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Bell S, Abedini J, Ceger P, Chang X, Cook B, Karmaus AL, Lea I, Mansouri K, Phillips J, McAfee E, Rai R, Rooney J, Sprankle C, Tandon A, Allen D, Casey W, Kleinstreuer N. An integrated chemical environment with tools for chemical safety testing. Toxicol In Vitro 2020; 67:104916. [PMID: 32553663 PMCID: PMC7393692 DOI: 10.1016/j.tiv.2020.104916] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/29/2020] [Accepted: 06/10/2020] [Indexed: 12/27/2022]
Abstract
Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.
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Affiliation(s)
- Shannon Bell
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Jaleh Abedini
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Patricia Ceger
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Bethany Cook
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Agnes L Karmaus
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Isabel Lea
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Jason Phillips
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Eric McAfee
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - Ruhi Rai
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John Rooney
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Catherine Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Arpit Tandon
- Sciome LLC, 2 Davis Dr., Research Triangle Park, NC 27709, USA.
| | - David Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
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Stathias V, Turner J, Koleti A, Vidovic D, Cooper D, Fazel-Najafabadi M, Pilarczyk M, Terryn R, Chung C, Umeano A, Clarke DJB, Lachmann A, Evangelista JE, Ma’ayan A, Medvedovic M, Schürer SC. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res 2020; 48:D431-D439. [PMID: 31701147 PMCID: PMC7145650 DOI: 10.1093/nar/gkz1023] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/17/2019] [Accepted: 11/04/2019] [Indexed: 12/21/2022] Open
Abstract
The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.
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Affiliation(s)
- Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - John Turner
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Amar Koleti
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Dusica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Daniel Cooper
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Mehdi Fazel-Najafabadi
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Marcin Pilarczyk
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Raymond Terryn
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Caty Chung
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
| | - Afoma Umeano
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Daniel J B Clarke
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alexander Lachmann
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - John Erol Evangelista
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Avi Ma’ayan
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Mario Medvedovic
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
- To whom correspondence should be addressed. Tel: +1 305 243 6552;
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Matsuzaka Y, Uesawa Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance. Front Bioeng Biotechnol 2020; 7:485. [PMID: 32039185 PMCID: PMC6987043 DOI: 10.3389/fbioe.2019.00485] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 12/30/2019] [Indexed: 12/16/2022] Open
Abstract
The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.
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Affiliation(s)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
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