The Process Chain for Peptidomic Biomarker Discovery

Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.