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      Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure

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          Abstract

          The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products.  Establishing a product’s potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking.  Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website ( INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21 st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.

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          Most cited references55

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Reproducibility in science: improving the standard for basic and preclinical research.

            Medical and scientific advances are predicated on new knowledge that is robust and reliable and that serves as a solid foundation on which further advances can be built. In biomedical research, we are in the midst of a revolution with the generation of new data and scientific publications at a previously unprecedented rate. However, unfortunately, there is compelling evidence that the majority of these discoveries will not stand the test of time. To a large extent, this reproducibility crisis in basic and preclinical research may be as a result of failure to adhere to good scientific practice and the desperation to publish or perish. This is a multifaceted, multistakeholder problem. No single party is solely responsible, and no single solution will suffice. Here we review the reproducibility problems in basic and preclinical biomedical research, highlight some of the complexities, and discuss potential solutions that may help improve research quality and reproducibility. © 2015 American Heart Association, Inc.
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              Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference.

              The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell-context-specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high-throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Data CurationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: SoftwareRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: Data CurationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Data CurationRole: SoftwareRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Project AdministrationRole: Writing – Review & Editing
                Role: Data CurationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: SoftwareRole: Writing – Review & Editing
                Role: ConceptualizationRole: Project AdministrationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Funding AcquisitionRole: SupervisionRole: Writing – Review & Editing
                Role: ConceptualizationRole: Funding AcquisitionRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                5 September 2017
                2017
                : 6
                : 12
                Affiliations
                [1 ]PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
                [2 ]Douglas Connect GmbH, Zeiningen, Switzerland
                [3 ]SBX Corporation, Tokyo, Japan
                [1 ]Department of Neuroscience, Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, MS, UK
                [2 ]Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
                [1 ]Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, UK
                Philip Morris, Switzerland
                [1 ]Department of Neuroscience, Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, MS, UK
                [2 ]Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
                Philip Morris, Switzerland
                Author notes

                SB, TE, VB, JD, FB, DP and BH conceived and built the platform and web portal. SG conceived and managed the building of the system for knowledge mining and visualization. JH, PV and MCP conceived the idea and managed the project. AB reviewed existing resources and built Table 1. All authors wrote the manuscript text and approved the final manuscript.

                Competing interests: All authors are employees of Philip Morris International, Douglas Connect, or SBX Corporation and performed this work under a joint research collaboration funded by Philip Morris International.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0003-0680-4168
                Article
                10.12688/f1000research.10493.2
                5657032
                29123642
                2ffad843-6fdd-4434-a7d8-ed4f468ded31
                Copyright: © 2017 Boué S et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 October 2017
                Funding
                Funded by: Philip Morris International
                The research described in this article was funded by Philip Morris International.
                Categories
                Method Article
                Articles
                Data Sharing
                Toxicology

                systems toxicology,data sharing,harm reduction,open data,database,website

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