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Developing PreDICT – a fully integrated data platform for preclinical in vivo data: learning from experience

机译:开发预测 - 一种全集成的数据平台,用于临床前的体内数据:从经验中学习

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摘要

Highlights ? We describe a project that implemented a fully integrated solution for in vivo data. ? Discuss cultural and technical learnings as well as blockers to success. ? PreDICT provides a novel platform for animal level in vivo PK, PD and efficacy data. ? It enables best practice in the integration, management and retention of data. ? The challenges in maintaining such a platform are highlighted. In vivo models have been crucial for developing our understanding of key processes associated with human disease and developing novel therapeutics. These in vivo studies are becoming increasingly complex, requiring long-term efficacy data and additional supportive datasets such as pharmacokinetic profiles and analysis of multiple biomarkers of pharmacodynamic response and efficacy. Moreover, a new agent will be investigated in many different models and often in combination with other drugs. Despite advances across the industry integrating and analysing complex datasets, management of in vivo data remains an ongoing challenge across the industry. Here, we describe a project that has successfully delivered a working solution to integrate pharmacokinetic, biomarker and efficacy data, independent of therapy area.
机译:强调 ?我们描述了一个项目为体内数据实施了完全集成的解决方案。还讨论文化和技术学习以及阻止成功。还预测为体内PK,PD和功效数据提供了一种用于动物水平的新颖平台。还它可以在集成,管理和保留数据中实现最佳实践。还突出了维护这种平台的挑战。体内模型对于制定对与人类疾病相关的关键过程和发展新的治疗方法来说至关重要。这些在体内研究越来越复杂,需要长期疗效数据和其他支持性数据集,例如药代动力学谱和药效学反应的多种生物标志物的分析。此外,将在许多不同的模型中进行新试剂,通常与其他药物组合。尽管在整合和分析复杂数据集的业内进步,但体内数据的管理仍然是整个行业的持续挑战。在这里,我们描述了一个成功交付工作解决方案的项目,以整合药代动力学,生物标志物和疗效数据,与治疗区域无关。

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