Metabolomics has the potential to deliver diagnostic biomarkers for the detection and prognosis

of diseases, and the prediction of the efficacy and safety of pharmaceutical interventions [3, 4 and 5]. Metabolomics can also provide insights into the biochemical mechanisms of diseases and the modulation by drugs. It has become clear that health and disease are optimally studied from a systems perspective [6•, 7 and 8]. Only such an approach will allow a personalized medicine approach, and system fingerprints by metabolomics will play an important role in the future to follow the health state of an individual [9•]. At present, there are still significant challenges in answering biological questions [10, 11 and 12••]. We will discuss these challenges and indicate possible directions of solutions. Considering the number of metabolites used in a Quizartinib purchase clinical setting as biomarkers of disease onset and/or progression, the picture appears to be rather diverse. In the first place in clinical chemistry a very limited number of small metabolites Panobinostat such as glucose, cholesterol, creatinine, urea, etc., is being used for decades to assess an individual’s (pre-) disease condition. Secondly, in the field of inborn errors of metabolism an extensive repertoire of metabolites is used as biomarkers for diagnosis, progression and response to treatment [13]. Finally, in multifactorial disorders

like type 2 diabetes, metabolic syndrome or neurodegenerative disorders there is an urgent need for all types of biomarkers. Especially in this area of pathology metabolomics is in principle very well suited to identify and deliver biomarkers for clinical use. In general ‘omics’ technologies such as proteomics and genomics have hardly contributed to obtain clinically useful and accepted biomarkers, despite the vast number of papers (more than 150 000) published on this subject [12••]. Many of these omics-studies

are hampered by the fact that studies were not well designed, findings not validated in independent check details replica cohorts, but most important no proper clinical phenotyping is available. The latter is in contrast to the field of inborn errors of metabolism, where the cause is always monogenetic and the resulting clinical phenotypes extreme such as is the case in aminoacidopathies, organic acidurias or fatty acid oxidation disorders. In multifactorial diseases, the phenotype is more complex, as various genetic and environmental factors are involved, and the phenotype is probably highly dynamic as well. At present the clinical characterization is at a high generic level and consequently inclusion/exclusion criteria will encompass several subtypes. Biomarkers found from those studies can typically not be validated as the subgroup diversity will be different in the next study. This situation is probably better for drug-response biomarkers.