When untargeted analysis is considered pharmacometabolomics

When untargeted analysis is considered, pharmacometabolomics also serves as a tool to shape hypothesis, as multiple analytes are quantified simultaneously and pharmacometabolomic modeling appears not to be limited by prior understanding or hypotheses. Hence, pharmacometabolomic modeling can be a powerful hypothesis-generating scenario. Of course, an adequate monitoring of the analytical quality is always mandatory, if a cluster of metabolites associates to a well-defined physiopathological condition. In the context of a pharmacometabolomics-aided pharmacogenomics strategy, the breakthrough of metabotype-based findings is patient or therapy profiling that addresses genomic and environmental influences in a rather pathway-targeted way, even if the mechanism in question (disease, drug efficacy/toxicity) is not completely known. Konstantynowicz et al. (2012) reported that children with autistic spectrum disorders (ASD) demonstrated 3-fold greater plasma oxalate levels as well as 2.5-fold greater urinary oxalate concentrations compared with healthy individuals. As the authors commented “whether hyperoxalemia and hyperoxaluria may be involved in the pathogenesis of ASD in children or this is the outcome of an impaired renal excretion or an extensive intestinal absorption, or both, or whether oxalate crosses the blood STF-62247 barrier and disturbs CNS function in the autistic children remains unclear” (Konstantynowicz et al., 2012). Taking into account that the SLC26 gene family encodes anion exchangers and channels transporting a broad range of substrates, including oxalate (Alper and Sharma, 2013), genetics is expected to play a role. In any case, a low oxalate diet has resulted in the ease of ASD symptoms (improvements in expressive speech, reduced obsessive behavior) (Konstantynowicz et al., 2012), allowing disease handling on the basis of metabotypes. Pre-dose metabotypes can be also predictive of a post-dose phenotype (drug toxicity or efficacy), addressing environmental influences as well as the role of gut microbiome. This is the exact information that pharmacogenomics fails to achieve patient-tailored treatment. An overview of the human pharmacometabolomics, metabolomics, metabonomics, metagenomics and pharmacometabolomics-aided pharmacogenomics studies reported in the literature to late 2015 is depicted in Supplementary Table 1. What we envisage is merging omics and information technologies beyond data mining and analysis. Instead, we propose the synergy between artificial and human intelligence to (i) acquire pharmacometabolomic and pharmacogenomic data and thus, address the interplay of genomic and environmental influences, (ii) facilitate collaborative data analysis and (iii) guide sense- and decision-making towards rapid and efficient data output. A “one-stop-shop” crowd-sourced, cloud- or web-based platform (a standard around which a system can be developed) where the informatics community and/or biomedicine scientists could explore and validate such an approach could pave the way for better-informed and cost-effective studies. Omics data demand strict filtering as well as thorough analysis and interpretation. At the same time, biomedicine scientists need to efficiently and effectively collaborate and make decisions. For this, large-scale volumes of complex multi-faceted data need to be meaningfully assembled, mined and analyzed. Tsiliki et al. (2014) presented an innovative web-based collaboration support platform that adopts a hybrid approach on the basis of the synergy between artificial and human intelligence.
The Paradigm of Autoimmune Disease Autoimmune disease results from the incapacity of the immune system to discern endogenous substances from xenobiotics and affects 5% of the population in Western countries (Sinha et al., 1990). The etiology of autoimmune disease is currently unclear, perplexing differential diagnosis, patient stratification and decision-making in the clinic. Although a genetic component has been described, disease occurrence has been also associated with several environmental factors, gut microbiota, infections as well as gender bias (Shoenfeld and Isenberg, 1989). Overall, disease management options are limited and ultimately fail to protect patients from disease symptoms due to its chronic nature.