Today recent technological advances have

Today, recent technological advances have dramatically improved the prospect of applying broadly the concept of precision medicine. Large-scale databases, omics technologies, cellular assays, epigenetics, informatics as well as imaging technologies converge towards optimum disease prevention, diagnosis and treatment. Hence, disease classification is refined, often accompanied by diagnostic, prognostic and treatment implications (Collins and Varmus, 2015). Successful examples include the FDA-approved use of chip technologies for the detection of variations in patients\' CYP2D6, CYP2C19 and UGT GM6001 that are of fundamental importance in drug metabolism (Swen et al., 2007). Notwithstanding, clinical pharmacogenomics depends on validated actionable genomic data that will inform diagnosis, prognosis or treatment (Lander, 2015). We feel that forthcoming trials are needed to demonstrate pharmacodynamic associations with genomic variants (Caraco et al., 2008; Mega et al., 2011). Notably, the currently available screening tests are characterized by unacceptable positive and negative predictive values (less than a 50% success rate in terms of safety and efficacy prediction), implying that inter-individual variability is not exclusively shaped by genomics. Indeed, combining screening for genomic variants in the CYP2C19 and VKORC1 genes – the most effective pharmacogenomics screen to date – resulted in a mere 41% prediction of the variability in warfarin doses (Namazi et al., 2010).
Pharmacometabolomics in Precision Medicine Pharmacogenomics does not consider environmental influences on drug pharmacokinetics (absorption, distribution, metabolism, excretion) and/or pharmacodynamics — neither the role of gut microbiome (Walter and Ley, 2011; Gurwitz, 2013; Li and Jia, 2013). More recently, in an alternative, but yet complementary discipline, pharmacometabolomics aims to predict and/or evaluate drug metabolism (Everett, 2015; Nicholson et al., 2011). Pharmacometabolomics is the later term used synonymously with pharmacometabonomics, the prediction of the outcome of a drug or xenobiotic intervention in an individual based on a mathematical model of preintervention metabolite signatures (Clayton et al., 2006). Historically, Jeremy Everett and Jeremy Nicholson first defined metabonomics in 1999, during the course of collaboration between Pfizer R&D, UK and Imperial College London (Nicholson et al., 1999). Pharmacometabonomics was established in 2000 as a result of inconsistent findings among subgroups of animal models. The hypothesis of Clayton et al. was that post-dose drug metabolism and safety were related to pre-dose metabolic profile differences (Clayton et al., 2006). Pharmacometabolomics is based on metabolic phenotypes (metabotypes), which are considered as the net result of genetic, physiological, chemical, and environmental influences (Holmes et al., 2008; Everett et al., 2013). Metabolic profiles refer to a huge list of chemical entities, both endogenous and exogenous, such as peptides, amino acids, nucleic acids, carbohydrates, fatty acids, organic acids, vitamins, hormones, drugs, food additives, phytochemicals, and toxins (Wishart et al., 2007). Surprisingly enough, even though the overall number of endogenous metabolites has been reported to be extremely high (~100,000), the major metabolites relevant for clinical diagnostics and/or drug development have been estimated at 1400–3000 molecules (Xu et al., 2009). What is also rather interesting to note is that metabolites are not just end- or by-products, as they can regulate gene expression and/or affect cell biology. Betaine, for example, is an osmolyte (protects from environmental stress) and methyl donor (participates in methionine cycle in human liver and kidneys) (Craig, 2004; Friesen et al., 2007), while it is a positive regulator of mitochondrial respiration and cytochrome c oxidase activity (Lee, 2015). In the presence or absence of chemometrics, a pre-dose metabotype can assist modeling and prediction of inter-individual drug responses (Nicholson et al., 2011). Similarly, Kaddurah-Daouk et al. (2007) investigated the lipid profiles of 50 patients with schizophrenia, before and after olanzapine-, risperidone- and aripiprazole-treatment. Pre- and post-treatment profiles were compared resulting in the identification of baseline lipid alterations that correlated with acute treatment response (Kaddurah-Daouk et al., 2007). Clayton et al. (2009) demonstrated a clear connection between a pre-dose urinary metabolite profile of an individual, and the metabolic fate of a standard dose of acetaminophen. It was reported that in individuals with high bacterially mediated p-cresol generation, competitive O-sulfonation of p-cresol reduces the effective systemic capacity to sulfonate acetaminophen, implying that the effects of microbiome activity should be an integral part of pharmaceutical development and of personalized healthcare (Clayton et al., 2009). Another study has shown that pre-treatment metabotypes could be predictive of sertraline response (acute treatment) in patients with major depressive disorder (Kaddurah-Daouk et al., 2011). This is the principle of “metabotype-based pharmacokinetics/pharmacodynamics”. Everett (2015) provides a comprehensive overview.