Pharmacometabolomics is thought to provide information that complements that gained from other omics, namely genomics, transcriptomics, and proteomics.  Looking at the characteristics of an individual down through these different levels of detail, there is an increasingly more accurate prediction of a person's ability to respond to a pharmaceutical compound. The genome, made up of 25 000 genes, can indicate possible errors in drug metabolism; the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed; and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions. Pharmacometabolomics complements the omics with direct measurement of the products of all of these reactions, but with perhaps a relatively smaller number of members: that was initially projected to be approximately 2200 metabolites, but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is to more closely predict or assess the response of an individual to a pharmaceutical compound, permitting continued treatment with the right drug or dosage depending on the variations in their metabolism and ability to respond to treatment.
Pharmacometabolomic analyses, through the use of a metabolomics approach, can provide a comprehensive and detailed metabolic profile or “metabolic fingerprint” for an individual patient. Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations, providing a more realistic depiction of disease phenotypes. This approach can then be applied to the prediction of response to a pharmaceutical compound by patients with a particular metabolic profile.  Pharmacometabolomic analyses of drug response are often coupled or followed up with pharmacogenetics studies. Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms) within patients that may contribute to altered drug responses and overall outcome of a certain treatment. The results of pharmacometabolomics analyses can act to “inform” or “direct” pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level. This concept has been established with two seminal publications from studies of antidepressants  serotonin reuptake inhibitors  where metabolic signatures were able to define pathway implicated in response to the antidepressant and that lead to identification of genetic variants within a key gene within highlighted pathway as being implicated in variation in response. These genetic variants were not identified through genetic analysis alone and hence illustrated how metabolomics can guide and inform genetic data.