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

• 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
• 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

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 这一建议的辅导职业发展奖包括一个培训和研究计划，以促进博士。 Zackary福尔斯转变为一名独立的研究者，专注于为患者量身定制的翻译生物信息学 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。 由于向患有糖尿病的患者开两种或两种以上药物的高流行率， 阿片类药物使用障碍，这增加了这些患者发生药物不良反应（ADR）的可能性。 了解和预测药物间相互作用（DDI）和由此产生的ADR对患者的安全至关重要， 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DDI数据库使用 在这些软件中，工具是不完整的，因为它们仅结合了成对DDI。此外，大多数软件 工具不包含药物的生物作用机制信息，并忽略了相关患者- 具体的临床数据，如诊断，烟草使用等。福尔斯博士的目标是超越这些软件的效率 通过为每个患者的处方配置文件创建嵌入式表示， 关于处方药和与多种药物相关的患者水平临床数据的交互知识， ADR。这项研究的具体目标是预测和验证阿片类药物的新型脱靶蛋白， 其他常见的联合处方药物（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（Aim 2）中，设计一个患者个性化软件 使用深度学习架构来预测阿片类药物相关的多种药物相互作用引起的严重ADR (Aim 3）与临床决策支持系统集成，以造福患者和临床医生。该ap- 申请人详细制定了一个严格的计划，其中包括三个职业发展目标，以获得技能和专业知识 来实现他的研究目标。这些目标包括：目标1。获得成瘾研究和phar的知识- 与类阿片使用有关的精神病学，目标2。获得临床数据集的高级统计分析技能，以及 目标3.增加对图论和知识图实施的理解。导师团队和 福尔斯博士召集了许多合作者，包括拉姆·萨穆德拉拉教授作为主要导师， 说明申请人将调查的研究领域的专业知识，并在该领域拥有知识 这补充了他自己的理解，有助于这项建议的职业发展方面。福尔斯医生 才能，创造力和毅力，成为一个优秀的研究人员。本K 01的支持、指导 来自他的导师和合作者的团队，以及丰富的研究环境的影响将使 进一步发展他的技能和知识。他一定会完成他所有的职业发展目标 和研究目标，成为一个成功的独立调查员，并在他的职业生涯中茁壮成长。