Quantitative mass spectrometry imaging of drugs and metabolites: a multiplatform comparison

Abstract

Mass spectrometry imaging (MSI) provides insight into the molecular distribution of a broad range of compounds and, therefore, is frequently applied in the pharmaceutical industry. Pharmacokinetic and toxicological studies deploy MSI to localize potential drugs and their metabolites in biological tissues but currently require other analytical tools to quantify these pharmaceutical compounds in the same tissues. Quantitative mass spectrometry imaging (Q-MSI) is a field with challenges due to the high biological variability in samples combined with the limited sample cleanup and separation strategies available prior to MSI. In consequence, more selectivity in MSI instruments is required. This can be provided by multiple reaction monitoring (MRM) which uses specific precursor ion-product ion transitions. This targeted approach is in particular suitable for pharmaceutical compounds because their molecular identity is known prior to analysis. In this work, we compared different analytical platforms to assess the performance of MRM detection compared to other MS instruments/MS modes used in a Q-MSI workflow for two drug candidates (A and B). Limit of detection (LOD), linearity, and precision and accuracy of high and low quality control (QC) samples were compared between MS instruments/modes. MRM mode on a triple quadrupole mass spectrometer (QqQ) provided the best overall performance with the following results for compounds A and B: LOD 35.5 and 2.5 μg/g tissue, R2 0.97 and 0.98 linearity, relative standard deviation QC <13.6%, and 97–112% accuracy. Other MS modes resulted in LOD 6.7–569.4 and 2.6–119.1 μg/g tissue, R2 0.86–0.98 and 0.86–0.98 linearity, relative standard deviation QC < 19.4 and < 37.5%, and 70–356% and 64–398% accuracy for drug candidates A and B, respectively. In addition, we propose an optimized 3D printed mimetic tissue model to increase the overall analytical throughput of our approach for large animal studies. The MRM imaging platform was applied as proof-of-principle for quantitative detection of drug candidates A and B in four dog livers and compared to LC-MS. The Q-MSI concentrations differed <3.5 times with the concentrations observed by LC-MS. Our presented MRM-based Q-MSI approach provides a more selective and high-throughput analytical platform due to MRM specificity combined with an optimized 3D printed mimetic tissue model. Graphical abstract

Targeted Desorption Electrospray Ionization Mass Spectrometry Imaging for Drug Distribution, Toxicity, and Tissue Classification Studies

“…The increased sensitivity of the DESI-TQ setup for olanzapine and its metabolites stand in contrast to previous work published by Lamont et al who evaluated the LOD for two drug candidates on a similar instrument setup using liver tissue mimetics [ 18 ]. The use of such mimetic-based calibration standards is required for the reported quantitative MSI workflow and does enable to approximate the sensitivity of the different instrument setups delivering a quantitative readout.…”

Defining the Tumor Microenvironment by Integration of Immunohistochemistry and Extracellular Matrix Targeted Imaging Mass Spectrometry

“…Combinations of multiplexed IHC with targeted proteomic access could greatly expand our knowledge of cell subtypes in cancer [61]. Current quantitative approaches in imaging mass spectrometry include on-tissue spotting of standards, internal standards and the mimetic models [62][63][64][65][66]. An additional advancement to this method could be made by utilizing synthesized peptide standards of the ECM components to obtain quantitative information.…”

A review on quantitation-related factors and quantitation strategies in mass spectrometry imaging of small biomolecules

“…10,11 The repeatability is mainly affected by spot-to-spot random errors for sampling/ionization, tissue thickness or topological height differences within a tissue or between tissues, and also MS detection modes. 45,47,55 They were discussed in details in the following based on different MSI techniques.…”