Pharmacometabolomics may be used in a predictive manner to determine the correct course of action in regards to a patient about to undergo some type of drug treatment. This involves determining the metabolic profile of a patient prior to treatment, and correlating metabolic signatures with the outcome of a pharmaceutical treatment course. Analysis of a patient's metabolic profile can reveal factors that may contribute to altered drug metabolism, allowing for predictions of the overall efficacy of a proposed treatment, as well as potential drug toxicity risks that may differ from the general population.  This approach has been used to identify novel or previously characterized metabolic biomarkers in patients, which can be used to predict the expected outcome of that patient following treatment with a pharmaceutical compound. One example of the clinical application of pharmacometabolomics are studies that looked to identify a predictive metabolic marker for the treatment of major depressive disorder (MDD)., In a study with antidepressant Sertraline, the Pharmacometabolomics Network illustrated that metabolic profile at baseline of patients with major depression can inform about treatment outcomes. In addition the study illustrated the power of metabolomics for defining response to placebo and compared response to placebo to response to sertraline and showed that several pathways were common to both. In another study with escitalopram citalopram, metabolomic analysis of plasma from patients with MDD revealed that variations in glycine metabolism were negatively associated with patient outcome upon treatment with selective serotonin reuptake inhibitors (SSRIs), an important drug class involved in the treatment of this disease.