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Updated: Sep 10, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
Expanded evaluation of a robust on-line sample cleanup-LC-MS platform suited for new approach methodologies (NAM)
Marcus Kamande1, Julie Bekkhus1, Stian Kogler1
1Section of Chemical Life Science, Department of Chemistry, University of Oslo, Oslo, Norway.
Abstract:
Sample preparation in bioanalysis can require significant numbers of (manual) steps and consumables. Such procedures can be bottlenecks regarding cost, throughput, and greenness. This is also the case for analysis of new approach methodologies (NAMs) such as organoids and organ-on-a-chip systems. In this context, approaches tailored for measuring drugs in NAM-related cell culture media (CCM) are being developed. For mass spectrometry-based analysis, automated filtration/filter backflush solid-phase extraction liquid chromatography AFFL-SPE-LC is utilized. Case studies have shown that the "AFFL" platform allows for vast reductions in sample preparation efforts, as the "self-cleaning" filtration features and SPE conditions remove potentially clogging/contaminating materials from samples of biological origin, e.g., the salt and protein content in CCM. Here, broader demonstrations of the AFFL platform's traits for CCM analysis are provided, employing an extended panel of hydrophobic small-molecule drugs. The investigated AFFL platform shows reproducible chromatographic performance, as well as fit-for-purpose inter-matrix and inter-column robustness. Hundred-scale injections can be performed with satisfactory repeatability (e.g., retention time RSDs < 1%), with limited interference from the various CCM matrices investigated. Practical NAM applications are also demonstrated. The approach has clear advantages in greenness, obtaining an AGREE prep score of 0.71, contrasting the < 0.55 scores of more conventional/commercial approaches. The plastic consumable usage of our approach is 28 g/100 samples, a nearly 20-fold improvement over a previously established benchmark (500 g/100 samples). Taken together, these results demonstrate that AFFL provides a robust, low-waste, and scalable LC-MS-based workflow for chemical analysis of NAM-derived samples.
