Prescreening was performed using Shinyscreen () , an open source and freely available mass spectral processing software developed in house to extract MS1 data and the associated MS2 events and spectra. The following settings for extraction and automatic quality control were used: coarse precursor m/z error (±0.5 Da), fine precursor m/z error (±2.5 ppm), extracted ion chromatogram (EIC) m/z error (±0.001 Da), retention time tolerance (±0.5 min), MS1 intensity threshold (1.0×10 5 ), MS2 intensity threshold relative to MS1 peak intensity (0.05), signal-to-noise ratio (3), retention time shift tolerance (±0.5 min). Features that passed QC through manual curation including peak shape, peak width, peak intensity, and alignment of the MS1 and MS2 peaks were then analyzed using MetFrag to achieve tentative identifications. Scripts used for this work are available on GitLab. PubChemLite was used as database, available as a local .csv file , to find chemicals that match the exact mass (within 5 ppm) of the suspect pharmaceutical. Both in silico fragmentation (mzabs=0.001, frag_ppm=5) and experimental MS/MS matching through MoNA records (built within MetFrag) were performed to obtain the fragmenter (scoring term 1) and MoNA (scoring term 2) scores. Metadata were also collected for the candidates by querying the database for patent count (scoring term 3), number of PubMed references (scoring term 4), PubChem annotation count (scoring term 5),