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.

项目摘要这项指导职业发展奖的提案包括一项培训和研究计划，以促进博士。 Zackary Falls向独立研究者的过渡，专注于针对患者量身定制的转化生物信息学预测性分析与绿化成瘾的严重程度有关。 opioid流行是曼联的主要关注点由于针对患者的两种或多种药物的处方率高而加剧的状态 Opioid使用障碍，增加了这些患者发生不良药物反应（ADR）的可能性。了解和预测药物与药物相互作用（DDI）和由此产生的ADR对于患者的安全至关重要，但是临床实践中使用的ADR预测软件工具有许多局限性。首先，大多数使用的DDI数据库在这些软件工具中，由于它们仅合并了Paire -Wise DDI，因此不完整。另外，大多数软件工具不包含用于药物的动作信息的生物学机制，并忽略相关患者 - 特定的临床数据，例如诊断，烟草使用等。Falls博士旨在超过这些软件的效率随着为每个患者的处方药创建嵌入式表示形式，利用这两种药物 - 蛋白有关处方药和患者水平临床数据的相互作用知识与多药有关 ADR。这项研究的特定目的是预测和验证阿片类药物的新颖核心蛋白质蛋白其他常见的共同处方药（AIM 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（AIM 2），并设计患者个性化软件它使用深度学习结构来预测阿片类药物相关的多药相互作用引起的严重ADR （AIM 3）将与患者和临床医生的益处纳入临床决策支持系统。 ap- Plicant详细介绍了一个严格的计划，其中包含三个职业发展目标，以获得技能和专业知识为了完成他的研究目的。这些目标包括：目标1。在成瘾研究和phal中获得知识与阿片类药物使用有关的宏观学，目标2。获得临床数据集的高级统计分析技能，以及目标3。增加对图理论和知识图实现的理解。导师团队和由Falls博士组装的合作者，包括Ram Samudrala教授，是主要的，完美的申请人将在研究领域进行研究并在领域中具有知识的研究领域的专业知识。这补充了他自己的理解，以帮助这一提议的职业发展方面。福尔斯博士有成为一名优秀研究人员的才能，创造力和毅力。这个K01的支持，指导从他的导师和合作者团队中，以及丰富的研究环境的影响将使他一定会实现自己的所有职业发展目标研究的目的是，成为成功的独立研究者，并在他的职业生涯中浮现。