in vitro data and in silico models for predictive toxicology

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In Vitro Data and In Silico Models for Predictive Toxicology The SEURAT project Elisabet Berggren European Commission, Joint Research Centre / COACH

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Page 1: In vitro data and in silico models for predictive toxicology

In Vitro Data and In Silico Models for Predictive Toxicology

The SEURAT project

Elisabet Berggren European Commission, Joint Research Centre / COACH

Page 2: In vitro data and in silico models for predictive toxicology

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• Cluster of seven collaborative projects • 50 million Euro investment • Co-financed by EC and Cosmetics Europe • Over 70 research partners • 16 countries plus EC • 6 year programme

Seurat-1: towards replacement of in vivo repeated dose systemic toxicity testing

http://www.seurat-1.eu/

Page 3: In vitro data and in silico models for predictive toxicology

2 1 0 3 4 5 6 years

2nd PoC , 1st PoC updated

SEURAT-1 case study descriptions

2nd PoC Kick-off

Kick-Off Meeting Final Report

COACH

SEURAT-1 MAIN SEURAT-2

GC WG

DA WG JRC

SA WG

MoA WG

BK

WG

Data Ware- house

T&M Cat. 3rd PoC

1st PoC

The SEURAT-1 Roadmap

SEURAT-1 Annual Meeting

SEURAT-1 Annual Report

SC W

G

WE ARE HERE

http://www.seurat-1.eu/

Page 4: In vitro data and in silico models for predictive toxicology

The SEURAT strategy is to adopt a toxicological

mode-of-action framework to describe how

any substance may adversely affect human health,

and to use this knowledge to develop

complementary theoretical, computational and

experimental (in vitro) models that predict

quantitative points of departure needed for safety

assessment.

SEURAT - The Strategy

www.seurat-1.eu

Page 5: In vitro data and in silico models for predictive toxicology

Scientific Tools

Chemical

Cells exchange

… development underpinned by mode-of-action rationale

Bioreactors for engineering tissues

Models to link in vitro to in vivo biokinetics

Database on cosmetics ingredients and properties Genetically engineered reporter-gene cell lines Multi-scale models of organ toxicity

Protocols for stably differentiated iPSC Project data and protocol warehouse

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Adverse Outcome Pathway:

Structure the information in order to be able to PREDICT adverse health effects

Molecular Initiating Events

Key Events

Adverse Outcomes

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Level 1, KNOWLEDGE: Adverse Outcome Pathway (AOP) constructs

Level 3, APPLICATION: Predictive systems to support regulatory safety assessment

SEURAT-1 Proof of Concept on three levels:

Level 2, PREDICTION: Integrated systems including in vitro and computational methods to predict toxicity

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Visit AOP Wiki (https://aopkb.org) to explore currently mapped AOPs, improve them or add new ones.

Structure the AOP information in collaboration with the rest of the world

Page 9: In vitro data and in silico models for predictive toxicology

http://wiki.toxbank.net/wiki

ToxBank Wiki

Page 10: In vitro data and in silico models for predictive toxicology

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For the first time ever hiPS-derived hepatocytes was used for repeated dose toxicity studies (Holmgren et al. (2014) in Drug Metab. Disp., 42(9): 1401-1406

Development of an in vitro drug‐induced liver fibrosis model

Differentiation of stem cell-derived hepatocytes

A flavor of highlights from the SEURAT-1 projects

(MTX = Methotrexate) Spheroids with co-cultured HepaRG and Hepatic Stellate Cells showing accumulation of collagen

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Liver cell toxicity reporters to identify hepatotoxicant-induced cellular stress responses

A flavor of highlights from the SEURAT-1 projects 2

A toxicity reporter platform based on BAC (Bacterial Artificial Chromosome) engineering of the human HepG2 cells (Wink et al (2014) in Chem.Res. Toxicol., 27: 338-355.)

Prediction of steatosis through repeated dose exposure to 3D HepaRG system combined with

Biokinetic modelling

Page 12: In vitro data and in silico models for predictive toxicology
Page 13: In vitro data and in silico models for predictive toxicology

COSMOS database

o Open-access

o High-quality toxicity data (quality

controlled, curated structures)

o User-friendly query builder (chemical

name, structure, toxicity data)

o 44,765 unique chemical structures

o 12,538 toxicity studies for 1,660

compounds across 27 endpoints

Webinar and tutorial:

http://www.cosmostox.eu/what/COSMOSdb/

http://cosmosdb.cosmostox.eu/

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Page 14: In vitro data and in silico models for predictive toxicology

Models are Freely Available Through COSMOS KNIME WebPortal and Space

Documentation, user guidance, web tutorials

Page 15: In vitro data and in silico models for predictive toxicology

JRC, Ispra, June 2013

Page 16: In vitro data and in silico models for predictive toxicology

Scientific Tools

Chemical

Cells exchange

… how to translate them into solutions for safety assessment?

Page 17: In vitro data and in silico models for predictive toxicology

Safety Assessment Working Group

TTC One conceptual framework - three case studies:

Pieces of evidence and initial considerations

· Purpose of the assessment

· Exposure context· Expert knowledge and judgement based on existing evidence / data

General adversities Organ specific adversities

Toxicodynamics

· Target organ: full assessment based on

Adverse Outcome Pathway (AOP)1

· Non-target organ: limited assessment

Toxicokinetics

Assessment of ADME properties

Overall Assessment (including uncertainties and knowledge gaps)

Use of prediction for pre-defined purpose (with consideration of

acceptable uncertainty)

Improve assessment if necessary

1) The steps in the AOP (molecular initiating event, key events) will be assessed using a

selection of tools including in silico predictions and in vitro tests.

Hypothesis generation

regarding mode of action

Toxicodynamics

· Many biological targets (based on chemi-

cal structure, e.g. alkylating agents)

· Specific targets present in many cells /

tissues / organs (e.g. AhR-pathway)

Type of adversity

Definition of relevant dose range

Determination of point of departure

Evaluation

Result

AB INITIO

Page 18: In vitro data and in silico models for predictive toxicology

I. Chemical similarity of compounds that do not require metabolic transformation to exert a potential adverse human health effect

II. Chemical similarity involving metabolic transformation resulting in exposure to the same/similar proximal toxicant

III. Chemicals with general low or no toxicity

IV. Distinguishing chemicals in a structurally similar category with variable toxicities based on Mode of Action hypothesis

Four different scenarios

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Final Reporting

You are all welcome Register at: http://www.seurat-1.eu/

Horizon 2020 project: EUToxRisk21 Starting this autumn will continue what SEURAT-1 started.

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Thanks for the attention!