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The vision of the toxicology in the 21st century movement is to overcome the currently used animal tests and identify molecular pathways of toxicity, using human in vitro systems with the aim to provide the most relevant mechanistic information for human risk assessment. It is expected to translate key surrogate biomarkers to novel types of toxicity-related high throughput screening of the many thousands of compounds which need to be tested during development phases of the pharmaceutical industry and with regard to the REACH legislation in Europe. Systems biology, an emerging and increasingly popular field of research, appears to be the discipline of choice to integrate results from transcriptomics, proteomics, epigenomics and metabonomics technologies used to analyze samples from toxicological models. The challenges, however, with respect to data generation, statistical treatment, bioinformatic integration and interpretation or in silico modeling remain formidable. One of the main difficulties is the fact that the sheer number of molecular species is inflated enormously in the course of translation from genes to proteins due to post-translational modifications. Moreover, at the level of proteins, time scales of cellular reactions to toxic insults can be very fast, ranging from milliseconds to seconds. Linear dynamic ranges of concentration differences between conditions can also differ by several orders of magnitude. So, the search for protein biomarkers of toxicity requires sophisticated strategies for time-resolved quantitative differential approaches. The statistical principles, normalization of primary data and principal component and cluster analysis have been well developed for genomics/transcriptomics and partly for proteomics, but have not been widely adapted to technologies like metabonomics. Also, the integration of functional data, in particular data from mass spectrometry, with the aim of modeling pathways of toxicity for human risk assessment, is still at an infant stage., (Copyright © 2012 Elsevier B.V. All rights reserved.)

There is an urgent need for surrogate biomarkers in clinical diagnostics but also preclinical in toxicology of chemicals and efficacy testing of pharmaceuticals. On the background of the emerging fields of systems biology and theranostics, the importance of time scales and the synchronization of complex biological readouts become increasingly obvious. Systemic effects such as responses to stimuli, medical intervention, cellular stress, or toxicity elicit immediate molecular changes on the protein level. Early events include phosphorylation and oxidation of proteins. These posttranslational modifications have a direct impact on enzyme activities and protein-protein interactions. Only at downstream stages is gene transcription activated. Here we outline the analytical and statistical requirements dealing with complex patterns of protein expression and the extraction of protein surrogate biomarkers defining specific early stages of biological responses. We also present successful examples from research on aging and discuss prerequisites and necessary considerations while moving candidates to validation.

The separation of proteins with the aim of discovering surrogate biomarkers defining differences between various stages of biological materials is the core occupation of every project in Proteomics. There are numerous recent publications suggesting a wide array of separation technologies, ranging from 2-DE, MS-linked LC, CE or chip-based surface-enhanced laser desorption ionization claiming to be useful for this purpose, and addressing the urgent clinical, diagnostic or toxicological needs for such surrogates. However, many potential biomarkers emerging from proteomic studies did not survive validation in, for example, large-scale clinical studies or simply independent experiments, and at the same time being tested in settings with case numbers bigger than perhaps a few hundreds. The major problems of protein biomarkers are associated with the huge dynamic range of possible concentrations and the ever-increasing number of molecular species due to post-translational modifications. In particular, the chemical diversity of the latter imposes a necessity of improved resolution of separation technologies, because otherwise the crucial quantitative information is lost in pools of poorly resolved peptides. Here, we present and analyze some examples of successful developments of protein biomarkers, and show the prerequisites and necessary considerations while moving protein candidates from purely descriptive phenomena to a stage of validated surrogate biomarkers. This includes a detailed discussion of requirements regarding resolution of initial separation techniques, linear dynamic range and statistics of differential quantification, but also the subsequent clinical validation, testing the biomarker in clinical settings and using large numbers of patient samples.