A method and drug for screening natural drug monomers targeting Brucella.

Abstract

This invention provides a method and drug for screening natural drug monomers targeting Brucella. The steps include collecting small molecule compounds, constructing a screening model, obtaining a preliminary screening database, molecular docking, and obtaining the candidate drug asarazine based on the molecular docking results and docking free energy through molecular dynamics simulation. Experimental studies have shown that asarazine can synergistically exert an anti-brucellosis effect with doxycycline, thereby treating Brucella-induced infections. This invention provides a method for screening natural drug monomers targeting bacteria and discovers a novel therapeutic drug against Brucella.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a method for screening natural drug monomers targeting Brucella. Background Technology

[0002] Brucella is a Gram-negative bacillus that causes brucellosis, a zoonotic disease that can be transmitted through food, direct contact, or aerosols. Patients often present with symptoms such as fever, arthritis, and headache. Given its long incubation period and diverse symptoms, accurate diagnosis and treatment of brucellosis are crucial for healthcare systems.

[0003] However, finding effective treatments remains a challenge due to Brucella's high resistance to most antibiotics. Doxycycline may be a promising treatment option in this regard. Doxycycline is a broad-spectrum tetracycline antibiotic that inhibits bacterial growth and reproduction by suppressing protein synthesis. Studies have shown that doxycycline has some antibacterial activity against Brucella and may play a role in treating brucellosis. However, as with all antibiotics, excessive and inappropriate use can lead to bacterial resistance to doxycycline. To ensure that this barrier against multidrug-resistant Gram-negative pathogens is not broken, it is crucial to combine it with other natural drug monomers and seek suitable treatment strategies to delay the onset of doxycycline resistance or restore its antibacterial activity. Currently, the early screening of natural drug monomers for adjuvant antibiotic therapy has the following main drawbacks and limitations:

[0004] 1. Difficulty in constructing compound libraries and low screening efficiency: To obtain a sufficient number of candidate compounds, continuous screening, purification, and synthesis from natural products and other sources are required. Furthermore, due to the diversity of compound structures, constructing high-quality compound libraries also presents many challenges.

[0005] 2. Insufficient targeting and high uncertainty in drug screening results: Traditional drug screening methods often struggle to accurately understand the molecular mechanisms of disease treatment. Consequently, the screened compounds frequently exhibit non-specific effects and often produce side effects, limiting drug usability. Therefore, molecular simulation, omics, and other technologies are needed to improve the targeting and precision of the drug screening process.

[0006] 3. Challenges in processing large-scale and complex data: Modern drug development generates massive amounts of complex data. This includes various data types such as molecular, chemical, and biological databases, each with its own format and processing methods. Processing and managing this data requires efficient data science technologies and computational algorithms.

[0007] In summary, existing methods for screening natural drug monomers have many shortcomings and deficiencies, especially in the early stages, which need to be addressed as they affect the smooth development of novel treatments for Brucella. Therefore, continuous improvement of drug research methods is necessary, along with reducing drug development costs and screening for novel synergistic treatments against Brucella. Summary of the Invention

[0008] This invention provides a novel computer-aided screening method for Brucella, addressing the problems of insufficient targeting, low screening efficiency, and low accuracy in existing screening methods. Experimental studies have also revealed that asarazine and doxycycline have a synergistic effect and effectively inhibit Brucella.

[0009] The first aspect involves providing computer-aided screening for novel treatments against Brucella, including the following steps:

[0010] S10. Collect small molecule compounds from traditional Chinese medicine sources through databases and literature reviews;

[0011] S20. Utilize machine learning to build a screening model and screen out small molecule compounds that meet the drug-like properties to form a preliminary screening database;

[0012] S30. Use Autodock-vina software to screen small molecule compounds in the initial screening database that can spontaneously bind to the target protein as candidate compounds;

[0013] S40. Based on the molecular docking results and docking free energy between candidate compounds and target proteins, obtain a small molecule compound library;

[0014] S50. Perform molecular dynamics simulations on compounds in the small molecule compound library, evaluate the stability of docking results, and obtain candidate drugs;

[0015] S60. Experimental verification and investigation were conducted on the candidate drugs, and it was found that asarazolidin works in conjunction with doxycycline to exert an anti-brucellosis effect and can be used to treat brucellosis-induced infections.

[0016] This invention collects a large number of candidate compounds from existing databases, solving the problem of difficult compound library construction. The application of CADD improves screening efficiency. Furthermore, molecular docking experiments verify that the screened natural drug monomers have more specific effects than those screened by existing traditional methods, thus improving the targeting and accuracy of the drug screening process.

[0017] Preferably, in step S20, a random forest algorithm is used to train and obtain a screening model, and active compounds are screened out based on OB>20% and DL>0.1.

[0018] Preferably, the construction and application of the screening model includes:

[0019] S21. Data collection and preparation: Collect relevant small molecule compound activity data, including molecular structure and corresponding activity tags, physicochemical properties, and structural descriptors;

[0020] S22, Feature Selection and Extraction: Use chemical structure-based descriptors SMILES as feature inputs for the random forest algorithm;

[0021] S23. Training and test set partitioning: The dataset with known compound activity indicators OB and DL is divided into a training set for training the model and a test set for evaluating the model performance.

[0022] S24. Model selection and training: Random forest algorithm is used for training, and grid search function is used to optimize model parameters to obtain selected models;

[0023] S25. Model Evaluation and Validation: The trained screening model is evaluated using a test set. Evaluation metrics include accuracy, recall, and F1 score. In addition, the model performance is validated using cross-validation.

[0024] S26. Model Application and Prediction: The optimized screening model is used to predict the activity of the small molecule compounds in step S10. Based on the prediction results, compounds that meet the drug-like properties are screened to form a preliminary screening database.

[0025] Preferably, step S30 includes:

[0026] S31. Target preparation: Select a suitable target from the database. The target is a protein that has been crystallized and has a known structure. The preparation process includes removing water molecules, repairing missing amino acid residues, and adding hydrogen atoms.

[0027] S32. Preparation of ligand database: Select suitable ligand candidates from the initial screening database; the ligands are low molecular weight compounds, peptides, or fatty acids; the preparation process includes generating all possible conformations and rotations, and adding hydrogen atoms.

[0028] S33. Search Algorithm and Scoring Function: Autodock-Vina uses the Lamarckian genetic algorithm to search for the optimal ligand binding mode. This algorithm searches for the best ligand pose by employing population evolution and individual mutation.

[0029] S34. Binding Affinity Scoring: Each ligand pose is evaluated using a scoring function to assess its binding affinity to the target; the scoring function includes terms describing coordination geometry and non-covalent interactions.

[0030] S35. Results Analysis and Visualization: Based on the results of the scoring function, the binding ability of the ligands is ranked. Finally, candidate compounds are selected based on the score and other evaluation indicators.

[0031] Preferably, step S40 includes: based on the identified active sites, setting other conditions to default, selecting the Lamarckian genetic theoretical gorithm (LGA) calculation method, running AutodockVina with PyRx, performing high-throughput screening of candidate compounds, and obtaining a preliminary small molecule compound library based on a free energy less than -5.

[0032] Preferably, step S50 involves performing molecular dynamics simulations on the initially screened small molecule compound library using the GROMACS program, including:

[0033] S51. Molecular structure preparation: Obtain the molecular structure of the target drug from the small molecule compound library, and perform structural modification and preparation, including adding hydrogen atoms and repairing missing atoms or bonds.

[0034] S52, Force field parameterization: Selecting appropriate force fields for drug molecules and target proteins, including parameterization of interatomic interaction potential energy and atomic charge;

[0035] S53. Initial Conformation and Solvent Model: Determine the initial conformation of the drug molecule and generate it using experimental data or other computational methods; select an appropriate number and type of solvent molecules to simulate the actual solvent environment;

[0036] S54. Simulation Parameter Settings: Set the simulation time, temperature, pressure, ion concentration parameters, and simulation time step;

[0037] S55. Molecular dynamics simulation algorithm: Select an appropriate numerical integration algorithm to solve Newton's equations of motion and calculate the interactions between atoms based on the force field parameters.

[0038] Preferably, in step S50, through 20 repeated molecular dockings, more stable docking results are selected based on the distribution of RMSD values ​​of the docking results, and candidate drugs are further screened.

[0039] Preferably, a compound that effectively inhibits Brucella is obtained for the treatment of Brucella-induced infections. The compound includes asarazine and doxycycline, and the combined use of asarazine and doxycycline is more effective than the use of either one.

[0040] Secondly, a drug is provided, which is obtained using the screening method described in the first aspect and screened through basic experiments. It was found that the combination of asarazolidin and doxycycline has a synergistic effect and an effective inhibitory effect on Brucella.

[0041] Includes the following steps:

[0042] The checkerboard test was used to detect the synergistic effect of asarazolidin and doxycycline.

[0043] The effects of asarazine and doxycycline on Brucella growth were determined by absorbance analysis.

[0044] The effect of CFU colony count on the growth of Brucella and doxycycline.

[0045] The anti-infective effects of asarone and doxycycline in vivo.

[0046] In this embodiment of the invention, a method for screening natural drug monomers and a drug are provided, which have the following characteristics:

[0047] Beneficial effects:

[0048] 1. A large number of candidate compounds were collected through existing databases, solving the problem of difficulties in building compound libraries;

[0049] 2. The application of CADD improved screening efficiency;

[0050] 3. Through molecular docking experiments, the natural drug monomers screened out have been found to have more specific effects than those screened by existing traditional methods, thus improving the targeting and accuracy of the drug screening process.

[0051] 4. It can quickly screen out compounds that have an effective inhibitory effect on Brucella.

[0052] 5. It was found that the combination of asarazine and doxycycline has a synergistic effect and effectively inhibits Brucella. Attached Figure Description

[0053] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of this invention, are illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention.

[0054] in:

[0055] Figure 1 A schematic diagram illustrating the implementation steps of a method for screening natural drug monomers targeting Brucella;

[0056] Figure 2 The performance evaluation results of computer machine learning algorithm prediction models;

[0057] Figure 3 Results of molecular docking;

[0058] Figure 4 This is a result of molecular dynamics;

[0059] Figure 5 This is due to the synergistic effect of asarone and doxycycline;

[0060] Figure 6 The enhanced inhibitory effect of different concentrations of asarazine and / or doxycycline on Brucella;

[0061] Figure 7 The effect of asarin and / or doxycycline on the number of viable colonies of Brucella;

[0062] Figure 8 The effect of asarone and / or doxycycline on the number of viable Brucella colonies in cells;

[0063] Figure 9 The effects of asarazine and / or doxycycline on Brucella-infected mouse tissues. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0066] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0067] Early screening of natural drug monomers is often the key to the smooth progress of subsequent research and development and clinical trials. The main problems in the early screening of traditional natural drug monomers include: difficulty in building compound libraries, low screening efficiency, insufficient targeting of drug screening, difficulty in processing large-scale and complex data, and high uncertainty of screening results.

[0068] To address the aforementioned problems, this invention proposes a method for screening natural drug monomers. By applying CADD (Carbon Deposition and Analysis), the screening efficiency is improved. Further verification through molecular docking experiments shows that the screened natural drug monomers exhibit more specific effects than those screened using existing traditional methods, thus enhancing the targeting and precision of the drug screening process.

[0069] After introducing the basic principles of the present invention, various non-limiting embodiments of the present invention are described in detail below. It should be noted that the embodiments provided by the present invention are only shown to facilitate understanding of the spirit and principles of the present invention, and the embodiments of the present invention are not limited in any way. On the contrary, the embodiments of the present invention can be applied to any applicable system.

[0070] Example 1:

[0071] Please see Figure 1 This paper provides a method for screening natural drug monomers, comprising the following steps:

[0072] S10. Collect small molecule compounds from traditional Chinese medicine sources through databases and literature surveys. In this example, more than 17,000 small molecule compounds were collected.

[0073] S20. Utilize machine learning to build a screening model and screen out small molecule compounds that meet the drug-like properties to form a preliminary screening database.

[0074] There are many types of compounds, and some may not be suitable for drug development due to their inherent properties. Therefore, this step aims to screen for compounds with potential drug activity from among the many available compounds. OB (Oral Bioavailability) is the oral bioavailability of a drug. For a drug to exert its effect in the body, it needs to be fully dissolved in the gastrointestinal tract so that the drug molecules can be absorbed into the bloodstream and exert their effect. Therefore, the dissolution rate of a drug is closely related to the OB value. DL (Drug-likeness) is used to estimate the degree to which a compound is similar to other drugs, which helps optimize pharmacokinetics and drug properties such as solubility and chemical stability. In one embodiment, this step uses a random forest algorithm to train and build a screening model, and selects active compounds based on OB > 20% and DL > 0.1.

[0075] In one specific embodiment, the construction and application of the screening model includes:

[0076] S21. Data collection and preparation: Collect relevant small molecule compound activity data, including molecular structure and corresponding activity tags, physicochemical properties, and structural descriptors;

[0077] S22, Feature Selection and Extraction: Use chemical structure-based descriptors SMILES as feature inputs for the random forest algorithm;

[0078] S23. Training and test set partitioning: The dataset with known compound activity indicators OB and DL is divided into a training set for training the model and a test set for evaluating the model performance.

[0079] S24. Model selection and training: Random forest algorithm is used for training, and grid search function is used to optimize model parameters to obtain selected models;

[0080] S25. Model Evaluation and Validation: The trained screening model is evaluated using a test set. Evaluation metrics include accuracy, recall, and F1 score. In addition, the model performance is validated using cross-validation. Figure 2 This is a performance evaluation result of the screening model trained by the random forest algorithm. The prediction relevance is R square > 0.9, indicating that the prediction results are highly accurate.

[0081] S26. Model Application and Prediction: The optimized screening model is used to predict the activity of the small molecule compounds in step S10. Based on the prediction results, compounds that meet the drug-like properties are screened to form a preliminary screening database.

[0082] S30. Use Autodock-vina software to screen small molecule compounds in the initial screening database that can spontaneously bind to the target protein as candidate compounds;

[0083] Autodock-Vina software is an open-source program for virtual screening and molecular docking, used to predict the binding affinity between molecules and targets. Autodock-Vina uses a Lamarckian genetic algorithm to search for optimal binding modes between molecules and evaluates and ranks the binding affinity of candidate ligands using a scoring function. In one embodiment, step S30 further includes:

[0084] S31. Target preparation: Select a suitable target from the database. The target is a protein that has been crystallized and has a known structure. The preparation process includes removing water molecules, repairing missing amino acid residues, and adding hydrogen atoms.

[0085] S32. Preparation of ligand database: Select suitable ligand candidates from the initial screening database; the ligands are low molecular weight compounds, peptides, or fatty acids; the preparation process includes generating all possible conformations and rotations, and adding hydrogen atoms.

[0086] S33. Search Algorithm and Scoring Function: Autodock-Vina uses the Lamarckian genetic algorithm to search for the optimal ligand binding mode. This algorithm searches for the best ligand pose by employing population evolution and individual mutation.

[0087] S34. Binding Affinity Scoring: Each ligand pose is evaluated using a scoring function to assess its binding affinity to the target; the scoring function includes terms describing coordination geometry and non-covalent interactions.

[0088] S35. Results Analysis and Visualization: Based on the results of the scoring function, the binding ability of the ligands is ranked. Finally, candidate compounds are selected based on the score and other evaluation indicators.

[0089] like Figure 3 The display shows the molecular docking results between the compound and the target gene protein, as well as the types of chemical bonds formed.

[0090] S40. Based on the molecular docking results and docking free energy between candidate compounds and target proteins, obtain a small molecule compound library;

[0091] In one embodiment, step S40 includes: based on the identified active sites, with other conditions set to default, selecting the Lamarckian genetic theoretical gorithm (LGA) calculation method, running AutodockVina with PyRx, performing high-throughput screening of candidate compounds, and obtaining a preliminary small molecule compound library based on a free energy less than -5.

[0092] S50. Perform molecular dynamics simulations on compounds in the small molecule compound library, evaluate the stability of docking results, and obtain candidate drugs;

[0093] Drug molecular dynamics simulation is a computational chemistry method used to simulate the dynamic behavior of drug molecules over time and their interactions with target proteins. Its basic principle is based on Newton's equations of motion and statistical mechanics. By simulating the interactions between atoms in a molecule and their motion in a solvent, information about the structure, conformation, and interactions of the drug can be obtained. In one embodiment, step S50 further utilizes the GROMACS program to perform molecular dynamics simulations on the initially screened small molecule compound library, including:

[0094] S51. Molecular structure preparation: Obtain the molecular structure of the target drug from the small molecule compound library, and perform structural modification and preparation, including adding hydrogen atoms and repairing missing atoms or bonds.

[0095] S52, Force field parameterization: Selecting appropriate force fields for drug molecules and target proteins, including parameterization of interatomic interaction potential energy and atomic charge;

[0096] S53. Initial Conformation and Solvent Model: Determine the initial conformation of the drug molecule and generate it using experimental data or other computational methods; select an appropriate number and type of solvent molecules to simulate the actual solvent environment;

[0097] S54. Simulation Parameter Settings: Set the simulation time, temperature, pressure, ion concentration parameters, and simulation time step;

[0098] S55. Molecular dynamics simulation algorithm: Select an appropriate numerical integration algorithm to solve Newton's equations of motion and calculate the interactions between atoms based on the force field parameters.

[0099] In step S50, through 20 repeated molecular dockings, more stable docking results are selected based on the distribution of RMSD values ​​of the docking results, and candidate drugs are further screened.

[0100] like Figure 4 The results shown are from multiple molecular dynamics simulations. The RMSD values ​​are low and stable with little variation, indicating that the simulation results are relatively reliable.

[0101] S60. Conduct experimental verification of the candidate drug to obtain an effective active ingredient drug.

[0102] Example 2:

[0103] A method for screening natural drug monomers targeting Brucella is provided, which uses the method provided in Example 1 to obtain compounds that have an effective inhibitory effect on Brucella.

[0104] In this embodiment, step S30 specifically includes adjusting the charge of the compound, determining the root of the ligand, and selecting ligands with torsion-resistant bonds. For the Brucella target receptor, optimization involving hydrogen addition and amino acid addition, as well as charge calculation, are required. Using AutoDockTools, both the receptor and the small molecule are converted to PDBQT format. Molecular docking is then performed using AutoDock vina in command-line mode, with the receptor name, ligand name, docking center coordinates, and distance information provided.

[0105] The compound obtained through this embodiment is asarazine, which has an effective inhibitory effect on Brucella.

[0106] In step S60 of this embodiment, the following experiments were conducted on asarone and doxycycline.

[0107] Experiment 1: Checkerboard test to detect the synergistic effect of asarazolidin and doxycycline

[0108] Methods: In 96-well plates, the concentration of Asa was doubled in each column (0–256 μg / mL), and the concentration of Dox was doubled in each row (0–16 μg / mL). 100 μL of test strain suspension (5 × 10⁵ CFU / mL) was inoculated into each well, with a final volume of 200 μL. The plates were then incubated at 37 °C for 24 h.

[0109] Result: As Figure 5 As shown, different doses of Brucella Asa showed a synergistic effect with Dox at 16 μg / mL, and the Dox synergistic MIC values ​​of Brucella abortus 544 and Brucella abortus RB51 strains were relatively small. The Dox synergistic MIC of all strains was 0.5-1 μg / mL when the Asa dose was 256 μg / mL.

[0110] Experiment 2: Absorbance Detection of the Effects of Asarazolidin and Doxycycline on Brucella Growth

[0111] Methods: First, in 96-well plates, the Asa concentration was doubled in each column (0–256 μg / mL), and the Dox concentration was doubled in each row (0–16 μg / mL). 100 μL of test strain suspension (5 × 10⁵ CFU / mL) was inoculated into each well, resulting in a final volume of 200 μL. The plates were then incubated at 37°C for 24 h. Next, 100 μL of 5 × 10⁵ CFU / mL test strain suspension was inoculated into each well of the 96-well plate, and the volume was increased to 200 μL with tryptone soybean broth (TSB) containing the drug. Brucella was treated for 24 h with different concentrations of Asa (20 μg / mL), Dox (2 μg / mL), and Asa + Dox (0, 5, 10, 15, 20, 25, 30 μg / mL + 2 μg / mL). The OD600 nm value was then measured.

[0112] Result: As Figure 6 As shown, the addition of asarazine significantly reduced bacterial concentration, thus enhancing the inhibitory effect of doxycycline on the two Brucella species.

[0113] Experiment 3: Effects of CFU colony count on the growth of Brucella and doxycycline

[0114] Methods: To further demonstrate the inhibitory effects of asarazine and doxycycline on Brucella, Brucella cultured overnight was diluted in tryptone soybean broth (TSB) to an OD600 of 0.01. Brucella at OD600 = 0.01 was treated with Asa (20 μg / mL), Dox (2 μg / mL), and Asa+Dox (20 μg / mL + 2 μg / mL) for 24 h. Brucella was then inoculated onto tryptone soybean agar (TSA) plates, and the viable counts were determined at 0 and 24 h, with DMSO as a control.

[0115] Result: As Figure 7 As shown, the number of Brucella colonies in the untreated group increased significantly after 24 hours of culture. Doxycycline at 2 μg / mL and asarazine at 20 μg / mL had significant inhibitory effects on the number of Brucella colonies, but the inhibitory effect of the combined use of doxycycline and asarazine was significantly better than that of the single use of doxycycline and asarazine.

[0116] Experiment 4: Effects of CFU colony count on the growth of intracellular Brucella by asarone and doxycycline

[0117] Methods: To demonstrate that the drug can penetrate host cells to reach the intracellular growth niche of the pathogen, mouse macrophages J774A.1 were cultured in 24-well plates at a density of 1.5 × 10⁶ cells per well. 5 Cells were seeded at a density of 100 μg / mL using DMEM medium and 10% FBS at 37°C with 5% CO2. Bacteria were then infected in three replicate wells at a 100:1 infection ratio. After 1 h, cells were washed with PBS. Cells were incubated in DMEM containing 25 μg / mL gentamicin for 1 h to kill extracellular bacteria. The medium was removed again and replaced with fresh medium containing 10% FBS. After 24 h of incubation, cells were washed twice with DMEM medium. Subsequently, 10 μl of DMEM containing 25% FBS was added to each well, along with asarazolidin and doxycycline at the above concentrations, and incubated at different time intervals. The medium was then removed and cells were washed twice with FBS. To determine the intracellular bacterial load, cells were cultured in 250 μL of 0.1% Triton X-100 medium. TMThe cells were lysed, diluted, and cultured on Brucella agar. Colony forming units (CFU) were counted after incubation at 37°C in the presence of CO2 for 24, 48, and 72 hours.

[0118] Result: As Figure 8 As shown, the number of Brucella colonies in the untreated group was higher after 24 hours of culture. Doxycycline at 2 μg / mL had a good inhibitory effect on the number of Brucella colonies, while asarone at 20 μg / mL had a weaker inhibitory effect. However, the inhibitory effect of using doxycycline and asarone in combination was significantly better than that of using doxycycline and asarone alone.

[0119] Experiment 5: Anti-infective effects of asarazine and doxycycline in vivo

[0120] Methods: An infection model was established in mice by a single intraperitoneal injection of 1.7 × 10⁵ CFU / mouse of *Brucella abortus* 2308 bacterial suspension. Uninfected mice were injected with sterile 0.9% saline from day 1 to day 15, followed by gavage with 2% Tween 20 solution for 21 days after day 15. Untreated mice were gavage with 2% Tween 20 solution for 21 days after day 15 of infection. The asarone group was treated with 100 mg / kg asarone solution, and the doxycycline group was treated with 80 mg / kg doxycycline solution. In the combined group, mice were treated with both 100 mg / kg asarone solution and 80 mg / kg doxycycline solution, administered daily by gavage for 21 days. Mice were sacrificed after 38 days, and the kidneys, liver, lungs, and spleen were aseptically removed for HE staining to observe the *Brucella* infection status.

[0121] Result: As Figure 9 As shown, the tissue structures of the kidneys, livers, lungs, and spleens of mice in the uninfected group were clear; the tissues of mice in the infected group showed obvious damage and necrosis of varying degrees. The degree of tissue damage was reduced after the administration of asarizole and doxycycline, but the tissue structure of mice was significantly improved after the combined administration of asarizole and doxycycline.

[0122] Example 3: A drug is provided, obtained by the method described in Example 1 or Example 2, which contains asarazine and doxycycline and has an effective inhibitory effect on Brucella.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A medicament against Brucella, characterized in that, The components of the medicament include ginnalin and doxycycline.

2. The medicament according to claim 1, characterized in that, The ginnalin and doxycycline are packaged separately.

Images