**6. Bioinformatic and statistical analysis of candidate biomarkers**

Since the omics methods are extensively data-intensive and bulk, there is a definite need for bioinformatics and statistical analysis for organizing the data in conveniently accessible databases, which integrate huge number of data sets, and therefore need quality database manager software such as SQL for centralized storage and flexible web based access to the bulk data.

Noncommercial databases available on the web such as CEBS (chemical effects in biological systems, (http://cebs.niehs.nih.gov), PhenoGen (http://phenogen.uchsc.edu), along with and commercial databases like ArrayTrack and ArrayExpress (http://www.ebi.ac.uk/arrayexpress) help to generate large data sets. These are complemented by metabonomics databases (http:// 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 their data by enabling extensive data set comparisons.

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

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

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

posttranslational changes and limited protein detection capacity.

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

and end-stage histopathological analyses [54].

by its high costs and complex metabolite isolation procedures.

**6. Bioinformatic and statistical analysis of candidate biomarkers**

Since the omics methods are extensively data-intensive and bulk, there is a definite need for bioinformatics and statistical analysis for organizing the data in conveniently accessible databases, which integrate huge number of data sets, and therefore need quality database manager software such as SQL for centralized storage and flexible web based access to the

Noncommercial databases available on the web such as CEBS (chemical effects in biological systems, (http://cebs.niehs.nih.gov), PhenoGen (http://phenogen.uchsc.edu), along with and commercial databases like ArrayTrack and ArrayExpress (http://www.ebi.ac.uk/arrayexpress) help to generate large data sets. These are complemented by metabonomics databases (http://

**5.3. Metabonomics**

bulk data.

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 cell and tissue banking information for in vitro toxicity testing.


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

## **7. Conclusion and future perspective**

Improved toxicity testing methods complementing advanced in vitro assays are very crucial in reducing the rate of attrition in final stages of product development. To avoid failures and withdrawals, there is an absolute need for integration of all available technologies to minimize cumbersome process of trials and expenses and eventually reduce the increasing costs of bringing a new drug into market. Supplementing toxicology evaluation methods, such as histopathology, physiology and clinical chemistry with transcriptomics, proteomics and metabonomics could provide new insights into the mechanisms underlying toxicological pathologies. Integration of in vitro toxicology technologies, with systems biology methods resulted in 'systems toxicology'. Expansion of open source databases and analytical platforms is critical to the discovery of novel biomarkers of toxicity. So far, the available approaches for discovery of biomarkers included toxicogenomics, toxicoproteomics, metabonomics and bioinformatics analyses (systems biology approach) while the technologies available for quantification include ELISA, solid phase ELISA, Luminex technology and patterned paper technology [50]. Individual technologies have limited usefulness unless the data generated from these assay platforms and '-omics' discovery technologies are integrated. The discovery of DNA microarrays and protein chips has made information exchanges extraordinarily easy, convenient and quick. Integration of information from these powerful sources using analytical computing software products, noncommercial databases, and advances in hi-throughput technology is the future of the next phase in the identification, selection and qualification of novel biomarkers of toxicity.

**Author details**

Shiva Prasad Potta2

Jürgen Hescheler1\*

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Future of toxicogenomics lies in developing a more refined understanding of molecular mechanisms related to specific toxicologies, to elucidate molecular signatures associated with the prediction of biomarkers or panels of biomarkers with support from the field of transcrip‐ tomics, metabolomics, and proteomics. These analytical tools applied to the emerging human PSC-based in vitro platforms utilizing their organ-specific differentiated derivatives, such as CMs, hepatocytes and neurons, have a great potential to revolutionize the field of toxicology. However, the full potential of these human in vitro cell-based platforms in predicting toxicity of compounds in humans will be realized only with further improvements in derivation of highly standardized, well-defined and homogeneous cell populations that functionally and structurally strongly resemble their adult counterparts and development of sensitive and robust methods for accurate detection of toxicity.

### **Acknowledgements**

The work in the author's laboratories is supported by the European Union FP7 Program, Bundesministerium für Bildung und Forschung (BMBF), Else-Kröner-Fresenius Stiftung, Excellence Research Support Program of the University of Cologne and Köln Fortune Program.
