Discriminating Drug Safety Profiles in Polytherapy

描述（由申请人提供）：应用于自发上市后不良事件报告大型数据库的统计数据挖掘已证明在支持政府卫生监管机构和制药商的药物警戒工作方面具有实质性效用。本研究的目的是开发先进的统计方法，以帮助评估，在安全性数据挖掘的背景下，在涉及多种治疗的情况下，个别药物的不良事件的贡献。这项工作解决了药物警戒中的一个重要理论和实践问题，并建立在长期合作的基础上，这种合作导致了申请人在FDA和行业中开发和部署安全性数据挖掘软件。 在I期，重点是实施和评价一种方法，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-事件对的多药治疗调整评分，从而提供关于高信号评分是否可能是由于其他药物的存在/影响的指导。第二阶段涉及算法的扩展，将算法完全整合到申请人的安全数据挖掘系统中，以及编写教育和辅导材料。对公共卫生的主要好处是提高了解释潜在药物安全问题信号的能力，并优先考虑调查和评估这些信号的努力。

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.