Sepsis is a life-threatening condition caused by a dysregulated host response to infection, it is associated with significant morbidity, mortality, and with a high financial burden on global... Show moreSepsis is a life-threatening condition caused by a dysregulated host response to infection, it is associated with significant morbidity, mortality, and with a high financial burden on global healthcare systems. Bacterial infections are the primary cause of sepsis, but the growing prevalence of antimicrobial resistance complicates the effectiveness of antimicrobial treatments. Moreover, limited understanding of the host immune response during sepsis hinders the discovery of valuable biomarkers and drug targets. As such, there is an urgent need to improve the treatment of sepsis. To tackle this challenge, we have concentrated our efforts on optimizing current treatment strategies and on facilitating the discovery of novel host inflammatory response directed therapeutics. In this thesis, we have utilized quantitative pharmacological modeling approaches to assess the adequacy of current dose regimens and to evaluate antibiotic pharmacokinetic variability, thereby optimizing antimicrobial therapies for sepsis. Additionally, our researches had aimed to deepen our understanding of the underlying dynamics of sepsis pathology, enabling the identification of promising biomarkers and therapeutic targets for sepsis. Our work demonstrated how quantitative modeling strategies can support the design of optimized treatment strategies, and how systematic model-based integration of disease mechanisms can help to overcome the translational challenges in sepsis drug development. Show less
Lipid signaling is an essential biological event/process in a plethora of pathophysiological conditions. The underlying idea of this thesis is that many of the roles and the complex interplay of... Show moreLipid signaling is an essential biological event/process in a plethora of pathophysiological conditions. The underlying idea of this thesis is that many of the roles and the complex interplay of the individual signaling lipids in inflammatory processes and related conditions in health and disease is not well known, and therefore has to be studied integrally as a complex network. In order to study this complex interplay, an improved broad analytical method is necessary to analyze a wide range of different signaling lipid classes such as oxylipins, (nitro) free fatty acids, endocannabinoids, bile acids and different subclasses of lysophospholipids. Therefore, the aim of this thesis is to develop a better method to study signaling lipids, and to apply it to study the role of these molecules in several relevant biological questions for a better understanding of inflammation related pathophysiology including autoimmune diseases, neurodegeneration and regulatory effect of exercise training. Show less
To increase clinical success rate of drugs, a better understanding of drug action mechanism and disease dynamics is required. Metabolomics, which studies small molecules involved in biochemical... Show moreTo increase clinical success rate of drugs, a better understanding of drug action mechanism and disease dynamics is required. Metabolomics, which studies small molecules involved in biochemical processes in organisms, has shown to be a useful tool for this better understanding. In this thesis, we focus on the endocannabinoid system (ECS) and profiling its related metabolic pathways using liquid chromatography - mass spectrometry (LC-MS) based metabolomics techniques. The endocannabinoid system (ECS) is a signaling system involved in multiple physiological and pathological processes. Due to its wide distribution and complex network of metabolic interactions, the development of drugs targeting the ECS has seen high failure rates. To get a better understanding of the behavior of the ECS and related pathways, LC-MS platforms with wide coverage of the major ECS-related metabolites, or with high sensitivity that reaches low levels of metabolites, were developed and optimized. Furthermore, these metabolomics platforms were applied in clinical studies looking into cardiometabolic health, and revealed correlations between endogenous metabolite signaling, cardiometabolic health and the benefits of exercise. Show less
