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Gaining new insights from old data

Kirstine Nielson and colleagues from Professor Mogens Johannsen’s group at the Department of Forensic Medicine at Aarhus University in Denmark wanted to find out if it was possible to discover new information about drug metabolism and toxicology by retrospectively analyzing old UPLC-HR-TOFMS data. According to their recently published study, it is – if the data are robust and reliable.

The team carried out an untargeted metabolomics study on data from ultra-performance liquid chromatography high-resolution time-of-flight mass spectrometry (UPLC-HR-TOFMS). Their goals were to detect and profile metabolites of the illicit drug 3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) and identify changes in endogenous metabolites caused by the drug.

The data they analyzed had been collected over a two-year period during routine toxicological screening of blood from people suspected of driving under the influence of drugs. Their forensic screening method uses full scan time-of-flight measurements, which as opposed to targeted MRM-based methods, generate a lot of other data that could be potentially used in retrospective analyses.

The researchers used various methods to compare samples in which MDMA had been found with MDMA-free control samples. Total ion chromatogram data were preprocessed and normalized before undergoing multivariate analysis and statistical calculations. Metabolites were searched against various databases, and their identities were confirmed by matching the m/z-values, fragments and retention times to authentic standards.

Although the data came from samples that had been analyzed over a period of two years, the retention times varied by only ± 10 seconds, indicating that the UPLC-HR-TOFMS method was very robust and reliable. They based the success of their analysis methods on the ability to identify MDMA and up-regulation of its phase I and II metabolites in the MDMA user group. The team identified several endogenous metabolites that were up- or down-regulated differently between the groups. A network analysis of these metabolites revealed a pattern suggesting that MDMA exposure causes “increased energy demand, ASH-mediated neurotoxicity, and a different tryptophan metabolism”.

These data admittedly come from a heterogeneous collection with many unknown variations that might affect the results, including drug dose, time between drug dosing and blood sampling, rate of drug turnover, food intake, and activity level of the individuals. The team acknowledged the unconventional nature of their study and pointed out the importance of verifying the results in a controlled setting.

But despite these possible challenges, the technique does hold promise for future use in metabolomics studies and could help identify metabolites of new designer drugs for which little or no data are available. Many forensic laboratories routinely perform toxicology screening of blood by HR-TOFMS, and as long as their methods are robust and reliable, the data they generate day-to-day is potentially full of answers just waiting to be revealed.

 

Read the details in the original study.

 

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