Neurodegenerative diseases, including Parkinson’s disease (PD), are increasing in prevalence due to the aging population. Despite extensive study, these diseases are still not fully understood and... Show moreNeurodegenerative diseases, including Parkinson’s disease (PD), are increasing in prevalence due to the aging population. Despite extensive study, these diseases are still not fully understood and the lack of personalised treatment options that can target the cause of the diseases, rather than the symptoms, has led to a greater demand for improved disease understanding, therapies and diagnostic procedures. In this thesis, we use systems biology approaches to construct disease-specific models intended for biomarker discovery, therapeutic treatment strategy identification and drug repurposing in PD. Systems biology is a mathematical field of research that analyses biological systems via construction of a computational model using experimental data. This is achieved by integration of omics data, including genomics, proteomics, transcriptomics and metabolomics. A specific approach used to identify the physico- and biochemical bounds within a biological system is constraint-based modelling, which requires the input of absolute quantitative metabolomics data. To improve our absolute quantitative coverage of the metabolome, we developed and improved new quantitative metabolomics methods using a targeted mass spectrometry workflow to obtain data intended to be integrated into constraint-based metabolic models for the study of PD. Show less
Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The... Show morePersonalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future. Show less
