MSE with mass defect filtering for in vitro and in vivo metabolite identification

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MSE with mass defect filtering for in vitro and
in vivo metabolite identification

• Kevin P. Bateman, Jose Castro-Perez, etc.
• Rapid Commun. Mass Spectrom. 2007 21 1485-1496

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OUTLINE

• In vitro metabolism
• In vivo metabolism
• Conclusion

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In vitro metabolism

• For in vitro studies, indinavir was used as it is
  known to be extensively metabolized.
• Indinavir was incubated with hepatocytes isolated
  from rat. After 2 H, the incubation were quenched
  and centrifuged.
• LC/MS data was generated using a premier
  quadrupole time-of-flight hybrid mass
  spectrometer in ESI.

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Fig.1 Total ion chromatograms from the analysis
of the Indinavir hepatocyte incubation
(A) no filtering,
(B) generic 40mDa filtering around Indinavirs
fractional mass of 0.3701 (0.3301-0.4101Da) Mass
range 400-800Da
(C) structure-based filtering Fractional mass
filtering 0.3701-0.3278Da 0.3701-0.4022Da Mass
range 52350Da 79050Da
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Fig.2 Fragment ion MSE spectra
C25H31N3O3
C27H37N4O3
(A) Indinavir,
Shift by 15.9921 from 421.2364
(B) the peak at 6.1 min in Fig. 1 corresponding
to hydroxylated Indinavir,
Shift by 91.03 from 465.2885
(C) the peak at 6.6 min in Fig. 1 corresponding
to hydroxylated N-depyridomethylation of Indinavir
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In vivo metabolism

• Metabolite identification from in vivo samples is
  challenging because of the increased complexity
  of the matrices involved and the lower
  concentration of the metabolites
• Radiolabled compounds are usually used for in
  vivo metabolite identification, problem?
• L-006235 was chosen for in vivo study as it was
  previously shown to have several metabolites
  present in rat plasma after dosing the compound.
  Blood sample was collected at different
  post-dosing time. After centrifugation,
  quenching, the plasma was transferred to HPLC.
• The quantification of L-006235 in plasma was
  performed using a
• Q-TRAP LC/MS/MS mass spectrometer.

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(A) The total ion chromatogram for the analysis
of protein-precipitated rat plasma from the 1 h
time point post L-006235 oral administration.
(B) Extracted ion chromatogram (m/z 400600) for
the data shown in A.
(C) Chromatogram generated after mass defect
filtering the data shown in (A) using a window of
10mDa and -30mDa around the fractional mass of
L-006235.
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Mass spectra for M1
(A) Spectrum from the unfiltered data
(B) Spectrum generated after mass defect
filtering. Spectra were generated by combining
three spectra across the peak apex without
background subtraction.
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(A) Spectrum generated by combining the spectra
from the unfiltered data over the time period
from 2 to 6 min and subtracting the spectra
before 2 min and after 6 min.
(B) Spectrum generated by combining the spectra
from the mass defect filtered data over the time
period from 2 to 6 min and subtracting the
spectra before 2 min and after 6 min.
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Spectra of the metabolites detected for L-006235
and their proposed structures
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Conclusion

• The use of mass defect filtering in combination
  with UPLC and MSE is a powerful approach for both
  in vitro and in vivo metabolite identification.
• Calculation of mass ranges and mass defects for
  the compound of interest allows for focused
  filtering of the data set, leading to facile
  visualization of in vitro metabolites
• Careful consideration of the structure ensures
  that unexpected metabolites are not lost by the
  filtering process in vitro and in vivo studies
• Collection of fragmentation data through the use
  of MSE data acquisition allows for rapid
  assessment of metabolite structures, and this is
  further facilitated by UPLC.