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/.

Large scale analysis of in vivo toxicology studies has been hindered by the lack of a standardized digital format for data analysis. The SEND standard enables the analysis of data from multiple studies performed by different laboratories. The objective of this work is to develop methods to transform, sort, and analyze data to automate cross study analysis of toxicology studies. Cross study analysis can be applied to use cases such as understanding a single compound’s toxicity profile across all studies performed and/or evaluating on- versus off-target toxicity for multiple compounds intended for the same pharmacological target. This collaborative work between BioCelerate and FDA involved development of data harmonization/transformation strategies and analytic techniques to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND data sets from the BioCelerate Toxicology Data Sharing module of DataCelerate® were used for the analyses. Toxicity prof..., Deidentified SEND data was donated by companies participating in BioCelerate’s Toxicology Data Sharing Initiative (TDS module in DataCelerate®).The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds intended for the same pharmacological target. To facilitate cross-study analysis of toxicology studies, it is practical to categorize findings within organ systems to provide insights into target organ toxicity. In the proof-of-concept for this application, we focused on the target organs with compound-related effects, namely the kidney, liver, hematopoietic system, endocrine system, and reproductive tract (male). The body weights (BW), food and water consumption (FW), laboratory test results (LB), organ measurements (OM), and microscopic findings (MI) SEND domains were included in the analysis. Each parameter was then assigned to the relevant organ system(s) (Table 1) based on veterinary literature (Faqi 2017) (Stockham 2008), scientific literature on ..., , # Dataset for Cross Study Analyses of SEND Data: Toxicity Profile Classification

https://doi.org/10.5061/dryad.s1rn8pkgr

The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds (Compound A and Compound B) intended for the same pharmacological target.Â

Description of the data and file structure

The files contain data from toxicology studies performed in rats and dogs to support clinical development for two different drugs intended for the same pharmacological target. The studies were donated by the pharmaceutical companies involved in development of the compounds. All proprietary and identifying information has been removed and deidentified. Â

The toxicology data is organized based on the CDISC - Standard for Exchange of Nonclinical Data (SEND) data standard (https://www.cdisc.org/standards/foundational/send/sendig-v3-1) and stored in .json a...,

The Critical Path for Alzheimer's Disease (CPAD: http://c-path.org/programs/cpad/) CODR data base contains patient-level control arm data (6,500 patients; 24 clinical trials; MCI and AD), fully anonymized and remapped using CDISC SDTM v3.1.2 Standard. The database includes, but is not limited to, demographic information, APOE4 genotype, concomitant medications and cognitive scales (MMSE, ADAS-Cog, CDR-SB). Currently no AD fluid biomarker or imaging data are included.

The file observation_example.json lists the Observation resource as described in the Demonstrating the clinical study design with FHIR section in json format. (JSON 2 kb)

Supplementary tables and figures from the research work of harvesting metadata in a clinical enviroment. The tables include a crosswalk between the 4 different data formats FHIR, OMOP, openEHR and CDISC and a prioritization of the metadata items identified. The figures include the visualization of a priority scoring and an example of the prevented data loss by using the proposed convergence format.

List of acquired studies using CDISC.

The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1), BREAST-DIAGNOSIS, Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)) and the Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. Due to the nature of the available data not all SDTM conformance rules were applicable or adhered to. These Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool. Export datasets for the following SDTM domains were generated:

• DM (Demographics)
• DS (Disposition)
• MI (Microscopic Findings)
• PR (Procedures)
• SS (Subject Status)
• TU (Tumor/Lesion Identification)
• TR (Tumor/Lesion Results)

The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1), BREAST-DIAGNOSIS, Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)) and the Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. Due to the nature of the available data not all SDTM conformance rules were applicable or adhered to. These Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool. Export datasets for the following SDTM domains were generated:

• DM (Demographics)
• DS (Disposition)
• MI (Microscopic Findings)
• PR (Procedures)
• SS (Subject Status)
• TU (Tumor/Lesion Identification)
• TR (Tumor/Lesion Results)