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
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
