Individual and cohort pharmacological phenotype prediction platform

Abstract

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, andoutcome 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, includingdrug 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.

Description

[0001] Cross References to Related Applications [0002] This application requires (1) Provisional U.S. Application Serial No. 62 / 505,422, filed May 12, 2017, entitled "Individual and Cohort Pharmacological Phenotype Prediction Platform" and (2) Priority to and Benefit from Provisional U.S. Application Serial No. 62 / 633,355, entitled "Individual and Cohort Pharmacological Phenotype Prediction Platform," filed February 21, 2018, in which The entire disclosure of each document is hereby expressly incorporated by reference. technical field [0003] This application relates to pharmacological patient phenotypes, and more specifically, to a method for predicting patient And methods and systems for drug response phenotype of stratified cohorts of patients. Background technique [0004] Today, some patient drug responses can be predicted based on the patient's coding genome. Specific genetic traits can be mapped to specific responses to drugs, and drugs can be selected for pati...

AI Technical Summary

Problems solved by technology

Current systems also do not consider environmental and sociological traits that may modify genetic traits to determine pharmacological phenotypes

In addition, such systems do not utilize machine learning techniques to train the system to adapt to changes in biological characteristics and / or pharmacological phenotypes corresponding to biological characteristics over time

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