Pharmacometabolomics
Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from
Goals of Pharmacometabolomics
Pharmacometabolomics is thought to provide information that complements that gained from other
Pharmacometabolomic analyses, through the use of a
History
Although the applications of pharmacometabolomics to
Personalized Medicine
As
Current Applications
Predicting treatment outcome
Metabotype informs about treatment outcomes
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
The second major application of pharmacometabolomics is the analysis of a patient's metabolic profile following the administration of a specific therapy. This process is often secondary to a pre-treatment metabolic analysis, allowing for the comparison of pre- and post-treatment
Metabolite Quantification and Analysis
In order to identify and quantify metabolites produced by the body, various detection methods have been employed. 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.
Nuclear Magnetic Resonance Spectroscopy
NMR spectroscopy has been utilized for the analysis of biological samples since the 1980s, and can be used as an effective technique for the identification and quantification of both known and unknown metabolites. For details on the principles of this technique, see NMR spectroscopy. In pharmacometabolomics analyses, NMR is advantageous because minimal sample preparation is required. Isolated patient samples typically include blood or urine due to their minimally-invasive acquisition, however, other fluid types and solid tissue samples have also been studied with this approach.[38] Due to the minimal preparation of samples before analysis, samples can be potentially fully recovered following NMR analysis (If samples are kept refrigerated to avoid degradation). This permits samples to be repeatedly analysed with extremely high levels of reproducibility, as well as maintaining precious patient samples for an alternative analysis. The high reproducibility and precision of NMR, coupled with relatively fast processing time (greater than 100 samples per day), makes this process a relatively high-throughput form of sample analysis. 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.[38] Furthermore, the initial instrument costs are extremely high, for both NMR and MS equipment.[1]
Mass Spectrometry
An alternative approach to the identification and quantification of patient samples is through the use of mass spectrometry. 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. The mass spectrometry (MS) approach is typically coupled to gas chromatography (GC), in GC-MS or liquid chromatography (LC), in LC-MS, which aid in initially separating out the metabolite components within complex sample mixtures, and can allow for the isolation of particular metabolite subsets for analysis. GC-MS can provide relatively precise quantification of metabolites, as well as chemical structural information that can be compared to pre-existing chemical libraries.[1] GC-MS can be conducted in a relatively high-throughput manner (greater than 100 samples per day) with greater detection sensitivity than NMR analysis. A limitation of GC-MS for this application, however, is that processed metabolite components must be readily volatilized for sample processing.
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. For both GC-MS and LC-MS there are limitations in the reproducibility of metabolite quantification.[1] Furthermore, sample processing for downstream mass spectrometry (MS) analysis is much more intensive than in NMR application, and results in the destruction of the original sample (via trypsin digestion).[1]
Following identification and quantification of metabolites in individual patient samples, NMR and mass spectrometry (MS) output is compiled into a dataset. These datasets include information on the identity and levels of individual metabolites detected within processed samples, as well as characteristics of each metabolite during the detection process (e.g. mass-to-charge ratios for mass spectrometry (MS)-based analysis). Multiple datasets can be created and compiled into large databases for individual patients in order to monitor varying metabolic profiles over a treatment course (i.e. pre- and post-treatment profiles). Each database is then processed through a type of informatics platform with software designed to characterize and analyze the data to generate an overall metabolic profile for the patient. To generate this overall profile, computational programs are designed to:
- identify metabolic disease signatures[1]
- assess treatment class (pre- or post-treatment)[1]
- identify compounds present in a patient sample that may alter drug response, or be caused by a therapy[1]
- identify metabolite variables and interactions among these variables[1]
- map identified variables to known metabolic and biochemical pathways[1]
Limitations
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.[39] However, as long as meaningful metabolic signatures can be elucidated to create baseline values, there still exists a possible means of comparison.[10]
Issues surrounding the measurement of metabolites in an individual can also arise from the methodology of metabolite detection, and there are arguments both for and against NMR and mass spectrometry (MS). Other limitations surrounding metabolite analysis include the need for proper handling and processing of samples, as well as proper maintenance and calibration of the analytical and computational equipment. These tasks require skilled and experienced technicians, and potential instrument repair costs due to continuous sample processing can be costly. The cost of the processing and analytical platforms alone is very high, making it difficult for many facilities to afford pharmacometabolomics-based treatment analyses.
Implications for Health Care
Pharmacometabolomics may decrease the burden on the healthcare system by better gauging the correct choice of treatment drug and dosage in order to optimize the response of a patient to a treatment. Hopefully, this approach will also ultimately limit the number of
See also
- Pharmacogenetics
- Pharmacogenomics
- Personal genomics
- Drug development
- Metabolism
- Personalized medicine
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External links
- Genomics Directory: A one-stop biotechnology resource center for bioentrepreneurs, scientists, and students
- Pharmacometabolomics Research Network (PMRN)
- Human Metabolome Project:Project supported by Genome Alberta and Genome Canada
- Metabolomics Society: An organization dedicated to promoting the growth, use and understanding of metabolomics in the life sciences.
- Biological Magnetic Resonance Data Bank:A Repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules
- Scripps Center for Metabolomics and Mass Spectrometry