Comprehensive summary – Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing Stefan O. Mueller a,b,⇑ , Wolfgang Dekant c , Paul Jennings d , Emanuela Testai e , Frederic Bois f a Nonclinical Safety, Merck Serono, Merck KGaA, Darmstadt, Germany b Institute of Applied Biosciences, Toxicology, Karlsruhe Institute of Technology, Karlsruhe, Germany c Department of Toxicology, University of Würzburg, Würzburg 97078, Germany d Division of Physiology, Dept. of Physiology and Medical Physics, Innsbruck Medical University, Innsbruck 6020, Austria e Environment and Primary Prevention Department, Istituto di Superiore di Sanitá, Rome 00161, Italy f INERIS, DRC/VIVA/METO, Verneuil-en-Halatte, France article info Article history: Received 14 July 2014 Accepted 10 September 2014 Available online xxxx Keywords: Predict-IV Systems toxicology Cellular models Liver Kidney Central nervous system (CNS) abstract This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project ‘‘Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics’’ (Predict-IV; Grant 202222). The project’s overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV. Ó 2014 Elsevier Ltd. All rights reserved. We have focused on optimized cellular systems for three rele- vant target organs of toxicity, the liver, the kidney, and the central nervous system (CNS). For all models highly differentiated cells were treated in a repeat-dose fashion with clinically relevant tissue specific toxins for up to 14 days. For cross-omics analyses com- pounds were tested after 14 days treatment. All in all 27 compounds were tested for alterations in transcriptome, with a large subset of these also being assayed for metabolomic and proteomic altera- tions. For nine compounds a kinetic analysis was also carried out by quantifying the actual cell exposure by measuring concentra- tions in the cells, in the cell culture medium and adsorption to cell culture plastic. For the liver, three different hepatic models were employed: primary human hepatocytes (PHH), primary rat hepato- cytes (PRH) and the hepatoma cell line HepaRG. The different cell models were capable of metabolising the selected parent test compounds, albeit with different sensitivities. Bioinformatic analy- sis of the transcriptomic signatures also provided highly useful mechanistic information. For example analysis of chlorpromazine differentially regulated genes revealed inflammation/hepatitis, cho- lestasis and hyperplasia to be the major mechanisms, which is in line with what is known in vivo (Parmentier et al., 2013). A single model was chosen for the kidney, i.e. the non-transformed normal human proximal tubular cell line, RPTEC/TERT1. Nine compounds were tested for in depth ‘omic analysis where cells were cultured on microporous supports and differentiated for 10 days prior to treatment (Wilmes et al., 2013). Here, the bioinformatics analysis was focused on three areas; (1) mechanistic effects, (2) tissue specific effects and (3) potential clinical biomarkers. The results showed that the RPTEC/TERT1 cells coupled with pharmacokinetics and high content ‘omic approaches gives detailed and quantitative insights into both the pharmacological and toxicological effects of compounds. As CNS models, we analyzed two neuronal primary models including a 2D mouse model and a 3D aggregating rat model. Aggregating 3D brain cultures developed features of higher cellular organization, including neurons, astrocytes and oligoden- drocytes endowed with cytochrome P450-mediated metabolic capability. A stochastic time-concentration activity model for in vitro cytotoxicity has been developed describing transitions from healthy to stressed cells and from stressed cells to death (Renner et al., 2013). The 2D mouse model allows the measurement of neuronal activity by measuring electrophysiological alteration when cells are cultured on micro electrode arrays (Novellino et al., 2011). Pattern recognition of this data allowed for the development of ‘‘activity fingerprints’’ for the different compound classes. An in vitro blood–brain barrier (BBB) was also evaluated for its potential integration into the in vitro testing strategy for drug-induced http://dx.doi.org/10.1016/j.tiv.2014.09.016 0887-2333/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Current address: Sandoz Biopharmaceuticals, Industriestr. 25, 83607 Holzkirchen, Germany. Tel.: +49 80244764742. E-mail address: Stefan.o.mueller@t-online.de (S.O. Mueller). Toxicology in Vitro xxx (2014) xxx–xxx Contents lists available at ScienceDirect Toxicology in Vitro journal homepage: www.elsevier.com/locate/toxinvit Please cite this article in press as: Mueller, S.O., et al. Comprehensive summary – Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing. Toxicol. in Vitro (2014), http://dx.doi.org/10.1016/j.tiv.2014.09.016