High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification

Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.


Figure S1 .
Figure S1.(A) Total ion current chromatogram (TIC) and (B-N) selected MS1 spectra presumably reflecting polyethoxylated castor oil of a user of the immunosuppressive drug cyclosporine A.

Figure S2 .
Figure S2.Pareto-scaled (A) scores and (B) loadings plots for unsupervised principal component analysis of unnormalized MS1-level feature data of 570 stable kidney transplant recipients.In pane A, users of cyclosporine A are indicated in black and nonusers are indicated in light grey, as reflect analytically-confirmed exposure statuses.Regarding the latter, this analytical conformation concerns a level 1 metabolite identification according to the Metabolomics Standards Initiative (L.W. Sumner,et al. Metabolomics, 2007, 3, 211-221)  for which we used a cyclosporine A reference standard (Sigma Aldrich, Cat.No. PHR1092; CID 49867938) and a commercial software tool (i.e., SCIEX PeakView, version 2.2.0.11391) using previously-published settings (F.Klont, et al.J Clin Epidemiol, 2021, 135,  10-16).

Figure S3 .
Figure S3.Scatter plots presenting unnormalized LC-MS peak areas of two MS1-level signals associated with (presumed) exposure to cyclosporine A capsules, as is presented on the y-axis, and usage of cyclosporine A, as is presented on the x-axis.With respect to the two signals,(A, C) the first one reflects the most intense peak presented in Fig.S1K(peak 'i' in Fig.S1A), and (B,D) the second one reflects the most intense peak presented in Fig.S1F(peak 'd' in Fig.S1A).With respect to the exposure status, this reflects (A,B) self-reported drug use or (C,D) analytically-confirmed presence of the drug of interest in the corresponding urine sample.In case of cyclosporine A, such analytical conformation concerns a level 1 metabolite identification according to the Metabolomics Standards Initiative (L.W.Sumner, et al.Metabolomics, 2007, 3, 211-221)  for which we used a cyclosporine A reference standard (Sigma Aldrich, Cat.No. PHR1092; CID 49867938) and a commercial software tool (i.e., SCIEX PeakView, version 2.2.0.11391) using previously-published settings (F.Klont, et al.J Clin  Epidemiol, 2021, 135, 10-16).

Figure S4 .
Figure S4.(A) Total ion current chromatogram (TIC) and (B-I) selected MS1 spectra reflecting polyethylene glycol of a user of the short-acting benzodiazepine drug temazepam.

Figure S7 .
Figure S7.Scatter plots presenting unnormalized LC-MS peak areas of two MS1-level signals associated with (presumed) exposure to temazepam, as is presented on the y-axis, and usage of temazepam, as is presented on the x-axis.With respect to the two signals,(A, C) the first one reflects the most intense peak presented in Fig.S4F(peak 'e' in Fig.S4A), and (B,D) the second one reflects the most intense peak presented in Fig.S4H(peak 'g' in Fig.S4A).With respect to the exposure status, this reflects (A,B) self-reported drug use or (C,D) analytically-confirmed presence of the drug of interest in the corresponding urine sample.In case of temazepam, such analytical conformation concerns the identification of temazepam glucuronide (CID 76973794) which reflects a level 2 metabolite identification according to the Metabolomics Standards Initiative (L.W.Sumner, et al.Metabolomics, 2007, 3, 211-221)  for which we employed a commercial spectral library (i.e., SCIEX 'Forensic', version 1.1) and a commercial software tool (i.e., SCIEX PeakView, version 2.2.0.11391) using previously-published settings (F.Klont, et al.J Clin Epidemiol, 2021, 135, 10-16).

Figure S8 .
Figure S8.(A) Total ion current chromatogram (TIC) and (B) a selected MS1 spectrum of a selfdeclared user of the laxative agent macrogol 3350.

Figure S9 .
Figure S9.Scatter plots presenting unnormalized LC-MS peak areas of two MS1-level signals associated with (presumed) exposure to macrogol 3350, as is presented on the y-axis, and selfreported usage of macrogol 3350, as is presented on the x-axis.With respect to the two signals, both are among the most intense peaks presented in Fig. S8B.

Figure S23 .
Figure S23.Scatter plots presenting MLR-(top), TAS-(middle), and Median-normalized (bottom) feature data of the MS1-level precursors on the y-axis and the results of previously-conducted routine measurements (in an ISO 15189 certified laboratory) on the x-axis for (A) the endogenous muscle breakdown product creatinine and (B) the exogenous phase I nicotine metabolite cotinine.Plots on the left represent results obtained by directly normalizing the data after feature finding whereas normalization factors of the plots on the right where obtained after first filtering data using the '80% rule' as described by Bijlsma et al. (Analytical Chemistry,2006, 78,[567][568][569][570][571][572][573][574].