62 results about "Extraction drug" patented technology

Systems and methods are provided for aerosolizing a pharmaceutical formulation. According to one method, respiratory gases are prevented from flowing to the lungs when attempting to inhale. Then, respiratory gases are abruptly permitted to flow to the lungs. The flow of respiratory gases may then be used to extract a pharmaceutical formulation from a receptacle and to place the pharmaceutical formulation within the flow of respiratory gases to form an aerosol.

The invention relates to the extraction of pharmaceutically active components from plant materials, and more particularly to the preparation of a botanical drug substance (BDS) for incorporation in to a medicament. It also relates to a BDS, for use in pharmaceutical formulations. In particular it relates to BDS comprising cannabinoids obtained by extraction from cannabis

A method is described for the extraction of products having a pharmacological activity, in particular an anti-tumoral activity, from spermatophyte plants. Said products, which are to be used in the pharmaceutical field, consist of complex mixtures of compounds characterized by one or more stilbene groups, variously hydroxylated and/or glucosidated, and of compounds derived from said group by natural enzymatic biosynthetical processes (stilbenoids). The following compounds are preferred: C-Res, glucosidated C-Res, epsilon-viniferine, H-gnetine, r-2-viniferine, r-viniferine, hopeaphenol, Ampelopepsin A and glucosidated T-Res.

The present invention discloses a Chinese medicine for effectively curing disease due to exopathy and wind-heat type common cold. Said Chinese medicine is made up by using 10 Chinese medicinal materials of scutellaria root, coptis root, forsythia fruit, arctium seed, notopterygium root and others through a certain preparation process. Said invention also provides its preparation process and concrete steps.

The invention provides a drug-target interaction prediction model method based on deep embedding learning of a molecular graph and a sequence, and the method comprises the steps: building a map neural network based on an attention mechanism and an attention-oriented bidirectional LSTM to predict the interaction, wherein in order to achieve the more effective training, a pre-training model BERT is utilized to extract embedded vector representation of each sub-sequence from a protein sequence, and meanwhile, a local breadth-first search algorithm is designed to extract sub-graph information of a drug molecular graph, so that a graph neural network learns higher feature information. On one hand, in the aspect of drug molecules, better spatial features can be learned based on the molecular graph; on the other hand, the protein sequence data size is large, larger protein space can be covered, and the generalization ability is improved.

The invention discloses a medicine extraction device. The liquid outlet of a first extraction tank is communicated with the liquid inlet of a first concentrator; and a concentrate outlet of the first concentrator is communicated with a concentrate inlet of an alcohol precipitation tank. The medicine extraction device is characterized in that in a liquid collector of the first concentrator, the position of the outlet of a liquid outlet pipe is lower than and far away from the position of a vacuum pipe in the liquid collector; condensate, flowing outlet from the liquid outlet pipe, directly flows in a liquid accommodating cavity at the bottom of the liquid collector, and then flows in a liquid reception tank for preservation to avoid or reduce the material leakage problem due to leadout of the condensate by a vacuum system; and at the same time, the collected condensate can be directly discharged through a water drainage pump without implementing the device evacuation and the secondary vacuum pumping, so that the operation is easy, the energy consumption is reduced, and the medicine extraction efficiency of the medicine extraction device is improved. Meanwhile, the invention further discloses a technique for extracting medicines by using the device; and the technique is suitable for extracting natural medicines.

A method of extracting a combination of a drug and an adverse event related to the drug includes: for each of positive example combinations, negative example combinations and combinations that are neither positive examples nor negative examples, which are combinations of drug and disease, extracting medical events from medical information data about a patient and generating attribute data based on time-series information about the medical events; and learning a discriminant model based on attribute data of the positive and negative examples; and inputting attribute data corresponding to the combinations that are neither positive examples nor negative examples to the discriminant model to determine scores.

The invention discloses an externally-used medicament for the treatment of burns, bedsore and chronic ulcer, as well as the process for preparation, the medicament is prepared from the following medicinal herbs: Chinese angelica root 30-80 parts, coptis root 30-180 parts, garden burnet root 30-120 parts, giant knotweed rhizome 30-90 parts, rheum officinale 30-90 parts, borneol 30-90 parts, emollient 30-90 parts and water 3000-5000 parts.

A method and apparatus for providing the information of adverse drug effects. The method includes: extracting at least a first information and a second information in basic information of a drug from a drug information source; matching the drug with a particular drug-related concept in a structured and normalized terminology system according to the first and the second information; extracting, from the drug information source, the information of Adverse Drug Effects associated with the drug; and matching the information of Adverse Drug Effects with a particular disorder-related concept in the structured and normalized terminology system; wherein the matching is along different paths in at least two disorder-related classified hierarchies. The invention can extract, standardize, and normalize information relating to adverse drug effects to help the integration, search, calculation, and propagation thereof.

The invention is a spasmolysis and pain easing medicine and its manufacturing method, it extracts medicine active substance enoxolone glycyrrhetinic acid from liquorice and extracts active substance paeoniflorin from peony according to the curing mechanism of peony and liquorice. And then are blended with medicine carrier and/or excepient, thus the current medicine with the same curative effect to peony and liquorice soup can be produced; it has a small volume, convenient, and has no toxin and side effects.

The present invention relates to fish processing method, and is process of preparing fine fish powder and superfine fish powder and extracting medicine component from fish. Under low temperature, fish is crushed and dried to obtain basic fish powder. The basic fish powder is further airflow crushed to obtain superfine fish powder, and the superfine fish powder is supercritical CO2 extracted to obtain medicine component. The present invention has whole fish processed to obtain fine fish powder and superfine fish powder and can maintain all the effective components and nutritive components of fish. The supercritical CO2 extraction process with cell wall crushing powder as material can obtain the medicinal effective component with no contamination, purity over 99 % and extraction rate over 98 %.

The invention relates to a preparation method of four-season common cold capsules. According to the invention, ranges of factors suitable for enzymolysis are summarized through single factor tests; and an optimal technology for extracting active components of the four-season common cold capsules is optimized through orthogonal tests. According to the invention, an extract is crushed into micro-powder with grain sizes smaller than 50 micrometers by using a jet mill; the micro-powder is added to a pharmaceutically acceptable excipient, and the mixture is well mixed; and the mixture is filled into capsules. According to the invention, active components of the medicine are extracted by using a composite enzymolysis method. The method is advantaged in that: the active components are fully extracted; the bioavailability of the active components is high; the disintegration speed of the active components is high, the absorption of the active components is good; the content of the active components is high; the quality of the active components is stable, and the reproducibility of the active components is good.

The invention relates to a kind of Chinese traditional medicine for treating psoriasis. The formulation for said medicine and the weight proportion are: flower of honeysuckle 1, flower of honeysuckle 1, lotus seed core 1. The process comprises following steps: extracting effective component from raw material with normal method, getting dried material; there are two methods for said medicine preparation: (1)loading extract into capsule, 0.3g for each capsule, taking two capsules per time and three times per day half an hour before eating with warm water; (2)adding adhesive agent and water into extract, producing pills by hand, or sheeting with pelleting machine, getting compound medicine tablet, the weight of tablet is 0.3g, two tablets per time and three times one day, taking it half an hour before eating with warm or boiled water. The medicine is characterized by preparation with Chinese traditional medicine, good effect, no side effect and suitability for popular people.

The invention relates to the field of drug-target interaction prediction technology and deep learning, in particular to a drug-target interaction prediction algorithm based on graph convolution and similarity. Comprising the following steps: 1) acquiring related data of drugs, proteins and DTI through public databases such as DygBank and RCSB; 2) extracting intermolecular structure information through global structure similarity; 3) extracting monomer characteristic information of the drug molecules and the protein molecules through DeepChem; 4) obtaining a drug-target interaction prediction result through feature dimension reduction, feature fusion and normalization processing; and 5) parameter adjustment and optimization are performed on the model, the prediction accuracy is continuously improved, and the optimal model is used for assisting research on drug discovery, drug verification, drug side effects and the like.

The invention relates to the technical field of the neurology department, in particular to a medicine for treating neurasthenia. The medicine is prepared from components in percentage by mass as follows: 2%-4% of trimetozine, 1%-5% of midecamycin, 30%-40% of nabumetone, 15%-17% of taurine, 2%-6% of ketotifen, 23%-25% of catechol, 3%-5% of nicotinamide, 1%-3% of oleanolic acid, 1.5%-2.5% of quetiapine and 2.5%-11.5% of loxoprofen. The invention also discloses a preparation method of the medicine. The medicine comprises the steps as follows: step 1, preparing materials; step 2, drying and grinding the materials into powder; step 3, performing crushing treatment; step 4, preparing an ethanol solution; step 5, performing concentration and drying; step 6, extracting the medicine. The medicine can effectively improve organism functions and enhance immunity, has significant effects on the neurasthenia, has no toxic or side effects on the human body, can relieve anxiety and cognitive defect symptoms and has effects of clearing heat, reducing internal heat, regulating flow of vital energy and dredging collaterals.

The present invention relates to the field of pharmaceutical universal apparatuses, particularly to an ultra-high pressure extraction apparatus for traditional Chinese medicines. The apparatus comprises a pressure container, a booster pump and a frame. A sealed pressurized cover is arranged on the top of the pressure container, a feed liquid tank is arranged in the pressure container, the pressure container is arranged on the frame, the bottom of the feed liquid tank is connected with a delivery pipe, the delivery pipe is connected with a pressure media tank, the pressure media tank is connected with the booster pump, the right of the booster pump is connected with a booster controller, the bottom of the booster controller is connected with a reversing valve, and the bottom of the reversing valve is connected with an oil tank. By adopting an ultra-high pressure technology, the ultra-high pressure technology is a relatively ideal medicine extraction method in recent years. The apparatus has the characteristics of being fast, efficient, low in energy consumption and environmental-friendly if medicines are extracted. The apparatus provides mass-transfer power depending on pressure, avoids medicine damage caused by nonuniform heating and ensures the biological activity of medicines. The whole apparatus is compact in structure, simple in principle and worth being promoted.

Aiming at the limitation of the existing drug relocation technology, the invention provides a drug-disease association prediction method and system, and the method partially reduces the sparsity of an existing drug-disease original association matrix by applying a weight K nearest neighbor algorithm; meanwhile, a linear feature extraction mode and a nonlinear feature extraction mode are integrated when the similarity feature information of the medicine and the disease is extracted, so that more comprehensive similar feature information is obtained, and the relationship between the medicine and the disease can be fully reflected; and a bipartite graph diffusion method is adopted to calculate a drug-disease associated prediction score, so that excellent prediction performance is obtained.

The invention relates to the extraction of pharmaceutically active components from plant materials, and more particularly to the preparation of a botanical drug substance (BDS) for incorporation in to a medicament. It also relates to a BDS of given purity, for use in pharmaceutical formulations. In particular it relates to BDS comprising cannabinoids obtained by extraction from cannabis.

The invention discloses a method for predicting drug target interaction, and the method is used for predicting the affinity score of a drug target pair based on an end-to-end model (MATT-DTI) of a multi-attention module. The model is wanted by two self-attention module distribution modeling drugs and interaction of drug target pairs. MATT-DTI, as shown in Figure 1, firstly, features of a drug sequence are extracted by using a related self-attention model and a convolutional neural network through a correlation relationship between coding atoms, so that deep expression of a drug is obtained; secondly, MATT-DTI obtains deep expression of a target sequence through coding of a convolutional neural network model; and finally, establishing a module based on a multi-head self-attention mechanism, and extracting interaction information of a drug target pair through similarity modeling between drug deep expression and target deep expression so as to obtain a predicted affinity score.

The invention provides a novel compound composition. The novel compound composition comprises a HIF-1a inhibitor and an ROS inducer. When the two medicines are independently used, the response rates to tumors are relatively low; the combined medicine generates a good effect of inhibiting tumor growth according to an effective administration dosage and an administration mode; the survival rate of apatient is obviously improved; especially, the response rate in osteosarcoma and ovarian cancer is obviously improved; the situation that malignant advanced patients have no drug for use is solved, asolid pharmacological basis is provided for subsequent development of novel small molecular compounds for HIF-1a inhibitors and ROS targets, and in addition, available inhibitors and inducers in theprior art are multiple in variety, the implementation means is simple, and the cost is low.