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Cross study analyses of SEND data: toxicity profile classification.

Mark A CarfagnaCm Sabbir AhmedSusan ButlerTamio FukushimaWilliam HouserNikolai K JensenBrianna PaisleyStephanie Leuenroth-QuinnKevin M SnyderSaurabh VisputeWenxian WangMd Yousuf Ali
Published in: Toxicological sciences : an official journal of the Society of Toxicology (2024)
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.
Keyphrases
  • data analysis
  • electronic health record
  • big data
  • oxidative stress
  • emergency department
  • machine learning
  • high throughput