Mapping the Drugome: predictive network approaches to drug safety surveillance

绘制药物组图：药物安全监测的预测网络方法

• 批准号: 8231483
• 负责人: Aurel B. Cami
• 金额: $ 13.08万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8231483
• 关键词: Advanced Development; Adverse drug effect; Adverse effects; Adverse event; Biological; Biometry; Boxing; Cessation of life; Data; Databases; Development; Drug Interactions; Electronics; Encapsulated; Evaluation; Event; Goals; Graph; Health; Hydrophobicity; Individual; Insurance Carriers; Knowledge; Machine Learning; Maps; Mentors; Methods; Modeling; Morbidity - disease rate; Motivation; Neighborhoods; Network-based; Pathway interactions; Performance; Pharmaceutical Preparations; Phase; Predictive Value; Principal Investigator; Process; Property; Public Health; Qualifying; Research; Research Personnel; Research Project Grants; Safety; Sales; Screening procedure; Sensitivity and Specificity; Source; Speed; Surveillance Methods; Taxonomy; Testing; Time; Training; Translational Research; Validation; Work; base; biomedical informatics; body system; career development; design; drug mechanism; drug structure; drug withdrawal; experience; heuristics; improved; mortality; network models; novel; post-market; predictive modeling; prevent; programs; prospective; research study; research to practice; software development

DESCRIPTION (provided by applicant): This application for the K99/R00 "Pathway to Independence" program aims to support Dr. Aurel Cami's development into an independent investigator in the field of Biomedical Informatics. The proposed career development period consists of a two-year mentored phase and a three-year independent phase. During the first phase, mentored by Drs. Reis, Kohane, Szolovits, and Manzi, the applicant will obtain formal training in drug safety and in biostatistics/machine learning methods for developing advanced predictive network models. This training will provide the groundwork for the proposed research project. The overall goal of this project is to develop novel surveillance approaches that leverage the inherent network structure of drug safety relationships across the entire drugome in order to predict adverse events earlier and more accurately. The project has four specific aims: 1. Construct and characterize integrated Drug Safety Network representations of the drugome. 2. Develop predictive network models to identify unknown drug-AE associations. 3. Develop predictive network models to identify unknown drug-drug-AE interactions. 4. Systematically evaluate model performance, both retrospectively and prospectively. The preliminary research studies have shown that predictive network models can sensitively and specifically detect drug-AE associations and drug-drug interactions, providing strong motivation for the proposed research. With a background in predictive network modeling and public health drug surveillance, Dr. Cami is highly qualified to conduct the proposed research. This project brings together a broad, interdisciplinary team of mentor, co-mentors and advisors with extensive combined experience in every aspect of the proposed research.

描述（由申请人提供）： 这项针对K99/R00“独立途径”计划的申请旨在支持Aurel Cami博士的发展成为生物医学信息学领域的独立研究者。拟议的职业发展期包括为期两年的指导阶段和一个三年的独立阶段。在第一阶段，由Drs指导。 Reis，Kohane，Szolovits和Manzi，申请人将获得药物安全和生物统计学/机器学习方法的正式培训，以开发先进的预测网络模型。该培训将为拟议的研究项目提供基础。该项目的总体目标是开发新型的监视方法，以利用整个药物组中药物安全关系的固有网络结构，以便更早，更准确地预测不良事件。该项目具有四个特定的目的：1。构建和表征药物组的综合药物安全网络表示。 2。开发预测网络模型以识别未知的药物AE关联。 3。开发预测性网络模型以识别未知的药物 - 毒素相互作用。 4。回顾性和前瞻性地评估模型性能。初步研究表明，预测网络模型可以敏感，特定地检测药物AE的关联和药物 - 药物相互作用，从而为拟议的研究提供了强大的动力。 Cami博士具有预测网络建模和公共卫生药物监视的背景，因此有资格进行拟议的研究。该项目汇集了一个广泛的跨学科团队，由指导者，联席会员和顾问团队在拟议的研究的各个方面具有丰富的综合经验。