High resolution, accurate mass LC-MS/MS technology combines with mass defect filtering software to improve the speed and efficiency of early-stage drug discovery and late-stage drug development.
An integral part of drug discovery and development is the ability to identify drug metabolites formed during various metabolic reaction stages. These metabolites may have intrinsic pharmacological activity or display specific toxicity. Liquid chromatography coupled with mass spectrometry (LC-MS/MS) has become the technique of choice for drug metabolite identification because of its sensitivity and ability to analyze complex mixtures. Although analytical sensitivity has improved enormously over the past decade, detecting and identifying drug metabolites in the presence of very complex biological matrices remains a challenge.
With the availability of high resolution and accurate mass data from novel mass spectrometry technology, researchers can resolve and identify metabolite peaks from background matrix ions. Coupling an orbitrap mass analyzer to a linear ion trap mass spectrometer simplifies metabolite identification because it not only enables rapid, parallel data acquisition with high resolution and mass accuracy, but it also provides structurally diagnostic fragmentation information. Post-data-acquisition processing techniques, such as mass defect filtering (MDF),1,2 remove the vast majority of matrix-related background ions and reduce the number of false-positives. This article describes the power of this approach—specifically, multiple mass defect filters (MMDF)—when used to identify metabolites from high resolution data.
Mass defect filtering
The term “mass defect” originates from the fact that only the carbon isotope 12C has an integer value for its atomic weight (12, or 12.0000 to be precise). Mass defect refers to the difference between the exact mass of an element (or a compound) and its closest integer value (Table 1). The mass defect filter method is a post-data-acquisition filtering technique that is used to discriminate metabolite ions from matrix ions, and is based upon the mass defect of the parent drug and its metabolites. In order to perform such filtering successfully, it is critically important to use high resolution, accurate mass data.
All pharmaceutical compounds have a mass defect that is associated with their metabolites. Based on the molecular weight of the parent compound, one can estimate the range of molecular weight in which potential metabolites will fall. An MDF is then used to filter out all ions that fall outside this expected molecular weight mass range, as well as those that are within the predicted molecular weight range, but exceed the expected mass defect range. This data reduction technique, utilizing the high resolution, accurate mass data to obtain a smaller, more refined data set, allows users to focus solely on species that are potential drug metabolite candidates.
The method can be extended to include multiple mass defect filters in which several filters are used to identify multiple metabolites of interest over a wide range of mass defects. The benefit of MMDF is that metabolites from Phase I and Phase II can be captured specifically and concurrently. It can also help identify metabolites from hydrolysis or N-dealkylation. In the following experiment, multiple mass defect filters were used to identify metabolites in biotransformation studies of the drug irinotecan in rat hepatocytes.
Irinotecan, combined with 5-fluorouracil and leucovorin, is approved by the US Food and Drug Administration (FDA) as a first-line therapy in the treatment of metastatic carcinoma of the colon or rectum.3 It is a water-soluble carbamate prodrug of camptothecin (CPT-11; 7-ethyl-10-[4-(1-piperidino)-1-piperidino] carbonyloxy-camptothecin) and is activated in vivo to SN-38, a potent topoisomerase I inhibitor.3,4 In this study, irinotecan-treated hepatocytes were analyzed using a Thermo Scientific LTQ Orbitrap XL mass spectrometer. Both collision-induced dissociation (CID) and high-energy, collision-induced dissociation (HCD) mass spectra were collected for potential metabolites and MMDFs were then used to process the raw data.
Incubation was carried out using hepatocytes pooled from one male and one female rat, with a cell density of 0.5 million/ml and 10 µm of irinotecan in a 1 ml incubation solution. The solution was shaken overnight and quenched using dry ice, after which 200 µl of chilled acetonitrile was added and the solution was vortexed and centrifuged. From the 1 ml of supernatant, 10 µl was directly injected for each LC-MS/MS run.
Thermo Scientific MetWorks metabolite identification software mines LC-MS/MS data for potential xenobiotic metabolites. It displays the distribution of parent and potential metabolites and calculates accurate masses for parent compounds and potential biotransformations using standard molecular formulae. The software can apply up to six different MDFs and can detect low levels of metabolite ions that are three to four orders of magnitude less abundant than the background matrix ions.
Using MMDF in combination with the various dissociation methods available on LTQ Orbitrap series mass spectrometers, including Higher-energy Collisional Dissociation in the HCD collision cell and Collision-Induced Dissociation ion in the linear ion trap, 13 irinotecan metabolites were identified from the incubation sample with their mass accuracies all less than 3 ppm. All 13 metabolites were found with peak areas less than 1% of that of the parent and are well buried in the original chromatogram (Figure 2a). The most abundant metabolite peaks become visible after applying a single MDF (Figure 2b); however, at this stage, peaks from background matrix ions that are unrelated to the metabolite are still prominent. This is due to the fact that in order to use only one MDF to capture all of the metabolites—including those from the hydrolysis product SN-38—a relatively wide mass defect and molecular weight range has to be used. As a result, a portion of the background ions remains after a single MDF.
Next, four different mass defect filters are applied to the original data (Figure 2c). One of the resulting peaks is a hydroxylation metabolite at 603.2805 m/z that elutes after 8.45 minutes and has an intensity that is less than 15% relative to the base peak in the same spectrum. After MMDF processing, only the hydroxylation peak and trace of the parent peak remain in the spectrum while all the background ions are eliminated.
It should be noted that the discriminating power demonstrated here is a result of both the high resolution, accurate mass capabilities of the mass spectrometer and the use of MMDF.
Multiple mass defect filtering is an invaluable tool for rapidly identifying drug metabolites. The technique is particularly selective because of its high resolution and accurate mass measurement of mass deficiencies, which are highly specific for a parent drug compound. Using multiple mass defect filters is more effective than using a single mass defect filter to uncover metabolites from high-resolution mass spectra, as demonstrated in biotransformation studies of the cancer drug Irinotecan in rat liver cells, where 13 metabolites whose peak areas were less than 1% of that of the parent were identified. Multiple mass defect filters make it possible for metabolites from Phases I and II to be captured specifically and concurrently, even when the products from such processes have mass defects that are significantly different from their parent compound. Recent improvements in MetWorks metabolite identification software maximize the power of MMDF and allow users to accelerate the search for expected and unexpected biotransformations.
1. Zhang, et al., J Mass Spectrom. 2003, 38 (10), 1110-1112.
2. Zhu, et al., Drug Metabolism and Disposition. 2006, 34 (10), 1722-1733.
3. Sanghani, et al., Drug Metabolism and Disposition, 2004, 32(5), 505-511.
4. Haaz, M.C., C. Riché, L.P. Rivory, and J. Robert, Drug Metabolism and Disposition, 1998, 26(8), 769-774.
About the Author
Yingying Huang, PhD, specializes in mass spectrometry applications for life science. Her current focus is hardware and software solutions involving LC-MS technologies that support metabolite identification in drug discovery and drug development.
This article was published in Drug Discovery & Development magazine: Vol. 12, No. 2, February, 2009, pp. 29-31.
Filed Under: Drug Discovery