• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Mapping the Alzheimer's Disease Clinical Trial Space
【24h】

Mapping the Alzheimer's Disease Clinical Trial Space

机译：绘制阿尔茨海默氏病临床试验空间

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Over the past 20 years, the pharmaceutical industry has realized a disproportional trend in new drug approvals at the cost of increased spending. While insights into underlying mechanisms of disease continues to grow, the accumulation and integration of this knowledge complicates its application to clinical practice in the form of clinical trial protocol design. As knowledge is gained from the successes and failures of basic research and subsequent clinical trials, the landscape of the therapeutic space evolves: novel drug targets are assessed, new treatment approaches are developed, and patient populations are refined. This has forced researchers to rethink strategies which facilitate the efficient development of next-generation clinical trials, by leveraging lessons-learned from outputs of past research. Conventional protocol repositories such as ClinicalTrials.gov do not allow for this level of precise inquiry from its plain-text search and document retrieval functionality. Instead, it is useful to devise analytical tools which quantify and visualize temporal shifts in treatment strategies for the purpose of better understanding the evolution of complex therapeutic spaces at various degrees of resolution, through the development of a novel protocol mining framework. This framework is explored through its application to the complex Alzheimer's Disease (AD) therapeutic space. In light of recent high-profile AD clinical trial failures, the need for leveraging lessons-learned from trial results is apparent, since the century-old therapeutic space is dynamic and multi-faceted.;ARRASTRA, a Python framework, has been developed for mining and analyzing large corpora of semantically annotated documents, specifically for this purpose, clinical trial protocols. Plain-text protocols are augmented with natural language processing (NLP) and semantic annotation routines. By mining protocol collections and expressing them as temporally-directed similarity networks, graph theory can be employed to quantify the evolution of a therapeutic space at both the individual protocol level (nodes), and high-level themes (clusters). Firstly, the ability of a protocol to influence the genesis of new trials are quantified by their position in this network, and are analyzed over time by identifying significant "bursts" within those metrics. Secondly, the evolution of a therapeutic space itself is visualized as temporal changes at the graph cluster level using a "MetroMap" visualization, which tracks sub-therapeutic themes as new ideas form and old ideas are abandoned. Finally, these graph metrics when integrated with additional protocol metadata, are used to predict the viability of themes using survival models. Overall, the ARRASTRA framework provides novel fine-grained insights into the changing nature of a therapeutic space by repurposing the content of clinical trial protocol repositories in ways not previously explored.
机译：在过去的20年中，制药业以增加支出为代价实现了新药审批的不成比例趋势。尽管对疾病潜在机制的见解不断增长，但这种知识的积累和整合使它以临床试验方案设计的形式在临床实践中的应用变得复杂。随着从基础研究和后续临床试验的成功与失败中获得知识，治疗领域的面貌也在不断发展：评估新的药物靶标，开发新的治疗方法并改善患者群体。这迫使研究人员通过利用从过去的研究成果中学到的经验教训，重新思考促进下一代临床试验有效发展的策略。常规协议存储库（例如ClinicalTrials.gov）不允许从纯文本搜索和文档检索功能进行这种精确的查询。取而代之的是，设计一种分析工具来量化和可视化治疗策略中的时间变化是有用的，目的是通过开发新的协议挖掘框架来更好地理解各种分辨率下复杂治疗空间的演变。通过将其应用于复杂的阿尔茨海默氏病（AD）治疗空间，探索了该框架。鉴于最近备受瞩目的AD临床试验失败，由于具有百年历史的治疗空间是动态且多方面的，因此需要利用从试验结果中学到的经验教训是显而易见的。ARRASTRA是为以下目的而开发的Python框架：挖掘和分析大量带有语义注释的文档，特别是为此目的的临床试验协议。纯文本协议增加了自然语言处理（NLP）和语义注释例程。通过挖掘协议集合并将其表示为时间定向的相似性网络，可以使用图论在单个协议级别（节点）和高级主题（集群）上量化治疗空间的演化。首先，通过协议在新网络中的位置来量化协议影响新试验起源的能力，并通过在这些指标内确定明显的“爆发”来对其进行分析。其次，使用“ MetroMap”可视化将治疗空间本身的演变可视化为图簇级别的时间变化，该可视化跟踪新的思想形式和旧思想被废弃时的亚治疗主题。最后，这些图形指标与其他协议元数据集成后，可用于使用生存模型预测主题的可行性。总体而言，ARRASTRA框架通过以以前未曾探索的方式重新利用临床试验方案存储库的内容，从而提供了对治疗空间变化性质的新颖细粒度见解。

著录项

  • 作者

    Schultz, Timothy J.;

  • 作者单位

    Drexel University.;

  • 授予单位 Drexel University.;
  • 学科 Information science.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 325 p.
  • 总页数 325
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号