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Drug Metabolism

During drug discovery and drug development, it is important to establish how the body metabolizes a drug; therefore rapid identification of metabolites from in vitro or in vivo samples becomes essential [81]. The classic way to perform metabolic studies is to use 14C or 3H radiolabeled drugs. Liquid chromatography with online radioactivity detection is applied to collect the metabolites, which after further purification are identified by mass spectrometry and nuclear magnetic resonance spectroscopy (Fig. 1.38). One of the advantages of the radiolabeled parent drug is that the response of the radioactivity detector is directly proportional to the amount of metabolite. Also due to the high specificity of the radioactivity detector urine or plasma can be directly injected onto the LC system.

Metabolic stability of drugs has become an important parameter in drug discovery and hundreds of samples can be rapidly generated using in vitro systems such as hepatocytes and microsomes. For structural elucidation, nuclear magnetic resonance spectroscopy is the technique of choice, but it does not allow high throughput analysis and sensitivity is still in the microgram range. LC-MS has therefore become the technique of choice. Ideally one would require a mass spectrometer with fast acquisition capabilities in positive and negative mode, selective scan modes, multiple stage MS and accurate mass measurements. Such an ideal instrument is currently not available and therefore drug metabolism studies require multi-instrument strategies.

Fig. 1.38 LC separation with radioactivity detection of an urine sample. The response of the various peaks is directly proportional to the amount of metabolites present in the sample. Peaks HUI-HU3: human urine metabolites.

When working with non-radiolabeled drugs the major challenge is to find metabolites in the biological matrices. Because the enzymes responsible for metabolism are quite well characterized metabolic changes can partially be predicted. For example hydroxylation of the parent drug is in many cases the principal metabolic pathway. From a mass spectrometric point of view it results in an increase of 16 units in the mass spectrum. In the full-scan mode an extracted ion current profile can be used to screen for potential metabolites. In a second step a product ion spectrum is recorded for structural interpretation. Ideally, one would like to obtain relative molecular mass information and the corresponding product ion spectrum in the same LC-MS run. This information can be obtained by data dependant acquisition (DDA), as illustrated in Fig. 1.39.

In this case the survey scan was set as a full scan and the dependent scan as a product ion scan. The problem with data dependent acquisition is to determine the selection criteria. In most cases the system picks up the most abundant ion in the full scan spectrum. An inclusion list with masses of potential metabolites or exclusion list of known interferences significantly improves the procedure. In the example shown in Fig. 1.39, a procedure called dynamic background subtraction (DBS) was applied. This procedure considers chromatographic peak shapes and monitors not the most abundant signal in the spectrum but the largest increase of an ion in a spectrum. The advantage is that once a signal of a peak has

Fig. 1.39 LC-MS data dependent analysis of vinpocetin in rat urine using dynamic background substraction (DBS) on a triple quadrupole linear ion trap. (A) Full scan MS (survey scan) trace. (B) Enhanced product ion scan (dependent scan). The major peak at 3.9 min corresponds to apovinpocetin, the minor one at 2.9 min to the hydroxylation product of apovinpocetin (m/z 339).

Fig. 1.39 LC-MS data dependent analysis of vinpocetin in rat urine using dynamic background substraction (DBS) on a triple quadrupole linear ion trap. (A) Full scan MS (survey scan) trace. (B) Enhanced product ion scan (dependent scan). The major peak at 3.9 min corresponds to apovinpocetin, the minor one at 2.9 min to the hydroxylation product of apovinpocetin (m/z 339).

Fig. 1.40 Schematic of online LC-MS analysis combined with fraction collection into 96-well plate. Depending on the online MS data, further MS experiments are performed with chip-based infusion at 200 nL min-1.

reached its maximum it switches automatically to the next mass. This is particularly important with co-eluting peaks of different intensities, as illustrated in Fig. 1.39B. It is then possible to obtain a good product ion spectrum of the small peak eluting at 4.0 min (m/z 339). In drug metabolism not only is the sensitivity of the mass spectrometer important but the selectivity is also crucial, particularly when working with plasma samples.

Most methods of metabolite identification are done with online LC-MS. As mentioned earlier there is no ideal mass spectrometer for this type of work and the sample has to be reanalyzed several times on different types of mass spectrometer. The consequence is that metabolic investigation is often time-consuming. A concept has been described by Staack et al. [82] (Fig. 1.40) where, during the LC-MS run, fractions are collected onto a 96-well plate.

Either the information obtained during the data-dependent acquisition is sufficient or a fraction of interest can be re-analyzed by chip-based infusion at a flow rate ca. 200 nl min-1. Due to the miniaturization sample consumption is very low (typically 1-3 ml) and acquisition time is no longer critical. Therefore various MS experiments can be performed on various instruments, including MSn and accurate mass measurements. An additional advantage is that the eluent can be removed and the infusion solvent can be optimized for positive or negative ion detection or for deuterium exchange measurements.

Advances in high resolution mass analyzers (TOF, FT-ICR, orbitrap) have greatly improved the detection and identification of metabolites based on accurate mass measurements. In single MS mode accurate mass determination is mainly used to differentiate between isobaric ions. Combined with LC-MS, it allows the detection of predicted metabolites by performing extracted ion current profiles

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