Data-integration for Endpoints, Chemoinformatics and Omics (DECO) using DIAMONDS
PRESENTING AUTHOR:
Dinant Kroese, Ph.D, Senior Scientist
INSTITUTION / COMPANY :
Risk Analysis for Products In Development (RAPID)
AUTHOR(S):
ED Kroese, EP van Someren, J Venhorst, HE Buist, S Bosgra, JTWE Vogels and RH Stierum- Risk Analysis for Products In Development (RAPID), TNO, Zeist, the Netherlands; H Kamp, G Montoya-Parra- Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany; G Patlewicz - DuPont Haskell Global Centers for Human Health and Environmental Sciences, Newark, DE, USA; J Polman4, D Jennen- Department of Toxicogenomics, Maastricht University, the Netherlands
Innovations in many industrial sectors involve the development of new chemical entities with improved properties. Though the regulatory frameworks in place may differ for each industrial sector, all strive to prevent harmful effects to exposed humans. Safe limit values are often driven by complex regulatory endpoints such as repeated dose toxicity, carcinogenicity, or reproductive toxicity. Until recently, the hazards of these complex endpoints could only be identified and quantified on a per compound basis by in vivo animal studies.
Today, developments in toxicological sciences, systems biology and computational chemistry, and their integration provide opportunities to predict toxicological profiles of chemicals with highly reduced in vivo testing. At TNO, we are developing DIAMONDS (Data Infrastructure for Applying Models ON Design and Safety), a data infrastructure with statistical and computational tools aimed at predicting complex toxicological endpoints through integrated analysis.
In addition, DIAMONDS includes in vitro effect-specific screening models for ‘biological verification’ for verification of in silico-based toxicity predictions, thus reducing the uncertainty often associated with in silico models for complex endpoints. Part of DIAMONDS was developed in the Cefic-LRI AIMT3 DECO project.
A transparent framework was created for improving prediction of repeated dose toxicity by integrating chemoinformatic data with biological information from ‘omics’ and high-throughput screening (HTS) technologies. The investigated prediction approaches consisted of unsupervised clustering approaches for grouping chemical analogues and supervised class prediction approaches to develop classifiers for different liver toxicity endpoints. Results showed that by integrating different data types the clustering of analogues could be improved.
Furthermore, the conducted classification approaches showed that using omics data leads to a better prediction of repeated dose toxicity. In the FP7 project ChemScreen we explored a battery approach to identify reproductive and developmental toxicants, but also to biologically verify grouping and read across of structurally-related chemicals.
For a good prediction of toxic potency, quantitative in vitroin vivo extrapolation (QIVIVE) proved to be indispensable and is presently being incorporated into DIAMONDS.
Dinant Kroese, Ph.D, Senior Scientist
Risk Analysis for Products In Development (RAPID)
ED Kroese, EP van Someren, J Venhorst, HE Buist, S Bosgra, JTWE Vogels and RH Stierum - Risk Analysis
for Products In Development (RAPID), TNO, Zeist, the Netherlands;
H Kamp, G Montoya-Parra - Experimental Toxicology
and Ecology, BASF SE, Ludwigshafen, Germany;
G Patlewicz - DuPont Haskell Global Centers for Human Health and Environmental Sciences, Newark, DE, USA;
J Polman4, D Jennen - Department of Toxicogenomics, Maastricht University, the Netherlands
>> SEE THE PRESENTATION <<
Innovations in many industrial sectors involve the development of new chemical entities with improved properties. Though the regulatory frameworks in place may differ for each industrial sector, all strive to prevent harmful effects to exposed humans. Safe limit values are often driven by complex regulatory endpoints such as repeated dose toxicity, carcinogenicity, or reproductive toxicity. Until recently, the hazards of these complex endpoints could only be identified and quantified on a per compound basis by in vivo animal studies.
Today, developments in toxicological sciences, systems biology and computational chemistry, and their integration provide opportunities to predict toxicological profiles of chemicals with highly reduced in vivo testing. At TNO, we are developing DIAMONDS (Data Infrastructure for Applying Models ON Design and Safety), a data infrastructure with statistical and computational tools aimed at predicting complex toxicological endpoints through integrated analysis.
In addition, DIAMONDS includes in vitro effect-specific screening models for ‘biological verification’ for verification of in silico-based toxicity predictions, thus reducing the uncertainty often associated with in silico models for complex endpoints. Part of DIAMONDS was developed in the Cefic-LRI AIMT3 DECO project.
A transparent framework was created for improving prediction of repeated dose toxicity by integrating chemoinformatic data with biological information from ‘omics’ and high-throughput screening (HTS) technologies. The investigated prediction approaches consisted of unsupervised clustering approaches for grouping chemical analogues and supervised class prediction approaches to develop classifiers for different liver toxicity endpoints. Results showed that by integrating different data types the clustering of analogues could be improved.
Furthermore, the conducted classification approaches showed that using omics data leads to a better prediction of repeated dose toxicity. In the FP7 project ChemScreen we explored a battery approach to identify reproductive and developmental toxicants, but also to biologically verify grouping and read across of structurally-related chemicals.
For a good prediction of toxic potency, quantitative in vitro in vivo extrapolation (QIVIVE) proved to be indispensable and is presently being incorporated into DIAMONDS.
E. Dinant Kroese (1956) studied Medicinal Chemistry at the Free University of Amsterdam (1984), and obtained his PhD for a thesis on chemical carcinogenesis at Leiden State University (1990). He is a Board Certified Toxicologist since 1997 and has over 25 years of experience in the field of toxicological risk assessment, of which ten in the design and conduct of carcinogenicity studies. He acted as specialist reviewer in this field at the National Institute of Public Health and the Environment (RIVM; 1988 to 1999). He participated in many working groups (EC, OECD), e.g. on Global Harmonisation of Classification and Labelling systems (GHS) and in drafting Technical Guidance Documents on Testing Strategies, Effects Assessment, and Risk Characterization in this field, was member of the Specialized Experts Group (advising the EU C&L WG on problematic C and M substances; 1993-1997). In 1999 he moved to TNO (inter alia acting as project leader Existing Substances (ESR), chief notification reviewer of New Substances (NONS) and project leader of several TNO research projects including one preparing for REACH), and preparing for REACH. He had significant involvement in REACH Implementation Projects, i.e. drafting guidance on data requirements (subgroup C&M), and deriving a DNEL/DMEL (RIP 3.2 and 3.3). Since the last 6 years he was engaged in EU FP projects like OSIRIS, and ChemScreen, on Integrated Testing and Evaluation Strategies for CMR substances, and in Cefic LRI projects on using omics information in toxicological assessments. All this work is published in well over 100 reports and manuscripts, and presented at various platforms.