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A translational bioinformatics approach to elucidate and mitigate polypharmacy induced adverse drug reactions

阐明和减轻复方用药引起的药物不良反应的转化生物信息学方法

基本信息

批准号：
10664024
负责人：
Zackary Michael Falls
金额：
$ 20.93万
依托单位：
STATE UNIVERSITY OF NEW YORK AT BUFFALO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10664024
关键词：
Achievement Affinity Age Aptitude Architecture Area Basic Science Benzodiazepines Binding Binding Proteins Bioinformatics Biological Biological Assay Buprenorphine Cause of Death Cessation of life Chemical Models Chronic Disease Clinical Clinical Data Clinical Decision Support Systems Clinical Informatics Clinical Skills Complement Computer Analysis Computer software Counseling Creativeness Data Data Set Databases Development Diagnosis Docking Drug Combinations Drug Interactions Drug Prescriptions Drug usage Electronic Health Record Environment Ethnic Origin Event FDA approved Financial Hardship Funding Gender Goals Grant Graph Health Healthcare Systems High Prevalence Human Impaired cognition In Vitro Incidence K-Series Research Career Programs Knowledge Knowledge acquisition Medicine Mentors Methadone Methodology Methods Naltrexone Opiate Addiction Opioid Outcome Overdose Overdose reduction Pathway interactions Patients Performance Pharmaceutical Preparations Pharmacology Polypharmacy Predictive Analytics Proteins Protocols documentation Quality of life ROC Curve Race Regimen Reporting Research Research Personnel Research Proposals Sampling Severities Site Software Tools Statistical Data Interpretation Testing Tobacco use Training United States Validation Ventilatory Depression addiction adverse drug reaction career career development clinical decision support clinical practice clinically significant cohort cost deep learning deep learning model design drug discovery experience falls graph theory improved individual patient knowledge graph medication-assisted treatment new therapeutic target novel novel therapeutics opioid epidemic opioid overdose opioid use opioid use disorder patient safety pharmacologic predictive modeling prescription opioid relapse patients side effect skills standard of care sustained recovery treatment guidelines vector

项目摘要

PROJECT SUMMARY This proposal for a mentored career development award consists of a training and research plan to facilitate Dr. Zackary Falls' transition to an independent investigator focusing on translational bioinformatics for patient tailored predictive analytics related to opioid addiction severity. The opioid epidemic is a major concern in the United States that is exacerbated due to the high prevalence of prescribing two or more drugs to patients living with opioid use disorder, which increases the likelihood of adverse drug reactions (ADRs) occurring in these patients. Knowing and predicting drug–drug interactions (DDIs) and resulting ADRs is critical for the safety of patients, but ADR prediction software tools used in clinical practice have many limitations. Firstly, most DDI databases used in these software tools are incomplete because they incorporate only pair–wise DDIs. Additionally, most software tools do not incorporate biological mechanism of action information for the drugs and omit relevant patient– speciﬁc clinical data such as diagnoses, tobacco use, etc. Dr. Falls aims to exceed the efﬁcacy of these software with the creation of embedded representations for each patient's prescription proﬁle, leveraging both drug–protein interaction knowledge about the prescription drugs and patient level clinical data pertaining to polypharmacy and ADRs. The speciﬁc aims of this research are to predict and validate novel off–target proteins for opioids and other commonly co–prescribed medications (Aim 1), extract polypharmacy interactions and ADR relationships from electronic health records of opioid prescription patients (Aim 2), and design a patient personalized software that uses deep–learning architecture to predict severe ADRs caused by opioid related polypharmacy interactions (Aim 3) to be integrated with clinical decision support systems for the beneﬁt of patients and clinicians. The ap- plicant has detailed a rigorous plan containing three career development goals for gaining the skills and expertise to accomplish his research aims. These goals include: Goal 1. Gain knowledge in addiction research and phar- macology as it relates to opioid use, Goal 2. Acquire advanced statistical analysis skills for clinical datasets, and Goal 3. Increase understanding of graph theory and knowledge graph implementation. The team of mentors and collaborators that has been assembled by Dr. Falls, including Prof. Ram Samudrala as primary mentor, perfectly accounts for expertise in research areas that the applicant will be investigating and have knowledge in domains that complement his own understandings to aid in the career development aspect of this proposal. Dr. Falls has the aptitude, creativity, and perseverance to become an excellent researcher. The support of this K01, guidance from his terriﬁc team of mentors and collaborators, and the inﬂuence of a rich research environment will enable him to further develop his skills and knowledge. He will surely accomplish all of his career development goals and research aims, become a successful independent investigator, and ﬂourish in his career.

项目总结