posted on 2021-07-23, 14:05authored byJohn R.
F. B. Connolly, Jordi Munoz-Muriedas, Cris Lapthorn, David Higton, Johannes P. C. Vissers, Alison Webb, Claire Beaumont, Gordon J. Dear
Identifying isomeric metabolites
remains a challenging and time-consuming
process with both sensitivity and unambiguous structural assignment
typically only achieved through the combined use of LC–MS and
NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce
timely and accurate data using a single technique to identify drug
metabolites, including isomers, without the requirement for in-depth
interpretation (cf. MS/MS data) using an automated
computational pipeline by comparison of experimental collision cross-section
(CCS) values with predicted CCS values. An ion mobility enabled Q-Tof
mass spectrometer was used to determine the CCS values of 28 (14 isomeric
pairs of) small molecule glucuronide metabolites, which were then
compared to two different in silico models; a quantum
mechanics (QM) and a machine learning (ML) approach to test these
approaches. The difference between CCS values within isomer pairs
was also assessed to evaluate if the difference was large enough for
unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and
ML-based models and experimentally determined CCS values. The predicted
CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference
in CCS values between isomer pairs. Of the 14 isomeric pairs, only
one (naringenin glucuronides) gave a sufficient difference in CCS
values for the QM model to distinguish between the isomers with some
level of confidence, with the ML model unable to confidently distinguish
the studied isomer pairs. An evaluation of analyte structures was
also undertaken to explore any trends or anomalies within the data
set.