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A computational drug metabolite detection using the stable isotopic mass-shift filtering with high resolution mass spectrometry in pioglitazone and flurbiprofen.

Uchida M, Kanazawa M, Ogiwara A, Sezaki H, Ando A, Miyamoto Y - Int J Mol Sci (2013)

Bottom Line: With high resolution MS, the approach became more accurate.The approach detected two unexpected metabolites in pioglitazone, i.e., the hydroxypropanamide form and the aldehyde hydrolysis form, which other approaches such as metabolite-biotransformation list matching and mass defect filtering could not detect.We demonstrated that the approach using computational alignment and stable isotopic mass-shift filtering has the ability to identify drug metabolites and is useful in drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Toxicology and Pharmacokinetics Laboratories, Pharmaceutical Research Laboratories, Toray Industries, Inc., 6-10-1 Tebiro, Kamakura, Kanagawa 248-8555, Japan. Youhei_Miyamoto@nts.toray.co.jp.

ABSTRACT
The identification of metabolites in drug discovery is important. At present, radioisotopes and mass spectrometry are both widely used. However, rapid and comprehensive identification is still laborious and difficult. In this study, we developed new analytical software and employed a stable isotope as a tool to identify drug metabolites using mass spectrometry. A deuterium-labeled compound and non-labeled compound were both metabolized in human liver microsomes and analyzed by liquid chromatography/time-of-flight mass spectrometry (LC-TOF-MS). We computationally aligned two different MS data sets and filtered ions having a specific mass-shift equal to masses of labeled isotopes between those data using our own software. For pioglitazone and flurbiprofen, eight and four metabolites, respectively, were identified with calculations of mass and formulas and chemical structural fragmentation analysis. With high resolution MS, the approach became more accurate. The approach detected two unexpected metabolites in pioglitazone, i.e., the hydroxypropanamide form and the aldehyde hydrolysis form, which other approaches such as metabolite-biotransformation list matching and mass defect filtering could not detect. We demonstrated that the approach using computational alignment and stable isotopic mass-shift filtering has the ability to identify drug metabolites and is useful in drug discovery.

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Related in: MedlinePlus

Representative MS/MS spectra of eight pioglitazone metabolites and their estimated chemical structures. Precursor ions were m/z 315 (A), 389 (B), 373 (C), 272 (D), 373 (E), 373 (F), 332 (G) and 355 (H) with the positive ion mode. Each arrow indicates a possible site of fragmentation, with the corresponding ion.
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f6-ijms-14-19716: Representative MS/MS spectra of eight pioglitazone metabolites and their estimated chemical structures. Precursor ions were m/z 315 (A), 389 (B), 373 (C), 272 (D), 373 (E), 373 (F), 332 (G) and 355 (H) with the positive ion mode. Each arrow indicates a possible site of fragmentation, with the corresponding ion.

Mentions: Structural analysis was carried out using a product ion scanning against ions regarded as metabolites by triple quadrupole mass spectrometry. In pioglitazone, precursor ions were set at m/z 315, 389, 373, 272, 332 and 355, and collisionally dissociated with nitrogen gas being set at 30 eV. In flurbiprofen, precursor ions were set at m/z 435, 275, 259 and 419, and dissociated in the same way as pioglitazone. Product ion spectra and estimated chemical structures of these precursors are shown in Figures 6 and 7. In pioglitazone, major product ions from the dissociation of O-alkyl chain in the middle of its structure were found in all of the metabolites (Figure 6). In flurbiprofen, typical product ions from the dissociation of acyl-glucuronide were found in M1 and M4 (Figure 7A,D). Based on these results, eight and four metabolites were proposed in pioglitazone and flurbiprofen, respectively. Additionally, values of mass defects from parent were calculated for each metabolite. These results are summarized in Table 3. With two other approaches, a metabolite-biotransformation list matching and a mass defect filtering, we could not find any other metabolite except those found by the mass-shift filtering in the study. The mass-shift detection captured several unexpected metabolites, M1 and M2 in pioglitazone, which the two other approaches did not detect. A metabolite-biotransformation list matching and a mass defect filtering are both widely used and facilitate metabolite identifications in drug discovery [8,9]. However, usability of the metabolite-biotransformation list is sometimes limited because of the unexpected metabolic pathways of drug candidates [8,9]. Mass defect filtering sometimes also led to lost metabolites because some have unexpected high mass defects from their parents [11]. In pioglitazone, M1, the hydroxypropanamide form, and M2, the aldehyde hydrolysis form were both considered to be metabolites and also have relatively high mass defects, 43.6 and −10.2 mDa. The results of the study showed that the approach using the stable isotopic mass-shift filtering directly detects signals derived from labeled isotopes and is not influenced by the metabolic complexity. In this study, we also confirmed no existence of particular metabolites that lost the labeled site via chemical and biological desorption at the phenyl group of pioglitazone-d4 and the methyl group of furbiprofen-d3 using the software by other mass-shift parameters; 3 and 2 Da for pioglitazone, and 1 and 2 Da for flurbiprofen. It would be more suitable to label the parent drug at the core frame of its structure with stable isotopes, e.g., carbon-13, to avoid loss of labels during metabolism [18].


A computational drug metabolite detection using the stable isotopic mass-shift filtering with high resolution mass spectrometry in pioglitazone and flurbiprofen.

Uchida M, Kanazawa M, Ogiwara A, Sezaki H, Ando A, Miyamoto Y - Int J Mol Sci (2013)

Representative MS/MS spectra of eight pioglitazone metabolites and their estimated chemical structures. Precursor ions were m/z 315 (A), 389 (B), 373 (C), 272 (D), 373 (E), 373 (F), 332 (G) and 355 (H) with the positive ion mode. Each arrow indicates a possible site of fragmentation, with the corresponding ion.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3821582&req=5

f6-ijms-14-19716: Representative MS/MS spectra of eight pioglitazone metabolites and their estimated chemical structures. Precursor ions were m/z 315 (A), 389 (B), 373 (C), 272 (D), 373 (E), 373 (F), 332 (G) and 355 (H) with the positive ion mode. Each arrow indicates a possible site of fragmentation, with the corresponding ion.
Mentions: Structural analysis was carried out using a product ion scanning against ions regarded as metabolites by triple quadrupole mass spectrometry. In pioglitazone, precursor ions were set at m/z 315, 389, 373, 272, 332 and 355, and collisionally dissociated with nitrogen gas being set at 30 eV. In flurbiprofen, precursor ions were set at m/z 435, 275, 259 and 419, and dissociated in the same way as pioglitazone. Product ion spectra and estimated chemical structures of these precursors are shown in Figures 6 and 7. In pioglitazone, major product ions from the dissociation of O-alkyl chain in the middle of its structure were found in all of the metabolites (Figure 6). In flurbiprofen, typical product ions from the dissociation of acyl-glucuronide were found in M1 and M4 (Figure 7A,D). Based on these results, eight and four metabolites were proposed in pioglitazone and flurbiprofen, respectively. Additionally, values of mass defects from parent were calculated for each metabolite. These results are summarized in Table 3. With two other approaches, a metabolite-biotransformation list matching and a mass defect filtering, we could not find any other metabolite except those found by the mass-shift filtering in the study. The mass-shift detection captured several unexpected metabolites, M1 and M2 in pioglitazone, which the two other approaches did not detect. A metabolite-biotransformation list matching and a mass defect filtering are both widely used and facilitate metabolite identifications in drug discovery [8,9]. However, usability of the metabolite-biotransformation list is sometimes limited because of the unexpected metabolic pathways of drug candidates [8,9]. Mass defect filtering sometimes also led to lost metabolites because some have unexpected high mass defects from their parents [11]. In pioglitazone, M1, the hydroxypropanamide form, and M2, the aldehyde hydrolysis form were both considered to be metabolites and also have relatively high mass defects, 43.6 and −10.2 mDa. The results of the study showed that the approach using the stable isotopic mass-shift filtering directly detects signals derived from labeled isotopes and is not influenced by the metabolic complexity. In this study, we also confirmed no existence of particular metabolites that lost the labeled site via chemical and biological desorption at the phenyl group of pioglitazone-d4 and the methyl group of furbiprofen-d3 using the software by other mass-shift parameters; 3 and 2 Da for pioglitazone, and 1 and 2 Da for flurbiprofen. It would be more suitable to label the parent drug at the core frame of its structure with stable isotopes, e.g., carbon-13, to avoid loss of labels during metabolism [18].

Bottom Line: With high resolution MS, the approach became more accurate.The approach detected two unexpected metabolites in pioglitazone, i.e., the hydroxypropanamide form and the aldehyde hydrolysis form, which other approaches such as metabolite-biotransformation list matching and mass defect filtering could not detect.We demonstrated that the approach using computational alignment and stable isotopic mass-shift filtering has the ability to identify drug metabolites and is useful in drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Toxicology and Pharmacokinetics Laboratories, Pharmaceutical Research Laboratories, Toray Industries, Inc., 6-10-1 Tebiro, Kamakura, Kanagawa 248-8555, Japan. Youhei_Miyamoto@nts.toray.co.jp.

ABSTRACT
The identification of metabolites in drug discovery is important. At present, radioisotopes and mass spectrometry are both widely used. However, rapid and comprehensive identification is still laborious and difficult. In this study, we developed new analytical software and employed a stable isotope as a tool to identify drug metabolites using mass spectrometry. A deuterium-labeled compound and non-labeled compound were both metabolized in human liver microsomes and analyzed by liquid chromatography/time-of-flight mass spectrometry (LC-TOF-MS). We computationally aligned two different MS data sets and filtered ions having a specific mass-shift equal to masses of labeled isotopes between those data using our own software. For pioglitazone and flurbiprofen, eight and four metabolites, respectively, were identified with calculations of mass and formulas and chemical structural fragmentation analysis. With high resolution MS, the approach became more accurate. The approach detected two unexpected metabolites in pioglitazone, i.e., the hydroxypropanamide form and the aldehyde hydrolysis form, which other approaches such as metabolite-biotransformation list matching and mass defect filtering could not detect. We demonstrated that the approach using computational alignment and stable isotopic mass-shift filtering has the ability to identify drug metabolites and is useful in drug discovery.

Show MeSH
Related in: MedlinePlus