In vivo drug metabolite identification in preclinical ADME studies by means of UPLC/TWIMS/high resolution-QTOF MS^E and control comparison: cost and benefit of vehicle-dosed control samples

Abstract

• 1. Liquid chromatography (LC)–high resolution mass spectrometry (HRMS) techniques proved to be well suited for the identification of predicted and unexpected drug metabolites in complex biological matrices.

• 2. To efficiently discriminate between drug-related and endogenous matrix compounds, however, sophisticated postacquisition data mining tools, such as control comparison techniques are needed. For preclinical absorption, distribution, metabolism and excretion (ADME) studies that usually lack a placebo-dosed control group, the question arises how high-quality control data can be yielded using only a minimum number of control animals.

• 3. In the present study, the combination of LC-traveling wave ion mobility separation (TWIMS)-HRMS^E and multivariate data analysis was used to study the polymer patterns of the frequently used formulation constituents polyethylene glycol 400 and polysorbate 80 in rat plasma and urine after oral and intravenous administration, respectively.

• 4. Complex peak patterns of both constituents were identified underlining the general importance of a vehicle-dosed control group in ADME studies for control comparison. Furthermore, the detailed analysis of administration route, blood sampling time and gender influences on both vehicle peak pattern as well as endogenous matrix background revealed that high-quality control data is obtained when (i) control animals receive an intravenous dose of the vehicle, (ii) the blood sampling time point is the same for analyte and control sample and (iii) analyte and control samples of the same gender are compared.

Keywords:

• Absorption
• distribution
• formulation constituents
• high resolution mass spectrometry
• metabolism and excretion
• multivariate data analysis
• orthogonal projections to latent structures-discriminant analysis (OPLS-DA)
• polyethylene glycol 400
• polysorbate 80
• principal component analysis

Acknowledgements

The authors wish to thank Julia Schmid for conducting the animal studies, Annamaria Grimminger for supporting sample preparation and Stefan Blech for helpful discussions. In addition, we are grateful to Simon Cubbon, Jayne Kirk and David Eatough (Waters, Manchester, UK) for their excellent assistance in UNIFI data analysis.

Declaration of interest

The authors report no conflict of interests and take the responsibility for the content of the publication.

Supplementary material available online Supplementary Figures S1–S8, and Tables S1 and S2.