Improvments and evaluation of data processing in LC-MS metabolomics : for application in in vitro systems pharmacology

Abstract: The resistance of established medicines is rapidly increasing while the rate of discovery of new drugs and treatments have not increases during the last decades (Spiro et al. 2008). Systems pharmacology can be used to find new combinations or concentrations of established drugs to find new treatments faster (Borisy et al. 2003). A recent study aimed to use high resolution Liquid chromatography–mass spectrometry (LC-MS) for in vitro systems pharmacology, but encountered problems with unwanted variability and batch effects(Herman et al. 2017). This thesis builds on this work by improving the pipeline and comparing alternative methods and evaluating used methods. The evaluation of methods indicated that the data quality was often not improved substantially by complex methods and pipelines. Instead simpler methods such as binning for feature extraction performed best. In-fact many of the preprocessing method commonly used proved to have negative or neglect-able effects on resulting data quality. Finally the recently introduced Optimal Orthonormal System for Discriminant Analysis (OOS-DA) for batch removal was found to be a good alternative to the more complex Combat method.