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Analyzing Trace-Level Impurities of a Pharmaceutical Intermediate Using ,,, an LCQ Deca Ion Trap Mass Spectrometer and the Mass Frontier ,,, Software Package

Figure 3: Proposed chemical structure of (a) the hydroxychloro impurity (b) the dihydro impurity and (c) the dichloro impurity.

Sensitivity and signal-to-noise ratios were such that detection of all four of the compounds was possible in a single Data-Dependent MS2 experiment.

Dynamic Exclusion, a feature of the Data Dependent acquisition software that allows the instrument to acquire MS2 data for an analyte in the presence of more intense co-elutors, was enabled for this analysis. Without this feature, the MS2 spectra of the dihydro impurity would not have been acquired, due to the presence of the more intense co-eluting unmodified intermediate. A Reject List containing the m/zs 391, 392, and 393 was also employed during this analysis to prevent these background ions from triggering MS2 data acquisition. It was not necessary to specify MS2 precursor masses prior to the analysis due to the Data Dependent nature of the acquisition. This eliminated the need to acquire a preliminary MS spectrum and then manually search it for MS2 precursor masses, a process that is both time-consuming and prone to failure when lowlevel analytes are present in the sample.


Figure 4: Comparison of the experimental and theoretical isotope patterns in the protonated molecular ion regions of (a) the hydroxychloro impurity (b) the dihydro impurity and (c) the dichloro impurity. The experimental isotope patterns were obtained from the full-scan MS data and are displayed on the left in the figure. The theoretical isotope patterns, displayed on the right in the figure, were generated by the Isotope Vie
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