Use of compounds in the preparation of drugs for treating proliferative diseases

Abstract

The present application discloses a use of a compound in the preparation of a drug for treating a proliferative disease, wherein the compound is a general compound shown in formula (1) and its stereoisomers, or a pharmaceutically acceptable salt, solvate, hydrate, polymorph, cocrystal, tautomer, isotope-labeled derivative and prodrug of the general compound and its stereoisomers, wherein the structure of formula (1) is: wherein R1, R2 and R3 independently represent a single substituent or multiple substituents at any substitutable position on the ring. The compound screened in the present application has a strong effect of inhibiting CDK12 kinase activity and can specifically target CDK12 protein kinase. It can be used to prepare preparations or drugs for proliferative diseases including tumors and for preparing drugs targeting CDK12 protein. The drug has good anti-cancer effect and strong universality, providing more options for clinical pharmaceutical preparations.

Description

Technical Field

[0001] The present application relates to the technical field of drug preparation, and in particular to the use of a compound in the preparation of drugs for treating proliferative diseases. Background Art

[0002] Drug discovery is the process of identifying new candidate compounds with potential therapeutic effects, and the determination of drug-target interactions (DTI) between drug molecules and target proteins is an essential step in the drug discovery process. The efficacy of drug molecules depends on their affinity for target proteins or receptors. Drug molecules without any interaction or affinity for target proteins will not provide a therapeutic response.

[0003] Proliferative diseases mainly include malignant diseases such as cancer, which pose a great threat to people's health. Cyclin-dependent kinases (CDKs) are serine / threonine kinases whose activity depends on the interaction with cyclin regulatory subunits. Taking breast cancer, a common disease in women, as an example, researchers have found that small molecules that target and inhibit CDK12 / 13 can block the occurrence and development of triple-negative breast cancer through the DDR effect. Although existing technologies already have CDK12 small molecule inhibitors, such as THZ531 and THZ1, it is still difficult to meet diverse clinical needs. Summary of the Invention

[0004] In response to the above problems, the embodiments of the present application provide a use of a compound in the preparation of a drug for treating proliferative diseases, aiming to provide a protein inhibitor for the preparation of a drug for treating proliferative diseases, providing many options for clinical needs.

[0005] The present application provides an embodiment of a compound for use in preparing a drug for treating a proliferative disease, wherein the compound is a compound of the general formula shown in formula (1) and its stereoisomers, or a pharmaceutically acceptable salt, solvate, hydrate, polymorph, cocrystal, tautomer, isotope-labeled derivative, and prodrug of the compound of the general formula and its stereoisomers, wherein the structure of formula (1) is:

[0006]

[0007] Wherein, R1, R2 and R3 each independently represent a single substituent or multiple substituents at any substitutable position on the ring.

[0008] Here, "stereoisomers" refer to isomers of a compound that differ in the arrangement of their atoms in space. Isomers are compounds that have the same molecular formula but differ in the nature or sequence of bonding of their atoms or the arrangement of their atoms in space.

[0009] "Pharmaceutically acceptable salts" refer to salts that are suitable for use in contact with the tissues of humans and lower animals without undue toxicity, irritation, allergic reactions, etc., within the scope of sound medical judgment and commensurate with a reasonable benefit / risk ratio. Pharmaceutically acceptable salts are well known in the art. Pharmaceutically acceptable salts of the compounds of the present invention include those produced by suitable inorganic and organic acids and bases. Examples of pharmaceutically acceptable non-toxic acid addition salts are amino salts formed with inorganic acids such as hydrochloric acid, hydrobromic acid, phosphoric acid, sulfuric acid, and perchloric acid, or with organic acids such as acetic acid, oxalic acid, maleic acid, tartaric acid, citric acid, succinic acid, or malonic acid, or by using other methods known in the art (such as ion exchange). Other pharmaceutically acceptable salts include adipates, alginates, ascorbates, aspartates, etc., which are not described in detail.

[0010] "Solvate" refers to an association formed between one or more solvent molecules and the compound of the present invention. Conventional solvents include water, methanol, ethanol, acetic acid, DMSO, THF, ether, etc.

[0011] "Hydrate" refers to an association formed when the solvent molecule is water. When the solvent is water, the term "hydrate" may be used. Generally, the number of water molecules contained in the hydrate of a compound is in a defined ratio to the number of compound molecules in the hydrate. A given compound may form more than one type of hydrate, including, for example, monohydrates, oligohydrates (e.g., hemihydrates), and polyhydrates. It should be noted that the hydrates described herein retain the biological effectiveness of the non-hydrated form of the compound.

[0012] "Tautomers" refer to compounds that are interchangeable forms of a particular compound structure and differ in the shift of hydrogen atoms and electrons. Thus, the two structures can remain in equilibrium via the movement of π electrons and atoms (usually H). Tautomeric forms may be relevant to achieving optimal chemical reactivity and biological activity for a compound of interest.

[0013] "Prodrug" refers to a compound that is converted in vivo to a compound of formula (I). Such conversion is affected by hydrolysis of the prodrug in the blood or by enzymatic conversion to the parent structure in the blood or tissues.

[0014] For any professional terms or nouns not explained in this application, please refer to the existing definitions in the field.

[0015] Optionally, in some embodiments of the present application, R1, R2 and R3 are independently selected from one or more of the following groups:

[0016] Hydrogen, methyl, ethyl, propyl, isopropyl, n-butyl, isobutyl, cyano, hydrogen, methyl, ethyl, propyl, isopropyl, n-butyl, isobutyl, cyano, -COOH, -CONHNHR, -OCH3, -NHCOR, -Br, -Cl, -F.

[0017] Optionally, in some embodiments of the present application, R1, R2 and R3 each independently represent hydrogen.

[0018] Optionally, in some embodiments of the present application, one or more of the compounds are used as active ingredients to prepare a pharmaceutical composition for treating proliferative diseases.

[0019] Optionally, in some embodiments of the present application, based on the total molar mass of the pharmaceutical composition, the molar mass of the compound is 5-10 micromoles.

[0020] Optionally, in some embodiments of the present application, the molar mass of the compound is 9.1 micromoles.

[0021] Optionally, in some embodiments of the present application, the proliferative disease includes breast cancer.

[0022] Optionally, in some embodiments of the present application, the breast cancer is triple-negative breast cancer.

[0023] Optionally, in some embodiments of the present application, the proliferative disease includes ovarian cancer.

[0024] Optionally, in some embodiments of the present application, the drug for treating proliferative diseases is a drug targeting CDK12 protein.

[0025] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0026] The present application provides the use of a specific compound in the preparation of a drug for treating proliferative diseases. The specific compound has a specific structure as shown in formula (1). Experiments have shown that the compound has a strong inhibitory effect on CDK12 kinase activity and can specifically target CDK12 protein kinase. The compound and its derivatives can be used to prepare preparations or drugs for proliferative diseases including tumors, especially breast cancer or ovarian cancer, as well as for preparing drugs targeting CDK12 protein. The drugs have strong anti-cancer effects and universal applicability, providing more options for clinical pharmaceutical manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0028] Figure 1 A schematic flow chart of a method for screening small molecule compounds as protein inhibitors according to one embodiment of the present application is shown;

[0029] Figure 2 A schematic structural diagram of an affinity prediction model according to the present application is shown;

[0030] Figure 3 Shown based on Figure 2 Schematic diagram of the data flow of the affinity prediction model shown;

[0031] Figure 4 A schematic diagram showing the results of the compound represented by formula (1) inhibiting the activity of CDK12 protein kinase; DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0034] 1. Briefly introduce the selection process of small molecule compounds in this application.

[0035] This application proposes a method for screening small molecule compounds that serve as protein inhibitors. By introducing artificial intelligence methods, combining molecular docking simulation and experimental means, rapid screening of small molecule inhibitors is achieved.

[0036] Specifically, such as Figure 1 As shown, Figure 1 A schematic flow chart of a method for screening small molecule compounds as protein inhibitors according to an embodiment of the present application is shown. Figure 1 It can be seen that the rapid screening of small molecule inhibitors includes steps S110 to S140:

[0037] Step S110: Obtain the protein sequence of the target protein and multiple candidate small molecules.

[0038] The present application is applicable to various proliferative diseases, especially tumors. For the sake of convenience, the following description will be made using common breast cancer as an example.

[0039] Breast cancer is the most common cancer among women worldwide. Currently, conventional treatments for breast cancer are based on the expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor 2 receptor (HER2 / ERBB2), with targeted blocking of receptor function. There is a branch of breast cancer characterized by the lack of ER, PR, and overexpression of HER2, the so-called triple-negative breast cancer (TNBC), which tests negative for all three receptors and accounts for approximately 20% of all breast cancers. These targeted therapies are ineffective against it. For patients with TNBC, surgery and chemotherapy are the main treatments. Although early-stage TNBC has good treatment effects, late-stage breast cancer has a higher recurrence rate and lower overall survival rate compared to other breast cancer subtypes.

[0040] Cyclin-dependent kinases (CDKs) are serine / threonine kinases whose activity depends on interactions with regulatory subunits of cyclins. Recent studies have shown that CDK12 is associated with the expression of a group of DNA damage response (DDR) genes. CDK12 promotes an increase in elongation rate, reduces the probability of cleavage at internal polyadenylation sites, and increases the probability of cleavage at 3' polyadenylation sites. Many DNA damage repair genes, such as members of the BRCAness gene set, have multiple intronic polyadenylation sites and are therefore particularly sensitive to CDK12 inhibition. Recently, researchers have discovered that small molecules that target and inhibit CDK12 / 13 can block the occurrence and development of triple-negative breast cancer through DDR effects.

[0041] Triple-negative breast cancer (TNBC) specimens contain mutations similar to those in ovarian cancer. Both are prone to mutations in genes involved in DNA damage repair, leading to genetic instability. Frequently mutated genes include p53 (80%) and BRCA1 (30%), while CDK12 is mutated in approximately 1.5% of TNBC patients. Furthermore, TNBC patients with defects in HR-related genes, including CDK12, may benefit from treatment with PARP1 / 2 inhibitors.

[0042] An increasing number of studies have found that CDK12 gene amplification and loss-of-function mutations occur in breast cancer. Loss-of-function mutations of CDK12 can lead to genetic instability. Breast cancer patients with CDK12 gene mutations may also be more sensitive to PARP1 / 2 inhibitors. Therefore, CDK12 may serve as a new and promising target for breast cancer treatment.

[0043] CDK12 is a transcription-associated CDK. Its primary function is to phosphorylate the C-terminal domain (CTD) of RNA polymerase II. It also plays an important role in DNA damage repair, mRNA splicing, cell growth, and differentiation. Studies have found that CDK12 gene mutations and overexpression are present in various malignant tumors. The subsequent development of two different CDK12 inhibitors has been helpful in studying the physiological functions of CDK12. Therefore, it is believed that CDK12 is an important transcription factor involved in cell cycle regulation, pre-mRNA splicing, and DNA damage repair. Its gene mutation is closely related to tumorigenesis.

[0044] The structure and function of CDK12: The gene encoding CDK12 is located on autosome 17 and encodes 1,490 amino acids with a molecular weight of 164 kDa. Its main structure consists of a proline-rich motif (PRM), an arginine / serine-rich motif (RS), and a catalytic kinase domain. The kinase domain, located in the central portion of the protein and consisting of approximately 300 amino acids, is responsible for substrate phosphorylation.

[0045] It is currently known that CDK12 has three main functions: (1) CDK12 phosphorylates RNA polymerase PoLⅡ to promote transcription elongation; (2) CDK12 interacts with RNA processing factors to regulate splicing; (3) it mediates the phosphorylation of RNA polymerase II and mRNA 3' end processing during transcription, regulating the polyadenylation of introns.

[0046] Studies have shown that knocking out CDK12 silences the expression of its downstream genes, including cellularly important DNA damage repair genes such as BRCA1, ATR, FANCI, and FANCD2. This creates a state similar to a "DNA damage repair gene deficiency," making cells more sensitive to external conditions that cause DNA damage. Furthermore, CDK12 regulates exon splicing by localizing on nuclear speckles and pre-mRNA spliceosomes. Overall, CDK12 primarily participates in DNA damage response or stress response by regulating genomic transcription and expression.

[0047] Therefore, for triple-negative breast cancer, CDK12 protein was selected as the target protein. In some embodiments of this application, the protein sequence of CDK12 protein was obtained from the uniprot database; candidate small molecules were downloaded from the Asinex, Chembridge, Chemdiv, Lifechemicals, Maybridge, Otava, and Specs chemical databases. In some embodiments of this application, a total of 4,527,236 small molecule compound structures were obtained. For subsequent use, the small molecules can be converted into a specific form, such as SMILES form.

[0048] Step S120: Based on the affinity prediction model, determining the interaction probability between the protein sequence and each of the candidate small molecules, and screening a plurality of preliminary screening small molecules from the plurality of candidate small molecules according to the interaction probability.

[0049] Then, based on the affinity prediction model, the interaction probability between each candidate small molecule and the protein sequence of CDK12 protein is predicted. The interaction probability can be understood as the possibility of interaction between the interaction probabilities.

[0050] In some embodiments of the present application, the affinity prediction model can be any neural network model known in the art, which can be a classification model or a regression model. If the affinity prediction model is a classification model, the output label is "yes" or "no," that is, whether a candidate small molecule interacts with a protein sequence; if the affinity prediction model is a regression model, the output label is a probability value, such as 60%. In this case, multiple candidate small molecules with a probability greater than a certain threshold can be selected as initial screening small molecules.

[0051] In other embodiments of the present application, an affinity prediction model is designed based on the Transformer architecture, which was originally designed for neural machine translation tasks. The Transformer is an autoregressive encoder-decoder model that combines multi-head attention layers and positional feedforward functions to solve sequence-to-sequence tasks. Many pre-trained models are limited to seq2seq tasks, but this application is inspired by its powerful ability to capture features between two sequences and defines a method for predicting the probability of interaction (CPI) by treating compounds and proteins as two sequences.

[0052] Figure 2 A schematic diagram of the structure of an affinity prediction model according to the present application is shown. Figure 2It can be seen that the affinity prediction model 200 includes at least an encoding layer 210, a decoding layer 220, and a fully connected layer 230; the output end of the encoding layer 210 is connected to the second input end of the decoding layer 220, and the output end of the decoding layer 220 is connected to the input end of the fully connected layer 230. The input end of the encoding layer 210 and the first input end of the decoding layer 220 both serve as the input end of the affinity prediction model 200, and the output end of the fully connected layer 230 serves as the output end of the affinity prediction model 200.

[0053] This application is in Figure 2 On the basis of the affinity prediction model shown, the data flow path is redefined. Specifically, the affinity prediction model is used to determine the interaction probability between the protein sequence and each candidate small molecule, including: converting the protein sequence into a real-valued vector, and converting each candidate small molecule into an atomic vector; for one candidate small molecule, the real-valued vector is input into the input end of the encoding layer, and the real-valued vector passes through the encoding layer and enters the second input end of the decoding layer from the output end of the encoding layer, and the atomic vector corresponding to the candidate small molecule is input into the first input end of the decoding layer, so as to determine the interaction vector between the candidate small molecule and the protein sequence based on the input content of the first input end and the second input end of the decoding layer; the interaction vector is allowed to enter the fully connected layer, so as to determine the interaction probability between the protein sequence and the candidate small molecule based on the fully connected layer.

[0054] First, the protein sequence and each candidate small molecule need to be represented by vectors. Specifically, the protein sequence is converted into a real-valued vector, and each candidate small molecule is converted into an atomic vector.

[0055] When converting the protein sequence into a real-valued vector, the protein sequence can be split into an amino acid sequence; based on a word vector generation method, the obtained amino acid sequence is converted into a real-valued vector. Specifically, in order to convert the protein sequence into a sequential representation, the protein sequence is first split into overlapping3-gram amino acid sequences, and then all words are translated into real-valued vector embeddings through the pre-training method word2vec. Word2vec is an unsupervised technology used to learn high-quality distributed vector representations that describe complex syntactic and semantic word relationships. By integrating Skip-Gram and CBOW, word2vec can ultimately map words to low-dimensional real-valued vectors, where words with similar semantics are mapped to vectors close to each other.

[0056] When converting each of the candidate small molecules into an atomic vector, each atom in the candidate small molecule can be converted into a feature vector of a preset length; based on the image neural network, the feature vector is converted into the atomic vector according to the features between adjacent atoms in the candidate small molecule. Specifically, first use graphic software (such as RDKit, a chemical open source tool) to convert the features of each atom in the small molecule compound into a vector representation of length 34, and record the vector as a feature vector; then use GNN (image neural network) to learn the representation of each atom by integrating the features of adjacent atoms. GNN was originally designed to solve the problem of semi-supervised node classification, and can be used here to solve the problem of molecular representation. GNN can be used to integrate the features between adjacent atoms and convert the feature vector of the small molecule into an atomic vector. The real-valued vector and the original vector are then used as the input of the affinity prediction model, and the output of the affinity prediction model is the interaction probability of each candidate small molecule with the protein sequence.

[0057] Figure 3 Shown based on Figure 2 The data flow diagram of the affinity prediction model shown in FIG. 1 is different from the prior art in that the present application redefines the data flow process in the affinity prediction model. Figure 3 It can be seen that in this application, real-valued vectors and atomic vectors are used as inputs of the encoding layer and decoding layer respectively. Specifically, taking a candidate small molecule as an example, the real-valued vector of the protein sequence is input into the input end of the encoding layer 210, so that the real-valued vector undergoes the encoding layer 210, and after being processed by the encoding layer 210, enters the second input end of the decoding layer 220 from the output end of the encoding layer 210; at the same time, the atomic vector corresponding to the candidate small molecule is input into the first input end of the decoding layer 220. The role of the decoding layer is to learn the interaction function of the real-valued vector and the atomic vector, that is, the interaction function can be learned by the decoder of the transformer, and the decoder of the transformer consists of a self-attention layer and a feedforward layer (not shown in the figure). The protein sequence (real-valued vector) is the input of the encoder, the atomic sequence (atomic vector) is the input of the decoder, and the output of the decoder is an interaction sequence (interaction vector) containing interaction features and the same length as the atomic sequence.

[0058] Then, the interaction vector is input into the fully connected layer 230, which can process the interaction vector to obtain a prediction result, which can characterize the probability of interaction between the small molecule compound and the protein. The prediction result can also be a score, such as the TransformerCPI score, which can retain small molecule compounds with scores above a certain score.

[0059] In some embodiments of the present application, the masking operation of decoder 220 can also be modified to ensure that the affinity prediction model has access to the entire sequence (real-valued vectors and atomic vectors), which can convert the transformer from an autoregressive task to a classification task. If modified to a classification task, the prediction result can be a "yes" or "no" label. If yes, the candidate small molecule is determined as a preliminary screening small molecule; if no, the candidate small molecule is excluded.

[0060] In addition, in some embodiments of the present application, in order to understand more abstract representations of proteins, the original self-attention layer in the encoder can be replaced by a relatively simple structure. Considering that the traditional Transformer architecture usually requires a large training corpus and is prone to overfitting on small or medium-sized data sets, in some embodiments of the present application, a gated convolutional network with Conv1D and linear gating is used because it can show better performance.

[0061] In some embodiments of the present application, a total of 240,000 small molecules with a transformer CPI score ≥ 6 were selected through the prediction of a trained deep learning-based affinity prediction model, and the small molecules obtained after the affinity prediction model were recorded as primary screening small molecules.

[0062] Step S130: obtaining simulation results of molecular docking simulation between the target protein and each of the pre-screened small molecules, and screening multiple target small molecules from the multiple pre-screened small molecules according to the simulation results.

[0063] After the initial screening by the affinity prediction model, many (tens of thousands) of pre-screened small molecules remain. If these pre-screened small molecules are directly tested, it will still require a lot of manpower and time. Therefore, the virtual screening technology of molecular docking simulation can be used to further screen multiple target small molecules, and then conduct experimental screening.

[0064] Molecular docking simulation can be performed according to the following method: obtaining the receptor structure of the protein sequence of the target protein and the ligand structure corresponding to each of the pre-screened small molecules; defining the binding pocket of the target protein according to the binding site of the receptor structure; defining the coordination relationship between each of the ligand structures and the binding pocket, and performing molecular docking simulation; screening multiple target small molecules from multiple pre-screened small molecules according to a preset virtual high-throughput threshold and a single-point energy threshold.

[0065] The receptor structure of the protein sequence of the target protein can be selected from a specified crystal structure of the protein sequence as the receptor structure. Specifically, the crystal structure: PDB code: 6CKX can be selected as the receptor structure for molecular docking.

[0066] The ligand structure can be optimized by performing structural optimization on the various configurations of each of the pre-screened small molecules to obtain the single point energy of each configuration of each of the pre-screened small molecules, wherein the multiple configurations include at least one of: ionic state configuration, tautomeric configuration and stereo configuration; the configuration with the lowest single point energy of each of the pre-screened small molecules is used as the ligand structure corresponding to each of the pre-screened small molecules. For example, if the affinity prediction model is selected to predict the top 240,000 small molecules (pre-screened small molecules) in affinity, The Lig Prep module in Maestro software further processes and optimizes the structure, minimizing various ionic states, tautomerism, and stereo configurations of the structure, and generates a structure with the optimal structure and the lowest energy as the ligand structure.

[0067] After the receptor structure and ligand structure are prepared, the binding pocket of the target protein can be defined according to the binding site of the receptor structure; the coordination relationship between each ligand structure and the binding pocket can be defined to perform molecular docking simulation; and multiple target small molecules can be screened from multiple pre-screened small molecules according to the preset virtual high-throughput threshold and single-point energy threshold. The Glide module of the software is used for molecular docking. The binding pocket is defined according to the binding site of the small molecule. The default parameters of the program are used in the process of grid generation. The precision of the step-by-step screening is set to retain the top 10% of HTVS and the top 10% of SP. The Canvas module in the software clusters the obtained compounds and selects multiple compounds as target small molecules. The number of target small molecules obtained through molecular docking simulation is about several to dozens, including the compound shown in formula (1)

[0068]

[0069] Wherein, R1, R2 and R3 each independently represent a single substituent or multiple substituents at any substitutable position on the ring.

[0070] Step S140: obtaining experimental results of the activity inhibition experiment of each target small molecule on the target protein, and screening protein inhibitors of the target protein from the multiple target small molecules according to the experimental results.

[0071] Finally, based on experimental verification of multiple target small molecules, the general formula compound shown in (1) and its stereoisomers, or pharmaceutically acceptable salts, solvates, hydrates, polymorphs, cocrystals, tautomers, isotope-labeled derivatives and prodrugs of the general formula compound and its stereoisomers were selected as small molecule inhibitors.

[0072] 2. Experiments on the inhibitory effect of small molecule compounds on CDK12 kinase activity.

[0073] Finally, based on the experimental results of the activity inhibition experiments of each target small molecule, one or several small molecule compounds with the best effect are screened out from multiple target small molecules and used as protein inhibitors.

[0074] Experiment 1:

[0075] The inhibition experiment of CDK12 kinase activity was carried out on 50 target small molecule compounds obtained by molecular docking simulation. Specifically, the ability of each target small molecule to inhibit CDK12 kinase activity can be detected by the ADP Glo method. After screening, it was found that the general formula compound shown in formula (1) has a good kinase activity inhibitory effect on CDK12 protein kinase, especially when R1, R2 and R3 independently represent hydrogen. The effect is significant. Figure 4 As shown, Figure 4 The schematic diagram shows the results of the inhibition of CDK12 protein kinase activity by substance T. Figure 4 It can be seen that the half effective inhibitory concentration (IC50) of small molecule compound T in inhibiting CDK12 kinase is 168.7 nM.

[0076] Experiment 2:

[0077] CCK8 cell assay: HER2-positive, CDK12-amplified breast cancer cell line BT-474 was seeded in a 96-well plate with 10,000 cells per well. Different concentrations of small molecule inhibitors screened using the aforementioned method were added to the plate in triplicate. After 48 hours, 10 μl of CCK8 detection reagent was added to each well and incubated for 3 hours. Cell viability was measured using a microplate reader: the cell viability was calculated as (OD experimental - OD blank) / (OD positive control - OD blank) * 100%.

[0078] In the CCK8 cell assay, the compound represented by formula (1) (R1, R2 and R3 independently represent hydrogen) showed an inhibitory effect in the Her2-positive breast cancer cell line BT-474, with IC50 = 9.1 μM.

[0079] The target small molecules in the experiment were all commercially available products.

[0080] 3. Preparation of Pharmaceutical Compositions

[0081] The compound or composition of the present invention can be taken alone or in combination with other therapeutic drugs or symptomatic drugs. When the compound of the present invention has a synergistic effect with other therapeutic drugs, its dosage should be adjusted according to the actual situation.

[0082] When preparing a pharmaceutical composition, the general compound represented by formula (1) and its stereoisomers, or one or more of the pharmaceutically acceptable salts, solvates, hydrates, polymorphs, cocrystals, tautomers, isotope-labeled derivatives, and prodrugs of the general compound and its stereoisomers can be used as active ingredients to prepare a pharmaceutical composition for treating a proliferative disease. Pharmaceutically acceptable solid or liquid excipients and / or auxiliary materials can also be added to prepare any dosage form suitable for human or animal use.

[0083] In the present application, the dosage of the pharmaceutical composition is any therapeutically acceptable dose; in some embodiments, based on the total molar mass of the pharmaceutical composition, the molar mass of the compound is 5-10 micromoles; preferably, the molar mass of the compound is 9.1 micromoles.

[0084] In summary: The present application provides the use of a specific compound in the preparation of a drug for treating proliferative diseases. The specific compound has a specific structure as shown in formula (1). Experiments have shown that the compound has a strong inhibitory effect on CDK12 kinase activity and can specifically target CDK12 protein kinase. The compound and its derivatives can be used to prepare preparations or drugs for proliferative diseases including tumors, especially breast cancer or ovarian cancer, as well as for preparing drugs targeting CDK12 protein. The drugs have strong anti-cancer effects and universality, providing more options for clinical pharmaceutical manufacturing.

[0085] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. Use of a compound in the preparation of a medicament for treating a proliferative disease, characterized in that: The compound is a compound of the general formula shown in formula (1): wherein R1, R2 and R3 each independently represent hydrogen; The proliferative disease is Her2-positive breast cancer.

2. The use according to claim 1, characterized in that One or more of the compounds are used as active ingredients to prepare a pharmaceutical composition for treating proliferative diseases.

3. The use according to claim 2, characterized in that Based on the total molar mass of the pharmaceutical composition, the molar mass of the compound is 5-10 micromoles.

4. The use according to claim 3, characterized in that The molar mass of the compound is 9.1 μmol.

5. The use according to any one of claims 1 to 4, characterized in that The drug for treating proliferative diseases is a drug targeting CDK12 protein.

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