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

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.

[1]  Hui Gong,et al.  CEBS: a comprehensive annotated database of toxicological data , 2016, Nucleic Acids Res..

[2]  Julia Hoeng,et al.  3-D nasal cultures: Systems toxicological assessment of a candidate modified-risk tobacco product. , 2016, ALTEX.

[3]  M. Peitsch,et al.  Evaluation of the Tobacco Heating System 2.2. Part 1: Description of the system and the scientific assessment program. , 2016, Regulatory toxicology and pharmacology : RTP.

[4]  Sharon Munn,et al.  Adverse outcome pathway development from protein alkylation to liver fibrosis , 2016, Archives of Toxicology.

[5]  Ashraf Elamin,et al.  Systems Toxicology Assessment of the Biological Impact of a Candidate Modified Risk Tobacco Product on Human Organotypic Oral Epithelial Cultures. , 2016, Chemical research in toxicology.

[6]  S. Friend,et al.  Crowdsourcing biomedical research: leveraging communities as innovation engines , 2016, Nature Reviews Genetics.

[7]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[8]  Manuel C. Peitsch,et al.  Comprehensive systems biology analysis of a 7-month cigarette smoke inhalation study in C57BL/6 mice , 2016, Scientific Data.

[9]  John E. Doe,et al.  Use of the RISK21 roadmap and matrix: human health risk assessment of the use of a pyrethroid in bed netting , 2015, Critical reviews in toxicology.

[10]  M. Peitsch,et al.  Biological impact of cigarette smoke compared to an aerosol produced from a prototypic modified risk tobacco product on normal human bronchial epithelial cells. , 2015, Toxicology in vitro : an international journal published in association with BIBRA.

[11]  Judy Strickland,et al.  Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy , 2015, Archives of Toxicology.

[12]  D. Drubin Great science inspires us to tackle the issue of data reproducibility , 2015, Molecular biology of the cell.

[13]  T. Rabesandratana REGULATORY SCIENCE. Europe's food watchdog embraces transparency. , 2015, Science.

[14]  Michelle R. Arkin,et al.  Tackling reproducibility in academic preclinical drug discovery , 2015, Nature Reviews Drug Discovery.

[15]  Georgia Tsiliki,et al.  The eNanoMapper database for nanomaterial safety information , 2015, Beilstein journal of nanotechnology.

[16]  Yeyejide A. Adeleye,et al.  Implementing Toxicity Testing in the 21st Century (TT21C): Making safety decisions using toxicity pathways, and progress in a prototype risk assessment. , 2015, Toxicology.

[17]  Ashraf Elamin,et al.  A 7-month cigarette smoke inhalation study in C57BL/6 mice demonstrates reduced lung inflammation and emphysema following smoking cessation or aerosol exposure from a prototypic modified risk tobacco product. , 2015, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[18]  J. Ioannidis,et al.  Reproducibility in Science: Improving the Standard for Basic and Preclinical Research , 2015, Circulation research.

[19]  Chris T. A. Evelo,et al.  diXa: a data infrastructure for chemical safety assessment , 2014, Bioinform..

[20]  Gary W Miller,et al.  Data sharing in toxicology: beyond show and tell. , 2015, Toxicological sciences : an official journal of the Society of Toxicology.

[21]  M. Peitsch,et al.  Characterization of the Vitrocell® 24/48 in vitro aerosol exposure system using mainstream cigarette smoke , 2014, Chemistry Central Journal.

[22]  Marcia McNutt,et al.  Journals unite for reproducibility , 2014, Science.

[23]  Martin Hofmann-Apitius,et al.  CSEO – the Cigarette Smoke Exposure Ontology , 2014, J. Biomed. Semant..

[24]  Julia Hoeng,et al.  A 28-day rat inhalation study with an integrated molecular toxicology endpoint demonstrates reduced exposure effects for a prototypic modified risk tobacco product compared with conventional cigarettes. , 2014, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[25]  Elizabeth Iorns,et al.  New forms of checks and balances are needed to improve research integrity , 2014, F1000Research.

[26]  Roch Giorgi,et al.  Reproducibility issues in science, is P value really the only answer? , 2014, Proceedings of the National Academy of Sciences.

[27]  Julia Hoeng,et al.  Case study: the role of mechanistic network models in systems toxicology. , 2014, Drug discovery today.

[28]  Marco Biasini,et al.  Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10 , 2014, Proteins.

[29]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[30]  J. Couchman Peer Review and Reproducibility. Crisis or Time for Course Correction? , 2014, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[31]  H. Kitano,et al.  Toward an integrated software platform for systems pharmacology , 2013, Biopharmaceutics & drug disposition.

[32]  Emilio Benfenati,et al.  The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing , 2013, Molecular informatics.

[33]  Sebastian Hoffmann,et al.  Evidence-based toxicology for the 21st century: opportunities and challenges. , 2013, ALTEX.

[34]  Julia Hoeng,et al.  A network-based approach to quantifying the impact of biologically active substances. , 2012, Drug discovery today.

[35]  Imran Shah,et al.  Toxicology ontology perspectives. , 2012, ALTEX.

[36]  H. Kitano,et al.  Software for systems biology: from tools to integrated platforms , 2011, Nature Reviews Genetics.

[37]  M. Peitsch,et al.  Verification of systems biology research in the age of collaborative competition , 2011, Nature Biotechnology.

[38]  Pantelis Sopasakis,et al.  Collaborative development of predictive toxicology applications , 2010, J. Cheminformatics.

[39]  Thomas Hartung,et al.  Lessons Learned from Alternative Methods and their Validation for a New Toxicology in the 21st Century , 2010, Journal of toxicology and environmental health. Part B, Critical reviews.

[40]  Sebastian Hoffmann,et al.  Integrated Testing Strategy (ITS) - Opportunities to better use existing data and guide future testing in toxicology. , 2010, ALTEX.

[41]  R. Judson,et al.  The Toxicity Data Landscape for Environmental Chemicals , 2008, Environmental health perspectives.

[42]  Thomas C. Wiegers,et al.  Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks , 2008, Nucleic Acids Res..

[43]  Nigel W. Hardy,et al.  The first RSBI (ISA-TAB) workshop: "can a simple format work for complex studies?". , 2008, Omics : a journal of integrative biology.

[44]  Steven K. Gibb Toxicity testing in the 21st century: a vision and a strategy. , 2008, Reproductive toxicology.

[45]  A. Califano,et al.  Dialogue on Reverse‐Engineering Assessment and Methods , 2007, Annals of the New York Academy of Sciences.

[46]  Alfonso Valencia,et al.  Overview of BioCreAtIvE: critical assessment of information extraction for biology , 2005, BMC Bioinformatics.

[47]  D. Eddy Evidence-based medicine: a unified approach. , 2005, Health affairs.