CN114081882B - A-FABP protein inhibitor and application thereof - Google Patents
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Abstract
The invention discloses an A-FABP protein inhibitor and application thereof in the technical field of metabolic diseases and cardiovascular and cerebrovascular complications thereof. The invention discloses an A-FABP protein inhibitor, the active component of which is cobicistinib. The invention applies the clinical medicine cobitinib to carry out targeted inhibition on the A-FABP protein, and inhibits the proinflammatory biological activity of the A-FABP protein by inhibiting JNK/c-Jun signal pathway phosphorylation. The invention develops new application on the basis of the existing medicine, can save a large amount of early research and development investment, greatly shortens the research and development period of the medicine, and provides candidate therapeutic medicines for various metabolic diseases according to the research result.
Description
Technical Field
The invention belongs to the technical field of metabolic diseases and cardiovascular and cerebrovascular complications thereof, and particularly relates to an A-FABP protein inhibitor and application thereof.
Background
The adipocyte-type fatty acid binding protein (A-FABP) is one of the members of the apolipoprotein family, has a molecular weight of 14.6KD and is mainly expressed in mature adipocytes and macrophages. The A-FABP protein mainly functions as a carrier of free fatty acid molecules and regulates the storage and decomposition of fat in fat cells; regulate lipid accumulation in macrophages and promote the expression of a variety of inflammatory factors, including MCP-1, TNF-alpha, IL-6, IL-1 beta, and the like. The A-FABP protein can be secreted to the outside of cells and blood to promote inflammatory reaction, and is closely related to the occurrence and development of various metabolic diseases, such as obesity, diabetes, lipid metabolism disorder, nonalcoholic steatohepatitis, atherosclerosis and the like. In animal experiments, mice subjected to bone marrow transplantation with A-FABP gene knockout have been shown to improve atherosclerosis comprehensively and have no metabolic side effects; meanwhile, the mice with the A-FABP gene mutation to reduce the expression level of the A-FABP have lower triglyceride level, reduce the risk of cardiovascular diseases and reduce type 2 diabetes caused by obesity. The related research also clarifies the molecular mechanism that the A-FABP protein up-regulates the expression of various inflammatory factors in macrophages through JNK/c-Jun/AP-1 signal axis, and proves that the expression level of the A-FABP protein in blood and brain tissues is increased after ischemic stroke occurs, so that the expression of the inflammatory factors is promoted, and the neuroinflammation after the stroke is aggravated. Therefore, the A-FABP can be used as a potential therapeutic target for improving metabolic diseases and cardiovascular and cerebrovascular complications caused by the metabolic diseases.
Over the past decade, hundreds of a-FABP inhibitors have been reported, including nicotinic acid, arylquinolines, bicyclic pyridines, ureas, derivatives of aromatic compounds, and other novel heterocyclic compounds, some of which have been shown to be effective in animal studies for the treatment of atherosclerosis and diabetes related diseases. Several potent small molecules have been identified as a-FABP inhibitors, such as BMS309403 and HTS01037, among others. However, only BMS309403 is systematically studied in vitro and in vivo disease models, and although the inhibition effect of BMS309403 on A-FABP is well verified in relevant mouse disease models, no further clinical test can be carried out due to cardiotoxicity, so that no therapeutic drug aiming at the target of A-FABP enters clinical research.
In recent years, researchers have focused on natural sources and FDA approved drugs for the development of a-FABP inhibitors. The traditional drug design method is long in time-consuming and high in cost, and researchers focus on the computer technology, so that the application of computer-aided drug design technologies such as Machine Learning (ML) and Molecular Docking (Molecular Docking) in drug design is rapidly developed. Molecular docking is a common computer-aided drug research technology, is a method for designing drugs by the characteristics of receptors and the interaction mode between the receptors and drug molecules, and has high accuracy. For example, in 2014, levofloxacin (Levofloxacin) was screened from FDA drug libraries (about 1500) by molecular docking and biochemical detection to verify the inhibitory activity of Levofloxacin on A-FABP. Machine learning is a multi-field cross subject and comprises a multi-field subject such as probability theory, statistics and the like, and a naive Bayes model is a classical classification model, only necessary parameters need to be estimated according to a small amount of training data, and the machine learning has good classification learning capability.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an A-FABP protein inhibitor and application thereof.
Cobicistinib (Cobimetinib) is an oral small-molecule mitogen-activated extracellular signal-regulated kinase (MEK) inhibitor, which is approved by FDA 11/10/2015 for use in combination with BRAFV600 inhibitor Vemurafenib (Vemurafenib) in the treatment of non-surgically resectable or metastatic melanoma with BRAFV600E or BRAFV600K positive mutations. The approved dosage form is a tablet, 20mg in size. Although the anti-melanoma effect of cobicistinib is variously confirmed, the effect of cobicistinib in preparing the A-FABP protein inhibitor is not reported. The chemical structural formula of cobitinib is shown as the following formula:
in a first aspect, the invention provides the use of cobicistinib as an inhibitor of the a-FABP protein.
In a second aspect, the invention provides an A-FABP protein inhibitor, the active ingredient of which is cobicistinib.
The third aspect of the invention provides an application of cobicisinib in preparing a medicament for preventing and/or treating diseases with A-FABP as a treatment target.
In the technical scheme of the invention, the disease using A-FABP as a treatment target is metabolic disease or cardiovascular and cerebrovascular complications caused by metabolic disease.
Further, the metabolic disease is selected from one of obesity, type 2 diabetes, lipid metabolism disorder, nonalcoholic steatohepatitis, atherosclerosis, metabolic syndrome, hyperglycemia, insulin resistance, dyslipidemia, hyperlipidemia, hypertriglyceridemia, hypercholesterolemia, and fatty liver;
the cardiovascular and cerebrovascular complications caused by the metabolic disease are selected from one of cerebral apoplexy, atherosclerosis and myocardial infarction.
In a fourth aspect, the invention provides a pharmaceutical composition for preventing and/or treating diseases with A-FABP as a treatment target, wherein the active ingredient of the pharmaceutical composition comprises cobicistinib.
In the technical scheme of the invention, the disease taking A-FABP as a treatment target is metabolic disease or cardiovascular and cerebrovascular complications caused by metabolic disease.
Further, the metabolic disease is selected from one of obesity, type 2 diabetes, lipid metabolism disorder, non-alcoholic steatohepatitis, atherosclerosis, metabolic syndrome, hyperglycemia, insulin resistance, dyslipidemia, hyperlipidemia, hypertriglyceridemia, hypercholesterolemia, and fatty liver;
the cardiovascular and cerebrovascular complications caused by the metabolic disease are selected from one of cerebral apoplexy, atherosclerosis and myocardial infarction.
The invention has the beneficial effects that:
1. the invention screens a new A-FABP inhibitor cobitinib (Cobimetinib) from the latest FDA marketed drug library (about 2600 types) by a naive Bayesian classification model-based machine learning method and combining molecular docking, and verifies the inhibition effect of a candidate compound on the biological activity of the A-FABP by a cell experiment. The invention develops new application on the basis of the existing medicine, can save a large amount of early research and development investment, greatly shortens the research and development period of the medicine, and solves the problems that the existing small molecular A-FABP protein inhibitor has more side effects and cannot be clinically tested further.
2. The invention applies the clinical medicine cobitinib to carry out targeted inhibition on the A-FABP protein, and inhibits the proinflammatory biological activity of the A-FABP protein by inhibiting JNK/c-Jun signal pathway phosphorylation. The invention is suitable for preclinical and clinical research with A-FABP protein as a therapeutic target. The research result provides a new idea for developing A-FABP inhibitors and provides candidate therapeutic drugs for various metabolic diseases.
Drawings
FIG. 1 shows a virtual screening model of A-FABP, which is constructed by using a naive Bayesian classification model based on ligands and a molecular docking method based on structures, aiming at an A-FABP target.
FIG. 2 is a visual representation of A-FABP inhibition based on active and inactive compounds generated using t-distribution random neighbor embedding (t-SNE) based on Morgan fingerprints (4096 bits).
FIG. 3 is a three-dimensional graph (B) of the binding mode of the protein small molecule and a two-dimensional graph (C) of the binding mode of the protein small molecule of the compound structural formula (A) obtained by screening.
FIG. 4 is a graph showing the inhibitory effect of candidate compounds on A-FABP activation of JNK/c-Jun signaling pathway phosphorylation.
FIG. 5 is a graph of inhibition of phosphorylation of the JNK/c-Jun signaling pathway by A-FABP by cobinib.
FIG. 6 is a graph showing the toxic effect of candidate compounds on cells.
Detailed Description
In order that the invention may be more clearly understood, it will now be further described with reference to the following examples and the accompanying drawings. The examples are for illustration only and do not limit the invention in any way. In the examples, each raw reagent material is commercially available, and the experimental method not specifying the specific conditions is a conventional method and a conventional condition well known in the art, or a condition recommended by an instrument manufacturer.
Example 1: screening A-FABP inhibitor from FDA drug library
Aiming at the target point A-FABP, a naive Bayesian classification model based on ligand and a molecular docking method based on structure are used for constructing a virtual screening model of A-FABP (figure 1).
Drugs approved by the FDA for marketing were collected in the drug Bank database (https:// go. Drug Bank. Com), for a total of 2595 compounds for further study. The collection of a-FABP small molecule inhibitors from the ChEMBL database as well as the existing literature defines compounds with activity. Compounds collected from the ChEMBL database did not overlap with compounds in the FDA dataset. The corresponding bait (defined as inactive) was automatically generated by the DUD-E online database (http:// dude. Docking. Org /). As shown in table 1, the data set for active compounds and the data set for inactive compounds are expressed as 1: a ratio of 4 was randomly assigned to the training set and the test set. In the invention, a single compound structural formula is represented by high-dimensional binary text (4096 bits, radius =2) by using Morgan finger prints (RDKit 2019.03.1) through an RDKit software package (RDKit 2019.03.1) of Python language, in all data sets, an A-FABP small molecule inhibitor and a bait are respectively marked as '1' and '0', and then are fed to a random neighbor embedding (t-SNE) algorithm of t distribution to investigate whether the diversity and the spatial distribution condition of a data set compound for machine learning meet the modeling requirements of a subsequent machine learning model.
Table 1 constructed data set
Furthermore, 542 molecular descriptors of Discovery Studio 2016 (DS 2016) and MOE 2014.9 software are selected as descriptor sets to be used for constructing the model. Each compound is characterized by a combination of descriptors from the software described above.
The crystal structure of the human A-FABP Protein (2 NNQ) was downloaded from the PDB (Protein Data Bank) database. The molecular docking process in the present invention was performed in DS 2016 software. Before molecular docking, the A-FABP protein crystal is pretreated to remove water molecules and ligand molecules in a crystal compound and hydrogenate to protonate a protein structure. And docking and scoring are carried out on the A-FABP protein crystal and the compound in the FDA database by utilizing a LibDock and CDOCKER docking method in DS software, and the reliability of the docking method is evaluated according to a Root-mean-square deviation (RMSD) value between initial conformations.
The invention performs Pearson correlation analysis in constructing a machine learning classification model to identify descriptors that are highly correlated with activity: first, descriptors with values that appear more than 50% high frequency are eliminated (removing the lack of diversity table descriptors); second, descriptors with correlation coefficient activity less than 0.1 are excluded. If the absolute value of the correlation coefficient between two descriptors is greater than 0.9, the descriptor with the lower correlation coefficient and with activity is deleted. And finally, further optimizing the remaining 31 molecular descriptors by using a stepwise linear regression method, performing machine learning by using a naive Bayesian classification model, generating a large number of Boolean features (Boolean features) by inputting the descriptors in the learning process, and adding weights calculated for each feature by using probability estimation after Laplacian-adjusted (Laplacian-adjusted) to provide probability estimation so as to construct a good classification model for predicting the activity of the compound. Therefore, virtual screening of the components in the FDA approved drug library based on the naive bayes classification model was performed, and a total of 369 compounds were expected to have inhibitory activity against a-FABP protein. A total of 369 compounds were ranked by Bayesian scoring EstPgood (0. Ltoreq. EstPgood. Ltoreq.1) (see Table 2).
And (3) carrying out molecular docking on the small molecules screened by the machine learning, and selecting the protein with the high-resolution protein number of 1tou as a docking receptor protein. The final candidate compounds were judged by scoring scores (Kcal/mol) and interaction patterns using BMS309403 as a reference (as shown in Table 3). Four compounds with the highest scores were screened by protein-ligand interaction pattern analysis: cobicistinib (Cobimetinib, currently approved for the treatment of melanoma), larotinib (larotrietinib, currently approved for the treatment of solid tumors), pantoprazole (Pantoprazole, currently approved for the treatment of gastric ulcer, duodenal ulcer, etc.), vildagliptin (Vildagliptin, currently approved for the treatment of type 2 diabetes) (compound structural formula, three-dimensional map of protein small molecule binding pattern and two-dimensional map of protein small molecule binding pattern are shown in fig. 3).
TABLE 2 Bayesian classification model score results (select top 30 compounds)
TABLE 3 molecular docking score
Example 2: cell-based assay for A-FABP inhibitory Activity of candidate Compounds
Mouse and human A-FABP protein are 132 amino acids, homology is 91.7%, and the amino acid sequence is the same with several key amino acid sites (Arg 106, arg126, tyrl 128) combined with the ligand, therefore, the candidate compound can be verified by the inhibition effect of mouse A-FABP protein.
RAW264.7 cells (mouse monocyte macrophage cell line) were cultured using high-glucose DMEM with 10% fetal bovine serum (FBS, gibco, usa). Cells were divided into three groups: (1) blank control group; (2) A-FABP protein (1. Mu.g/ml) treatment group; (3) A-FABP protein (1. Mu.g/ml) + candidate compound treatment group. Candidate compounds were diluted to two concentrations (10. Mu.M, 100. Mu.M). Incubating the candidate compound with A-FABP protein for 30min and then adding to the cells for 30min at 37 ℃ in a 5% CO2 incubator.
38g Brewer thioglycolate medium (BD, lot 5243569) was dissolved in 1000ml distilled water, and the solution was boiled to completely dissolve the solution, autoclaved at 121 ℃ for 15min to prepare a 3.8% Brewer thioglycolate medium, which was stored at room temperature in the dark. Injecting 1ml3.8% Brewer thioglycollate culture medium into mouse abdominal cavity, and waiting for 3 days; killing the mouse by cervical dislocation, and injecting 5ml of precooled D-PBS into the abdominal cavity of the mouse; gently massaging on abdominal cavity, sucking out liquid into 15ml centrifuge tube, centrifuging at 400 Xg for 10min, maintaining the whole process at low temperature, discarding supernatant to obtain primary macrophage with high purity, and resuspending and culturing with high glucose DMEM containing 10% fetal bovine serum (FBS, gibco, USA). After 24h incubation in a 5% CO2 incubator at 37 ℃ C, the culture was continued for 24h with the change, and the primary macrophages were grouped and treated in the same manner as described above for cell grouping and treatment.
Total cellular protein was extracted separately using RIPA tissue/cell lysate (Solarbio, cat # R0020) and processed as follows: firstly, protein samples are separated by SDS-polyacrylamide gel electrophoresis and transferred to a PVDF membrane; then, the cells were incubated overnight at 4 ℃ with Phospho-SAPK/JNK (Thr 183/Tyr 185) (81E 11) Rabbit mAb (CST, # 4668), SAPK/JNK Antibody (CST, # 9252) and Phospho-c-Jun-S63 Rabbit mAb (ABClonal, AP 0105), JUN Rabbit pAb (ABClonal, A169905) and β -Actin (8H 10D 10) Mouse mAb (CST, # 3700), respectively; then, using horseradish peroxidase labeled Anti-rabbitIgG, HRP-linked Antibody (CST, # 7074) and Anti-mouse IgG, HRP-linked Antibody (CST, # 7076) to incubate for 1h at room temperature; finally, a picture of the western blot was taken using ECL plus in conjunction with autoradiography.
The results show that: compared with the blank control group, the phosphorylation of JNK/c-Jun is enhanced by the A-FABP protein treatment group, and from the four candidate compounds, the cobicisib has the obvious effect of inhibiting the A-FABP from activating the JNK/c-Jun phosphorylation, while the effects of the lapatinib, pantoprazole and vildagliptin on inhibiting the A-FABP activity are not obvious (as shown in figure 4).
Further, cobitinib concentration gradients were set to (1. Mu.M, 10. Mu.M, 30. Mu.M, 100. Mu.M), respectively, and changes in phosphorylation of JNK/c-Jun signaling pathway in RAW264.7 cells were detected by the same method as described above. Combined consideration, cobitinib (10. Mu.M, 30. Mu.M, 100. Mu.M) was found to have an inhibitory effect on A-FABP activation JNK/c-Jun phosphorylation (see FIG. 5). Taken together, cobicistinib acts as a potential a-FABP inhibitor.
Example 3 detection of cytotoxicity of candidate Compounds
Cell Counting Kit-8 Kit (Bioss, BA 00208) is used for detecting cytotoxicity, RAW264.6 cells are cultured according to the requirements, and experimental groups are set up: (1) blank control group (medium + CCK8 reagent, no cell) (2) reagent control group (cell + medium + CCK8 reagent) (3) solvent control group (DMSO 100 μ M) (cell + medium + DMSO + CCK8 reagent) (4) a-FABP protein (1 μ g/ml) treatment group (cell + medium + a-FBP4 protein + CCK8 reagent) (5) a-FABP protein (1 μ g/ml) + candidate compound treatment group (cell + medium + a-FBP4 protein + candidate compound + CCK8 reagent), and the candidate compound was diluted to two concentrations (10 μ M, 100 μ M). Incubating the candidate compound with A-FABP protein for 30min at 37 deg.C in a CO2 incubator, 5% and then adding to the cells for 30min. Then, CCK8 reagent was added thereto, and the mixture was treated at 37 ℃ and 5% by volume of CO2 for 2 hours. Finally, absorbance (OD) at 450nm was measured using a microplate reader.
The results show that: comparing the absorbance of the blank control group, the reagent control group and the solvent control group, and finding that the solvent DMSO (100 mu M) has no obvious toxic effect on cells; when the absorbances (OD) of the solvent control group and the A-FABP protein + candidate compound treatment group are compared, the low/high concentration of the candidate compounds except for the cobinib has no obvious toxic effect on cells, while the low concentration (10 mu M) of the cobinib has no obvious toxic effect on the cells, and the high concentration (100 mu M) of the cobinib has strong toxic effect on the cells. Meanwhile, the toxic effect on RAW264.7 cells was detected by the same method for the concentration gradients of cobicistinib (1. Mu.M, 10. Mu.M, 30. Mu.M and 100. Mu.M), and it was found that cobicistinib has no obvious toxic effect on cells except that high concentration (100. Mu.M) has strong toxic effect on cells (FIG. 6). This result indicates that the inhibitory effect of cobicistinib on JNK/c-Jun phosphorylation in fig. 4 and 5 is not due to cytotoxic effects.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (1)
1. Application of cobicistinib in-vitro preparation of A-FABP protein inhibitor.
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Non-Patent Citations (5)
| Title |
|---|
| "A-FABP mediates adaptive thermogenesis by promoting intracellular activation of thyroid hormones in brown adipocytes";Lingling Shu et al.;《NATURE COMMUNICATIONS》;20170127;全文 * |
| "ERK inhibition with PD184161 mitigates brain damage in a mouse model of stroke";A. Gladbach et al.;《J Neural Transm》;20131214;全文 * |
| "N-Benzyl-indolo carboxylic acids: Design and synthesis of potent and selective adipocyte fatty-acid binding protein (A-FABP) inhibitors";T. Barf et al.;《Bioorganic & Medicinal Chemistry Letters》;20090130;全文 * |
| "Serum adipocyte fatty acid–binding protein associated with ischemic stroke and early death";A.W.K. Tso et al.;《Neurology》;20110511;全文 * |
| "急性缺血性脑卒中患者血清A-FABP、CXCL12水平变化及临床意义";柴玉梅等;《临床和实验医学杂志》;20191231;第18卷(第24期);全文 * |
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