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SEND 数据的交叉研究分析:毒性谱分类。

Cross study analyses of SEND data: toxicity profile classification.

机构信息

Eli Lilly & Company, Indianapolis, IN 46285, United States.

US Food & Drug Administration, Silver Spring, MD 20901, United States.

出版信息

Toxicol Sci. 2024 Aug 1;200(2):277-286. doi: 10.1093/toxsci/kfae072.

Abstract

A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories. This work describes methods to analyze data and automate cross-study analysis of toxicology studies. Cross-study analysis can be used to understand a single compound's toxicity profile across all studies performed and/or to evaluate on-target versus off-target toxicity for multiple compounds intended for the same pharmacological target. This work involved development of data harmonization/transformation strategies to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND datasets from the BioCelerate database were used for the analyses. Toxicity profiles for key organ systems were developed for liver, kidney, male reproductive tract, endocrine system, and hematopoietic system using SEND domains. A cross-study analysis dashboard with a built-in user-defined scoring system was created for custom analyses, including visualizations to evaluate data at the organ system level and drill down into individual animal data. This data analysis provides the tools for scientists to compare toxicity profiles across multiple studies using SEND. A cross-study analysis of 2 different compounds intended for the same pharmacological target is described and the analyses indicate potential on-target effects to liver, kidney, and hematopoietic systems.

摘要

创建了一个SEND 毒理学数据转换、协调和分析平台,以使用来自多个研究的数据提高与预期靶标、物种和给药持续时间相关的独特发现的识别能力。缺乏用于数据分析的标准化数字格式阻碍了对体内毒理学研究的大规模分析。CDISC SEND 标准允许分析来自不同实验室进行的多项研究的数据。这项工作描述了分析数据和自动化毒理学研究交叉研究分析的方法。交叉研究分析可用于了解在所有进行的研究中单一化合物的毒性特征,和/或评估针对同一药理学靶标的多个化合物的靶标内与靶标外毒性。这项工作涉及开发数据协调/转换策略,以实现对数值和分类 SEND 数据的交叉研究分析。使用 SEND 域为肝脏、肾脏、男性生殖道、内分泌系统和造血系统开发了关键器官系统的毒性概况。创建了一个带有内置用户定义评分系统的交叉研究分析仪表板,用于自定义分析,包括可视化效果,可在器官系统级别评估数据,并深入研究单个动物数据。该数据分析为科学家提供了使用 SEND 比较多个研究中的毒性概况的工具。描述了针对同一药理学靶标 2 种不同化合物的交叉研究分析,分析表明对肝脏、肾脏和造血系统存在潜在的靶标效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05a0/11285163/1fbcf4c03c1e/kfae072f1.jpg

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