 We began by identifying publicly available datasets from transcriptome profiling experiments that analyzed blood leukocyte samples from adult and child sepsis patients. After data processing, we used Pathprint to translate these gene expression patterns to the pathway activity level. By comparing samples at the pathway level the Pathprint method is robust to batch effect and allows for comparison across multiple array platforms. After identifying age-associated differences in pathway activity, we used them to facilitate drug discovery by constructing targeted Pathway Drug Networks (PDNs). This novel method works by incorporating our target pathways into a base network built upon the correlation in the expression of >16,000 disease, pathway, and drug gene signatures across >50,000 individual microarrays. The resultant network neighborhood was used to identify drugs with positive or negative association with high-survival (child) or high-mortality (adult) pathways, respectively. We validated top drug leads by curating and analyzing prior data collected in pre-clinical models of sepsis and also by directly testing their ability to improve survival in a mouse model of fatal endotoxemia 