Considering the role of the environment and

Considering the role of the environment and gut microbiome in the pathobiology of the disease (Li and Jia, 2013; Wilson, 2009), a “pharmacometabolomics-aided pharmacogenomics” strategy could be of great benefit, as pharmacogenomics alone fails to address environmental influences. Taking into account host–microbiome interactions as well as the implications of gut microbiota for nutrition, data-intensive and cognitively complex settings and processes that limit human ability are anticipated. Can we delineate inter-individual variability towards differential diagnosis? Can we highlight the disease mechanisms in question to assist disease management? We consider immune disease as a model that presents multiple challenges, which could only be met by a multidisciplinary strategy built on the synergy of artificial and human intelligence. Some potential case scenarios are presented below. Celiac disease is a complex chronic immune-mediated disorder of the small intestine. Gluten has been identified as the environmental trigger of the disease (Di Sabatino and Corazza, 2009). Today, the presence of HLA-DQ2 and HLA-DQ8 coupled to a positive biopsy and serological clemastine fumarate manufacturer upon a gluten-containing diet is used for diagnosis. However, the HLA-DQ2 and HLA-DQ8 genes are necessary, but not sufficient for the development of celiac disease. To date, a few studies report differential metabotypes between healthy individuals and celiac patients. In 2009, a metabolic signature of celiac disease was defined, according to which differential serum levels of glucose and ketonic bodies suggested alterations of energy metabolism, whereas alterations of gut microbiota were also evident following urine data analysis (Bertini et al., 2009). In agreement with our view that the gut microflora of the small bowel is altered in celiac patients or presents peculiar species with their own microbial metabolome, Di Cagno et al. (2011) extensively explored the duodenal and fecal microbiota of celiac children, performing a molecular, phenotype as well as metabolome characterization. Rheumatoid arthritis is another chronic inflammatory disorder that typically affects the lining of joints, causing a painful swelling that can eventually result in bone erosion and joint deformity (Longo et al., 2015). Anti-tumor necrosis factor (anti-TNF) therapies are highly effective in rheumatoid arthritis. Yet, many patients exhibit only a partial or no therapeutic response. Kapoor et al. (2013) investigated the possibility a pre-dose patient\'s metabotype could predict responses to anti-TNF agents. Findings were rather informative. Another metabolomic analysis identified serum biomarkers to evaluate methotrexate treatment in patients with early rheumatoid arthritis (Wang et al., 2012). Autoimmune hepatitis is one of the most common chronic liver diseases caused by the activation of host\'s immune system against its own hepatocytes (Hadzic and Hierro, 2014). Notably, wrong diagnosis is a key issue as there are no accurate biomarkers to discriminate autoimmune hepatitis from other diseases that have similar symptoms, such as drug induced liver disease. Moreover, a recent study revealed that nonalcoholic steatohepatitis shares the same autoimmune antibodies with autoimmune hepatitis (Czaja, 2013). In 2014, Wang et al. successfully identified nine metabolites that serve as disease biomarkers for its diagnosis and distinguish between similar or overlapping liver diseases (accuracy of 93%) (Wang et al., 2014). Systemic lupus erythematosus is a chronic inflammatory disease characterized by multi-system involvement and diverse clinical presentation. Interestingly, the metabolic disturbances that underlie the disease are currently unknown. In a thorough study, Wu et al. (2012) compared the metabotypes of patients against their healthy counterparts to show that disease metabolome exhibited profound lipid peroxidation, reflective of oxidative damage.
A “Pharmacometabolomics-aided Pharmacogenomics” Workflow