780 results about "Anticancer drug" patented technology

Platinum-acridines and analogs thereof as cytotoxic agents for cancer treatment. Also provided methods of using hMATE1 (SLC47A1) as a biomarker to identify tumors that are likely to respond to the agents, and epigenetically sensitizing tumor tissue to anticancer drugs targeting this membrane transporter.

The invention relates to the field of natural products, in particular to a dimer compound of guaiane type sesquiterpenes and uric acid as well as an extraction and separation method and application of the dimer compound. According to the invention, a new dimer compound of guaiane type sesquiterpene and uric acid is extracted and separated from carpesium abrotanoides in Guizhou, and it is found that the dimer compound has relatively strong anti-tumor activity. The compound has strong inhibitory activity on breast cancer, prostate cancer, nasopharynx cancer, liver cancer, lung cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, skin cancer, cervical cancer, ovarian cancer, kidney cancer and esophageal cancer, and provides an optional scheme for broad-spectrum anti-cancer drugs. Moreover, in human prostate cancer cells PC3, human cervical cancer cells Hela, human liver cancer cells HepG2 and human glioma cells U87 which are resistant to paclitaxel, the compound also shows strong anti-cancer activity, which indicates that the compound has the potential of treating patients resistant to paclitaxel.

Provided are methods for the treatment of SCLC patients by administering therapeutic amounts of lurbinectedin by intravenous infusion. Also provided are methods of treating cancer by administering lurbinectedin in combination with other anticancer drugs, in particular topoisomerase inhibitors. The invention further relates to the administration of lurbinectedin in combination with anti-emetic agents for effective control of symptoms related to nausea and vomiting, reduced lurbinectedin dosages to achieve a safer administration and an increase in the number of treatment cycles. Stable lyophilized formulations of lurbinectedin are also provided.

The invention relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The present invention relates to: a method for evaluating the efficacy of an anti-cancer drug, a method for screening an anti-cancer drug, a system for evaluating the efficacy of an anti-cancer drug, or a system for screening an anti-cancer drug, using a mixture of peripheral blood mononuclear cells (PBMC) and a cancer organoid. Specifically, the present invention relates to: a method for evaluating the efficacy of an anti-cancer drug, a method for screening an anti-cancer drug, a system for evaluating the efficacy of an anti-cancer drug, or a system for screening an anti-cancer drug, using a mixture of cytotoxic T cells differentiated from PBMC and cancer organoids. According to the method for evaluating the efficacy of an anti-cancer drug, the screening method, the efficacy evaluation system, or the screening system of the present invention, a tumor microenvironment, i.e. The form in which cancer cells are present in a living body, can be very closely reproduced. Furthermore, since PBMCs derived from normal individuals are used and differentiated into cancer patient-specific cytotoxic T cells, the burden of collecting biological samples such as blood and tissue samples from cancer patients can be reduced, and the efficacy of an anti-cancer drug can be evaluated with high accuracy.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention discloses an anti-cancer drug synergistic effect prediction method based on deep learning, and the method can employ a network model which comprises a data input module, a drug sub-network module, a CrossNet module and a collaborative score prediction module. The data input is used for inputting drug pair-cell line characteristic data and cell line types, tissue types and cancer subtype data under the combined action of the two drugs, and the drug pair-cell line characteristic data comprises drug chemical characteristic data of the two drugs and cancer cell line gene expression data under the combined action of the two drugs; the input of the medicine sub-network module and the input of the CrossNet module are data output by the data input module; and the input of the collaborative score prediction module is the output of the drug sub-network module and the output of the CrossNet module. The method provided by the invention can more accurately and reliably predict whether the anticancer drugs have a synergistic effect.

The invention belongs to the technical field of biomedical high-molecular polymer materials, and discloses a pH and temperature dual-response lignin-based amphiphilic polymer and micelle as well as a preparation method and an application of the pH and temperature dual-response lignin-based amphiphilic polymer and micelle. The preparation method of the polymer comprises the following steps: purifying the alkali lignin, preparing a macroinitiator AL-Br based on the alkali lignin, and then preparing the lignin-based amphiphilic polymer with double responses of pH and temperature. The polymerization process is controllable, and the usage amount of copper is lower than 500 ppm. The lignin polymer disclosed by the invention can be self-assembled into nano-scale micelles in a water medium, the structure is stable, and AgNPs and an entrapped hydrophobic anticancer drug CPT can be simultaneously prepared in situ. The obtained single nano-carrier system has good silver nanoparticle stabilizing capacity, in-vitro pH and temperature double-response drug release capacity and excellent cytotoxicity and antibacterial capacity, can achieve the anti-tumor and antibacterial purposes at the same time, and is well applied to preparation of drugs with anti-tumor and antibacterial effects at the same time.

The invention relates to the field of biological medicine, in particular to IMSFs hydrogel, a ferroptosis induction system, a preparation method and application. The IMSFs hydrogel comprises a hydrogel body, and ferroferric oxide nanoparticles and sorafenib are loaded in the hydrogel body in a wrapping manner. The IMSFs hydrogel can be used as an injectable cascade ferroptosis anti-cancer drug. The preparation method of the IMSFs hydrogel comprises the following steps: firstly, preparing a silk fibroin-hyaluronic acid hydrogel body: dissolving silk hydrogel and hyaluronic acid in a lithium bromide solution; bDDE is added into the dissolved solution, and incubation is carried out; then dialyzing and washing with deionized water to obtain a silk fibroin-hyaluronic acid hydrogel body; and then loading the sorafenib nanoparticles and the ferroferric oxide nanoparticles into the silk fibroin-hyaluronic acid hydrogel body. The IMSFs hydrogel disclosed by the invention integrates magnetic thermal response and drug controlled release, and can be injected to a tumor site, so that the treatment effect on TNBC is remarkably improved.

The invention discloses a drug-combined anticancer composition and application of the composition in preparation of anticancer drugs. According to the present invention, the drug combination composition comprises the PARP inhibitor Olaparib and the calcium ion antagonist amlodipine maleate, the PARP inhibitor Olaparib and the calcium ion antagonist amlodipine maleate provide the synergistic effect, and compared with the single PARP inhibitor, the drug combination composition has characteristics of high stomach cancer sensitivity, significant gastric cancer efficiency improving, and wide application prospect. The Amlodipine in the drug combination composition provided by the invention enhances the curative effect of the PARP inhibitor Olaparib on the gastric cancer.

The invention belongs to the technical field of microbial medicine, and provides a primary multidrug resistance human far-end bile duct cancer cell line CBC3T-6 Homo sapiens and application thereof.The human far-end bile duct cancer cell line CBC3T-6 Homo sapiens is separated from differentiated adenocarcinoma in a far-end bile duct, the human far-end bile duct cancer cell line CBC3T-6 Homo sapiens is already subcultured to 50 generations at present, the long-term in-vitro culture character is stable, and on the premise that the biological characteristics of bile duct cancer are reserved, the human far-end bile duct cancer cell line CBC3T-6 Homo sapiens can be used for preparing the primary multidrug resistance human far-end bile duct cancer cell line CBC3T-6 Homo sapiens. The compound has the characteristics of short multiplication time, high mouse subcutaneous tumor formation rate and the like, and has different degrees of drug resistance to gemcitabine, 5-fluorouracil, oxaliplatin and cis-platinum in first-line anti-cancer drugs for bile duct cancer. The method not only enriches an extrahepatic bile duct cancer cell bank, provides a new cell model for basic research and clinical drug research and development of bile duct cancer, but also provides powerful scientific research data for research developed based on the genetic background of Chinese population. Therefore, the primary multidrug resistant cell line has higher scientific research and clinical application values.

The present invention relates to a novel anoctamin 1 (ANO1) inhibitory compound and has been completed by confirming that the novel compound may inhibit brain tumor by inhibiting the expression or activity of ANO1 which is a calcium-dependent chloride channel. Thus, according to the present invention, a novel compound usable as an ANO1 inhibitor may be provided. In addition, the compound of the present invention may simultaneously inhibit ANO1 and EGFR in brain tumor cells, and thus may be used as a dual-target anticancer agent for ANO1 and EGFR, and may also be used as a combined preparation for EGFR-targeted therapy, and thus is expected to be usable in various ways in the field of brain tumor prevention and treatment.

This invention relates to the field of anticancer drugs, and more particularly to an indole-3-aryl ketone derivative, its preparation method, and its applications. The indole-3-aryl ketone derivative is compound I-VI, which can be used to prepare drugs for the prevention and / or treatment of cancer, including at least one of breast cancer, liver cancer, lung cancer, colorectal cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, cervical cancer, ovarian cancer, kidney cancer, and esophageal cancer. This invention screened the activity of various compounds and, for the first time, discovered that compound I-VI exhibits excellent inhibitory activity against multiple different types of human cancer cells, providing a potential option for broad-spectrum anticancer drugs.

The invention provides an anti-cancer drug collaborative prediction method based on knowledge graph embedding, GNN and Bi-LSTM, and belongs to the technical field of anti-cancer drug collaborative prediction. In order to solve the problem of poor collaborative prediction effect of anti-cancer drugs, the technical scheme adopted by the invention comprises the following steps: respectively obtaining corresponding relations of drug, protein, cell line and tissue interaction from a database; constructing a knowledge graph according to the corresponding relationship, and grouping all the drug combinations based on the collaborative scores of different drug combinations; based on a drug combination grouping result and the constructed knowledge graph, extracting a neighbor entity set interacting with the drug or the cell line, constructing an attention layer of the knowledge graph, and iteratively updating the initial entity embedding representation of each entity in the neighbor entity set; taking an entity neighborhood structure generated by iterative updating as input, extracting a high-order structure and a semantic relationship through a graph neural network, and learning medicine and neighborhood representation thereof; the method is applied to collaborative prediction of anti-cancer drugs.

The invention discloses an anti-cancer drug sensitivity prediction method and system based on bipartite graph and hypergraph learning, and belongs to the technical field of computer software. The method comprises the following steps: acquiring cell line and drug data and drug and cell line reaction original IC50 values by using an open source database, and standardizing the IC50 values; constructing a bipartite graph of the cell line and the drug, and obtaining embedded representation of the drug and the cell line by applying a graph convolutional network; super nodes are introduced to construct a cell line-drug isomerism hypergraph, and node features are learned through GCN; and finally, fusing the output of the GCN layer by applying an attention mechanism, and predicting the final association probability of the cell line-drug pair in combination with a full-connection layer. According to the method, combined exploration and collaborative optimization of the bipartite graph and the hypergraph are achieved, meanwhile, potential and complex relations between the medicine and the cell line are explored through the hypergraph, the quality of node feature representation is jointly improved, and therefore the association score between the medicine and the cell line is predicted more accurately.

The invention provides a novel anti-cancer drug TNBG-sugar conjugate as well as preparation and application thereof, and belongs to the field of chemical medicines. The conjugates are compounds as shown in a formula I and a formula IV, or salts or stereoisomers thereof. The anticancer drug TNBG-sugar conjugate and a preparation prepared from the anticancer drug TNBG-sugar conjugate matched with appropriate auxiliary materials or auxiliary components have a good inhibition effect on cells of cancers such as lung cancer and liver cancer, are low in toxicity or basically non-toxic to normal cells, can be used for preventing and treating the cancers such as the lung cancer and the liver cancer, and have a good application prospect.

The present invention relates to a use of THBS1 as a novel combination drug target that can overcome drug resistance of a targeted anticancer agent. A THBS1 inhibitor according to the present invention inhibits the drug resistance of a target anticancer agent and thus increases an anticancer effect when administered in combination with a target anticancer agent. Accordingly, the present invention can overcome resistance to a targeted anticancer agent and increase a treatment success rate of anticancer drugs for cancer patients, thereby suggesting new possibilities for treatment strategies using targeted anticancer drugs and contributing to the realization of precision medicine.

The present invention relates to a use of a novel compound for the prevention, amelioration, or treatment of head and neck cancer. The novel compound of the present invention not only significantly inhibits the growth, proliferation, migration, and metastasis of head and neck cancer cells, but also simultaneously inhibits ANO1 and EGFR in head and neck cancer cells, thereby exerting a stronger anticancer effect through dual inhibition against these two proteins. In addition, when used in combination with an anticancer agent or the like, the compound can enhance the efficacy of the anticancer drugs and suppress drug resistance. Accordingly, the compound can be used as a dual-target anticancer agent for ANO1 and EGFR on its own, or as a combination agent with anticancer drugs, and is expected to be widely applicable in the field of prevention and treatment of head and neck cancer.

The invention relates to the field of anti-cancer drugs, in particular to an indole-3-aryl ketone derivative as well as a preparation method and application thereof. The indole-3-aryl ketone derivative is a compound I-VI, the indole-3-aryl ketone derivative can be used for preparing drugs for preventing and / or treating cancers, and the cancers comprise at least one of breast cancer, liver cancer, lung cancer, colorectal cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, cervical cancer, ovarian cancer, kidney cancer and esophageal cancer. According to the invention, activity screening is carried out on various compounds, the fact that the compounds I-VI have excellent inhibitory activity on various different types of human cancer cells is found for the first time, and an optional scheme is provided for broad-spectrum anti-cancer drugs.

The invention belongs to the field of gene therapy, and relates to a DNA (deoxyribonucleic acid) molecule for targeted degradation of target protein and application. The DNA molecule comprises at least one group of D-PROTAC units, each group of D-PROTAC units comprises at least one first DNA chain segment for coding a ubiquitin ligase targeting peptide and at least one second DNA chain segment for coding a target protein targeting peptide, and a third DNA chain segment for coding a connecting peptide is optionally arranged between the adjacent first DNA chain segment and second DNA chain segment. The gene therapy specific PROTAC molecular model is successfully constructed, the huge potential of a gene therapy method in the aspects of research, development and application of new PROTAC anticancer drugs is proved, the action mode of the PROTAC technology is innovated, and a new treatment application scene is expanded for gene therapy.

The present invention relates to the technical field of drug sensitivity assessment, and specifically discloses an anticancer drug sensitivity assessment method based on semi-supervised learning, the method comprising: obtaining basic data, and preprocessing the basic data to obtain initial features; constructing an anticancer drug sensitivity assessment model based on a semi-supervised multi-task deep neural network framework and the initial features; based on the anticancer drug sensitivity assessment model, combining Monte Carlo and integral gradient algorithms, obtaining the contribution of the initial features to drug sensitivity. The present invention uses a comprehensive multi-task learning framework to share feature representations between different tasks, optimize neural networks from different angles, thereby improving the accuracy and robustness of the model, and introduces Monte Carlo and integral gradient algorithms to solve the black box problem of deep learning models, provide feature interpretability, and help researchers understand the calculation process of the model, so as to discover the advantages of key features that affect the final results.

The invention discloses a ChemBERTa-FP anti-cancer drug prediction method based on combination of deep learning and the chemical field. The ChemBERTa-FP anti-cancer drug prediction method comprises the following steps: step 1, acquiring data of a CI-60DTP database and data of a ChEMBL database; step 2, ChemBERTa and BERTbase are used as a basic pre-training model; a BERT base pre-trains a deep bidirectional language representation model, and an additional output layer is used for adjusting the pre-trained BERT base model, so that a model is created for a wide range of tasks under the condition that major architecture modification is not carried out; meanwhile, ChemBERTa is used as a basic pre-training model, RoBERTa is used as a basic model, pre-training is carried out on a PubChem 77M data set, and a specific chemical language SMILES is added in training; 3, the dimension represented by the model vector of ChemBERTa is 384, the number of heads is 12, and the number of layers of Transformer is 6. According to the invention, two deep learning models, namely ChemBERTa and BERT-base, are innovatively utilized in the field of anti-cancer molecule screening, which is the first application of the models.

Disclosed is an anticancer composition using a flavone derivative. In the present invention, the flavone derivative exhibits cytotoxicity against: H827, H1299, H522, and A549, which are human non-small cell lung cancer cell lines; MCF-7, which is a human breast cancer cell line; and PANC-1, which is a human pancreatic cancer cell line, in a time-dependent manner and in a treatment concentration-dependent manner, and not only exhibits a synergistic effect when used in combination with other conventional anticancer agents, such as osimertinib, which is an anticancer agent targeting for lung cancer, tamoxifen, which is a hormone agent against breast cancer, and gemcitabine, which is a cytotoxic anticancer agent against pancreatic cancer, but also exhibits the effect of synergistically inhibiting the expression of HIF-1α when used alone or in combination with an existing anticancer agent.

[Problem] To provide a novel compound promising as a prodrug-type anticancer agent that exhibits a sufficient anticancer effect and is capable of reducing adverse effects. [Solution] A compound represented by general formula (I) or a pharmaceutically acceptable salt of the compound. Formula (I): Y-T-(X1)n

The present invention relates to a polypeptide-drug conjugate that binds to IL4Ra and a use thereof, and provides: a polypeptide comprising a ligand that specifically binds to IL4Ra and a temperature-responsive disordered domain; and a polypeptide-drug conjugate in which a cytotoxic drug is bound to the polypeptide. More specifically, the polypeptide, in which an IL4RA-binding ligand and a temperature-responsive disordered domain are repetitively linked, exhibits high selectivity in binding to IL4Ra, and the polypeptide-drug conjugate, in which a cytotoxic drug is bound to the polypeptide, demonstrates significantly enhanced cancer cell-killing efficacy and anti-tumor activity compared to unbound drugs or conventional anticancer agents, and has been confirmed to suppress drug resistance in cancer cells that are resistant to the anticancer agent temozolomide. Accordingly, the polypeptide-drug conjugate can be provided as an effective anticancer therapeutic agent for the treatment of tumors overexpressing IL4Ra and as a pharmaceutical composition for inhibiting drug resistance in cancer cells exhibiting resistance to anticancer drugs.

The invention discloses a synthetic lethal prediction method based on non-generality forgetting and low-rank interaction multi-mode adaptive fusion, and relates to the technical field of bioinformatics, computational biology and drug target screening. The method comprises the following steps: firstly, constructing a multi-view gene embedding expression based on a gene graph structure, a hypergraph structure and a knowledge graph; then constructing a difference vector and an interaction vector for any gene pair so as to characterize the difference and potential complementary relationship between the genes; multi-source information fusion is realized by using a deep learning model containing interactive attention, a forgetting gate and a multi-modal fusion mechanism; on this basis, multi-task optimization is carried out through joint classification loss, modal interaction loss and redundancy suppression loss, and optimal discrimination is realized through a dynamic threshold search strategy; and finally, outputting a sorting result of the candidate synthetic lethal gene pairs according to the prediction score. According to the method, the accuracy and generalization ability of synthetic lethal relationship prediction can be effectively improved, and a high-reliability calculation auxiliary tool is provided for anti-cancer drug target screening and gene therapy strategy design.

The invention belongs to the technical field of medicines, and discloses preparation and application of a paclitaxel-loaded liposome nano targeting preparation. The paclitaxel-entrapped nano-particles are prepared by adopting a film dispersion method, and micromolecular hyaluronic acid is entrapped on the surfaces of the lipid nano-particles by utilizing the charge effect. The bionic nano-drug provided by the invention is helpful for solving the problems of poor solubility, low bioavailability, high toxicity and the like of the anticancer drug paclitaxel. The hyaluronic acid has affinity with a glycoprotein CD44 receptor on the surface of a tumor cell, so that the nanoparticles are targeted and concentrated at a tumor part, the anti-tumor effect is improved, and the systemic toxicity of paclitaxel is reduced. The preparation process is simple, the cost is low, the stability is good, the repeatability is high, and the preparation method also has a better development prospect in the aspect of clinical application transformation.

The invention provides an anticancer drug collaborative prediction method MultiFusion Syn based on multi-scale feature fusion, and relates to the technical field of anticancer drug collaborative prediction. According to the method, synergistic effect prediction is converted into a multi-scale feature fusion classification task: a pre-trained graph neural network DGCL is utilized to extract drug fine-grained structure features, ChemBERTa is combined to obtain semantic features, and adaptive fusion is carried out through an attention mechanism; meanwhile, a residual network is adopted to extract biological characteristics of the cell line, drug-cell bidirectional interaction is constructed by means of double-end cross attention, and fine fusion is performed through a gating network; the model introduces a pre-training encoder and realizes knowledge migration in combination with parameter freezing. On a reference data set, MultiFusionSyn is evaluated by indexes such as AUC, PRAUC, ACC and BACC, and the performance of MultiFusionSyn is superior to that of an existing advanced method through verification of an independent test set.

The present invention. relates to a biomarker comprising Lactobacillus salivarius or Bacteroides flavius for responsiveness prediction and prognosis of immune anticancer drug treatment and a method for responsiveness prediction and prognosis of immune anticancer drug treatment using same. In the present invention, Lactobacillus salivarius and Bacteroides plebeius strains were selected as strains that inhibit the therapeutic effect of immune anticancer agents in a cohort of patients treated with immune anticancer agents who have received fecal microorganism transplantation. By detecting the strain, the present invention has the effects of predicting immune anticancer drug treatment responsiveness or prognosis after immune anticancer drug treatment and securing the efficiency of existing fecal microorganism transplantation. In addition, the selected strain is a strain present in the human intestine and has excellent safety due to low therapeutic side effects, and thus is expected to be advantageously used for treatment of autoimmune diseases and the like through immune activity inhibition.

This scheme belongs to the field of organic synthesis technology, discloses the synthesis method of anticancer medicine darolutamide, and the synthesis method designs a new synthesis route: using formula (S1) compound as starting material, first converted into formula (1ab) or formula (1ac) compound, and then converted into formula (TM-1) compound; the obtained formula (TM-1) compound reacts with formula (1ad) or formula (1ae) compound converted from formula (S5) compound to synthesize formula (TM-2) compound, and then converted into formula (TM-3 compound); the obtained formula (TM-3) compound reacts with formula (S6) compound to synthesize formula (TM-4) compound, and then converted into formula (TM-5) darolutamide. The synthesis process does not need to use expensive palladium catalysts or ligands, so there is no problem of heavy metal residues or heavy metal toxicity in the drug product caused by palladium catalysts in the pharmaceutical process, and there is no need to remove trace metal residues from the target product; the raw materials used are cheap and easy to obtain, the reaction system is green and environmentally friendly, simple to handle, high yield, and good purity.