Anti-osteoarthritis medicine virtual screening method and anti-osteoarthritis medicine

Abstract

The invention relates to an anti-osteoarthritis drug virtual screening method and an anti-osteoarthritis drug. Based on RNA-seq transcriptome sequencing of a new generation of high-throughput sequencing technology, a new target spot Foxd1 capable of curing osteoarthritis (OA) is found through bioinformatics and database big data, a novel targeted drug capable of effectively improving the OA condition is screened out for the Foxd1, and the treatment effect of the drug is verified.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer-aided drug design. Specifically, the present invention relates to a method for virtual screening of anti-osteoarthritis drugs and anti-osteoarthritis drugs. Background Art

[0002] Osteoarthritis (OA), also known as degenerative osteoarthrosis, degenerative arthritis, senile arthritis, hypertrophic arthritis, etc. As a degenerative disease of the entire joint, it is caused by many factors such as aging, obesity, strain, trauma, congenital joint abnormalities, and joint deformities, resulting in degradation and damage of articular cartilage, and reactive hyperplasia of the joint margin and subchondral bone. The clinical manifestations are slowly developing joint pain, tenderness, stiffness, joint swelling, limited mobility, and joint deformity, etc. It is the most common disease of the locomotor system and an important cause of joint disability in the elderly, bringing a heavy burden to the patient's family and social economy. According to statistics, more than 300 million people worldwide are affected by OA, and there are more than 300 million OA patients globally. The overall prevalence of primary OA in people over 40 years old in China has reached as high as 46.3%. Moreover, with the continuous aggravation of the aging degree of China's population, the prevalence of OA shows an increasing trend. Although OA is the most common joint disease, the current treatment methods are very limited and the treatment effects are not satisfactory, and there is no treatment method to inhibit or even reverse the condition of OA.

[0003] Currently, for the treatment of osteoarthritis, drug treatment is the mainstay in clinical practice, mainly relying on symptomatic pain relief and supportive treatment. The application of non-steroidal anti-inflammatory drugs is the most common treatment method in clinical practice. Commonly used drugs include the oral drug celecoxib [Puljak L, Marin A, Vrdoljak D, Markotic F, Utrobicic A, Tugwell P. Celecoxib for osteoarthritis. Cochrane Database Syst Rev. 2017 May 22;5(5):CD009865.doi:10.1002 / 14651858.CD009865.pub2.PMID:28530031;PMCID:PMC6481745.], and the topical and intra-articular injection of diclofenac [Hagen M, Baker M. Skin penetration and tissue permeation after topical administration of diclofenac. Curr Med Res Opin. 2017 Sep;33(9):1623-1634.doi:10.1080 / 03007995.2017.1352497.Epub 2017 Jul 18.PMID:28681621.; Nishida Y, Kano K, Nobuoka Y, Seo T. Efficacy and Safety of Diclofenac-Hyaluronate Conjugate (Diclofenac Etalhyaluronate) for Knee Osteoarthritis: A Randomized Phase III Trial in Japan. Arthritis Rheumatol. 2021 Sep;73(9):1646-1655.doi:10.1002 / art.41725.Epub 2021 Jul 27.PMID:33749997;PMCID:PMC8456865.], etc.Glucosamine is also a commonly used drug for the treatment of osteoarthritis [Fransen M, Agaliotis M, Nairn L, Votrubec M, Bridgett L, Su S, Jan S, March L, Edmonds J, Norton R, Woodward M, Day R; LEGS study collaborative group. Glucosamine and chondroitin for knee osteoarthritis: a double-blind randomised placebo-controlled clinical trial evaluating single and combination regimens. Ann Rheum Dis. 2015 May; 74(5):851-8. doi:10.1136 / annrheumdis-2013-203954. Epub 2014 Jan 6. PMID:24395557.]. Diacerein controls and improves the condition of osteoarthritis by interfering with interleukin-1 and has anti-inflammatory, anti-catabolic and anabolic properties on cartilage and synovium [Pavelka K, Bruyère O, Cooper C, Kanis JA, Leeb BF, Maheu E, Martel-Pelletier J, Monfort J, Pelletier JP, Rizzoli R, Reginster JY. Diacerein: Benefits, Risks and Place in the Management of Osteoarthritis. An Opinion-Based Report from the ESCEO. Drugs Aging. 2016 Feb; 33(2):75-85. doi:10.1007 / s40266-016-0347-4. Erratum in: Drugs Aging. 2017 May; 34(5):413. PMID:26849131; PMCID:PMC4756045.].Diacerein alleviates oxidative stress and osteoarthritis in a mouse model by inhibiting peroxisome proliferator-activated receptor-γ [Chen X, Zhu X, Dong J, Chen F, Gao Q, Zhang L, Cai D, Dong H, Ruan B, Wang Y, Jiang Q, Cao W. Reversal of Epigenetic Peroxisome Proliferator-Activated Receptor-γ Suppression by Diacerein Alleviates Oxidative Stress and Osteoarthritis in Mice. Antioxid Redox Signal. 2022 Jul;37(1-3):40-53. doi: 10.1089 / ars.2021.0219. PMID: 35196878.].

[0004] Long-term application of non-steroidal anti-inflammatory drugs has many side effects. The gastrointestinal tract is prone to adverse reactions such as nausea, vomiting, diarrhea, abdominal distension, and abdominal pain. In severe cases, there are mucosal ulcers such as peptic ulcer, bleeding, and perforation. In addition, it will also affect the kidneys, easily causing renal insufficiency, interstitial nephritis, or renal necrosis, which will lead to electrolyte disorders and water and sodium retention. Other adverse reactions include decreased peripheral blood white blood cells, coagulation disorders, aplastic anemia, liver function damage, and a few may have allergic reactions and tinnitus and hearing loss. In terms of the effectiveness of treating knee and hip OA, glucosamine is no better than placebo in relieving pain or function. Currently, there is no literature published on the effective evidence of glucosamine in treating OA pain, and it has no effect on controlling the development of OA. The drugs mentioned above can at most improve symptoms and control pain, but cannot promote the recovery of the lesion and control the development of the disease. In actual clinical applications, it has been found that the analgesic effect of diacerein is inferior to that of non-steroidal anti-inflammatory drugs, and the analgesia is not good. And currently, there is no substantial clinical evidence to show that diacerein can protect cartilage, improve the cartilage degeneration of OA patients clinically, and control the disease progression. Currently, most drugs on the market only treat the symptoms, and there is no drug that can protect cartilage and improve the condition of OA, and it is impossible to prevent or delay the progression of OA. The currently popular field of molecular targeted drugs is considered to have great promise to become a new breakthrough in the treatment of OA.

[0005] Exploring the pathophysiological changes induced by inflammation, finding the key targets of the inflammatory pathway, promoting anabolism, inhibiting catabolism, and promoting the generation of chondrocyte extracellular matrix will be a very promising method for the treatment of OA. Based on the current lack of drugs to improve the condition of OA, this invention precisely found the protective factor for OA - Foxd1 through RNA-seq transcriptome sequencing analysis. Through bioinformatics and big data analysis of databases, molecular targeted drugs for the target of Foxd1 were screened. On the basis of symptomatic treatment to improve the quality of life of patients, it improves the degenerative changes of OA cartilage, alleviates and even reverses the progression of OA, focusing on the current pain points of OA treatment. Summary of the Invention

[0006] In view of this, the present invention provides a virtual screening method for anti-osteoarthritis drugs, and also provides the drugs screened by the method and their applications in anti-osteoarthritis.

[0007] In the first aspect, the present invention provides a virtual screening method for anti-osteoarthritis drugs, using Foxd1 as the target for virtual screening of anti-osteoarthritis drugs.

[0008] Specifically, the virtual screening method includes:

[0009] S1: Construct the 3D structure of the P122 - P225 region of Foxd1, perform protonation treatment and structure optimization;

[0010] S2: Select the small molecule binding pocket on the surface of the model as the docking area for virtual screening;

[0011] S3: Select the small molecules to be screened from the compound library, perform 3D structure transformation and optimization, and establish a compound library of multi-conformations of the compounds to be screened;

[0012] S4: Dock the compounds in the compound library to be screened into the active pocket, perform docking scoring and ranking screening, and obtain candidate compounds with potential activity after layer-by-layer filtration screening.

[0013] In one embodiment, the amino acid sequence of the P122 - P225 region of Foxd1 is SEQ ID NO.1 (PLVKPPYSYIALITMAILOSPKKRLTLSEICEFISGRFPYYREKFPAWONSIRHNLSLNDCFVKIPREPGNPGKGNYWTLDRESADMFDNGSFLRRRKRFKRQP).

[0014] In one embodiment, the 3D structure of the P122 - P225 region of Foxd1 in step S1 is constructed by AlphaFold.

[0015] In one embodiment, in step S1, the QuickPrep plug-in in the MOE software is used to protonate the structure and optimize the structure.

[0016] In one embodiment, in step S2, the apopdb2receptor tool of Openeye is used to process and generate the receptor file for virtual screening. Preferably, the length, width, and height of its docking box are respectively Its volume is The volume of its inner contour is

[0017] In one embodiment, the compound library in step S3 can be any compound library, such as the targetmol compound library, the ZINC compound library, the drug compound library already approved and marketed by the FDA, and the Maybridge compound library.

[0018] In one embodiment, step S3 includes performing molecular cleaning on the compound library to remove salt ions, metal ions, and small fragment compounds, and obtaining the 3D structure of the compound through energy minimization.

[0019] In one embodiment, step S3 further includes hydrogenation and charging of the 3D structure of the compound obtained through energy minimization; preferably, the AMBER10-EHT force field is used in this process.

[0020] In one embodiment, step S3 further includes using the omega2 plug-in in the Openeye software to generate multiple conformations of small molecule compounds.

[0021] In one embodiment, step S4 includes using the virtual screening software FRED to perform batch molecular docking for each conformation of the established docking region, and obtaining the affinity scoring value and interaction mode of each compound with the FoxD1 protein. Preferably, the running parameters such as -save_component_scores are selected as true, -hitlist_size is selected as 30,000, and -docked_molecule_file is selected to be retained in the sdf format, and the remaining parameters are all default parameters.

[0022] In one embodiment, step S4 further includes calculating and analyzing the properties of small molecule compounds through Stardrop software, such as molecular weight, water solubility (logS), lipophilicity-hydrophilicity distribution coefficient (logP), surface accessible area (TPSA), oral bioavailability (HIA), cardiac toxicity (hERG) inhibition rate index, CYP2C9 enzyme degradation level, drug interaction risk (2D6), blood-brain barrier permeability (BBB), hydrogen bond property, molecular flexibility and other indexes. Evaluation, scoring and screening are carried out by using the screening criteria (DrugScore) for oral non-central nervous system drugs and the druglikeness five-rule score value (LipiskinScore).

[0023] In one embodiment, in the first round of screening, active compounds and natural product compounds with an affinity less than -22 kcal / mol and fragment compounds with an affinity less than -20 kcal / mol are selected.

[0024] In one embodiment, in the second round of screening, compounds with an affinity less than -25 kcal / mol, a DrugScore greater than 0.2 and a LipinskiScore greater than 0.6 are selected.

[0025] In one embodiment, in the third round of screening, compounds with an affinity not greater than -26 kcal / mol, a molecular weight not greater than 500 Da, a water solubility logS greater than 0, a lipophilicity logP not greater than 5, a surface accessible area TPSA not greater than 100, an oral bioavailability HIA not negative, an hERG not greater than 7, a 2C9 not greater than 6, a 2D6 not very high, a BBB not greater than 0, the number of hydrogen bond donors not exceeding 5, and the number of rotatable bonds not greater than 10 are selected.

[0026] In one embodiment, in the fourth round of screening, compounds that can form strong interactions with FoxD1 are selected. Preferably, the interactions are analyzed by using the protein ligand interface fingerprint (PLIF) method. Having more than 2 interactions between the compound to be screened and FoxD1 is considered a strong interaction.

[0027] In a second aspect, the present invention provides a compound for treating osteoarthritis, which comprises at least one of the candidate compounds screened by the method of the present invention.

[0028] The present invention also provides the use of the candidate compounds screened by the method of the present invention in the preparation of drugs for treating osteoarthritis.

[0029] Preferably, the candidate compound is selected from TD13757.

[0030] The CAS registry number of TD13757 is 1164503-47-2, and its English name is (Z)-5-(1-acetyl-2-oxoindolin-3-ylidene)-3-(1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)-2-thioxothiazolidin-4-one, and its Chinese name is (Z)-5-(1-acetyl-2-oxoindolin-3-ylidene)-3-(1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)-2-thioxothiazolidin-4-one, and its structural formula is:

[0031]

[0032] In one embodiment, the candidate compound further comprises a pharmaceutically acceptable salt thereof.

[0033] In one embodiment, the osteoarthritis is pro-inflammatory cytokine-induced osteoarthritis. Preferably, the pro-inflammatory cytokines include TNF-α, IL-1β, IL-6, IL-17, IL-18, IL-8.

[0034] In one embodiment, the osteoarthritis is anterior cruciate ligament transection-induced osteoarthritis.

[0035] Beneficial effects

[0036] Based on RNA-seq transcriptome sequencing of the new generation of high-throughput sequencing technology, through bioinformatics and database big data, the present invention found a new target - Foxd1 that can cure OA, and improved the condition of OA by screening a new type of targeted drug against Foxd1. The present invention also verified the in vitro and in vivo anti-OA effects of the screened compound TD13757, and confirmed the feasibility of the Foxd1 target and screening method of the present invention in the treatment of OA. Aiming at the pain points of curing OA in the current medical field, by exploring the pathophysiological changes induced by inflammation, finding the key targets of the inflammatory pathway, promoting the anabolic metabolism of the chondrocyte extracellular matrix, inhibiting its catabolic metabolism, thereby promoting the generation of the chondrocyte extracellular matrix and improving the condition of OA patients. The present invention provides a molecular targeted drug for gene expression and is expected to become an effective means for treating OA. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 : Amino acid sequence and 3D structural model of the DNA-binding region of FoxD1.

[0038] Figure 2: Small molecule binding pocket on FoxD1 surface and pocket properties.

[0039] Figure 3 : Distribution map of affinity scoring values of screened compounds with FoxD1.

[0040] Figure 4 : Compound sources and affinity scoring values. Select active compounds and natural product compounds with an affinity less than -22 kcal / mol and fragment compounds with an affinity less than -20 kcal / mol, a total of 7,053 compounds (marked in red).

[0041] Figure 5 : Scoring functions of Drug Score and Lipinski Score.

[0042] Figure 6 : Distribution of DrugScore, LipinskiScore, and DockingScore values and structural diversity grouping of 7,053 screened compounds. The 991 compounds that meet the screening criteria are marked in red.

[0043] Figure 7 : Analysis of the druggability properties of 991 compounds.

[0044] Figure 8 : Interaction fingerprint map (PLIF) of 184 compounds with the target. 16 interacting amino acids and 44 interacting fingerprint maps, among which strong interactions are formed with 11 amino acids.

[0045] Figure 9 : Interaction fingerprint map (PLIF) of 105 compounds with the target forming strong interactions.

[0046] Figure 10 : Interaction mode of compound TD13757 with FoxD1. A, Complex structure of FoxD1 protein / TD13757, where the compound is shown in green and the FoxD1 protein is shown as a molecular surface area map. B, Interaction mode diagram of the compound with important amino acids, where amino acids S129, R216, and R217 form hydrogen bond interactions and Π hydrogen bond interactions with the compound (for easy display, some interacting amino acids are hidden). C, 2D mode diagram of the complex interaction and amino acids around the compound. D, Structural formula of compound TD13757 and its affinity scoring value (-29.14 kcal / mol).

[0047] Figure 11 : TD13757 increases the content of proteoglycan in OA chondrocytes.

[0048] Figure 12: TD13757 significantly increased the mRNA levels of cartilage-related marker factors in rat OA chondrocytes; ** indicates.

[0049] Figure 13 : TD1375 significantly increased the content of the chondrocyte extracellular matrix.

[0050] Figure 14 : TD1375 alleviated the OA condition in rats.

[0051] Figure 15 : TD13757 reduced the OARSI score in OA rats; *** indicates P < 0.001.

[0052] Figure 16 : Micro-CT images of OA rats and the treatment situation. Detailed implementation manners

[0053] The present invention is described in more detail below to facilitate the understanding of the present invention.

[0054] It should be understood that the terms or words used in the specification and claims should not be construed as having the meanings defined in the dictionary, but should be understood as having meanings consistent with their meanings in the context of the present invention based on the following principles: The concept of the terms can be appropriately defined by the inventor for the best description of the present invention. Preferred methods and materials are described below, but methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention. The materials, methods, and examples disclosed herein are illustrative only and are not intended to be limiting.

[0055] Example 1: Virtual screening method for anti-osteoarthritis drugs

[0056] 1.1 The DNA-binding region of FoxD1 is V124 - L215. Although FoxD1 has no crystal structure, the AlphaFold model can well predict its P122 - P225 region. The 3D structure of this region is a compact structure composed of three α-helices and two folds, which contains the DNA-binding region ( Figure 1 ).

[0057] Select the 3D structure (AF model) of the P122 - P225 region constructed by AlphaFold. Use the QuickPrep plugin in the MOE software to protonate the structure and optimize it, and save it as an independent file for subsequent analysis. (Detect the possible small molecule compound binding pockets (pockets) on the surface of these models through the SiteFinder plugin in MOE. Analysis reveals that there is a small molecule binding pocket on the surface of this model, and its position and properties (size, number of hydrophobic atoms, pocket quality score value, amino acids included, etc.) are as Figure 2 shown. The Pocket1 region of this model is used as the molecular screening site for subsequent steps.

[0058] 1.2 Virtual screening

[0059] 1.2.1 Receptor file processing: Based on the structural model of the P122 - P225 region, a receptor file was generated after protonation and structure optimization. Use MOE - site finder to determine a small molecule binding pocket (Pocket1) on this model and select it as the docking region for this virtual screening. The apopdb2receptor tool of Openeye (Release 3.2.0.2) (https: / / www.eyesopen.com / ) is used to process and generate the receptor file for virtual screening. The length, width, and height of its docking box are Its volume is The volume of its inner contour is

[0060] 2.2.2 Preparation of the small molecule compound library: The compound library for this screening is the targetmol compound library (T001). This compound library contains 20054 compounds. First, perform molecular cleaning on the compound library to remove impurities such as salt ions, metal ions, and small fragments. Then, obtain the 3D structure of the compounds through energy minimization, add hydrogen and charges. The AMBER10 - EHT force field is used in this process. To ensure the global conformations of small molecule compounds during virtual screening, use the omega2 (Ver 3.0.1.2) plugin in the openeye software to generate multiple conformations for small molecule compounds. Among them, 449 compounds cannot generate multiple conformations. Finally, on average, 34 conformations are generated for each compound and saved as a multiple conformation file. This file contains the 3D structures of each conformation, compound information, compound ID numbers, and compound sources.

[0061] 2.2.3 Virtual screening: Use the virtual screening software FRED (Ver 3.2.0.2) to perform batch molecular docking for each conformation on the established target screening region, where the running parameters such as -save_component_scores are selected as true, -hitlist_size is selected as 30,000 (that is, the maximum number of compounds finally retained does not exceed 30,000), -docked_molecule_file is selected to be retained in sdf format, and the remaining parameters use the default parameters. Finally, we obtain the affinity scoring values and interaction modes of each compound with the FoxD1 protein and save them in a file. This file contains the docking modes and affinity scoring values of the compounds.

[0062] 2.3 Analysis of screening results

[0063] 2.3.1 Screening of druggability properties. By analyzing the affinity scoring values of molecular docking, it is found that the affinity scoring values of these compounds with FoxD1 are normally distributed, and the affinities of most compounds are between -30 and -10 kcal / mol, and the median value is -22 kcal / mol ( Figure 3 ). These compounds come from the active compound library, natural product library and fragment library, and their affinity distributions are as Figure 4 shown. Based on this, 7,053 compounds with an affinity less than -22 kcal / mol for active compounds and natural product compounds and an affinity less than -20 kcal / mol for fragment compounds are selected for subsequent property and binding mode analysis.

[0064] First, use the Stardrop software (Version 6.5.0) to calculate and analyze the properties of small molecule compounds, such as water solubility (logS), octanol-water partition coefficient (logP), molecular weight, molecular flexibility, hydrogen bond properties, total polar surface area (TPSA), CYP2C9 enzyme degradation level, hERG inhibition rate index, blood-brain barrier permeability (BBB), human intestinal absorption (HIA), drug interaction risk (2D6), etc. Further, use the screening criteria for oral non-central nervous system drugs (DrugScore) and the druglikeness five-rule scoring value (LipiskinScore) for evaluation, scoring and screening ( Figure 5 ). These two scoring functions are the mathematical statistical results of the above-described properties, and their scoring values range from 0 to 1, and the higher the value, the better the druggability properties of the compound.

[0065] We hope that the compounds to be screened are structurally diverse, so that it is more likely to obtain more potential hit compounds with completely new skeletons. Based on the common structural similarity analysis, 7,053 compounds were clustered. By continuously adjusting the similarity threshold, these compounds could finally be divided into 344 groups at a similarity threshold of 0.6. Based on three properties, namely DrugScore, LipinskiScore, and DockingScore ( Figure 6 ), compounds with an affinity less than -25 kcal / mol in each of the 344 groups were selected. At the same time, the DrugScore of these compounds was greater than 0.2 and the LipinskiScore was greater than 0.6. Finally, 991 compounds were retained.

[0066] Furthermore, the specific drug-like indicators of these 991 compounds were analyzed, such as polar surface area (TPSA), molecular weight (MW), water solubility (logS), lipophilicity (logP), cardiac toxicity (hERG), drug metabolism risk (2C9, 2D6), oral bioavailability (HIA), blood-brain barrier permeability (BBB), number of hydrogen bond donors, number of rotatable bonds, and affinity value, etc. ( Figure 7 ). Among these compounds, there were 22 compounds with a molecular weight greater than 500 Da, 139 compounds with a water solubility logS less than 0, 53 compounds with a lipophilicity logP greater than 5, 188 compounds with a TPSA greater than 100, 0 compounds with poor absorption (HIA(-)), 19 compounds with cardiac toxicity (HERG>7), 258 compounds with metabolism risk (2C9>6) and 2D6 (very high), 68 compounds that may penetrate the blood-brain barrier (BBB>0), 13 compounds with the number of hydrogen bond donors exceeding 5, 13 compounds with the number of rotatable bonds greater than 10, and 498 compounds with an affinity greater than -26 kcal / mol. After removing these less ideal compounds, 198 compounds were finally obtained.

[0067] 2.3.2 Interaction mode analysis: To analyze the interaction mode between small molecule compounds and the target, the protein ligand interface fingerprint (PLIF) method was used to analyze the interaction sites and force types between 198 candidate compounds and FoxD1. PLIF can describe the interactions between the target and the compound, such as hydrogen bonds, ionic bonds, and surface contact energy, in the form of a fingerprint map, thereby characterizing the interaction mode in the entire compound library. Based on this, we can also rule out some low-probability interactions.

[0068] Among the 198 compounds, 184 compounds have interactions with the target such as hydrogen bonds and ionic bonds and can be characterized by fingerprint spectra. The PLIF results show ( Figure 8 ), 16 amino acids in FoxD1 are involved in the interaction with the compounds and form 44 interactive fingerprint spectra. Among them, the high-frequency interactive amino acid sites are S129 and R217, and the two together participate in the interaction of 67 compounds. Among these interactions, 11 amino acids can form strong interactions with the compounds, contributing 15 interactive fingerprint spectra ( Figure 9 ). Amino acids S129 and R217 are still the hot amino acids in the strong interactions, and 105 compounds can form strong interactions with FoxD1, which is the final result of this screening. This result includes information such as the 3D pharmacophore conformation information of small molecules, compound ID, affinity score value, druggability score value, druggability attribute value, and physical and chemical attributes of the compounds. By comparing this result with the 3D structure of the FoxD1 DNA structural region based on the AF algorithm, the interaction information of the target-small molecule docking can be viewed.

[0069] Compound TD13757 was selected, and its affinity score value with FoxD1 is -29.14 kcal / mol. It binds to the pocket1 region. TD13757 forms hydrogen interactions and Π-hydrogen bond interactions with amino acids S129, R216, and R217, and there are mainly positively charged / polar amino acids around it ( Figure 10 ).

[0070] Example 2: Anti-osteoarthritis effect of the compound

[0071] Through various biological experiments, it has been observed that compound TD13757 has a significant curative effect on improving the condition of osteoarthritis and controlling the progression of osteoarthritis.

[0072] 1. Verification by in vitro experiments

[0073] The small molecule compound TD13757 with a final concentration of 1 μM was co-cultured with TNF-α-induced rat OA chondrocytes for 5 days, and Alcian blue staining was performed. It was observed that the small molecule compound TD13757 significantly increased the content of proteoglycan ( Figure 11 ). The above-mentioned rat OA cells co-cultured with TD13757 were subjected to RT-PCR analysis, and it was found that the mRNA levels of cartilage-related marker factors Col2a1, Acan, and Sox9 in rat OA chondrocytes were significantly increased ( Figure 12 , n = 3 for each group).

[0074] 2. Verification of the in vivo animal model (TNF-α-induced OA model mice)

[0075] TNF-α was injected into the knee joints of normal rats to make them OA model rats. 200 μL of TD13757 with different concentrations (100 nM, 1 μM, 10 μM) was injected into the knee joints of the model rats. After 4 weeks, the samples were taken, fixed, decalcified, histologically stained, and histologically analyzed under a microscope, and compared with the normal group and the negative control group. The experiment found that the content of the extracellular matrix of chondrocytes in the experimental group was significantly increased compared with the negative control group, indicating an increase in the synthesis of the extracellular matrix of chondrocytes and a significant reduction in the degree of cartilage damage. The OA condition of the rats was alleviated and recovered. Among them, 1 μM of TD1375 significantly increased the content of the extracellular matrix of chondrocytes, which was closest to the content of the extracellular matrix of chondrocytes in the normal group mice ( Figure 13 ).

[0076] 3. Verification of the in-vivo animal model (OA model rats induced by anterior cruciate ligament transection)

[0077] Anterior cruciate ligament transection was performed on normal rats to make them OA model rats. 200 μL of TD13757 with a concentration of 1 μM was injected into the knee joints of the ACLT model rats. After 4 weeks, the samples were taken, fixed, decalcified, histologically stained, and histologically analyzed under a microscope, and compared with the normal group and the negative control group. The experiment found that the content of the extracellular matrix of chondrocytes in the experimental group was significantly increased compared with the negative control group, indicating an increase in the synthesis of the extracellular matrix of chondrocytes and a significant reduction in the degree of cartilage damage. The OA condition of the rats was alleviated and recovered ( Figure 14 ). Quantitative analysis - OARSI score was performed on the results of the above histological staining. It was found that after intra-articular injection of TD13757 to the OA model rats of ACLT, their OARSI scores were significantly decreased, and the OA condition of the rats was alleviated ( Figure 15 ). The Micro-CT imaging evidence for this in-vivo experiment also showed the same result ( Figure 16 ).

[0078] After the in-vivo experiment was completed, HE staining was performed on the liver, heart, spleen, and kidneys of the rats treated with TD13757 by knee joint injection. It was found that the tissue and cell morphological structures of the above organs were all normal, and no toxicity of TD13757 was observed, preliminarily verifying the biosafety of TD13757.

[0079] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the method of the present invention, several improvements and supplements can be made, and these improvements and supplements should also be regarded as the protection scope of the present invention.

Claims

1. A virtual screening method for anti-osteoarthritis drugs, the virtual screening method comprising: S1: Construction of the 3D structure of the P122-P225 region of Foxd1, protonation treatment and structural optimization; S2: Select the small molecule binding pockets contained in the model surface as the docking area for virtual screening; S3: Select small molecules to be screened from the compound library, perform 3D structure transformation and optimization, and establish a multi-conformation compound library to be screened; S4: Dock the compounds in the library of compounds to be screened to the active pockets, perform docking scoring and sorting screening, and obtain candidate compounds with potential activity after layers of filtering and screening.

2. The screening method according to claim 1, characterized in that The amino acid sequence of the P122-P225 region of the Foxd1 is SEQ ID NO.

1.

3. The screening method according to claim 1, characterized in that In step S1, the 3D structure of the P122-P225 region of Foxd1 is constructed by AlphaFold; in step S1, the QuickPrep plug-in in the MOE software is used to protonate and optimize the structure.

4. The screening method according to claim 1, characterized in that In step S2, the apopdb2receptor tool of Openeye is used to process and generate a receptor file for virtual screening.

5. The screening method according to claim 1, characterized in that Step S3 includes performing molecular cleaning on the compound library to remove salt ions, metal ions, and small fragment compounds, and obtaining the 3D structure of the compound through energy minimization processing; Step S3 also includes hydrogenating and charging the 3D structure of the compound obtained through energy minimization processing; Step S3 also includes using the plug-in omega2 in the Openeye software to generate multiple conformations of small molecule compounds.

6. The screening method according to claim 1, characterized in that Step S4 includes using the virtual screening software FRED to perform batch molecular docking of each conformation on the established docking region to obtain the affinity score and interaction mode of each compound with the FoxD1 protein.

7. The screening method according to claim 1, characterized in that Step S4 also includes calculating and analyzing the properties of small molecule compounds by Stardrop software, and evaluating, scoring and screening by using the screening standard DrugScore for oral non-central nervous system drugs and the five-principle scoring value LipiskinScore for drug-like compounds.

8. The screening method according to claim 7, characterized in that The first round of screening selected active compounds and natural product compounds with an affinity less than -22 kcal / mol and fragment compounds with an affinity less than -20 kcal / mol; the second round of screening selected compounds with an affinity less than -25 kcal / mol, and a DrugScore greater than 0.2 and a LipinskiScore greater than 0.6; the third round of screening selected compounds with an affinity not greater than -26 kcal / mol, a molecular weight not greater than 500 Da1, a water solubility logS greater than 0, a lipid solubility logP not greater than 5, a surface accessible area TPSA not greater than 100, an oral availability HIA not negative, hERG not greater than 7, 2C9 not greater than 6, 2D6 not very high, BBB not greater than 0, the number of hydrogen bond donors not greater than 5, and the number of rotatable bonds not greater than 10; the fourth round of screening selected compounds that can form a strong interaction with FoxD1.

9. Use of the candidate compound screened by the method according to any one of claims 1 to 8 in the preparation of an anti-osteoarthritis drug.

10. The use according to claim 9, characterized in that: The candidate compound is selected from TD13757, whose structural formula is:

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