。 <Example of drug distribution analysis focused only on the object>
As an example of drug distribution analysis focused only on the object region, drug concentration distribution analysis within the region of the object that is the target of the drug will be described.
First, only the area of the object that is the target of the drug is set as the analysis target area. Next, only the drug signal in the target area is detected and quantified, and the drug concentration is quantified. By assigning a drug to each object from the object coordinates and the drug detection position, the average drug concentration in the object can be calculated.
＜オブジェクトに関連付けた薬物分布の比較解析について＞
　薬物分布とオブジェクトの関連付けは、特に比較解析をする際に有効となる。
　例えば関連付けた薬物の種類が異なる薬物分布を比較解析する場合、関連付けたオブジェクトの種類が異なる薬物分布を比較解析する場合、関連付けたオブジェクトの種類も薬物の種類も異なる薬物分布を比較解析する場合などである。 <Comparative analysis of drug distribution associated with objects>
The association between drug distribution and objects is especially useful for comparative analysis.
For example, when comparing and analyzing drug distributions with different types of associated drugs, when comparing and analyzing drug distributions with different types of associated objects, when comparing and analyzing drug distributions with different types of associated objects and types of drugs, etc. Is.
＜オブジェクトに関連付けた薬物分布の比較解析例（１）＞
　オブジェクト内側領域・外側領域に関連付けた薬物分布の比較解析の例を説明する。
　工程３で解析した薬物の空間分布を、工程４で抽出したオブジェクトを内側領域と外側領域にそれぞれ関連付ける。このとき、明視野ＤＡＢ（ジアミノベンジジン：3,3’-diaminobenzidine）染色画像に対し、ＤＡＢの色ベクトルを用いた色分解、閾値処理によりオブジェクト領域を抽出してもよい。オブジェクト内側領域と外側領域にそれぞれ関連付けた薬物分布を比較解析することで、オブジェクト内側領域と外側領域での薬物濃度平均値、代表値及び頻度分布（ヒストグラム）の比較をすることができる。 <Example of comparative analysis of drug distribution associated with an object (1)>
An example of comparative analysis of drug distribution associated with the inner and outer regions of an object will be described.
The spatial distribution of the drug analyzed in step 3 is associated with the object extracted in step 4 to the inner region and the outer region, respectively. At this time, the object region may be extracted from the bright-field DAB (diaminobenzidine) stained image by color separation using the DAB color vector and threshold processing. By comparing and analyzing the drug distributions associated with the inner region and the outer region of the object, it is possible to compare the average drug concentration, the representative value, and the frequency distribution (histogram) in the inner region and the outer region of the object.
　なお、後述する工程４を経由して行う、前記オブジェクトに関連づけた薬物濃度分布を解析する工程３が、当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の解析であり、かつ、前記解析の方法が代表値算出又は相関解析であることが薬物動態解析・薬効薬理解析等の観点から好ましい。 The step 3 for analyzing the drug concentration distribution associated with the object, which is performed via step 4 described later, is the analysis of the drug concentration distribution associated with the object inner region and the object outer region, respectively. It is preferable that the analysis method is representative value calculation or correlation analysis from the viewpoint of pharmacokinetic analysis, pharmacological analysis and the like.
　当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の「代表値算出」とは、それぞれの領域に関連付けた薬物濃度分布の平均値、最頻値、中央値、最大値などの代表値を算出することである。 The "representative value calculation" of the drug concentration distribution associated with the inner region of the object and the outer region of the object is a representative of the average value, mode value, median value, maximum value, etc. of the drug concentration distribution associated with each region. To calculate the value.
　当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の「相関解析」とは、上記代表値の相関性を解析することである。 The "correlation analysis" of the drug concentration distribution associated with the inner region of the object and the outer region of the object is to analyze the correlation of the above representative values.
＜オブジェクトに関連付けた薬物分布の比較解析例（２）＞
　第２のオブジェクトからの空間分布に応じた第１のオブジェクトに関連付けた薬物分布の比較解析の例を説明する。
　第２のオブジェクトを腫瘍領域、第１のオブジェクトを免疫細胞とすることで、腫瘍領域からの距離が異なる免疫細胞に着目した薬物分布（腫瘍領域浸潤免疫細胞薬物分布）を比較解析することができる。 <Example of comparative analysis of drug distribution associated with an object (2)>
An example of comparative analysis of the drug distribution associated with the first object according to the spatial distribution from the second object will be described.
By using the tumor region as the second object and the immune cells as the first object, it is possible to compare and analyze the drug distribution (tumor region infiltrating immune cell drug distribution) focusing on immune cells having different distances from the tumor region. ..
≪工程４≫：オブジェクト情報抽出工程
　工程４は、前記生体試料画像から少なくとも解析対象である前記薬物のターゲットとなるオブジェクト又は当該ターゲットと関連性を有する非ターゲット・オブジェクトの情報を抽出する工程である。なお、オブジェクト情報を得るオブジェクトは複数種であることが比較解析の点から好ましい。 << Step 4 >>: Object information extraction step Step 4 is a step of extracting information on at least the target object of the drug to be analyzed or a non-target object related to the target from the biological sample image. .. It is preferable that there are a plurality of types of objects for which object information is obtained from the viewpoint of comparative analysis.
　オブジェクト情報の抽出方法は、オブジェクト情報が抽出できれば特に限定されないが、例えば明視野ＤＡＢ（ジアミノベンジジン）染色画像に対し、ＤＡＢの色ベクトルを用いた色分解・閾値処理によりオブジェクト情報を抽出することができる。
　また、細胞輪郭を明視野で抽出後、オブジェクト特異的に発現するバイオマーカーの発現量が閾値以上のものを選別することによってもオブジェクト情報を抽出することができる。 The method for extracting the object information is not particularly limited as long as the object information can be extracted. For example, the object information can be extracted from the bright field DAB (diaminobenzidine) stained image by color separation / threshold processing using the DAB color vector. can.
In addition, object information can also be extracted by extracting cell contours in a bright field and then selecting those having an expression level of a biomarker specifically expressed in an object of a threshold value or more.
　オブジェクト情報の抽出の精度を上げるために、工程２と同様にノイズ除去処理や、領域設定処理などの前処理を行ってもよい。 In order to improve the accuracy of extracting object information, preprocessing such as noise reduction processing and area setting processing may be performed as in step 2.
　なお、例えば第２のオブジェクトからの空間分布に応じた第１のオブジェクトに関連付けた薬物濃度の比較解析のために、第１のオブジェクトを第２のオブジェクトからの空間分布に応じて２つ以上のグループ（エリア）を分けてそれぞれについて情報を抽出することも好ましい態様である。 It should be noted that, for example, for comparative analysis of the drug concentration associated with the first object according to the spatial distribution from the second object, the first object is divided into two or more according to the spatial distribution from the second object. It is also a preferable embodiment to divide the groups (areas) and extract information for each group.
　第２のオブジェクトは、例えば腫瘍領域や浸潤がんの浸潤領域などとすることができる。第１オブジェクトは、例えば免疫細胞や細胞識別器や識別マーカー染色を用いて抽出したがん細胞などとすることができる。 The second object can be, for example, a tumor area or an infiltrating area of an invasive cancer. The first object can be, for example, an immune cell, a cell classifier, a cancer cell extracted using a discrimination marker stain, or the like.
　例えば腫瘍領域（第２のオブジェクト）からの距離閾値によって免疫細胞（第１のオブジェクト）を腫瘍内部・辺縁・遠位の３グループに分けることができる。また、各第１のオブジェクトを中心とした腫瘍細胞の最近傍距離に基づいてグループを分けることもできる。 For example, immune cells (first object) can be divided into three groups: internal to tumor, marginal area, and distal to tumor, depending on the distance threshold from the tumor area (second object). It is also possible to divide the groups based on the nearest neighbor distance of the tumor cells centered on each first object.
≪工程５≫：オブジェクト分布解析工程
　工程５は、オブジェクトに関する空間分布及び統計分布を解析する工程であり、工程４を経由してされる。工程５を有することは関連因子の解析の点から好ましい。なお、オブジェクト分布を解析するオブジェクトは複数種であることが比較解析の点から好ましい。 << Step 5 >>: Object distribution analysis step Step 5 is a step of analyzing the spatial distribution and the statistical distribution of the object, and is performed via the step 4. Having step 5 is preferable from the viewpoint of analysis of related factors. It is preferable that there are a plurality of types of objects for which the object distribution is analyzed from the viewpoint of comparative analysis.
　本発明において、オブジェクト分布解析の対象としては、例えばバイオマーカー発現量、オブジェクトの空間分布及びこれらの統計分布情報などを挙げることができる。
　オブジェクトの空間分布は、座標位置、分布領域面積、密度、増減、局在パターンなどを含み、統計分布情報は頻度分布、平均値などの集約値などを含む。 In the present invention, objects of object distribution analysis include, for example, biomarker expression level, spatial distribution of objects, and statistical distribution information thereof.
The spatial distribution of an object includes coordinate positions, distribution area area, density, increase / decrease, localization pattern, etc., and statistical distribution information includes aggregated values such as frequency distribution, mean value, and the like.
　オブジェクト分布の解析は、工程４で抽出したオブジェクト情報を基に、区画単位特徴量を算出することにより行うことができる。 The object distribution can be analyzed by calculating the feature amount for each section based on the object information extracted in step 4.
≪工程６≫：薬物-オブジェクト分布関連性解析工程
　工程６は、前記工程３において得た前記薬物濃度分布と前記工程４において得た前記オブジェクトの前記空間分布との関連性を解析する工程である。工程６を有することは薬物動態と関連因子の相関性解析の観点から好ましい。関連性を解析する薬物及びオブジェクトは複数種であることが比較解析の点から好ましい。 << Step 6 >>: Drug-object distribution relationship analysis step Step 6 is a step of analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4. .. Having step 6 is preferable from the viewpoint of correlation analysis between pharmacokinetics and related factors. From the viewpoint of comparative analysis, it is preferable that there are a plurality of types of drugs and objects for which the relationship is analyzed.
　薬物濃度分布とオブジェクト分布の関連性を解析する方法として、例えば、検出薬物シグナルをオブジェクトに帰属させた後に薬物濃度閾値単位でのオブジェクトの空間分布を解析する方法がある。また、薬物の空間分布傾向とオブジェクト分布傾向の相関性を解析する方法もある。 As a method of analyzing the relationship between the drug concentration distribution and the object distribution, for example, there is a method of analyzing the spatial distribution of the object in the drug concentration threshold unit after assigning the detected drug signal to the object. There is also a method for analyzing the correlation between the spatial distribution tendency of drugs and the object distribution tendency.
　薬物濃度分布とオブジェクト分布の関連性を解析する方法の具体例として、薬物濃度分布対オブジェクト密度分布の解析について説明する。薬物濃度特徴量をオブジェクト密度特徴量で除する（薬物濃度特徴量÷オブジェクト密度特徴量）ことで正規化した薬物濃度分布を得ることができる。この分布が均一であれば、オブジェクトの密度と薬物濃度が相関していると評価することができる。オブジェクト密度は、面積率の他、個数/単位区画でもよい。 As a specific example of the method for analyzing the relationship between the drug concentration distribution and the object distribution, the analysis of the drug concentration distribution vs. the object density distribution will be described. A normalized drug concentration distribution can be obtained by dividing the drug concentration feature by the object density feature (drug concentration feature ÷ object density feature). If this distribution is uniform, it can be evaluated that the density of the object and the drug concentration are correlated. The object density may be the number / unit section as well as the area ratio.
≪工程７≫：詳細解析箇所選定工程
　工程７は、工程３又は前記工程６において得た解析結果に基づいて詳細解析箇所を選定する工程である。工程７を有することは、より精度の高い濃度算出が行える領域を選定できる点から好ましい。 << Step 7 >>: Detailed analysis point selection step Step 7 is a step of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6. It is preferable to have the step 7 from the viewpoint that a region where the concentration can be calculated with higher accuracy can be selected.
　詳細解析箇所の選定は、目視による選定、オブジェクト領域面積と指定箇所数による選定などの方法によって行うことができる。 The detailed analysis location can be selected by visual selection, selection based on the object area area and the specified number of locations, and so on.
≪工程８≫：詳細解析箇所解析工程
　工程８は、選定した詳細解析箇所を解析する工程である。工程８を有することは、より精度の高い薬物濃度算出が行える点から好ましい。
　工程７で選定した詳細解析箇所を、例えば高倍率撮影や３Ｄ撮影によって画像を取得することで、工程３、工程５又は工程７において行った解析よりもより詳細な解析を行うことができる。 << Step 8 >>: Detailed analysis point analysis step Step 8 is a step of analyzing the selected detailed analysis point. It is preferable to have the step 8 from the viewpoint that the drug concentration can be calculated with higher accuracy.
By acquiring an image of the detailed analysis location selected in step 7 by, for example, high-magnification imaging or 3D imaging, it is possible to perform more detailed analysis than the analysis performed in step 3, step 5 or step 7.
≪工程９≫：連続切片画像位置合わせ工程
　工程９は、連続する複数の組織等の切片の位置合わせによる画像位置合わせをする工程である。工程９を有することは、１枚の切片では多重染色ができないケースでも、複数の切片が連続した画像として比較解析が可能になるという点で好ましい。 << Step 9 >>: Continuous Section Image Alignment Step Step 9 is a step of aligning images by aligning sections of a plurality of continuous tissues. Having step 9 is preferable in that even in a case where multiple staining cannot be performed with one section, comparative analysis can be performed as a continuous image of a plurality of sections.
≪工程１０≫：生体試料画像間について比較解析をする工程
　工程１０は、工程１において取得した複数の生体試料画像に対して、当該生体試料画像間について比較解析をする工程である。工程１０を有することは、経時変化等を評価できる点から好ましい。
　例えば、薬物投与後の経過時間が異なる複数の生体試料画像間で比較解析を行うことにより、薬物分布の経時変化を捉えることができ、薬物の増減、集積／拡散方位速度などを評価することができる。 << Step 10 >>: Step of comparative analysis between biological sample images Step 10 is a step of comparatively analyzing a plurality of biological sample images acquired in step 1 between the biological sample images. It is preferable to have the step 10 from the viewpoint of being able to evaluate changes over time and the like.
For example, by performing comparative analysis between multiple biological sample images with different elapsed times after drug administration, it is possible to capture changes in drug distribution over time, and to evaluate drug increase / decrease, accumulation / diffusion azimuth velocity, and the like. can.
≪工程１１≫：注目領域を選定する工程
　工程１１は、薬物濃度分布又はオブジェクト分布の解析結果に基づいて、注目領域を選定する工程である。工程１１を有することは、解析結果と関連する因子を特定するため、薬物分布の特徴が異なる箇所の生体内空間ヘテロ性を比較解析する観点から好ましい。
　ここで、「注目領域」とは、工程１２における関連バイオマーカーの同定に有用と思われる領域、例えば薬物高集積細胞集団などのことである。 << Step 11 >>: Step of selecting the region of interest Step 11 is a step of selecting the region of interest based on the analysis result of the drug concentration distribution or the object distribution. Having step 11 is preferable from the viewpoint of comparative analysis of in vivo spatial heterogeneity at locations having different characteristics of drug distribution in order to identify factors related to the analysis results.
Here, the “region of interest” is a region that is considered to be useful for identifying the relevant biomarker in step 12, such as a highly drug-accumulated cell population.
≪工程１２≫：関連バイオマーカー同定工程
　工程１２は、複数の前記注目領域の間の遺伝子発現差分から関連バイオマーカーを同定する工程である。工程１２を有することは、計測対象とした薬物集積や特定バイオマーカー発現として表出していない生体内の変化を網羅的に解析できる点から好ましい。
　例えば、工程１１で選定した薬物高集積細胞集団を、レーザーダイセクション・ＮＧＳ（次世代シーケンシング）による遺伝子変異解析などを行って遺伝子の発現状態の差を解析し、関連バイオマーカーを同定する。 << Step 12 >>: Related biomarker identification step Step 12 is a step of identifying the related biomarker from the gene expression difference between the plurality of regions of interest. It is preferable to have the step 12 from the viewpoint that it is possible to comprehensively analyze changes in the living body that are not expressed as drug accumulation or specific biomarker expression as a measurement target.
For example, the drug highly accumulated cell population selected in step 11 is subjected to gene mutation analysis by laser dissection / NGS (next generation sequencing) to analyze the difference in gene expression state and identify related biomarkers.
（１．３）実施形態
　以下において、上記各種工程の実施形態及びそれらを組み合わせた実施形態について説明する。 (1.3) Embodiments In the following, embodiments of the above-mentioned various steps and embodiments in which they are combined will be described.
（１．３．１）実施形態１：蛍光画像取得方法の例
　以下、典型的例として、分布解析対象薬物の投与後生体から採材した組織切片に対し免疫染色(蛍光色素等)等により薬物位置を特定可能にし、蛍光顕微鏡などの撮像装置を用いて画像取得する例について説明する。
　具体的には、例えば薬物染色工程、オブジェクト染色工程、フォーカシング工程、及び画像取得工程を行うことにより生体試料画像を取得する例について説明する。 (13.1) Embodiment 1: Example of a fluorescent image acquisition method Hereinafter, as a typical example, a tissue section collected from a living body after administration of a drug to be distributed is subjected to immunostaining (fluorescent dye, etc.). An example in which the position can be specified and an image is acquired using an imaging device such as a fluorescence microscope will be described.
Specifically, an example of acquiring a biological sample image by performing, for example, a drug staining step, an object staining step, a focusing step, and an image acquisition step will be described.
＜薬物染色工程＞
　生体試料内の薬物の位置を特定可能とするために、薬物を染色する工程である。 <Drug staining process>
This is a step of staining a drug so that the position of the drug in a biological sample can be identified.
[１]免疫染色剤（抗体－蛍光ナノ粒子の結合体）
　免疫染色剤としては、蛍光標識の効率を向上させて蛍光の劣化につながる時間経過をなるべく抑えるために、一次抗体及び蛍光ナノ粒子が間接的に、つまり抗原抗体反応などを利用した、共有結合以外の結合によって連結される複合体を用いることが好ましい。染色操作を簡便にするため、免疫染色剤として、一次抗体又は二次抗体に蛍光ナノ粒子が直結している複合体を用いることもできる。 [1] Immunostaining agent (antibody-fluorescent nanoparticles conjugate)
As an immunostaining agent, in order to improve the efficiency of fluorescent labeling and suppress the passage of time leading to deterioration of fluorescence as much as possible, the primary antibody and fluorescent nanoparticles indirectly, that is, using an antigen-antibody reaction or the like, other than covalent bonds. It is preferable to use a complex linked by the binding of. In order to simplify the staining operation, a complex in which fluorescent nanoparticles are directly linked to the primary antibody or the secondary antibody can also be used as the immunostaining agent.
　免疫染色剤の一例として、［目的物質に対する一次抗体］…［一次抗体に対する抗体（二次抗体）］～［蛍光ナノ粒子］が挙げられる。
　“…”は抗原抗体反応により結合していることを表し、“～”が示す結合の態様としては特に限定されず、例えば、共有結合、イオン結合、水素結合、配位結合、抗原抗体結合、ビオチンアビジン反応、物理吸着、化学吸着などが挙げられ、必要に応じてリンカー分子を介していてもよい。 Examples of the immunostaining agent include [primary antibody against the target substance] ... [antibody against the primary antibody (secondary antibody)] to [fluorescent nanoparticles].
“…” Indicates that the bond is bound by an antigen-antibody reaction, and the mode of binding indicated by “～” is not particularly limited. For example, covalent bond, ion bond, hydrogen bond, coordination bond, antigen-antibody bond, Examples thereof include biotin avidin reaction, physical adsorption, chemical adsorption, etc., and may be mediated by a linker molecule if necessary.
[２]抗体
　一次抗体には、目的物質を抗原として特異的に認識して結合する抗体（ＩｇＧ）を用いることができる。例えば、ＨＥＲ２を目的物質とする場合は抗ＨＥＲ２抗体を、ＨＥＲ３を目的物質とする場合は抗ＨＥＲ３抗体を、それぞれ用いることができる。
　二次抗体には、一次抗体を抗原として特異的に認識して結合する抗体（ＩｇＧ）を用いることができる。
　一次抗体及び二次抗体はいずれも、ポリクローナル抗体であってもよいが、定量の安定性の観点から、モノクローナル抗体が好ましい。抗体を産生する動物（免疫動物）の種類は特に限定されるものではなく、従来と同様、マウス、ラット、モルモット、ウサギ、ヤギ、ヒツジなどから選択すればよい。 [2] Antibodies As the primary antibody, an antibody (IgG) that specifically recognizes and binds to the target substance as an antigen can be used. For example, when HER2 is the target substance, an anti-HER2 antibody can be used, and when HER3 is the target substance, an anti-HER3 antibody can be used.
As the secondary antibody, an antibody (IgG) that specifically recognizes and binds to the primary antibody as an antigen can be used.
Both the primary antibody and the secondary antibody may be polyclonal antibodies, but monoclonal antibodies are preferable from the viewpoint of quantitative stability. The type of animal (immune animal) that produces an antibody is not particularly limited, and may be selected from mice, rats, guinea pigs, rabbits, goats, sheep, and the like as in the past.
[３]蛍光ナノ粒子
　蛍光ナノ粒子とは、励起光の照射を受けて蛍光発光するナノサイズの粒子であって、目的物質を１分子ずつ輝点として表すのに十分な強度の蛍光を発光しうる粒子である。蛍光ナノ粒子として、蛍光物質集積ナノ粒子（ＰＩＤ：Ｐｈｏｓｐｈｏｒ　Ｉｎｔｅｇａｔｅｄ　Ｄｏｔ　ｎａｎｏｐａｒｔｉｃｌｅｓ）を使用することができる。 [3] Fluorescent nanoparticles Fluorescent nanoparticles are nano-sized particles that fluoresce and emit light when irradiated with excitation light, and emit fluorescence with sufficient intensity to represent the target substance as bright spots one by one. Fluorescent particles. As the fluorescent nanoparticles, phosphor integrated nanoparticles (PID: Phosphor Integrated Dot nanoparticles) can be used.
[３．１]蛍光物質集積ナノ粒子
　蛍光物質集積ナノ粒子は、有機物又は無機物でできた粒子を母体とし、複数の蛍光物質（例えば、上記量子ドット、有機蛍光色素など）がその中に内包されている及び／又はその表面に吸着している構造を有する、ナノサイズの粒子である。蛍光物質集積ナノ粒子としては、量子ドット集積ナノ粒子、蛍光色素集積ナノ粒子などが使用される。 [3.1] Fluorescent substance-accumulated nanoparticles Fluorescent substance-accumulated nanoparticles are based on particles made of organic or inorganic substances, and a plurality of fluorescent substances (for example, the above-mentioned quantum dots, organic fluorescent dyes, etc.) are contained therein. Nano-sized particles having a structure that is and / or is adsorbed on the surface thereof. As the fluorescent substance-accumulated nanoparticles, quantum dot-accumulated nanoparticles, fluorescent dye-accumulated nanoparticles and the like are used.
　蛍光物質集積ナノ粒子に用いられる蛍光物質としては、２００～７００ｎｍの範囲内の波長の紫外～近赤外光により励起されたときに、４００～９００ｎｍの範囲内の波長の可視～近赤外光の発光を示すことが好ましく、母体と蛍光物質とが、互いに反対の電荷を有する置換基又は部位を有し、静電的相互作用が働くものであることが好適である。 Fluorescent material used for integrated nanoparticles is visible to near-infrared light with a wavelength in the range of 400 to 900 nm when excited by ultraviolet to near-infrared light with a wavelength in the range of 200 to 700 nm. It is preferable that the mother body and the fluorescent substance have substituents or sites having opposite charges and that an electrostatic interaction acts.
　蛍光物質集積ナノ粒子の平均粒径は特に限定されないが、３０～８００ｎｍ程度のものを用いることができる。平均粒径が３０ｎｍ未満の場合には、集積粒子に含まれる蛍光物質が少なく、目的物質の定量的評価が困難となり、８００ｎｍを超える場合には、病理組織での目的物質との結合が困難となるためである。なお、平均粒径は、４０～５００ｎｍの範囲内であることがより好ましい。ここで、平均粒径を４０～５００ｎｍとしたのは、４０ｎｍ未満の場合には、高価な検出系が必要となり、５００ｎｍを超える場合には、物理的な大きさから定量範囲が狭まるためである。 The average particle size of the nanoparticles accumulating fluorescent substances is not particularly limited, but those having a diameter of about 30 to 800 nm can be used. When the average particle size is less than 30 nm, the amount of fluorescent substance contained in the accumulated particles is small, which makes quantitative evaluation of the target substance difficult, and when it exceeds 800 nm, it is difficult to bind to the target substance in the pathological tissue. This is to become. The average particle size is more preferably in the range of 40 to 500 nm. Here, the reason why the average particle size is set to 40 to 500 nm is that if it is less than 40 nm, an expensive detection system is required, and if it exceeds 500 nm, the quantification range is narrowed due to the physical size. ..
　なお、粒径のばらつきを示す変動係数（＝（標準偏差／平均値）×１００％）は特に限定されないが、１５％以下のものを用いることが望ましい。粒径のばらつきは小さい程、蛍光輝点の輝度のばらつきが小さく、後述するように蛍光輝度をもとに目的物質の発現量を定量的に評価することができる。平均粒径は、走査型電子顕微鏡（ＳＥＭ）を用いて電子顕微鏡写真を撮影し十分な数の粒子について断面積を計測し、各計測値を円の面積としたときの円の直径を粒径として求めることができる。 The coefficient of variation (= (standard deviation / average value) x 100%) indicating the variation in particle size is not particularly limited, but it is desirable to use one of 15% or less. The smaller the variation in particle size, the smaller the variation in the brightness of the fluorescent bright spot, and as will be described later, the expression level of the target substance can be quantitatively evaluated based on the fluorescence brightness. For the average particle size, take an electron micrograph using a scanning electron microscope (SEM), measure the cross-sectional area of a sufficient number of particles, and use the diameter of the circle as the area of the circle as the area of each measured value. Can be obtained as.
[３．１．１]母体
　母体のうち、有機物としては、メラミン樹脂、尿素樹脂、アニリン樹脂、グアナミン樹脂、フェノール樹脂、キシレン樹脂、フラン樹脂など、一般的に熱硬化性樹脂に分類される樹脂；スチレン樹脂、アクリル樹脂、アクリロニトリル樹脂、ＡＳ樹脂（アクリロニトリル－スチレン共重合体）、ＡＳＡ樹脂（アクリロニトリル－スチレン－アクリル酸メチル共重合体）など、一般的に熱可塑性樹脂に分類される樹脂；ポリ乳酸などのその他の樹脂；多糖を例示することができる。
　母体のうち、無機物としては、シリカ、ガラスなどを例示することができる。 [3.1.1] Mother Body Among the mother bodies, the organic substances are generally classified as thermosetting resins such as melamine resin, urea resin, aniline resin, guanamine resin, phenol resin, xylene resin and furan resin. Resins generally classified as thermoplastic resins such as styrene resin, acrylic resin, acrylonitrile resin, AS resin (acrylonitrile-styrene copolymer), ASA resin (acrylonitrile-styrene-methyl acrylate copolymer); poly Other resins such as lactic acid; polysaccharides can be exemplified.
Examples of the inorganic substance in the mother body include silica and glass.
[３．１．２]量子ドット集積ナノ粒子
　量子ドット集積ナノ粒子とは、上記量子ドットが、上記母体の中に内包されている、及び／又はその表面に吸着している構造を有する。
　量子ドットが母体に内包されている場合、量子ドットは母体内部に分散されていればよく、母体自体と化学的に結合していてもよいし、していなくてもよい。 [3.1.2] Quantum dot integrated nanoparticles The quantum dot integrated nanoparticles have a structure in which the quantum dots are contained in the mother body and / or are adsorbed on the surface thereof.
When the quantum dots are contained in the mother body, the quantum dots may or may not be chemically bonded to the mother body itself as long as they are dispersed inside the mother body.
　量子ドットとしては、ＩＩ－ＶＩ族化合物、ＩＩＩ－Ｖ族化合物又はＩＶ族元素を含有する半導体ナノ粒子が使用される。例えば、ＣｄＳｅ、ＣｄＳ、ＣｄＴｅ、ＺｎＳｅ、ＺｎＳ、ＺｎＴｅ、ＩｎＰ、ＩｎＮ、ＩｎＡｓ、ＩｎＧａＰ、ＧａＰ、ＧａＡｓ、Ｓｉ、Ｇｅなどが挙げられる。 As the quantum dots, semiconductor nanoparticles containing a II-VI group compound, a III-V group compound, or an IV group element are used. For example, CdSe, CdS, CdTe, ZnSe, ZnS, ZnTe, InP, InN, InAs, InGaP, GaP, GaAs, Si, Ge and the like can be mentioned.
　上記量子ドットをコアとし、その上にシェルを設けた量子ドットを用いることもできる。以下、本明細書中シェルを有する量子ドットの表記法として、コアがＣｄＳｅ、シェルがＺｎＳの場合、ＣｄＳｅ／ＺｎＳと表記する。例えば、ＣｄＳｅ／ＺｎＳ、ＣｄＳ／ＺｎＳ、ＩｎＰ／ＺｎＳ、ＩｎＧａＰ／ＺｎＳ、Ｓｉ／ＳｉＯ２、Ｓｉ／ＺｎＳ、Ｇｅ／ＧｅＯ２、Ｇｅ／ＺｎＳなどを用いることができるが、これらに限定されない。 It is also possible to use a quantum dot having the above quantum dot as a core and a shell provided on the core. Hereinafter, as the notation of the quantum dot having a shell in the present specification, when the core is CdSe and the shell is ZnS, it is expressed as CdSe / ZnS. For example, CdSe / ZnS, CdS / ZnS, InP / ZnS, InGaP / ZnS, Si / SiO 2 , Si / ZnS, Ge / GeO 2 , Ge / ZnS and the like can be used, but the present invention is not limited thereto.
　量子ドットは必要に応じて、有機ポリマーなどにより表面処理が施されているものを用いてもよい。例えば、表面カルボキシ基を有するＣｄＳｅ／ＺｎＳ（インビトロジェン社製）、表面アミノ基を有するＣｄＳｅ／ＺｎＳ（インビトロジェン社製）などが挙げられる。 If necessary, the quantum dots may be surface-treated with an organic polymer or the like. For example, CdSe / ZnS having a surface carboxy group (manufactured by Invitrogen), CdSe / ZnS having a surface amino group (manufactured by Invitrogen), and the like can be mentioned.
　量子ドット集積ナノ粒子は、公知の方法により作成することが可能である。例えば、量子ドットを内包したシリカナノ粒子は、ニュー・ジャーナル・オブ・ケミストリー３３巻５６１ページ（２００９）に記載されているＣｄＴｅ内包シリカナノ粒子の合成を参考に合成することができる。 Quantum dot integrated nanoparticles can be produced by a known method. For example, silica nanoparticles encapsulating quantum dots can be synthesized with reference to the synthesis of CdTe-encapsulating silica nanoparticles described in New Journal of Chemistry Vol. 33, p. 561 (2009).
　量子ドットを外包したシリカナノ粒子は、ケミカル・コミュニケーション ２６７０ページ（２００９）に記載されているＣｄＳｅ／ＺｎＳを５－ａｍｉｎｏ－１－ｐｅｎｔａｎｏｌとＡＰＳでキャッピングした粒子を表面に集積したシリカナノ粒子の合成を参考に合成することができる。 For silica nanoparticles enclosing quantum dots, refer to the synthesis of silica nanoparticles in which particles capped with 5-amino-1-pentanol and APS of CdSe / ZnS described on page 2670 (2009) of Chemical Communication are integrated on the surface. Can be synthesized into.
　量子ドットを内包したポリマーナノ粒子は、ネイチャー バイオテクノロジー１９巻６３１ページ（２００１）に記載されているポリスチレンナノ粒子への量子ドットの含浸法を用いて作製することができる。 Polymer nanoparticles containing quantum dots can be produced by using the method of impregnating polystyrene nanoparticles with quantum dots described in Nature Biotechnology Vol. 19, page 631 (2001).
[３．１．３]蛍光色素集積ナノ粒子
　蛍光色素集積ナノ粒子とは、蛍光色素が、上記母体の中に内包されている、及び／又はその表面に吸着している構造を有する。 [3.1.3] Fluorescent dye-accumulated nanoparticles The fluorescent dye-accumulated nanoparticles have a structure in which a fluorescent dye is contained in the mother body and / or is adsorbed on the surface thereof.
　蛍光色素としては、ローダミン系色素分子、スクアリリウム系色素分子、シアニン系色素分子、芳香環系色素分子、オキサジン系色素分子、カルボピロニン系色素分子、ピロメセン系色素分子などの有機蛍光色素を例示することができる。 Examples of the fluorescent dye include organic fluorescent dyes such as rhodamine-based dye molecules, squarylium-based dye molecules, cyanine-based dye molecules, aromatic ring-based dye molecules, oxazine-based dye molecules, carbopyronine-based dye molecules, and pyrromesen-based dye molecules. can.
　具体的には、Ａｌｅｘａ　Ｆｌｕｏｒ（登録商標、インビトロジェン社製）系色素分子、ＢＯＤＩＰＹ（登録商標、インビトロジェン社製）系色素分子、Ｃｙ（登録商標、ＧＥヘルスケア社製）系色素分子、ＨｉＬｙｔｅ（登録商標、アナスペック社製）系色素分子、ＤｙＬｉｇｈｔ（登録商標、サーモサイエンティフィック社製）系色素分子、ＡＴＴＯ（登録商標、ＡＴＴＯ－ＴＥＣ社製）系色素分子、ＭＦＰ（登録商標、Ｍｏｂｉｔｅｃ社製）系色素分子、ＣＦ（登録商標、Ｂｉｏｔｉｕｍ社製）系色素分子、ＤＹ（登録商標、ＤＹＯＭＩＣＳ社製）系色素分子、ＣＡＬ（登録商標、ＢｉｏＳｅａｒｃｈ　Ｔｅｃｈｎｏｌｏｇｉｅｓ社製）系色素分子などを用いることができる。 Specifically, Alexa Fluor (registered trademark, manufactured by Invigen) dye molecule, BODIPY (registered trademark, manufactured by Invigen) dye molecule, Cy (registered trademark, manufactured by GE Healthcare) dye molecule, HiLyte (registered). Trademark, Anaspec) dye molecule, DyLight (registered trademark, Thermoscientific) dye molecule, ATTO (registered trademark, ATTO-TEC) dye molecule, MFP (registered trademark, Mobitec) ) -Based dye molecule, CF (registered trademark, manufactured by Biotium) -based dye molecule, DY (registered trademark, manufactured by DYOMICS) -based dye molecule, CAL (registered trademark, manufactured by BioSearch Technologies) -based dye molecule and the like can be used. ..
　なお、蛍光色素が母体に内包されている場合、蛍光色素は母体内部に分散されていればよく、母体自体と化学的に結合していてもよいし、していなくてもよい。 When the fluorescent dye is contained in the mother body, the fluorescent dye may or may not be chemically bonded to the mother body itself as long as it is dispersed inside the mother body.
　蛍光色素集積ナノ粒子は、公知の方法により作成することが可能である。例えば、蛍光色素を内包したシリカナノ粒子は、ラングミュア８巻２９２１ページ（１９９２）に記載されているＦＩＴＣ内包シリカ粒子の合成を参考に合成することができる。ＦＩＴＣの代わりに所望の蛍光色素を用いることで種々の蛍光色素集積ナノ粒子を合成することができる。 Fluorescent dye-accumulated nanoparticles can be produced by a known method. For example, silica nanoparticles encapsulating a fluorescent dye can be synthesized with reference to the synthesis of FITC-encapsulating silica particles described in Langmuir Vol. 8, p. 2921 (1992). By using a desired fluorescent dye instead of FITC, various fluorescent dye-accumulated nanoparticles can be synthesized.
　蛍光色素を内包したポリスチレンナノ粒子は、米国特許第４３２６００８号明細書（１９８２）に記載されている重合性官能基をもつ有機色素を用いた共重合法や、米国特許第５３２６６９２号明細書（１９９２）に記載されているポリスチレンナノ粒子への蛍光色素の含浸法を用いて作製することができる。 For polystyrene nanoparticles containing a fluorescent dye, a copolymerization method using an organic dye having a polymerizable functional group described in US Pat. No. 4,326,008 (1982) or US Pat. No. 5,326,692 (1992) can be used. ), The polystyrene nanoparticles can be prepared by using the method of impregnating the polystyrene nanoparticles with a fluorescent dye.
[４]組織切片の染色方法の例
　染色方法の一例について説明する。この染色方法が適用できる組織切片（単に「切片」ともいい、病理切片などの切片も含まれる。）の作製法は特に限定されず、公知の手順により作製されたものを用いることができる。 [4] Example of Staining Method for Tissue Section An example of the staining method will be described. The method for preparing a tissue section to which this staining method can be applied (also referred to simply as a “section” and including a section such as a pathological section) is not particularly limited, and a tissue section prepared by a known procedure can be used.
[４．１]標本作製工程
[４．１．１]脱パラフィン処理
　キシレンを入れた容器に、切片を浸漬させ、パラフィン除去する。温度は特に限定されるものではないが、室温で行うことができる。浸漬時間は、３分以上３０分以下であることが好ましい。また必要により浸漬途中でキシレンを交換してもよい。 [4.1] Specimen preparation process
[4.1.1] Deparaffin treatment The section is immersed in a container containing xylene to remove paraffin. The temperature is not particularly limited, but it can be carried out at room temperature. The immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, xylene may be replaced during immersion.
　次いでエタノールを入れた容器に切片を浸漬させ、キシレン除去する。温度は特に限定されるものではないが、室温で行うことができる。浸漬時間は、３分以上３０分以下であることが好ましい。また必要により浸漬途中でエタノールを交換してもよい。 Next, immerse the section in a container containing ethanol to remove xylene. The temperature is not particularly limited, but it can be carried out at room temperature. The immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, ethanol may be replaced during immersion.
　水を入れた容器に、切片を浸漬させ、エタノール除去する。温度は特に限定されるものではないが、室温で行うことができる。浸漬時間は、３分以上３０分以下であることが好ましい。また必要により浸漬途中で水を交換してもよい。 Immerse the section in a container containing water and remove ethanol. The temperature is not particularly limited, but it can be carried out at room temperature. The immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, the water may be replaced during the immersion.
[４．１．２]賦活化処理
　公知の方法に倣い、目的物質の賦活化処理を行う。
　賦活化条件に特に定めはないが、賦活液としては、０．０１Ｍのクエン酸緩衝液（ｐＨ６．０）、１ｍＭのＥＤＴＡ溶液（ｐＨ８．０）、５％尿素、０．１Ｍのトリス塩酸緩衝液などを用いることができる。 [4.1.2] Activation treatment The activation treatment of the target substance is performed according to a known method.
The activation conditions are not particularly specified, but the activating solution includes 0.01 M citric acid buffer (pH 6.0), 1 mM EDTA solution (pH 8.0), 5% urea, and 0.1 M Tris-hydrochloric acid buffer. A liquid or the like can be used.
　ｐＨ条件は用いる組織切片に応じてｐＨ２．０～１３．０の範囲から、シグナルが出て、組織の荒れがシグナルを評価できる程度となる条件で行う。通常はｐＨ６．０～８．０で行うが、特殊な組織切片では例えばｐＨ３．０でも行う。 The pH condition is such that a signal is output from the range of pH 2.0 to 13.0 depending on the tissue section to be used and the tissue roughness is such that the signal can be evaluated. Normally, the pH is 6.0 to 8.0, but for special tissue sections, for example, pH 3.0 is also used.
　加熱機器はオートクレーブ、マイクロウェーブ、圧力鍋、ウォーターバスなどを用いることができる。温度は特に限定されるものではないが、室温で行うことができる。温度は５０～１３０℃、時間は５～３０分で行うことができる。 The heating equipment can be an autoclave, microwave, pressure cooker, water bath, etc. The temperature is not particularly limited, but it can be carried out at room temperature. The temperature can be 50 to 130 ° C. and the time can be 5 to 30 minutes.
　次いでＰＢＳを入れた容器に、賦活処理後の切片を浸漬させ、洗浄を行う。温度は特に限定されるものではないが、室温で行うことができる。浸漬時間は、３分以上３０分以下であることが好ましい。また必要により浸漬途中でＰＢＳを交換してもよい。 Next, the section after activation treatment is immersed in a container containing PBS and washed. The temperature is not particularly limited, but it can be carried out at room temperature. The immersion time is preferably 3 minutes or more and 30 minutes or less. If necessary, the PBS may be replaced during the immersion.
[４．２]免疫染色工程
　免疫染色工程では、目的物質を染色するために、目的物質に直接的又は間接的に結合しうる部位を有する蛍光ナノ粒子を含む免疫染色剤の溶液を、切片に載せ、目的物質との反応を行う。免疫染色工程に用いる免疫染色剤の溶液については、この工程の前にあらかじめ調製しておけばよい。 [4.2] Immunostaining Step In the immunostaining step, in order to stain the target substance, a solution of an immunostaining agent containing fluorescent nanoparticles having a site that can directly or indirectly bind to the target substance is applied to a section. Place and react with the target substance. The solution of the immunostaining agent used in the immunostaining step may be prepared in advance before this step.
　なお、複数の目的物質を検出しようとする場合は、目的物質に対応した複数の免疫染色剤によって免疫染色を行う。この場合に用いる複数の免疫染色剤は、蛍光物質集積ナノ粒子を用いた免疫染色剤（ＰＩＤ染色剤）を少なくとも１つ含むものであればよく、抗体及び蛍光物質（蛍光波長）とが互いに異なれば、ＰＩＤ染色剤を複数用いた多重染色や、ＰＩＤ染色剤と有機蛍光物質や量子ドットなどの蛍光標識体を用いた免疫染色剤とのを組み合わせた多重染色によって、複数の目的物質を検出することも可能である。この場合は、各免疫染色剤の溶液をそれぞれ調製し、切片に載せ、目的物質との反応を行うが、切片に載せる際にそれぞれの免疫染色剤の溶液をあらかじめ混合してもよいし、別々に順次載せてもよい。 When trying to detect multiple target substances, immunostaining is performed with multiple immunostaining agents corresponding to the target substances. The plurality of immunostaining agents used in this case may be those containing at least one immunostaining agent (PID stain) using fluorescent substance-accumulated nanoparticles, and the antibody and the fluorescent substance (fluorescent wavelength) are different from each other. For example, multiple target substances are detected by multiple staining using a plurality of PID stains or a combination of a PID stain and an immunostaining agent using a fluorescent label such as an organic fluorescent substance or a quantum dot. It is also possible. In this case, a solution of each immunostaining agent is prepared, placed on a section, and reacted with the target substance. However, the solution of each immunostaining agent may be mixed in advance when the solution is placed on the section, or separately. It may be placed sequentially in.
　複数の免疫染色剤を用いる場合、蛍光物質集積ナノ粒子の励起／発光波長と、他の免疫染色剤の蛍光標識体の励起／発光波長は、クロストークを無視できる程度に離れていることが望ましい。 When multiple immunostainers are used, it is desirable that the excitation / emission wavelengths of the fluorescent substance-accumulated nanoparticles and the excitation / emission wavelengths of the fluorescent labels of other immunostainers are so far apart that crosstalk can be ignored. ..
　免疫染色工程を行う上での条件、すなわち免疫染色剤の溶液に組織切片を浸漬する際の温度及び浸漬時間は、従来の免疫染色法に準じて、適切なシグナルが得られるよう適宜調整することができる。温度は特に限定されるものではないが、室温で行うことができる。反応時間は、３０分以上２４時間以下であることが好ましい。 The conditions for performing the immunostaining step, that is, the temperature and soaking time when immersing the tissue section in the solution of the immunostaining agent, should be appropriately adjusted so as to obtain an appropriate signal according to the conventional immunostaining method. Can be done. The temperature is not particularly limited, but it can be carried out at room temperature. The reaction time is preferably 30 minutes or more and 24 hours or less.
　上述したような処理を行う前に、ＢＳＡ含有ＰＢＳなど公知のブロッキング剤やＴｗｅｅｎ２０などの界面活性剤を滴下することが好ましい。 It is preferable to drop a known blocking agent such as PBS containing BSA or a surfactant such as Tween 20 before performing the treatment as described above.
[４．３]標本後処理工程
　免疫染色工程を終えた組織標本は、観察に適したものとなるよう、固定化・脱水、透徹、封入などの処理を行うことが好ましい。 [4.3] Specimen post-treatment step It is preferable that the tissue specimen after the immunostaining step is subjected to treatments such as immobilization / dehydration, permeation, and encapsulation so as to be suitable for observation.
　固定化・脱水処理は、組織切片を固定処理液（ホルマリン、パラホルムアルデヒド、グルタールアルデヒド、アセトン、エタノール、メタノールなどの架橋剤）に浸漬すればよい。透徹処理は、固定化・脱水処理を終えた組織切片を透徹液（キシレンなど）に浸漬すればよい。封入処理は、透徹処理を終えた組織切片を封入液に浸漬すればよい。 For immobilization / dehydration treatment, the tissue section may be immersed in a fixing treatment solution (crosslinking agent such as formalin, paraformaldehyde, glutaraldehyde, acetone, ethanol, methanol, etc.). For the permeation treatment, the tissue sections that have been immobilized and dehydrated may be immersed in a permeation solution (xylene or the like). The encapsulation treatment may be performed by immersing the tissue section that has been subjected to the permeation treatment in the encapsulation liquid.
　これらの処理を行う上での条件、例えば組織切片を所定の処理液に浸漬する際の温度及び浸漬時間は、従来の免疫染色法に準じて、適切なシグナルが得られるよう適宜調整することができる。 The conditions for performing these treatments, for example, the temperature and the soaking time when the tissue section is immersed in a predetermined treatment solution, may be appropriately adjusted so as to obtain an appropriate signal according to the conventional immunostaining method. can.
＜オブジェクト染色工程＞
　生体試料内のオブジェクトの位置を特定可能とするために、オブジェクトを染色する工程である。オブジェクトの染色は、上記の薬物染色工程と同様に免疫染色により行うことができるが、その他の標準的な方法によっても行うことができる。
　例えば、細胞質・間質・各種線維・赤血球・角化細胞を赤～濃赤色に染色する、エオジンを用いた染色や、細胞核・石灰部・軟骨組織・細菌・粘液を青藍色～淡青色に染色する、ヘマトキシリンを用いた染色であってもよい。これら２つの染色を同時に行う方法はヘマトキシリン・エオジン染色（ＨＥ染色）として知られている。 <Object dyeing process>
This is a step of staining an object so that the position of the object in the biological sample can be specified. Staining of objects can be performed by immunostaining as in the drug staining step described above, but can also be performed by other standard methods.
For example, staining with eodin, which stains cytoplasm, interstitial, various fibers, erythrocytes, and keratinized cells in red to deep red, and indigo to pale blue in cell nuclei, lime, cartilage tissue, bacteria, and mucus. It may be stained with hematoxylin for staining. The method of simultaneously performing these two stainings is known as hematoxylin / eosin staining (HE staining).
　オブジェクト染色工程を含める場合は、薬物染色工程の後に行うようにしてもよいし、薬物染色工程の前に行うようにしてもよい。 When the object dyeing step is included, it may be performed after the drug dyeing step or before the drug dyeing step.
[４．４]蛍光画像取得工程
　顕微鏡の明視野画像取得部によって生体試料の明視野画像を取得し、これに基づいて解析対象となる領域を設定し、明視野画像を基準にフォーカシングを行う。さらに、蛍光画像取得部によって生体試料に蛍光標識された蛍光物質集積ナノ粒子に励起光を照射し、検出された蛍光物質集積ナノ粒子からの蛍光発光に基づき蛍光画像を取得する。 [4.4] Fluorescent image acquisition step A bright-field image of a biological sample is acquired by a bright-field image acquisition unit of a microscope, a region to be analyzed is set based on this, and focusing is performed based on the bright-field image. Further, the fluorescent image acquisition unit irradiates the fluorescent substance-accumulated nanoparticles fluorescently labeled on the biological sample with excitation light, and acquires a fluorescent image based on the fluorescence emission from the detected fluorescent substance-accumulated nanoparticles.
（１．３．２）実施形態２
　本発明の薬物分布状態解析法は、薬物分布状態解析法薬物を含む生体試料画像に基づく薬物分布状態解析法であって、前記生体試料画像を取得する工程と、前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程と、前記薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程とを有することを特徴とする。 (1.3.2) Embodiment 2
The drug distribution state analysis method of the present invention is a drug distribution state analysis method based on a biological sample image containing a drug, and is a step of acquiring the biological sample image and a drug signal from the biological sample image. Based on the step of detecting / quantifying and obtaining drug state information, and analyzing at least the spatial distribution or statistical distribution including the concentration distribution of the drug associated with the region including the analysis target in the biological sample image. It is characterized by having a process.
　以下において、画像取得、薬物シグナル検出、及び薬物分布解析の工程順に従った解析方法により得た解析結果に基づき作成したヒストグラム等に基づき、面積率による薬物濃度分布の均一性及び適正濃度範囲を評価する実施形態例について説明する（図２及び図３参照）。 In the following, the uniformity and appropriate concentration range of the drug concentration distribution by area ratio will be evaluated based on the histogram created based on the analysis results obtained by the analysis method according to the process order of image acquisition, drug signal detection, and drug distribution analysis. An example of the embodiment will be described (see FIGS. 2 and 3).
＜画像取得＞
　分布解析対象の薬物の投与後の生体から採材した組織切片に対し免疫染色(蛍光色素等）等により薬物位置を特定可能にし、蛍光顕微鏡などの撮像装置を用いて画像取得する。
　例えば蛍光物質集積ナノ粒子による薬物の染色切片の蛍光画像取得である。 <Image acquisition>
Distribution analysis The position of the drug can be specified by immunostaining (fluorescent dye, etc.) on the tissue section collected from the living body after administration of the drug, and the image is acquired using an imaging device such as a fluorescence microscope.
For example, acquisition of a fluorescent image of a section stained with a drug using nanoparticles accumulating fluorescent substances.
＜薬物シグナル検出＞
　薬物シグナルを検出する際には、薬物情報の抽出精度を上げるために前処理を行っても良い。また、蛍光解析時はハイパスフィルタ（ＨＰＦ：high-pass filter:高域通過濾波器）等の信号処理による自家蛍光ノイズの除去処理を行うことがノイズに対するシグナル強度の比（Ｓ／Ｎ）の向上の観点で好ましい。 <Drug signal detection>
When detecting a drug signal, pretreatment may be performed in order to improve the extraction accuracy of drug information. In addition, during fluorescence analysis, the ratio of signal intensity to noise (S / N) can be improved by removing self-fluorescent noise by signal processing such as a high-pass filter (HPF: high-pass filter). It is preferable from the viewpoint of.
　切片輪郭、腫瘍領域及び浸潤領域など特定の領域に解析対象を限定する処理として、領域を判別するためのマーカー染色を抽出しても良いし、機械学習を用いた識別を行ってもよいし、手作業でも良い。 As a process for limiting the analysis target to a specific region such as a section contour, a tumor region, and an infiltration region, a marker stain for discriminating the region may be extracted, or discrimination may be performed using machine learning. Manual work is also acceptable.
＜薬物分布解析＞
　薬物染色強度から薬物濃度の空間分布情報を抽出する蛍光染色の輝度積分値算出、蛍光染色の輝点検出・蛍光粒子算出による薬物濃度定量等を行う。
　薬物の空間分布及び濃度分布の統計算出については、ヒストグラムによる薬物濃度の均一性評価、濃度閾値処理による薬効空間又は毒性空間面積率評価等を行う。 <Drug distribution analysis>
Calculation of the integrated brightness of fluorescent staining that extracts spatial distribution information of drug concentration from drug staining intensity, detection of bright spots of fluorescent staining, quantification of drug concentration by calculation of fluorescent particles, etc. are performed.
For statistical calculation of drug spatial distribution and concentration distribution, uniformity evaluation of drug concentration by histogram, drug effect space or toxicity spatial area ratio evaluation by concentration threshold processing, etc. are performed.
　ヒストグラムによる薬物濃度の均一性評価例としては、濃度最頻値を基準として既定の頻度割合（例えば９５［％］等）を満たす薬物濃度幅を薬物濃度分布バラつき性能とし、基礎評価（至適濃度範囲内であるか等）を行う。なお、最頻値が複数となるような薬物は不均一な分布特性を持ち、薬効安定性が低いと判断する（図３Ａ参照）。 As an example of evaluating the uniformity of drug concentration using a histogram, the drug concentration range that satisfies a predetermined frequency ratio (for example, 95 [%], etc.) based on the most frequent concentration value is defined as the drug concentration distribution variation performance, and the basic evaluation (optimal concentration) is performed. Whether it is within the range, etc.). It should be noted that a drug having a plurality of modes has a non-uniform distribution characteristic, and it is judged that the drug efficacy stability is low (see FIG. 3A).
　濃度閾値処理による薬効空間又は毒性空間面積率の評価例としては、例えば面積率を判断基準として薬効又は毒性の評価をする。また、効果範囲の空間ばらつきによる薬効評価をすることもできる（図３Ｂ参照）。 As an example of evaluation of the medicinal space or toxic space area ratio by the concentration threshold treatment, for example, the medicinal effect or toxicity is evaluated using the area ratio as a judgment criterion. It is also possible to evaluate the drug efficacy based on the spatial variation in the effective range (see FIG. 3B).
（１．３．３）実施形態３
　下記工程順に従った解析を行うことにより、オブジェクト領域内に限定した薬物濃度分布解析をして薬物のターゲットであるオブジェクトにおける集積状態を評価する実施形態例について説明する（図４参照）。 (13.3) Embodiment 3
An embodiment example in which the drug concentration distribution analysis limited to the object region is performed and the accumulation state in the object which is the target of the drug is evaluated by performing the analysis according to the following process order will be described (see FIG. 4).
＜画像取得＞
　薬物位置を特定可能にした画像と、オブジェクト位置を特定可能にした画像を取得する。 <Image acquisition>
An image in which the drug position can be specified and an image in which the object position can be specified are acquired.
＜薬物シグナル検出＞
　実施形態２と同様の方法で行う。 <Drug signal detection>
This is performed in the same manner as in the second embodiment.
＜オブジェクト情報抽出＞
　例えば明視野ＤＡＢ（ジアミノベンジジン）染色画像に対し、ＤＡＢの色ベクトルを用いた色分解・閾値処理によりオブジェクト情報を抽出することができる。
　また、細胞輪郭を明視野で抽出後、オブジェクト特異的に発現するバイオマーカーの発現量が閾値以上のものを選別することによってもオブジェクト情報を抽出することができる。 <Object information extraction>
For example, object information can be extracted from a bright-field DAB (diaminobenzidine) stained image by color separation / threshold processing using a DAB color vector.
In addition, object information can also be extracted by extracting cell contours in a bright field and then selecting those having an expression level of a biomarker specifically expressed in an object of a threshold value or more.
　＜薬物分布解析＞
　薬物の標的となるオブジェクト領域内に絞った薬物濃度分布について定量解析をする（図５参照。）。
　例えば、オブジェクト領域内のみを区画解析対象領域とする方法、区画内のオブジェクト領域内の蛍光のみを濃度定量に使用する方法、オブジェクト座標と薬物検出位置から、オブジェクト単位で薬物を帰属させ、単位を濃度／オブジェクトとした薬物濃度分布を算出する方法がある。 <Drug distribution analysis>
Quantitative analysis is performed on the drug concentration distribution narrowed down to the object region targeted by the drug (see FIG. 5).
For example, a method in which only the object area is set as the area to be analyzed, a method in which only the fluorescence in the object area in the area is used for concentration determination, a drug is assigned to each object from the object coordinates and the drug detection position, and the unit is set. There is a method of calculating the drug concentration distribution as a concentration / object.
　（１．３．４）実施形態４
　オブジェクト領域内外での薬物濃度差比較し、薬物ターゲットへの薬物集積の有意差を判定する実施形態について説明する（図６、図７及び図８参照。）。 (1.3.4) Embodiment 4
An embodiment of comparing drug concentration differences inside and outside the object region and determining a significant difference in drug accumulation in a drug target will be described (see FIGS. 6, 7, and 8).
＜画像取得、薬物シグナル検出及びオブジェクト情報抽出＞
　実施形態２と同様の方法で行う。 <Image acquisition, drug signal detection and object information extraction>
This is performed in the same manner as in the second embodiment.
＜薬物とオブジェクトの分布関連解析＞
　例えばオブジェクトの内側領域及び外側領域において区画単位の薬物濃度の定量、領域内の濃度の総和、及び単位面積あたり平均薬物濃度等を算出し、オブジェクト内外にそれぞれ関連付けた薬物分布・薬物濃度の比較解析をする（図７及び図８参照。）。 <Distribution-related analysis of drugs and objects>
For example, quantification of drug concentration in each section in the inner region and outer region of the object, total concentration in the region, average drug concentration per unit area, etc. are calculated, and comparative analysis of drug distribution and drug concentration associated with each other inside and outside the object. (See FIGS. 7 and 8).
　（１．３．５）実施形態５
　薬物空間分布とオブジェクト空間分布の相関性についての解析、例えばオブジェクトの密度分布を加味した薬物濃度分布の解析をし、薬物分布に影響を及ぼす関連因子の評価をする実施形態例について説明する(図９参照)。 (1.3.5) Embodiment 5
An embodiment example in which the correlation between the drug space distribution and the object space distribution is analyzed, for example, the drug concentration distribution in consideration of the object density distribution is analyzed, and the related factors affecting the drug distribution are evaluated will be described (Fig.). 9).
＜画像取得、薬物シグナル検出、薬物分布解析、オブジェクト情報抽出及びオブジェクト分布解析＞
　上記各工程は、実施形態２と同様に行う。 <Image acquisition, drug signal detection, drug distribution analysis, object information extraction and object distribution analysis>
Each of the above steps is performed in the same manner as in the second embodiment.
＜薬物とオブジェクト分布関連解析＞
　例えばオブジェクト密度特徴量で正規化した薬物濃度マップの作成、薬物特徴量で正規化したオブジェクト密度マップ等の作成等による解析をする。なお、オブジェクト密度は面積率の他、個数/単位区画もあり得る。
　例えば密度正規化後の分布が均一であれば、オブジェクトの密度と薬物濃度が相関していると評価される。 <Drug and object distribution association analysis>
For example, analysis is performed by creating a drug concentration map normalized by an object density feature, creating an object density map normalized by a drug feature, and the like. In addition to the area ratio, the object density may be the number / unit section.
For example, if the distribution after density normalization is uniform, it is evaluated that the density of the object and the drug concentration are correlated.
（１．３．６）実施形態６
　第２のオブジェクト位置に応じたオブジェクト領域内の薬物濃度の比較により、腫瘍浸潤免疫細胞内への薬物の集積状態を確認する実施形態例について説明する（図１０及び図１１参照。）。 (1.3.6) Embodiment 6
An embodiment of confirming the accumulation state of the drug in the tumor infiltrating immune cells by comparing the drug concentration in the object region according to the position of the second object will be described (see FIGS. 10 and 11).
＜画像取得＞
　例えば免疫チェックポイント阻害薬として抗ＰＤ－１抗体を投与した生体試料の画像を取得する。この際、抗ＰＤ－１抗体は蛍光染色し、免疫細胞はＤＡＢなどで染色し、その蛍光画像及び明視野画像を取得する。
　投与する薬物は、後述する第１のオブジェクトに応じて、種々変えることが好ましい。例えば第１オブジェクトが、がん細胞の場合は、抗ＨＥＲ２抗体や抗ＰＤ－Ｌ１抗体などとしてもよい。
　複数オブジェクトの抽出のために、複数の染色（蛍光多色）を行っても良い。染色を行わず、又はヘマトキシリン・エオジン染色（ＨＥ染色）若しくはヘマトキシリン染色（Ｈ染色）などのみで形態情報からオブジェクト抽出してもよい。 <Image acquisition>
For example, an image of a biological sample administered with an anti-PD-1 antibody as an immune checkpoint inhibitor is acquired. At this time, the anti-PD-1 antibody is fluorescently stained, the immune cells are stained with DAB or the like, and the fluorescent image and the bright field image are obtained.
It is preferable that the drug to be administered varies depending on the first object described later. For example, when the first object is a cancer cell, it may be an anti-HER2 antibody, an anti-PD-L1 antibody, or the like.
Multiple stains (fluorescent multicolor) may be performed to extract multiple objects. Objects may be extracted from the morphological information without staining or only with hematoxylin / eosin staining (HE staining) or hematoxylin staining (H staining).
＜薬物シグナル検出、薬物分布解析＞
　実施形態２と同様に行う。抗ＰＤ－１抗体のシグナルを検出・定量し、薬物状態情報を取得する。 <Drug signal detection, drug distribution analysis>
This is performed in the same manner as in the second embodiment. The signal of anti-PD-1 antibody is detected and quantified, and drug status information is acquired.
＜オブジェクト情報抽出＞
　例えば第２のオブジェクトとして腫瘍領域の情報を抽出し、腫瘍領域からの距離に応じて、エリアを腫瘍領域内部、腫瘍領域近縁部、腫瘍領域遠隔部を区別する。それぞれのエリアに属する免疫細胞を第１のオブジェクトとして情報を抽出する。さらに、標的細胞情報、例えば腫瘍領域浸潤ＣＤ８陽性細胞等の情報を得る。
　上記の例では第２オブジェクトを腫瘍領域としたが、他に浸潤がんの浸潤領域などとしてもよい。また、上記の例では第１オブジェクトを免疫細胞としたが、他にがん細胞などとしてもよい。 <Object information extraction>
For example, information on the tumor region is extracted as a second object, and the area is distinguished from the inside of the tumor region, the vicinity of the tumor region, and the remote portion of the tumor region according to the distance from the tumor region. Information is extracted with immune cells belonging to each area as the first object. Furthermore, information on target cells, such as tumor region infiltrating CD8-positive cells, is obtained.
In the above example, the second object is the tumor region, but it may also be the infiltrating region of the invasive cancer. Further, in the above example, the first object is an immune cell, but it may also be a cancer cell or the like.
＜薬物シグナル検出、薬物分布解析＞
　例えば図１１Ａの画像を基に作成した図１１Ｃのオブジェクト単位薬物濃度分布ヒストグラムや、図１１Ｂの画像を基に作成した図１１Ｄのオブジェクト単位薬物濃度分布ヒストグラムを得て第１オブジェクトに関連付けた薬物分布を解析することで、腫瘍領域近位の免疫細胞（腫瘍浸潤免疫細胞）の抗ＰＤ－１抗体濃度が低い場合は、抗腫瘍免疫応答を示す免疫細胞（腫瘍浸潤免疫細胞）への治療薬到達率が低いため、免疫チェックポイント阻害性が低いと評価する。 <Drug signal detection, drug distribution analysis>
For example, an object-based drug concentration distribution histogram of FIG. 11C created based on the image of FIG. 11A and an object-based drug concentration distribution histogram of FIG. 11D created based on the image of FIG. 11B are obtained and the drug distribution associated with the first object is obtained. By analyzing Since the rate is low, it is evaluated as having low immune checkpoint inhibition.
（１．３．７）実施形態７
　マウスを用いて薬物分布及びオブジェクト分布等の経時変化を調べる実験例について説明する（図１２～図１５参照。）。
　抗ＨＥＲ２抗体薬物複合体（抗体と薬物（ペイロード）が結合したもの）をマウスに投与後、所定経過時間毎にマウスからサンプルを採取し、蛍光物質集積ナノ粒子（ＰＩＤ）染色し、蛍光画像を取得する（図１２Ａ及び図１２Ｂ参照。）。その後、各サンプルについて連続切片を用いたオブジェクト検出と、薬物定量解析をする。 (13.7) Embodiment 7
An experimental example for investigating changes over time such as drug distribution and object distribution using a mouse will be described (see FIGS. 12 to 15).
After administering the anti-HER2 antibody drug conjugate (bonded antibody and drug (loading)) to the mouse, a sample is taken from the mouse at predetermined elapsed time, stained with fluorescent substance-accumulated nanoparticles (PID), and a fluorescent image is obtained. Acquire (see FIGS. 12A and 12B). After that, object detection using continuous sections and drug quantitative analysis are performed for each sample.
　染色によりＨＥＲ２陽性／陰性領域を特定し、それぞれの領域で２種類の薬物分布（濃度）を測定し、時系列のサンプル間で濃度を比較する(図１３Ａ及び図１３Ｂ参照)。 HER2 positive / negative regions are identified by staining, two types of drug distribution (concentration) are measured in each region, and the concentrations are compared between time-series samples (see FIGS. 13A and 13B).
　次に、本実施形態７における解析方法の一例として、連続切片を用いたＨＥＲ２陰性／陽性領域の抽出し、顕微鏡による拡大倍率４０倍の撮影画像を用いた解析におけるＰＩＤスコア算出法について説明する（図１４参照）。 Next, as an example of the analysis method in the seventh embodiment, a PID score calculation method in an analysis using a HER2 negative / positive region extracted using a continuous section and an image taken with a microscope at a magnification of 40 times will be described ( See FIG. 14).
　ＨＥＲ２（ＤＡＢ）染色切片の位置合わせをした後に、ＨＥＲ２陽性領域を抽出（ＤＡＢ染色領域検出）する。次に、ＨＥＲ２陽性領域の画像を重ね合わせした後、ＨＥＲ２陽性・陰性領域の視野を指定し、拡大倍率４０倍で顕微鏡画像を得る。撮影視野に対応するＨＥＲ２陽性領域を生成し、ＨＥＲ２陽性領域画像に基づきＰＩＤスコアを算出する。薬物投与後の時間別の組織切片における陽性及び陰性領域のＰＩＤスコアを比較する（図１４参照）。 After aligning the HER2 (DAB) stained sections, the HER2 positive region is extracted (DAB stained region detection). Next, after superimposing the images of the HER2-positive region, the field of view of the HER2-positive / negative region is specified, and a microscope image is obtained at a magnification of 40 times. A HER2-positive region corresponding to the field of view to be photographed is generated, and a PID score is calculated based on the HER2-positive region image. The PID scores of the positive and negative regions in the tissue sections by time after drug administration are compared (see FIG. 14).
　なお、図１５に、抗ＨＥＲ２抗体薬物複合体投与後の時間経過による薬物（ペイロード）がオブジェクトに到達し、更に集積（局在化）及び拡散する状態変化をヒストグラムから解析する方法を示す。横軸に薬物（ペイロード）に標識している蛍光色素の蛍光輝度をとり、縦軸に頻度をとったグラフから、時間の経過に伴い、高輝度帯の頻度が低下し、最頻値のピークが高輝度側へシフトしていることが分かる。
　高輝度蛍光領域頻度の低下は集積ペイロード減少を、最頻値の輝度上昇は遊離ペイロードの拡散（ペイロードが無かった領域が減ることによる領域全体の輝度上昇）を示していると考えられる。 In addition, FIG. 15 shows a method of analyzing from a histogram a state change in which a drug (payload) reaches an object and is further accumulated (localized) and diffused with the passage of time after administration of the anti-HER2 antibody drug conjugate. From the graph in which the horizontal axis shows the fluorescence intensity of the fluorescent dye labeled on the drug (papment) and the vertical axis shows the frequency, the frequency of the high-intensity band decreases with the passage of time, and the peak of the mode value. Can be seen to shift to the high brightness side.
It is considered that the decrease in the frequency of the high-intensity fluorescent region indicates the decrease in the integrated payload, and the increase in the brightness of the mode indicates the diffusion of the free payload (the increase in the brightness of the entire region due to the decrease in the region without the payload).
２　薬物分布状態解析システム
　本発明の薬物分布状態解析システムは、本発明の薬物分布状態解析法を実施するための工程手段を有することを特徴とする。 2. Drug distribution state analysis system The drug distribution state analysis system of the present invention is characterized by having a process means for carrying out the drug distribution state analysis method of the present invention.
　すなわち、前記工程１～３を実施する各工程手段は最低限必要であるが、その他目的に応じて種々の工程手段を含めた構成の解析システムとすることが好ましい。
　例えば、前記工程１～１２の各工程に対応する工程手段又は工程部のいずれかを含む構成の解析システムであることが好ましい。 That is, each process means for carrying out the steps 1 to 3 is at least necessary, but it is preferable to use an analysis system having a configuration including various process means depending on other purposes.
For example, it is preferable that the analysis system has a configuration including any of the process means or the process unit corresponding to each of the processes 1 to 12.
　なお、ここで、「工程手段」とは、本発明の薬物分布状態解析法を実施するための各工程における手段として必要な化学物質、測定/分析のため機器、イメージング装置、情報処理装置等をいう。 Here, the "process means" refers to chemical substances required as means in each process for carrying out the drug distribution state analysis method of the present invention, devices for measurement / analysis, imaging devices, information processing devices, and the like. say.
　本発明は、生体試料からデジタル画像を取得し、薬物の時空間分布又は微小環境との相関を一定のモデル・ルールに従い自動的に解析する薬物分布状態解析法及び薬物分布状態解析システムに利用することができる。 The present invention is used in a drug distribution state analysis method and a drug distribution state analysis system that acquires digital images from biological samples and automatically analyzes the correlation between the spatiotemporal distribution of drugs or the microenvironment according to certain model rules. be able to.

Claims (20)

  1. 　薬物を含む生体試料画像に基づく薬物分布状態解析法であって、
    　前記生体試料画像を取得する工程１と、
    　前記生体試料画像から薬物シグナルを検出・定量し、薬物状態情報を得る工程２と、
    　前記薬物状態情報に基づき、少なくとも前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布を含む空間分布又は統計分布を解析する工程３とを有することを特徴とする薬物分布状態解析法。     It is a drug distribution state analysis method based on a biological sample image containing a drug.
    Step 1 of acquiring the biological sample image and
    Step 2 of detecting and quantifying a drug signal from the biological sample image and obtaining drug state information,
    A drug distribution state characterized by having at least a step 3 of analyzing a spatial distribution or a statistical distribution including a concentration distribution of the drug associated with a region including an analysis target in the biological sample image based on the drug state information. Analysis method.
  2. 　前記各工程に加えて、前記生体試料画像から少なくとも解析対象である前記薬物のターゲットとなるオブジェクト又は当該ターゲットと関連性を有する非ターゲット・オブジェクトの情報を抽出する工程４を更に有し、前記工程３においては、前記オブジェクト又は非ターゲット・オブジェクトに関連付けられた前記薬物の濃度分布を、前記生体試料画像における解析対象を含む領域に関連付けられた前記薬物の濃度分布として解析することを特徴とする請求項１に記載の薬物分布状態解析法。 In addition to the above steps, the step 4 further includes a step 4 of extracting information on at least a target object of the drug to be analyzed or a non-target object having a relationship with the target from the biological sample image. 3. The invention is characterized in that the concentration distribution of the drug associated with the object or the non-target object is analyzed as the concentration distribution of the drug associated with the region containing the analysis target in the biological sample image. Item 1. The drug distribution state analysis method according to Item 1.
  3. 　前記工程４を経由して行う、前記オブジェクトに関連づけた薬物濃度分布を解析する工程３が、当該オブジェクト内側領域及び当該オブジェクト外側領域にそれぞれ関連付けた薬物濃度分布の解析であり、かつ、前記解析の方法が代表値算出又は相関解析であることを特徴とする請求項２に記載の薬物分布状態解析法。 The step 3 for analyzing the drug concentration distribution associated with the object, which is performed via the step 4, is the analysis of the drug concentration distribution associated with the object inner region and the object outer region, respectively, and is the analysis. The drug distribution state analysis method according to claim 2, wherein the method is representative value calculation or correlation analysis.
  4. 　前記オブジェクト内側領域にバイオマーカー陽性細胞領域を有し、前記オブジェクト外側領域にバイオマーカー陰性細胞領域を有することを特徴とする請求項３に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 3, further comprising a biomarker-positive cell region in the object inner region and a biomarker-negative cell region in the object outer region.
  5. 　前記オブジェクトに関連付けた薬物濃度分布を解析する工程３が、各オブジェクトからの距離又は方位のいずれか少なくとも一つの指標で区別したエリアにそれぞれ関連付けた薬物濃度分布を比較解析する工程であることを特徴とする請求項１から請求項４までのいずれか一項に記載の薬物分布状態解析法。 The step 3 for analyzing the drug concentration distribution associated with the object is a step for comparing and analyzing the drug concentration distribution associated with each area distinguished by at least one index of distance or orientation from each object. The drug distribution state analysis method according to any one of claims 1 to 4.
  6. 　前記各工程に加えて、前記工程４を経由して前記オブジェクトに関する空間分布を解析する工程５を更に有することを特徴とする請求項１から請求項５までのいずれか一項に記載の薬物分布状態解析法。 The drug distribution according to any one of claims 1 to 5, further comprising a step 5 of analyzing the spatial distribution of the object via the step 4 in addition to the steps. State analysis method.
  7. 　前記各工程に加えて、前記工程３において得た前記薬物濃度分布と前記工程４において得た前記オブジェクトの前記空間分布との関連性を解析する工程６を更に有することを特徴とする請求項１から請求項６までのいずれか一項に記載の薬物分布状態解析法。 Claim 1 is characterized in that, in addition to each of the above steps, a step 6 of analyzing the relationship between the drug concentration distribution obtained in the step 3 and the spatial distribution of the object obtained in the step 4 is further provided. The drug distribution state analysis method according to any one of claims 6 to 6.
  8. 　前記薬物又は前記オブジェクトのいずれか若しくは両方が複数種であることを特徴とする請求項１から請求項７までのいずれか一項に記載の薬物分布状態解析法。 The drug distribution state analysis method according to any one of claims 1 to 7, wherein either or both of the drug and the object are a plurality of types.
  9. 　特定の前記オブジェクトに関連する異なる種類の前記薬物の濃度分布を比較解析することを特徴とする請求項８に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 8, wherein the concentration distribution of different types of the drug related to the specific object is comparatively analyzed.
  10. 　前記比較解析する複数の薬物が、抗体薬物複合体とペイロードであることを特徴とする請求項９に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 9, wherein the plurality of drugs to be comparatively analyzed are an antibody drug conjugate and a payload.
  11. 　前記比較解析する前記オブジェクトが複数種あり、異なるオブジェクト種間の前記薬物濃度分布を比較解析することを特徴とする請求項８に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 8, wherein there are a plurality of types of the objects to be comparatively analyzed, and the drug concentration distribution between different object types is comparatively analyzed.
  12. 　前記比較解析する前記オブジェクトが複数種あり、第２のオブジェクトからの空間分布に応じた、第１のオブジェクトに関連付けた前記薬物濃度分布を比較解析することを特徴とする請求項１１に記載の薬物分布状態解析法。 The drug according to claim 11, wherein there are a plurality of types of the objects to be comparatively analyzed, and the drug concentration distribution associated with the first object is comparatively analyzed according to the spatial distribution from the second object. Distribution state analysis method.
  13. 　前記比較解析する前記オブジェクトが複数種あり、種類の異なる前記オブジェクト間での、それぞれ異なる薬物種の濃度分布を比較解析することを特徴とする請求項８に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 8, wherein there are a plurality of types of the objects to be comparatively analyzed, and the concentration distributions of different drug types are compared and analyzed among the different types of objects.
  14. 　前記各工程に加えて、前記工程３又は前記工程６において得た解析結果に基づいて詳細解析箇所を選定する工程７を更に有することを特徴とする請求項１から請求項１３までのいずれか一項に記載の薬物分布状態解析法。 One of claims 1 to 13, further comprising a step 7 of selecting a detailed analysis point based on the analysis result obtained in the step 3 or the step 6 in addition to the steps. The drug distribution state analysis method described in the section.
  15. 　前記各工程に加えて、前記選定した詳細解析箇所を解析する工程８を更に有することを特徴とする請求項１４に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 14, further comprising a step 8 for analyzing the selected detailed analysis location in addition to the above steps.
  16. 　前記生体試料画像を取得する工程１が、連続する複数の切片からそれぞれ解析対象となる薬物状態情報及びオブジェクト情報を個別に取得する工程であり、かつ、前記各工程に加えて、前記連続する複数の切片の位置合わせによる画像位置合わせをする工程９を更に有することを特徴とする請求項１から請求項１５までのいずれか一項に記載の薬物分布状態解析法。 The step 1 of acquiring the biological sample image is a step of individually acquiring drug state information and object information to be analyzed from a plurality of consecutive sections, and in addition to the above steps, the continuous plurality of steps. The drug distribution state analysis method according to any one of claims 1 to 15, further comprising a step 9 of aligning an image by aligning the sections of the above.
  17. 　前記各工程に加えて、前記工程１において取得した複数の生体試料画像に対して、当該生体試料画像間での薬物分布について比較解析をする工程１０を更に有することを特徴とする請求項１から請求項１６までのいずれか一項に記載の薬物分布状態解析法。 The first aspect of the present invention is characterized in that, in addition to each of the above steps, there is further a step 10 of comparatively analyzing the drug distribution among the biological sample images obtained in the plurality of biological sample images obtained in the first step. The drug distribution state analysis method according to any one of claims 16.
  18. 　前記各工程に加えて、前記薬物濃度分布又はオブジェクト分布の解析結果に基づいて、注目領域を選定する工程１１を更に有することを特徴とする請求項１から請求項１７までのいずれか一項に記載の薬物分布状態解析法。 The item according to any one of claims 1 to 17, further comprising a step 11 for selecting a region of interest based on the analysis result of the drug concentration distribution or the object distribution in addition to each of the above steps. The described drug distribution state analysis method.
  19. 　前記各工程に加えて、複数の前記注目領域の間の遺伝子発現差分から関連バイオマーカーを同定する工程１２を更に有することを特徴とする請求項１８に記載の薬物分布状態解析法。 The drug distribution state analysis method according to claim 18, further comprising a step 12 of identifying a related biomarker from a gene expression difference between a plurality of the regions of interest in addition to the above steps.
  20. 　薬物を含む生体試料画像に基づく薬物分布状態解析システムであって、
    　請求項１から請求項１９までのいずれか一項に記載の薬物分布状態解析法を実施するための工程手段を有することを特徴とする薬物分布状態解析システム。     A drug distribution state analysis system based on images of biological samples containing drugs.
    A drug distribution state analysis system comprising a process means for carrying out the drug distribution state analysis method according to any one of claims 1 to 19.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010044129A1 (en) * 2000-03-14 2001-11-22 Jian Ling Methodology of using raman imaging microscopy for evaluating drug action within living cells
JP2011016730A (en) * 2009-07-07 2011-01-27 Japan Health Science Foundation Method of screening microtubule growth inhibitor, and microtubule growth inhibitor
JP2015513551A (en) * 2012-03-05 2015-05-14 ブラッコ・イメージング・ソシエタ・ペル・アチオニＢｒａｃｃｏ Ｉｍａｇｉｎｇ Ｓ．Ｐ．Ａ． Dynamic contrast-enhanced MRI methods and agents for assessing macromolecular transport into pathological tissues
WO2018159212A1 (en) * 2017-02-28 2018-09-07 コニカミノルタ株式会社 Method for detecting constituent component of antibody-drug conjugate
WO2019221062A1 (en) * 2018-05-17 2019-11-21 コニカミノルタ株式会社 Method for evaluating medicine
JP2020060544A (en) * 2018-10-04 2020-04-16 国立大学法人大阪大学 Safety evaluation method for alpha-ray nuclear medicine treatment agent and screening method
WO2020122102A1 (en) * 2018-12-14 2020-06-18 コニカミノルタ株式会社 Method for forecasting arrival of drug inside diseased tissue

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010044129A1 (en) * 2000-03-14 2001-11-22 Jian Ling Methodology of using raman imaging microscopy for evaluating drug action within living cells
JP2011016730A (en) * 2009-07-07 2011-01-27 Japan Health Science Foundation Method of screening microtubule growth inhibitor, and microtubule growth inhibitor
JP2015513551A (en) * 2012-03-05 2015-05-14 ブラッコ・イメージング・ソシエタ・ペル・アチオニＢｒａｃｃｏ Ｉｍａｇｉｎｇ Ｓ．Ｐ．Ａ． Dynamic contrast-enhanced MRI methods and agents for assessing macromolecular transport into pathological tissues
WO2018159212A1 (en) * 2017-02-28 2018-09-07 コニカミノルタ株式会社 Method for detecting constituent component of antibody-drug conjugate
WO2019221062A1 (en) * 2018-05-17 2019-11-21 コニカミノルタ株式会社 Method for evaluating medicine
JP2020060544A (en) * 2018-10-04 2020-04-16 国立大学法人大阪大学 Safety evaluation method for alpha-ray nuclear medicine treatment agent and screening method
WO2020122102A1 (en) * 2018-12-14 2020-06-18 コニカミノルタ株式会社 Method for forecasting arrival of drug inside diseased tissue

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YUKARI TSUBATA, HAYASHI MITSUHIRO, TANINO RYOSUKE, AIKAWA HIROAKI, OHUCHI MAYU, TAMURA KENJI, FUJIWARA YASUHIRO, ISOBE TAKESHI, HA: "Evaluation of the heterogeneous tissue distribution of erlotinib in lung cancer using matrix-assisted laser desorption ionization mass spectrometry imaging", SCIENTIFIC REPORTS, vol. 7, no. 1, 1 December 2017 (2017-12-01), pages 1 - 6, XP055717813, DOI: 10.1038/s41598-017-13025-8 *

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