toxicology ontology development supporting evidence-based ... · embl-ebi industry programme...
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Evidence-based Toxicology EBTC Session
2 September 2013 EuroTox 2013, Interlaken, CH
Toxicology Ontology Development supporting Evidence-based Approaches in Predictive Toxicology
Barry Hardy (Douglas Connect)
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Overview
1. Ontology & Evidence-based approaches 2. Ontology-based Applications 3. OpenTox and ToxBank 4. Ontology-based Weight of Evidence 5. Systematic Description of Investigations 6. Integrated Analysis
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Message
Evidence-based approaches (EBTC) + Open Standards for Data, Software, and
Ontology (OpenTox) + Application (Use Cases – Systematic Reviews,
Weight of Evidence) will result in Significant Progress in Predictive
Toxicology and Safety Assessment
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Definition of Ontology
In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts. It can be used to model a domain and support reasoning about concepts.
In theory, an ontology is a "formal, explicit specification of a shared conceptualisation". An ontology provides a shared vocabulary, which can be used to model a domain, that is, the type of objects and/or concepts that exist, and their properties and relations.
en.wikipedia.org/wiki/Ontology_information_science
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Systematic Review
A systematic review is a literature review focused on a research question that tries to identify, appraise, select and synthesize all high quality research evidence relevant to that question. ...
Systematic reviews often, but not always, use statistical techniques (meta-analysis) to combine results of the eligible studies, or at least use scoring of the levels of evidence depending on the methodology used. ... A systematic review uses an objective and transparent approach for research synthesis, with the aim of minimizing bias. While many systematic reviews are based on an explicit quantitative meta-analysis of available data, there are also qualitative reviews which adhere to the standards for gathering, analyzing and reporting evidence.
en.wikipedia.org/wiki/Systematic_review
Requires a common open public toxicology ontology supporting the review process, the capturing of the results and their synthesis into applications supporting decision-making and judgement.
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Weight of Evidence
“An evidence based approach involves an assessment of the relative values/weights of different pieces of the available information that have been retrieved and gathered in previous steps. To this end, a value needs to be assigned to each piece of information. These weights/values can be assigned either in an objective way by using a formalised procedure or by using expert judgement. The weight given to the available evidence will be influenced by factors such as the quality of the data, consistency of results, nature and severity of effects, relevance of the information for the given regulatory endpoint. One definition for weight of evidence is: ‘the process of considering the strengths and weaknesses of various pieces of information in reaching and supporting a conclusion concerning a property of the substance.’ Within the REACH legislation, the so-called weight of evidence approach is a component of the decision-making procedure on substance properties and thus an important part of the chemical safety assessment.»
ECHA echa.europa.eu/documents/10162/13655/pg_report_weight_of_evidence_en.pdf
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Need for Ontology
Again a Weight of Evidence requires
A common open public toxicology ontology supporting the review process, the capturing of the results and their synthesis into applications supporting decision-making and judgement.
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Toxicology Ontology Developments and Roadmap
• See perspectives and roadmap published in A Toxicology Ontology Roadmap ALTEX 29(2), 129- 137 and Toxicology Ontology Perspectives 139 - 156 (2012)
• Available online in Open Access mode from www.altex.ch • Barry Hardy (Douglas Connect and OpenTox), Gordana Apic
(Cambridge Cell Networks), Philip Carthew (Unilever), Dominic Clark (EMBL-EBI), David Cook (AstraZeneca), Ian Dix (AstraZeneca & Pistoia Alliance), Sylvia Escher (Fraunhofer Institute for Toxicology & Experimental Medicine), Janna Hastings (EMBL-EBI), David J. Heard (Novartis), Nina Jeliazkova (Ideaconsult), Philip Judson (Lhasa Ltd.), Sherri Matis-Mitchell (AstraZeneca), Dragana Mitic (Cambridge Cell Networks), Glenn Myatt (Leadscope), Imran Shah (US EPA), Ola Spjuth (University of Uppsala), Olga Tcheremenskaia (Istituto Superiore di Sanità), Luca Toldo (Merck KGaA), David Watson (Lhasa Ltd.), Andrew White (Unilever), Chihae Yang (Altamira)
Based on Proceedings from the Toxicology Ontology Roadmap Workshop
EMBL-EBI Industry Programme Workshop 16 -17th November 2010, Hinxton, UK
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Semantic Reflections
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OpenTox committed to creating a Semantic Web for Predictive Toxicology (with its API 1.1 development in 2009)
Linked Data is a term used to describe the exposing, sharing, and connecting of data on the Semantic Web using: URIs a generic means to identify entities in the world HTTP a simple yet universal mechanism for retrieving resources RDF a generic graph-based data model with which to structure and link data
Linked Data needs: 1. Provision of a URI that describes a Data Resource 2. Use of HTTP to retrieve useful data from the URI 3. A Data Format described with standardised
semantics (so relationships are enabled) e.g. RDF 4. Data should provide links to other Data (through
URIs)
DBpedia = Linked Data approach applied to Wikipedia
Linked Data approach can also be applied to other resource types e.g., for algorithms or models as done in OpenTox… Linked Resource approach enables Knowledge Creation, Combination and Analysis
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Feature GET POST PUT DELETE
Compound
GET POST PUT DELETE
Dataset GET POST PUT DELETE
Ontology
GET POST PUT DELETE
Algorithm
GET POST PUT DELETE
Model
GET POST PUT DELETE
AppDomain GET POST PUT DELETE
Validation
GET POST PUT DELETE
Report
GET POST PUT DELETE
Overview of Application Programming Interfaces
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Interacting Components create Solutions
Adaptor Solution in Jeddah, 2008
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What you can do with it ...
Simple building of predictive toxicology
applications based on well-established
methods and databases
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What you can do with it ...
Simple building of predictive toxicology
applications based on well-established
methods and databases
Distributed applications, integrating
wide range of data, models, prediction
methods
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What you can do with it ...
Simple building of predictive toxicology
applications based on well-established
methods and databases
Distributed applications, integrating
wide range of data, models, prediction
methods
Integration into workflow systems for
computational biology
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Toxicological Ontology: graphical representation
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Bioclipse Visualisation Workbench
O. Spjuth, L. Carlsson, M. Eklund, E. Ahlberg Helgee, and Scott Boyer. Integrated decision support for assessing chemical liabilities. J Chem Inf Model. 2011 Aug 22;51(8):1840-7.
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Bioclipse - OpenTox Interoperation
Model discovery
predictions
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Synergy Drug Design Collaboration Pilot
Screened Library
Refine Predictions
CERF
Data
Data
Toxicity Predictions
Toxicity Assays
Decision Dashboard
(Safer) Drug Leads
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Recording of Collaborative R&D
Controlled Vocabularies
Visualisation
Collaborative Electronic Laboratory Notebook (ELN)
Hardy and Affentranger, Drug Discovery Today.
2013 Jul;18(13-14):681-6.
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SAM ICT Architecture
CERF Enterprise Service Bus
Consensus Rule Editor
CEPS Complex Event Pattern Service
Collaboration Pattern Assistant
Data, Protocols,
Results Data
Data
Events
Consensus Rule
Outcome
Collaboration Pattern Suggestions
• There is a data conflict. • Method A is not
providing accurate predictions.
• New information is available.
• etc.
• Hold a meeting! • Discuss a new strategy! • Contact partner Y!
Data is exchanged as ontology-‐based messages
SAM partners generate data/protocols using external tools and pla;orms (e.g. OpenTox)
Data
Hardy and Affentranger, Drug Discovery Today.
2013 Jul;18(13-14):681-6.
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Ontology Description of Predictions
1. Data Values and Properties 2. Predictions 3. Prediction Rules 4. Recommendation Rules 5. Consensus 6. Uncertainty 7. Learning
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Event-driven Weight of Evidence
Hardy and Affentranger, Drug Discovery Today. 2013 Jul;18(13-14):681-6.
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1. A library of compounds is entered to the ELN
ELN
Synergy
OpenTox
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2. Each compound is assigned a data structure in ELN
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3. ELN passes compounds to OpenTox and SYNERGY
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4. OpenTox computes toxicity predictions
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Models
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5. OpenTox sends back a report to ELN
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6. ELN sends the results to SYNERGY
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Resolving Inconclusives
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Hit, high confidence
Not a hit, high confidence
Inconclusive results, further study needed
ELN
Synergy
OpenTox
1 1 1
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Resolving Inconclusives
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OpenTox
!
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Resolving Inconclusives
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Human Adverse Events Data
Adverse Event Groups Group Name HepaHc funcHon abnormal FuAbn Liver disorder HepaHc necrosis Nec CytolyHc hepaHHs
Hepa HepaHHs HepaHHs acute HepaHHs toxic Cholestasis
CholJa Jaundice HepaHHs cholestaHc jaundice cholestaHc Yellow skin HepaHc failure
HepFail HepaHHs fulminant Acute hepaHc failure Hepatorenal failure Hepatotoxicity
HepTox Hepatomegaly Hyperbilirubinaemia Hepatosplenomegaly
CombinaHon Rule for Event Groups: Associate a drug with a group if the sum of individual event values is larger of equal to 0.4.
Event-‐drug pair values in Titanium PredicHons: 0 : no associaHon (0) 0.35-‐0.4 : non-‐significant associaHon (0) > 0.4 : significant associaHon (1) CombinaHon Rule for Event Groups:
Associate a drug with a group if the sum of individual event values is non-‐zero
Event-‐drug pair values in Titanium Data: 0: no associaHon 1: significant associaHon
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Combining Predictions and Experimental Data
CombinaHon Rule for Event Group PredicHons: Associate a drug with a group if either the Pharmatrope or the Leadscope predciHon is posiHve (or both)
AERS Consensus: Count the number of Adverse Event Group Consensus associaHons. If more than one is posiHve, the AERS Consensus is posiHve.
OpenTox Consensus: NegaHve if both carcinogenicity and the micronucleus assay predicHons are negaHve, OR if the Cramer Rule classificaHon is Class I. PosiHve otherwise.
TCAMS Cytotoxicity: PosiHve if > 30% growth inhibiHon at 10 µM.
TCAMS AnHmalarial AcHvity: PosiHve if > 80% growth inhibiHon of P. Falciparum DD2 at 2 µM.
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Investigator
Phase 2: Integrated data analysis
Phase 1: Unified data access Outline of the ToxBank Data Warehouse
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ToxBank Infrastructure System Vision
Warehouse
Gold Compounds Database
Biobank
Users access compounds, biological materials, data and models for experimental planning and integrated analysis of experimental results
Data Models
SOPs Compounds
SOPs Biological Materials
Data Models
RES
www.toxbank.net
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ISA-Tab
Within ToxBank we use ISA-Tab to provide a systematic description of SEURAT-1 experiments and data: 1. I - Investigations 2. S - Studies 3. A - Assays Terms in protocols and metadata may be linked to ontologies during dataset preparation. All datasets are described and uploaded to ToxBank in a linked way with the ISATab. Processed data can be prepared to facilitate integration for Meta Analysis.
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Use of SEURAT-configured ISAcreator to prepare datasets
Templates are used to describe different experiments in a standardised way
SEURAT-1 information
Investigation information
Publications
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Use of SEURAT-configured ISAcreator to prepare datasets
SEURAT-1 information
Investigation information
Publications
Templates for different assays
Specify experimental factors
Materials and results, with links to files containing the raw or processed data
Each step linked to a SEURAT-1 protocol
Terms mapped to ontologies
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Use of SEURAT-configured ISAcreator to prepare datasets
Mapped to terms in ontologies
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ISATab archives are created for each investigation
Test results (a… files) with links to data table or native file (e.g. CEL files)
Overall investigation design and
information (i… files)
Study description (s… files)
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New data may be combined with reviewed community data on reference compounds
wiki.toxbank.net
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ToxBank Wiki Reference Information Resource
wiki.toxbank.net
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ToxBank integrates systems biology concepts into toxicological assessment
Pekka Kohonen,[a] Emilio Benfenati,[b] David Bower,[c] Rebecca Ceder,[a] Michael Crump,[c] Kevin Cross,[c] Roland C. Grafstrçm,[a] Lyn Healy,[d] Christoph Helma,[e]
Nina Jeliazkova,[f] Vedrin Jeliazkov,[f] Silvia Maggioni,[b] Scott Miller,[c] Glenn Myatt,[c] Michael Rautenberg,[e] Glyn Stacey,[d] Egon Willighagen,[a] Jeff Wiseman,[g]
and Barry Hardy*[h]; [a]Karolinska Institutet, Institute for Environmental Medicine, Molecular Toxicology,Stockholm, Sweden; [b], Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy; [c] Leadscope, Columbus, USA; [d] National Institute for Biological Standards and Control, Potters Bar, UK; [e]In silico toxicology, Basel, Switzerland; [f]Ideaconsult, Sofia, Bulgaria; [g]Pharmatrope,Wayne, USA; [h]Douglas Connect, Zeiningen, Switzerland.
Figure 1. Multiple tools will be, step by step, implemented into an innovative toxicity testing strategy based on mode-of-action.
Figure 2. Clustering of ToxBank Gold Compounds by biological similarity using chemical-genome links from Comparative Toxicogenomics Database (CTD). Compounds with similar Mode-of-Action cluster together.
Systems toxicology - principles Understanding the toxicological interactions in biological systems under compound challenges
Based on developments in high-throughput biology v ‘Omics profiling: gene expression, proteins, metabolites and others v cell-based screening: High-Throughput and High-Content analyses
Risk assessment carried out primarily using v in vitro v In silico methods
Conclusions - great potential to contribute to v toxicity evaluation based on Mode-of-Action v decreased need for animal experiments
ToxBank builds databases and data management solutions to aid in systems toxicology-based risk assessment
Clustering by Gene Clustering by Gene Ontology
BA
Figure 3. A) Enriched gene ontology (GO) categories of genes associated with the oxidizing agent mode-of-action (MOA) B) Protein-protein association network around the Asah1 protein Associated with phospholipid binding MOA.
Kohonen P. et al. The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing. Molecular Informatics. 17 JAN 2013, DOI: 10.1002/minf.201200114.
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Molecular function
Binding 19 genes adjP=6.61e-01
Catalytic activity 9 genes adjP=6.75e-01
Electron carrier activity 3 genes adjP=1.75e-02
Transporter activity
Nucleoside binding 3 genes adjP=6.75e-01
Nucleotide binding
Protein binding 12 genes adjP=6.75e-01
Oxidoreductase activity 5 genes adjP=1.75e-02
Transmembrane transporter activity 3 genes adjP=6.61e-01
Phospholipid Binding
Oxidative Agent
Clustering by Gene Ontology associations from CTD*
*CTD = Comparative Toxicogenomics Database (www.ctd.org)
Kohonen P. et al. The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing. Mol. Inf.17 JAN 2013.
Public Data Analysis
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onlinelibrary.wiley.com/doi/10.1002/minf.201200114/full
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Collaborating Partners Douglas Connect,
Switzerland (Coordinator)
In Silico Toxicology, Switzerland
Ideaconsult, Bulgaria
Istituto Superiore di Sanità, Italy
Technical University of Munich, Germany
Albert Ludwigs University Freiburg, Germany
National Technical University of Athens, Greece
David Gallagher, UK Institute of Biomedical Chemistry of the Russian
Academy of Medical Sciences, Russia
Seascape Learning & JNU, India
Fraunhofer Institute for Toxicology & Experimental Medicine, Germany
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ToxBank Acknowledgements
UK Stem Cell Bank, NIBSC-HPA Ideaconsult Ltd
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Message
Evidence-based approaches (EBTC) + Open Standards for Data, Software, and
Ontology (OpenTox) + Application (Use Cases – Systematic Reviews,
Weight of Evidence) will result in Significant Progress in Predictive
Toxicology and Safety Assessment