Addressing gaps in clinically useful evidence on drug-drug interactions

潜在的药物相互作用（PDDI）是药物不良反应的重要因果关系。 事件不幸的是，目前的资源提供者是不完整和不准确的。我们 提出了一个新的PDDI知识表示范式，我们假设将产生更多的 临床相关的证据比目前可能的。从我们广泛的机构， 初步工作，我们将建立一个框架，实现新的范式使用他汀类药物， 精神药物（抗抑郁药和抗精神病药）。我们预计，该框架将 可推广到涉及其他药物的PDDI，包括使用来自 药理学和生物信息学。我们将推进三个研究目标，同时建立 示范框架。第一个研究目标是导出一个新的元数据标准， 代表PDDI知识，满足药剂师工作的信息需求， 不同的护理环境。信息需求调查将产生临床场景， 通知并随后验证新标准。我们将设计标准，使其反映 语义Web社区的最佳思想，并将有很高的可能性广泛传播 领养然后，我们将联合收割机与语义注释和最佳实践相结合 用于发布关联数据，以创建他汀类药物和精神药物的语义Web知识库 PDDI。第二个研究目标是比较语义网上的PDDI证据与 现有PDDI知识资源的完整性、准确性和通用性。我们将验证 将他汀类药物和精神药物PDDI断言与相关证据联系起来的机制， 语义网。由于药物基因组学可以影响许多PDDI，我们还将链接到一个 该证据的可互操作表示。两名药剂师将比较 和质量的PDDI证据的语义网与三个现有的资源，使用 PDDI证据评分工具。第三个研究目的是探讨一个填充的过程 临床上有用的PDDI知识的空白无法用现有证据填补。我们将 利用基于共识的方法选择高优先级PDDI并评估其临床 回顾性队列研究的相关性。我们将扩展关联数据PDDI知识 以这些研究的结果为基础，并通过 试点门户网站。拟议的工作将通过更有效地利用 PDDI证据，填补了药物安全知识的重要空白，并促进了药物安全领域的创新。 药品信息检索

Potential drug-drug interactions (PDDIs) represent a significant causality for adverse drug events. Unfortunately, current resources for providers are incomplete and inaccurate. We propose a new PDDI knowledge representation paradigm that we hypothesize will yield more clinically relevant evidence than is currently possible. Starting from our extensive body of preliminary work, we will build a framework that implements the new paradigm using statins and psychotropics (antidepressants and antipsychotics). We expect that the framework will be generalizable to PDDIs involving other drugs, including those predicted using methods from pharmacology and bioinformatics. We will advance three research aims while building the exemplar framework. The first research aim is to derive a new meta-data standard for representing PDDI knowledge that satisfies the information needs of pharmacist working in different care settings. An information needs inquiry will result in clinical scenarios that will then inform, and later validate, the new standard. We will design the standard so that it reflects the best thinking of Semantic Web community and will have a high likelihood of widespread adoption. We will then combine the new standard with semantic annotation and best practices for publishing Linked Data to create a Semantic Web knowledge base of statin and psychotropic PDDIs. The second research aim is to compare PDDI evidence on the Semantic Web with existing PDDI knowledge resources for completeness, accuracy and currency. We will validate a mechanism for linking statin and psychotropic PDDI assertions to relevant evidence on the Semantic Web. Because pharmacogenomics can impact many PDDIs, we will also link to an interoperable representation of this evidence. Two pharmacists will then compare the coverage and quality of the PDDI evidence on the Semantic Web with three existing resources using a new PDDI evidence scoring tool. The third research aim is to investigate a process for filling in gaps in clinically useful PDDI knowledge that cannot be filled with available evidence. We will utilize a consensus-based approach to select high priority PDDIs and evaluate their clinical relevance by retrospective cohort studies. We will extend the Linked Data PDDI knowledge base with the results of these studies, and make the knowledge base publicly available via a pilot web portal. The proposed work will contribute to public health by making more effective use of PDDI evidence, filling in important gaps in drug safety knowledge, and spurring innovations in drug information retrieval.