Although the applications of pharmacometabolomics to personalized medicine are largely only being realized now, the study of an individual's metabolism has been used to treat disease since the Middle Ages. Early physicians employed a primitive form of metabolomic analysis by smelling, tasting and looking at urine to diagnose disease. Obviously the measurement techniques needed to look at specific metabolites were unavailable at that time, but such technologies have evolved dramatically over the last decade to develop precise, high-throughput devices, as well as the accompanying data analysis software to analyze output. Currently, sample purification processes, such as liquid or gas chromatography, are coupled with either mass spectrometry (MS)-based or nuclear magnetic resonance (NMR)-based analytical methods to characterize the metabolite profiles of individual patients. Continually advancing informatics tools allow for the identification, quantification and classification of metabolites to determine which pathways may influence certain pharmaceutical interventions. One of the earliest studies discussing the principle and applications of pharmacometabolomics was conducted in an animal model to look at the metabolism of paracetamol and liver damage. NMR spectroscopy was used to analyze the urinary metabolic profiles of rats pre- and post-treatment with paracetamol. The analysis revealed a certain metabolic profile associated with increased liver damage following paracetamol treatment.  At this point, it was eagerly anticipated that such pharmacometabolomics approaches could be applied to personalized human medicine. Since this publication in 2006, the Pharmacometabolomics Research Network led by Duke University researchers and that included partnerships between centers of excellence in metabolomics, pharmacogenomics and informatics (over sixteen academic centers funded by NIGMS) has been able to illustrate for the first time the power of the pharmacometabolomics approach in informing about treatment outcomes in large clinical studies and with use of drugs that include antidepressants, statins, antihypertensives, antiplatelet therapies and antipsychotics. Totally new concepts emerged from these studies on use of pharmacometabolomics as a tool that can bring a paradigm shift in the field of pharmacology. It illustrated how pharmacometabolomics can enable a Quantitative and Systems Pharmacology approach. 
Pharmacometabolomics has been applied for the treatment of numerous human diseases, such as schizophrenia, diabetes, neural disease, depression and cancer.