117 results about "Trial drug" patented technology

The invention relates to an information management method and device for a drug clinical test, a medium and equipment, and relates to the technical field of information management. Determining an abnormal reason of the abnormal clinical test information according to the target abnormal dimension and the corresponding target fluctuation dimension in which the clinical test information is easy to be abnormal; when the abnormal reason is that an error occurs when the abnormal clinical test information is input, a suitable input form corresponding to the abnormal clinical test information is determined, and the clinical test information of the test abnormal dimension is obtained again based on the suitable input form; and when the abnormal reason is that the body of the target subject is abnormal in the drug clinical test, sending an abnormal early warning to a terminal of a clinical test worker. The method has the effect of improving the accuracy of the collected clinical test information.

The invention discloses a drug formula optimization system based on artificial intelligence, and relates to the technical field of drug formula optimization, and the system comprises a data input module which is used for receiving and processing drug component information from a drug research and development database, clinical test results and laboratory test data, and extracting key features based on the drug component information, the medicine component information comprises biological activity, chemical properties and formula ratio of the compound. The drug formula optimization system based on artificial intelligence integrates data input, intelligent analysis, formula iteration, simulation test and decision optimization, realizes full-process automatic processing of drug formula optimization, reduces manual intervention and tedious experimental processes, shortens the drug research and development period, improves the drug formula efficiency, and reduces the labor intensity of a user. And the drug formula effect can be accurately predicted.

The invention provides a risk detection method and system for adverse events of a drug clinical test. The method comprises the following steps: constructing a clinical biological model according to a body health state, body parameters and a historical examination report of a subject; recording the trial information accepted by the subject, and generating a trial database; according to the physiological monitoring equipment and the health monitoring system, body function parameters and biological indexes of the subject after drug testing are monitored in real time, and drug testing data are obtained; screening and optimizing the drug test data based on a genetic algorithm to obtain drug test reaction characteristics; determining a risk score according to the reagent database and the reagent reaction characteristics; and dividing different risk levels according to the risk scores, and carrying out risk early warning according to the risk levels. According to the method, through screening of the drug reaction characteristics and calculation of the risk score, the monitoring efficiency of adverse events is improved, the data analysis capability is enhanced, and the scientificity and accuracy of the result are ensured.

Treatment of Cancer Methods for treatment of cancer, in particular soft tissue sarcomas (STS), are described, as well as compounds, compositions, and combined preparations for use in such methods. An effective amount of: a LAG-3 protein, or a derivative thereof that is able to bind to MHC class II molecules; a programmed cell death protein-1 (PD-1) pathway inhibitor; and radiotherapy, is administered to a subject. Optionally the subject has undergone, is undergoing, or is to undergo, surgery for treatment of the cancer. A clinical trial study is described in which a triple combination of LAG-3 derivative eftilagimod alpha, a PD-1 pathway inhibitor, and radiotherapy was administered. The combination was surprisingly effective in the treatment of STS in patients who have completed 10 weeks of treatment, followed by surgery 2-3 weeks later. A substantial number of near complete pathological responses were observed, which are rarely seen in STS patients with standard therapeutic approaches including radiotherapy.

The invention provides a clinical test drug alerting method and system, and relates to the technical field of clinical drug trials, the clinical test drug alerting method adopts multiple algorithms, and can help researchers to better monitor, analyze and manage drug-related problems in a test. The corresponding system comprises a drug interaction analysis end, an adverse event monitoring and early warning end, a safety assessment and risk management end, a real-time data monitoring and alarm end and a data analysis and prediction end. The drug interaction analysis end, the adverse event monitoring and early warning end, the safety assessment and risk management end, the real-time data monitoring and alarm end and the data analysis and prediction end are in data communication through a private network and a private line network. The clinical test drug warning system can effectively monitor and manage the safety of drugs, timely discover and warn potential safety problems, and take corresponding measures to guarantee the safety and welfare of subjects.

The invention relates to the technical field of drug clinical trials, in particular to a drug clinical trial design auxiliary system based on a generative pre-training model, and the system comprises a knowledge fusion module, a real-time updating module, a clinical trial generation module and a clinical trial optimization module. According to the method, key drug data and biomarkers are integrated, a test design process is automated and optimized, and potential relevance among data is analyzed through a graph attention network, so that the analysis capability of a complex clinical data structure is enhanced, the quality and application efficiency of input data are ensured, the scientificity and adaptability of a test scheme are promoted, and the method is suitable for large-scale popularization and application. According to the method, the test related data and the parameter adjusting function are automatically updated, so that the design can reflect the latest achievement of drug research and development in time, the real-time performance and accuracy of the test design are improved, through the automatically generated test parameter design, human intervention is reduced, the error rate is reduced, and the overall efficiency of the test design process is improved.

The invention relates to an intelligent drug management method and system in a drug clinical test process, and the method comprises the steps: obtaining a clinical test scheme, determining a drug clinical test standard according to the clinical test scheme, searching potential subjects, and transmitting a recommendation list to the potential subjects; in the clinical test process, acquiring multi-dimensional clinical test information of a subject corresponding to the target drug, extracting target data associated with the safety of the subject corresponding to the clinical test scheme in the multi-dimensional clinical test information, acquiring risk index data through the target data, and performing risk assessment; after a clinical test is finished, visit information is configured according to a clinical test scheme, the visit information comprises a visit base line, a visit window, a visit period and visit examination items, a visit schedule table is generated based on the visit information, remote monitoring is further carried out under the condition of being not affected by area and time factors, and the visit schedule table is displayed on the basis of all doctor-seeing data of a subject. A subject panoramic data view is formed.

The invention relates to a risk management and decision support method, system and platform for the pharmaceutical industry. The method comprises strategic investment portfolio management, supervision affair, clinical drug development, pharmaceutical economics, investment strategy optimization, risk management, merger and purchase full-duty investigation and stock markets. The system uses artificial intelligence and diverse data from public and private sources, including clinical trials, regulatory decisions, economic data, pharmacological data, and company data. The system integrates a plurality of decision modules for clinical development, such as clinical risk, supervision risk, pharmacological risk and economic risk. This risk factor network generates predictive and normative information for strategic decisions.

To smoothly select a candidate suited to conditions for a clinical trial subject.SOLUTION: A clinical trial and test subject selection support system comprises: a candidate extraction unit 12 that extracts a candidate suited to subject conditions and notifies a pharmacy terminal 4 of the candidate, by using personal data including personal attribute information, diagnostic result information, case history information, and medication history information of potential clinical trial and test subjects; and a questionnaire answer creation unit 13 that creates answers to a questionnaire by using the personal data of the extracted candidate and the medication history information input from the pharmacy terminal 4. The clinical trial and test subject selection support system not only extracts the candidate suited to the subject conditions by using the personal data of the potential clinical trial and test subjects, but also creates the answers to the questionnaire that are to be subsequently created by the extracted candidate. The clinical trial and test subject selection support system creates the answers to the questionnaire on the basis of the accurate personal data of the candidate and the accurate medication history information additionally input from a pharmacy, and thereby can accurately determine the suitability of the candidate as a clinical trial and test subject on the basis of the details of the answers.SELECTED DRAWING: Figure 2

The present disclosure generally relates to methods and systems for improving personalized medicine. In one aspect, a method for assessing eligibility of a patient to a clinical trial is provided based on evaluation of molecular profiles of patients and matching them with the predicted response to a therapy tested in the clinical trial. Embodiments include determining impact of a therapy tested in the clinical trial on the molecular profile of the patient based on the number and types of biomarkers and their mutual interaction between each other and drugs of the therapy.

A drug combination composition comprising ACT001 has a synergistic anti-tumor effect in a glioblastoma (GBM) animal model, effectively protects or improves, in the process of a phase II clinical trial of treating a GBM patient who was sensitive to temozolomide (TMZ) but subsequently relapses, basic immune functions of said patient, such as relieving lymphocyte reduction caused by chemotherapy, alleviating myelosuppression of TMZ, and suppressing inhibitory Treg lymphocytes in peripheral blood, and reduces DNA damage repair capability in GBM cancer cells, thereby improving the sensitivity of GBM to TMZ.

The invention disclosed concerns a fixed dose combination (FDC) of pertuzumab, trastuzumab, and, optionally, recombinant human hyaluronidase (rHuPH20), which is administered subcutaneously to patients. The final efficacy and safety data for the FeDeriCa clinical trial, United States Prescribing Information (USPI) (including home-use) methods, and primary analysis of the PHranceSCa clinical trial are disclosed and claimed.

The invention relates to the technical field of medical management, in particular to a clinical test drug full-process block chain tracing and intelligent management system, which comprises a drug identity identification module, a flow direction recording module, a state monitoring module, an authority distribution module and a closed-loop management module. According to the invention, through standardized generation and dynamic verification of the unique identifier of the drug, unified and standardized management of identity information is realized, traceability deviation caused by inconsistent codes in multiple links is avoided, operation time and personnel information are recorded in a circulation path and rules are compared, clear and transparent drug destination is ensured, and omission and abnormal risks are reduced. The state is recognized in real time, the target is compared, the abnormity is early warned in time, the safety and controllability of the process are improved, the authority of participants is differentially recognized, the strategy is dynamically adjusted, fine management is achieved, potential safety hazards caused by over-wide or over-narrow authority are prevented, the recovery path and resource distribution are optimized, and the recovery efficiency and integrity are improved. And closed-loop management of the whole life cycle of the medicine is formed.

The present invention relates to pharmacogenetics, more specifically to strategies involving biomarkers associated with the clinical response to a compound before or during treatment of a cartilage disorder, such as osteoarthritis. The present invention more particularly relates to the combination of JSW measurements and level of specific proteins present in the blood, serum, synovial fluid or in the urine, which can be used in strategies such as patients' enrichment in clinical trials, patients' selection strategy before or during treatment or for adapting the treatment of a patient in the frame of treatments for cartilage disorder, such as osteoarthritis.

The application discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and a Transformer, and relates to a drug synergistic effect prediction method. In order to solve the problem that a traditional drug combination discovery process mainly depends on clinical trials, which is not only time-consuming and laborious, but also high in cost and risky to patients, the application comprises the following steps: extracting drug data samples, generating a data set, dividing training and test sets, performing network training and testing; constructing a biomedical knowledge graph; converting a sequence list of a drug structure into a graph by using Rdkit; mining a subgraph of the knowledge graph by using a multi-hop subgraph mining network; learning feature representations of the knowledge network and the drug molecular graph by using a relation-aware Transformer, and performing fusion; performing synergistic effect prediction between drug pairs by using a multilayer perception machine; inputting drug pairs in a training set into the above model; inputting drug pairs in a test set into the prediction model to obtain a prediction result. The application belongs to the technical field of drug synergistic effect prediction.

The utility model relates to the technical field of medicine production, and discloses clinical test medicine subpackaging equipment which comprises an L-shaped mounting plate, a subpackaging box is fixed to one side of the top of the L-shaped mounting plate, a screening mechanism for conducting vibration screening on medicine is arranged in the subpackaging box, and a driving device for driving the screening mechanism is arranged on one side of the subpackaging box. The screening mechanism comprises sliding grooves formed in the two ends of the inner wall of the subpackaging box, guide rods are fixed to the two sides of the inner walls of the two sliding grooves, sliding blocks are arranged on the outer walls of the two guide rods in a sliding mode, the same screening frame is fixed to the faces, close to one another, of the four sliding blocks, and rolling wheels are rotationally connected to the tops and the bottoms of the multiple sliding blocks. According to the medicine screening device, through left-right shaking of the sliding blocks, medicine can be effectively and evenly dispersed in the screening frame, tablets are prevented from being adhered together, mistaken weighing or mistaken packaging caused by adhesion of the medicine is effectively avoided, and potential quality safety hazards are reduced.

The invention provides a drug research and development and individualized treatment platform based on an artificial neural network and a multi-modal artificial intelligence technology, and the platform is characterized in that the platform comprises the following specific steps: S1, data preparation and preprocessing: collecting multi-modal drug data, target data and clinical data, and carrying out the standardization processing; s2, constructing and training a model, generating drug molecules by using VAE and GAN, predicting interaction between a drug and a target spot by using GNN, optimizing an individualized treatment scheme through reinforcement learning, predicting adverse drug reactions by using self-supervised learning, and protecting sensitive data by using federal learning and differential privacy to realize cross-mechanism cooperation; s3, verifying and optimizing the model, verifying the performance of the model through laboratory and clinical tests, and optimizing through feedback data; and S4, system deployment and application: all modules are integrated to a drug research and development and treatment platform, and the efficiency and precision of drug development and individualized treatment are improved.

The present invention discloses the use of combined GC triamcinolone enema in the treatment of immune checkpoint inhibitor-related immune enteritis. The present invention proves through clinical trial cases that intravenous administration of GC methylprednisolone combined with (triamcinolone) enema administration is beneficial to the improvement of the condition of immune checkpoint inhibitor-related immune enteritis. Therefore, combined GC (triamcinolone) enema can be used in related fields such as immune checkpoint inhibitor-related immune enteritis. The present invention provides a new drug and treatment idea for the treatment of immune checkpoint inhibitor-related immune enteritis; the present invention opens up a new use for GC (triamcinolone) enema administration and provides a new treatment method for immune checkpoint inhibitor-related immune enteritis, having good clinical application prospects. The combined medication scheme provided by the present invention has been fully clinically verified in terms of its effectiveness and safety; it is easily acceptable to patients and has unique advantages.

The invention discloses application of glucoraphanin to preparation of a product for reducing biological accumulation of perfluorooctane sulfonate, and belongs to the technical field of biological medicine. The occurrence probability of the high PFOS level in the body of a participant frequently eating broccoli is confirmed to be remarkably reduced through population epidemiological experiments, and then the PFOS level in the body of the participant is confirmed to be remarkably reduced after glucoraphanin intervention through clinical RCT random control experiments. The glucoraphanin-intervened serum has the advantages that the glucoraphanin-intervened serum is used as a main body, the metabolic change of steroid, ceramide and bile acid in the glucoraphanin-intervened serum is confirmed to be closely related to the reduction of the PFOS level through lipidomics analysis, and finally, the glucoraphanin is confirmed to be capable of effectively reducing the PFOS in-vivo accumulation and improving the liver injury caused by the PFOS through animal experiments and an AI reverse targeting technology; and the glucoraphanin and the in-vivo metabolite sulforaphane thereof have relatively strong binding capacity with a liver-specific PXR receptor, so that a good technical foundation is laid for research and development of drugs for treating diseases related to PFOS accumulation.

This invention is a method, system, and platform for risk management and decision support in pharmaceuticals, including strategic portfolio management, regulatory affairs, clinical drug development, pharmacoeconomic, investment strategy optimization, risk management, due diligence for mergers and acquisitions, and the stock market. The system uses artificial intelligence and diverse data from open and private sources, including clinical trials, regulatory decisions, economic data, pharmacological data, and corporate data. It integrates multiple decision-making modules for clinical development, such as clinical risk, regulatory risk, pharmacological risk, and economic risk. This network of risk factors generates predictive and prescriptive information for strategic decision-making.

The invention relates to the technical field of digital processing, in particular to a patient information automatic processing system and method. The method comprises the following steps: S1, acquiring first acquisition information, wherein the first acquisition information comprises historical case information; s2, according to the historical case information and the key information of the medicine to be clinically tested, screening a plurality of tested patients matched with the medicine to be clinically tested; s3, acquiring second acquisition information; s4, according to the second collection information, safety level judgment is carried out on each tested patient, safety levels judged by a second processing module include low safety, medium safety and high safety, and the clinical drug test is stopped for the tested patients judged to be low safety and the like; s5, screening the information of each tested patient in the clinical drug test stage into conventional data and key data; according to the invention, the accuracy of clinical drug tests and the safety of tested patients are ensured.

The invention provides a digital organoid construction method, equipment, a medium and a program product, and further provides a method, equipment, a medium and a program product for developing and predicting therapeutic schedule and / or therapeutic drug sensitivity based on a model, and relates to the field of intelligent medical treatment. The constructed digital organoid is widely applied to daily clinical practice, and the model can help to select the most suitable scheme from candidate schemes; in clinical trials, the model can help select patients who may respond to new test regimens, even previously abandoned regimens, and the model can predict drug response even if the treatment regimens contain new drugs not contained in the training set.

To make an electronic device take an appropriate action in response to an action of a user.SOLUTION: An action control system disclosed herein comprises an input unit 292 for receiving information input, a processing unit 294 for performing specific processing using a sentence generation model for generating a sentence, and an output unit 296 configured to control an action of an electronic device to output a result of the specific processing. When request information requesting generation of a clinical trial implementation plan for a pharmaceutical is input, the processing unit 294 performs the specific processing comprising generating instruction information for instructing the sentence generation model to generate the clinical trial implementation plan for the pharmaceutical, including necessary information required to generate the clinical trial implementation plan for the pharmaceutical as received by the input unit 292, and inputting the instruction information to the sentence generation model; and causes the output unit 296 to output the generated clinical trial implementation plan for the pharmaceutical as a result of the specific processing.SELECTED DRAWING: Figure 2B

To overcome the problem in the prior art that a siRNA applied to a human liver LOC157273 gene cannot be directly applied to a primate T2DM experiment in a clinical trial, a siRNA for inhibiting LOC157273 gene expression in liver cells is provided. The siRNA comprises a sense strand and an antisense strand corresponding to the sense strand, wherein the sense strand is selected from "sequence 1", and the antisense strand is selected from "sequence 2", or a siRNA having 80% or more homology with sequences of the sense strand and the antisense strand. In addition, also provided are a composition and a use of the siRNA for inhibiting LOC157273 gene expression in liver cells. The provided siRNA for inhibiting LOCI57273 gene expression in liver cells can silence LOC157273 genes in both human liver cells and primate liver cells, solving the problem in the prior art that a siRNA applied to a human liver LOC157273 gene cannot be directly applied to a primate T2DM experiment in a clinical trial, verifying the efficacy and pharmacokinetic effects of therapeutic drugs, thereby promoting the development of new drugs for the treatment of T2DM.

This invention discloses a clinical trial drug cabinet and a clinical trial drug management method. The clinical trial drug cabinet includes a connected cabinet body and a cabinet door, with the interior of the cabinet body and the cabinet door forming a storage space. A weighing platform divides the storage space into multiple layers of clinical trial drug storage spaces. Each layer of clinical trial drug storage space is equipped with an electronic tag reader. By cooperating with the weighing platform and the electronic tag reader, it is possible to determine which clinical trial drug is currently being placed in or taken out of the cabinet, as well as the original storage space where the clinical trial drug was placed, or from which clinical trial drug was taken out. This effectively avoids drug management chaos and improves drug storage and retrieval efficiency.

The present invention belongs to the field of drug design and specifically discloses a virtual screening method for small molecule inhibitors targeting the YTHDF1 protein, comprising: obtaining the YTHDF1 protein structure and generating multiple conformational proteins through homology modeling; obtaining a small molecule that binds to the YTHDF1 protein and generating a bait molecule to form a ligand molecule; docking the ligand molecule with the binding site on YTHDF1 and characterizing the complex through PLEC fingerprinting; using a machine learning algorithm to construct a specificity scoring function, scoring and screening the complexes of the docked compounds to be screened and the YTHDF1 molecule, and then performing affinity prediction to screen small molecule inhibitors targeting YTHDF1. The virtual screening method provided by the present invention can screen YTHDF1-targeting molecules with high affinity on a large scale and accurately, providing candidate molecules for drug development for YTHDF1-related diseases and laying the foundation for clinical trials and drug optimization.

The utility model discloses a waste liquid treatment device for drug clinical trials, which comprises a waste liquid treatment barrel and a barrel cover arranged at the top of the waste liquid treatment barrel, one side of the top of the barrel cover is fixedly connected with a liquid injection hopper, and the center of the bottom of the waste liquid treatment barrel is fixedly connected with a liquid outlet pipe; at least four supporting legs making contact with the ground are arranged on the surface of the positioning sleeve in a circumferential array mode, and a stirring shaft is rotationally connected to the center of the interior of the waste liquid treatment barrel. The device has the advantages of efficient mixing and flexible adjustment, a complex gear system is driven by a power motor, the rotary stirring function of a stirring shaft is realized, and a mechanism that a lifting threaded sleeve drives an annular sleeve and a second stirring blade to move up and down is introduced, so that a more comprehensive mixing effect is provided; and the design of a sealing ring and a lifting limiting rod ensures the safety and stability of equipment operation.

The application discloses a kind of based on artificial intelligence's pharmaceutical clinical test safety evaluation method, comprising the following steps: collecting heterogeneous clinical survival data with deletion characteristics and time series drift characteristics, and constructing original survival data set;Based on propensity score model and survival outcome model, implement deletion correction, obtain individual non-compliance risk score;Through stratified weighting rule and two-side order-preserving prediction mechanism, calibrate and update individual risk prediction interval;Calculate individual risk upper and lower bound probability, define three-domain risk threshold rule, generate real-time risk decision result through three-domain decision mapping and dynamically evaluate overall safety trend.The application improves the robustness and interpretability of clinical safety risk decision.

The invention provides a clinical test source data traceability tracking operation system, and relates to the technical field of drug clinical tests. Comprising a data identification and index end, a data acquisition and recording end, a data change tracking end, a data traceability and traceability end, a data verification and auditing end, a data access and authority control end, a data backup and recovery end and a data report and analysis end. The data identification and index end, the data acquisition and recording end, the data change tracking end, the data traceability and tracing end, the data verification and auditing end, the data access and authority control end, the data backup and recovery end and the data report and analysis end are in data communication through a private network and a private line network. The system aims at monitoring and managing links such as sources, acquisition, recording and change of clinical test data so as to ensure the reliability and credibility of the data.

This invention provides a drug clinical trial subject management system and method. The system includes: a learnable model construction module for building a learnable model; a subject screening module for inputting multi-dimensional characteristics of subjects into the learnable model for analysis and screening to obtain target subjects; an informed consent management module for managing the informed consent signing information of target subjects; a subject grouping module for grouping target subject data; a trial process management module for real-time monitoring and management of post-trial observation data of grouped target subjects; and a learnable model relearning module for retraining and updating the learnable model after the completion of the current clinical trial. This provides more efficient and accurate subject screening and grouping services for clinical trials, helping to improve the success rate and research value of clinical trials, and enhancing the accuracy of model predictions.