Glucagon receptor antagonists and their uses

Abstract

The present invention relates to the pharmaceutical technical field and provides a compound and its use as a glucagon receptor antagonist. The compound includes compound A or an isomer, metabolite, prodrug, pharma- ceutically acceptable ester or salt of compound A. The glucagon receptor antagonist of the present invention has a new backbone structure and has a low 50% inhibitory concentration (IC50) for the glucagon receptor. JPEG2024500683000053.jpg44170

Description

[Technical field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) The present embodiments claim priority to a Chinese patent application having application number 202110194113.X, filed with the China Patent Office on February 20, 2021, the entire contents of which are incorporated by reference into the present embodiments.

[0002] The present invention relates to the field of pharmaceutical technology, specifically to a glucagon receptor antagonist and its use. [Background technology]

[0003] Glucagon is a polypeptide hormone consisting of 29 amino acids secreted from the α cells of the islets of Langerhans in the pancreas. It acts mainly on the liver, specifically binding to the glucagon receptor (GCGR) on the surface of liver cells, promoting glycogenolysis and gluconeogenesis in the liver and increasing blood glucose, thereby counteracting the hypoglycemic effect of insulin. The glucagon receptor is a G-protein coupled receptor that is mainly present on the surface of liver cells, and by binding to glucagon, it activates adenylate cyclase (AC) and phospholipase C (PLC), increases the intracellular concentration of cyclic adenosine monophosphate (cAMP), and activates cAMP-dependent protein kinase (PKA).

[0004] Glucagon receptor antagonists bind to the glucagon receptor, inhibiting the binding of glucagon to the glucagon receptor and suppressing the function of glucagon, thereby reducing the response activity of cAMP, which is advantageous in lowering blood glucose levels and regulating blood glucose balance.

[0005] At present, many glucagon receptor antagonists have been reported, but most of them have similar structures, and there is a lack of compounds with novel structures as candidate drugs. Note that the information disclosed in the background art section above is only used to deepen understanding of the background of the present invention. Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a glucagon receptor antagonist having a novel structure and use of the glucagon receptor antagonist.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by the practice of the invention. [Means for solving the problem]

[0008] According to one aspect of the present invention, there is provided a compound, the compound being represented by the following structural formula A, or an isomer, metabolite, prodrug, pharma- ceutically acceptable ester, or pharma- ceutically acceptable salt thereof, [ka] wherein R1 is H, F, Cl, Br or I; R2 is H or -O-(CH2) m -CH3, and m is an integer from 0 to 2; R3 is H or -S-(CH2) n -CH3, n is an integer from 0 to 2; and R4 and R5 are both [ka] Form.

[0009] According to a specific embodiment of the present invention, the compound is a compound represented by the following structural formula A1 or A2: [ka] [ka]

[0010] According to one aspect of the present invention, there is provided a pharmaceutical composition comprising the compound described above and a pharma- ceutically acceptable carrier.

[0011] According to one aspect of the invention there is provided the use of a compound or pharmaceutical composition as described above in the preparation of a medicament for use as a glucagon receptor antagonist.

[0012] Said drug is used for treating diseases related to glucagon metabolism imbalance.Specifically, said compound is used for treating diabetes, more specifically for treating type I diabetes and / or type II diabetes; glucagon level imbalance; and / or hyperglycemia.

[0013] The medicament is applied to a mammal, particularly a human.

[0014] According to one aspect of the invention there is provided a compound for use as a glucagon receptor antagonist, said compound being as hereinbefore defined.

[0015] According to an embodiment of the invention, said compounds are used for treating diseases related to glucagon metabolism imbalance as defined above.

[0016] According to one aspect of the present invention, there is provided a method for treating diseases associated with glucagon metabolism imbalance or regulating blood glucose levels, said method comprising administering a therapeutically effective dose of the above-mentioned compound or pharmaceutical composition to an individual in need thereof.

[0017] The diseases associated with imbalance of glucagon metabolism include diabetes (type I diabetes and / or type II diabetes); imbalance of glucagon levels; and / or hyperglycemia.

[0018] The individual is a mammal, in particular a human. According to one aspect of the present invention, there is provided a glucagon receptor antagonist, the glucagon receptor antagonist comprising Compound A or an isomer, acid, ester, metabolite, prodrug, or pharma- ceutically acceptable salt of Compound A; the structural formula of Compound A is: [ka] and; wherein R1 is independently ||-H, ||-F, ||-Cl, ||-Br, or ||-I; R2 is independently ||-H or ||-O-(CH2) m -CH3, and the value of m is 0 to 2; R3 is independently ||-H or ||-S-(CH2) n -CH3, and the value of n is 0 to 2; R4 and R5 are both [ka] and; || represents the point of attachment to the parent molecule. According to an embodiment of the present invention, R1 is ∥-Cl. According to an embodiment of the present invention, R2 is ||-O-CH3. According to an embodiment of the present invention, R3 is ||-S-CH3. According to an embodiment of the present invention, R1 is ||-Cl, R2 is ||-O-CH3, R3 is ||-H, and R4 and R5 are both [ka] Forming, i.e. The compound A is [ka] It is.

[0019] According to an embodiment of the present invention, R1 is ||-H, R2 is ||-H, R3 is ||-S-CH3, and R4 and R5 are both [ka] Forming, i.e. The compound A is [ka] It is.

[0020] According to one aspect of the present invention, there is provided a use of compound A for the preparation of a medicament for treating diabetes or other glucagon-related metabolic disorders, said compound A having the structural formula: [ka] and; wherein R1 is independently ||-H, ||-F, ||-Cl, ||-Br, or ||-I; R2 is independently ||-H or ||-O-(CH2) m -CH3, and the value of m is 0 to 2; R3 is independently ||-H or ||-S-(CH2) n -CH3, and the value of n is 0 to 2; R4 and R5 are both [ka] and; || represents a connection point.

[0021] According to one aspect of the present invention, there is provided a pharmaceutical composition comprising a pharma- ceutically acceptable carrier and a glucagon receptor antagonist as described above.

[0022] According to some embodiments of the invention, the pharmaceutical composition is a solution, tablet, capsule or injectable liquid.

[0023] According to some embodiments of the invention, the pharmaceutical composition is administered by injection or orally.

[0024] According to one aspect of the invention, there is provided a method of modulating the glucagon receptor in an individual comprising administering to an individual in need thereof a glucagon receptor inhibiting dose of a glucagon receptor antagonist as described above.

[0025] According to one aspect of the invention there is provided a method of modulating the glucagon receptor in an individual comprising administering to the individual in need thereof a glucagon receptor inhibiting dose of a pharmaceutical composition as described above.

[0026] In some embodiments of the invention, the individual is a mammal.

[0027] According to one aspect of the invention, there is provided a method of regulating blood glucose levels in an individual comprising administering to the individual in need thereof a glucagon receptor antagonist as described above in a glucagon receptor inhibiting dose.

[0028] According to one aspect of the invention, there is provided a method of regulating blood glucose levels in an individual comprising administering to the individual in need thereof a glucagon receptor inhibiting dose of a pharmaceutical composition as described above.

[0029] In some embodiments of the invention, the individual is a mammal.

[0030] According to one aspect of the present invention there is provided the use of a glucagon receptor antagonist as described above for the preparation of a medicament for the treatment of diabetes or other glucagon-related metabolic disorders. Effect of the Invention

[0031] As can be seen from the above technical solutions, the compounds that can be used as glucagon receptor antagonists in the exemplary embodiments of the present invention have at least the following advantages and positive effects:

[0032] The compound has a novel skeletal structure and a low 50% inhibitory concentration for the glucagon receptor, and therefore can be used as a promising candidate drug.

[0033] It is to be understood that, in the present invention, the foregoing general description and the following detailed description are merely exemplary and explanatory and are not intended to be limiting of the present invention. [Brief description of the drawings]

[0034] [Figure 1] The 1H NMR spectrum of compound A1 is shown. [Diagram 2] 1 shows an exemplary two-dimensional view of the interaction of Compound A1 with the glucagon receptor. [Diagram 3] The 1H NMR spectrum of compound A2 is shown. [Figure 4] FIG. 4 exemplarily shows a two-dimensional view of the interaction of compound A2 with the glucagon receptor. [Diagram 5] 1 is a curve graph showing experimental results of the response of Compound A1 and Compound A2 to cyclic adenosine monophosphate (cAMP). [Figure 6] 1 shows a flow chart of an artificial intelligence-based antagonist prediction method in an exemplary embodiment of the present invention. [Figure 7] 1 shows a flowchart of an artificial intelligence-based antagonist prediction method in another exemplary embodiment of the present invention. [Figure 8] FIG. 2 illustrates an exemplary structural diagram of a generator network of an adversarial neural network in one exemplary embodiment of the present invention. [Figure 9] FIG. 2 illustrates an exemplary structural diagram of a classifier network of an adversarial neural network in an exemplary embodiment of the present invention. [Figure 10] 1 shows a flowchart of a method for training a generator network of an adversarial neural network in an exemplary embodiment of the present invention. [Figure 11] 1 shows a flowchart of a method for training a generator network of an adversarial neural network in another exemplary embodiment of the present invention. [Figure 12] 1 shows a flowchart of a virtual screening method in an exemplary embodiment of the present invention. [Figure 13]1 is a flow chart of a method for detecting activity of a target compound in an exemplary embodiment of the present invention. [Figure 14] The graph shows curves of experimental results of the response of 30 compounds in Table 2 to cyclic adenosine monophosphate (cAMP) (A corresponds to C1 to C10, B corresponds to C11 to C20, and C corresponds to C21 to C30, with L-168,049 serving as the positive control for all compounds). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0035] The above drawings are incorporated in and constitute a part of the specification, illustrate embodiments in accordance with the present invention, and together with the specification are used to explain the principles of the present invention. It is self-evident that the above drawings are only some embodiments of the present invention, and those skilled in the art can derive other drawings from these drawings without creative efforts.

[0036] Hereinafter, exemplary embodiments will be described more comprehensively with reference to the drawings. However, it is understood that the exemplary embodiments can be implemented in various forms and are not limited to the examples described herein; on the contrary, these embodiments are provided to make the present invention more comprehensive and complete, and to comprehensively convey the concept of the exemplary embodiments to those skilled in the art.

[0037] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to fully understand the embodiments of the present invention. However, those skilled in the art will recognize that the technical solutions of the present invention may be implemented without one or more of the specific details, or may employ other methods, components, devices, steps, etc. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present invention.

[0038] The flowcharts shown in the drawings are merely illustrative and do not necessarily include all contents and operations / steps, and do not necessarily need to be performed in the order described. For example, some operations / steps may be separated, some operations / steps may be merged or partially merged, and the actual execution order may be changed according to actual circumstances.

[0039] Glucagon and insulin are two hormones with opposing effects secreted by pancreatic islet cells. Glucagon is secreted by the alpha cells of the pancreatic islet, and insulin is secreted by the beta cells of the pancreatic islet. These two hormones are They interact and regulate each other to maintain blood glucose homeostasis. The role of glucagon is to increase blood glucose. For example, excessive secretion of insulin can decrease secretion of glucagon, causing a decrease in blood glucose; conversely, excessive secretion of glucagon can decrease secretion of insulin, causing an increase in blood glucose.

[0040] Glucagon works together with insulin to maintain normal levels of blood sugar, and disturbances in the balance between glucagon and insulin levels can lead to a variety of diseases, including diabetes and ketoacidosis.

[0041] After glucagon binds to the glucagon receptor, it transduces the signal into the cell and activates the phosphorylation enzyme in liver cells through the cAMP-PK system to promote glycogenolysis. Therefore, reducing the expression of glucagon receptors can regulate blood glucose levels.

[0042] The glucagon receptor antagonist according to the embodiment of the present invention specifically binds to the glucagon receptor, thereby inhibiting the binding of glucagon to the glucagon receptor and regulating blood sugar balance.

[0043] Thus, the present invention provides compounds that can be used as glucagon receptor antagonists.

[0044] The compound used as the glucagon receptor antagonist may be selected from Compound A having the following structural formula, an isomer, metabolite, or prodrug of Compound A, a pharma- ceutically acceptable ester, and a salt thereof. Specifically, the general structural formula of Compound A is: [ka] and During the ceremony, R1 can be H, F, Cl, Br or I. When R1 is H, there is no substitution on the benzene ring of the general formula. When R1 is F, Cl, Br or I, there is substitution on the benzene ring of the general formula. In some embodiments of the present invention, R1 is Cl or H. R2 is H or -O-(CH2) m -CH3, and m is an integer of 0 to 2. When R2 is H, there is no substitution at R2 on the benzene ring in the general formula. When m is 0, R2 is -O-CH3. When m is 1, R2 is -O-CH2-CH3. When m is 2, R2 is -O-(CH2)2-CH3. In some embodiments of the present invention, R2 is H or -O-CH3. R3 is -H or -S-(CH2) n -CH3, and n is an integer from 0 to 2. When R3 is H, there is a substitution at R3 on the benzene ring in the general formula. When m is 0, R3 is -S-CH3. When m is 1, R3 is -S-CH2-CH3. When m is 2, R3 is -S-(CH2)2-CH3. In some embodiments of the present invention, R3 is H or -S-CH3. R4 and R5 are both [ka] Form. The isomers of compound A include optical isomers and geometric isomers. Specifically, the optical isomer is one in which R4 and R5 are both chiral substituents. [ka] When forming [ka] Not limited to, [ka] It refers to something that may be.

[0045] In the present invention, the geometric isomer refers to cis-trans isomers. Specifically, the geometric isomers of the present invention are formed by different arrangements of groups at double bonds or ring carbon atoms of the parent molecule and different arrangements of groups at double bonds or ring carbon atoms of the R4 and R5 substituents.

[0046] An ester of compound A is obtained by the reaction of compound A with an organic acid. Here, the ester has a functional group of ester, -COO-, and is obtained by a dehydration condensation reaction. For example, an organic acid reacts with -OH in compound A to obtain an ester. The organic acid may be formic acid, acetic acid, propionic acid, etc. A metabolite of compound A refers to a product obtained by metabolizing a part of the functional group of compound A. Here, a metabolite refers to a substance produced or consumed by a metabolic process, and does not substantially affect the pharmaceutical properties of compound A.

[0047] A prodrug of compound A, also called a precursor drug, drug precursor, or precursor pharmaceutical, is a compound obtained by modifying the chemical structure of compound A, which is inactive or has low activity in vitro, and which exerts its medicinal effect by releasing an active drug through enzymatic or non-enzymatic conversion in vivo. That is, a prodrug of compound A is usually a functional derivative of compound A, and is easily converted to compound A in vivo.

[0048] A solvate of compound A refers to a form in which compound A is diluted in a particular solvent. Common solvents include liquid substances that can dissolve compound A, such as water, ethanol, and dimethyl sulfoxide.

[0049] Pharmaceutically acceptable salts of Compound A include salts formed in two ways:

[0050] (1) Salts formed by an acidic functional group (e.g., -OH) present in compound A of the present invention and a suitable inorganic or organic cation (alkali), such as a salt formed by compound A of the present invention and an alkali metal or alkaline earth metal, an ammonium salt of compound A of the present invention, and a salt formed by compound A of the present invention and a nitrogen-containing organic base. (2) Salts formed by an alkaline functional group (e.g., -NH-, etc.) present in compound A of the present invention and a suitable inorganic or organic anion (acid), such as a salt formed by a compound of the present invention and an inorganic acid or an organic carboxylic acid.

[0051] Thus, pharma- ceutically acceptable salts of compound A of the present invention include alkali metal salts such as sodium salts, potassium salts, lithium salts, etc.; alkaline earth metal salts such as calcium salts, magnesium salts, etc.; other metal salts such as aluminum salts, iron salts, zinc salts, copper salts, nickel salts, cobalt salts, etc.; inorganic alkali salts such as ammonium salts; tert-octylamine salts, dibenzylamine salts, morpholine salts, glucosamine salts, phenylglycine alkyl ester salts, ethylenediamine salts, N-methylglucosamine salts, guanidine salts, diethylamine salts, triethylamine salts, dicyclohexylamine salts, N,N'-dibenzylethylenediamine salts, chloroprocaine salts, procaine salts, diethanolamine salts, N-benzylphenethylamine salts, piperazine salts, etc. salts of organic alkali salts such as salts of tetramethylamine and tris(methylol)aminomethane; salts of inorganic acids such as hydrohalides such as hydrofluoride, hydrochloride, hydrobromide and hydroiodide, as well as nitrates, perchlorates, sulfates and phosphates; salts of lower alkanesulfonates such as mesylates, trifluoromethanesulfonates and ethanesulfonates; salts of arylsulfonates such as benzenesulfonates and parabenzenesulfonates; salts of organic carboxylates such as acetates, malates, fumarates, succinates, citrates, tartrates, oxalates and maleates; salts of amino acids such as glycinates, trimethylglycinates, arginates, ornithines, glutamates and aspartates, but are not limited thereto.

[0052] In an exemplary embodiment, R1 is -Cl, R2 is -O-CH3, R3 is -H, and R4 and R5 together are [ka] wherein compound A is [ka] This is designated as compound A1.

[0053] The molecular formula of the above compound A1 is C 20 H 19The compound is ClN2O4S and its name is (7S)-N-(3-chloro-4-methoxyphenyl)-4-hydroxy-7-methyl-2-oxo-5,6,7,8-tetrahydro-1H-[1]benzothiolo[2,3-b]pyridine-3-carboxamide.

[0054] InChI is InChI=1S / C20H19ClN2O4S / c1-9-3-5-11-14(7-9)28-20-15(11)17(24)16(19(26)23-20)18(25)22-10-4-6-13(27-2)12(21)8-10 / h4,6,8-9H,3,5,7H2,1-2H3,(H,22,25)(H2,23,24,26) / t9- / m0 / s1.

[0055] The SMILES sequence is COc1ccc(NC(=O)c2c(O)c3c4c(sc3[nH]c2=O)C[C@@H](C)CC4)cc1Cl.

[0056] Compound A1 can be prepared according to any known method, and in the following examples, commercially available compound A1 is used.

[0057] FIG. 1 shows the structure of compound A1. 1 1 H NMR spectrum, in which the solvent is dimethyl sulfoxide (DMSO), shows that the molecular structure of compound A1 is the structure represented by formula A1 above.

[0058] Figure 2 exemplarily shows a two-dimensional diagram of the interaction between compound A1 and glucagon receptor. In the figure, ARG412, LYS405, ARG346, ASN404, SER350, THR353, LEU399, LEU403, LYS349 and GLN408 are all amino acid residues in the amino acid sequence of glucagon receptor. Here, in the protein sequence, the amino group and carboxyl group between amino acids are dehydration bonded, and some groups of amino acids are involved in the formation of peptide bonds, so the remaining structural part is called amino acid residue.

[0059] LYS405, ARG346, SER350 and LYS349 all interact with compound A1. During the interaction, LYS405 and ARG346 are hydrogen donors and form hydrogen bond interactions with the O atom in compound A1. SER350 is a hydrogen acceptor and forms hydrogen bond interactions with the H atom on compound A1. LYS349 and the O atom in compound A1 - are attracted to each other by salt bridges, i.e., ionic bonds. Therefore, any of the above amino acid residues can interact with compound A1.

[0060] Furthermore, according to the mutation experiments reported in the related literature (Jazayeri A, Andrew S. Dore, Lamb D, et al. Extra-helical binding site of a glucagon receptor antagonist [J] Nature, 2016, May 12; 533 (7602): 274-277), LYS405, ARG346, SER350 and LYS349 were confirmed to be key residues in the binding pocket. Compound A1 in this example can interact with the above residues, so that compound A1 is well bound to the binding pocket.

[0061] In another exemplary embodiment, R1 is -H, R2 is -H, R3 is -S-CH3, and R4 and R5 are both [ka] wherein compound A is [ka] and is designated as compound A2.

[0062] The molecular formula of the above compound A2 is C 15 H 12 The compound is N2O3S2 and its name is 3-(azepan-1-ylcarbonyl)-N-(2,5-difluoro-phenyl)-7-methyl-1,8-naphthyridin-4-amine.

[0063] InChI is InChI=1S / C15H12N2O3S2 / c1-21-9-4-2-3-8(7-9)16-14(19)11-12(18)13-10(5-6-22-13)17-15(11)20 / h2-7H,1H3,(H,16,19)(H2,17,18,20).

[0064] The SMILES sequence is CSc1cccc(NC(=O)c2c(O)c3sccc3[nH]c2=O)c1.

[0065] Compound A2 can be prepared according to any known method, and the following examples use commercially available Compound A2.

[0066] FIG. 3 shows the structure of compound A2. 1 1 H NMR spectrum, in which the solvent is dimethyl sulfoxide (DMSO), shows that the molecular structure of compound A2 is the structure represented by formula A2 above.

[0067] Figure 4 exemplarily shows the two-dimensional diagram of the interaction between compound A2 and glucagon receptor.In the figure, THR341, HIE340, ARG346, LYS405, ASN404, SER350, LEU399, GLN408, LYS349 and LEU403 are all amino acid residues in glucagon receptor amino acid sequence.Here, ARG346, LYS405, SER350 and LYS349 all interact with compound A2.

[0068] LYS405 and ARG346 are hydrogen donors and form hydrogen bond interactions with the O atom in compound A2. SER350 is a hydrogen acceptor and forms hydrogen bond interactions with the H atom on compound A2. LYS349 and the O atom in compound A2 - are attracted to each other by salt bridges, i.e., ionic bonds. Therefore, any of the above amino acid residues can interact with compound A2.

[0069] Compound A2 in this example can interact with the above residues, so that compound A2 is well bound to the binding pocket.

[0070] To measure the antagonistic activity of compound A in the above examples against glucagon receptor, a cellular activity test was carried out, the specific embodiment is as follows:

[0071] A known human glucagon receptor (hGCGR) antagonist drug was selected as a positive control, and compound A in the above example, a glucagon peptide segment (i.e., human full-length glucagon) was mixed with human embryonic kidney cells containing a target receptor molecule, and the glucagon peptide segment was bound to hGCGR, activating the hGCGR signal pathway and releasing the second messenger cyclic adenosine monophosphate (cAMP), in which compound A exerts an antagonistic effect of suppressing the activity of hGCGR.

[0072] In an exemplary embodiment, a known hGCGR antagonist drug (L-168,049) is selected as a positive control. The name of L-168,049 is 4-[3-(5-bromo-2-propoxyphenyl)-5-(4-chlorophenyl)-1H-pyrrol-2-yl]pyridine, which is a non-competitive glucagon receptor antagonist. In view of the presence of human glucagon receptor on the cell membrane of human embryonic kidney cell HEK293, HEK293 is used to measure the drug activity of the human glucagon antagonist. In this example, different concentrations of compound A of the present invention and a positive control are mixed with glucagon peptide segments and HEK293 cells, respectively, and the second messenger cAMP is detected by homogeneous time resolved fluorescence (HTRF), and the 50% inhibitory concentration value of each compound is calculated based on the intensity value of HTRF.

[0073] In an exemplary embodiment, the activity of compound A is tested by using HTRF technology.Specifically, when glucagon peptide segment activates glucagon receptor, the second messenger cyclic adenosine monophosphate (cAMP) is released in glucagon receptor cells, and the second messenger cAMP can be detected by HTRF, and the 50% inhibitory concentration (IC50) of test compound can be calculated according to the fluorescence intensity.

[0074] In an exemplary embodiment, 11 identical concentration gradients were set for compounds A1 and A2, respectively, and the activities of compounds A1 and A2 were obtained by triple HTRF-based (second messenger) cyclic adenosine monophosphate (cAMP) response experiments, and the obtained experimental data are shown in Table 1.

[0075] [Table 1]

[0076] The data in Table 1 is processed by software to obtain Figure 5, with the concentration gradient on the horizontal axis and the inhibition rate on the vertical axis. Figure 5 shows a curve graph of the experimental results of the response of the test compound to cyclic adenosine monophosphate (cAMP). Specifically, the response inhibition rates of L-168,049, compound A1 and compound A2 are shown in the figure. Here, L-168,049 is a positive control drug, compound A1 is represented by C13, and compound A2 is represented by C14. As can be seen from the figure, both compound A1 and compound A2 can inhibit the activity of glucagon receptor well.

[0077] Specifically, according to the measurement results, the IC50 of L-168,049 is 2.082E-08M, the IC50 of compound A1 is 4.465E-07M, and the IC50 of compound A2 is 4.225E-07M.

[0078] Here, the 50% inhibitory concentration (IC50) values ​​of Compound A1 and Compound A2 are as small as 446.5 nM and 422.5 nM, respectively, and therefore can be used as candidate drugs for glucagon receptor antagonists having novel structures.

[0079] The present invention further provides the use of compound A as described above for the preparation of a medicament for treating diabetes or other glucagon-related metabolic disorders.

[0080] Here, examples of glucagon-related metabolic disorders include diabetes (type I diabetes, type II diabetes), hyperglycemia, hyperinsulinemia, β-cell rest, β-cell dysfunction (i.e., the compound can improve β-cell function by restoring early response), dietary hyperglycemia, cell apoptosis (i.e., the compound can prevent cell apoptosis), and impaired fasting glucose (IFG). Glucose), metabolic syndrome, hypoglycemia, hyper / hypokalemia, glucagon level imbalance (i.e., the compound can normalize glucagon levels), abnormal LDL / HDL ratio (i.e., the compound can improve LDL / HDL ratio), reduced snacking, eating disorders, weight loss, polycystic ovarian syndrome (PCOS), obesity due to diabetes, latent autoimmune diabetes (potential approval of autoimmune diabetes), insulitis, islet transplantation, childhood diabetes, gestational diabetes, end-stage diabetic complications, micro / macroproteinuria, kidney disease, retinopathy, neuropathy, diabetic foot ulcers, glucagon administration These include, but are not limited to, decreased intestinal motility, short bowel syndrome, antidiarrheal, increased gastric secretion, decreased blood flow, erectile dysfunction, glaucoma, postoperative stress, organ tissue damage due to blood reperfusion after ischemia (i.e., the compound can improve organ tissue damage due to blood reperfusion after ischemia), ischemic heart damage, cardiac dysfunction, congestive heart failure, stroke, myocardial infarction, arrhythmia, premature death, anti-apoptosis, wound healing, impaired glucose tolerance (IGT), insulin resistance syndrome, syndrome X, hyperlipidemia, dyslipidemia, hypertriglyceridemia, hyperlipoproteinemia, hypercholesterolemia, arteriosclerosis, and the like.

[0081] The present invention further provides a pharmaceutical composition comprising a glucagon receptor antagonist as described above and a pharma- ceutically acceptable carrier.

[0082] As used herein, a pharma- ceutically acceptable carrier refers to a substance that is compatible with the recipient individual and suitable for delivering an active agent to a target without affecting the activity of the agent.

[0083] The pharmaceutical composition is applied to mammals, and further to humans. Unless otherwise specified, the following administration individuals are the same as those described herein.

[0084] Pharmaceutically acceptable carriers include any and all solvents, diluents and other liquid vehicles, dispersing or suspending aids, surfactants, pH adjusters, isotonic solutions, thickening or emulsifying agents, preservatives, solid binders, lubricants and the like, that are appropriate for the particular dosage form desired.

[0085] Remington: The Science and Practice of Pharmacy, 20th edition, edited by AAGennaro, Lippincott Williams & Wilkins, 2000, disclosed various carriers for preparing pharmaceutically acceptable compositions and their known preparation techniques. Strickley, Pharmaceutical Research, 21(2)201-230(2004) described pharmaceutically acceptable excipients used in commercial products to dissolve compounds for oral or parenteral administration. As long as any conventional carrier medium is not incompatible with the glucagon receptor antagonist of the present invention, e.g., does not produce any undesirable biological effect or interact in a deleterious manner with any other component of the pharmaceutically acceptable composition, its use falls within the scope of the present invention.

[0086] Specific examples of substances that function as pharmaceutical pharma- ceutically acceptable carriers include ion exchangers, alumina, aluminum stearate, lecithin, serum proteins (such as human serum albumin), buffer substances (such as phosphates, carbonates, magnesium hydroxide, aluminum hydroxide), glycine, sorbic acid or potassium sorbate, partial glyceride mixtures of saturated vegetable fatty acids, water, pyrogen-free water, salts or electrolytes (such as protamine sulfate, disodium hydrogen phosphate, potassium hydrogen phosphate, sodium chloride, zinc salts), colloidal silica, magnesium trisilicate, polyvinylpyrrolidone, polyacrylic esters, waxes, polyethylene-polyoxypropylene, polyvinylpyrrolidone ... Pyrene block polymer, lanolin, sugar (lactose, glucose, sucrose, mannitol, etc.), starch (corn starch, potato starch, etc.), cellulose and its derivatives (sodium carboxymethylcellulose, ethylcellulose, cellulose acetate, etc.), powdered tragacanth gum, malt, gelatin, talc, excipients (cocoa butter, suppository wax, etc.), oils (peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil, soybean oil, etc.), diols (propylene glycol, polyethylene glycol, etc.), esters (ethyl oleate, ethyl laurate, etc.), agar, alginic acid, isotonic saline, Ringer's solution solution), alcohols (such as ethanol, isopropanol, cetyl alcohol, glycerol, etc.), cyclodextrins (such as hydroxypropyl β-cyclodextrin, sulfobutyl ether β-cyclodextrin, etc.), lubricants (such as sodium lauryl sulfate, magnesium stearate, etc.), petroleum hydrocarbons (such as mineral oil, paraffin oil, etc.) At the discretion of the formulator, colorants, release agents, coating agents, sweeteners, flavoring and fragrance agents, preservatives and antioxidants can also be present in the composition.

[0087] The pharmaceutical compositions in the embodiments of the present invention can be prepared by methods known in the art, such as conventional granulation, mixing, dissolving, encapsulation, lyophilization, emulsification, among others. The pharmaceutical compositions can be manufactured in various forms, including particles, precipitates or particulates, powders (including freeze-dried, rotary dried or spray-dried powders, amorphous powders), tablets, capsules, syrups, suppositories, injections, emulsions, elixirs, suspensions or solutions. They can be administered by injection or orally.

[0088] Embodiments of the present invention further provide methods of modulating the glucagon receptor in an individual comprising administering to an individual in need thereof a glucagon receptor inhibiting dose of a glucagon receptor antagonist as described above.

[0089] Embodiments of the present invention further provide methods of modulating glucagon receptors in an individual, comprising administering to an individual in need thereof a glucagon receptor inhibiting dose of the pharmaceutical composition described above.

[0090] Here, the individual is a mammal. Further, the individual is a human.

[0091] Embodiments of the present invention further provide a method of regulating blood glucose levels in an individual comprising administering to an individual in need thereof a glucagon receptor inhibiting dose of a glucagon receptor antagonist as described above.

[0092] Embodiments of the present invention further provide a method of regulating blood glucose levels in an individual comprising administering to the individual in need thereof a glucagon receptor inhibiting dose of the pharmaceutical composition described above.

[0093] An embodiment of the present invention further provides the use of the glucagon receptor antagonists described above for the preparation of a medicament for the treatment of diabetes or other glucagon-related metabolic disorders.

[0094] Here, the glucagon-related metabolic disorders include diabetes (type I diabetes, type II diabetes), hyperglycemia, hyperinsulinemia, β-cell rest, β-cell dysfunction (i.e., the compound can improve β-cell function by restoring early response), dietary hyperglycemia, cell apoptosis (i.e., the compound can prevent cell apoptosis), impaired fasting glucose (IFG), and the like. Glucose), metabolic syndrome, hypoglycemia, hyper / hypokalemia, glucagon level imbalance (i.e., the compound can normalize glucagon levels), abnormal LDL / HDL ratio (i.e., the compound can improve LDL / HDL ratio), reduced snacking, eating disorders, weight loss, polycystic ovarian syndrome (PCOS), diabetic obesity, latent autoimmune diabetes (potential approval of LADA), insulitis, islet transplantation, pediatric diabetes, gestational diabetes, end stage diabetic complications, micro / macroproteinuria, nephropathy, retinopathy, neuropathy, diabetic foot ulcers, glucagon administration These include, but are not limited to, decreased intestinal motility due to ischemia, short bowel syndrome, antidiarrheal, increased gastric secretion, decreased blood flow, erectile dysfunction, glaucoma, postoperative stress, organ tissue damage due to blood reperfusion after ischemia (i.e., the compound can improve organ tissue damage due to blood reperfusion after ischemia), ischemic heart damage, cardiac dysfunction, congestive heart failure, stroke, myocardial infarction, arrhythmia, premature death, anti-apoptosis, wound healing, impaired glucose tolerance (IGT), insulin resistance syndrome, syndrome X, hyperlipidemia, dyslipidemia, hypertriglyceridemia, hyperlipoproteinemia, hypercholesterolemia, arteriosclerosis, and the like.

[0095] In another illustrative embodiment, an insulin receptor antagonist can be selected as compound A1 or compound A2 according to an artificial intelligence-based antagonist prediction method.

[0096] The method for predicting antagonists based on artificial intelligence will be specifically described below.

[0097] Referring to Figure 6, Figure 6 is a flow chart of an antagonist prediction method based on artificial intelligence in an exemplary embodiment of the present invention. The antagonist prediction method based on artificial intelligence provided by this embodiment includes steps S210 to S240.

[0098] In step S210, the generator network of the trained Generative Adversarial Neural Network performs a molecular structure prediction process on the target drug-like molecule to obtain a pseudo-drug molecule, where the Generative Adversarial Neural Network is trained according to the drug-like molecule and the existing antagonists corresponding to the target receptor molecule.

[0099] For example, the drug-like molecule and the existing antagonist corresponding to the target receptor molecule are used as training samples of the generative adversarial neural network. The drug-like molecule in the training sample is input to a generator network in the generative adversarial neural network, and molecular structure prediction is performed on the drug-like molecule based on the generator network to obtain a predicted drug molecule. The predicted drug molecule, the drug-like molecule, and the existing antagonist are input to a discriminator network in the generative adversarial neural network, and parameters of the generative adversarial neural network are optimized according to the output of the discriminator network of the generative adversarial neural network to obtain a trained generative adversarial neural network.

[0100] Based on the generative adversarial neural network obtained by the training, the chemical formula of the target drug-like molecule is obtained, and graph encoding is performed to obtain a corresponding graph representation. The graph representation corresponding to the target drug-like molecule is input into a generator network of the trained generative adversarial neural network, and a molecular structure prediction is performed based on the generator network of the trained generative adversarial neural network, and then the graph representation of the pseudo-drug molecule is output.

[0101] In step S220, a database of small molecules to be screened is determined based on the pseudo-drug molecules.

[0102] For example, the two-dimensional pseudo-drug molecule is converted into a three-dimensional molecule by the force field transformation method of chemical simulation software to obtain a small molecule database, which is used as the drug database for virtual screening.

[0103] In step S230, a virtual screening process is performed on the small molecule database based on the target receptor molecule to obtain the target drug small molecule.

[0104] For example, by using a target receptor molecule as a target, an optimized search algorithm is used to calculate the degree of affinity between the drug molecule and the target in the small molecule database based on the shape characteristics, size characteristics, and electrostatic properties of the target protein active pocket, and then, based on the degree of affinity, a target drug small molecule is selected from the small molecule database as a target corresponding to the target receptor molecule, i.e., as a target drug small molecule.

[0105] In step S240, a cellular activity test is performed on the target drug small molecule to obtain a predicted antagonist of the target receptor molecule.

[0106] The artificial intelligence-based antagonist prediction technology scheme according to the embodiment shown in Figure 6, on the one hand, obtains a pseudo-drug molecule of a target receptor molecule based on a deep learning model, thereby increasing the diversity of the chemical space structure of the pseudo-drug molecule, so that the skeleton structure of the antagonist determined based on the pseudo-drug molecule is significantly different from the skeleton structure of existing drugs, which is favorable for improving the diversity of drugs. On the other hand, a small molecule database to be selected is determined based on the pseudo-drug molecule, and a virtual selection process is performed on the small molecule database based on the target receptor molecule, so that a suitable conformation in the protein active pocket of the target receptor can be directly searched for based on the target drug small molecule, which is favorable for ensuring the rationality of the designed antagonist.

[0107] In the following illustrative examples, human glucagon is taken as the target receptor molecule, and of course, the target receptor molecule may be any specific type of receptor, and is not limited thereto.

[0108] Illustratively, FIG. 7 shows a flowchart of an antagonist prediction method based on artificial intelligence in another exemplary embodiment of the present invention. Referring to FIG. 7, the antagonist prediction method includes a pseudo-drug molecule acquisition part 31, a virtual selection part 32 for the pseudo-drug molecule, and a biopsy part 33 for the selected target drug small molecule. Here, the pseudo-drug molecule acquisition part 31 may be determined by a trained Generative Adversarial Network (GAN). For example, graph encoding is performed based on the chemical formula of the drug-like molecule to obtain a graph representation corresponding to the drug-like molecule. For example, atoms are represented as points on a graph, and covalent bonds are represented as lines connecting the points. When the graph representation corresponding to the drug-like molecule is input into a trained generator network of the GAN, the trained generator network of the GAN can output a graph representation corresponding to the pseudo-drug molecule.

[0109] In the exemplary embodiment, the network structure of the GAN adopted in one exemplary embodiment of the present invention will be described first. For example, Figures 8 and 9 respectively show the network structures of the generator network and the classifier network of the GAN according to one embodiment of the present invention.

[0110] Referring to FIG. 8, the generator network (Generator) according to this embodiment may include a convolution layer, a pooling layer, a feature completion layer, a deconvolution layer, and a feature normalization layer. Here, in the neural network architecture, deep features of the graph representation of the drug-like molecule can be extracted through convolution and pooling processes, and logical topological relationships in the molecular diagram can be learned through the deep features. For example, the vertices of the molecular diagram represent atoms, and the lengths of the edges of the molecular diagram represent the bond lengths of the molecules. Through model training, the vertices, the change rules of the edge lengths, and the relationships between the vertices and the edge lengths are learned. However, compared to the graph representation of the drug-like molecule at the time of input, the feature images of the molecular diagram are reduced due to multiple convolution and pooling processes, thereby causing information loss. Therefore, in order to reduce information loss, for each downsampling (as in 41 to 410 in FIG. 8), corresponding upsampling (i.e., feature completion layer, deconvolution layer, as in 411 to 420 in FIG. 8) is performed. Therefore, in the generator network, the upsampling parameters correspond to the downsampling parameters, so that the image is reduced in the upsampling stage and the corresponding image is enlarged in the downsampling stage. That is, the generator network adopts a Unet network structure (as shown by the "U" dotted line in FIG. 8) to reduce the loss of original information in the network transmission process, thereby reducing the phenomenon that the image content output by the generator network does not match the image content input to the generator network due to the mismatch between the downsampling parameters and the upsampling parameters, and finally improves the prediction accuracy of pseudo-drug molecules.

[0111] In an exemplary embodiment, a tanh function can be used as the activation function in the generator network shown in Figure 8. For example, the output data of the upsampling 420 can be mapped between -1 and 1 by the tanh function.

[0112] In an exemplary embodiment, referring to FIG. 9, a discriminator network of a GAN according to an exemplary embodiment of the present invention includes multiple convolution layers (such as 501-510 in FIG. 9). It should be noted that in this exemplary technical solution, Wasserstein GAN (WGAN) can also be adopted, so that the discriminator network does not use a sigmoid function as an activation function in downsampling 510, thereby effectively avoiding the loss gradient of the generator network from vanishing.

[0113] In the exemplary embodiment, Fig. 8 and Fig. 9 are merely exemplary network structures of the generator network and the discriminator network. In actual implementation, the network structure can be adjusted according to actual needs, so that the network structure of the neural network for generating pseudo-drug molecules in the present invention is extensible.

[0114] Based on the network structure of the WGAN, FIG. 10 is a flowchart of a method for training an adversarial neural network in an exemplary embodiment of the present invention, including steps S610 to S630.

[0115] In step S610, the drug-like molecules and existing antagonists corresponding to the target receptor molecules are obtained as training samples for the adversarial neural network.

[0116] For example, when the target receptor molecule is human glucagon receptor (abbreviated as hGCGR), obtain existing antagonists of hGCGR, such as reported antagonists, and obtain a large number of drug-like molecules. For example, obtain 1 million drug-like molecules in a commercial or open source small molecule database (such as ZINC). Use the existing antagonists of hGCGR and the 1 million drug-like molecules as a training sample set.

[0117] Before the training samples are input into the WGAN, a graph encoding process is first performed on each training sample to obtain a corresponding graph representation. Illustratively, in order to facilitate processing in the WGAN, the drug-like molecules and existing antagonists in the training sample set are graph-encoded one by one in the manner of an adjacency tensor A and an annotation matrix X. For example, the carbon atoms of one cyclopentane can be encoded as the vertices of a pentagon, and the edges of the pentagon correspond to the covalent bonds of the cyclopentane molecule (as shown in the pseudo-drug molecule acquisition part 31 of FIG. 7).

[0118] In step S620, the drug-like molecules in the training sample are input into a generator network in the generative adversarial neural network, and molecular structure prediction is performed on the drug-like molecules based on the generator network to obtain predicted drug molecules.

[0119] In an exemplary embodiment, Figure 11 is an exemplary framework diagram of a model training method according to one embodiment of the present invention. Referring to Figure 11, the generative adversarial deep neural network includes a generator network G710 and a discriminator network D720.

[0120] The generator network G710 performs molecular structure prediction processing (e.g., convolution processing, deconvolution processing, normalization processing, etc.) on the drug-like molecule M (specifically, a graph representation corresponding to the drug-like molecule M), and the output of the generator network G710 is a predicted drug molecule M' (specifically, a graph representation corresponding to the predicted drug molecule M'). Here, the generator network G710 performs molecular structure prediction processing on the drug-like molecule M, and the training goal is to obtain a predicted drug molecule for which the classification result of the classifier network D720 is "true", that is, the classification result of the classifier network D720 is that the probability that the predicted drug molecule M' is an antagonist of the target receptor molecule is greater than 0.5. In other words, the training goal of the generator network G710 is to train the predicted drug molecule to be closer to the existing antagonist, thereby achieving the effect of "passing the fake as the real thing" (the classifier network D720 determines that the predicted drug molecule M' is an antagonist of the target receptor molecule as "true").

[0121] Next, referring to FIG. 12, in step S630, the predicted drug molecules, drug-like molecules and existing antagonists are input into a classifier network in a generative adversarial deep neural network, and parameters of the generative adversarial neural network are optimized based on the output of the classifier network of the generative adversarial network.

[0122] In one exemplary embodiment, the predicted drug molecule M' (specifically, a graph representation corresponding to the predicted drug molecule M') and the existing antagonist B (specifically, a graph representation corresponding to the existing antagonist B) are input to the discriminator network D720, and the discriminator network D720 determines whether the predicted drug molecule M' is an antagonist of the target receptor molecule. For example, if the discriminator network D720 determines that the predicted drug molecule M' is an antagonist of the target receptor molecule, it indicates that the predicted drug molecule M' currently generated by the generator has high accuracy (i.e., achieves the effect of "passing fake as real"), so the optimization of the generator network parameters can be stopped. If the discriminator network D720 determines that the predicted drug molecule M' is not an antagonist of the target receptor molecule, it indicates that the accuracy of the predicted drug molecule M' currently generated by the generator should be further improved (i.e., has not yet reached the effect of "passing fake as real"), so the optimization of the generator network parameters needs to be continued.

[0123] In another exemplary embodiment, the output of the classifier network includes a first classification result for a predicted drug molecule and a second classification result for an existing antagonist, where the first classification result includes a probability output by the classifier network that the predicted drug molecule M' is an antagonist of a target receptor molecule; and the second classification result includes a probability output by the classifier network that the existing antagonist B is an antagonist of a target receptor molecule.

[0124] In one exemplary embodiment, referring to FIG. 11 , the classifier network D720 is configured to receive the predicted drug molecule M' (specifically, a graph representation corresponding to the predicted drug molecule M') and an existing antagonist B (specifically, a graph representation corresponding to the existing antagonist B). The classifier network D720 determines the probability that the predicted drug molecule M' is an antagonist of the target receptor molecule (i.e., the first classification result) according to the difference between the predicted drug molecule M' and the existing antagonist B. If the probability value is greater than 0.5, the first classification result indicates that the predicted drug molecule M' is an antagonist of the target receptor molecule, i.e., the classification result output by the classifier network is that the predicted drug molecule M' is "true"; correspondingly, if the probability value is less than or equal to 0.5, the first classification result indicates that the predicted drug molecule M' is not an antagonist of the target receptor molecule, i.e., the classification result output by the classifier network is that the predicted drug molecule M' is "false".

[0125] The classifier network D720 also predicts the probability that the existing antagonist B is an antagonist of the target receptor molecule (i.e., the second classification result). If the probability value is greater than 0.5, the second classification result indicates that the existing antagonist B is an antagonist of the target receptor molecule, i.e., the classification result output by the classifier network is that the existing antagonist B is "true"; correspondingly, if the probability value is less than or equal to 0.5, the second classification result indicates that the existing antagonist B is not an antagonist of the target receptor molecule, i.e., the classification result output by the classifier network is that the existing antagonist B is "false".

[0126] Here, the training goal of the classifier network D720 is to classify existing antagonists as "true" (the probability value of the second classification result is greater than 0.5) and to classify predicted drug molecules as "false" (the probability value of the first classification result is less than or equal to 0.5).

[0127] In one exemplary embodiment, parameters of a generative adversarial neural network are optimized based on the first identification result and the second identification result.

[0128] In an exemplary embodiment, referring to Fig. 11, the first and second classification results are determined based on the difference in logical topology between the predicted drug molecule M' and the existing antagonist B, and the parameters of the generator network G710 and the parameters of the classifier network D720 are optimized based on the first and second classification results. For example, the loss function is as shown in Equation (1).

number

[0129] where x i and z i represent the data corresponding to the existing antagonist B and the data corresponding to the predicted drug molecule M′, respectively; D represents the discriminator network D; G represents the generator network G; i is a positive integer less than or equal to m; and m is the number of samples.

[0130] The optimization target of the loss function is min G max G First, the parameters of the generator network G are fixed, the parameters of the discriminator network D are adjusted, and the parameters of the discriminator D are optimized as shown in equation (2).

number

[0131] Here, D(x i ) represents the second discrimination result (i.e., the probability that the existing antagonist B is an antagonist of the target receptor molecule), and the larger the value, the better; D(G(z i )) represents the first discrimination result (i.e., the probability that the predicted drug molecule A' is an antagonist of the target receptor molecule), and the smaller the value, the better (i.e., log(1-D(G(z i The larger the better).

[0132] For example, max D By using Loss, the first classification result output by the classifier network D is that the probability that the predicted drug molecule M' is an antagonist of the target receptor molecule is less than or equal to 0.5, and the second classification result is that the probability that the existing antagonist B is an antagonist of the target receptor molecule is greater than 0.5.

[0133] Next, we fix the parameters of the discriminator D and adjust the parameters of the generator network G. The generator network G detects the existing antagonist B (corresponding data is x i Since the optimization process of the generator network G does not involve the processing of logD(x i ) is not involved. Specifically, we optimize the parameters of the generator G as shown in equation (3).

number

[0134] Here, D(G(z i )) represents the first discrimination result (i.e., the probability that the predicted drug molecule A' is an antagonist of the target receptor molecule), and the larger the value, the better (i.e., log(1-D(G(z i The smaller the better).

[0135] For example, min G After the predicted drug molecule A' output by the generator G after parameter adjustment by Loss is input to the discriminator D, the first predicted discrimination result output by the discriminator D is that the probability that the predicted drug molecule A' is an antagonist of the target receptor molecule is greater than 0.5.

[0136] Through continuous iterative calculation, the loss value meets the preset requirements, and the generator network G of the GAN (also referred to as the molecular map growing model) after training can grow pseudo-drug molecules similar to the training set.

[0137] In an exemplary embodiment, when WGAN is adopted to perform the model prediction, the Earth Mover (EM) distance may be used as the loss function, which can make the training converge more easily since the loss function does not need to calculate log values ​​any more.

[0138] A specific embodiment of determining a pseudo-drug molecule based on the molecular diagram growth model obtained by the training (i.e., the trained GAN or WGAN generator network) is as follows: obtain the chemical formula of the target drug-like molecule and perform graph encoding to obtain a corresponding graph representation; input the graph representation corresponding to the target drug-like molecule into the molecular diagram growth model, perform molecular structure prediction based on the molecular diagram growth model, and then output the graph representation of the pseudo-drug molecule.

[0139] In addition, what is output by the molecular diagram growth model is a graphical representation corresponding to the pseudo-drug molecule, and in order to facilitate the subsequent virtual selection, it needs to be decoded to obtain a standard format for the pseudo-drug molecule, such as the Simplified Molecular Input Line Entry System (SMILES) for inputting and representing molecular reactions, or the Structure Data File (SDF) format.

[0140] Therefore, a decoding process is performed on the encoded pseudo-drug molecule output from the molecular diagram growth model to obtain a chemical formula of the pseudo-drug molecule that meets a preset format.

[0141] Continuing with reference to FIG. 7, after the pseudo drug molecule acquisition part 31, the process proceeds to a virtual selection part 32 for the pseudo drug molecules, and the following introduces an embodiment of performing virtual selection for the pseudo drug molecules.

[0142] Exemplarily, referring to FIG. 6, in step S220, a small molecule database to be selected is determined based on the pseudo drug molecule; and in step S230, a virtual selection process is performed on the small molecule database based on the target receptor molecule to obtain a target drug small molecule.

[0143] Here, the small molecule database is the database used for virtual screening. For example, the molecular diagram growth model is used to generate 10 million pseudo drug molecules, and the standard format of these pseudo drug molecules is obtained through decoding process. In order to favorably directly search for suitable conformation in protein active pocket based on the pseudo drug molecule, the pseudo drug molecule needs to be converted into a three-dimensional pseudo drug molecule (such as mol2 format). In an exemplary embodiment, the two-dimensional pseudo drug molecule is converted into a molecule with a three-dimensional structure by the force field conversion method of chemical simulation software to obtain the small molecule database, which can be used as the drug database for virtual screening.

[0144] In an exemplary embodiment, 100,000 non-pseudo drug molecules can be randomly selected from an existing database (such as the ChemBridge database) for control purposes. The non-pseudo drug molecules are integrated into the small molecule database and used together as a drug database for virtual screening.

[0145] In an exemplary embodiment, considering the principle of virtual selection, which is to select candidate compounds that can be used as targets from a small molecule database based on features such as the shape characteristics, size characteristics, and electrostatic properties of the protein active pocket of the target (target receptor molecule), before performing virtual selection with the above-mentioned new molecular database, it is necessary to preprocess the target receptor molecule, such as filling in amino acid residues in the target receptor molecule, hydrogenating the target receptor molecule, and imparting charges to the target receptor molecule, in order to ensure the smooth progress of virtual selection.

[0146] For example, human glucagon receptor molecule hGCGR (PDB number: 5EE7) is downloaded as a target receptor molecule from an existing database (such as PDB database). The following preprocessing is performed on hGCGR: the amino acid residues of the hGCGR protein molecule are complemented using Schrodinger software, and the target receptor molecule hGCGR is subjected to hydrogenation processing, charge addition processing, etc., and the preprocessed hGCGR protein molecule is stored in a three-dimensional format (such as mol2 format).

[0147] In addition, in order to ensure the smooth progress of virtual screening, the format of the target receptor molecule is unified with the format of the drug molecule in the small molecule database, for example, both are unified into the Mol2 format.

[0148] In an exemplary embodiment, referring to FIG. 12, a virtual selection process is carried out on the small molecule database and the target receptor molecule in the three-dimensional format based on molecular docking technology. Here, the molecular docking technology is based on the principle of the lock-and-key model, in which the receptor protein 81 corresponds to the "lock" and the small molecule 82 corresponds to the "key". The virtual selection is carried out according to whether the two can be integrated into 83. For example, the target receptor molecule hGCGR is taken as the target, and an optimized search algorithm is used to calculate the affinity between the drug molecule and the target in the small molecule database based on the shape characteristics, size characteristics, and electrostatic properties of the target protein active pocket, and then the target drug small molecule is selected from the small molecule database as a target corresponding to the target receptor molecule hGCGR, i.e., as a candidate compound, based on the affinity.

[0149] For example, each small molecule in the small molecule database is scored using a scoring function depending on whether it is a potential target drug, and each small molecule is sorted in descending order depending on the score, and multiple (e.g., 30) small molecules with the highest rankings among the results sorted in descending order are used as target drug small molecules (see Table 2). [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4] [Table 2-5] [Table 2-6]

[0150] Here, C13 in Table 2 is the compound A1, and C14 is the compound A2.

[0151] Next, referring to FIG. 7, after the pseudo drug molecule acquisition section 31 and the virtual selection section 32 for the pseudo drug molecule, we proceed to the biopsy section 33 for the selected target drug molecule (see Table 2), and the following introduces an example of performing activity measurement on the selected target drug molecule.

[0152] Exemplarily, referring to FIG. 6, in step S240, a cellular activity test is performed on the target drug small molecule to obtain an antagonist of the target receptor molecule.

[0153] The target drug small molecules C1-C30 are obtained by outsourcing or synthesis for carrying out the following activity tests.

[0154] In an illustrative example, where the target receptor molecule is the human glucagon receptor, FIG. 13 shows a specific embodiment of performing a cellular activity test on the targeted drug small molecule.

[0155] In step S910, select an antagonist drug of hGCGR as a positive control, mix each target drug small molecule and glucagon peptide segment with human embryonic kidney cells containing target receptor molecule, and bind the glucagon peptide segment with hGCGR to activate the hGCGR signal pathway, thereby releasing the second messenger cyclic adenosine monophosphate (cAMP), where the target drug molecule exerts an antagonistic effect to suppress the activity of hGCGR.

[0156] In an exemplary embodiment, a hGCGR antagonist drug (e.g., L-168,049) is selected as a positive control, and considering that human glucagon receptors exist on the cell membrane of human embryonic kidney cells (e.g., HEK293), human embryonic kidney cells can be used to test the drug activity of human glucagon antagonists. A target drug small molecule, a glucagon peptide segment, is mixed with HEK293 cells containing a target receptor molecule, and the glucagon peptide segment is used to bind with hGCGR and activate the hGCGR signal pathway, thereby promoting the release of cAMP, a second messenger. Here, the target drug molecule exerts an antagonistic effect to suppress the activity of hGCGR.

[0157] In step S920, the second messenger cAMP is detected by homogeneous time-resolved fluorescence (HTRF), and the IC50 value of each target drug small molecule is determined based on the intensity value of HTRF.

[0158] In an exemplary embodiment, homogeneous time resolved fluorescence (HTRF) technology is used to test the activity of each targeted drug small molecule (30 targeted drug small molecules as shown in Table 2). Specifically, after glucagon peptide segment activates glucagon receptor, the second messenger cyclic adenosine monophosphate (cAMP) is released in glucagon receptor cells, and the second messenger cAMP can be detected by HTRF, and the 50% inhibitory concentration (IC50) of drug molecule can be calculated based on the fluorescence intensity.

[0159] In step S930, an antagonist of the target receptor molecule is determined from the target drug small molecule based on the IC50 value.

[0160] In an exemplary embodiment, for the 30 targeted drug small molecules shown in Table 2, 11 identical concentration gradients were set up for each targeted drug small molecule, and the activity of each targeted drug small molecule was obtained by triple HTRF-based (second messenger) cyclic adenosine monophosphate (cAMP) response experiments, and the obtained experimental data are shown in Table 3.

[0161] [Table 3-1] [Table 3-2] [Table 3-3] [Table 3-4] [Table 3-5] [Table 3-6] [Table 3-7] [Table 3-8] [Table 3-9] [Table 3-10] [Table 3-11] [Table 3-12] [Table 3-13] [Table 3-14] [Table 3-15]

[0162] The data in Table 3 were processed by software to obtain Figure 14 and Table 4, with the concentration gradient on the horizontal axis and the inhibition rate on the vertical axis. In the figure, the response inhibition rates of L-168,049 and 30 target drug small molecules are shown, respectively. Here, L-168,049 is the positive control drug. As can be seen from this figure, both C13 and C14 have activity, and the activity values ​​of the remaining 28 target drug small molecules are not shown.

[0163] [Table 4]

[0164] Here, Table 4 shows the 50% inhibitory concentration IC50 of 30 tested target drug small molecules. For example, the 50% inhibitory concentration IC50 values ​​of compound C13 and compound C14 are small, 446.5 nm / L (nM) and 422.5 nM, respectively, and therefore can be used as a human glucagon receptor antagonist with a novel structure.

[0165] This technical solution integrates the advantages of deep learning and virtual selection, and uses deep learning model to predict and obtain pseudo drug molecules, and increases the diversity of the chemical space structure of the pseudo drug molecules, so that the skeleton structure of the antagonist determined based on the pseudo drug molecules is significantly different from the skeleton structure of existing drugs, which is favorable to increasing drug diversity. At the same time, a small molecule database to be selected is determined based on the pseudo drug molecules, and a virtual selection process is performed on the small molecule database based on the target receptor molecules, so that the appropriate conformation in the protein active pocket of the target receptor can be directly searched for based on the target drug small molecule, which is favorable to ensure the rationality of the designed antagonist. In this way, this technical solution can reduce the cost and time of drug development, and is favorable to improving drug diversity.

[0166] Those skilled in the art can easily conceive other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention. These variations, uses, or adaptations follow the general principles of the present disclosure and include common knowledge or customary technical means well known in the art that are not disclosed in the present disclosure. The specification and examples should be considered as merely illustrative, with the scope and spirit of the present invention being pointed out in the following claims.

[0167] It should be understood that the present invention is not limited to the specific configurations shown in the above-mentioned drawings, and various modifications and changes can be made without departing from the scope of the present invention.

Claims

1. A compound represented by the following structural formula A1 or A2, its isomer, metabolite, or pharma- ceutically acceptable salt. 【Chemistry 1】 A glucagon receptor antagonist comprising: A glucagon receptor antagonist, wherein the isomer is an optical isomer or a geometric isomer.

2. 13. A pharmaceutical composition comprising a pharma- ceutically acceptable carrier and the glucagon receptor antagonist of claim 1.

3. The pharmaceutical composition is a solution, tablet or capsule. The pharmaceutical composition according to claim 2.

4. The pharmaceutical composition is administered by injection or orally. The pharmaceutical composition according to claim 3.

5. For use in the treatment of diabetes, glucagon imbalance and / or hyperglycemia. A glucagon receptor antagonist according to claim 1 or a pharmaceutical composition according to any one of claims 2 to 4.

6. For use in mammals, A glucagon receptor antagonist according to claim 1 or a pharmaceutical composition according to any one of claims 2 to 4.

7. For use in humans 7. The glucagon receptor antagonist or pharmaceutical composition of claim 6.

8. A composition for use in a method for treating a disease associated with glucagon metabolic imbalance or a method for regulating blood glucose levels, comprising a therapeutically effective dose of the glucagon receptor antagonist of claim 1 or the pharmaceutical composition of any one of claims 2 to 4.

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