**5. Omics strategies to develop biomarkers for RDT**

### **5.1. Toxicogenomics**

maturation of Ca2+handling [38]. Further improvements of differentiation methods will enable generation of more homogeneous and mature CM populations thus increasing their validity for RDT testing. The overall predictability of drug efficacy and toxicity using iPSC-CMs and disease specific iPSC-CMs has been recently reported by several groups [29, 39-41]. An absolute requirement for CMs to be used for RDT is to be able to stably maintain the beating cardiac phenotype for a prolonged period of time under defined conditions. Both hiPSC-CMs and hESC-CMs display beat rate variability similar to that of a human heart sinoatrial node [42]. However, recently, variability in action potentials and sodium currents in response to lidocaine and tetrodotoxin was shown in late stage in vitro differentiated human iPSC-CMs [32], thus

Bioanalytics is a very promising tool in the application of in vitro cardiotoxicty assays. Novel bioanalytical tools for discovery of biomarkers of cardiotoxicity include the field potential QT scanning, using cellular oxygen uptake for monitoring the metabolic state of CMs, using surface plasmon resonance (SPR) biosensing for key CM biomarkers, and also exploiting realtime multi-wavelength fluorimetry [12]. Novel imaging technologies and physiological analyses such as impedance measurements [43] and microelectrode arrays (MEAs) [44] give an insight into major in vitro cellular events such as migration, proliferation, cell morphology,

So far, hepatotoxicity is evaluated on day 28 or 90 in in vivo RDT tests by analysis of clinical parameters, hematology, and histopathology. RDT tests evaluate chronic effects on organ toxicity to establish a NOAEL which is used in calculation of the substance safety parameters [25]. The extrapolation of the quantitative risk assessment for cosmetic ingredients using data derived from animal studies to in vitro systems could be done by considering a margin of safety (MoS) value of at least 100 for intra-species and inter-species variation [25]. Human PSCs represent a promising human cellular model which could help in increasing the safety and predictability of RDT testing. Combined with this cell model, toxicogenomic technologies would help predict biomarkers in an evidence-based approach. During these RDT tests, the animals are observed for indications of toxicity. Afterwards, necropsy, blood analysis and histopathology of the organs of the animals are performed [17]. However, these parameters can turn out to be insensitive and potentially generate false negative results [15, 16]. Unex‐ pected hepatotoxicity may be seen in the clinical trials or even when the product is already on the market because careful examinations of idiosyncratic (person specific) or non-idiosyncratic inter-drug interactions are either ignored or overseen [45]. This is also probably because of dose-dependent reactions and other unknown peculiar drug interactions. There is need for novel screening methods that can address these hepato-toxicological hazards early in the development [46]. Most studies relied on the use of liver slices as an in vitro model for toxicity testing due to limited availability of tissue samples. However, the human PSC-derived hepatocytes have the potential to replace these in vitro models and be applied for toxicity

cell–cell interactions and colony formation, relevant to biomarker discovery.

**4. Stem cell-derived hepatocytes for toxicity testing**

warranting some caution and further analyses.

186 Pluripotent Stem Cell Biology - Advances in Mechanisms, Methods and Models

Proteomics, genomics and metabonomics, either alone or in combination have the potential for developing biomarkers in applied toxicology. Toxicogenomics refers to the areas men‐ tioned above and is a thorough-mean for hi-throughput discovery of biomarkers using latest technologies [50]. Transcriptomics measures the levels of both coding and non-coding RNAs using hi-throughput technology such as microarrays. This whole genome gene expression analysis can measure the levels of expression of a gene at any stage, in any tissue and in any vitro model. Examples of toxicogenomics applications include prediction of genotoxicity or carcinogenicity, target organ toxicity and endocrine disruption. Expression profiling of any selected cellular systems exposed to new test substances is compared against controls to identify, classify and validate toxic compound and its effects. Bioinformatic analyses of the data sets obtained from above can be used to predict the patterns and signatures of a toxin (e.g. biological processes or signaling pathways affected by a toxin). Furthermore, the data sets can be matched up against existing databases for predicting and carving out a mode-of-action for the toxin. The main disadvantage of this approach is limited reproducibility and also it is semi quantitative and detects only changes in gene expression. Therefore, mRNA expression profiling cannot be used as a standalone method in identifying potential biomarkers of RDT.

EU FP7 project Predict-IV is evaluating the integration of 'omics' technologies, biomarkers and high content imaging for the early prediction of toxicity of pharmaceuticals in vitro. The aim is to identify general molecular response pathways that result from toxic drug effects that are independent of the cell/tissue type [51]. Detection of endpoints and biomarkers of RDT using in vitro systems (DETECTIVE) is a unique large scale SEURAT-1 cluster project aimed at establishing screening pipeline of high content, high throughput as well as classical functional and "-omics" technologies to detect human biomarkers for RDT in in vitro test system (http:// www.detect-iv-e.eu/). Other-omics technologies such as microRNA analysis and epigenetics also play a vital role.

#### **5.2. Proteomics**

Drug induced toxicity can also exhibit various effects at the proteome level. Classification of such endpoints is difficult using traditional RDT methods. Proteomics improves the classifi‐ cation by identifying individual proteins or such protein panels that reflect the specific toxic pathway mechanisms. Proteomics-based in vitro toxicity assays measure drug-induced changes by comparing in vitro to in vivo effects thus validating the suitability of in vitro models. There is an absolute need for integration of standard RDT tests with the 'omics' applications. Current proteomic technologies include gel-based (1-DE or 2-DE) and gel-free (LC-MS/MS) techniques [17]. Recently thalidomide-specific proteomics signatures during human ESCs differentiation were identified using two-dimensional electrophoresis coupled with Tandem Mass spectrometry [52]. Proteomic studies are quantitative, sensitive and are more accurate and powerful in detecting protein biomarkers of RDT. Main pitfalls include posttranslational changes and limited protein detection capacity.

www.hmdb.ca). PrestOMIC is proteome-specific open-source that is a user-friendly database, where researchers can upload and share data with the scientific community using a custom‐ izable browser [60]. Such a database helps researchers to increase the exposure and impact of

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Another open-source systems biology application called SysBio-OM, integrates information from the CEBS database with other open source projects, including MAGE-OM (micro-array gene expression object model) and PEDRo (proteomics experiment data repository), to model profiling of protein, and metabolite expression and protein-protein interactions following insult [61]. SysTox-OM is a more specific application that performs expression profiling of genome, proteome, and metabolome, after the introduction of a toxicant. Different omics approaches and some of the crucial data bases are summarized in Table.1. While incorporating toxicological endpoints such as – clinical chemistry, hematology, observations and histopa‐ thology, it profiles the phenotype [62]. With this application, one can identify a single toxic phenotype, classify, and compare gene and protein expression profiles in an organ after administration of each drug. It is also possible to predict a common toxicologic pathway, mechanism or a biomarker. ToxBank is an EU FP7 project aimed at establishing a dedicated web-based warehouse for toxicity data management and modeling along with establishing a

http://ctdbase.org/

http://toxico.nibio.go.jp/english/

http://toxnet.nlm.nih.gov/index.html http://www.ebi.ac.uk/arrayexpress/ http://wwwdev.ebi.ac.uk/fg/dixa/index.html http://www.fda.gov/ScienceResearch/ BioinformaticsTools/Arraytrack/default.htm

http://www.ebi.ac.uk/pride/archive/ https://www.proteomicsdb.org/ http://gpmdb.thegpm.org/ http://peptide.nist.gov/

http://www.ebi.ac.uk/metabolights/index

http://fiehnlab.ucdavis.edu/projects/binbase\_setupx

http://www.hmdb.ca/ http://bigg.ucsd.edu/

http://www.bml-nmr.org/ http://www.massbank.jp/ http://gmd.mpimp-golm.mpg.de/

https://ntp.niehs.nih.gov/drugmatrix/index.html

their data by enabling extensive data set comparisons.

cell and tissue banking information for in vitro toxicity testing.

**Databases Web links**

Comparative Toxicogenomics Database (CTD)

Human Metabolome Database (HMDB)

Golm Metabolome Database (GMD)

**Table 1.** Summary of different Omics approaches and corresponding databases

Open TG-GATEs DrugMatrix® TOXNET ArrayExpress diXa Data Warehouse ArrayTrack™

PRIDE ProteomicsDB GPMDB NIST

BiGG MetaboLights MMCD

SetupX & BinBase BML-NMR MassBank

**Toxicogenomics**

**Proteomics**

**Metabonomics**

#### **5.3. Metabonomics**

Human PSCs offer a potential alternative test system for the identification of developmen‐ tal toxicants [53]. Metabonomics refers to profiling of diverse metabolic complement of a biofluid or tissue using analytical tools such as high-field NMR together with mass spectrometry [54]. Subsequent statistical modeling and analysis of a multivariate spectral profiles obtained using NMR [55] in combination with LC-MS and UPLC helps to distin‐ guish the phenotypes and metabolites of interest. These metabolites might represent new biomarkers for toxicity. Previously, some of metabolites were identified to be biomarkers for a variety of pathological diseases [56]. Metabonomics-based approaches have proved to be highly successful in furthering our understanding of research in the field of drug metabolism, drug pathways and toxicology [54, 55]. In addition, metabonomics provides a useful link between 'omics' platforms such as genomics, transcriptomics and proteomics and end-stage histopathological analyses [54].

The Consortium for Metabonomic Toxicology (COMET) project (a collaboration between five pharmaceutical companies and Imperial College London) focused on pre-clinical toxicological research and resulted in the generation of an extensive 1H NMR biofluid spectral database which was used for screening of toxins and also to build an expert system for prediction of target organ toxicity [57]. A follow-up project, COMET-2, is currently investigating the detailed biochemical mechanisms of toxicity, and seeks a better understanding of inter-subject variation in metabonomics analyses [55]. Several groups have developed Metabonomics-based robust human ESC in vitro test systems for predicting human developmental toxicity biomarkers and pathways [58, 59]. Metabonomics is the most relevant and robust omics platform to study both in vivo and in vitro toxicology. It is possible to detect metabolites with accuracy but it is limited by its high costs and complex metabolite isolation procedures.
