GC-MS Analysis of pharmaceutical drugs in blood samples using Deconvolution
GC-MS Analysis of pharmaceutical drugs in blood samples using Deconvolution
For over 50 years, mass spectrometers have been used as gas chromatographic (GC) detectors. During this time, the capability and reliability of the mass spectrometer (MS), and the reproducibility of analytical data, has increased tremendously.
A modern GC-MS system is capable of performing an analysis on a complex matrix of 25 compounds in less than 30 minutes. Both the qualitative and quantitative information is provided to the analyst in a short period of time. The GC-MS data system is used to process each GC peak found during the analysis and to compare the mass spectrum from each GC peak to a mass spectrum from a library of standards stored in a database.
A GC-MS analysis can fail, however, when acquired spectra are “contaminated” with mass spectral peaks that arise from co-eluting compounds and ionization chamber contaminants. These peaks can pose a serious problem for automated identification methods where they can cause identifications to be missed by reducing the spectrum comparison factor below some pre-set identification threshold. In addition, the presence of such peaks in a spectrum adds to the risk of making false identifications.
Since the inception of GC-MS, there has been a continuing interest in extracting “pure” component spectra from complex chromatograms. A deconvolution software – AMDIS (Automatic Mass Spectral Deconvolution and Identification System) in our case - is used to automatically find any of a set of target compounds in a gas chromatography / mass spectrometry (GC-MS) spectrum. The basic steps are the following:
- AMDIS first deconvolutes the GC-MS data file to find all of the separate components
- Each of these components is then compared against a library (or libraries) of target compounds
- The match factor between the target spectrum and the deconvoluted component spectrum is then reported
- If this match factor is above a user set value (for example 80%), then there is positive identification.
In our case, a blood sample was analyzed by GC-MS and AMDIS for compounds of pharmacological interest. Several compounds were identified (see video below) amongst them: venlafaxine (antidepressant, Effexor), diazepam (anti-anxiety, Valium, Diastat) and levomepromazine (neuroleptic, Nosinan, Nozinan, Levoprome).
The match factor for a positive identification was set to above 80%. The actual analysis time was 25 min (GC-MS) and 5 min (deconvolution process using AMDIS and several spectra libraries such as NIST, AAFSDRUG, SWGDRUG, WILEY, MPW2007).
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References
- D. Harvey, “Modern Analytical Chemistry”, McGraw-Hill Companies Inc., 2000
- R.D. Brown, “Introduction to Chemical Analysis”, McGraw-Hill Companies Inc, 1982
- R. G. Dromey et al., Anal. Chem., 48, 1368-1375, (1976)
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