SEND harmonization &amp; cross-study analysis: A proposal to better harvest the value from SEND data

Carfagna, Mark A.; Bjerregaard, Thomas G.; Fukushima, Tamio; Houser, William; Sloan, Cheryl; Snyder, Kevin M.; Anderson, Jesse; Page, Todd J.

doi:10.1016/j.yrtph.2019.104542

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…As there is substantial value in developing tools to facilitate cross-study analysis of SEND data, the BioCelerate consortium has led efforts since 2018 in partnership with the US FDA to identify solutions to enable analytic approaches for SEND datasets ( Carfagna et al, 2020 ). Inconsistent application of the available standards in toxicology data prevents analysis of data across studies, accurate statistical calculations, and sharing of data across organizations.…”

Section: Introductionmentioning

confidence: 99%

sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis

Snyder,

Ahmed,

Ali

et al. 2024

Front. Toxicol.

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The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public–private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR.sendigR reference manual: https://phuse-org.github.io/sendigR/.sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.

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Section: Introductionmentioning

confidence: 99%

sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis

Snyder,

Ahmed,

Ali

et al. 2024

Front. Toxicol.

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“…Recently, BioCelerate has partnered with the US FDA to work together to propose approaches to address some of these issues that prevent more efficient cross-study analysis; six use cases were created for this evaluation. 6 One use case is the development and maintenance of historical control databases. Historical control data are defined as the combined observations for all animals in control groups (not treated with an experimental test article or with an agent intended to induce a known toxicity) across multiple studies and conditions under which these data were collected (e.g., species, study duration, route of administration).…”

Section: Introductionmentioning

confidence: 99%

“…Since 2018, the BioCelerate consortium has been engaged in efforts to identify solutions to enable these analytic approaches for SEND data sets. Recently, BioCelerate has partnered with the US FDA to work together to propose approaches to address some of these issues that prevent more efficient cross-study analysis; six use cases were created for this evaluation . One use case is the development and maintenance of historical control databases.…”

Section: Introductionmentioning

confidence: 99%

Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals

Carfagna

Anderson

Eley

et al. 2020

Chem. Res. Toxicol.

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Implementation of the Clinical Data Interchange Standards Consortium (CDISC)’s Standard for Exchange of Nonclinical Data (SEND) by the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) has created large quantities of SEND data sets and a tremendous opportunity to apply large-scale data analytic approaches. To fully realize this opportunity, differences in SEND implementation that impair the ability to conduct cross-study analysis must be addressed. In this manuscript, a prototypical question regarding historical control data (see Table of Contents graphic) was used to identify areas for SEND harmonization and to develop algorithmic strategies for nonclinical cross-study analysis within a variety of databases. FDA CDER’s repository of >1800 sponsor-submitted studies in SEND format was queried using the statistical programming language R to gain insight into how the CDISC SEND Implementation Guides are being applied across the industry. For each component needed to answer the question (defined as “query block”), the frequency of data population was determined and ranged from 6 to 99%. For fields populated <90% and/or that did not have Controlled Terminology, data extraction methods such as data transformation and script development were evaluated. Data extraction was successful for fields such as phase of study, negative controls, and histopathology using scripts. Calculations to assess accuracy of data extraction indicated a high confidence in most query block searches. Some fields such as vehicle name, animal supplier name, and test facility name are not amenable to accurate data extraction through script development alone and require additional harmonization to confidently extract data. Harmonization proposals are discussed in this manuscript. Implementation of these proposals will allow stakeholders to capitalize on the opportunity presented by SEND data sets to increase the efficiency and productivity of nonclinical drug development, allowing the most promising drug candidates to proceed through development.

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“…SEND allows for various visualizations ( Snyder et al 2024 ), such as the ones created for this work using R Shiny. Visualizations can be systematically generated from SEND formatted data, simplifying work for the analyst who can be given control over the visualizations through a dashboard ( Carfagna et al 2020 ). To generate these visualizations, data normalization is necessary to accurately compare various types of studies and data from different species.…”

mentioning

confidence: 99%

Cross study analyses of SEND data: toxicity profile classification

Carfagna,

Ahmed,

Butler

et al. 2024

Toxicological Sciences

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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.

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SEND harmonization & cross-study analysis: A proposal to better harvest the value from SEND data

Cited by 10 publications

References 5 publications

sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis

sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis

Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals

Cross study analyses of SEND data: toxicity profile classification

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