Discriminating Drug Safety Profiles in Polytherapy

综合治疗中药物安全性的区别

• 批准号: 6738316
• 负责人: DAVID M FRAM
• 金额: $ 9.97万
• 依托单位: LINCOLN TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-07 至 2004-11-06
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6738316
• 关键词: clinical research; combination chemotherapy; computer program /software; computer system design /evaluation; drug adverse effect; drug interactions; human data; information retrieval; method development; patient safety /medical error; public health; statistics /biometry

DESCRIPTION (provided by applicant):Statistical data mining applied to large databases of spontaneous post-marketing adverse event reports has demonstrated substantial utility in supporting pharmacovigiiance efforts at government health authorities and pharmaceutical manufacturers. The objective of this research is to develop advanced statistical methods to help assess, in the safety data mining context, the contribution of individual drugs to adverse events in situations involving polytherapy. This work addresses an important theoretical and practical problem in pharmacovigilance, and builds on a long-standing collaboration which has led to the development and deployment of applicant's safety data mining software at FDA and in industry. In Phase I, the focus is on the implementation and evaluation of a method utilizing information from three-way (drug-drug-event) combinations to calculate a polytherapy adjustment score for each drug-event pair and thus to provide guidance on whether a high signal score may be due to the presence/influence of other drugs. Phase II involves extensions to the algorithm, full integration of the algorithm into applicant's safety data mining system, and preparation of educational and tutorial materials. The key benefit to public health is an improved ability to interpret signals of potential drug safety problems, and to prioritize efforts to investigate and evaluate these signals.

描述(由申请人提供)：统计数据挖掘应用于自发上市后不良事件报告的大型数据库，在支持政府卫生当局和制药制造商的药物监督工作方面显示出巨大的效用。这项研究的目的是开发先进的统计方法，在安全数据挖掘的背景下，帮助评估在涉及多种疗法的情况下，个别药物对不良事件的贡献。这项工作解决了药物警戒中的一个重要的理论和实践问题，并建立在长期合作的基础上，这一合作导致了FDA和行业开发和部署申请人的安全数据挖掘软件。在第一阶段，重点是实施和评估一种方法，该方法利用来自三向(药物-药物-事件)组合的信息来计算每个药物-事件对的综合治疗调整分数，从而提供关于高信号分数是否可能是由于其他药物的存在/影响的指导。第二阶段涉及对算法的扩展，将算法完全整合到申请者的安全数据挖掘系统中，以及准备教育和教程材料。对公众健康的关键好处是提高了解读潜在药物安全问题信号的能力，并优先努力调查和评估这些信号。