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Carfagna MA, Ahmed CS, Butler S, Fukushima T, Houser W, Jensen N, Paisley B, Leuenroth-Quinn S, Snyder K, Vispute S, Wang W, Ali MY. Cross study analyses of SEND data: toxicity profile classification. Toxicol Sci 2024; 200:277-286. [PMID: 38851876 DOI: 10.1093/toxsci/kfae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2024] Open
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.
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Affiliation(s)
| | - Cm Sabbir Ahmed
- US Food & Drug Administration, Silver Spring, MD 20901, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States
| | - Susan Butler
- US Food & Drug Administration, Silver Spring, MD 20901, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States
| | | | - William Houser
- Bristol Myers Squibb, New Brunswick, NJ 08901, United States
| | | | | | | | - Kevin Snyder
- US Food & Drug Administration, Silver Spring, MD 20901, United States
| | | | - Wenxian Wang
- Bristol Myers Squibb, New Brunswick, NJ 08901, United States
| | - Md Yousuf Ali
- US Food & Drug Administration, Silver Spring, MD 20901, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States
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Snyder K, Ahmed CMS, Ali MY, Butler S, DeNieu M, Houser W, Paisley B, Rosentreter M, Wang W, Larsen B. sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis. FRONTIERS IN TOXICOLOGY 2024; 6:1392686. [PMID: 39077556 PMCID: PMC11284615 DOI: 10.3389/ftox.2024.1392686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/03/2024] [Indexed: 07/31/2024] Open
Abstract
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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Affiliation(s)
- K. Snyder
- US Food and Drug Administration, Silver Spring, MD, United States
| | - C. M. Sabbir Ahmed
- US Food and Drug Administration, Silver Spring, MD, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
| | - Md Yousuf Ali
- US Food and Drug Administration, Silver Spring, MD, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
| | - S. Butler
- US Food and Drug Administration, Silver Spring, MD, United States
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
| | | | - W. Houser
- Bristol Myers Squibb, New Brunswick, NJ, United States
| | - B. Paisley
- Eli Lilly & Co, Indianapolis, IN, United States
| | | | - W. Wang
- Bristol Myers Squibb, New Brunswick, NJ, United States
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Penn S, Lomax J, Karlsson A, Antonucci V, Zachmann CD, Kanza S, Schurer S, Turner J. An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows. J Biomed Semantics 2023; 14:10. [PMID: 37568227 PMCID: PMC10416407 DOI: 10.1186/s13326-023-00288-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 04/29/2023] [Indexed: 08/13/2023] Open
Abstract
With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct intelligent analysis. Research has demonstrated that the use of semantic technologies such as ontologies to structure and enrich scientific data can greatly improve this potential. However, whilst there are many ontologies that can be used for this purpose, there is still a vast quantity of scientific terminology that does not have adequate semantic representation. A key area for expansion identified by the authors was the pharmacokinetic/pharmacodynamic (PK/PD) domain due to its high usage across many areas of Pharma. As such we have produced a set of these terms and other bioassay related terms to be incorporated into the BioAssay Ontology (BAO), which was identified as the most relevant ontology for this work. A number of use cases developed by experts in the field were used to demonstrate how these new ontology terms can be used, and to set the scene for the continuation of this work with a look to expanding this work out into further relevant domains. The work done in this paper was part of Phase 1 of the SEED project (Semantically Enriching electronic laboratory notebook (eLN) Data).
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Affiliation(s)
- Steve Penn
- Pfizer Inc, 1 Portland Street, Cambridge, MA 02139 USA
| | - Jane Lomax
- Scibite an Elsevier Company, Scibite Ltd, Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1DR UK
| | - Anneli Karlsson
- Scibite an Elsevier Company, Scibite Ltd, Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1DR UK
| | | | - Carl-Dieter Zachmann
- Sanofi-Aventis Deutschland GmbH, R&D / Integrated Drug Discovery, Industriepark Hoechst, Frankfurt am Main, H831 C.0156, 65926 Germany
| | - Samantha Kanza
- Department of Chemistry, University of Southampton, Highfield Campus, University Road, Southampton, SO17 1BJ UK
| | - Stephan Schurer
- Department of Cellular and Molecular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136 USA
| | - John Turner
- Department of Cellular and Molecular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136 USA
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4
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Steger-Hartmann T, Kreuchwig A, Wang K, Birzele F, Draganov D, Gaudio S, Rothfuss A. Perspectives of data science in preclinical safety assessment. Drug Discov Today 2023:103642. [PMID: 37244565 DOI: 10.1016/j.drudis.2023.103642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.
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Affiliation(s)
| | - Annika Kreuchwig
- Investigational Toxicology, Bayer AG, Pharmaceuticals, 13353 Berlin, Germany
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Fabian Birzele
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Dragomir Draganov
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Stefano Gaudio
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Andreas Rothfuss
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
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Sato G, Matsumoto I, Watanabe M, Kinomoto T, Nakajima M, Yamamoto M, Inoue Y, Kobayashi K, Tanaharu T, Kiya A, Yamaguchi T, Hayashi H, Motoyama K, Suzuki M, Watanabe K. Qualitative analysis of actual Standard for Exchange of Nonclinical Data (SEND) datasets for Data Domains: Proposition from Japan Pharmaceutical Manufacturers Association SEND Taskforce Team on standardization of nonclinical data. Regul Toxicol Pharmacol 2021; 122:104919. [PMID: 33753112 DOI: 10.1016/j.yrtph.2021.104919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/01/2021] [Accepted: 03/12/2021] [Indexed: 10/21/2022]
Abstract
The Standard for Exchange of Nonclinical Data (SEND) has been adopted by the US FDA, which has required pharmaceutical companies who are developing new drugs for the US market to implement SEND. The Japan Pharmaceutical Manufacturers Association (JPMA) SEND Taskforce Team responded to this situation by starting a project to better understand the contents of SEND datasets. The project focused on domains generally included in the SEND domains for single- and repeat-dose general toxicology studies, and surveyed what kind of information are populated in which domains and in what way. The qualitative analysis of the results indicated that variations exist based on whether or not an individual variable was populated and on how the variable was populated. The Taskforce Team recommends reducing variations not only in the SEND datasets but also in the descriptions in the study protocol and/or final study report. Reduction of such variations should lead to higher quality datasets with powerful and increased searchability so that accumulated SEND datasets should become more valuable. These efforts would provide regulatory agencies with easier review of SEND datasets, which contributes to efficient development of new drug candidates.
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Affiliation(s)
- Gen Sato
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba-shi, Ibaraki, 300-2635, Japan.
| | - Izumi Matsumoto
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Sumitomo Dainippon Pharma Co., Ltd., 3-1-98 Kasugade-naka, Konohana-ku, Osaka, 554-0022, Japan
| | - Mayumi Watanabe
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Daiichi Sankyo Co., Ltd., 1-16-13, Kitakasai, Edogawa-ku, Tokyo, 134-8630, Japan
| | - Toshiko Kinomoto
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; ZERIA Pharmaceutical Co., Ltd., 2512-1, Numagami, Oshikiri, Kumagaya-shi, Saitama, 360-0111, Japan
| | - Mikio Nakajima
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Asahi Kasei Pharma Corporation, 632-1 Mifuku Izunokuni-shi, Shizuoka, 410-2321, Japan
| | - Masakatsu Yamamoto
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; MSD K.K., Urban Ace Kitahama Bldg. 2-3-7 Hiranomachi, Chuo-ku, Osaka-city, Osaka, 541-0046, Japan
| | - Yuki Inoue
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Otsuka Pharmaceutical Co., Ltd., 463-10 Kagasuno, Kawauchi-cho, Tokushima, 771-0192, Japan
| | - Keita Kobayashi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Kowa Company, Ltd., 332-1, OhnoShinden, Fuji-city, Shizuoka, 417-8650, Japan
| | - Takashi Tanaharu
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Bristol-Myers Squibb K.K., Shinjuku i-Land Tower, 5-1, Nishi-Shinjuku 6-chome, Shinjuku-ku, Tokyo, 163-1327, Japan
| | - Akinori Kiya
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Shionogi & Co., Ltd., 3-1-1, Futaba-Cho, Toyonaka, Osaka, 561-0825, Japan
| | - Takafumi Yamaguchi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Eli Lilly Japan K.K., LILLY PLAZA ONE Bldg., 5-1-28, Isogamidori, Chuo-ku, Kobe, 651-0086, Japan
| | - Hitomi Hayashi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Nippon Kayaku Co., Ltd., 31-12, Shimo 3-chome, Kita-ku, Tokyo, 115-0042, Japan
| | - Keiko Motoyama
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Janssen Pharmaceutical K.K., 3-5-2 Nishi-kanda, Chiyoda-ku, Tokyo, 101-0065, Japan
| | - Mutsumi Suzuki
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Kyowa Kirin Co., Ltd., 1188, Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan
| | - Kazuto Watanabe
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Chugai Pharmaceutical Co., Ltd., 1-135 Komakado, Gotemba, Shizuoka, 412-8513, Japan
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