Pharmacometabolomics informs viromics towards precision medicine

Nowadays, we are experiencing the big data era with the emerging challenge of single data interpretation. Although the advent of high-throughput technologies as well as chemo- and bio- informatics tools presents pan-omics data as the way forward to precision medicine, personalized health care and ta...

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Bibliographic Details
Published in:Frontiers in pharmacology 2016-10, Vol.7
Main Authors: Aggeliki Balasopoulou, George P Patrinos, Theodora Katsila
Format: Article
Language:English
Subjects:
Metagenomics
pharmacogenomics
pharmacometabolomics
Pharmacometagenomics
precision medicine
Viromics
Online Access:Get full text
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Summary:Nowadays, we are experiencing the big data era with the emerging challenge of single data interpretation. Although the advent of high-throughput technologies as well as chemo- and bio- informatics tools presents pan-omics data as the way forward to precision medicine, personalized health care and tailored-made therapeutics can be only envisaged when interindividual variability in response to/ toxicity of xenobiotics can be interpreted and thus, predicted. We know that such variability is the net outcome of genetics (host and microbiota) and environmental factors (diet, lifestyle, polypharmacy, microbiota) and for this, tremendous efforts have been made to clarify key-molecules from correlation to causality to clinical significance. Herein, we focus on the host-microbiome interplay and its direct and indirect impact on efficacy and toxicity of xenobiotics and we inevitably wonder about the role of viruses, as the least acknowledged ones. We present the emerging discipline of pharmacometabolomics-informed viromics, in which pre-dose metabotypes can assist modeling and prediction of interindividual response to/ toxicity of xenobiotics. Such features, either alone or in combination with host genetics, can power biomarker discovery so long as the features are variable among patients, stable enough to be of predictive value, and better than pre-existing tools for predicting therapeutic efficacy/ toxicity.
ISSN:1663-9812
DOI:10.3389/fphar.2016.00411