Drug screening method and device based on blood-brain barrier injury model and medium

Abstract

The invention provides a drug screening method and device based on a blood-brain barrier injury model and a medium, and the method comprises the steps: collecting experimental metadata and biological sample measurement data of the blood-brain barrier injury model, the experimental metadata comprises an identifier of a candidate drug, an animal identifier, a body weight, an Evans blue injection volume, circulation time, perfusion time and a sampling position, and the biological sample measurement data comprises a brain tissue weight and a fluorescence intensity measurement value; converting the biological sample measurement data into a quantitative index of blood brain barrier leakage, wherein the quantitative index is the Evans blue leakage amount per gram of brain tissue; and generating a prediction effect of the candidate drugs according to experimental metadata and the quantitative indexes. The blood-brain barrier leakage can be rapidly and accurately evaluated through Evans blue staining and fluorescent quantitative technologies.

Description

Technical Field

[0001] This application relates to the field of drug screening technology, and in particular to a drug screening method, electronic device and computer-readable storage medium based on a blood-brain barrier injury model. Background Technology

[0002] Radiation-induced brain injury (RIBI) is a common and serious complication following head, neck, or brain radiotherapy. Its core pathological changes include disruption of the blood-brain barrier (BBB), increased vascular permeability, and inflammatory / edematous responses in brain tissue. These pathological processes affect patients' cognitive and neurological functions and directly determine the efficacy of interventional drugs or protective strategies. Therefore, establishing a method for rapidly, accurately, and quantitatively assessing BBB integrity in animal models is crucial for studying the mechanisms of radiation-induced brain injury, comparing the effects of candidate drugs, and promoting clinical translation. Existing in vivo assays (such as DCE-MRI, two-photon microscopy, and fluorescent tracer tissue sections) and in vitro BBB models each have their advantages, but it is often difficult to simultaneously balance cost, throughput, quantitative accuracy, and physiological relevance. Literature and existing technologies generally face problems such as "in vivo / in vitro disconnect, insufficient quantification, high subjectivity, and low throughput," which limits the realization of large-scale drug screening and standardized evaluation. Summary of the Invention

[0003] In view of this, it is necessary to provide a drug screening method, electronic device and computer-readable storage medium based on a blood-brain barrier injury model that can overcome at least one of the above-mentioned defects.

[0004] In a first aspect, embodiments of this application provide a drug screening method based on a blood-brain barrier injury model, the method comprising:

[0005] The experimental metadata and biological sample measurement data of the blood-brain barrier injury model were collected. The experimental metadata included the identification of the candidate drug, animal identification, body weight, Evans blue injection volume, circulation time, perfusion time and sampling location. The biological sample measurement data included brain tissue weight and fluorescence intensity measurements.

[0006] The biological sample measurement data are converted into a quantitative indicator of blood-brain barrier leakage, wherein the quantitative indicator is the amount of Evans blue leakage per gram of brain tissue.

[0007] The predicted effects of the candidate drugs are generated based on experimental metadata and the quantitative indicators.

[0008] In one embodiment, the dosage of the Evans blue injection volume is 4-6 mg / kg.

[0009] In one embodiment, the concentration of the Evans blue solution is 0.5-5% by mass, preferably 2% by mass.

[0010] In one embodiment, before converting the biological sample measurement data into a quantitative indicator of blood-brain barrier leakage, the method further includes:

[0011] Homogenize the sample using a 10-50% (w / v) aqueous solution of trichloroacetic acid to obtain a supernatant, and then extract Evans blue bound to plasma albumin.

[0012] In one embodiment, the volume of the trichloroacetic acid aqueous solution added to each brain tissue biological sample is 500 μL to 1000 μL.

[0013] In one embodiment, the homogenization process using a 10-50% (w / v) aqueous solution of trichloroacetic acid to obtain a supernatant includes:

[0014] The solution after adding the trichloroacetic acid aqueous solution was centrifuged at 15,000 relative centrifugation force for 20 minutes at 4 degrees Celsius.

[0015] In one embodiment, converting the biological sample measurement data into a quantitative indicator of blood-brain barrier leakage includes:

[0016] The supernatant was irradiated with a fluorescence microplate reader at an excitation wavelength of 620 nm and an emission wavelength of 680 nm to obtain the fluorescence intensity.

[0017] In one embodiment, the method further includes

[0018] The fluorescence intensity is converted into the absolute content of Evans blue according to a preset equation.

[0019] Secondly, embodiments of this application provide an electronic device, including:

[0020] Processor; and

[0021] A memory having computer-readable instructions stored thereon for controlling the processor to execute the drug screening method based on the blood-brain barrier injury model as described in the first aspect.

[0022] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that instruct a device to perform the drug screening method based on the blood-brain barrier injury model as described in the first aspect.

[0023] This application provides a drug screening method, electronic device, and computer-readable storage medium based on a blood-brain barrier injury model. It can achieve rapid and accurate assessment of blood-brain barrier leakage through Evans blue staining and quantitative fluorescence technology. Specifically, it collects experimental metadata and biological sample measurement data, converts the measurement data into an absolute quantitative index of blood-brain barrier leakage, and generates a prediction of the efficacy of candidate drugs based on the index. This solves the problems of high cost, complex process, strong subjectivity, and insufficient quantification in existing blood-brain barrier assessment methods. Attached Figure Description

[0024] Figure 1 This is a schematic flowchart of a drug screening method based on a blood-brain barrier injury model provided in an embodiment of this application.

[0025] Figure 2a This is a schematic diagram of a blood-brain barrier injury control group provided in an embodiment of this application.

[0026] Figure 2b This is a schematic diagram of a blood-brain barrier injury irradiation group provided in an embodiment of this application.

[0027] Figure 2c This is a schematic diagram of a blood-brain barrier injury irradiation and drug administration group provided in an embodiment of this application.

[0028] Figure 3 A schematic diagram of the Evans Blue standard curve provided for an embodiment of this application.

[0029] Figure 4 A schematic diagram of blood-brain barrier leakage provided in an embodiment of this application.

[0030] Figure 5 A schematic diagram of an electronic device provided in an embodiment of this application.

[0031] Explanation of main component symbols

[0032] Electronic devices 20

[0033] Processor 21

[0034] Memory 22

[0035] Method steps S100-300 Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0037] It should be noted that, in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0038] It should be noted that in the embodiments of this application, the terms "first," "second," etc., are used only for descriptive purposes and should not be construed as indicating or implying relative importance, nor as indicating or implying order. Features specified as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0039] Based on the embodiments described in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] Radiation-induced brain injury (RIBI) is a common and serious complication following head, neck, or brain radiotherapy. Its core pathological changes include blood-brain barrier (BBB) ​​disruption, increased vascular permeability, and inflammatory / edematous responses in brain tissue. These pathological processes affect patients' cognitive and neurological functions and directly determine the efficacy of interventional drugs or protective strategies. Therefore, establishing a rapid, accurate, and quantifiable method for assessing BBB integrity in animal models is crucial for studying the mechanisms of radiation-induced brain injury, comparing the effects of candidate drugs, and promoting clinical translation. Existing in vivo assays (such as DCE-MRI, two-photon microscopy, and fluorescent tracer tissue sections) and in vitro transmembrane models each have their advantages, but it is often difficult to simultaneously balance cost, throughput, quantitative accuracy, and physiological relevance. Literature and existing technologies generally face problems such as "in vivo / in vitro disconnect, insufficient quantification, high subjectivity, and low throughput," which limits the realization of large-scale drug screening and standardized evaluation.

[0041] Currently, detection technologies for blood-brain barrier leakage after radiation-induced brain injury can be mainly divided into two categories: in vivo detection methods and in vitro detection methods. Each has its own application scenarios and inherent limitations, and it is still difficult to achieve an ideal balance between sensitivity, throughput, cost, and physiological relevance.

[0042] In vivo detection methods aim to directly assess the integrity and leakage status of BBB in live animals, but existing in vivo technologies each have their own shortcomings.

[0043] (1) Small animal magnetic resonance imaging (MRI): Gadolinium-based contrast agents are usually injected intravenously and dynamic contrast-enhanced MRI (DCE-MRI) is performed to assess BBB patency. The advantage of this method is that it can achieve non-invasive in vivo three-dimensional imaging and spatiotemporal dynamic analysis, but its disadvantages include high equipment and operating costs, long scanning and data processing cycles, complex data analysis procedures, and limited sensitivity to small or focal leaks.

[0044] (2) Two-photon microscopy imaging: This technique has high resolution and real-time visualization capabilities, and can be used to observe the local dynamic process of blood-brain barrier leakage. However, two-photon imaging often requires craniotomy to prepare cranial windows, which is complicated and invasive; the imaging depth is limited, mainly suitable for shallow cortical observation, and it is difficult to cover deep brain regions; in addition, quantitative analysis is greatly affected by field of view selection and sampling bias, making it difficult to form comparable whole-brain quantitative indicators.

[0045] (3) Fluorescent tracer combined with tissue sectioning: This is a widely used traditional method that involves injecting a fluorescent tracer in vivo, preparing tissue sections after sampling, and performing fluorescence imaging and semi-quantitative analysis. The process includes sampling, freezing or paraffin sectioning, microscopic imaging, and image analysis. It involves many steps and has high variability. The results are highly dependent on the operator's experience and usually show semi-quantitative characteristics (such as the ratio of fluorescent area). It is difficult to obtain objective, absolute quantitative data on whole-brain leakage that can be directly used for large-scale comparisons.

[0046] In vitro detection methods aim to assess the controllability of BBB function using in vitro cell or tissue models, but they suffer from insufficient physiological relevance.

[0047] The Transwell chamber / co-culture model is a commonly used platform for in vitro studies of BBB permeability. It simulates the barrier structure by co-culturing a monolayer of brain microvascular endothelial cells with pericytes or astrocytes, and assesses permeability using translayer fluorescence tracer permeability or transcellular electrical resistance (TEER). The advantages of this method are controllable conditions and ease of quantification and permeability comparison. However, it is inherently a simplified system and cannot reproduce the hemodynamics, immune responses, and complex interactions between intact neurovascular units present in vivo. Therefore, its predictive value for dynamic changes in vivo after radiation injury is limited.

[0048] Based on the aforementioned analysis of existing technologies, the mainstream methods in this field for detecting radiation-induced blood-brain barrier (BBB) ​​leakage still have several key drawbacks that restrict their widespread application and screening efficiency, as follows:

[0049] There is a disconnect between in vivo and in vitro methods. While in vitro models (such as the Transwell co-culture system) facilitate precise quantification under controlled conditions, their microenvironment differs significantly from the complex physiological states of living organisms (such as hemodynamics, systemic immune responses, and interactions of intact neurovascular units), limiting the reliability of translating in vitro data into in vivo and even clinical applications. In contrast, in vivo detection methods can reflect real biological processes, but they are insufficient in terms of cost, throughput, and quantitative accuracy to meet the needs of drug screening. There is a lack of effective connection and comparable quantitative standards between the two methods.

[0050] High costs and high equipment barriers. While advanced imaging technologies such as small animal magnetic resonance imaging (DCE-MRI) and two-photon microscopy can provide advantages in spatial or temporal resolution, their high equipment procurement, maintenance, and operation costs make it difficult for research institutions and industrial screening platforms to deploy them widely, thus hindering the availability of such technologies in large-scale candidate drug screening.

[0051] The process is complex and time-consuming. Existing in vivo methods involve a large number of manual operations and complex steps from sample preparation (such as craniotomy, long-term imaging or tissue section preparation) to signal acquisition and subsequent data analysis. The entire process is time-consuming and inefficient, making it difficult to meet the needs of drug screening scenarios that require rapid iteration and high-throughput evaluation.

[0052] There are many confounding factors and limited throughput. Some in vivo high-resolution imaging methods (such as microscopy that requires craniotomy) may introduce invasive interferences (such as meningeal or cerebral blood vessel rupture), thus becoming confounding factors affecting leakage assessment. At the same time, these methods are complex and time-consuming to operate, with low throughput, making them difficult to apply to experimental designs that require large sample size validation or multi-dose, multi-time point comparisons.

[0053] Interpretation is highly subjective and lacks absolute quantitative indicators. Traditional qualitative or semi-quantitative analysis of tissue sections / fluorescence microscopy relies heavily on the operator's experience in interpreting results and lacks unified absolute quantitative standards (such as the amount of leakage per gram of tissue based on tissue weight). This makes it difficult to conduct objective and accurate quantitative comparisons between different treatment groups, thereby weakening the comparability and reliability of research conclusions.

[0054] In summary, existing in vivo and in vitro detection methods have complementary but mutually exclusive limitations in terms of sensitivity, cost, throughput, operational complexity, and quantitative comparability. There is an urgent need for a detection and analysis framework that can provide standardized, traceable absolute quantitative indicators, simplify experimental procedures, and support high-throughput screening and statistical / algorithm-driven decision support while ensuring biological relevance, so as to significantly improve the efficiency and reliability of blood-brain barrier injury research and drug screening.

[0055] This application provides a drug screening method, electronic device, and computer-readable storage medium based on a blood-brain barrier injury model. It enables rapid and accurate assessment of blood-brain barrier leakage using Evans blue staining and quantitative fluorescence techniques. Specifically, it collects experimental metadata and biological sample measurement data, converts the measurement data into an absolute quantitative index of blood-brain barrier leakage (μg / g brain tissue), and generates candidate drug efficacy predictions based on this index. This solves the problems of high cost, complex procedures, strong subjectivity, and insufficient quantification in existing blood-brain barrier assessment methods. The method can complete the entire process from sample processing to quantitative results within 2-3 hours, exhibiting high sensitivity (linear range 0.1-100 μg / g) and high throughput. It is not only suitable for screening and evaluating drugs for treating blood-brain barrier injury but can also be extended to the development of blood-brain barrier protective drugs in various neurological disease models such as multiple sclerosis, Alzheimer's disease, brain tumors, and cerebral ischemia-reperfusion injury, significantly reducing research barriers and improving drug screening efficiency and accuracy.

[0056] Figure 1 This is a schematic flowchart of a drug screening method based on a blood-brain barrier injury model provided in one embodiment of this application. Figure 1 The drug screening method based on the blood-brain barrier injury model shown includes at least the following steps: S100: collecting experimental metadata and biological sample measurement data of the blood-brain barrier injury model; S200: converting the biological sample measurement data into quantitative indicators of blood-brain barrier leakage; S300: generating predicted effects of candidate drugs based on experimental metadata and quantitative indicators.

[0057] S100: Collect experimental metadata and biological sample measurement data of the blood-brain barrier injury model.

[0058] In this embodiment of the application, the drug screening method based on the blood-brain barrier injury model includes collecting experimental metadata and biological sample measurement data of the blood-brain barrier injury model in step S100. The experimental metadata includes the candidate drug identifier, animal identifier, body weight, Evans blue injection volume, circulation time, perfusion time, and sampling location. The biological sample measurement data includes brain tissue weight and fluorescence intensity measurements.

[0059] Specifically, a blood-brain barrier injury animal model is first established. A 0.5-5% (w / v) Evans blue saline solution (preferably 2%) is injected into the tail vein of the animal model. The injection volume is determined based on the animal's body weight at a ratio of 4-6 mL / kg, allowing the Evans blue to circulate in vivo for 2-3 hours. Subsequently, a large amount of saline is perfused through the heart until the outflow becomes colorless, to completely remove any unleashed Evans blue dye from the cerebral blood vessels. Then, the entire brain or specific brain region tissue (such as the hippocampus or cortex) is removed and accurately weighed. Throughout this process, experimental metadata is systematically recorded, including key information such as experimental batch identifier, animal identifier, body weight, Evans blue injection volume, circulation time, perfusion time, and sampling location.

[0060] Understandably, using 1.5mL low-adsorption brown centrifuge tubes for sample processing is crucial. The brown tube walls effectively block light, preventing the attenuation of Evans blue fluorescence signals, while the low-adsorption surface minimizes sample loss, ensuring subsequent quantitative accuracy. Simultaneously, strictly adhering to standardized procedures to record experimental metadata provides necessary parameters for subsequent data correction and analysis, effectively eliminating systematic errors caused by operational differences.

[0061] S200: Converts biological sample measurement data into a quantitative indicator of blood-brain barrier leakage.

[0062] In this embodiment of the application, the drug screening method based on the blood-brain barrier damage model includes converting biological sample measurement data into a quantitative index of blood-brain barrier leakage in step S200. The quantitative index is the amount of Evans blue leakage per gram of brain tissue.

[0063] Specifically, the weighed brain tissue was placed in a 1.5 mL low-absorption brown centrifuge tube, and 10-50% (w / v) trichloroacetic acid (TCA) aqueous solution was added for tissue homogenization. The tissue was centrifuged at 15,000 x g for 20 minutes at 4°C to completely precipitate the proteins. Subsequently, the supernatant was carefully transferred to a black, transparent polystyrene microplate, and the fluorescence intensity was detected using a fluorescence microplate reader at an excitation wavelength of 620 nm and an emission wavelength of 680 nm. According to the standard curve equation Y = 6E-05X-0.0001, (R... 2 The concentration of Evans blue in the supernatant (Y, μg / μL) can be calculated using a value ≥0.9921. Multiplying the concentration Y by the volume of the trichloroacetic acid supernatant used (e.g., 600 μL) gives the absolute amount of Evans blue in the brain tissue (unit: μg). Where X is the measured fluorescence intensity, 6E-05 is the slope of the standard curve, and -0.0001 is the intercept of the standard curve.

[0064] Then, the blood-brain barrier leakage can be calculated using the formula: blood-brain barrier leakage (μg / g) = [absolute content of Evans blue in brain tissue (μg) / weight of brain tissue (g)].

[0065] Understandably, the use of trichloroacetic acid solution efficiently precipitates proteins, fully releasing and dissolving Evans blue bound to plasma albumin in the supernatant, while simultaneously terminating any enzymatic reactions and stabilizing the sample. The 96-well plate design with a black body and transparent bottom effectively prevents crosstalk between wells, ensuring the accuracy of the detection results. The linear range of this quantitative indicator is 0.1-100 μg / g, sensitively capturing early minor damage and accurately quantifying severe leakage, providing precise evidence for drug efficacy evaluation.

[0066] S300: Generates predicted effects of candidate drugs based on experimental metadata and quantitative indicators.

[0067] In this embodiment of the application, the drug screening method based on the blood-brain barrier injury model includes generating the predicted effect of candidate drugs based on experimental metadata and quantitative indicators in step S300.

[0068] Specifically, the blood-brain barrier leakage of different treatment groups (control group, irradiation group, and irradiation combined with drug administration group) was statistically compared to calculate the improvement rate of blood-brain barrier damage by candidate drugs, construct dose-response curves, and determine the median effective dose (ED50). Further, the biological sample measurement data were preprocessed and corrected, including blank correction, intra- and inter-plate calibration, and normalization based on experimental covariates. Normalization was achieved using a linear mixed-effects model or a Bayesian correction model to eliminate batch effects and operational bias. Based on this, additional modal data (such as small animal magnetic resonance imaging data, two-photon microscopy imaging data, etc.) were acquired and preprocessed. The additional modal data and quantitative indicators were time / space aligned and feature registered to construct statistical or machine learning models, generating predictions of the candidate drug's efficacy and corresponding uncertainty assessments.

[0069] Understandably, this step enables drug efficacy evaluation based on objective and precise absolute quantitative data (leakage per unit brain tissue), overcoming the shortcomings of traditional methods such as strong subjectivity and insufficient quantification. By prioritizing candidate drugs using an objective function combining expected information gain and experimental cost, subsequent experimental design can be efficiently guided, significantly improving drug screening efficiency. This method is not only applicable to the screening of drugs for treating blood-brain barrier damage, but can also be extended to the development of blood-brain barrier protective drugs in various neurological disease models such as multiple sclerosis, Alzheimer's disease, brain tumors, and cerebral ischemia-reperfusion injury, demonstrating broad application prospects.

[0070] In the embodiments of this application, the dosage of Evans blue injection volume is 4-6 mg / kg.

[0071] In the embodiments of this application, the concentration of the Evans blue solution is 0.5-5% by mass / volume, preferably 2% by mass / volume.

[0072] In this embodiment of the application, before converting the biological sample measurement data into a quantitative indicator of blood-brain barrier leakage, the method further includes: homogenizing the sample with a 10-50% (w / v) aqueous solution of trichloroacetic acid to obtain a supernatant, and extracting Evans blue bound to plasma albumin to obtain a supernatant containing free Evans blue for quantitative analysis.

[0073] In this embodiment, the volume of trichloroacetic acid aqueous solution added to each brain tissue biological sample is 500 μL-1000 μL. Preferably, 600 μL of trichloroacetic acid aqueous solution can be added to each brain tissue biological sample for homogenization.

[0074] In this embodiment of the application, a 10-50% (w / v) aqueous solution of trichloroacetic acid is used for homogenization to obtain a supernatant, including: centrifuging the solution after adding the aqueous solution of trichloroacetic acid at 4 degrees Celsius with a relative centrifugal force of 15000 for 20 minutes.

[0075] In this embodiment of the application, the biological sample measurement data is converted into a quantitative indicator of blood-brain barrier leakage, including: irradiating the supernatant with a fluorescence microplate reader at an excitation wavelength of 620 nm and an emission wavelength of 680 nm to obtain fluorescence intensity.

[0076] In this embodiment of the application, the method further includes converting the fluorescence intensity into the absolute content of Evans blue according to a preset equation.

[0077] This application discloses a reagent and consumable combination (hereinafter referred to as the "reagent combination") for tracer-based quantitative analysis of blood-brain barrier leakage and drug screening. The reagent combination includes solution A (staining solution), solution B (extraction solution), a dedicated centrifuge tube set, and a 96-well microplate for fluorescence detection. Solution A is a physiological saline solution containing 0.5–5% (w / v) Evans Blue (EB). This invention preferably uses a concentration range of approximately 2% to balance detection sensitivity and in vivo distribution kinetics. The main function of solution A as a tracer solution is to provide a large molecular tracer that can bind to plasma proteins, maintaining intravascular confinement in areas of intact blood-brain barrier but allowing leakage in areas of damaged blood-brain barrier, thereby providing a biological signal basis for subsequent quantitative assessment.

[0078] It is understood that using the concentration range and preferred values ​​described in this invention can reduce non-specific background while obtaining sufficient detection signals, improve the signal-to-noise ratio of inter-group comparisons, and facilitate the consistency of multiple replicate experiments.

[0079] In this embodiment, solution B is a 10–50% (w / v) aqueous solution of trichloroacetic acid (TCA), used as a solution for sample post-processing and extraction. Its function is to efficiently precipitate tissue-bound proteins and release Evans blue bound to plasma albumin from the protein complex into the measurable supernatant matrix, while simultaneously terminating the enzymatic reaction to maintain sample chemical stability. This extraction system works synergistically with the staining system, enabling tracers leaking from the tissue to be enriched in a measurable form and enter the assay channel, thereby achieving continuous quantitative determination and comparative analysis.

[0080] It is understood that using the TCA extraction system within the scope of this invention can improve tracer recovery and enhance sample stability, thereby improving assay repeatability and quantitative accuracy.

[0081] In this embodiment, 1.5 mL low-adsorption brown centrifuge tubes are used as sample processing and storage containers. The light-blocking properties of the brown tube walls help prevent optical quenching or degradation of the photosensitive tracer during processing and short-term storage, while the low-adsorption surface reduces non-specific adsorption of proteins and tracers on the container walls, maximizing the retention of measurable components in the supernatant for accurate quantification. The centrifuge tube assembly is compatible with the following detection and data processing workflow and facilitates data link traceability (e.g., recording tube location, sample batch, operator information, and other metadata), thereby supporting subsequent quality control and statistical correction.

[0082] Understandably, using a dedicated low-adsorption, light-proof container can significantly reduce the variability introduced by the sample processing steps and improve the precision and repeatability of the measurement.

[0083] In this embodiment, the 96-well plate used for fluorescence / optical reading is preferably a polystyrene microplate with a black body and a transparent bottom. This plate structure can simultaneously ensure low inter-well crosstalk and compatibility with bottom imaging or bottom-reading microplate readers during detection. The black plate wall effectively suppresses inter-well fluorescence scattering and crosstalk, while the transparent bottom supports bottom-reading mode, thereby improving optical path consistency and sensitivity. In this embodiment, it is recommended to design blank wells, internal control wells, and standard wells in each detection plate to achieve in-plate calibration, sensitivity monitoring, and standard curve construction.

[0084] Understandably, by selecting a suitable board type and combining it with the quality control holes within the board, systematic errors can be reduced at the board level and the comparability of readings from different batches can be improved.

[0085] In this embodiment, the aforementioned reagent combination constitutes a chemical foundation layer from tracer to assay, which, together with a structured data acquisition and quality control process, supports subsequent quantitative analysis and modeling. Conceptually, solution A is used for in vivo tracer to generate a leakage signal within the tissue, while solution B is used to release the leakage signal from the tissue complex and stabilize it into a measurable sample. Dedicated centrifuge tube sets and 96-well plates serve as consumables for sample processing and detection, ensuring optical and adsorption stability during the detection process. To ensure the comparability of results and the quality of data used for modeling, this embodiment recommends recording key metadata (including reagent batch number, processing batch, sample identifier, detection plate position, and original reading values, etc.) during the data acquisition phase, and reserving standard wells in the detection plate for standard curve conversion and in-plate calibration based on internal parameters for each batch.

[0086] Understandably, the synergy between reagent combinations and metadata management can significantly improve the reliability of quantitative labels (e.g., “leakage per unit of tissue”), thereby providing stable and interpretable input for statistical models and machine learning pipelines.

[0087] The following exemplary embodiment describes the practical process of the drug screening method based on the blood-brain barrier injury model provided in this application.

[0088] This embodiment describes a workflow for evaluating blood-brain barrier injury and screening drugs based on tracer leakage quantification. The overall workflow includes six main steps: tracer administration and in vivo circulation, perfusion and tissue sampling, sample pretreatment and supernatant extraction, fluorescence / optical detection, standard curve conversion, and calculation of unit tissue leakage based on tissue weight. To ensure data comparability and reliable input for subsequent modeling, the entire workflow also includes detailed metadata recording, intra- and inter-plate calibration, sample quality control, and data auditing links to support statistical comparisons between groups, multimodal fusion, and machine learning-driven prioritization of candidate drugs. This embodiment can be used in a parallel setup with control and treatment groups to evaluate the impact of radiation damage on blood-brain barrier permeability and provide quantitative basis for the initial screening and prioritization of candidate drugs.

[0089] Please refer to the following: Figures 2a to 2c . Figure 2a This is a schematic diagram of a blood-brain barrier injury control group provided in an embodiment of this application. Figure 2b This is a schematic diagram of a blood-brain barrier injury irradiation group provided in an embodiment of this application. Figure 2c This is a schematic diagram of a blood-brain barrier injury irradiation and drug administration group provided in one embodiment of this application. Figure 2c The drug used was rapamycin (1 mg / kg, intraperitoneal injection).

[0090] In this embodiment, regarding tracer administration and in vivo circulation, the tracer is injected into the body according to a dosing strategy related to the test animal's body weight, and a certain period of in vivo circulation is maintained after administration to allow the tracer to bind to plasma proteins and leak into the brain parenchyma in areas with impaired blood-brain barriers. After injection, the tracer is allowed to circulate in vivo for a period of time (2-3 hours). Evans blue binds to plasma albumin in the blood, forming an Evans blue-albumin complex. In areas with intact blood-brain barriers, this large molecular complex cannot pass through; however, in areas with impaired blood-brain barriers, the complex leaks into the brain parenchyma. Different treatment groups, such as a control group, an irradiation group, and an irradiation-combined-drug group, are simultaneously set up to compare leakage differences between groups. All metadata, including administration routes, administration times, circulation duration, animal identification, and body weight information, is structured and stored in the experimental database for use as covariates in subsequent data correction and statistical models.

[0091] Understandably, standardized dosing strategies and detailed metadata recording can significantly improve the comparability of intergroup comparisons and provide the necessary information for subsequent batch effect correction, thereby reducing confounding effects caused by individual differences or operational factors.

[0092] In this embodiment, during the perfusion and tissue sampling process, in vivo perfusion is used to remove as much tracer as possible from the cerebral blood vessels before the whole brain or a predetermined brain region of interest (such as the hippocampus or cortex) is extracted. Information such as sampling location, tissue weight, and sampling time is accurately recorded. Standardized descriptions of sampling locations and preservation of images or diagrams facilitate spatial alignment and regional comparison across samples.

[0093] Understandably, by reducing residual tracers in blood vessels through perfusion and accurately recording metadata such as tissue weight, subsequent quantitative indicators can better reflect intraplasmic leakage, thereby improving the biological relevance of measurements and the reliability of statistical comparisons.

[0094] In this embodiment, the sample processing and supernatant extraction section involves appropriate pretreatment and homogenization of the collected tissue to release the tracer that has penetrated into the tissue. Subsequently, reagent-based or physical methods such as separation / precipitation are used to obtain a detectable supernatant, which is then transferred to a suitable detection container. Key parameters throughout the sample processing (e.g., solvent type, container type, sample volume ratio, operator, and batch number) are recorded for traceability. The beneficial effects of this step are: standardized sample processing procedures and comprehensive metadata recording reduce human error and provide necessary covariates for subsequent intra- / inter-plate calibration and statistical normalization, thereby enhancing data consistency and reproducibility.

[0095] In this embodiment, during the fluorescence / optical detection process, the processed sample supernatant is placed on a standardized detection carrier and the signal intensity is read. Simultaneously, blank wells, internal reference wells, and standard wells are designed on each detection plate for in-plate calibration and sensitivity monitoring. The raw detection readings and plate position information are automatically imported into a database, and basic quality checks (e.g., blank verification and repeatability testing) are performed on the in-plate data after detection.

[0096] Understandably, in-board calibration and automatic data entry can minimize equipment and human error, improve data integrity, and support subsequent standardized conversion processes and abnormal sample identification.

[0097] Please refer to the following: Figure 3 , Figure 3 A schematic diagram of the Evans Blue standard curve provided for an embodiment of this application.

[0098] In this embodiment, during the standard curve conversion process, a mathematical fitting model is constructed based on a pre-prepared series of tracer standards to convert the relative detection signal into the absolute content of the tracer. During the conversion process, quality indicators such as goodness of fit are recorded to assess the reliability of the results. If the fit is abnormal or the goodness of fit is below a preset threshold, a quality control process can be triggered, such as retesting or labeling the batch of data for subsequent weighting or rejection.

[0099] Understandably, standardizing relative signals to absolute content facilitates direct comparison between different plates / batch, while a goodness-of-fit monitoring mechanism helps to identify and handle abnormal data in advance, thereby ensuring the robustness of downstream analysis.

[0100] Please refer to the following: Figure 4 , Figure 4 A schematic diagram of blood-brain barrier leakage provided in an embodiment of this application.

[0101] In this embodiment, the calculation and use of "unit tissue leakage" is based on the absolute content of tracer in the sample and the corresponding brain tissue weight. This "unit tissue leakage" is used as the final quantitative indicator in this embodiment, and is also used as a label or feature for inter-group comparisons and machine learning models. The clarification of this indicator allows the effects of drug intervention to be compared and ranked using a uniform and interpretable numerical value.

[0102] Understandably, using "tracer content per unit tissue weight" as an absolute indicator is not only convenient for biological interpretation, but also beneficial for statistical modeling and multimodal data fusion, thereby improving the discriminative ability and comparability of candidate drug screening.

[0103] Finally, this embodiment generates a standardized electronic report for each batch. The report includes the original readings, standard curve fitting results, unit tissue leakage, quality control records, and subsequent model outputs (such as candidate ranking, confidence intervals, uncertainty measures, and experimental recommendations), and is stored and exported in a machine-readable format.

[0104] Figure 5 This is an electronic device 20 provided in one embodiment of this application. For example... Figure 5 As shown, the electronic device 20 includes at least the following components: a processor 21 and a memory 22.

[0105] In this embodiment, the memory 22 is used to store executable instructions of the processor 21, which, when configured to execute instructions, implement... Figure 1 The drug screening method based on the blood-brain barrier injury model is shown.

[0106] In one embodiment of this application, the program operating in the electronic device 20 may be a program that controls a central processing unit (CPU) or similar device to achieve the functions described in the above-described embodiments of the present invention (a program that enables the computer to function). Information processed by these devices is then temporarily stored in random access memory (RAM) during processing, and subsequently stored in various ROMs such as read-only memory (Flash ROM) and hard disk drives (HDDs), and read, corrected, and written by the CPU as needed.

[0107] It should be noted that a portion of the electronic device 20 described above can also be implemented using a computer. In this case, the program for implementing the control function can be recorded on a computer-readable recording medium, and the program recorded on the recording medium can be read into the computer and executed.

[0108] It should be noted that the term "computer" as used here refers to a computer built into electronic device 20, employing hardware including an operating system and peripheral devices. Furthermore, "computer-readable recording media" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard drives built into the computer.

[0109] Furthermore, a "computer-readable recording medium" can include: a medium that dynamically stores a program for a short period of time, such as a communication line used when transmitting a program via a network such as the Internet or a communication line such as a telephone line; or a medium that stores a program for a fixed period of time, such as volatile memory inside a computer that serves as a server or client in this case. In addition, the aforementioned program can be a program used to implement the above-mentioned functions, or it can be a program that can implement the above-mentioned functions by combining with programs already recorded in the computer.

[0110] Furthermore, the electronic device 20 in the above embodiments can also be implemented as an assembly (device group) composed of multiple devices. Each device constituting the device group can possess some or all of the functions or functional blocks of the electronic device 20 in the above embodiments. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device 20.

[0111] It is understood that the drug screening method, electronic device 20, and computer-readable storage medium based on the blood-brain barrier injury model provided in this application can achieve rapid and accurate assessment of blood-brain barrier leakage through Evans blue staining and quantitative fluorescence technology. Specifically, it collects experimental metadata and biological sample measurement data, converts the measurement data into an absolute quantitative index of blood-brain barrier leakage (μg / g brain tissue), and generates candidate drug efficacy predictions based on this index. This solves the problems of high cost, complex process, strong subjectivity, and insufficient quantification in existing blood-brain barrier assessment methods. The method can complete the entire process from sample processing to quantitative results within 2-3 hours, and has high sensitivity (linear range 0.1-100 μg / g) and high throughput characteristics. It is not only suitable for screening and evaluating drugs for treating blood-brain barrier injury, but can also be extended to the development of blood-brain barrier protective drugs in various neurological disease models such as multiple sclerosis, Alzheimer's disease, brain tumors, and cerebral ischemia-reperfusion injury, significantly reducing the research threshold and improving the efficiency and accuracy of drug screening.

[0112] Those skilled in the art should recognize that the above embodiments are only used to illustrate this application and are not intended to limit this application. Any appropriate changes and variations made to the above embodiments within the essential spirit and scope of this application fall within the scope of protection claimed in this application.

Claims

1. A drug screening method based on a blood brain barrier damage model, characterized by, The method comprises: collecting experimental metadata and biological sample measurement data of the blood-brain barrier injury model, the experimental metadata comprising identification of a candidate drug, animal identification, body weight, Evans blue injection volume, circulation time, perfusion time, and sampling position, and the biological sample measurement data comprising brain tissue weight and fluorescence intensity measurement value; converting the biological sample measurement data into a quantitative index of blood-brain barrier leakage, the quantitative index being the amount of Evans blue leakage per gram of brain tissue; generating a predicted effect of the candidate drug according to the experimental metadata and the quantitative index.

2. The drug screening method based on a blood brain barrier injury model according to claim 1, wherein, The dosage of the Evans blue injection volume is 4-6 mg / kg.

3. The drug screening method based on a blood brain barrier damage model according to claim 2, wherein, The concentration of the Evans blue solution is 0.5-5 mass / volume percent, preferably 2 mass / volume percent.

4. The drug screening method based on a blood brain barrier damage model according to claim 3, wherein, Before the converting the biological sample measurement data into a quantitative index of blood-brain barrier leakage, the method further comprises: homogenizing treatment using 10-50 mass / volume percent trichloroacetic acid aqueous solution to obtain supernatant and extract Evans blue bound to plasma albumin to obtain supernatant containing free Evans blue for quantitative analysis.

5. The drug screening method based on a blood brain barrier impairment model according to claim 4, wherein The volume of the trichloroacetic acid aqueous solution added in each piece of brain tissue biological sample is 500 μL-1000 μL.

6. The drug screening method based on a blood brain barrier injury model according to claim 5, wherein The homogenizing treatment using 10-50 mass / volume percent trichloroacetic acid aqueous solution to obtain supernatant comprises: centrifuging the solution after adding the trichloroacetic acid aqueous solution at 4 degrees Celsius at 15000 relative centrifugal force for 20 minutes.

7. The drug screening method based on a blood brain barrier injury model according to claim 6, wherein The converting the biological sample measurement data into a quantitative index of blood-brain barrier leakage comprises: irradiating the supernatant by a fluorescence microplate reader at an excitation wavelength of 620 nm and an emission wavelength of 680 nm to obtain fluorescence intensity.

8. The drug screening method based on a blood brain barrier injury model according to claim 7, wherein, The method further comprises converting the fluorescence intensity into absolute content of Evans blue according to a preset equation.

9. An electronic device, comprising: comprise: a processor; and a memory having computer readable instructions stored thereon for controlling the processor to execute the blood-brain barrier injury model-based drug screening method according to any one of claims 1-8. comprise instructions instructing a device to execute the blood-brain barrier injury model-based drug screening method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, ​

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