Addressing gaps in clinically useful evidence on drug-drug interactions

潜在的药物相互作用(PDDIs)是导致药物不良反应的重要原因。 事件。不幸的是，目前为供应商提供的资源是不完整和不准确的。我们 提出了一种新的PDDI知识表示范式，我们假设它将产生更多 临床上相关的证据比目前可能的更多。从我们广泛的身体开始 前期工作，我们将使用他汀类药物和 精神药物(抗抑郁药和抗精神病药物)。我们预计该框架将是 可推广到涉及其他药物的PDDIs，包括使用以下方法预测的那些 药理学和生物信息学。我们将推进三个研究目标，同时建设 范例框架。第一个研究目标是制定一个新的元数据标准 表示满足药师信息需求的PDDI知识 不同的护理环境。信息需求查询将导致临床场景，然后 告知并随后验证新标准。我们将设计该标准，以使其反映 最好的语义网社区思想，并将有很高的可能性广泛传播 领养。然后，我们将把新标准与语义注释和最佳实践结合起来 用于发布链接数据以创建他汀类药物和精神药物的语义网知识库 PDDIS。第二个研究目标是比较语义Web上的PDDI证据和 现有的PDDI知识资源的完整性、准确性和时效性。我们将验证 他汀类药物和精神药物PDDI断言与有关证据的联系机制 语义网。由于药物基因组学可以影响许多PDDIs，我们还将链接到 这一证据的可互操作陈述。然后，两名药剂师将比较覆盖范围 和语义Web上的PDDI证据的质量，以及三个现有资源使用 新的PDDI证据评分工具。第三个研究目标是调查填报过程 临床上有用的PDDI知识的空白，无法用现有的证据填补。我们会 利用基于共识的方法选择高优先级的PDDIs并评估其临床应用 通过回溯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来促进公共卫生 PDDI证据，填补了药物安全知识的重要空白，并刺激了 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.