Addressing gaps in clinically useful evidence on drug-drug interactions

描述（由申请方提供）：潜在药物相互作用（PDDI）代表药物不良事件的显著因果关系。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证据比目前可能的。从我们广泛的前期工作开始，我们将建立一个框架，使用他汀类药物和精神药物（抗抑郁药和抗精神病药）实施新的范式。我们预计，该框架将推广到涉及其他药物的PDDI，包括使用药理学和生物信息学方法预测的药物。在建立范例框架的同时，我们将提出三个研究目标。第一个研究目标是获得一个新的元数据标准，以满足在不同的护理环境中工作的药剂师的信息需求，代表PDDI知识。信息需求调查将产生临床场景，然后通知并随后验证新标准。我们将设计标准， 它反映了语义Web社区的最佳思想，并且很有可能被广泛采用。然后，我们将联合收割机与语义注释和发布关联数据的最佳实践相结合的新标准，以创建他汀类药物和精神药物PDDI的语义Web知识库。第二个研究目标是比较PDDI证据语义Web与现有的PDDI知识资源的完整性，准确性和货币。我们将验证一种机制，用于将他汀类药物和精神药物PDDI断言与语义Web上的相关证据联系起来。由于药物基因组学可以影响许多PDDI，我们还将链接到该证据的可互操作表示。然后，两名药剂师将使用新的PDDI证据评分工具，将语义网上PDDI证据的覆盖范围和质量与现有的三种资源进行比较。第三个研究目标是调查填补临床有用的PDDI知识空白的过程， 与现有的证据。我们将利用基于共识的方法选择高优先级PDDI，并通过回顾性队列研究评价其临床相关性。我们将利用这些研究的结果扩展关联数据PDDI知识库，并通过试点门户网站公开提供知识库。拟议的工作将通过更有效地利用PDDI证据，填补药物安全知识的重要空白，并促进药物信息检索的创新，为公共卫生做出贡献。

DESCRIPTION (provided by applicant): 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.