Though of tremendous benefit, the overuse of antibiotics has lead to the emergence of antibiotic resistance and may cause adverse events that impact on patient healthcare. Current diagnostic tools for facilitating the appropriate use of antibiotics in patients are inadequate.We will establish a broad-based strategy (not limited to a particular antibiotic group) that can be implemented on a broad scale to increase the effectiveness of antimicrobial therapy, reduce adverse events, and help limit the emergence of antimicrobial resistance in children and adults.At the heart of the TAILORED-Treatment project is a prospective clinical study in which we will recruit 1200 patients (>2000 patient samples) presenting with respiratory tract infections and/or sepsis. Patient cohorts will include equal representation of genders, children and adults. State-of-the-art molecular technologies will be applied to characterize host-pathogen interactions at the genomic, transcriptomic, proteomic and clinical level, resulting in a large-scale and unique multi-dimensional dataset. The consortium partners will develop new computational tools to interrogate this data, in order to provide new insights into personalized host-pathogen interactions, including the discovery of novel biomarkers for physicians to use in patient diagnosis and disease monitoring.Concurrently, using this data, we will construct a predictive personalized treatment algorithm that will lead to informed and personalized antimicrobial treatment regimens (indication, dosage, and duration) that are tailored to the needs (type of infection, presence of novel biomarkers etc) of children and adults presenting with respiratory infections and sepsis.This unique multi-dimensional dataset and personalized predictive treatment algorithm will be built into an easily navigable web-based, free-to-use, decision support system for use by physicians to explore and assist in patient-tailored antimicrobial treatment decisions.