Molecular networking (GNPS)

A molecular network was created with the feature based molecular networking workflow (https://ccms‐ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking/) on GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=b3966445abad4658a3cdc63c8198a666). The data were filtered by removing all MS² product ions within ± 17 m/z of the precursor m/z. MS² spectra were window filtered by choosing only the top 6 fragment ions in the ± 50 m/z window throughout the spectrum. The precursor m/z tolerance was set to 0.02 m/z and a MS² product ion m/z tolerance of 0.02 m/z. A network was then created where edges were filtered to have a cosine score above 0.7 and more than 4 matched peaks. Further, edges between two nodes were kept in the network if and only if each of the nodes appeared in each other’s respective top 10 most similar nodes. Finally, the maximum size of a molecular family (i.e., network component) was set to 100, and the lowest scoring edges were removed from molecular families until the molecular family size was below this threshold. The spectra in the network were then searched against GNPS spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0.7 and at least 4 matched peaks.

Curves were generated in blood plasma, urine, and feces samples using code (R language) available at GitHub (see below). The MS¹ features corresponding to the drug and drug metabolites were subsetted from the MS¹ feature table (“quant.csv”) generated for feature‐based molecular networking by linking the chemical annotations from GNPS (based on MS² spectral matching) or the monoisotopic mass (confirmed by manual interpretation of the MS² product ion spectra). MS¹ features detected in urine were normalized by the peak area of creatinine to compensate for variable concentration in random urine collection. MS¹ features detected in fecal samples were normalized by the peak area of stercobilin, a heme catabolite responsible for the brown color of feces, to compensate for variable amounts in material extracted. Blood samples were analyzed without normalization as a fixed amount of volume was extracted in each sample. The results were subsequently plotted using the ggplot2 library in R. Note, nonfinite values were dropped when applying log₁₀‐scaling to the y‐axis to facilitate interpretation as the peak areas for the drug and drug metabolites span multiple orders of magnitude.