Patents by Inventor Narathip Reamaroon
Narathip Reamaroon has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11984208Abstract: Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies.Type: GrantFiled: January 22, 2020Date of Patent: May 14, 2024Assignee: REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Brian D. Athey, Gerald A. Higgins, Alex Ade, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
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Publication number: 20220020466Abstract: Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies.Type: ApplicationFiled: September 22, 2021Publication date: January 20, 2022Inventors: Brian D. Athey, Gerald A. Higgins, Alex Ade, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
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Patent number: 10867702Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.Type: GrantFiled: December 23, 2019Date of Patent: December 15, 2020Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
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Publication number: 20200294623Abstract: Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies.Type: ApplicationFiled: January 22, 2020Publication date: September 17, 2020Inventors: Brian D. Athey, Gerald A. Higgins, Alex Ade, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
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Publication number: 20200234810Abstract: Methods for identifying patients diagnosed with treatment resistant or refractory depression, pain or other clinical indications who are eligible to receive N-methyl-D-aspartate receptor antagonist, glycine receptor beta (GLRB) modulator, or ?-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-based therapies to include determining the appropriate medication, an optimal dose for each patient, and determining which patients are not eligible to receive the therapy. The pharmacogenomic clinical decision support assays include targeted single nucleotide polymorphisms and clinical values or a combination of targeted single nucleotide polymorphisms, targeted ketamine-specific expansion and contraction of topologically associated domains, and clinical values. The methods described herein allow for a more effective determination of which patients will experience drug efficacy and which patients will experience adverse drug events.Type: ApplicationFiled: January 22, 2020Publication date: July 23, 2020Inventors: Brian D. Athey, Alex Ade, Gerald A. Higgins, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
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Publication number: 20200135337Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.Type: ApplicationFiled: December 23, 2019Publication date: April 30, 2020Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
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Patent number: 10553318Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.Type: GrantFiled: February 5, 2019Date of Patent: February 4, 2020Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
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Publication number: 20190172584Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.Type: ApplicationFiled: February 5, 2019Publication date: June 6, 2019Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
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Patent number: 10249389Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.Type: GrantFiled: May 11, 2018Date of Patent: April 2, 2019Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
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Publication number: 20180330824Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.Type: ApplicationFiled: May 11, 2018Publication date: November 15, 2018Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon