Let's work towards a #SaferWorldbyDesign - Knowledge-integrated Toxicology and Safety Assessment, Barry Hardy, OpenTox Asia 2019, Hyderabad, India. To make a good decision we need to bring both expertise and relevant information together to form the basis for a structured well-informed discussion leading to best judgement based on available evidence and opinions formed on it. Such a knowledge integration is required in many areas of toxicology and safety assessment based on scientific knowledge generated by a growing number of alternative testing research methods and initiatives. Integration may include evidence from in vitro or in silico methods, biology or chemistry, science and engineering, human health or environment-oriented, and requires both effective organisation of knowledge and communications to reach common understandings.
The requirements, context and format for applications may vary e.g., asking and answering scientific questions, carrying out a risk assessment on products, or performing a regulatory decision or submission. All applications however require a sound reproducible scientific basis and the use of good practices in characterising experiments, organising data and describing concepts in our ontology and knowledge framework.
There is a growing opportunity to develop knowledge integration based on combining emerging concepts and frameworks e.g., OpenTox for data integration and resource interoperability, adverse outcome pathways for mapping data to events, evidence-based methods including systematic review applied to weight of evidence and read across methods for combining evidence from chemical or biological mechanistic categories.
We also need community frameworks to bring different disciplines together for fruitful interactions and discussions.
In this seminar we will review recent developments related to integrated testing and assessment including applications to chemicals, cosmetic ingredients and nanotechnology and including 3Rs goals of reducing and replacing animal testing.
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Let's work towards a #SaferWorldbyDesign - Knowledge-integrated Toxicology and Safety Assessment
1. Barry Hardy, President, OpenTox Association
and CEO, Edelweiss Connect GmbH. and Inc. (formerly Douglas
Connect GmbH.)
2 March, 2019
OpenTox Asia 2019
CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad
Knowledge-integrated Toxicology
and Safety Assessment
3. or not … you need to understand the local context .. and
yourself and your behaviour
4. The Caprivi Delta Project
https://barryhardy.blogs.com/theferryman/2009/02/experiences-from-expedition-work-in-the-caprivi-delta.html
5. The Tamboti Tree Case
https://barryhardy.blogs.com/theferryman/2011/03/the-tamboti-tree-use-case-bioclipse-meets-opentox.html
6. Bioclipse-OpenTox Integration – See Application example in Chapter in Open Source Software in Life Science
Research: Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond
(Woodhead Publishing Series in Biomedicine) edited by Lee Harland and Mark Forster (30 Oct 2012)
Bioclipse – OpenTox Drug Design Workbench
8. Collaborative development of predictive toxicology applications
Journal of Cheminformatics 2010, 2:7 doi:10.1186/1758-2946-2-7
Barry Hardy, Nicki Douglas, Christoph Helma, Micha Rautenberg,
Nina Jeliazkova, Vedrin Jeliazkov, Ivelina Nikolova, Romualdo Benigni, OlgaTcheremenskaia, Stefan
Kramer, Tobias Girschick, Fabian Buchwald,
Joerg Wicker, Andreas Karwath, Martin Gutlein, Andreas Maunz,
Haralambos Sarimveis, Georgia Melagraki, Antreas Afantitis, Pantelis Sopasakis, David Gallagher,
Vladimir Poroikov, Dmitry Filimonov, Alexey Zakharov,
Alexey Lagunin, Tatyana Gloriozova, Sergey Novikov, Natalia Skvortsova, Dmitry Druzhilovsky, Sunil
Chawla, Indira Ghosh, Surajit Ray, Hitesh Patel and Sylvia Escher
Open Access publication available at
www.jcheminf.com/content/2/1/7
Proposed the components and specifications for predictive toxicology.
The OpenTox Framework (reported 2010)
9. OpenTox and Open Components and Standards
Feature
GET
POST
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Compound
GET
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Dataset
GET
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Ontology
GET
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Algorithm
GET
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Model
GET
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AppDomain
GET
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Validation
GET
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Report
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www.jcheminf.com/content/2/1/7
Investigation
(Study, Assay)
GET
POST
PUT
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<- ToxBank API addition to cover biological data
Authorisation &
Authentication
GET
POST
PUT
DELETE
10. Consensus on need for Common Language and Ontology
• 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
19. Upload your data from a CSV file
https://csvtoapi.edelweiss.douglasconnect.com/
20. Annotate your data from the CSV file
https://csvtoapi.edelweiss.douglasconnect.com/
21. Publish your Data to a Workflow (through an automatically
created API)
https://csvtoapi.edelweiss.douglasconnect.com/
22. EUToxRisk case study program
Training Testing / Application
Cmpd X is part of a group of cmpds
with similar MoA/shared KE
CmpdXispartofagroupof
cmpdswithsimilarstructure
Cases of initial information
AO blinded (test)
AO unknown (real)
Case 3
(biol RAX)
grouping &
prediction
algorithms
Hazardprediction
Riskassessment
RAX
ab initio
procedure
optimisationcycles
Case 2
(QSAR)
Case 1
Case 4
(ab initio)
Project
case
studies
mechanistic
rationale
Case 1
‚Case 2‘
Case 3
Case 4
(ab initio)
Case 3 Case 4
(ab initio)
NoYES
Case 1 Case 2
YESNo
Project industry
use case studies
• Four different case study scenarios based on available information.
• Iterative approach to optimize test system selection & IATA
development.
25. Garuda and Edelweiss Connect share the same vision:
A community-driven, connected platform providing databases,
novel algorithms, software and services improving the work of researchers
www.edelweissconnect.com/portfolio/opentox-gadget-garuda
OpenTox - Garuda Application Development
28. Associated Partner Programme
● The Programme aims at strengthening the working ties between the OpenRiskNet
Consortium members and other organisations within relevant scientific and
technology communities.
● Any organisation such as a university, institute, consortium, non-governmental
organisations (NGOs), as well as small and medium enterprises (SMEs) or large
commercial companies can become an Associate Partner of OpenRiskNet.
● Types of users:
○ Service providers - integrate their databases and software tools into the
OpenRiskNet infrastructure
○ Early adopters - use the infrastructure for their predictive toxicology and risk
assessment tasks
○ Technology partners - develop services and tools on which the OpenRiskNet e-
infrastructure will be based
https://openrisknet.org/associated-partner-programme/
30. OpenRiskNet Case studies
● DataCure - Data curation and creation of pre-
reasoned datasets and searching
● ModelRX - Modelling for Prediction or Read
Across
● SysGroup - A systems biology approach for
grouping compounds
● MetaP - Metabolism Prediction
● AOPLink - Identification and Linking of Data
related to AOPWiki
● TGX - Toxicogenomics-based prediction and
mechanism identification
● RevK - Reverse dosimetry and PBPK prediction
https://openrisknet.org/development/case-studies/
31. NanoCommons – development of a Community-based
NanoSafety Knowledge Research Infrastructure
NanoCommons
32.
33. NanoCommons Knowledge Hub
1. Ingest data from nanosafety labs
2. Act as a data warehouse, storing all ingested data
3. Apply internal QA, reformatting and homogenisation
4. Develop concepts for data curation
5. Integrate related datasets (for using common or
reference NMs)
6. Apply automated annotation pipelines
7. Provide data access via a variety of mechanisms
8. Specifically feed the needs of stakeholders and Users
developing new analytical modules
9. Assimilate & disseminate results back to the
community
10. Maintain provenance and links at all stages
43. Conversation with Weida Tong (FDA) at SOT 2017 on reproducibility
…
… leading to a ToxHQ
collaboration project
we discussed at the
Global Summit on
Regulatory Science in
Brasilia
https://www.linkedin.com/pulse/towards-reproducible-silico-practice-via-opentox-barry-hardy
44. What is Reproducibility?
Reproducibility is the ability of an entire
analysis of an experiment or study to be
duplicated, either by the same researcher or
by someone else working independently,
whereas reproducing an experiment is called
replicating it. Reproducibility and replicability
together are among the main principles of
the scientific method.
https://en.wikipedia.org/wiki/Reproducibility
46. SaferSkinTM on the Blockchain – a Trusted Reproducible
Integrated Testing Strategy for Skin Sensitization
Advances in Computational Toxicology: Methodologies and Applications in Regulatory Science, editor
Huixiao Hong (US FDA) available August 2019
https://www.amazon.com/Advances-Computational-Toxicology-Methodologies-
Applications/dp/303016442X/ref=sr_1_fkmrnull_2?keywords=huixiao+hong&qid=1551303509&s=gateway&sr=8-2-fkmrnull
49. INTERVALS: Scientific data transparency applied to Industry
Aim: establish a community and a public repository for 21st
-
century preclinical and clinical (systems) inhalation toxicology
assessment data and results that supports open data principles.
Publications
Reports
Protocols Data
Educatio
n
Peer review Community
www.intervals.science
Boué S, Exner T, Ghosh S et al. Supporting evidence-
based analysis for modified risk tobacco products through
a toxicology data-sharing infrastructure [version 2;
referees: 2 approved] F1000Research 2017, 6:12 (doi:
10.12688/f1000research.10493.2)
www.intervals.science
50. INTERVALS: a scientific platform to share protocols,
results, data, and investigate diseases and pathways
www.intervals.science