AstraZeneca, Södertälje SE-151 85, Sweden.
J Transl Med. 2013 Oct 8;11:250. doi: 10.1186/1479-5876-11-250.
Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding.Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be.The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes.The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early.
综合理解临床前和临床数据对于做出明智的决策和降低药物开发过程中的淘汰率至关重要。在药物开发过程中产生的数据量和种类都大大增加了。为了有效利用所有可用的数据和当前的知识,开发了一种新的信息模型和可视化工具。知识图谱通过添加两个概念将临床前、临床、疗效和安全性数据整合在一起:不同学科的知识和蛋白结合。内部和公开可用的数据被收集和处理,以允许灵活和交互式的可视化。暴露被表示为化合物的未结合浓度,并且通过包含关于什么是有生物学意义的治疗效果的专家意见来归一化和缩放治疗效果。知识图谱已经在多个不同治疗领域的项目团队中进行了回顾性和前瞻性的应用,促进了多个学科之间对临床前和临床数据的讨论。该图谱允许对化合物进行直接比较,并用于支持候选药物的选择,以及与比较药物和竞争药物的区分、临床数据的反向翻译、理解临床前模型和测定的可预测性、多年来主要终点的漂移情况的审查,以及对化合物进行评估或基准测试,比较多个属性。知识图谱的概念允许灵活地整合和可视化相关数据,以便在药物开发的各个阶段进行科学和明智的决策。该概念可用于沟通、决策、知识管理,以及作为向前和向后翻译的工具,这将有助于提高对特定项目或疾病领域组合的竞争优势的理解。此外,它还建立了一个知识和翻译的连续体,从而通过尽早识别不良候选药物来降低临床开发的淘汰率和成本。