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.