19 results about "Pharmacogenomics" patented technology

The present disclosure provides methods, compositions and systems for identifying variant targets of interest, for example, in clinically actionable genes. Disclosed herein are representative assays for identifying gene variants, for example, that are implicated in one or more drug metabolism pathways. The methods, composition and systems disclosed herein enable a highly streamlined and cost-effective workflow for moving forward the emerging field of personalized medicine and related fields of study such as pharmacogenomics.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.

The present invention relates to the field of pharmacogenomics and in particular to detecting the presence or absence of methylated ANKRD13B and / or FOXF2 DNA derived from a tumor in blood or blood-derived samples or in other body fluids that contain DNA released from a tumor. This detection is useful for a minimally invasive diagnosis of cancers and the invention provides methods and oligonucleotides suitable for this purpose.

The invention discloses a pharmacogenomics report generation method and device, a medium and a product, and relates to the field. The method comprises the following steps: in response to the completion of a drug genome experiment of a first object, obtaining experimental result data of the drug genome experiment, and determining experimental result interpretation data based on a first drug gene association database; inputting the experimental result interpretation data, the object attribute information corresponding to the first object, the medical record information and the first medicine information into a pre-trained medication risk assessment model to obtain a medication risk assessment result of the first object; and generating a pharmacogenomics report of the first object based on the medication risk assessment result, the experimental result interpretation data, the object attribute information, the medical record information and the first drug information. According to the technical scheme of the invention, the problems of high threshold and low efficiency of the existing pharmacogenomics report generation are solved, the pharmacogenomics report can be automatically generated, the accuracy of the report content is improved, and the report generation cost is reduced.

A method and device for predicting a genotype of a target gene, the method comprising: a baseline correction step; a feature extraction step, comprising obtaining a cluster center of a specific region of the target gene according to a corrected depth of each capture site, and taking the cluster center as a feature value of the corresponding region; and a prediction step, comprising predicting a genotype of the target gene in sequencing data of a sample to be tested according to the feature value. The method has the advantages of fast analysis speed, high accuracy, wide application range, no need for reference set correction, avoidance of the influence of sequencing fluctuation on the analysis result, ease of implementation of high-throughput screening typing at a population level, and applicability to individualized medication guidance for more populations and acceleration of large-scale research and development of pharmacogenomics.

The present invention relates to the field of pharmacogenomics, which applies one or more genomic biomarkers and the related diagnostic methods, devices, reagents, systems, and kits, for predicting varied individual responses such as, for example, efficacy or adverse effect, to therapeutic agents, e.g., inhibitors of certain neurotransmitter transporters such as liafensine.

The invention relates to the field of virology and pharmacogenomics, in particular to a molecular marker combination and application thereof in preparation of a bavirin drug resistance monitoring kit. The molecular marker combination is a single nucleotide polymorphism located at the 603rd nucleotide site of the reference sequence and a single nucleotide polymorphism located at the 913th nucleotide site of the reference sequence of the RSVF gene. And the 603rd nucleotide is changed from adenine to cytosine, so that the sensitivity of the virus to ribavirin is changed. And the 913th nucleotide is changed from cytosine to adenine, so that the sensitivity of the virus to ribavirin is changed.

A health analysis method has the steps of obtaining test data of a patient sample, cleaning the test data by removing data items independent of the health analysis, obtaining statistics of the cleaned test data, and determining a health analysis result based on the obtained statistics of the cleaned test data and statistics of the health data. Analysis results of one or more genes and / or species of a patient sample are obtained using one or more artificial intelligence (AI) methods.

Disclosed are methods for treating pain and tracking response as disclosed are methods for determining pain, including predicting future medical care facility visits for pain.

The present invention relates to a clinical decision support system comprising a processing unit in communication with a memory. The processing unit is configured to receive and process clinical data associated with a patient, which comprises medical records, biometric or physiological data, and / or currently prescribed medication information. The system computes personalized target lipid profile ranges based on the clinical data and medical guidelines stored in the memory. Preferably, the system receives pharmacogenetic analysis data and measured lipid profile values from patient blood tests. The processing unit compares the measured lipid values with the personalized target ranges and, if deviations are found, computes a list of potential medication treatments. This list is sorted based on the estimated effects of each treatment on the lipid profile values, the different between measured and objective lipid profile values, on the clinical data and / or on the pharmacogenetic analysis. The system further outputs data related to the personalized lipid ranges, the comparison of measured values, and / or the suggested medication treatments.

The determination of pharmacogenomics gene star alleles using high-throughput targeted genotyping includes obtaining input genetic sequence variation data from a high-throughput genotyping platform based on a pharmacogenomic genotyping of a sample, applying a Bayesian graphical model to determine a plurality of different star allele calls corresponding to the sample, and providing a respective quality score for each star allele call of the plurality of different star allele calls. For instance, the application of the Bayesian graphical model uses multi-solution integer programming to explore a model space of the Bayesian graphical model in a first phase that includes structural variant candidate identification and a second phase that includes star allele candidate identification based on the structural variant candidate identification, to determine the plurality of different star allele calls.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.

The invention discloses a test method of reaction of EPHX1 genotype based COPD to NAC. The test method comprises the following steps: extracting a DNA by a phenol / chloroform method and sequencing, dividing COPD patients into slow and very slow gene activity group and a quick and normal gene activity group according to EPHX1 gene polymorphism, giving 600 mg Bid of NAC for 1 year to both groups, performing baseline measurement of pulmonary function parameters, completing SGRQ and 6MWT, measuring FVC and FEV1 at a follow-up day every 12 weeks, completingSGRQ and 6MWT in 24 and 48 weeks, and recording the number of acute exacerbationof COPD in one year. The data were statistically analyzed with SPSS. The results show that NAC is effective in the treatment of slow and very slow EPHX1 gene activityCOPD patients. The test method provided by the invention provides a solid basis for demonstrating the effect and mechanism of NAC based on pharmacogenomics in the individualized treatment of COPD.

This invention relates to the fields of biotechnology and pharmacogenomics detection, and discloses a kit and application for gene detection of drugs used in general anesthesia. The kit contains a primer set for detecting genetic polymorphic loci related to commonly used perioperative anesthetic, analgesic, and muscle relaxant drugs. The primer set includes multiplex PCR amplification primers and single-base extension primers. The kit also includes multiplex PCR amplification reaction solution, SAP reaction solution, SAP enzyme, extension buffer, single-base extension termination mixture, extension reaction enzyme, desalting purification resin, standards, calibrators, deionized water, and instructions for use. This invention uses matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) to genotype SNP loci, obtaining genotype results for 103 genetic polymorphic loci in a single test. This allows for the prediction of individual differences in drug metabolism, drug efficacy, and adverse reaction risk, providing a basis for personalized medication in general anesthesia.

Methods are described that include an integrated, multi-scale, artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional subnetworks. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of drug pharmacogenomic networks is performed by selecting candidate SNPs with the aid of imputation, determining predictive causal relationships of the SNPs using machine learning and deep learning, probing spatial genomes as determined by chromosome conformation capture analysis using causal relationship SNPs, combining targeted genes controlled by the same cell and tissue-specific enhancers, and using different data sources and metrics to reconstruct pharmacogenomic networks based on results of genome-wide association studies. The pharmacogenomic networks are deconstructed into their constituent functional and adverse event subnetworks using a knowledge-based segmentation approach for application in clinical decision support, drug repurposing, and in silico drug discovery.

This invention presents a novel approach that integrates pharmacogenomics with multi-dimensional factor analysis to improve the detection of drug allergies and the adjustment of drug dosages tailored to individual patients. Genetic differences between individuals directly affect the production of proteins involved in the processes of absorption, distribution, metabolism, and excretion (ADME) of drugs in the body. This study has developed an analytical system that not only considers genetic factors but also includes external factors that influence drug efficacy, such as lifestyle habits, herbal consumption, and the effects of polypharmacy. The developed system utilizes a scoring process derived from the analysis of multiple data sources, including genetic data, treatment history, lifestyle information, and other clinical data. These scores assist in clinical decision making by evaluating the risk of adverse drug reactions and recommending appropriate drug dosage adjustments for each patient. This approach enhances the accuracy and safety of drug therapy.

A set of candidate drugs is selected based on one or more outcomes related to one or more diseases and variants linked with the selected set of candidate drugs are obtained. One or more protein sequences related to the selected set of candidate drugs are collated. Pairs of variants and protein sequences are generated for each drug of the set of candidate drugs. A contrastive learning model is trained using the generated pairs of variants and protein sequences and a downstream task is performed using the contrastive learning model.