CN114540306A - Construction method of drug screening model based on artificial intelligence and big data analysis - Google Patents
Construction method of drug screening model based on artificial intelligence and big data analysis Download PDFInfo
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Abstract
The invention belongs to the technical field of medicines, and particularly relates to a construction method of a medicine screening model based on artificial intelligence and big data analysis. The invention adopts a cell line strategy based on the cell activity of the constitutive organoid, so that the reporting system is in a highly activated state in the cell, no additional stimulation is needed, and the obtained stable cell model can be directly used for screening compounds; not only greatly reduced the cost of screening, also made the whole screening process from cell culture to detection simultaneously, shortened the cycle of screening, reduced the operating procedure, promoted system stability, high efficiency and ease for use, more be applicable to high flux drug screening.
Description
Technical Field
The invention relates to the technical field of drug screening, in particular to a method for building a drug screening model based on artificial intelligence and big data analysis.
Background
Organoids (organoids) are 3D cell cultures that highly mimic human organs in structure and function, can form spatial structures similar to organs and differentiate corresponding functions, and have the characteristics of cell proliferation and differentiation, self-renewal, self-assembly, long-term culture, genetic stability, and the like. Drug screening is a biochemical level and cellular level screening. Drug screening is divided into high-throughput and virtual drug screening.
The screening model is a pharmacological experiment model applied to drug screening experiments, and because drug screening requires that an experimental scheme has characteristics of standardization and quantification, common animal experiments in traditional pharmacological experiments are rarely applied to drug screening, and drug screening can be divided into biochemical level screening and cell level screening according to different experimental models. Cell level drug screening is a drug screening model closer to physiological conditions, the model is organoid cells simulating drug action, cell culture technology is applied to obtain required cells, the cells interact with candidate compounds, and the compound action capacity is measured by detection technology similar to biochemical level screening, so that the compounds are screened.
The screening model is a pharmacological experiment model applied to drug screening experiments, and because drug screening requires that an experimental scheme has characteristics of standardization and quantification, common animal experiments in traditional pharmacological experiments are rarely applied to drug screening, and drug screening can be divided into biochemical level screening and cell level screening according to different experimental models. Cell level drug screening is a drug screening model closer to physiological conditions, the model is organoid cells simulating drug action, cell culture technology is applied to obtain required cells, the cells interact with candidate compounds, and the compound action capacity is measured by detection technology similar to biochemical level screening, so that the compounds are screened.
When various diseases occur, the medicines are used for treatment to relieve and solve the pain caused by the diseases, but the problem of screening active medicines is a common concern in the medical field, and researchers are always trying to find inhibitors with good specificity and low side effect for clinical application, alleviation and treatment of a large number of related patients; although high-throughput screening methods based on reporter systems have been developed and put into practice, and have achieved certain results, some insurmountable disadvantages still exist, such as the need for exogenous stimulation, which not only requires many steps, but also increases the screening cost, and also increases the instability of reporter systems.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to solve the technical problems that the culture of organoids needs to be stimulated by external sources, the screening cost is high, and the instability of a reporting system is increased.
In order to solve the technical problem, the invention adopts the following technical scheme: a construction method of a drug screening model based on artificial intelligence and big data analysis comprises a construction system and an experimental record system, wherein the construction system utilizes a report system carrier containing an organoid cell specific binding sequence and constructs a stable high-throughput drug screening cell model taking a specific target signal as a target point on the basis of active cells with constitutive specific target cells; the report system vector is constructed by inserting a response sequence containing an organoid cell specific binding sequence as a report system vector into a report vector multiple cloning site containing a luciferase report gene, and the construction system specifically comprises the following steps:
s1: culturing organoid cell models;
s2: preparing a medicament;
s3: screening transport concentration;
s4: grouping, administration and transportation experiments;
s5: performing statistical analysis;
in the step of S1, human tissues are taken and cut into pieces on ice, collagenase is added for resuspension, a shaker is used for digestion, cells are filtered, DMEM/F12 is added into filtrate for stopping digestion, centrifugation is carried out, and supernatant is removed; taking erythrocyte lysate for resuspending cells, centrifuging, removing supernatant, adding DMEM/F12 for resuspension, centrifuging, and removing supernatant; counting cells, mixing matrigel, dripping the matrigel in the center of a hole plate, placing a culture dish, and solidifying the matrigel; adding culture medium into each hole, and culturing in a cell culture box;
the experiment recording system comprises an experimenter information input module, an experiment information acquisition module, an information transcription module, an information arrangement module, an information processing module, an experiment information modification module, an integration and sending module and a database.
Preferably, the experimental personnel information input module is used for inputting information to each experimental personnel, wherein each experimental personnel is divided into an experimental personnel and a recording personnel, the experimental information acquisition module is respectively connected with the experimental personnel information input module and the information transcription module, the information processing module is respectively connected with the information arrangement module and the experimental information modification module, the database is respectively connected with the information arrangement module and the information processing module, and the experimental information modification module is connected with the integration and sending module;
all take notes to experimenter's mission information and operating procedure, the record of experimenter information can be to knowing the personnel record of experiment operation, prevents that experimental information from leaking, and the record of experimental procedure is convenient for carry out analysis and whole to the experiment, can carry out effectual integration to last experimental procedure, is convenient for finally reachd.
Preferably, in the step S4, the prepared drugs need to be grouped, the record of the drug grouping information, the drug administration information and the transportation experiment is transcribed to the information transcription module by the experiment information acquisition module, the corresponding experiment information of each experimenter in the experiment time period is arranged, the experiment record corresponding to each experiment information is transcribed to the text format, so as to obtain the experiment text information corresponding to each experimenter, the name of each experimenter and the experiment time period of each experimenter are sent to the information arrangement module, the information arrangement module collects the arranged experiment information together, the arranged experiment data is processed by the software of the experiment data processing class, the defects of the prepared drugs are judged by comparing the analyzed experiment data with the needed drugs, the experiment information modification module is used for modifying analyzed experiment operation and steps, enabling an experimenter to modify the experiment steps according to the analyzed experiment data, and then integrating and sending the modified experiment steps to the database through the integration sending module, so that the experiment data can be sorted and collected.
Preferably, the culture medium comprises matrigel and a cell suspension mixed with the matrigel; the volume ratio of the matrigel to the cell suspension is (3-4) to (5-10), the cell suspension contains cells derived from a cell line, and the cells are attached to the surface and the interior of the matrigel; and, in the cell suspension, the cell line-derived cell content is 80 to 1600 per 100. mu.l.
Preferably, the cell suspension is filtered using a collection screen with a pore size of 40-100 μm and the collection screen is back-flushed with a cell pellet collection fluid to obtain the tumor cell pellet.
Preferably, in the step of S2, the drug is formulated into a plurality of concentration gradients, and then the organoid model is administered with drug stimulation under different concentration gradients, and organoid samples are collected at different time points, observed for morphological changes, and tested for functional indicators of the corresponding organs.
Preferably, organ-like cells in logarithmic growth phase are cultured in vitro and divided into a normal saline group of 0.009 g.mL-1 and a NaCl different concentration gradient group: respectively 0.012, 0.015, 0.018, 0.021, 0.024, 0.027, 0.030, 0.033 and 0.036 g.mL < -1 > are subjected to 9 concentration gradients, the organoid cells are incubated for 3 to 10 hours by NaCl liquid medicine with corresponding concentration, the cell survival rate is detected by an MTT method, and the optimal NaCl incubation concentration for researching NaCl transportation is determined.
Preferably, the drug treatment of the organoid model further comprises: treating the organoid model with 0.1% DMSO as a blank control, collecting organoid samples of the blank control at different time points, observing morphological changes and detecting functional indexes of the organ;
by using the blank control, the effect of smoke with different concentration gradients and NaCl concentrations with different concentrations on organoid cell culture can be observed, and the effect contrast is more obvious.
Compared with the prior art, the invention has at least the following advantages:
1. the invention adopts a cell line strategy based on the cell activity of the constitutive organoid, so that the reporting system is in a highly activated state in the cell, and the obtained stable cell model can be directly used for screening compounds without using external stimulation; not only greatly reduced the cost of screening, also made the whole screening process from cell culture to detection simultaneously, shortened the cycle of screening, reduced the operating procedure, promoted system stability, high efficiency and ease for use, more be applicable to high flux drug screening.
2. According to the invention, through the experiment acquisition module, the information transcription module and the combination of the information arrangement module and the information processing module, real-time experiment acquisition is carried out on each experimenter in the experiment process, the experiment information of each experimenter is converted into text information through an experiment information recording and identifying technology, and meanwhile, the experiment text information of each experimenter is also arranged, so that the intelligent processing of experiment data information is realized, the experiment effect and the experiment recording efficiency are improved, the concentration of each experimenter in the experiment of each experimenter is ensured, and meanwhile, the time cost and the economic cost of the experiment processing of the experimenter are greatly saved.
3. The invention can accurately predict the occurrence of adverse drug reactions in the in-vitro evaluation of drug organ damage, reduces the development cost of new drugs, has the key of reducing adverse consequences brought to patients by the drugs, has short period and simple and convenient operation, can replace the traditional cell line model and animal model, and is used for the development of new drugs before clinic and the evaluation of organ toxicity of clinical drugs.
Drawings
FIG. 1 is an overall structure diagram of a method for constructing a drug screening model based on artificial intelligence and big data analysis according to the present invention;
FIG. 2 is a flow chart of a method for constructing a drug screening model based on artificial intelligence and big data analysis, which is provided by the invention;
FIG. 3 is a flow chart of a method for constructing a drug screening model based on artificial intelligence and big data analysis, which is provided by the invention;
FIG. 4 is a flow chart of a method for constructing a drug screening model based on artificial intelligence and big data analysis, which is provided by the invention;
FIG. 5 is a flow chart of a method for building a drug screening model based on artificial intelligence and big data analysis.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Reference will now be made in detail to embodiments of the present patent, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present patent and are not to be construed as limiting the present patent.
In the description of this patent, it is to be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings for the convenience of describing the patent and for the simplicity of description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the patent.
In the description of this patent, it is noted that unless otherwise specifically stated or limited, the terms "mounted," "connected," and "disposed" are to be construed broadly and can include, for example, fixedly connected, disposed, detachably connected, disposed, or integrally connected and disposed. The specific meaning of the above terms in this patent may be understood by one of ordinary skill in the art as appropriate.
Referring to fig. 1-5, a method for constructing a drug screening model based on artificial intelligence and big data analysis, comprising a construction system and an experimental record system, wherein the construction system utilizes a report system carrier containing an organoid cell specific binding sequence and constructs a stable high-throughput drug screening cell model taking a specific target signal as a target on the basis of an active cell with a constitutive specific target cell; the report system vector is constructed by inserting a response sequence containing an organoid cell specific binding sequence as a report system vector into a report vector multiple cloning site containing a luciferase report gene, and the construction system specifically comprises the following steps:
s1: culturing organoid cell models;
s2: preparing a medicament;
s3: screening transport concentration;
s4: grouping, administration and transfer experiments;
s5: performing statistical analysis;
s1, cutting human tissues on ice, adding collagenase for resuspension, carrying out table digestion, filtering cells, adding DMEM/F12 into filtrate to terminate digestion, centrifuging, and removing supernatant; taking erythrocyte lysate for resuspending cells, centrifuging, removing supernatant, adding DMEM/F12 for resuspension, centrifuging, and removing supernatant; counting cells, mixing matrigel, dripping the matrigel in the center of a hole plate, placing a culture dish, and solidifying the matrigel; adding culture medium into each hole, and culturing in a cell culture box;
the experiment recording system comprises an experimenter information input module, an experiment information acquisition module, an information transcription module, an information sorting module, an information processing module, an experiment information modification module, an integration and sending module and a database;
the cell line based on the cell activity of the constitutive organoid is adopted as a strategy, so that the reporting system is in a highly activated state in the cell, external stimulation is not required, and the obtained stable cell model can be directly used for screening compounds; not only greatly reduced the cost of screening, also made the whole screening process from cell culture to detection simultaneously, shortened the cycle of screening, reduced the operating procedure, promoted system stability, high efficiency and ease for use, more be applicable to high flux drug screening.
Referring to fig. 3, the experimenter information input module is used for inputting information to each experimenter, wherein each experimenter is divided into an experimenter and a recording person, the experimental information acquisition module is respectively connected with the experimenter information input module and the information transcription module, the information processing module is respectively connected with the information arrangement module and the experimental information modification module, the database is respectively connected with the information arrangement module and the information processing module, and the experimental information modification module is connected with the integration and sending module.
Referring to fig. 3, in the step S4, the prepared drugs need to be grouped, the record of drug grouping information, drug administration information and transfer experiment is transcribed to the information transcription module by the experiment information acquisition module, the corresponding experiment information of each experimenter in the experiment time period is arranged, the experiment record corresponding to each experiment information is transcribed into text format, so as to obtain the experiment text information corresponding to each experimenter, the name of each experimenter and the experiment time period of each experimenter are sent to the information arrangement module, the information arrangement module collects the arranged experiment information together, the arranged experiment data is processed by the software of the experiment data processing class, the defects of the prepared drugs are judged by comparing the analyzed experiment data with the needed drugs, the experimental information modification module is used for modifying the analyzed experimental operation and steps, so that an experimenter can modify the experimental steps according to the analyzed experimental data, and then the modified experimental steps are transmitted to the database through the integration transmission module, so that the experimental data can be sorted and collected;
convenient quick the record of handling to experimental information has realized the intelligent processing to experimental data information, has improved experiment effect and experiment record efficiency, through software analysis medicine and judge that the weak point of medicine can be quick right.
Referring to fig. 1 and 3, the experiment information modification module is configured to modify the analyzed experiment operations and steps, so that an experimenter can modify the experiment steps according to the analyzed experiment data, and then the modified experiment steps are integrated and sent to the database through the integration sending module, so that the experiment data can be collected.
Referring to FIG. 4, the medium includes matrigel and a cell suspension mixed with the matrigel; the volume ratio of the matrigel to the cell suspension is (3-4) to (5-10), the cell suspension contains cells from a cell line, and the cells are attached to the surface and the interior of the matrigel; furthermore, the cell suspension contains 80 to 1600 cells per 100. mu.l of cells derived from the cell line.
Referring to FIG. 4, the cell suspension was filtered using a collection screen with a pore size of 40-100 μm and the collection screen was back-washed with the cell mass collection fluid to obtain tumor cell masses.
Referring to fig. 1-2, in the step of S2, the drug is formulated into a plurality of concentration gradients, and then the organoid model is given drug stimulation under different concentration gradients, and organoid samples are collected at different time points, observed for morphological changes, and tested for functional indicators of the corresponding organs.
Referring to FIGS. 1-2, organoid cells in logarithmic growth phase were cultured in vitro and divided into a saline solution group of 0.009 g.mL-1 and NaCl gradient groups of different concentrations: respectively 0.012, 0.015, 0.018, 0.021, 0.024, 0.027, 0.030, 0.033 and 0.036 g.mL < -1 > are subjected to 9 concentration gradients, the organoid cells are incubated for 3 to 10 hours by NaCl liquid medicine with corresponding concentration, the cell survival rate is detected by an MTT method, and the optimal NaCl incubation concentration for researching NaCl transportation is determined.
Referring to fig. 1-2, the organoid model is subjected to pharmaceutical processing, further comprising: organoid models were given 0.1% DMSO treatment as blanks, organoid samples of the blanks were collected at different time points, morphological changes were observed and functional indices of the organs were examined.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (8)
1. A construction method of a drug screening model based on artificial intelligence and big data analysis comprises a construction system and an experimental record system, and is characterized in that the construction system utilizes a report system carrier containing an organoid cell specific binding sequence and constructs a stable high-throughput drug screening cell model taking a specific target signal as a target point on the basis of active cells with constitutive specific target cells; the report system vector is constructed by inserting a response sequence containing an organoid cell specific binding sequence as a report system vector into a report vector multiple cloning site containing a luciferase report gene, and the construction system specifically comprises the following steps:
s1: culturing organoid cell models;
s2: preparing a medicament;
s3: screening transport concentration;
s4: grouping, administration and transfer experiments;
s5: performing statistical analysis;
in the step of S1, cutting human tissues on ice, adding collagenase for resuspension, digesting by a shaker, filtering cells, adding DMEM/F12 into filtrate to terminate digestion, centrifuging, and removing supernatant; taking erythrocyte lysate for resuspending cells, centrifuging, removing supernatant, adding DMEM/F12 for resuspension, centrifuging, and removing supernatant; counting cells, mixing matrigel, dripping the matrigel in the center of a hole plate, placing a culture dish, and solidifying the matrigel; adding culture medium into each hole, and culturing in a cell culture box;
the experiment recording system comprises an experimenter information input module, an experiment information acquisition module, an information transcription module, an information arrangement module, an information processing module, an experiment information modification module, an integration and sending module and a database.
2. The method for building the drug screening model based on artificial intelligence and big data analysis according to claim 1, wherein the experimenter information entry module is used for entering information into each experimenter, wherein each experimenter is divided into an experimenter and a recording person, the experimental information acquisition module is respectively connected with the experimenter information entry module and the information transcription module, the information processing module is respectively connected with the information arrangement module and the experimental information modification module, the database is respectively connected with the information arrangement module and the information processing module, and the experimental information modification module is connected with the integration and transmission module.
3. The method for constructing a drug screening model based on artificial intelligence and big data analysis as claimed in claim 2, wherein the step of S4 is to group the prepared drugs, the experimental information collection module is used to transcribe the drug grouping information, the drug administration information and the records of the transportation experiment to the information transcription module, the experimental information corresponding to each experimenter in the experimental period is arranged, the experimental records corresponding to each experimental information are transcribed into a text format, the experimental text information corresponding to each experimenter is obtained, the experimental text information corresponding to each experimenter, the name of each experimenter and the experimental period of each experimenter are sent to the information arrangement module, the information arrangement module collects the arranged experimental information, and the arranged experimental data is processed by the software of the experimental data processing class, the defects of the prepared medicine are judged by comparing the analyzed experimental data with the required medicine, the experimental information modification module is used for modifying the analyzed experimental operation and steps, experimenters modify the experimental steps according to the analyzed experimental data, and then the modified experimental steps are transmitted to a database through the integration transmission module, so that the experimental data can be sorted and collected.
4. The method for building the drug screening model based on artificial intelligence and big data analysis of claim 1, wherein the culture medium comprises matrigel and cell suspension mixed with the matrigel; the volume ratio of the matrigel to the cell suspension is (3-4): 5-10), the cell suspension contains cells derived from a cell line, and the cells are attached to the surface and the interior of the matrigel; and, in the cell suspension, the cell line-derived cell content is 80 to 1600 per 100. mu.l.
5. The method for constructing a drug screening model based on artificial intelligence and big data analysis as claimed in claim 4, wherein the cell suspension is filtered by a collection screen with a pore size of 40-100 μm, and the collection screen is back-washed by a cell mass collection liquid to obtain the tumor cell mass.
6. The method for constructing a drug screening model based on artificial intelligence and big data analysis as claimed in claim 5, wherein in said step of S2, the drug is formulated into multiple concentration gradients, then the organoid model is given drug stimulation under different concentration gradients, and organoid samples are collected at different time points, its morphological changes are observed and the function index of the corresponding organ is detected.
7. The method of claim 6, wherein organ-like cells in logarithmic growth phase are cultured in vitro and divided into 0.009 g-mL saline group-1And NaCl different concentration gradient groups: respectively 0.012, 0.015, 0.018, 0.021, 0.024, 0.027, 0.030, 0.033, 0.036 g/mL-1Total 9 concentration gradients, corresponding concentration NaCl liquid medicine is taken to incubate the organoidAnd (3) detecting the cell survival rate by using an MTT method for 3-10 hours, and determining the optimal NaCl incubation concentration for researching NaCl transportation.
8. The method for constructing a drug screening model based on artificial intelligence and big data analysis as claimed in claim 7, wherein said organoid model is processed with drugs, further comprising: organoid models were given 0.1% DMSO treatment as blanks, organoid samples of the blanks were collected at different time points, observed for morphological changes and examined for functional indices of the organs.
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