The immune-mediated inflammatory diseases (IMID) are a range of disorders that generally develop in genetically susceptible individuals, with an autoimmune pathogenic mechanism. The term IMID encompasses a range of ostensibly unrelated conditions, with a collective population incidence of 5-7%, that share overlapping inflammatory mechanisms, the commonest of which include rheumatoid arthritis (RA), Inflammatory bowel disease (IBD), psoriasis, systemic lupus erythematosus (SLE) and multiple sclerosis (MS). Since the early 1990s, the development of immunomodulatory humanized antibodies, such as tumor necrosis factor-alpha (TNFα) inhibitors have revolutionized the treatment of IMIDs. The therapeutic aim in IMID treatment is to gain rapid control of inflammation, prevent tissue damage, improve quality of life and, if possible, achieve long-term disease remission. When successful targeted biologic therapy has been revolutionary in achieving these aims, but biologics are currently prescribed stochastically, through trial and error with low or absent response rates seen in 10-40% of patients depending on the disease and drug used. The heterogeneity of response observed with biologics suggests the existence of differing molecular etiology, so-called “disease endotypes”, phenotypically presenting as the same disease, but betraying differing underlying mechanism with differential drug response. Multi-Omic analysis, including RNA sequencing holds considerable promise for mechanistic insight into the personalisation of therapies for IMIDS, such as Psoriasis and Rheumatoid Arthritis. Tools and a TranSMART data warehouse framework are described, enabling multi-omic analysis in longitudinal transcriptome samples collected from Psoriasis and RA patients undergoing biologic treatment. Exploratory analysis of RNA-seq data identifies many differentially expressed genes which collectively highlight biological pathways or “endotypes” which offer potential new insights into the stratification of biologic therapies in IMIDs. We show evidence that the concept of both disease endotype and drug endotype can be used to predict therapeutic response and inform patient stratification.