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：
10507532
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/10507532
关键词：
Achievement Affinity Aptitude Architecture Area Basic Science Benzodiazepines Binding Binding Proteins Bioinformatics Biological Biological Assay Biological Pharmacology 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 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 Recovery Regimen Relapse Reporting Research Research Personnel Research Proposals Research Training Sampling Severities Site Software Tools Statistical Data Interpretation Supervision Testing Tobacco use Training United States Validation Ventilatory Depression addiction adverse drug reaction base career career development clinical decision support clinical practice cohort cost deep learning deep learning model design drug discovery experience falls graph theory improved individual patient informatics training knowledge graph medication-assisted treatment new therapeutic target novel novel therapeutics opioid epidemic opioid overdose opioid use opioid use disorder patient safety predictive modeling prescription opioid side effect skills standard of care 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.

项目概要这项指导性职业发展奖的提案包括一项培训和研究计划，以促进博士。扎卡里·福尔斯 (Zackary Falls) 转型为独立研究者，专注于为患者量身定制转化生物信息学与阿片类药物成瘾严重程度相关的预测分析阿片类药物的流行是美国的一个主要问题。由于向患有这种疾病的患者开出两种或两种以上药物的比例很高，导致这种情况加剧的国家阿片类药物使用障碍，增加了这些患者发生药物不良反应 (ADR) 的可能性。了解和预测药物相互作用 (DDI) 以及由此产生的 ADR 对于患者的安全至关重要，但是临床实践中使用的ADR预测软件工具有很多局限性，首先，大多数使用的DDI数据库。这些软件工具中的内容并不完整，因为它们仅包含成对的 DDI。工具不包含药物作用的生物机制信息并忽略相关患者 - 具体的临床数据，如诊断、烟草使用等。Dr. Falls 的目标是超越这些软件的功效为每个患者的处方概况创建嵌入式表示，利用药物-蛋白质关于处方药和与复方用药相关的患者水平临床数据的交互知识本研究的具体目的是预测和验证阿片类药物和药物的新型脱靶蛋白。其他常见的共同处方药物（目标 1），提取多药相互作用和 ADR 关系从阿片类药物处方患者的电子健康记录中获取信息（目标 2），并设计患者个性化软件使用深度学习架构来预测阿片类药物相关的多药相互作用引起的严重不良反应（目标 3）与临床决策支持系统集成，以造福患者和居民。 plicant 制定了详细的严格计划，其中包含三个职业发展目标，以获得技能和专业知识实现他的研究目标包括：目标 1. 获得成瘾研究和药物方面的知识。与阿片类药物使用相关的宏观学，目标 2。获得临床数据集的高级统计技能分析，以及目标 3. 提高对图论和知识图实现的理解导师团队和团队。 Falls 博士召集的合作者，包括作为主要导师的 Ram Samudrala 教授，完美地说明申请人将要研究的研究领域的专业知识以及该领域的知识这补充了他自己的理解，以帮助福尔斯博士的职业发展方面。成为一名优秀研究员的能力、创造力和毅力得到了K01的支持、指导。来自他的导师和合作者的领域团队，以及丰富的研究环境的影响力将使他进一步发展自己的技能和知识，他一定会实现所有的职业发展目标。和研究目标，成为一名成功的独立研究者，并在他的职业生涯中蓬勃发展。