Virtual screening method for anti-osteoarthritis drugs and anti-osteoarthritis drugs

Abstract

The present invention relates to a virtual screening method for anti-osteoarthritis drugs and anti-osteoarthritis drugs. Based on RNA-seq transcriptome sequencing using next-generation high-throughput sequencing technology, the method uses bioinformatics and database big data to identify a new target for curing osteoarthritis (OA)—Foxd1. The method then screens for novel targeted drugs that can effectively improve OA symptoms and verifies the therapeutic efficacy of the drugs.

Description

Technical Field

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

[0002] Osteoarthritis (OA), also known as degenerative osteoarthritis, degenerative arthritis, senile arthritis, and hypertrophic arthritis, is a degenerative disease affecting all joints. It is caused by factors such as aging, obesity, strain, trauma, congenital joint abnormalities, and joint deformities, leading to degenerative damage to articular cartilage and reactive proliferation of the joint margins and subchondral bone. Clinical manifestations include slowly progressive joint pain, tenderness, stiffness, joint swelling, limited mobility, and joint deformity. It is the most common disease of the musculoskeletal system and a major cause of joint disability in the elderly, placing a heavy burden on patients' families and the socioeconomic status of the population. According to statistics, OA affects over 300 million people worldwide. In my country, the overall prevalence of primary OA in people over 40 years old is as high as 46.3%. Furthermore, with the increasing aging of my country's population, the prevalence of OA is on the rise. Despite being the most common joint disease, current treatment options are very limited and ineffective. No treatments have yet been developed that can inhibit or even reverse OA.

[0003] Currently, the treatment of osteoarthritis is mainly based on drug therapy, which mainly relies on symptomatic analgesic supportive treatment. The use of nonsteroidal anti-inflammatory drugs is the main treatment method in clinical practice. Commonly used drugs include oral 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.], 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.2017Sep;33(9):1623-1634.doi:10.1080 / 03007995.2017.1352497.Epub 2017Jul 18.PMID:28681621.;NishidaY,Kano K,Nobuoka Y,Seo T.Efficacy and Safety of Diclofenac-HyaluronateConjugate(Diclofenac Etalhyaluronate) for Knee Osteoarthritis:A RandomizedPhase III Trial in Japan.Arthritis Rheumatol.2021Sep;73(9):1646-1655.doi:10.1002 / art.41725.Epub 2021Jul 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 randomized 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. Erratumin: Drugs Aging.2017May;34(5):413.PMID:26849131; PMCID:PMC4756045.].Diacerein alleviates oxidative stress and osteoarthritis in mouse models 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 use of nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with numerous side effects. Gastrointestinal adverse reactions include nausea, vomiting, diarrhea, abdominal distension, and pain. In severe cases, these drugs can cause mucosal ulcers, including peptic ulcers, bleeding, and perforation. Furthermore, they can affect the kidneys, potentially leading to renal insufficiency, interstitial nephritis, or renal necrosis, which can lead to electrolyte imbalances and water and sodium retention. Other adverse reactions include peripheral leukopenia, coagulopathy, aplastic anemia, liver damage, and, in rare cases, allergic reactions, tinnitus, and hearing loss. In terms of effectiveness in treating knee and hip OA, glucosamine is no more effective than placebo in relieving pain or function. Currently, there is no published evidence demonstrating its effectiveness in treating OA pain, and it is also ineffective in controlling OA disease progression. The aforementioned drugs can at best improve symptoms and control pain, but they cannot promote lesion recovery or control disease progression. In actual clinical practice, diacerein has been found to be less effective than NSAIDs in providing adequate analgesia. Currently, there is no substantial clinical evidence demonstrating that diacerein can protect cartilage, improve cartilage degeneration in OA patients, or control disease progression. Currently, most drugs on the market only treat symptoms, and no medications are capable of protecting cartilage, improving OA symptoms, or preventing or slowing OA progression. The currently popular field of molecularly targeted drugs is believed to hold great promise as a new breakthrough in OA treatment.

[0005] Exploring the pathophysiological changes induced by inflammation, identifying key targets in the inflammatory pathway, promoting anabolism, inhibiting catabolism, and promoting the production of cartilage extracellular matrix will be a very promising method for the treatment of OA. This invention is based on the current pain point of the lack of drugs to improve OA. Based on RNA-seq transcriptome sequencing analysis, it accurately found the protective factor for OA - Foxd1. Through bioinformatics and database big data analysis, it screened molecular targeted drugs targeting the Foxd1 target. On the basis of achieving symptomatic treatment to improve the quality of life of patients, it improves the degenerative changes of OA cartilage, alleviates or 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. The present invention also provides drugs screened by the method and their use in anti-osteoarthritis.

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

[0008] Specifically, the virtual screening method includes:

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

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

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

[0012] 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 layer-by-layer filtering and screening.

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

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

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

[0016] In one embodiment, in step S2, the apopdb2receptor tool of Openeye is used to process and generate a receptor file for virtual screening. Preferably, the length, width and height of the docking box are Its volume is Its inner contour volume 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 FDA-approved drug compound library, 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 comprises hydrogenating and charging the 3D structure of the compound obtained by energy minimization; preferably, the AMBER10-EHT force field is used in this process.

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

[0021] In one embodiment, step S4 includes using the virtual screening software FRED to perform batch molecular docking for each conformation in the established docking region, obtaining affinity scores and interaction patterns for each compound with the FoxD1 protein. Preferably, the run parameters, such as -save_component_scores, are set to true, -hitlist_size is set to 30,000, and -docked_molecule_file is set to retain the sdf format. All other parameters are set to default.

[0022] In one embodiment, step S4 further comprises calculating and analyzing the properties of the small molecule compound using Stardrop software, such as molecular weight, water solubility (logS), lipid-water distribution coefficient (logP), surface accessible area (TPSA), oral availability (HIA), cardiotoxicity (hERG) inhibition rate index, CYP2C9 enzyme degradation level, drug interaction risk (2D6), blood-brain barrier permeability (BBB), hydrogen bonding properties, molecular flexibility, etc. Evaluation, scoring, and screening are performed using the screening criteria for oral non-central nervous system drugs (DrugScore) and the five drug-like principle scoring value (LipiskinScore).

[0023] In one embodiment, the first round of screening selects 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.

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

[0025] In one embodiment, the third round of screening selects compounds with an affinity no greater than -26 kcal / mol, a molecular weight no greater than 500 Da1, a water solubility logS greater than 0, a lipid solubility logP no greater than 5, a surface accessible area TPSA no greater than 100, an oral availability HIA not negative, hERG no greater than 7, 2C9 no greater than 6, 2D6 not very high, BBB no greater than 0, the number of hydrogen bond donors no more than 5, and the number of rotatable bonds no greater than 10.

[0026] In one embodiment, the fourth round of screening selects compounds that form strong interactions with FoxD1. Preferably, the interactions are analyzed using the protein ligand interface fingerprint (PLIF) method. Compounds that form two or more interactions with FoxD1 are considered to have strong interactions.

[0027] In a second aspect, the present invention provides an anti-osteoarthritis compound comprising 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 compound screened by the method of the present invention in the preparation of anti-osteoarthritis drugs.

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

[0030] The Cas registration number of TD13757 is 1164503-47-2, the 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, the Chinese name is (Z)-5-(1-acetyl-2-oxoindolin-3-ylidene)-3-(1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrrol-4-yl)-2-thioxothiazolidin-4-one, and the 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 osteoarthritis induced by proinflammatory cytokines. Preferably, the proinflammatory cytokines include TNF-α, IL-1β, IL-6, IL-17, IL-18, and IL-8.

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

[0035] Beneficial effects

[0036] The present invention is based on RNA-seq transcriptome sequencing using a new generation of high-throughput sequencing technology. Through bioinformatics and database big data, a new target that can cure OA, Foxd1, is found, and the OA condition is improved by screening out a new targeted drug targeting Foxd1. The present invention also verifies the in vitro and in vivo anti-OA effects of the screened compound TD13757, confirming the feasibility of the Foxd1 target and screening method of the present invention in the treatment of OA. The present invention targets the pain points of curing OA in the current medical field, explores the pathophysiological changes induced by inflammation, searches for key targets of the inflammatory pathway, promotes the anabolism of the extracellular matrix of cartilage cells, inhibits its catabolism, thereby promoting the production of the extracellular matrix of cartilage cells, and improving the condition of OA patients. The present invention provides a molecular targeted drug for gene expression, which is expected to become an effective means of treating OA. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0039] Figure 3 : Distribution of affinity scores of screening compounds to FoxD1.

[0040] Figure 4 Compound origin and affinity score. Active compounds and natural product compounds with an affinity less than -22 kcal / mol, as well as fragment compounds with an affinity less than -20 kcal / mol, were selected, totaling 7,053 compounds (marked in red).

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

[0042] Figure 6 DrugScore, LipinskiScore, and DockingScore distributions of 7053 screened compounds, along with their structural diversity groups. Compounds marked in red represent 991 compounds that met the screening criteria.

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

[0044] Figure 8 : PLIFs of 184 compounds with their targets. 16 interacting amino acids and 44 interacting fingerprints, of which 11 formed strong interactions with amino acids.

[0045] Figure 9 :105 compounds formed strong interaction fingerprints (PLIF) with targets.

[0046] Figure 10 : Interaction pattern of compound TD13757 and FoxD1. A, Structure of the FoxD1 protein / TD13757 complex, where the compound is shown in green and the FoxD1 protein is shown as a molecular surface area diagram. B, Interaction pattern diagram of the compound and key amino acids, where amino acids S129, R216, and R217 form hydrogen bond interactions and π hydrogen bond interactions with the compound (for ease of display, some interacting amino acids are hidden). C, 2D pattern diagram of the complex interaction and surrounding amino acids. D, Structural formula and affinity score of compound TD13757 (-29.14 kcal / mol).

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

[0048] Figure 12:TD13757 induced a significant increase in the mRNA levels of cartilage-related markers in rat OA chondrocytes; ** indicates.

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

[0050] Figure 14 :TD1375 alleviates OA symptoms in rats.

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

[0052] Figure 16 : Micro-CT images of OA rats and treatment conditions. DETAILED DESCRIPTION

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

[0054] It should be understood that the terms or words used in the specification and claims should not be understood to have the meaning defined in the dictionary, but should be understood to have the meaning consistent with its meaning in the context of the present invention on the basis of the following principles: the concept of the term 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 the methods and materials 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 there is no crystal structure of FoxD1, the AlphaFold model can well predict its P122-P225 region. The 3D structure of this region has a compact structure composed of three α helices and two folds, which contains the DNA binding region ( Figure 1 ).

[0057] The 3D structure (AF model) of the P122-P225 region constructed by AlphaFold was selected. The structure was protonated and optimized using the QuickPrep plug-in in the MOE software and saved as a separate file for subsequent analysis (the SiteFinder plug-in in MOE was used to detect the possible small molecule binding pockets on the surface of these models. The analysis found that the model surface contains a small molecule binding pocket, and its location and properties (size, number of hydrophobic atoms, pocket quality score, amino acid content, etc.) are as follows Figure 2 The Pocket1 region of this model was used for subsequent molecular screening sites.

[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 treatment and structural optimization. A small molecule binding pocket (Pocket 1) on the model was identified using MOE-site finder and selected as the docking area for this virtual screening. The apopdb2receptor tool of Openeye (Release 3.2.0.2) (https: / / www.eyesopen.com / ) was used to process and generate the receptor file for virtual screening. The length, width, and height of the docking box were Its volume is Its inner contour volume is

[0060] 2.2.2 Preparation of small molecule compound library: The compound library for this screening is the targetmol compound library (T001). This compound library contains 20,054 compounds. First, the compound library is molecularly cleaned to remove impurities such as salt ions, metal ions, and small fragments. Then, the 3D structure of the compound is obtained through energy minimization, and hydrogenation and charge addition are performed. The AMBER10-EHT force field is used in this process. In order to ensure the global conformation of small molecule compounds during virtual screening, the plug-in omega2 (Ver 3.0.1.2) in the openeye software is used to generate multiple conformations of small molecule compounds. Among them, 449 compounds could not generate multiple conformations. In the end, an average of 34 conformations were generated for each compound and saved as a multi-conformation file. The file contains the 3D structure, compound information, compound ID number and compound source of each conformation.

[0061] 2.2.3 Virtual Screening: We used the virtual screening software FRED (Version 3.2.0.2) to perform batch molecular docking for each conformation within the defined target screening region. We set the following parameters for the docking parameter: -save_component_scores to true, -hitlist_size to 30,000 (i.e., a maximum of 30,000 compounds were retained), and -docked_molecule_file to sdf format. All other parameters remained default. Finally, we obtained the affinity score and interaction pattern for each compound with FoxD1 and saved them in a file. This file contains the compound's docking pattern and affinity score.

[0062] 2.3 Analysis of screening results

[0063] 2.3.1 Druggability screening. By analyzing the affinity scores of molecular docking, it was found that the affinity scores of these compounds with FoxD1 were normally distributed, with the affinity of most compounds ranging from -30 to -10 kcal / mol, and the median value at -22 kcal / mol ( Figure 3 ). These compounds come from active compound libraries, natural product libraries and fragment libraries, and their affinity distribution is as follows Figure 4 As shown, on this basis, 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 were selected, totaling 7053 compounds for subsequent property and binding mode analysis.

[0064] First, the properties of small molecule compounds were calculated and analyzed using Stardrop software (Version 6.5.0), such as water solubility (logS), lipid-water distribution coefficient (logP), molecular weight, molecular flexibility, hydrogen bonding properties, surface accessible area (TPSA), CYP2C9 enzyme degradation level, hERG inhibition rate index, blood-brain barrier permeability (BBB), oral availability (HIA), drug interaction risk (2D6), etc. Further, the screening criteria for oral non-central nervous system drugs (DrugScore) and the five-principle scoring value of drug-like drugs (LipiskinScore) were used for evaluation, scoring and screening ( Figure 5 ). These two scoring functions are mathematical statistical results of the properties described above, and the score values ​​range from 0 to 1. The higher the value, the better the drugability of the compound.

[0065] We hope that the compounds screened have structural diversity, which may make it more likely to obtain more potential hit compounds with new skeletons. Based on the common structural similarity analysis, 7053 compounds were clustered. By continuously adjusting their similarity thresholds, these compounds were finally divided into 344 groups at a similarity threshold of 0.6. Based on the three attributes of DrugScore, LipinskiScore and DockingScore ( Figure 6 ), we selected 344 compounds with an affinity of less than -25 kcal / mol in each group, and at the same time, the DrugScore of these compounds was greater than 0.2 and the LipinskiScore was greater than 0.6, and finally 991 compounds were retained.

[0066] Furthermore, the specific drug-like properties of these 991 compounds were analyzed, such as polar surface area (TPSA), molecular weight (MW), water solubility (logS), lipid solubility (logP), cardiotoxicity (hERG), drug metabolism risk (2C9, 2D6), oral availability (HIA), blood-brain barrier permeability (BBB), number of hydrogen bond donors, number of rotatable bonds, and affinity value. Figure 7 Among these compounds, 22 had a molecular weight greater than 500 Da1, 139 had a water-soluble logS less than 0, 53 had a lipid-soluble logP greater than 5, 188 had a TPSA greater than 100, 0 had poorly absorbed compounds (HIA(-)), 19 had cardiotoxicity (HERG>7), 258 had metabolic risks (2C9>6) and 2D6 (very high), 68 had the potential to penetrate the blood-brain barrier (BBB>0), 13 had more than 5 hydrogen bond donors, 13 had more than 10 rotatable bonds, and 498 had an affinity greater than -26 kcal / mol. After removing these less-than-ideal compounds, 198 compounds were finally obtained.

[0067] 2.3.2 Interaction pattern analysis: To analyze the interaction patterns between small molecule compounds and targets, 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 in the form of fingerprints, such as hydrogen bonds, ionic bonds, and surface contact energy, and thus characterize the interaction patterns in the entire compound library. Based on this, we can also exclude interactions with low probability.

[0068] Among the 198 compounds, 184 compounds have hydrogen bond, ionic bond and other interactions with the target and can be characterized by fingerprint. PLIF results show that ( Figure 8 ), 16 amino acids in FoxD1 participate in the interaction of compounds and form 44 interaction fingerprints. Among them, the high-frequency interaction amino acid sites are S129 and R217, which participate in the interaction of 67 compounds. Among these interactions, 11 amino acids can form strong interactions with compounds and contribute 15 interaction fingerprints ( Figure 9 ), amino acids S129 and R217 remained strong interaction hotspots, and 105 compounds were found to form strong interactions with FoxD1, representing the final results of this screening. This result includes the small molecule's 3D pharmacodynamic conformational information, compound ID, affinity score, druggability score, druggability attribute values, and compound physicochemical properties. Comparing this result with the 3D structure of the FoxD1 DNA region derived from the AF algorithm reveals target-small molecule docking interaction information.

[0069] The compound TD13757 was selected, and its affinity score for FoxD1 was -29.14 kcal / mol. It binds to the pocket 1 region and forms hydrogen interactions and π hydrogen bond interactions with amino acids S129, R216, and R217. It is surrounded by positively charged / polar amino acids ( Figure 10 ).

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

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

[0072] 1. In vitro experimental verification

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

[0074] 2. Validation of in vivo experimental animal model (TNF-α-induced OA model mouse)

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

[0076] 3. Validation of in vivo experimental animal model (OA model mouse induced by anterior cruciate ligament rupture)

[0077] Normal rats underwent anterior cruciate ligament rupture to make them OA model rats. 200 μL of 1 μM TD13757 was injected into the knee joint cavity of ACLT model rats. Four weeks later, the samples were fixed, decalcified, and histologically stained. Microscopic histological analysis was performed and compared with the normal group and negative control group. The experiment found that the content of cartilage extracellular matrix in the experimental group was significantly increased compared with the negative control group, reflecting an increase in the synthesis of cartilage extracellular matrix and a significant decrease in the degree of cartilage damage. The OA condition of rats was alleviated and recovered ( Figure 14 ). We also conducted a quantitative analysis of the results of the above histological staining - OARSI score, and found that after intra-articular injection of TD13757 in ACLT OA model mice, their OARSI scores were significantly reduced, and the OA condition of the rats was alleviated ( Figure 15 ). 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 kidney of rats treated with TD13757 injected into the knee joint. It was found that the tissue and cell morphology of the above organs were normal, and no toxicity of TD13757 was observed, which preliminarily verified the biosafety of TD13757.

[0079] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the method of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A virtual screening method for anti-osteoarthritis drugs, 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 pocket 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-conformational compound library to be screened; S4: docking the compounds in the library of compounds to be screened to the active pocket, performing docking scoring and sorting screening, and obtaining candidate compounds with potential activity after layer-by-layer filtering and screening; wherein, the amino acid sequence of the P122-P225 region of the Foxd1 is SEQ ID NO.

1.

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

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

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

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

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

7. The screening method according to claim 6, wherein The first round of screening selected active compounds and natural product compounds with an affinity of less than -22 kcal / mol and fragment compounds with an affinity of less than -20 kcal / mol; the second round of screening selected compounds with an affinity of 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 no greater than -26 kcal / mol, a molecular weight no greater than 500 Da1, a water solubility logS greater than 0, a lipid solubility logP no greater than 5, a surface accessible area TPSA no greater than 100, an oral availability HIA not negative, hERG no greater than 7, 2C9 no greater than 6, 2D6 not very high, BBB no greater than 0, the number of hydrogen bond donors no more than 5, and the number of rotatable bonds no more than 10; the fourth round of screening selected compounds that can form a strong interaction with FoxD1.

8. Use of the candidate compound screened by the method according to any one of claims 1 to 7 in the preparation of an anti-osteoarthritis drug.

9. The use according to claim 8, characterized in that The candidate compound is selected from TD13757, whose structural formula is: 。

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