Construction method and application of animal model for drug-induced liver injury research

Abstract

The invention discloses a construction method and application of an animal model for drug-induced liver injury research, and belongs to the technical field of animal models.The construction method comprises the steps that S1, a liver injury inducer is applied to an experimental animal; s2, collecting animal samples; s3, performing 16S rDNA high-throughput sequencing on the intestinal content sample to obtain a specific differential intestinal flora marker related to liver injury; s4, carrying out inflammatory factor detection on the blood sample to obtain a specific difference inflammatory factor marker related to liver injury; s5, performing non-targeted metabonomics detection on the liver tissue sample to obtain a specific differential liver metabolism marker related to the liver injury; s6, performing multivariate correlation analysis on changes of the markers; through multi-omics integration and network association analysis, a research system capable of systematically analyzing a liver injury complex mechanism and performing drug effect evaluation is constructed, and the research system has remarkable advantages in systematicness of mechanism research, distinction degree of drug evaluation and universality of methodology.

Description

Technical Field

[0001] This invention relates to the field of animal model technology, and in particular to a method for constructing and applying an animal model for studying drug-induced liver injury. Background Technology

[0002] Drug-induced liver injury (DILI) is a common drug-induced disease in clinical practice and one of the main causes of acute liver failure, but its mechanism of action remains unclear. To further investigate the pathogenesis of DILI, identify potential therapeutic targets, and evaluate the efficacy of hepatoprotective drugs, constructing animal models that can mimic the key characteristics of human DILI is an indispensable research tool.

[0003] Currently, DILI models are typically constructed by administering known liver injury inducers to laboratory animals such as rats and mice. Evaluation criteria primarily rely on the detection of blood biochemical indicators and pathological examination of liver tissue. While these indicators can directly reflect the damage to hepatocytes, existing evaluation indicators mostly reflect the post-injury state, and their sensitivity and specificity need improvement. They are insufficient to comprehensively and systematically reveal the complex, multi-factorial interactive pathological nature of DILI.

[0004] Therefore, there is an urgent need for a novel method for constructing animal models of drug-induced liver injury, which can not only establish a stable liver injury phenotype, but also simultaneously capture the characteristic changes in the model in multiple dimensions such as gut microbiota, liver metabolic network and host systemic response, and quantitatively analyze their intrinsic relationships. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing an animal model for drug-induced liver injury research and its application, in order to solve the key technical problem that existing animal model construction methods have a single evaluation dimension and rely on downstream biochemical and pathological results, which makes it impossible to synchronously and systematically capture and analyze the multidimensional characteristics and their intrinsic interactions of gut microbiota, liver metabolic network and host systemic response, and thus make it difficult to reveal the complex essential mechanism of drug-induced liver injury (DILI).

[0006] To achieve the above objectives, the present invention provides the following solution: A method for constructing an animal model for studying drug-induced liver injury, the key of which includes the following steps: S1. Administering liver injury inducers to experimental animals to induce liver injury; S2. After modeling, collect intestinal contents, blood, and liver tissue samples from the animals. S3. Perform 16S rDNA high-throughput sequencing on the above intestinal contents samples to obtain specific differential intestinal flora markers associated with liver injury. S4. Detect inflammatory factors in the above blood samples to obtain specific differentially expressed inflammatory factor markers related to liver injury; S5. Perform non-targeted metabolomics detection on the above liver tissue samples to obtain specific differential liver metabolic markers related to liver injury. S6. Perform multivariate association analysis on the changes in the above-mentioned specific differential gut microbiota markers, the above-mentioned specific differential inflammatory factor markers related to liver injury, and the above-mentioned specific differential liver metabolic markers, and use the changes in the association among the three as the core basis for evaluating the above-mentioned animal models.

[0007] Specifically, the liver injury inducer mentioned above in step S1 is acetaminophen.

[0008] Furthermore, in step S3, the specific differential gut microbiota markers mentioned above are determined by comparing the relative abundance of gut microbiota in the model group and the control group at the genus or species level, and screening for microbiota with statistically significant differences.

[0009] Furthermore, in step S3, the aforementioned specific differential gut microbiota markers include at least one of Lactobacillus, Bacteroides, or Akkermansia.

[0010] Furthermore, in step S4, the above detection includes detecting at least one of inflammatory factors, traditional liver injury markers, and oxidative stress markers; wherein the inflammatory factors include at least one of interleukin-6 and tumor necrosis factor-α; the traditional liver injury markers include at least one of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase; and the oxidative stress markers include at least one of malondialdehyde and superoxide dismutase.

[0011] Furthermore, in step S5, the aforementioned specific differential liver metabolic markers are endogenous small molecule metabolites related to energy metabolism, lipid metabolism, or amino acid metabolism.

[0012] Preferably, in step S6, the multivariate association analysis is Spearman correlation analysis.

[0013] Preferably, step S6 specifically includes: calculating the Spearman rank correlation coefficients between each pair of the above-mentioned specific differential gut microbiota markers, specific differential inflammatory factor markers, and specific differential liver metabolic markers, and screening for statistically significant correlation pairs.

[0014] The above-described construction method can be used to screen or evaluate hepatoprotective drugs.

[0015] Specifically, the above applications include: establishing animal models using the above construction method, administering candidate drugs to the animal models, and evaluating the hepatoprotective effect of the candidate drugs by detecting and comparing changes in specific differential gut microbiota markers, specific differential inflammatory factor markers, and specific differential liver metabolic markers in the animal models before and after drug administration, as well as changes in their multivariate associations.

[0016] The present invention discloses the following technical effects: This invention provides a method for constructing animal models and conducting multidimensional evaluations of drug-induced liver injury. By integrating gut microbiota sequencing, serum multifactorial profiling, and liver metabolomics, and combining this with multivariate association analysis, it solves the technical problems of traditional models having limited dimensionality, insufficient mechanistic elucidation, and homogenized drug efficacy evaluations. Specific technical effects are as follows: First, this invention constructs an integrated research model capable of systematically revealing the pathological associations of the gut-liver axis. Traditional models rely on single indicators such as serum transaminases, which are insufficient to reflect the systemic and complex nature of the disease. This invention, by simultaneously collecting and analyzing gut microbiota, blood inflammatory factors, and liver metabolites, for the first time comprehensively depicts a multidimensional pathological atlas of drug-induced liver injury within the same system. Through multivariate association analysis, this invention discovers a highly synergistic network of changes among biomarkers of different dimensions, thereby integrating traditionally isolated indicators into an intrinsically linked pathological system, providing a comprehensive research model for elucidating the interaction mechanisms between the gut and the liver.

[0017] Secondly, this invention establishes an evaluation system capable of deeply distinguishing drugs with different mechanisms of action, particularly suitable for studying compound traditional Chinese medicine compositions. Traditional efficacy evaluations only focus on a few indicators such as transaminases, failing to differentiate the specific action links of drugs within the disease network. However, applying the animal model and system of this invention to evaluate three drugs—Uyghur liver-protecting granules (HBG), bicyclol, and N-acetylcysteine—revealed that HBG, while lowering ALT, also significantly restored intestinal flora balance and comprehensively improved liver metabolic disorders, demonstrating multi-target overall regulatory characteristics. Bicyclol mainly exhibited anti-inflammatory and cholestasis-reducing effects; N-acetylcysteine ​​focused on detoxification, and both had limited effects on improving the intestinal microecology. Therefore, the animal model and rating system of this invention can keenly identify the differences in the effects of different drugs at different pathological stages, providing a more precise research tool for studying the mechanisms of compound traditional Chinese medicine compositions and innovative drug mechanism research.

[0018] Third, the standardized process for modeling, detection, and analysis established in this invention has good reproducibility and scalability. Based on mature experimental procedures and a high-throughput detection platform, this invention establishes a standardized technical solution for the entire process from multi-omics data acquisition to networked analysis. It is not only applicable to the acetaminophen-induced liver injury model, but its technical approach can also be extended to the study of liver injury caused by other etiologies.

[0019] In summary, this invention, through multi-omics integration and network correlation analysis, constructs a research system capable of systematically analyzing the complex mechanisms of liver injury and conducting in-depth pharmacodynamic evaluation. It demonstrates significant advantages in terms of the systematic nature of mechanism research, the discriminative power of drug evaluation, and the universality of methodology. Detailed Implementation

[0020] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0021] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0022] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0023] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0024] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0025] Example 1 This embodiment provides a method for constructing an animal model for studying drug-induced liver injury, the specific steps of which include: S1. Administering acetaminophen (APAP), a liver injury inducer, to experimental animals to induce liver injury: In this embodiment, 30 eight-week-old SPF-grade male SD rats were randomly divided into three groups: The control group, the silymarin positive control group (hereinafter referred to as the positive control group), and the model group each consisted of 10 animals. During the feeding process, each group was kept in a standard laboratory animal room with a temperature of 22℃±2℃, humidity of RH50±10%, and a 12-hour light-dark cycle, and was allowed free access to food and water.

[0026] S11, Pretreatment Stage (15 consecutive days): Control group and model group: The same volume of normal saline was administered daily by gavage for 15 consecutive days (the administration volume was 10 mg / kg). Positive drug group: 45 mg / kg of silymarin suspension was administered by gavage daily to establish drug protection in the animals of the positive drug group.

[0027] S12, Acute Liver Injury Modeling Phase (performed 30 minutes after the last dose on day 15): Blank control group: Subcutaneous injection of an equal volume of physiological saline; Model group and positive drug group: Both rats were given a single subcutaneous injection of APAP solution at a dose of 500 mg / kg to establish an acute liver injury model. This procedure was performed uniformly after all animals had completed 15 days of pretreatment to ensure that the only difference between the groups of animals before they suffered liver damage was whether or not they had received prior drug protection.

[0028] S2. After modeling, collect intestinal contents, blood, and liver tissue samples from the experimental animals: At 24 hours after APAP injection (i.e., day 16 of the entire experiment), endpoint samples were collected from animals in each group, including: Anesthesia and euthanasia: Rats were anesthetized by intraperitoneal injection of 3% sodium pentobarbital, and euthanized by cervical dislocation after deep anesthesia. Blood sample collection: 5 mL of blood was collected via the abdominal aorta and injected into a coagulation tube. After standing at room temperature for 30 min, the blood was centrifuged at 3000 r / min for 15 min (4℃) to separate the serum. The serum was then aliquoted into sterile centrifuge tubes and stored at -80℃. Liver tissue sample collection: The whole liver was quickly dissected and rinsed with pre-cooled 0.9% saline; adjacent parts of the same liver lobe were taken: about 100 mg was quick-frozen in liquid nitrogen and stored at -80℃ for metabolomics; about 100 mg was fixed in 4% paraformaldehyde (more than 24 hours) for paraffin embedding and HE staining for pathological examination. Intestinal contents sample collection: Take about 200 mg of cecal contents, place them in a sterile cryovial, flash freeze in liquid nitrogen, and store at -80°C.

[0029] S3. Perform 16S rDNA high-throughput sequencing on intestinal contents samples to obtain specific differentially expressed gut microbiota markers associated with liver injury: Total DNA was extracted from fecal genomic DNA extraction kits using magnetic beads, and PCR amplification of the V3-V4 region of the bacterial 16S rRNA gene was performed using primers 338F and 806R. The purified amplicon was subjected to 2×250bp paired-end sequencing on the Illumina NovaSeq platform. The QIIME2 workflow was used to perform quality control, noise reduction, feature table generation, and species annotation based on the SILVA 138 database on the raw data to obtain the raw data of the relative abundance of each bacterial community at the genus level for each group of samples. To screen for bacterial communities with statistically significant differences, the original abundance data of each genera in the model group and the blank control group were first compared. The non-parametric Kruskal-Wallis H test was used to analyze the significance of differences between groups, and P1<0.05 was set as the threshold for statistically significant differences. After the test was completed, the mean and standard deviation of each experimental group were recorded in Table 1 for descriptive analysis of the central tendency and dispersion of the data in each group. The statistical significance (P1 value) is not calculated from the standard deviations of the two groups, but is an inferential conclusion obtained by calculating the Kruskal-Wallis H test based on all data, including the original abundance of each sample, the mean within the group, and the dispersion.

[0030] After screening using the Kruskal-Wallis H test, the intergroup comparison P values ​​for Lactobacillus, Bacteroides, and Akkermansia were 0.003, 0.005, and 0.008, respectively, while the P value for Prevotella was 0.23. The mean and standard deviation results are shown in Table 1.

[0031] Table 1: Statistical results of the relative abundance of specific differential markers of gut microbiota at the genus level in each group of rats As shown in Table 1, compared with the control group, the mean relative abundance of *Lactobacillus* and *Ackermania* decreased by 65.4% and 73.0% respectively in the model group, showing a significant decreasing trend; while the mean relative abundance of *Bacteroides* increased by 61.9%, showing a significant increasing trend; the mean relative abundance of the positive control group was between that of the model group and the control group, showing a downward trend. Regarding dispersion, the within-group standard deviations for each genera remained at relatively low levels, indicating good consistency within the groups and controllable individual differences.

[0032] Based on the statistical test results above and the descriptive trends in Table 1, it can be seen that the relative abundance of Lactobacillus, Bacteroides, and Akkermansia in the gut microbiota of the model group animals was significantly different from that of the control group, and they were identified as specific differential gut microbiota markers related to liver injury; Prevotella showed no significant difference among the groups.

[0033] S4. Analyze blood samples to obtain specific differential markers associated with liver damage: Serum samples were collected for the detection of traditional liver injury markers, inflammatory factors, and oxidative stress markers. To validate the model and screen differential markers, the Mann-Whitney U test was used to compare the differences between the model group and the blank control group, with P<0.05 set as the threshold for statistical significance. The screening results, mean values, and standard deviations of each indicator in each group are shown in Table 2.

[0034] Specific testing items include: (1) Traditional liver injury markers: detection of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities, and alkaline phosphatase (ALP) activities; (2) Inflammatory markers: Serum interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) concentrations were detected using an ELISA kit; (3) Oxidative stress markers: Detect serum malondialdehyde (MDA) content and superoxide dismutase (SOD) activity.

[0035] Table 2: Results of detection of specific differential markers in rat serum of each group As shown in Table 2, compared with the blank group, the mean values ​​of all blood markers in the model group showed extremely significant differences. Among them, liver injury markers (ALT, AST, ALP) and inflammation / oxidative stress markers (TNF-α, IL-6, MDA) were significantly increased, while the activity of the antioxidant enzyme SOD was significantly decreased. Moreover, the standard deviation within each indicator group showed that the data dispersion was within a reasonable range.

[0036] Compared with the model group, all indicators in the positive drug group showed significant regression and significant improvement, indicating that the model has a sensitive response to the therapeutic effect of classic hepatoprotective drugs.

[0037] The standard deviations of the data within each experimental group were within a reasonable range relative to their means, indicating that individual variation within the groups was controllable and the data were stable and reliable. Based on the combined statistical test results (P2<0.01) and the descriptive trends in Table 2, the above seven indicators were identified as specific differential blood biomarkers associated with liver injury.

[0038] S5. Perform non-targeted metabolomics analysis on liver tissue samples to obtain specific differential liver metabolic biomarkers associated with liver injury: (1) Sample pretreatment: Accurately weigh 50 mg of frozen liver tissue, add 400 μL of pre-cooled methanol-acetonitrile-water solution (volume ratio 2:2:1, containing 0.1% formic acid), and homogenize in a low-temperature grinder. After vortexing, sonicate in an ice bath for 10 min, and let stand at -20℃ for 1 h to precipitate proteins; then, centrifuge at 4℃ and 14000g for 15 min, and take the supernatant to dry under nitrogen. Reconstitute with 100 μL of acetonitrile-water solution (volume ratio 1:1), centrifuge again, and take the supernatant for analysis.

[0039] (2) Test conditions: Instrumental analysis: Analysis was performed using an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry system. Chromatographic conditions: ACQUITY UPLC HSS T3 column; column temperature 40℃; mobile phase A: water (containing 0.1% formic acid by mass), mobile phase B: acetonitrile (containing 0.1% formic acid by mass); flow rate 0.3 mL / min; gradient elution.

[0040] Mass spectrometry conditions: electrospray ionization source, positive and negative ion modes for separate acquisition; ion source temperature 500℃; declustering voltage 80V; collision energy 35eV; mass scan range m / z 50-1000.

[0041] (3) Data processing and differential metabolite screening: The raw mass spectrometry data were imported into Progenesis QI software for peak extraction, alignment, and normalization to generate a data matrix containing mass-to-charge ratio, retention time, and peak intensity.

[0042] The matrix was imported into SIMCA-P software for multivariate statistical analysis: First, unsupervised principal component analysis was performed to observe the overall separation trend. Then, supervised partial least squares discriminant analysis was used to maximize the separation between the model group and the blank control group. Statistically significant differentially expressed metabolites were screened using a variable importance projection VIP value > 1.0 and a p-value < 0.05 (t-test). Differentially expressed metabolites were identified by comparing secondary mass spectrometry fragments with the HMDB and METLIN databases.

[0043] Pathway enrichment analysis: The screened differential metabolites were imported into the Metabo Analyst platform for KEGG pathway enrichment analysis. The key perturbed metabolic pathways were identified with a P3 value of <0.05 between the model group and the blank group.

[0044] Table 3: Results of liver metabolic marker detection with significant differences between the model group and the blank group As shown in Table 3, compared with the control group, the levels of liver metabolic markers in the model group showed significant changes. Among them, bile acid metabolites (glycocholic acid and taurocholic acid) accumulated significantly, energy metabolism intermediates (acetylcarnitine and citric acid) were significantly consumed, and the levels of amino acid metabolites (glutamate and aspartic acid) and lactate were abnormally elevated, indicating the typical pathological features of cholestasis, energy metabolism disorders, and amino acid metabolism disorders in liver injury.

[0045] Compared to the model group, the levels of various metabolites in the positive control group significantly reverted to those in the control group, such as a significant decrease in glycocholic acid and glutamate levels, and a significant increase in acetylcarnitine levels. This indicates that the classic hepatoprotective drug silymarin can partially reverse the specific metabolic disorders caused by liver injury, further validating the sensitivity of this model in evaluating drug efficacy at the metabolic level.

[0046] The standard deviation of the data within each experimental group is within a reasonable range relative to its mean, indicating that the detection method is stable, the variation among individuals within the group is controllable, and the data is stable and reliable.

[0047] S6. Conduct multivariate association analysis and use changes in association as the core basis for evaluating the model: To construct a three-dimensional interaction network of gut microbiota, host inflammation, and liver metabolism, it is necessary to screen out key biomarkers that are more representative of each dimension from the differential biomarkers identified in steps S3 to S5, and use them as key three-dimensional biomarkers for core association analysis.

[0048] The selection of key three-dimensional biomarkers was based on the results in Tables 1-3, following the basic principle of significant trends and statistical significance: (1) Gut microbiota dimension: Lactobacillus and Akkermansia were selected from Table 1. Both species showed a highly significant decreasing trend in the model group, which is consistent with the characteristics of severe depletion of protective microbiota.

[0049] (2) Host inflammation and injury dimension: ALT activity was selected from Table 2. As the most direct indicator of the degree of hepatocyte injury, this indicator showed the largest increase in activity in the model group, and the P value was highly significant.

[0050] (3) Liver metabolism dimension: Glycinecholic acid and glutamate were selected from Table 3. Glycinecholic acid is the core marker of bile acid metabolism disorder, and glutamate is the key representative of amino acid metabolism abnormality. Both showed extremely significant upregulation in the model group.

[0051] Based on the above screening, the data on Lactobacillus abundance, Akkermansia abundance, ALT activity, glycocholic acid level, and glutamate level from all individual samples (n=30) were integrated. Spearman rank correlation analysis was used to calculate the correlation coefficients r and P4 values ​​between each pair of the above markers in the blank group, model group, and positive drug group, respectively, to reveal the specificity and dynamic changes of the association network. The analysis results are shown in Table 4.

[0052] Table 4: Spearman correlation analysis results of key three-dimensional biomarkers in different groups As can be seen from the results in Table 4, this invention establishes a tight multi-dimensional interaction network in the model group.

[0053] In the model group, the correlations among all 10 pairs of core biomarkers were statistically significant (P4 < 0.05), forming a highly correlated system. Within this network, changes in the two protective gut bacteria (Lactobacillus and Akkermansia) were highly synchronized (r = 0.88, P4 = 0.001), and both showed significant negative correlations with the liver injury marker ALT and two liver metabolic disorder markers (glycocholic acid and glutamate); while ALT showed a significant positive correlation with the two metabolites. These results indicate that in drug-induced liver injury, the synergistic dysregulation of the gut microbiota, severe hepatocyte damage, and hepatic bile acid and amino acid metabolism disorders are not isolated events, but rather constitute an intrinsically interconnected pathophysiological network.

[0054] Furthermore, the association network constructed in this invention exhibits clear disease model specificity and can be reversed by effective drug intervention. In the blank control group, no significant correlations were found between any of the biomarker pairs, indicating that the network is a specific outcome under pathological conditions. In the positive control group, the network structure changed significantly; the number of significantly correlated biomarker pairs decreased, and the strength of the still significant correlations was also significantly weakened. This demonstrates that effective hepatoprotective drug treatment can not only improve individual indicators but also significantly alleviate the strength of this multidimensional pathological association overall, promoting the system's recovery towards normalcy.

[0055] Example 2 This embodiment aims to demonstrate how to use the animal model and multidimensional evaluation system constructed in Embodiment 1 to evaluate and compare the efficacy of candidate hepatoprotective drugs with different mechanisms of action.

[0056] S1. Animal grouping, modeling, and drug administration: Referring to step S1 of Example 1, 50 8-week-old SPF-grade male SD rats were randomly divided into 5 groups of 10 rats each: blank group, model group, Weiyao Hugan Buzure Granules group (HBG group), Bicyclol group, and N-acetylcysteine ​​group (NAC group). The feeding conditions were the same as in step S1 of Example 1.

[0057] S11, Pretreatment Stage (15 consecutive days): Control group and model group: The same volume of physiological saline was administered by gavage daily.

[0058] HBG group: Administered 100mg / kg of liver-protecting Buzure granules suspension by gavage daily.

[0059] Bicyclol group: Bicyclol suspension was administered by gavage at a dose of 150 mg / kg daily.

[0060] NAC group: N-acetylcysteine ​​solution was administered by gavage at a dose of 300 mg / kg daily.

[0061] The administration volume was 10 mL / kg.

[0062] S12, Acute Liver Injury Modeling Phase (performed 30 minutes after the last dose on day 15): Control group: Subcutaneous injection of an equal volume of physiological saline.

[0063] Model group and each treatment group: A rat acute liver injury model was established by a single subcutaneous injection of APAP solution at a dose of 500 mg / kg.

[0064] S2, Sample Collection: 24 hours after APAP injection, in accordance with step S2 of Example 1, endpoint samples were collected from all animals, including blood samples, liver tissue samples and intestinal contents samples, and the processing methods and storage conditions were exactly the same.

[0065] S3. Detection and analysis of key three-dimensional biomarkers: To efficiently evaluate and compare the effects of different drugs, this embodiment uses the most representative key three-dimensional biomarkers identified in Example 1 for detection, namely: Gut microbiota dimension: relative abundance of Lactobacillus and relative abundance of Akkermansia.

[0066] Liver injury and inflammation dimension: serum ALT activity.

[0067] Liver metabolism dimension: content of glycocholic acid (GCA) and glutamate (Glu) in liver tissue.

[0068] Detection methods: Gut microbiota detection followed the 16S rDNA sequencing and data analysis procedure in step S3 of Example 1; ALT detection followed the serum biochemical detection method in step S4 of Example 1; glycocholic acid and glutamate detection followed the non-targeted metabolomics detection and targeted validation method in step S5 of Example 1. The detection results of the above five key biomarkers in each group were statistically analyzed and compared with the model group and the blank group. The results are shown in Table 5.

[0069] Table 5: Effects of different hepatoprotective drugs on key three-dimensional biomarkers of APAP-induced liver injury in rats As shown in Table 5, all three drugs significantly reduced serum ALT, a traditional indicator of liver injury (P<0.01), with the bicyclol group exhibiting the lowest mean ALT activity. Based on this, it would be concluded that all three drugs effectively alleviate hepatocellular damage, with bicyclol being slightly superior in reducing transaminase levels. This would completely mask the differences in other important pathological dimensions.

[0070] The multi-dimensional evaluation system provided by this invention reveals information far exceeding that of traditional single indicators.

[0071] HBG showed significant advantages in improving gut microbiota dysbiosis caused by APAP damage. It not only restored the abundance of Lactobacillus and Akkermansia to a level that was not statistically different from the control group, but also the magnitude of the restoration was significantly better than that of the bicyclol group and the NAC group.

[0072] In correcting liver-specific metabolic disorders, HBG also showed the most comprehensive performance, with the most significant correction of abnormally elevated glycocholic acid and glutamate levels, with glutamate levels approaching those of the control group.

[0073] In comparison, bicyclol is effective in improving bile acid metabolism, but its corrective effect on amino acid metabolism disorders is weaker than that of HBG; NAC, as a direct detoxifying agent, can alleviate cholestasis, but its effect on improving intestinal flora and amino acid metabolism is limited.

[0074] Therefore, the model and multi-dimensional evaluation system constructed using this invention can clearly distinguish the therapeutic characteristics of different hepatoprotective drugs. HBG exhibits multi-target and holistic regulatory effects in the model, significantly repairing the gut microbiota barrier and comprehensively improving the liver's metabolic environment while alleviating hepatocellular damage. In contrast, traditional chemical drugs such as bicyclol and the antidote NAC show different focuses and limitations. Relying solely on traditional indicators such as ALT fails to identify the unique protective effects of HBG in both the upstream (gut microbiota) and downstream (liver metabolic network) of the gut-hepatic axis, leading to superficial and highly homogenized evaluations of drug efficacy.

[0075] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for constructing an animal model for studying drug-induced liver injury, characterized in that, Includes the following steps: S1. Administering liver injury inducers to experimental animals to induce liver injury; S2. After modeling, collect intestinal contents, blood, and liver tissue samples from the animal. S3. Perform 16S rDNA high-throughput sequencing on the intestinal contents sample to obtain specific differential intestinal flora markers associated with liver injury; S4. Detect inflammatory factors in the blood sample to obtain specific differential inflammatory factor markers related to liver injury; S5. Perform non-targeted metabolomics detection on the liver tissue sample to obtain specific differential liver metabolic markers related to liver injury; S6. Perform multivariate association analysis on the changes in the specific differential gut microbiota markers, the specific differential inflammatory factor markers related to liver injury, and the specific differential liver metabolic markers, and use the changes in the association among the three as the core basis for evaluating the animal model.

2. The construction method according to claim 1, characterized in that, The liver injury inducer mentioned in step S1 is acetaminophen.

3. The construction method according to claim 1, characterized in that, The specific differential gut microbiota markers mentioned in step S3 are determined by comparing the relative abundance of gut microbiota at the genus or species level between the model group and the control group, and screening for microbiota with statistically significant differences.

4. The construction method according to claim 1, characterized in that, In step S3, the specific differential gut microbiota markers include at least one of Lactobacillus, Bacteroides, or Akkermansia.

5. The construction method according to claim 1, characterized in that, In step S4, the detection includes detecting at least one of inflammatory factors, traditional liver injury markers, and oxidative stress markers; wherein the inflammatory factors include at least one of interleukin-6 and tumor necrosis factor-α; the traditional liver injury markers include at least one of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase; and the oxidative stress markers include at least one of malondialdehyde and superoxide dismutase.

6. The construction method according to claim 1, characterized in that, In step S5, the specific differential liver metabolic markers are endogenous small molecule metabolites related to energy metabolism, lipid metabolism, or amino acid metabolism.

7. The construction method according to claim 1, characterized in that, In step S6, the multivariate association analysis is Spearman correlation analysis.

8. The construction method according to claim 7, characterized in that, Step S6 specifically includes: calculating the Spearman rank correlation coefficients between each pair of the specific differential gut microbiota markers, specific differential inflammatory factor markers, and specific differential liver metabolic markers, and screening for statistically significant correlation pairs.

9. The application of the construction method as described in any one of claims 1 to 8 in screening or evaluating hepatoprotective drugs.

10. The application according to claim 9, characterized in that, The application includes: establishing an animal model using the construction method, administering the candidate drug to the animal model, and evaluating the hepatoprotective effect of the candidate drug by detecting and comparing changes in specific differential gut microbiota markers, specific differential inflammatory factor markers, and specific differential liver metabolic markers in the animal model before and after drug administration, as well as changes in their multivariate associations.

Images