[1][2] It refers to the direct measurement of metabolites in an individual's bodily fluids, in order to predict or evaluate the metabolism of pharmaceutical compounds, and to better understand the pharmacokinetic profile of a drug.
[8] All three approaches require the quantification of metabolites found in bodily fluids and tissue, such as blood or urine, and can be used in the assessment of pharmaceutical treatment options for numerous disease states.
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.
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.
[1] Continually advancing informatics tools allow for the identification, quantification and classification of metabolites to determine which pathways may influence certain pharmaceutical interventions.
[1] 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.
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.
This field is currently being employed in a predictive manner to determine the potential responses of therapeutic compounds in individual patients, allowing for more customized treatment regimens.
It is anticipated that such pharmacometabolomics approaches will lead to the improved ability to predict an individual's response to a compound, the efficacy and metabolism of it as well as adverse or off-target effects that may take place in the body.
This allows for the identification of the metabolic processes and pathways that are being altered by the treatment either intentionally as a designated target of the compound, or unintentionally as a side effect.
Most often, these involve the use of nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS), providing universal detection, identification and quantification of metabolites in individual patient samples.
Although both processes are used in pharmacometabolomic analyses, there are advantages and disadvantages for using either nuclear magnetic resonance (NMR) spectroscopy- or mass spectrometry (MS)-based platforms in this application.
One disadvantage of this technique is the relatively poor metabolite detection sensitivity compared to MS-based analysis, leading to a requirement for greater initial sample volume.
This approach offers excellent precision and sensitivity in the identification, characterization and quantification of metabolites in multiple patient sample types, such as blood and urine.
LC-MS initially separates out the components of a sample mixture based on properties such as hydrophobicity, before processing them for identification and quantification by mass spectrometry (MS).
Overall, LC-MS is an extremely flexible method for processing most compound types in a somewhat high-throughput manner (20-100 samples a day), also with greater sensitivity than NMR analysis.
To generate this overall profile, computational programs are designed to: Along with the emerging diagnostic capabilities of pharmacometabolomics, there are limitations introduced when individual variability is looked at.
The ability to determine an individual's physiological state by measurement of metabolites is not contested, but the extreme variability that can be introduced by age, nutrition, and commensal organisms suggest problems in creating generalized pharmacometabolomes for patient groups.