50 results about "Trial drug" patented technology

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 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 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 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.

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 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.

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 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.

Disclosed is an application of Teruravone in the preparation of a drug for treating the amyotrophic lateral sclerosis disease. Teruravone can significantly increase the survival rate of TDP-43 M337V stably transfected cells and reduce the LDH leakage rate, and has a potency superior to that of Edaravone, indicating that Teruravone possesses stronger neuroprotective activity. Preliminary clinical trials indicate that after oral administration of Teruravone to an ALS patient for 3 months, the Norris Scale score and the neurofilament light chain protein level in the blood remained generally stable, and the decline in ALSFRS-R score was significantly slowed. Thus, the clinical effect of delaying the disease course of ALS is achieved, and no serious adverse effects were observed.

Provided are a crystal form of a compound, a preparation method therefor and a use thereof. The compound has a structure as shown in formula I. The crystal form has high crystalline purity, low hygroscopicity, good solid-state stability and good solubility, is beneficial to the preparation and storage of pharmaceutical formulations, and has good application value and drug development potential. In addition, clinical trial results further demonstrate that the crystal form B of the compound as shown in formula I has good pharmacokinetic properties, low clinical toxicity, good safety, high oral bioavailability, and good therapeutic efficacy against multiple diseases.

The invention relates to application of a medicine for inhibiting glioma progression promoted by chronic pressure stress, and relates to the technical field of biological medicine, chronic pressure stress is proved to be a key risk factor for promoting glioma progression, but a cell molecular mechanism behind the chronic pressure stress is not clear yet. Therefore, a targeting drug capable of specifically intervening in the process is lacked clinically. Existing immunotherapy such as adoptive NK cell therapy is poor in effect in glioma clinical tests, it is prompted that pressure stress possibly induces a strong immunosuppression microenvironment, and the technical scheme with a C5aR inhibitor Avacopan as the core is provided. According to the means, the activity of a key node C5aR1 in a pressure stress signal channel is blocked through a pharmacological method, functional inhibitory stress-related macrophages (SAMs) are cleared in a targeted manner, so that immunosuppression is reversed, glioma growth driven by chronic pressure stress can be selectively inhibited, and a synergistic interaction effect with an NK cell therapy is shown in an in-vivo experiment.

Disclosed in the present invention are a crystal form of a compound, and a preparation method therefor and the use thereof. The compound has a structure as represented by formula I. The crystal form has high crystal purity, low hygroscopicity, good solid-state stability and good solubility, and is thus beneficial for the preparation and storage of a pharmaceutical formulation and has great application value and has the prospect of becoming a medicine. In addition, the results of clinical trials further show that the crystal form A of the compound as represented by formula I of the present invention has great pharmacokinetic properties, low clinical toxicity, good safety, high oral bioavailability and good therapeutic effects on various diseases.

The invention provides a PD classification identification optimization method based on I-III stage drug clinical test nursing, and relates to the technical field of drug clinical test nursing management. The method comprises the following steps: collecting I-III phase drug clinical test nursing related data, and screening PD data; dividing the PD into three categories, namely test drug configuration or use deviation, sample collection or treatment deviation and vital sign detection deviation, and judging the severity; the PD core root is analyzed in combination with test item types, medicine dosage forms and nursing working modes; the matching system comprises a data acquisition module, a classification identification module, a root analysis module, an optimization strategy output module and a dynamic monitoring module. According to the method, the defects of fuzzy PD classification, inaccurate root analysis and insufficient prevention and control strategy pertinence in the prior art are overcome, the PD risk point can be accurately positioned, the PD occurrence rate is effectively reduced, the clinical test nursing quality is improved, the test risk is reduced, and the method has good practicability and popularization value.

The invention provides an inhibitor of a targeted esophageal cancer pathway. The inhibitor has a specific structural general formula, can specifically target an esophageal cancer pathway, and inhibits growth and proliferation of tumor cells. The preparation process comprises the steps of raw material preparation, reaction and purification, is simple and efficient, and is suitable for large-scale production; research and development of the inhibitor are based on deep understanding of esophageal cancer pathways, and survival mechanisms of tumor cells can be effectively interfered by accurately regulating and controlling key signal transduction paths. Clinical tests show that the inhibitor has a remarkable inhibition effect on various types of esophageal cancers, is small in side effect and has a good clinical application prospect. In addition, the preparation process is simple, convenient and economical, the high purity of the product can be ensured, the production efficiency is improved, the production cost is reduced, and favorable conditions are provided for large-scale industrial production. Therefore, the invention provides an innovative and effective choice for the treatment of esophageal cancer.

A computer-based method and system for predicting preclinical pharmacokinetics of a therapeutic agent. Dosage regimen data associated with a dosage regimen of a therapy is received. Also received is medical data associated with a subject. Model input data is formed using the dosage regimen data and the medical data. Using a machine learning model and the model input data, a predicted pharmacokinetic (PK) outcome output is generated. The machine learning model may be a neural ordinary differential equation (NODE) system. A recommendation for a clinical trial is formed, using the predicted PK output.

This invention belongs to the field of biomedicine, specifically relating to the application of APOL2 (Apolipoprotein L2) inhibitors in the preparation of products for treating liver fibrosis. The inventors isolated a series of natural tetrodopane-type diterpenes from *Euphorbia pekinensis*, a plant in the Euphorbiaceae family. Anti-liver fibrosis-related activity tests on this series of diterpenes revealed that they significantly inhibited the expression of fibronectin, type I collagen, and α-smooth muscle actin in LX-2 cells. In animal studies, their therapeutic effect was superior to that of pirfenidone, a phase II clinical trial drug for treating liver fibrosis, and they showed no significant toxicity. Mechanistic studies showed that TD1 is an APOL2 inhibitor, and knocking out APOL2 protein in vivo can alleviate the progression of liver fibrosis. In summary, this series of tetrodopane-type diterpenes, especially TD1, shows promise as a candidate drug for treating liver fibrosis and provides a new target for researching novel drugs for treating liver fibrosis.

The invention relates to an azaindole compound as well as a preparation method and application thereof, and belongs to the field of medicines. The invention provides a compound shown in a general formula I or a pharmaceutically acceptable salt thereof. The azaindole compound provided by the invention has a novel chemical structure, most of the compounds have high-level inhibitory activity on TNIK in vitro, and the inhibitory activity of most of the compounds on TNIK is superior to that of the disclosed compounds and a TNIK inhibitor INS018055 entering a clinical II stage at present. The compound can be used for preventing and / or treating various diseases such as cancers, nervous system diseases, fibrotic diseases or metabolic diseases, and has a very wide application prospect.

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. Systems and methods described herein relate to DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states on cellular subsystems which are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to design of synergistic drug combinations, which can be validate systematically. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

This invention discloses an intelligent management method and system applicable to drug clinical trials, relating to the field of drug clinical trial management technology. It includes collecting raw project activity records containing information such as trial site number and subject identification from the drug clinical trial execution system to form a collection. This collection undergoes multi-dimensional structured processing to generate a unified project view comprising subject time-series activity logs, trial site operational status snapshots, clinical document association networks, and key event sequences. Compliance modeling, effectiveness assessment, compliance checks, and logical verification are performed on the four types of views respectively. After generating corresponding specialized results, these are input into an intelligent analysis model for fusion and deduction, ultimately outputting a comprehensive project report including project risk warnings, process deviation location, and resource gap alerts. This invention achieves multi-dimensional integration and specialized fusion analysis of drug clinical trial data, optimizing the data analysis and application methods of clinical trial management.

This invention discloses an intelligent monitoring drug transport box and its operating method for drug clinical trials, which can ensure real-time area monitoring, opening permission management, and data traceability of drugs during transport. Its control unit includes: a data transmission security module, an encrypted communication module, a temporary opening authorization module, and a location-based service module.

The invention relates to the technical field of specimen centrifugal devices, and discloses a rapid centrifugal device for a drug clinical test specimen, and the rapid centrifugal device comprises a plurality of fixing blocks rotationally connected to the outer walls of a plurality of spherical connecting rods, a plurality of springs are fixedly connected to the outer walls of the plurality of fixing blocks, and a rotating assembly comprises a spherical hinge base fixedly connected to the inner wall of a shell. The inner wall of the spherical hinge base is rotatably connected with a ball rod, the outer wall of the ball rod is fixedly connected with a rotor base bearing, the outer walls of the springs are fixedly connected with the outer wall of the rotor base bearing, and when the rotor mechanism inclines towards one side and applies pressure downwards, the pressure is applied to the spring fixedly connected with the bottom of the side; the spring can finally apply upward pressure to the connecting ring, and the base moves downwards to apply downward pressure to the connecting ring on the other side, so that the upward pressure applied to the connecting ring by the base on the other side is balanced, and at the moment, the inclination angle of a part of the rotor mechanism can be reduced.

The invention relates to the field of medicine management and tracing, and discloses a clinical test medicine management and tracing system platform which comprises a basic information management module, a medicine storage management module, a medicine circulation tracing module, a data recording and tracing query module, a compliance and report module and a blind state management module. The output end of the basic information management module is connected with the input end of the medicine storage management module, the output end of the medicine storage management module is connected with the input end of the medicine circulation tracing module, and the output end of the medicine circulation tracing module is connected with the input end of the data recording and tracing query module. The output end of the data recording and tracing query module is connected with the input end of the compliance and report module, the output end of the compliance and report module is connected with the input end of the blind state management module, supervision rules such as GCP, NMPA and FDA are built in the system, violation operation is automatically intercepted in key links such as warehousing, dispensing and transferring, and compliance risks caused by human errors are avoided.

Provided are processes suitable for preparing the KRAS G12C inhibitor, (5aSa,17aRa)-20-Chloro-2-[(2S,5R)-2,5-dimethyl-4-(prop-2-enoyl)piperazin-1-yl]-14,17-difluoro-6-(propan-2-yl)-11,12-dihydro-4H-1,18-(ethanediylidene)pyrido[4,3-e]pyrimido[1,6-g][1,4,7,9]benzodioxadiazacyclododecin-4-one, (the compound of Formula (I)), to provide sufficient quantities of drug substance to support clinical trials and commercial operations. Also provided are certain synthetic intermediates useful in preparing the compound of Formula (I). The present disclosure also provides crystalline polymorphic forms of the compound of Formula (I) as well as processes for their preparation.

The present disclosure relates to the field of methods for the determination of drug toxicity, and more particularly cardiotoxicity associated with an anti-cancer agent. Further, the present disclosure provides related methods of treatment and stratifying patients with cancer for a clinical trial utilising such agents.

The invention discloses a clinical test digital intelligent mobile laboratory which comprises a chassis and a compartment. Front wheels and rear wheels are arranged on the chassis, a cab is arranged at the front end of the chassis, and a driving device for driving the front wheels to rotate is installed at the front end of the chassis. The compartment is arranged on the chassis, three partition plates are arranged in the compartment and divide the compartment into a power supply area, a sample storage area, a medicine intravenous dispensing area and a pharmacy area, and side doors are arranged on the two partition plates close to the tail of the compartment. The power supply area is located on the front portion of the compartment and internally provided with a mute generator. The pharmacy area is positioned at the tail part of the carriage; a medical low-temperature box and a medical refrigerator are arranged in the pharmacy area; an ultralow-temperature refrigerator and a centrifugal machine are arranged in the sample storage area; a biological safety cabinet is arranged in the medicine intravenous distribution area; two cabin doors are arranged on the side face of the compartment, one cabin door is used for entering and exiting the power supply area, the other cabin door is used for entering and exiting the sample storage area or the medicine intravenous dispensing area, and a hinged door is arranged at the tail of the compartment. According to the invention, a mobile laboratory is formed, and the efficiency of clinical tests is improved.

The invention relates to the technical field of artificial intelligence, and particularly discloses a drug clinical test file quality control and risk early warning method based on artificial intelligence, comprising the following steps: S1, collecting drug clinical test whole-process file data; s2, preprocessing the collected archive data; s3, feature extraction is carried out on the preprocessed data, and an optimal feature set is obtained through feature screening; s4, inputting the optimal feature set into a quality evaluation model for training, and then outputting an archive quality quantitative score; s5, inputting the quality quantitative score and the optimal feature set into a risk early warning model for training, and outputting a risk level; and S6, generating a quality assessment report, and pushing risk early warning information in real time. According to the drug clinical test file quality control and risk early warning method based on artificial intelligence, the AI model is combined with a dynamic monitoring mechanism, various risks occurring in a clinical test can be recognized in advance, and scientific decision support is provided for clinical test management.