Approach:• Identify gene changes for 30

steatotic compounds in TG Gates and analyse time dependencies of gene changes

• Include relevant ToxCast data• Compare with data for

compounds inducing steatosis in RDT studies from RepDose DB (162 compounds identified)

• Develop profile with data from OpenPhacts and map to signal pathways

• Develop profile with CT link, which predicts protein interactions and drug targets.

Plasma Cmax, unbound 2.14 mMLiver Cmax, unbound 0.89 mM

The EU-ToxRisk project has received funding from the European Union‘s Horizon 2020 research and innovation programme under grant agreement No 681002

Prediction of microvesicular liver steatosis:Read across case study with VPA analogues (case study 1)

AbstractIn this case study we aim to develop non-animal approaches, which can be used to reduce the uncertainty of read across predictions. We selected valproic acid (VPA), a short-branched carboxylic acid, as lead compound for this case study. VPA is used in the treatment of epilepsy in humans and induces microvesicular hepatic steatosis in the liver of some patients. This effect has also been reported after relatively short exposure (e.g. 5 days) and high dosing in repeated dose toxicity (RDT) animal studies. Impaired b-oxidation in mitochondria is described as adverse outcome pathway, probably caused by sequestration of coenzyme A. In this read across case study, we investigate whether the EU-ToxRisk in silico and in vitro models predict the in vivo potential of VPA analogues to induce microvesicular steatosis, qualitatively and quantitatively.

Selection of VPA analoguesDefine the structural and toxicological boundaries of the read across study by using in vivo positive (induce steatosis) and in vivo negative case compounds (no liver effects reported up to highest tested dose)

Rabea Gräpel*, Inga Tluczkiewicz, Ahmed Abdelaziz, Bob van de Water, Bart van de Burg, Ciaran Fisher, Oliver Hatley, Gopal Pawar, Daria Goldmann, Gerhard Ecker, Enrico Mombelli, Françoise Gautier, Frederic Bois, Laia Tolosa, Paul Jennings, Alice Limonciel, Regina Stöber, Richard Maclennan, Thomas Exner, Johann Lindberg, Thomas Braunbeck, Ulf Norinder, Olivier Taboreau, Cleo Tebby, Will Drewe, Tony Long, Wolfgang Moritz, Jens Kelm, Alejandro Aguayo, Luc Bischoff, KatherinaBrotzmann, Jan Hengstler, Sylvia Escher (*presenting author)

Model key events of the AOP network for steatosis – quantitatively

In vivo negative In vivo positive

differ in chain length

blocked in position 2

non-branched

Reverse dosimetry was used to calculate in vivo relevant dosing for VPA and 4-ene VPA. LOAEL data from rodent studies were used. A dose range for in vitro testing was selected accordingly. Different time points will be investigated in step 1 to select the optimal test protocol and test system (up to 72 hour exposure; +/- preincubation of fatty acids (FAs)). In step 2 all analogues will be tested accordingly. Several questions will be answered:• Interlab reproducibility by testing HepG2 cells in two labs• Organ specificity (kidney versus liver cells)• Best performing cell type (primary liver cells versus 2D + 3D liver cell

lines (HepaRG, HepG2, embryo toxicity tests with zebrafish)• HTS systems: reporter gene and CALUX assays

Selection of in vitro conditions(HepG2 cells -grey 5 days, black 6 days)

also DART data available

only DART data available

Calculation of bioavailable dosing by PBPK modeling (rodent)

no FA

FA 6

2 uM

FA +

VPA

6 m

M

FA 3

00 u

MVP

A

0

50

100

150 5-days experiment

6-days experiment

% L

ipid

accu

mu

lati

on

Building blocks In vivo data: define case study compounds based on existing in vivo

repeated dose studies in animals (e.g. from RepDose) In vitro: Test different in vitro systems for their predictivity of liver

steatosis, start with simple and evolve to more complex test systems Develop physiologically based pharmacokinetic (PBPK) models and

test at bioavailable, realistic dosing -> prerequisite for QIVIVE Test ADME parameters in vitro, where in vivo data are missing Use transcriptomics and pathway information to establish adverse

outcome pathways (AOP) Human anchoring: Use human samples from patients (plasma and

liver samples) and map outcomes of in vitro assays to human data.

ZebrafishembryosLiver models

Bile canaliculiDNA structure

Cyprotex: HepaRG

3D spheroid

LU: HepG2 GFP

reporter cell line

HULAFE: HepG2 cells

IFADO: PHH

stained with Hoechst33342 (nuclei) Phospholipidosis Red dye Neutral lipids Green dye

primary cells,seeding between two

layers of collagen

University Heidelberg

Kidney

MUI:RPTEC/TERT1

cells grown on plastic, with characteristic dome formation

ITEM: PCLiS

In vitro testing

AOP and modeling

tissues slices freshlyprepared from

human liver

human

rat_in_vivosingle

rat_in_vivorepeated

rat_in_vitro

Gene overlap in TG Gates

Dose response to 18 receptorendpoints from ToxCast

BDS:Calux-assay

HTS